Fit for purpose application of currently existing animal models in the discovery of novel epilepsy therapies

Epilepsy Research 126 (2016) 157–184

Contents lists available at www.sciencedirect.com

Epilepsy Research journal homepage: www.elsevier.com/locate/epilepsyres

Review article

Fit for purpose application of currently existing animal models in the discovery of novel epilepsy therapies Wolfgang Löscher a,b,∗ a b

Department of Pharmacology, Toxicology, and Pharmacy, University of Veterinary Medicine Hannover, Hannover, Germany Center for Systems Neuroscience, Hannover, Germany

a r t i c l e

i n f o

Article history: Received 6 October 2015 Received in revised form 6 March 2016 Accepted 30 May 2016 Available online 1 August 2016 Keywords: Epilepsy Antiepileptic drugs Anti-seizure drugs Epileptogenesis Antiepileptogenic drugs Pharmacoresistance Epilepsy-associated comorbidities Biomarkers Adverse drug effects

a b s t r a c t Animal seizure and epilepsy models continue to play an important role in the early discovery of new therapies for the symptomatic treatment of epilepsy. Since 1937, with the discovery of phenytoin, almost all anti-seizure drugs (ASDs) have been identified by their effects in animal models, and millions of patients world-wide have benefited from the successful translation of animal data into the clinic. However, several unmet clinical needs remain, including resistance to ASDs in about 30% of patients with epilepsy, adverse effects of ASDs that can reduce quality of life, and the lack of treatments that can prevent development of epilepsy in patients at risk following brain injury. The aim of this review is to critically discuss the translational value of currently used animal models of seizures and epilepsy, particularly what animal models can tell us about epilepsy therapies in patients and which limitations exist. Principles of translational medicine will be used for this discussion. An essential requirement for translational medicine to improve success in drug development is the availability of animal models with high predictive validity for a therapeutic drug response. For this requirement, the model, by definition, does not need to be a perfect replication of the clinical condition, but it is important that the validation provided for a given model is fit for purpose. The present review should guide researchers in both academia and industry what can and cannot be expected from animal models in preclinical development of epilepsy therapies, which models are best suited for which purpose, and for which aspects suitable models are as yet not available. Overall further development is needed to improve and validate animal models for the diverse areas in epilepsy research where suitable fit for purpose models are urgently needed in the search for more effective treatments. © 2016 Elsevier B.V. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Validated animal models for antiepileptic drug discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 2.1. The maximal electroshock seizure (MES) test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 2.2. The subcutaneous (sc) pentylenetetrazole (PTZ) seizure test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 2.3. The kindling model of temporal lobe epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 2.4. Use of validated animal models for evaluating drug combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 2.5. Other animal models used in preclinical ASD development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Abbreviations: AES, American Epilepsy Society; ASD, anti-seizure drug; ASP, Anticonvulsant Screening Program; BLA, basolateral amygdala; CC, convulsant current; CD, convulsive dose; BBB, blood-brain barrier; CNS, central nervous system; ED, effective dose; ETSP, Epilepsy Therapy Screening Program; FPI, fluid percussion injury; GAD, glutamic acid decarboxylase; GAERS, genetic absence epilepsy rat from Strasbourg; ILAE, International League Against Epilepsy; iPSC, induced pluripotent stem cell; MAM, methylazoxymethanol acetate; MES, maximal electroshock seizure; NIH, National Institutes of Health; NINDS, National Institute of Neurological Disorders and Stroke; NMDA, N-methyl-d-aspartate; PET, positron emission tomography; Pgp, P-glycoprotein; PTZ, pentylenetetrazole; SE, status epilepticus; SNR, substantia nigra pars reticulata; SWD, spike-wave discharge; TBI, traumatic brain injury; TLE, temporal lobe epilepsy; TMEV, Theiler’s murine encephalomyelitis virus. ∗ Correspondence address: Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine Hannover, Bünteweg 17, D-30559 Hannover, Germany. E-mail address: [email protected] http://dx.doi.org/10.1016/j.eplepsyres.2016.05.016 0920-1211/© 2016 Elsevier B.V. All rights reserved.

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W. Löscher / Epilepsy Research 126 (2016) 157–184

Animal models of drug-resistant seizures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 3.1. Models of seizures that are difficult to control with ASDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 3.1.1. The 6-Hz psychomotor seizure model of partial epilepsy in mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 3.1.2. The lamotrigine-resistant kindled rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 3.1.3. The intrahippocampal kainate mouse model of therapy-resistant mesial TLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 3.2. Models based on selection of nonresponders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 3.2.1. The phenytoin-resistant kindled rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 3.2.2. The phenobarbital-resistant epileptic rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 3.2.3. ASD-resistant epileptic dogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 3.3. Models where the resistance develops over time (e.g., multiple-hit models) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 3.4. Models for discovery of novel drugs for refractory status epilepticus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Changes of efficacy during chronic drug administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Prediction of potential serious adverse reactions and their mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Inclusion of pharmacokinetic analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.1. Preclinical pharmacokinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.2. Estimation of effective plasma concentrations of new ASDs for first clinical trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.3. Animal models of epilepsy-associated blood-brain barrier dysfunction and its effect on drug distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Mechanism of action and drug target engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Identification of biomarkers in animal models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Models for discovery of antiepileptogenic or disease-modifying treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Multicenter clinical trials of novel drugs in rodent models? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Emerging models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.1. Models of pediatric epilepsies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.2. Genetic animal models of epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.3. Models for infection-induced epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.4. Models for studying epilepsy-associated cognitive deficits and psychiatric comorbidities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.5. The zebrafish model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 12.6. Patient-derived induced pluripotent stem cells to model epilepsies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Potential pitfalls of animal models in antiepileptic drug discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 13.1. The “old models identify old drugs” argument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 13.2. Seizure types used as endpoints for drug testing in animal models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 13.3. Lack of uniform seizure definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 13.4. The search for broad spectrum ASDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 13.5. Drug potency vs. efficacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 13.6. Mechanism of action of ASDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 13.7. Gaps and shortcomings in study design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

1. Introduction Despite large investments in drug development, the overall success rate of drugs during clinical development remains low (Denayer et al., 2014). This is particularly true for CNS drugs, for which the overall success rate is below 10% (Kola and Landis, 2004; Hay et al., 2014). One prominent explanation is flawed preclinical research, in which the use and outcome of animal models is pivotal to bridge the translational gap to the clinic (Kilkenny et al., 2010; Galanopoulou et al., 2012; Landis et al., 2012; Simonato et al., 2012). Therefore, the selection of validated and predictive animal models is essential to address the clinical question. This is also pivotal for development of anti-seizure drugs (ASDs; previously termed “antiepileptic drugs”) (Galanopoulou et al., 2012; Simonato et al., 2012; Löscher et al., 2013; Simonato et al., 2014). Preclinical research has facilitated the discovery of valuable drugs for the symptomatic treatment of epilepsy. Yet, despite these therapies, seizures are not adequately controlled in about a third of all affected individuals, and comorbidities still impose a major burden on quality of life (Löscher and Schmidt, 2011; Galanopoulou et al., 2012). More than a decade ago, translational medicine was invented both as a catchword and as a novel approach to improve success in drug development and ameliorate the low-output syndrome from collapsing pipelines (Wehling, 2011). Translational medicine describes the conditions and prerequisites for the transfer of in

vitro (e.g. cell culture) and in vivo (e.g. animal model) results in human applications (Wehling, 2011). Thus, it is a still-emerging attempt to define and analyze the processes governing innovative developments from ‘bench to bedside’ (Wehling, 2011). An essential requirement for translational medicine to improve success in drug development is the availability of animal models with high predictive validity for a therapeutic drug response. For this requirement, the model, by definition, needs not to be a perfect replication of the clinical condition, but it is important that the validation provided for a given model is “fit for purpose” (Denayer et al., 2014; Wartha et al., 2014; Willner and Belzung, 2015). A major concern in many disease areas, including epilepsy, is the poor reproducibility of preclinical data for compounds progressing from academic laboratories to industrial development programs and, ultimately, to clinical trials (Ioannidis, 2005; Benatar, 2007; Fisher et al., 2009; Kimmelman and London, 2011; Mullard, 2011; Philip et al., 2009; Prinz et al., 2011; Galanopoulou et al., 2012; Perrin, 2014). Thus, guidelines that improve and standardize the design, reporting, and validation of data across preclinical therapy development are important, and such guidelines are currently developed for many preclinical research areas, including preclinical ASD studies in animal models (Kilkenny et al., 2010; Philip et al., 2009; Galanopoulou et al., 2012; Landis et al., 2012; Simonato et al., 2012; Perrin, 2014). The aim of this review is to critically discuss the translational value of currently used animal models of seizures and epilepsy,

W. Löscher / Epilepsy Research 126 (2016) 157–184

Fig. 1. The maximal electroshock seizure (MES) test in naive mice and rats. Stimulation via transauricular electrodes is illustrated, but often transcorneal stimulation is used in this test. A generalized tonic-clonic seizure with hindlimb extension as illustrated in “A” is typically used as endpoint in this test. Hindlimb extension is typically followed by clonic seizures as shown in “B”. Rodent drawings are from Peterson (1998).

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ples of the opposite scenario were N-methyl-d-aspartate (NMDA) receptor antagonists, such as MK-891 (dizocilpine) and D-CPP-ene, which were potent anti-seizure drugs in the MES test, but did not suppress focal kindled seizures and failed in proof-of-concept trials in patients with partial seizures (Löscher and Schmidt, 1994). Despite its ∼80 years of use and many criticisms (e.g., Löscher and Schmidt, 2011), the MES test remains a “gold standard” in ASD discovery, because it is well suited for screening and is quite effective in identifying drugs that block tonic–clonic seizures in patients (Bialer and White, 2010). However, in relation to the problem of treatment-resistant epilepsy specifically (i.e. the situation in which ASDs do not work), the MES test is not fit for purpose and a different approach is needed (see below). Furthermore, following initial screening by the MES test, various additional models are needed to differentiate the anti-seizure effect of a novel drug from already existing ASDs. As shown by the example of levetiracetam, novel drugs that are not effective in the MES test should not be discarded, but may prove effective in more sophisticated models, such as kindling (see Section 2.3), which are predictive for anti-seizure efficacy in patients with partial seizures. 2.2. The subcutaneous (sc) pentylenetetrazole (PTZ) seizure test

particularly what animal models can tell us about ASDs in patients and what limitations exist. Some of these aspects have been dealt with in several recent reviews (Löscher, 2011; Löscher and Schmidt, 2011; Galanopoulou et al., 2012; Simonato et al., 2012; O’Brien et al., 2013; Wilcox et al., 2013; Harward and McNamara, 2014; Simonato et al., 2014), but the focus of the present paper will be the fit for purpose paradigm that is key for success in clinical translation (Denayer et al., 2014; Wartha et al., 2014; Willner and Belzung, 2015). Furthermore, this review should guide researchers in both academia and industry regarding what can and cannot be expected from animal models in preclinical ASD development, which models are best suited for which purpose, and for which aspects suitable models are as yet not available. 2. Validated animal models for antiepileptic drug discovery Despite the innumerable animal models of seizures or epilepsy that are available, only three models are validated in that they identified novel treatments that were subsequently found effective in patients. 2.1. The maximal electroshock seizure (MES) test The first of these models, the maximal electroshock seizure (MES) test, was used by Merritt and Putnam in the 1930s to systematically screen hundreds of compounds for anti-seizure efficacy in cats, leading to the discovery of phenytoin (Putnam and Merritt, 1937), which was the first ASD to be tested in animals before it was given to humans. The MES test was later modified for mice and rats by Toman et al. (1946), and is still the most commonly used first screen in the search for new ASDs (Bialer and White, 2010). In this test, tonic-clonic seizures are induced by transcorneal or, less often, transauricular application of a short (0.2 s) suprathreshold electrical stimulus in normal mice (50 mA) or rats (150 mA) (Fig. 1). The endpoint in this test is tonic hindlimb extension, and the test is thought to be a predictive model for generalized tonic-clonic seizures (Bialer and White, 2010). Anti-seizure ED50 s of various clinically used and investigational drugs in mice are shown in Table 1. In addition, it was proposed that the MES test may also predict ASDs with efficacy against partial seizures (Krall et al., 1978a; Krall et al., 1978b), but the lack of anti-MES activity of several ASDs (e.g., levetiracetam, tiagabine, vigabatrin; see Table 1) that subsequently were shown to suppress partial seizures in epilepsy patients strongly argues against this idea (Löscher, 2011). Exam-

The second validated seizure model for the early detection of anti-seizure activity is the sc pentylenetetrazole (PTZ; metrazol) seizure test, which is thought to be useful to identify drugs that block generalized nonconvulsive (absence, myoclonic) seizures (White et al., 2006), but with limitations discussed below. An interesting translational aspect of this convulsant is that PTZ, which acts predominantly by antagonizing GABAergic inhibition via an effect at the GABAA receptor, has clinically been used to stimulate the brain stem for circulatory and respiratory stimulation, for convulsive therapy in patients with major depression (before the invention of electroconvulsive therapy), and as a diagnostic activation method in patients with epilepsy (Löscher, 2009). In the sc PTZ seizure test, the convulsive dose of PTZ inducing a clonic seizure of at least 5 s duration in 97% of the animals (CD97 ) is subcutaneously injected into normal mice or rats, and animals are observed for a post-injection period of usually 30 min for the occurrence of such a “threshold” seizure (Fig. 2). In 1944, Everett and Richards used the PTZ seizure model in mice to demonstrate the anti-seizure effect of trimethadione, which was subsequently demonstrated to block absence seizures in humans and introduced for this indication in 1946. Everett and Richards (1944) also showed that phenytoin was ineffective in the PTZ model, which is consistent with its lack of efficacy against absence seizures in patients. Thus, two simple animal models, the MES and PTZ tests, could be used to differentiate ASDs with different clinical effects, which subsequently formed the basis for Swinyard (1949) and Swinyard et al. (1952) to propose the MES and sc PTZ tests in mice and rats as standard procedures for predicting clinical anti-seizure activity of investigational drugs. Some years after the discovery of trimethadione, the PTZ test was crucial to identify the succinimides (Chen et al., 1951), including phensuximide, methsuximide, and ethosuximide, which rapidly replaced oxazolidinediones such as trimethadione because of their superior tolerability. For several decades, the MES and PTZ models were also used as initial preclinical screens for anti-seizure activity of investigational drugs in the Anticonvulsant Screening Program (ASP) of the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH; Bethesda, MD). This program was initiated in 1975 to stimulate the discovery and development of new chemical entities for the symptomatic treatment of human epilepsy (Krall et al., 1978a,b; White et al., 2006). In addition to evaluating drugs for anti-seizure activity in the MES and sc PTZ tests, this program included tests to detect “minimal neurologi-

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Table 1 Anti-seizure ED50 s of anti-seizure drugs (ASDs) in the maximal electroshock (MES) test, the s.c. pentylenetetrazole (PTZ) seizure test, and the 6-Hz test in mice as taken from the literature. See text for details of these tests. Except otherwise indicated, all data are from male mice. Because the mouse strain may markedly affect the efficacy of ASDs in the 6-Hz test (Leclercq and Kaminski, 2015a,b), mouse strains used in this test (CF-1, NMRI, C57Bl/6J, ICR, Swiss) are indicated. Drug

Sodium channel modulators Phenytoin Phenytoin Phenytoin

Anti-seizure ED50 (mg/kg i.p.) in mice

Reference

MES

s.c. PTZ

6-Hz 22 mA

6-Hz 32 mA

6-Hz 44 mA

5.64

>50

9.4 (CF-1)

>60 (CF-1) ∼30a (NMRI) 31 (CF-1) 26 (NMRI) 1.4 (C57Bl/6) 47.9 (CF-1) 19.5 (NMRI) 24 (Swiss)

>60 (CF-1) >81 (CF-1) 46 (NMRI) >81 (C57Bl/6)

Carbamazepine Carbamazepine Oxcarbazepine

7.81

>50

14

n.e.

Eslicarbazepine acetate

23

n.e.

Lamotrigine Lacosamide Zonisamide

7.47 4.5 40.5

>40 n.e. >250

4.4 (CF-1)

>60 (CF-1) 9.9 (CF-1) 97 (CF-1)

>60 (CF-1)

T-type calcium channel modulators n.e. Ethosuximide

136

86.9 (CF-1)

167 (CF-1)

>600 (CF-1)

Alpha2-delta-subunit calcium channel modulators 78b Gabapentin

47b

78 (Swiss)

31

23 (Swiss)

13.5

26 (CF-1) 50 (NMRI)

>5 >7000

0.26 940

0.66 (CF-1)

25.6

0.02

21.8

13.2

0.04 (CF-1) 1.6 (NMRI) 14.8 (CF-1) 23.6 (NMRI; m) 17.3 (NMRI; f)

Pregabalin

11.6c

Potassium channel modulators 9.3 Retigabine Retigabine GABAmimetic compounds Tiagabine Vigabatrin Clonazepam Diazepam Phenobarbital Phenobarbital AMPA receptor antagonists Perampanel SV2A modulators Levetiracetam Levetiracetam Levetiracetam

Brivaracetam Drugs with multiple actions Valproate Valproate Felbamate Topiramate Unknown mechanism Carisbamate

1.6

0.94

>500

>500

113

30

263

220

35.5 33

126 >800

7.9

20.4

15.9

4.6 (CF-1)

Barton et al. (2001) Bankstahl et al. (2013) Leclercq and Kaminski (2015a) Barton et al. (2001) Löscher et al. (2006) Schmutz et al. (1994); Florek-Luszczki et al. (2015) Soares-da-Silva et al. (2015) Barton et al. (2001) Bialer et al. (2009) White et al. (2002) Barton et al. (2001) Taylor (2002); Florek-Luszczki et al. (2015) Vartanian et al. (2006); Florek-Luszczki et al. (2015)

33 (CF-1)

Bialer et al. (2009) Bankstahl et al. (2013)

18.3 (NMRI; f)

Barton et al. (2001) Löscher (1980); Dalby and Nielsen (1997) Barton et al. (2001) Löscher et al. (2006) Barton et al. (2001) Bankstahl et al. (2013)

2.1

2.8 (ICR)

Hanada et al. (2011)

19.4 (CF-1) 17.6 (NMRI; f) 30 (CF-1) 4.8 (NMRI) 24 (C57Bl/6)

1089 (CF-1) >20 (NMRI; f) 286 (CF-1) 30 (NMRI) 260 (C57Bl/6) 4.4 (CF-1)

Barton et al. (2001) Bankstahl et al. (2013 Leclercq and Kaminski (2015a) Bialer et al. (2009)

41.5 (CF-1)

126 (CF-1) 127 (CF-1) 73.8 (CF-1) >300 (CF-1)

310 (CF-1)

Barton et al. (2001) Löscher et al. (2006) Barton et al. (2001) Barton et al. (2001)

20.7 (CF-1)

21.4 (CF-1)

27.6 (CF-1)

Bialer et al. (2009)

Abbreviations: m, male; f, female; ne, not effective. a But truncated dose-response. b But >1750 mg/kg reported by Dalby and Nielsen (1997). c But >400 mg/kg reported by Luszczki et al. (2012).

cal deficit” such as the rotarod test to calculate the “protective (or therapeutic) index” between anti-seizure and neurotoxic doses of test compounds. Since its initiation, the ASP program has evolved and various additional models have been implemented, including models of ASD-resistant seizures (see Section 3), in order to identify potentially interesting and effective compounds that may be missed when only using the MES and sc PTZ tests (White et al.,

2006; Wilcox et al., 2013). Furthermore, the sc PTZ test has been abandoned as an initial screen (Wilcox et al., 2013), because it did not correctly predict the effect of several novel ASDs, including lamotrigine and levetiracetam, to suppress absence seizures and resulted in false positive data (e.g., tiagabine, vigabatrin) for other ASDs (Table 1). This indicates that the validated predictive value of a given animal model may be restricted to certain chem-

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161

Fig. 2. The subcutaneous (sc) pentylenetetrazole (PTZ) seizure test in naive mice and rats. The animal figure illustrates a clonic seizure with loss of righting, which is used as endpoint in this test.

ical categories of compounds. However, timed intravenous PTZ infusion is still widely used to determine seizure threshold, particularly when testing investigational drugs for proconvulsant activity during safety evaluation (Löscher, 2009). Intravenous PTZ seizure threshold is also included in the initial differentiation phase of the ASP (Wilcox et al., 2013). In addition, determination of PTZ seizure threshold might be useful to assess altered seizure susceptibility as a biomarker of epileptogenesis (see Section 9). 2.3. The kindling model of temporal lobe epilepsy The third validated model is the kindling model of temporal lobe epilepsy (TLE) first described by Goddard et al. (1969). Whereas the MES and PTZ models induce seizures in healthy, neurologically intact rodents, kindling is a chronic model in which the repeated application of electrical stimuli via a depth electrode in the limbic system (amygdala or hippocampus) of rats induces permanently enhanced seizure susceptibility and other enduring brain alterations that are similar to those occurring in human TLE (Sato et al., 1990) (Fig. 3). The kindling model is one of the few (and often the only) chronic models that are currently used by most ASD discovery programs, including the NIH/NINDSsponsored ASP in the U.S. (Löscher, 2011). Its validation is based on the fact that levetiracetam, which is ineffective in the MES and PTZ tests, is highly effective at blocking kindled seizures (Löscher and Hönack, 1993). Efficacy against kindled seizures correctly predicted the anti-seizure effect of this drug in patients with partial epilepsy (Klitgaard and Verdru, 2007). Furthermore, the kindling model correctly predicted the clinical utility of various other ASDs against partial seizures in patients with epilepsy (Löscher, 2011). Approaches to replace the classical kindling model, which is costly and laborious, by easier models such as corneal kindling have not been successful, because the predictive value of such models, if any, is not clear (Löscher, 2011). 2.4. Use of validated animal models for evaluating drug combinations Monotherapy is considered the gold standard for drug treatment of epilepsy. However, most patients with refractory epilepsy are eventually treated with drug combinations, necessitating rational choice of ASDs for such combinations (French and Faught,

2009). The concept of “rational polypharmacy” is an often discussed but rather inadequately proven goal of epilepsy treatment in patients not responding to a single ASD (Leppik, 1996; Deckers et al., 2000; French and Faught, 2009; Brodie and Sills, 2011; Barker-Haliski et al., 2014; Gidal, 2015).The principle underlying rational polypharmacy is that the combination of two medications with different mechanisms of action may result in supra-additive or synergistic anti-seizure effects, with infra-additive toxicity (Barker-Haliski et al., 2014). In view of the large number of clinically available ASDs, it is not possible to evaluate all probable drug combinations in clinical trials, particularly because only fixed doses can be tested in such trials (French and Faught, 2009). Thus, optimal combinations of dosages, with the most benefit and least toxicity, may be missed. This difficulty can be circumvented in animal studies, including the MES and PTZ tests, which are widely used to investigate ASD combinations, employing appropriate methods of analysis such as isobolography (Deckers et al., 2000; Jonker et al., 2007; Czuczwar et al., 2009; Matsumura and Nakaki, 2014). Identifying preferred combinations of ASDs, particularly when starting a new drug, might be regarded as advantageous in terms of maximizing efficacy and minimizing adverse effects (Brodie and Sills, 2011). In such preclinical studies it is important to account for pharmacokinetic interactions by controlling for plasma drug levels (French and Faught, 2009). 2.5. Other animal models used in preclinical ASD development Various other animal models of seizures or epilepsy are employed in preclinical ASD development, including genetic models such as DBA/2 mice with audiogenic seizures, the genetic absence epilepsy rat from Strasbourg (GAERS), and the WAG/Rij genetic rat model of absence epilepsy. The genetic absence models (GAERS, WAG/Rij) have replaced the PTZ test in many ASD development programs (Löscher, 2011). Such models are useful to elucidating the potency and spectrum of anti-seizure activities against different types of epileptic seizures. They are, however, not considered validated and, as several other models discussed here (e.g., the MES and PTZ tests), do not allow evaluating whether a new investigational drug possesses higher efficacy for suppressing seizures compared to clinically established ASDs, particularly in difficult-to-treat types of epilepsy. However, one may argue that the GAERS and WAG/Rij models are sufficiently validated,

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Fig. 3. The kindling model of temporal lobe epilepsy. Seizures induced by once daily electrical stimulation of the amygdala or hippocampus typically progress through 5 stages (from nonconvulsive stage 1 and stage 2 to convulsive stage 3–5 motor seizures), which are rated by the Racine scale (Racine, 1972). The drawings of these stages have been kindly provided by Kerry W. Thompson. If daily stimulations are continued, rats progress into spontaneous recurrent seizures, demonstrating that the electrical stimulations induce epileptogenesis. However, in general, only the induced seizures during kindling acquisition or in the fully kindled stage are used for pharmacological testing.

because these two models correctly predicted activity of commonly used drugs for absence epilepsy (valproate, ethosuximide and levetiracetam) and predicted that phenytoin, carbamazepine and, in particular, vigabatrin and tiagabine would aggravate absence seizures. The only failure they have had so far has been lamotrigine that neither reduced nor aggravated spike-wave discharges (SWDs) in the rat models (van Rijn et al., 1994) but became a commonly used drug for treating childhood absence epilepsy.

3. Animal models of drug-resistant seizures The fact that – despite the development of >15 novel ASDs in the last 30 years and the high expectations associated with each novel drug – the percentage of pharmacoresistant epilepsy patients has not significantly changed, has led to increasing disappointment among clinicians, basic scientists, and people in industry (Löscher and Schmidt, 2011). In fact, “big pharma” has almost completely abandoned ASD development, which may halt any further improvement in the treatment of epilepsy unless we find ways out of this dilemma (Löscher and Schmidt, 2011; Löscher et al., 2013). About 30% of patients suffer from ASD-resistant seizures, so that there is an urgent need to develop and incorporate models of drugrefractory seizures into development of new ASDs. Based on the operational definition of ASD resistance in patients with epilepsy (Kwan et al., 2010), the term “pharmacoresistant” applied in the context of animal models can be defined as persistent seizure activity not responding or with very poor response to monotherapy with at least two current ASDs at maximum tolerated doses (Stables et al., 2003). Several models which fulfill this definition have been developed (Löscher, 2016), but as yet none of these models has been validated. In this respect, validation would mean that the higher efficacy of a novel drug in a model of ASD-resistant seizures translates into a higher clinical efficacy in patients with refractory epilepsy.

The available models of pharmacoresistant seizures are summarized in Table 2 and categorized in three groups, corresponding to clinical scenarios of treatment-resistant epilepsy: (1) models with seizures that are difficult to control with existing ASDs (i.e., severe types of seizures/epilepsy); (2) models based on selection of nonresponders (i.e., part of the animals in the model respond, while others do not respond to ASDs); and (3) models where the resistance develops over time (e.g. multiple-hit models). Representative models of each category will be described and discussed in the following. Some of the less well characterized models shown in Table 2 will not be discussed in detail here (see Löscher, 2016, for a review of these models), but some of them are shortly described in Section 12. Although none of the various models shown in Table 2 have helped to identify a novel, more efficacious ASD as yet, some of them have been important to elucidate potential mechanisms of ASDresistance, and some of these mechanisms seem to be operative in humans, too. Thus, in line with the fit for purpose paradigm, animal models of drug-resistant seizures may not only help to find new more effective ASDs but also, probably more importantly, may identify new targets for ASD discovery and validate drug candidates directed towards these targets (Löscher et al., 2013). However, it should be noted that the fact that many of the animal models discussed in the following are selectively refractory to a few drugs, but respond to others, is apparently not consistent with the clinical situation. The ILAE has suggested that a diagnosis of medically refractory epilepsy is warranted after a patient has failed trials of two appropriate mediations, alone or in combination (Kwan et al., 2010). The basis for this is clinical evidence that once a patient fails two appropriate drug trials, the chance of responding to a third is extremely small (∼10%), so that most patients failing two drugs are considered multiresistant (Kwan et al., 2011). It is not clear why this is different in most animal models of drug-resistant epilepsy and which consequences this has for the translational value of such animal models in general. One may argue that in relation to the

W. Löscher / Epilepsy Research 126 (2016) 157–184

163

Table 2 Overview of animal models of drug-resistant seizures discussed in this review. Model

Induction of seizures or epilepsy

Acute or chronic

Spontaneous recurrent seizures?

Selection of responders and nonresponders possible?

Models with seizures that are difficult to control with some/several ASDs 6-Hz Transcorneal No No Acute psychomotor electrical seizure stimulation model (44 mA) 6-Hz corneal No Not known Transcorneal Chronic kindling in electrical mice stimulation

Lamotrigineresistant kindled rat

Suitable to study mechanisms of resistance?

Seizures are resistant to

No

Phenytoin, lamotrigine

Yes

Seizures are responsive to

Usable for drug Representative screening? references

Retigabine, phenobarbital, carisbamate, brivaracetam, levetiracetam Levetiracetam, Valproate, carbamazepine clonazepam (limited efficacy against partial seizures) Lamotrigine, Valproate carbamazepine

Yes

Barton et al. (2001)

Yes (inter-mediate screening)

Leclercq et al. (2014)

No

Postma et al. (2000); Srivastava and White (2013) Riban et al. (2002); Klein et al. (2015)

Amygdalakindling

Chronic

No

No

Not clear

IntrahippocampalUnilateral kainate injection of kainate model in mice Posttraumatic Fluid seizures in percussion rats

Chronic

Yes

Yes

Yes

Carbamazepine, Diazepam, phenytoin valproate, phenobarbital

Yes (inter-mediate screening)

Chronic

Yes

Not known

Yes

No

Eastman et al. (2010)

Allylglycine Allylglycine i.p. Acute seizure (mice) or via fish water model in mice and (zebrafish) larval zebrafish MAM (methyl- Kainate Chronic azoxymethanol acetate) model of cortical dysplasia in rats

No

No

Not clear

Carbamazepine, Not known valproate (in part), carisbamate Levetiracetam, Diazepam, phenytoin, valproate topiramate

Yes

Leclercq et al. (2015)

No

Not known

Not known

Valproate

Not known

No

Smyth et al. (2002)

No

Yes

Yes

Phenytoin, phenobarbital, valproate, felbamate etc.

Levetiracetam

No

Löscher et al. (1993)

Yes

Yes

Yes

Phenobarbital, phenytoin

Lamotrigine

No

Brandt et al. (2004)

Yes

Yes

Yes

Phenobarbital, primidon, imepitoin, potassium bromide

Add-on therapy may reduce seizure frequency

No

Löscher et al. (1985)

No

Valproate, Not known phenobarbital, phenytoin Carbamazepine Levetiracetam, diazepam, perampanel, phenobarbital

Yes (inter-mediate screening) Yes (inter-mediate screening)

Blanco et al. (2009)

ACTH, phenytoin, NAX-5055

Yes

Models based on selection of ASD nonresponders PhenytoinAmygdalaChronic resistant kindling amygdalakindled rats Phenobarbital- SE induction Chronic resistant via BLA epileptic rat stimulation Dogs with Idiopathic or Chronic pharmacore- symptomatic sistant epilepsy epilepsy

Models where the resistance develops over time (e.g., multiple-hit models) PTZ seizure test Pilocarpine Chronic No No in epileptic rats 6-Hz seizure Chronic No No Pilocarpine test in epileptic mice Multiple-hit rat model of infantile spasms due to structural lesion

Doxorubicin Chronic and lipopolysaccharide intra-cerebrally

Yes

Not known

Not clear

Not done yet

Vigabatrin (transiently), CPP-115, carisbamate, rapamycin.

Bankstahl et al. (2013); Leclercq and Kaminski (2015a,b) Scantlebury et al. (2010); Galanopoulou and Moshe (2015)

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problem of treatment-resistant epilepsy specifically (i.e. the situation in which ASDs do not work), the animal models of epilepsy in current use are not fit for purpose and a different approach is needed. Indeed, none of the models described in the following has led to the discovery of a novel ASD that is more clearly effective in as yet pharmacoresistant patients. However, several of these models have been of immense value in advancing an understanding on mechanisms of ASD resistance. 3.1. Models of seizures that are difficult to control with ASDs Of these, the 6-Hz psychomotor seizure model in mice has been added to the initial identification screen of the ASP program (Wilcox et al., 2013). Furthermore, the lamotrigine-resistant kindled rat has been added to the initial differentiation phase of this program (Wilcox et al., 2013). In addition, the intrahippocampal kainate mouse model of therapy-resistant mesial TLE is currently being evaluated by the ASP (which recently was renamed as “Epilepsy Therapy Screening Program [ETSP]”). These three models will therefore be discussed in more detail below. 3.1.1. The 6-Hz psychomotor seizure model of partial epilepsy in mice The 6-Hz test in mice was first described more than 60 years ago by Toman (1951) and designated as the “psychomotor seizure test” because the convulsions resembled those seen clinically in psychomotor (i.e., complex-partial) seizures. However, subsequent studies with the relatively few available ASDs at that time indicated that the pharmacological profile of the 6-Hz test, particularly its resistance to phenytoin, was not consistent with the clinical profile of these ASDs in the treatment of psychomotor seizures (Brown et al., 1953), so the 6-Hz test was subsequently abandoned. Fifty years later, Barton et al. (2001) re-evaluated the utility of the 6-Hz model as a potential screen for therapy-resistant epilepsy. While the test did not discriminate between clinical classes of ASDs when used at the CC97 (22 mA), increasing the current intensity by 50% (i.e., 32 mA) decreased the sensitivity of the 6-Hz seizure to phenytoin and lamotrigine (see Table 1). At a current intensity of 2 × CC97 (i.e. 44 mA), only two ASDs, levetiracetam and valproate, displayed complete protection against 6-Hz seizures in CF-1 mice, although the efficacy of these drugs was markedly reduced when compared to the lower stimulation intensities (Barton et al., 2001; Table 1). Based on these observations, Barton et al. (2001) suggested that the 6-Hz stimulation may provide a useful model of therapy-resistant limbic seizures. However, more recent studies cast doubt on the value of the 6-Hz test as a model of drug-refractory partial seizures. As shown in Table 1, several clinically established and investigational ASDs, including retigabine, phenobarbital, brivaracetam and carisbamate, potently suppress 6-Hz seizures induced by 44 mA, but there is no clinical evidence that these drugs are particularly effective in patients with drug-refractory partial seizures (Löscher et al., 2013). The 6-Hz model seems to be predominantly resistant to sodium channel modulators such as phenytoin or lamotrigine, whereas drugs with other mechanisms, particularly GABAergic compounds, are quite effective (Table 2). Thus, this test is certainly not a model of drug-refractory seizures, but may help to discriminate drugs during development. When using this model, it is important to note that the genetic background of mice strongly affects drug resistance (Leclercq and Kaminski, 2015a). Thus, as shown in Table 1, in contrast to CF-1 mice as used by Barton et al. (2001), NMRI mice are not resistant to phenytoin in the 6-Hz test. Similarly, NMRI mice are much more responsive to levetiracetam in this test than CF-1 (or C57Bl/6) mice (Leclercq and Kaminski, 2015a) A real advantage of the 6-Hz model compared to the kindling or post-status epilepticus (post-SE) TLE models is its simplicity, allow-

ing screening of several compounds over a relatively short time. However, in contrast to epilepsy models, in which chronic brain alterations may alter the pharmacological responsiveness of the animals (Löscher and Schmidt, 1988), naive mice are used for the 6-Hz model. Thus, the 6-Hz model may be an interesting approach for ASD testing, but clearly is not suited to study the mechanisms leading to refractory epilepsy or to target mechanisms of pharmacoresistance (Table 2). Based on the extensive experiments of Barton et al. (2001), the 6-Hz model was added to the screening approach employed by the ASP/ETSP at the University of Utah (Wilcox et al., 2013). It remains to be determined whether this approach will contribute to the identification of novel ASDs that are subsequently found to possess clinical activity in patients with therapy-resistant epilepsy. More recently, Leclercq et al. (2014) demonstrated that it is possible to kindle mice via repeated corneal application of a 6Hz electrical stimulus. By comparing mice corneally kindled by 50-Hz stimuli vs. 6-Hz stimuli, all tested ASDs (clonazepam, valproate, carbamazepine, levetiracetam) showed a relatively lower potency in the 6 Hz kindling model, and a limited efficacy against partial seizures was observed with carbamazepine and levetiracetam (Table 2). Based on the observed low potency and limited efficacy of ASDs in 6 Hz fully kindled mice, the authors suggested that this model could be a useful tool in the discovery of novel ASDs targeting treatment-resistant epilepsy (Leclercq et al., 2014). In contrast, corneal kindling induced by 50 or 60 Hz stimuli does not seem to reproduce some of the treatment resistance aspects that had been reported in the amygdala kindling model (Löscher et al., 1986; Matagne and Klitgaard, 1998; Potschka and Löscher, 1999; Rowley and White, 2010). 3.1.2. The lamotrigine-resistant kindled rat Using the amygdala-kindling model in male Sprague-Dawley rats, Robert Post’s group reported that exposure to lamotrigine during kindling development leads to a reduced subsequent response to the drug in fully kindled animals (Postma et al., 2000). Weiss and Post (1991) reported the same phenomenon for carbamazepine in that rats that had been treated with carbamazepine during kindling were subsequently unresponsive to carbamazepine, while fully kindled seizures are normally very responsive to this ASD. They referred to this as “contingent tolerance” and suggested that early exposure to a low dose of lamotrigine or carbamazepine during the critical period of kindling acquisition might lead to pharmacoresistance. However they did not utilize their model to evaluate the effectiveness of other ASDs. More recently, Steve White’s group reassessed this approach, using groups of male Sprague Dawley rats that received either 0.5% methylcellulose (the drug vehicle) or lamotrigine (5 mg/kg, i.p.) 1 h before each amygdala kindling stimulation during kindling acquisition (Srivastava and White, 2013). Treatments were stopped once the groups were fully kindled. Two days later, rats were challenged with a higher dose of lamotrigine (15 mg/kg, i.p.) to verify lamotrigine-resistance in the lamotriginepretreated rats. The higher dose of lamotrigine blocked fully kindled seizures in the vehicle-treated rats but not seizures in the lamotrigine-pretreated group. Interestingly, carbamazepine (10, 20, and 40 mg/kg) displayed a dose-dependent anti-seizure effect in the vehicle-kindled group, but was less effective in lamotriginepretreated animals. In contrast, valproate (300 mg/kg) effectively blocked the behavioral seizures and decreased the afterdischarge duration in both vehicle and lamotrigine groups. The authors suggested that the lamotrigine-resistant, amygdala-kindled rat may represent a novel model of pharmacoresistant epilepsy (Srivastava and White, 2013). If “contingent tolerance” is indeed responsible for these findings, however, the lamotrigine-resistant kindled rats would model only a very selective type of drug resistance. Contingent tolerance

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indicates that tolerance is dependent upon the timing of the drug in relation to the kindling stimulation and will only occur if the drug is given before the stimulation (Löscher and Schmidt, 2006). Similar amounts of drug exposure, given after the seizure has occurred, do not result in a loss of efficacy of the drug, even after many drug administrations. Similar to other types of tolerance, contingent tolerance can lead to cross-tolerance, that is loss of response to a novel drug (i.e., one that has not previously been administered) due to tolerance development to the effects of another drug (Löscher and Schmidt, 2006). Cross-tolerance is thought to indicate shared or overlapping mechanisms of action of the two drugs, i.e., Na+ channel modulation in the case of lamotrigine and carbamazepine. Such cross-tolerance has been also reported when lamotrigine or carbamazepine was not given during but after kindling acquisition (Krupp et al., 2000; Srivastava et al., 2013). Whether cross-tolerance is a mechanism of ASD resistance in epilepsy patients is not known (Löscher and Schmidt, 2006).

3.1.3. The intrahippocampal kainate mouse model of therapy-resistant mesial TLE In recent years, the intrahippocampal kainate model of mesial TLE in NMRI, Swiss, C57Bl/6 or FVB/N mice has become a popular mouse model of epilepsy, because induction of SE by unilateral intrahippocampal injection of kainate is associated with almost no mortality, and most mice develop highly frequent electrographic seizures and less frequent convulsive seizures after SE (Baraban and Löscher, 2014). Riban et al. (2002) were the first to report that electrographic seizures recorded from the hippocampus in this model are resistant to major ASDs such as carbamazepine, phenytoin, and valproate, whereas diazepam was capable of suppressing these focal electrographic events. This finding was confirmed by subsequent studies, so Guillemain et al. (2012) proposed that the intrahippocampal mouse model of mesial TLE is suited as a model of difficult-to-treat focal seizures. In this model, due to the high recurrence of hippocampal discharges, ASDs can easily be tested during the 1–2 h that follow ASD injection and also during chronic treatment (Guillemain et al., 2012). This is a major advantage over other animal models of epilepsy with spontaneous seizures, in which, because of the low frequency of the seizures, continuous (24/7) video/EEG recordings over weeks are needed for drug efficacy studies (Löscher, 2011). Because of the advantages of the intrahippocampal kainate mouse model for drug testing, this model is currently being evaluated as a mouse model of therapy-resistant mesial TLE by the ASP/ETSP of the NIH/NINDS. We have established this model in recent years in our laboratory and examined whether epileptic mice differ in their individual response to ASDs (Klein et al., 2015). In a first step, we examined anti-seizure effects of 6 ASDs on spontaneous recurrent focal electrographic seizures and secondarily generalized convulsive seizures in epileptic FVB/N mice, showing that the focal nonconvulsive seizures were resistant to carbamazepine and phenytoin, whereas valproate and levetiracetam exerted moderate and phenobarbital and diazepam marked anti-seizure effects (Table 2). All ASDs seemed to suppress generalized convulsive seizures. Next we investigated the inter-individual variation in the anti-seizure effects of these ASDs and, in case of focal seizures, found responders and nonresponders to all ASDs except carbamazepine. Most nonresponders were resistant to more than one ASD. However, in contrast to our previous studies in rat epilepsy models (see below), it was not possible to select subgroups of epileptic mice that either responded or did not respond to a specific ASD such as phenytoin or phenobarbital (Klein et al., 2015). We are currently investigating how mouse strain, sex and different types of electrographic hippocampal seizures affect the response to ASDs in this model.

Fig. 4. A comparison of anti-seizure ED50 values (effective doses to suppress seizures in 50% of animals per group in mg/kg) of several ASDs for suppression of primarily generalized convulsive maximal electroshock seizures (MES) and different components of fully amygdala-kindled seizures in age-matched Wistar rats. As illustrated, all kindled seizure types were more difficult to suppress than MES in naive (nonkindled) rats. Furthermore, in kindled rats, focal kindled seizures (stage 1–3) were less sensitive to ASDs than secondarily generalized kindled (stage 4–5) seizures. Similar results were found with benzodiazepines (diazepam, clonazepam) and some investigational drugs, which are not illustrated in the figure. Data are from Löscher et al. (1986). Abbreviations: CBZ, carbamazepine; PB, phenobarbital; PHT, phenytoin; PRM, primidone; VPA, valproic acid.

3.2. Models based on selection of nonresponders Our own strategy in model development has been to identify models in which it is possible to select ASD-responders and -nonresponders from the same group of animals, thus allowing by direct subgroup-comparison determination of which mechanisms underlie the ASD-resistance in the nonresponders (Löscher, 1997; Löscher, 2006; Löscher, 2016). This strategy led to the development of the phenytoin-resistant kindled rat, the phenobarbital-resistant epileptic rat, and the characterization of ASD-resistant epileptic dogs, which will be described below. 3.2.1. The phenytoin-resistant kindled rat We first proposed kindling as a model to investigate intractable epilepsy in 1986 (Löscher, 1986; Löscher et al., 1986). Indeed, to our knowledge, this was the first animal model of pharmacoresistant epilepsy that was proposed for this purpose. In all of our studies, we used the original protocol described by Goddard et al. (1969) with once daily electrical stimulation of the basolateral amygdala (BLA) until all rats are “fully kindled”, i.e., exhibit the same maximum response (a secondarily generalized stage 5 seizure) upon stimulation (Fig. 3). By directly comparing standard ASDs in the kindling model and the standard MES test in age-matched female Wistar rats, we found that kindled seizures were less sensitive to anti-seizure treatment than primarily generalized seizures as produced in the MES test (Fig. 4). Furthermore, in the kindling model focal seizure stages were found to be much less responsive to ASDs than secondarily generalized seizures (Fig. 4), which is consistent with clinical experience. We proposed that search for novel compounds with high potency in the amygdala-kindling model may thus be a promising strategy in the development of new ASDs for patients with intractable epilepsy (Löscher, 1986; Löscher et al., 1986; Löscher and Schmidt, 1988). In follow-up studies in the amygdala-kindling model, using female rats of the Wistar outbred strain, we found that the individual response of fully kindled rats to maximum tolerated doses of phenytoin differs dramatically, that is that kindled seizures in some animals always respond while others do never respond to

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Fig. 5. The phenytoin-resistant kindled rat model. “A” illustrates the procedure to select phenytoin-responders and non-responders from large groups of amygdala-kindled Wistar rats. “B” illustrates data from the responder and nonresponder subgroups of one prospective experiment. Response to phenytoin or its prodrug fosphenytoin, was assessed by repeatedly determining the drug’s effect on the focal seizure threshold (afterdischarge threshold; ADT) in individual rats of a large group of fully kindled rats. The dose of phenytoin in each trial was 50 mg/kg i.p. (or 83.5 mg/kg fosphenytoin i.p.). ADT was determined by a staircase method 60 min after drug administration. Interval between two trials was 1 week. Control ADTs were determined in the same animals after vehicle injection. Following each drug injection, the plasma level of phenytoin was determined, and experiments indicating erroneous drug injection (∼5% of all experiments) were repeated. A responder was defined a priori as an animal showing at least 60% increase above predrug ADT in all three trials, whereas a nonresponder was defined as an animal not showing an increase of more than 20% in all three trials (which also occurred with repeated injections of vehicle). In reality, however, all phenytoin responders exhibited average ADT increases of 200–300% above predrug control, so that the response to phenytoin in large cohorts of kindled Wistar rats was an all-or-none phenomenon. Rats with variable responses (i.e., an ADT increase in one experiment but no ADT increase in another experiment) are not shown in “B”. The dramatic difference in anti-seizure effect of phenytoin between responders and nonresponders was not related to any difference in plasma drug levels, but both groups had plasma levels within the therapeutic range of phenytoin (indicated by the shaded area). “C” illustrates that the loss of anti-seizure efficacy in phenytoin-responders extended to several other ASDs with only one exception, i.e., levetiracetam. Loss of efficacy in nonresponders is indicated by comparing the drug-induced ADT increase in nonresponders with that obtained in responders. Except levetiracetam, all drugs were less effective (by at least 50%) in phenytoin-nonresponders compared to responders. For details see Löscher (2006).

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phenytoin (Rundfeldt et al., 1990). This finding was systematically explored by determining the effect of phenytoin on the threshold for induction of afterdischarges (ADT), i.e., the threshold for induction of focal seizure activity via an amygdala electrode in kindled rats. Phenytoin was repeatedly tested (3–4 times) in large groups of fully kindled rats in order to prove the reproducibility of its effect on ADT in individual animals (Fig. 5A). Confirming our preliminary observation (Rundfeldt et al., 1990), we found that the individual response of fully kindled rats to phenytoin markedly differs (Löscher and Rundfeldt, 1991), resulting in a subgroup of rats that consistently responded to treatment with an ADT increase whereas another subgroup never responded with an anti-seizure effect upon repeated drug administration (Fig. 5B). This phenomenon was highly reproducible in subsequent studies. Details of the experimental procedure are shown in Fig. 5 and its legend. Average data from more than 200 rats showed a consistent anti-seizure response to phenytoin (or its prodrug fosphenytoin) in only 16% of the animals, no anti-seizure response in 23%, and a variable response in the remaining 61% (Löscher, 1997; Löscher, 2002). These differences were not due to pharmacokinetic issues, but plasma drug analyses demonstrated that all rats had phenytoin levels within the therapeutic concentration range. Based on these data, we suggested that the three subgroups of amygdala-kindled rats model three different clinical scenarios (Löscher, 2006). The nonresponder subgroup models drug-refractory patients with TLE in which ASD treatment does not significantly reduce seizure frequency. The variable responder group models patients in which ASD treatment reduces seizure frequency but does not achieve complete control of seizures. The responder subgroup models patients which achieve complete control of seizures during ASD treatment. Following the identification of phenytoin resistant kindled Wistar rats, various clinically available ASDs were tested in such animals (Fig. 5C). Except levetiracetam, all examined ASDs were significantly less efficacious or not efficacious at all in phenytoin nonresponders compared to phenytoin responders, demonstrating that the phenytoin resistance of a subgroup of kindled Wistar rats extends to various ASDs (Löscher, 2006). This reflects the clinical situation in patients with TLE, because most patients who are refractory to one ASD are also resistant to other ASDs, including newly developed drugs. Overall, we concluded that kindled rats with phenytoin are a unique resource for the investigation of mechanisms for drug resistance in epilepsy, particularly because pathophysiological processes in phenytoin-resistant rats can be directly compared with those of kindled rats which reproducibly respond to this drug (Löscher, 1997). Fig. 7 summarizes the most important differences between phenytoin-responders and -nonresponders that we have found as yet in the kindling model and how they translate to respective changes in patients with pharmacoresistant TLE. Since we first characterized this model in Wistar rats, several other groups have used this model as well, reproduced our findings with phenytoin, and reported for instance differences in the expression of mitochondrial proteins in the hippocampus between phenytoin responders and nonresponders (Jiang et al., 2007), enhanced synaptic vesicle trafficking in the hippocampus of phenytoin-resistant rats (Zeng et al., 2009) and alterations of glutamate and GABA release in the hippocampus of phenytoin-nonresponders that resemble those found in patients with refractory TLE (Luna-Munguia et al., 2011). One of our findings in this model was that the expression of the efflux transporter P-glycoprotein (Pgp) is significantly higher in the focus (the amygdala) of phenytoin-nonresponders when compared to responders (Potschka et al., 2004). Subsequent experiments by another group showed that the increased Pgp expression in phenytoin nonresponders extended to the hippocampus, was associated with significantly reduced brain levels of phenytoin and carbamazepine

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(measured by microdialysis) and that this reduction in ASD levels could be counteracted by inhibiting Pgp with verapamil (Ma et al., 2013). More recently we tested whether individual amygdala-kindled Wistar rats also differ in their anti-seizure response to valproate and which mechanism may underlie the different response to this major ASD (Töllner et al., 2011). We found that good and poor valproate responders can be selected in kindled rats by repeatedly determining the effect of valproate on the ADT. Furthermore, there is a significant correlation between the anti-seizure response to valproate in kindled rats and its effect on the firing rate and pattern of GABAergic neurons in substantia nigra pars reticulata (SNR), a main basal ganglia output structure involved in seizure propagation, seizure control, and epilepsy-induced neuroplasticity. The less valproate is able to raise seizure threshold, the lower is the valproate-induced reduction of SNR firing rate and the valproateinduced regularity of SNR firing. These data demonstrated for the first time an involvement of the SNR in pharmacoresistant experimental epilepsy and emphasized the relevance of the basal ganglia as target structures for new treatment options. 3.2.2. The phenobarbital-resistant epileptic rat Prompted by the promising data from the kindling model, we investigated whether responders and nonresponders also occur in rats with spontaneous recurrent seizures, using a model in which SE is induced by sustained electrical stimulation of the BLA, which, after a latent period, leads to late spontaneous seizures (Brandt et al., 2003, 2004). Prolonged treatment of epileptic SpragueDawley rats with phenobarbital at maximal tolerated doses (see Fig. 6) resulted in two subgroups, responders and nonresponders (Brandt et al., 2004). Responders were defined by complete seizure suppression during treatment or a seizure suppression of at least 50% (in later studies 75%) compared to seizure frequency in the predrug and/or postdrug control periods. In three independent prospective studies by our group, about 40% of the rats were resistant to treatment with phenobarbital, demonstrating the reproducibility of this model (Brandt et al., 2004; Bethmann et al., 2007; Brandt and Löscher, 2014). A summary of the data from these 3 trials is illustrated in Fig. 6B. Similar figures were reported when experiments were performed by the group of H. Potschka in Munich, Germany (Fig. 6C). When the phenobarbital-resistant rats were subsequently treated with phenytoin, 83% of these rats were also resistant to the latter drug (Bethmann et al., 2007), thus fulfilling the minimum requirements for a model of drug-resistant epilepsy described above. Plasma drug levels and adverse effects of phenobarbital and phenytoin were comparable in responders and nonresponders, demonstrating that the resistance is restricted to the anti-seizure effect of these ASDs. The severity or duration of the initial brain insult (the SE) did not differ between responders and nonresponders, indicating that the different ASD response in the two subgroups is genetically determined. One may argue that the a priori definition of the responder and nonresponder groups, which was based on having or not having at least a 50–75% decrease in seizure frequency, may automatically result in a statistically significant difference between the averages of the data in the top half versus the bottom half of the whole group of rats tested, but this does not necessarily mean that the two subgroups represent two separate populations. Furthermore, based on the small numbers of rats in the responder and nonresponder subgroups in most of our experiments, one may argue that it is difficult to conclude whether the two groups identified are truly a result of a difference in response to PB or simply just random variability and clustering of spontaneous seizures that is known to occur in SE models even within the same animal over time (e.g., Williams et al., 2009). However, several findings support that the responder and nonresponder groups do truly represent distinct populations.

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Fig. 6. The phenobarbital-resistant epileptic rat. “A” illustrates the procedure to select phenobarbital-responders and non-responders from large groups of epileptic SpragueDawley rats. Rats were made epileptic in response to SE induced by sustained electrical stimulation of the BLA. The dosing protocol for phenobarbital (PB) consisted of an i.p. bolus dose of 25 mg/kg PB in the morning of the first treatment day, followed 10 h later by an administration of 15 mg/kg i.p., and then twice daily 15 mg/kg i.p. for the 13 subsequent days. “B” illustrates the effect of PB on spontaneous recurrent seizures (SRS) in 33 epileptic rats from 3 prospective studies. As shown in “A”, SRS were recorded over a period of two weeks before onset of PB treatment (predrug control), followed by drug treatment for two weeks, and then a two-week postdrug control period. SRS were continuously (24/7) recorded in the 33 rats over the 6 weeks of this experiment. A response to PB was defined by complete seizure suppression during treatment or a seizure suppression of at least 50–75% compared to seizure frequency in the predrug and postdrug control periods. The first graph in “B” illustrates individual seizure frequencies (SRS in 2 weeks) of all 33 rats of these experiments, while the second graph shows respective data from the 20 responders and the third graph data from the 13 nonresponders selected in these experiments from the 33 rats. Only the PB responders exhibited a significant difference in seizure frequency to control recordings (indicated by asterisk; P < 0.001), so that the response to this ASD was an all-or-none phenomenon. The fourth graph in “B” illustrates the average plasma concentration (mean ± SEM) of PB from the blood samples taken during the treatment period. Statistical analysis did not indicate a significant difference in PB plasma levels between groups. The shaded area indicates the therapeutic plasma concentration range of PB in patients with epilepsy, demonstrating that all rats exhibited PB plasma concentrations within this range. “C” illustrates a comparison of results of PB efficacy from 6 prospective trials of two independent groups of researchers in this model. The percentage of PB nonresponders selected by the two groups did not significantly differ when tested by Fisher’s exact test, indicating that the selection method used resulted in reproducible findings. For further details see Brandt and Löscher (2014).

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Fig. 7. Differences between ASD-responders and -nonresponders in two animal models of drug-resistant epilepsy. For comparison, alterations associated with ASD-resistance in patients are shown. Those alterations that occur both in the models and in patients are highlighted by the coloured boxes. For details see Löscher (2011) and Löscher et al. (2013).

First, PB responder and nonresponder subgroups in the BLA model have been shown in several independent prospective studies from two laboratories (Fig. 6C), demonstrating the reproducibility of this phenomenon. Second, most responders were seizure-free during treatment with PB, while nonresponders typically did not exhibit any decrease in seizure frequency in response to PB but often even increases in seizure frequency (see Fig. 6B). Thus, similar to the phenytoin-resistant amygdala kindled rats (Fig. 5), response to phenobarbital was an all-or-none phenomenon. Third, as described above, the PB nonresponders did also not respond to phenytoin (Bethmann et al., 2007). Fourth, PB nonresponders from the BLA model differ from PB responders in several aspects apart from the anti-seizure effect of PB (see below) and some of these differences are so marked that we do believe that the two subgroups represent distinct populations. One of the most marked differences was hippocampal damage, which was almost exclusively found in nonresponders (Fig. 7). Fig. 7 summarizes the studies that we have undertaken to identify mechanisms that could explain the resistance to phenobarbital in nonresponders. Interestingly, the average seizure frequency of phenobarbital-nonresponders was significantly higher than that of responders (Löscher and Brandt, 2010b), which is in line with clinical experience that the frequency of seizures in the early phase of epilepsy is a dominant risk factor that predicts refractoriness (Rogawski, 2013). However, resistance to treatment also occurred in rats that did not differ in seizure frequency from responders, indicating that disease severity alone is not sufficient to explain ASD resistance (Löscher and Brandt, 2010b). In addition to the difference in average seizure frequency, we found that phenobarbital-nonresponders differ from responders in behavioral and cognitive alterations (Gastens et al., 2008). Furthermore, we found that the majority (90%) of phenobarbitalnonresponders exhibit hippocampal damage, whereas such

damage was determined in only 7% of responders, so that neuron loss in the hippocampus, particularly in the dentate hilus, is a characteristic feature of phenobarbital-resistant rats (Volk et al., 2006; Bethmann et al., 2008). Again, these observations in our rat model are in line with clinical experience, in that psychiatric comorbidities and hippocampal sclerosis are predictors of ASD-resistance (Fig. 7). In addition to hippocampal damage, phenobarbitalnonresponders differ from responders in increased brain expression of the efflux transporter Pgp in the epileptic focus, which could explain why phenobarbital, which is a Pgp substrate, does not reach sufficiently high levels at its brain targets (Volk and Löscher, 2005). Furthermore, subunit expression and binding characteristics of GABAA receptors were altered in the nonresponders (Volk et al., 2006; Bethmann et al., 2008), which may be critically involved in the lack of antiepileptic efficacy of phenobarbital, because this drug predominantly acts at GABAA receptors (Rogawski and Löscher, 2004). These clear differences between phenobarbital-responders and nonresponders also indicate that the strict definition of response that we chose for selection is suitable for differentiating between pharmacoresistant and pharmacoresponsive rats. Interestingly, the only comparable difference between responders and nonresponders in the two models (kindling, post-SE TLE) explored by us was increased expression of Pgp at the blood-brain barrier (BBB) of nonresponders (Fig. 7). To directly address the possibility that Pgp is critically involved in ASD resistance, we performed a proof-of-concept experiment with the Pgp inhibitor tariquidar in phenobarbital-resistant epileptic rats (Brandt et al., 2006). Coadministration of tariquidar fully restored the anti-seizure activity of phenobarbital without altering its plasma pharmacokinetics or neurotoxicity, demonstrating that inhibiting Pgp in epileptic rats with proven drug resistance counteracts resistance (Brandt et al., 2006). Based on these promising

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data, we developed a strategy by which enhanced focal expression and functionality of Pgp can be identified in vivo by positron emission tomography (PET) in rats (Bankstahl et al., 2011). Translation of this strategy into the clinic resulted in the first in vivo imaging proof of enhanced focal Pgp in patients with refractory TLE (Feldmann et al., 2013). These data demonstrate again the principle of fit-forpurpose and illustrate how new diagnostic techniques developed in an animal model can be used to identify patients that may benefit from selective treatment, inhibition of Pgp (Feldmann et al., 2013). 3.2.3. ASD-resistant epileptic dogs Dogs with naturally occurring idiopathic or symptomatic epilepsy exhibit a striking similarity in etiology, clinical manifestation, and disease course when compared to human patients (Löscher et al., 1985; Löscher, 1997; Potschka et al., 2013; Baraban and Löscher, 2014). About 50% of dogs with partial and generalized convulsive seizures are not controlled by treatment with ASDs, so epileptic dogs have been proposed as a valuable large animal model of pharmacoresistant epilepsy that can be used to unravel mechanisms of resistance and evaluate new strategies for treatment (Löscher et al., 1985; Löscher, 1997; Potschka et al., 2013). However, clinical trials on new ASDs in epileptic dogs are as laborious and time-consuming as clinical trials in human patients, necessitating randomized trial designs in which the new drug is compared with either placebo or a standard comparator (Munana et al., 2010, 2012; Rundfeldt et al., 2015; Tipold et al., 2015). Recently, different treatments, including ASDs, vagal stimulation, and the ketogenic diet were compared with placebo or pseudoplacebo in epileptic dogs, and an unexpectedly high placebo rate was found, which was similar to that known from controlled clinical trials in humans with epilepsy (Munana et al., 2010, 2012). As a consequence, it is now clear that studies on new treatments in epileptic dogs should always include a “placebo” group receiving all manipulations (e.g., handling, injections, electrode implantation, seizure recording etc.) that are used for the new treatment. The same is, of course, important when performing studies in rodents. Although rarely termed “placebo controls”, most studies today use “sham controls” in which animals receive for instance the drug vehicle and all other manipulations except the drug to be evaluated. Numerous studies have shown that such sham controls may markedly differ in a variety of variables from “naive” (untreated) controls. Another important caveat that has to be considered when using dogs for long-term studies on ASDs is that dogs, similar to rodents, eliminate many drugs, including most ASDs, much more rapidly than humans (Baraban and Löscher, 2014). Thus, when using ASDs such as phenytoin, carbamazepine or valproate with half-lives too short for maintenance treatment in epileptic dogs, insufficient drug levels and, hence, no antiepileptic effects are obtained in this species. The few ASDs with sufficiently long half-lives in dogs for maintenance treatment include phenobarbital, primidone (because of its metabolism to phenobarbital), and potassium bromide, which is the reason why until recently only these old drugs were approved for treatment of canine epilepsy in the U.S. or Europe. This situation has changed by the recent approval of imepitoin for treatment of dogs with newly diagnosed epilepsy (Rundfeldt and Löscher, 2014). Furthermore, several newer ASDs, including levetiracetam, felbamate, zonisamide, topiramate, gabapentin, and pregabalin are used as add-on treatment in dogs with pharmacoresistant seizures (Potschka et al., 2013). Overall, use of epileptic dogs as a translational model of drug-refractory epilepsy, although seemingly attractive, has many limitations as discussed in detail recently (Potschka et al., 2013; Baraban and Löscher 2014). Naturally occurring canine SE has been proposed as a translational platform for evaluating investigational

compounds for eventual use in human trials (Leppik et al., 2011) and a controlled study on i.v. levetiracetam for treatment of SE in dogs has been published (Hardy et al., 2012). 3.3. Models where the resistance develops over time (e.g., multiple-hit models) Blanco and colleagues (Blanco et al., 2009) performed an acute MES or acute PTZ seizure test four weeks following a pilocarpineinduced SE in male Wistar rats. Whereas the efficacy of valproate, phenobarbital, and phenytoin in the MES test was not affected by previous SE, the response to all ASDs was significantly reduced in the PTZ test in comparison to a control group without pilocarpine administration (Table 2). The authors proposed that the induction of acute seizures with PTZ, but not with MES, in animals pretreated with pilocarpine might constitute an effective and valuable method to screen ASDs and to study mechanisms involved in pharmacoresistant TLE. If this finding can be reproduced, the strategy proposed by Blanco et al. (2009) would simplify the use of epileptic rodents in ASD testing, because induction of acute seizures in epileptic animals does not necessitate prolonged continuous (24/7) videoEEG recording that is usually needed to determine drug effects on spontaneous seizures. Based on the idea of the “two-hit model” approach reported by Blanco et al. (2009), we recently studied whether using the 6-Hz test in mice that have been made epileptic by pilocarpine increases the ASD resistance of this test (Bankstahl et al., 2013). To our surprise, 6-Hz seizures were not more resistant to ASDs (phenobarbital, retigabine, levetiracetam) in epileptic vs. nonepileptic mice, but instead showed an increased sensitivity to the anti-seizure effects of levetiracetam in epileptic animals. In a subsequently published study, Leclercq and Kaminski (2015b) confirmed the enhanced anti-seizure effect of levetiracetam on 6-Hz seizures in post-SE mice, while carbamazepine was less effective on 6-Hz seizures in pilocarpine mice vs. controls (Table 2). Interestingly, in the experiments reported by Leclercq and Kaminski (2015b), the anti-seizure effect of ASDs (phenytoin, carbamazepine, perampanel, levetiracetam, diazepam) in the 6-Hz test in post-SE mice depended on the mechanism of action and varied with time after SE. Early onset and infantile epileptic encephalopathies are usually associated with medically intractable or difficult to treat epileptic seizures and prominent cognitive, neurodevelopmental and behavioral consequences (Galanopoulou and Moshe, 2015). Several acute and chronic rodent models of infantile spasms have emerged that recapitulate various aspects of the disease and can be used for drug testing (Galanopoulou and Moshe, 2015). The chronic models include the multiple-hit rat model of infantile spasms shown in Table 2. This model was designed to mimic the more refractory form of infantile spasms due to structural lesions. The induction method utilized in this model included right intracerebroventricular infusions of the cytotoxic agent doxorubicin and right intracortical infusion of lipopolysaccharide to target white matter, both given on PN3 (Scantlebury et al., 2010). The multiplehit model has been extensively used for screening for new therapies given after the onset of spasms, as in clinical practice (Galanopoulou and Moshe, 2015). Similar to the infantile spasms from structural lesions, the observed spasms are more refractory to current therapies: ACTH has no effect while vigabatrin transiently reduces spasms (Scantlebury et al., 2010). Treatments that are not effective in human infantile spasms, like phenytoin, have no effect in this model. Promising effects have been found with carisbamate, the vigabatrin analog CPP-115, and the mTOR inhibitor rapamycin (Table 2). However, all these two-hit models are not models of progressive medically refractory epilepsy. Studies from epilepsy surgery cen-

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ters indicate that many patients with mesial TLE, and perhaps also focal neocortical epilepsy, who ultimately come to surgery, are initially drug-responsive, and become refractory over long periods of time, suggesting a progressive disorder (Berg et al., 2003; Schmidt and Löscher, 2005). Progression is a common scenario that needs to be better understood before it can be duplicated in the animal laboratory. Similar progression is demonstrated in animals kindled over long periods of time (“overkindling”), during which they eventually develop spontaneous seizures (Gorter et al., 2015); however, this is not a cost-effective model for screening drugs. Similarly, progression has been demonstrated in models of post-SE TLE (Gorter et al., 2015), but, again, these are too laborious models for drug screening.

3.4. Models for discovery of novel drugs for refractory status epilepticus Refractory SE that persists despite adequate administration of benzodiazepines and at least one ASD is an important and serious clinical problem with a mortality of up to 40% and the risk of severe long-term consequences (Rossetti and Lowenstein, 2011). Animal models of refractory SE are thus important to develop new therapies for this major medical emergency (Löscher, 2015). The pilocarpine, lithium-pilocarpine and kainate rodent models are widely used in this respect. In these models, resistance to benzodiazepines and other ASDs develops during SE so that SE that is longer than 30 min is difficult to suppress (Wasterlain and Chen, 2008; Löscher, 2015). Internalization of synaptic GABAA receptors during SE is thought to be a major cause of treatment failure, because synaptic GABAA receptor are the target for benzodiazepines and several other ASDs (Wasterlain et al., 2009). However, extrasynaptic GABAA receptors, which mediate tonic inhibition, are not internalized and could therefore represent a target for new SE therapies (Rogawski et al., 2013). In this respect, GABAA receptor positive allosteric modulator neuroactive steroids, such as allopregnanolone, are of interest, because, in contrast to benzodiazepines, they potentiate both synaptic and extrasynaptic GABAA receptors (Rogawski et al., 2013). Using a pharmacoresistant kainate pediatric model, Rogawski et al. (2013) demonstrated that when administered 40 min after kainate, only 25% of the animals that had received diazepam were SE-free, whereas all of the animals that had received allopregnanolone were SE-free at this time point. Based on this and various other observations in animal models (Rogawski et al., 2013) and early extremely promising anecdotal data in patients (Vaitkevicius et al., 2013; Broomall et al., 2014), allopregnanolone advanced to clinical trials in patients with refractory SE (Bialer et al., 2015). Allopregnanolone is a good example how fit-for-purpose models, in this case rodents models of refractory SE, can relatively rapidly lead to novel treatments. Another example from my group is the development of drug cocktails for termination of refractory SE (Brandt et al., 2015; Löscher, 2015). When using SE models for testing a drug’s efficacy to terminate SE, an important difference to drug testing for anti-seizure efficacy in other models, such as MES, PTZ or kindled seizures, has to be noted. In conventional seizure tests used for drug screening (see Section 2) and most other seizure models used in drug discovery, a drug is given at some time (e.g., 30 or 60 min) before induction of seizures, and effects on seizures are recorded after administration of different doses of the drug to allow calculation of anti-seizure ED50 s as shown in Table 1. In contrast, in studies on anti-seizure drugs in SE models, the drug is typically given after SE has been induced. This leads to important differences in drug efficacy, because many drugs are much more effective to suppress SE when administered before SE compared to administration some

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time after SE has started, which is in line with the hypothesis that SE may change the accessibility of drug targets (see above). 4. Changes of efficacy during chronic drug administration In most seizure models, investigational drugs are tested after administration of a single dose and the drug effect is then determined at one fixed time point (e.g., 30 min) following drug administration. However, treatment of patients with epilepsy is typically by chronic, daily drug administration, which may change drug efficacy. There are several different scenarios in this respect. (1) With several ASDs, particularly benzodiazepines, the antiseizure efficacy decreases during prolonged treatment due to development of adaptive processes (‘functional tolerance’) in the brain (Löscher and Schmidt, 2006). With some older ASDs, such as phenobarbital, carbamazepine or phenytoin, also “metabolic tolerance” may occur due to enhanced drug elimination by induction of ASD metabolizing enzymes. Tolerance is clinically advantageous when it concerns the adverse effects of ASDs but disadvantageous when it involves the antiepileptic efficacy itself. In mice and rats, tolerance to the anti-seizure and adverse effect of benzodiazepines and various other ASDs can be demonstrated in a variety of models of seizures or epilepsy with 1–4 weeks of daily drug administration, provided that effective drug concentrations are maintained during treatment (Löscher and Schmidt, 2006). In addition to benzodiazepines, evidence for loss of efficacy of those old and new ASDs for which functional tolerance was shown in animal models has also been reported in a small portion of patients with epilepsy, which should be taken into account when considering mechanisms of ASD resistance (Löscher and Schmidt, 2006). (2) With some drugs the anti-seizure efficacy may increase during prolonged treatment (Löscher and Schmidt, 1988); examples are primidone (due to accumulation of phenobarbital), valproic acid (reasons are unknown), and vigabatrin (due to accumulation of GABA by irreversible inhibition of its degradation). Consequently, determination of acute potency of such drugs underestimates their potency during prolonged treatment and, in case of new compounds, may thus lead to false decisions with respect to further preclinical or clinical development (Löscher, 2007). 5. Prediction of potential serious adverse reactions and their mechanisms Adverse effects of ASDs are common, can have a considerable impact on quality of life, and contribute to treatment failure in up to 40% of patients (Perucca and Meador, 2005). These include issues with CNS tolerability, hypersensitivity reactions and weight gain. Modern ASDs manifest these adverse events to varying degree but all reveal issues with CNS tolerability (Schmidt, 2009). This is likely because all current ASDs have been developed to counteract hyperexcitability of neurons, targeting mechanisms that also interfere with normal neurotransmission, which explains why they all, to a large extent, are associated with similar issues on CNS tolerability as doses are increased (Löscher et al., 2013). Furthermore, the classical preclinical screening models such as the MES and PTZ tests have consistently selected drugs with significant CNS side effects, apparently as a result of the models identifying compounds with specific molecular targets (Meldrum, 2002). The only exception seems to be levetiracetam, which was devoid of anti-seizure activity in the conventional screening models and has been shown to be well tolerated in preclinical testing and clinical studies (Klitgaard and Verdru, 2007). Another important aspect that may help to develop better tolerated ASDs is that epilepsy is associated with multiple changes in the function and subunit composition of ion channels and recep-

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tor molecules. This may not only result in loss of efficacy of drugs acting on such targets, but may also change their adverse effect profile (Löscher and Hönack, 1991; Löscher and Schmidt, 1994; Klitgaard et al., 2002; Meldrum, 2002). An early example illustrating this problem is that of the competitive antagonists of the NMDA subtype of glutamate receptors, which were well tolerated in healthy volunteers but induced serious CNS adverse effects in patients with epilepsy (Löscher and Schmidt, 1994). This enhanced potential for NMDA antagonists to induce severe adverse effects in epilepsy was correctly predicted in kindled rats, i.e., a chronic model of epileptogenesis, but not in nonepileptic rodents (Löscher and Hönack, 1991; Löscher and Schmidt, 1994). Thus, kindled or epileptic animals should be included in preclinical adverse effect testing (Klitgaard et al., 2002; Meldrum, 2002; Löscher et al., 2013), and for a comprehensive assessment of drug-induced impact on CNS function models beyond the classical rotarod test should be used. Future ASD discovery programs combining a focus on diseasespecific mechanisms with adverse effect testing in epileptic animals using relevant paradigms for CNS function seem to hold a promising potential for the identification of new epilepsy therapies with fewer adverse effects (Löscher et al., 2013). A difficult issue relates to the risk for serious adverse events that may only be discovered late in the adoption of new ASDs, such as idiosyncratic events or toxic effects that are difficult to identify and predict from preclinical development programs. Felbamate, vigabatrin, and most recently retigabine, are relevant examples. With respect to such adverse effects, the emerging evidence for the role of polymorphisms will certainly have a positive impact and could result in personalised medicine, whereby administration of the drug and dosage is tailored to an individual genotype (Löscher et al., 2013). 6. Inclusion of pharmacokinetic analyses 6.1. Preclinical pharmacokinetics Advances in preclinical in vitro and in vivo methodologies for predicting human pharmacokinetics have served to significantly reduce the drug failure rate due to poor pharmacokinetics, which was identified as a main cause for drug attrition in the early 1990s (Schuck et al., 2015). Furthermore, preclinical pharmacokinetic/pharmacodynamic modeling and simulation are essential parts of any drug development in the pharmaceutical industry (Schuck et al., 2015). In animal experiments, pharmacokinetic analyses allow to control whether a drug is sufficiently absorbed from injection sites, reaches the target organ – in the case of ASDs the brain – and whether active metabolites are involved in its action. However, pharmacokinetic principles are only rarely applied by academic scientists in studies on novel drugs or targets. As long as pharmacokinetic experiments are not performed, it is not possible to compare data between models and laboratories. For instance, differences in drug vehicles, the use of drug suspensions instead of solutions, and many other variables can lead to marked differences in a drug’s absorption and distribution and thus critically contribute to the lack of reproducibility of data (Löscher, 2007). 6.2. Estimation of effective plasma concentrations of new ASDs for first clinical trials Clinicians are often prone to dismiss animal data because the doses are so different to achieve the same effect in patients. Indeed, because rodents eliminate most drugs much more rapidly than humans, anti-seizure doses of ASDs (in mg/kg body weight) are usually much higher in rodent models of seizures or epilepsy than effective doses in epilepsy patients (Löscher, 2007). However,

determination of ASD plasma levels associated with anti-seizure effects in rodents after acute or chronic drug administration has demonstrated that effective plasma ASD levels are often remarkably similar in humans and rats (Löscher et al., 1991a,b; Löscher, 2007), which is illustrated in Fig. 8. To our knowledge, Brodie and Reid (1969) were the first to propose that, although the dose of drug necessary to produce the same effect may differ considerably between species, the plasma concentration is usually quite similar. This proposal became known as the “Brodie-Reid-Hypothesis” (Woodbury, 1983). Thus, plasma levels determined at time of anti-seizure effect in rodent models can be used for selecting adequate doses of a new ASD for first clinical trials by calculating the doses that will produce such plasma levels in humans (Löscher, 2011). This approach is key when a drug candidate is identified by screening and the mechanism of action is still unknown, whereas rationally designed drug candidates can rely more on biomarkers (e.g., PET) for identification of doses in humans that result in the same target occupancy that provided activity in the animal models. With respect to the ASDs shown in Fig. 8, there is, however, one exception, which is that the drug plasma levels associated with the anti-seizure effect of valproate in the MES and PTZ tests (∼200–300 ␮g/ml) are much higher than levels associated with its effect during chronic administration (50–100 ␮g/ml) both in models and in patients. This can be explained by the increase of valproate’s anti-seizure activity during prolonged treatment (see Section 4). Of course, many other details, including toxicity, have to be dealt with when selecting doses of an investigational drug for first use in humans, but, in view of the critical problems associated with dose finding in epilepsy patients, the information obtained by plasma level determinations in preclinical seizure models should be considered. Furthermore, even after a novel ASD has been approved for clinical use, animal models may be a better way to establish a therapeutic range than the not very precise current approach to look over a set of clinical trial data and then come up with a best estimate of a therapeutic range. In addition, the pharmacokinetics of ASD entry into the brain can best be studied by animal models. With approaches to new drug discovery increasingly being based on rational drug design, PET ligand development will often be associated with discovery of a drug candidate for a specific mechanism and this will permit PET studies to provide strong guidance for translation of preclinical data to clinical dose ranges. Furthermore, the advent of “humanized mice” that replicate many aspects of human drug metabolism provides invaluable experimental models that circumvent the limitation of mouse vs. human differences in drug metabolism to a considerable degree (Muruganandan and Sinal, 2008). 6.3. Animal models of epilepsy-associated blood-brain barrier dysfunction and its effect on drug distribution Seizures may transiently disturb the integrity or function of the BBB, both in animal models and patients with epilepsy, leading to increased permeability of the BBB (Cornford and Oldendorf, 1986; Duncan and Todd, 1991; Oby and Janigro, 2006; Friedman et al., 2009). It has been argued that seizure-induced overexpression of efflux transporters such as Pgp at endothelial cells and perivascular astrocytic end feet of the BBB is a “second line defense” mechanism to compensate for the transient leakiness of the BBB in response to seizures (Abbott et al., 2002; Löscher, 2005). However, it is important to ask if seizure-induced impairment of BBB integrity affects the brain penetration of ASDs. If so, pharmacokinetic studies in normal, non-epileptic rodents would result in misleading data on brain drug distribution and target engagement. Thus, this is an important aspect in any preclinical drug development strategy. Seizure-induced changes of BBB function and of cerebral blood perfusion have been repeatedly postulated to influence access of

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Fig. 8. Effective plasma concentrations of ASDs in rodent models of seizures or epilepsy and patients with epilepsy. Data from acute seizure models were determined at time of the anti-seizure effect following administration of ED50 s in the MES and sc PTZ tests in mice or rats. Data from chronic seizure models were determined in either the kindling model or rat models with spontaneous recurrent seizures. For all illustrated ASDs, a similar effective concentration range as in patients was found in animal models except for the anti-seizure effect of valproate in the acute MES and PTZ tests, in which much higher plasma drug concentrations (∼200–300 ␮g/ml) are needed for anti-seizure activity (not illustrated). Also, effective plasma concentrations of ethosuximide in seizure models were a bit above the therapeutic range in humans. Importantly, because of the more rapid elimination of ASDs in rodents, anti-seizure doses are considerably higher than in humans. Data are from Löscher (2007) and Patsalos et al. (2008).

ASDs to the epileptic tissue (e.g., Duncan and Todd, 1991; Carvey, 1998). The main consequences of seizure-induced BBB leakage are extravasation of albumin and increased brain uptake of highly protein-bound hydrophilic dyes (e.g., Evans blue), hydrophilic drugs, or contrast agents (e.g., gadolinium), which normally do not enter the brain (Cornford and Oldendorf, 1986; Kroll and Neuwelt, 1998; van Vliet et al., 2007). Thus, the BBB does not generally open (as suggested by terms such as “damage”, “disruption” or “breakdown”), but the “leakiness” is highly specific and thought to be mediated, at least in part, by enhanced micropinocytosis through endothelial cells, while the tight junctions may remain intact (Petito et al., 1977; Nitsch et al., 1986; Duncan and Todd, 1991). Furthermore, in patients with chronic epilepsy, thickening of the brain capillary basement membrane has been observed (Duncan and Todd, 1991), so that it is difficult to speculate how these complex seizure-induced alterations in the BBB affect ASD penetration into the brain. To our knowledge, only two studies have investigated the consequences of BBB disruption on ASD penetration into the brain (Marchi et al., 2009; Potschka et al., 2011). In our study (Potschka et al., 2011), which was prompted by earlier observations that extracellular brain levels of phenytoin are lower in kindled than non-kindled rats (Potschka and Löscher, 2002), microdialysis experiments were performed in amygdala-kindled rats and electrode-implanted, nonkindled rats with the microdialysis probe located directly adjacent to the stimulation/recording depth electrode in the amygdala. Penetration of phenytoin to the extracellular fluid in the focus region was investigated by microdialysis at different time points in relation to seizure activity elicited in kindled rats. Access of phenytoin to the kindled focus proved to be comparable in kindled rats two hours or fourteen days following a single generalized seizure compared to non-kindled, electrode-implanted control rats. When a single generalized seizure was elicited l0 min following phenytoin administration, average phenytoin brain dialysate levels were lower (up to 44%) than those of control animals. During self-sustained SE, which was induced by 30 min of electrical stimulation of the amyg-

dala and lasted 3 h, phenytoin access to the site of seizure initiation tended to be lower in the early phase following drug administration, but reached the control level two hours later. BBB disruption was demonstrated by enhanced brain uptake of Evans Blue. These data clearly demonstrate that seizure-induced alterations in BBB integrity and function do not increase functionally relevant brain levels of phenytoin in dialysates of the affected brain region, but rather decrease the free (non-protein bound) concentration of phenytoin in the extracellular compartment. Marchi et al. (2009) studied the effects of impaired BBB integrity on brain distribution of hydrophilic (deoxyglucose and sucrose) and lipophilic (phenytoin and diazepam) molecules. They tested the hypothesis that lipophilic and hydrophilic drug distribution is differentially affected by BBB damage. In vivo BBB disruption was performed in rats by intracarotid injection of hyperosmotic mannitol. Drugs were measured and correlated with brain water content and protein extravasation. BBB disruption resulted in extravasation of serum albumin and radiolabeled drugs. Total drug permeability increase was greater for lipophilic than hydrophilic compounds. However, BBB disruption markedly reduced the amount of free phenytoin in the brain, which is comparable to our microdialysis findings with phenytoin after seizure-induced BBB disruption (Potschka et al., 2011). Marchi et al. (2009) concluded that after BBB disruption, drug binding to protein is the main controller of total brain drug accumulation. Osmotic BBB disruption increased serum protein extravasation and reduced free phenytoin brain levels. Thus two studies independently found that the functionally relevant free concentration of the ASD phenytoin is reduced after BBB disruption (Marchi et al., 2009; Potschka et al., 2011), which is in contrast to the often suggested notion that BBB disruption should increase brain penetration of ASDs. As a consequence, kindled or epileptic rodents should be included in preclinical pharmacokinetic studies. While these studies suggest that seizure-induced BBB dysfunction does not necessarily increase functionally relevant brain levels of clinically approved or investigational ASDs, the increased extravasation of albumin associated with the BBB dysfunction may

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critically contribute to epileptogenesis (Heinemann et al., 2012). Thus, targeting the mechanisms of this albumin extravasation or the effects of albumin in the brain may provide interesting novel antiepileptogenic drugs. This has elegantly been demonstrated in a recent study in animal models, showing that the angiotensin II type 1 receptor antagonist, losartan, blocks albumin-induced inflammatory TGF-␤ signaling in the brain and prevents epilepsy in the albumin or BBB breakdown models of epileptogenesis (Bar-Klein et al., 2014). 7. Mechanism of action and drug target engagement In addition to in vitro preparations for studying mechanisms of action of ASDs, animal models are used for this purpose, for instance when studying long-term changes in neuronal networks and the way that ASDs work on networks to suppress seizures. Furthermore, the drug target engagement in the target tissue (the brain in the case of ASDs) can only be studied in vivo and is important to link preclinical to clinical readouts (Durham and Blanco, 2015). Demonstration of target engagement for an investigational drug is essential to unequivocally establish the validity of a given target for a specific disease indication (Durham and Blanco, 2015). There is a wide variety of methods to measure target engagement biomarkers. Imaging techniques like PET have received great attention since they can enable non-invasive target engagement assays compatible with human clinical studies (Durham and Blanco, 2015). Furthermore, animal models are important to establish new targets for epilepsy treatment (Löscher et al., 2013). For instance, data from models of neuroinflammation and epilepsy in animals may translate to further study of human inflammation-related epilepsies and their treatment (Vezzani et al., 2016). 8. Stratification In the absence of robust biomarkers of epilepsy or epileptogenesis other than seizures (see Section 9), the potential success of novel therapeutic agents for epilepsy is largely unknown until the drugs enter late-stage clinical studies. In order to increase the efficiency of future clinical development there is, therefore, a need to identify biomarkers of drug response that allow early prediction of efficacy and markers to aid the stratification of the patient population. Stratifying for patient selection in targeted trials will be essential to demonstrate efficacy for any novel drug emerging from hypothesis-driven, target-based drug design (Löscher et al., 2013). This is supported by the complexity of the epilepsies and underlying etiologies implying that stratification just for seizure type will not be sufficient. A relatively traditional approach is based on in vivo neuroimaging markers of subtle structural brain abnormalities to improve sample stratification in human clinical trials and potentially extend the range of patients that might benefit from treatment. A more modern approach is application of hypothesis-free analytical approaches (the ‘omics’) to well-defined phenotypes, which would lead to the stratification of the epilepsies along causal pathways. Identification of novel biomarkers linked to phenotypically well classified patients would be essential to provide a stratified approach to disease management beyond simple disease severity and causal pathways. For instance, if a certain mechanism is dysregulated and involved in a chronic, drug refractory condition one could perform imaging studies to select patients that reveal this dysregulation and test the drug candidate that target this mechanism in these patients. If at the same time PET studies are performed with a ligand specific for this mechanism (and this PET ligand may actually be used both for measuring target engagement and stratifying patients) one could ensure that the drug candidate

can provide sufficient target engagement and select doses for the clinical study that are known to correlate optimal efficacy in the preclinical models. This would permit performance of a clinical study that could determine whether or not modulation of a specific mechanism is relevant for improving seizure control in such a specified subpopulation of patients. Before any clinical study, all these strategies should also be used to stratify and characterize drug response in animal models. This is because, as noted above, not all animals in a model behave the same and subgroups likely represent different pheno- and/or genotypes of the epilepsy, which likely would affect pharmacological responses.

9. Identification of biomarkers in animal models Validated biomarkers of epileptogenesis and ictogenesis, as suggested by Engel et al. (2013), could be used to “(a) predict the development of an epilepsy condition, (b) identify the presence and severity of tissue capable of generating spontaneous seizures, (c) measure progression after the condition is established, (d) create animal models for more cost-effective screening of potential antiepileptogenic and anti-seizure drugs and devices, and (e) reduce the cost of clinical trials of potential antiepileptogenic interventions by enriching the trial population with patients at high risk for developing epilepsy.” In this regard, the wide range of etiologically relevant animal epilepsy models could play an important role in the identification and validation of biomarkers (White and Löscher, 2014). Furthermore, in addition to the potential uses of biomarkers described above, they could serve to identify potential mechanisms of ASD-resistance in an individual patient and thus guide personalised treatment. Information gleaned from animal studies could be translated to the appropriate human population. Similarly, coincident development of biomarkers in patients with epilepsy can then be back-translated to animal studies and utilized for therapy discovery. The importance of having access to validated biomarkers cannot be understated and efforts at both the clinical and preclinical level to achieve this goal are clearly required (White and Löscher, 2014). At present, the only robust biomarker for diagnosis of epilepsy is the occurrence of a seizure, whereas reliable biomarkers indicating development of epilepsy before seizure onset do not exist. In recent years, a number of biomarkers have emerged that may provide predictive insight into the process of epileptogenesis (Engel et al., 2013; White and Löscher, 2014). These include alterations in hippocampal MRI images as suggested by the FEBSTAT study, presence of interictal spikes and high-frequency oscillations in the EEG, altered seizure threshold, and the presence of blood markers suggestive of brain inflammation or neurodegeneration. In addition, genetic biomarkers have the potential to predict outcome and inform treatment for the selected patient population. With respect to drug resistance, the recent example described in Section 3.2.2 illustrates how a PET imaging method aimed to identify increased functionality of Pgp is developed in a fit-for-purpose animal model (Bankstahl et al., 2011) and then successfully translated to patients (Feldmann et al., 2013). For further progress in the identification of biomarkers of epileptogenesis, animal models need to be refined, so that not all animals in a model develop spontaneous seizures, but only part of them. For instance, as recently demonstrated for the pilocarpine model of TLE, this is possible by reducing the duration of SE by a drug cocktail which irreversibly terminates SE (Brandt et al., 2015). Studies using this refined model have indicated that, because of the complexity of epileptogenesis, it is unlikely that a single biomarker is sufficient for predicting development of epilepsy, but a combinatorial approach may be needed to overcome the challenge of individual variability and disease heterogeneity (Bröer and Löscher, 2015). In the latter

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study we found that a combinatorial assessment of changes in PTZ seizure threshold and behavioral alterations discriminate rats that do and do not develop epilepsy after SE with high selectivity and sensitivity. Slow intravenous infusion of PTZ has been shown to be a safe technique for determining seizure threshold in patients (Calcagni et al., 2002; Barba et al., 2007; Barba et al., 2012). An alternative, noninvasive method of determining alterations in brain excitability following brain insults in both laboratory animals and patients are cortical excitability measures on transcranial magnetic stimulation (TMS) (Hsieh et al., 2012; Bauer et al., 2014; Kimiskidis et al., 2014).

10. Models for discovery of antiepileptogenic or disease-modifying treatments The search for new ASDs has traditionally been directed to compounds that suppress seizures in a symptomatic fashion. There is no clinical evidence that any ASD is capable of preventing or modifying epilepsy after brain insults, such as traumatic brain injury (TBI) or stroke, although only relatively few ASDs have been investigated in this respect in clinical trials (Löscher and Brandt, 2010a; Löscher et al., 2015). In view of the complex molecular, morphological and functional alterations that are induced by brain insults and thought to be involved in the epileptogenic process leading to epilepsy, drugs that interfere with these alterations will most likely act by other mechanisms than ASDs that suppress seizures. As yet, prevention of epilepsy in patients at risk is an unmet clinical need, but various strategies for epilepsy prevention or disease-modification are being evaluated in animal models. The most widely used models in this respect are kindling, post-SE models of TLE, and models of TBI (Stables et al., 2002; Kharatishvili and Pitkänen, 2010; Löscher and Brandt, 2010a). Drug testing in such models sharply differs from testing of novel ASDs, in that drugs with potential anti-epileptogenic efficacy are tested immediately after the brain insult, before spontaneous seizures occur (Löscher and Brandt, 2010a). There is an enormous effort by several groups in the field to develop new strategies for antiepileptogenesis, and the progressively enhanced understanding of mechanisms underlying epileptogenesis will hopefully soon lead to effective treatments (Löscher et al., 2013). However, the choice of adequate animal models will be essential for testing such strategies. Most studies to date use SE models in this respect, which may lead to translational problems, because SE models are induced in healthy animals without an underlying etiology, which is uncommon for SE in humans. Furthermore, SE is a relatively rare cause of epilepsy in humans (Löscher et al., 2015). Probably even more importantly, in traditional SE models in rodents, such as the pilocarpine and kainate models with systemic administration of high doses of these convulsants, most of the animals develop epilepsy, which is associated with widespread brain damage and almost not latent period between SE and onset of seizures, so that the sensitivity of such models to identify antiepileptogenic drugs may be low (Löscher et al., 2015). This may explain the failure of innumerous studies in such models to prevent or significantly modify epilepsy after SE (Löscher and Brandt, 2010a; Löscher et al., 2015) More recently, refined SE models have been used, resulting in proof-of-concept that prevention of epilepsy after brain injury is possible (White and Löscher, 2014). Liu et al. (2013) used a mouse model of SE-induced epilepsy, in which kainate is injected into the basolateral amygdala, leading to limbic SE (which is interrupted after 40 min by diazepam and lorazepam) and development of spontaneous recurrent seizures after a seizure-free latent period of several days. They showed that inhibition of the SE-induced activation of brain-derived neurotrophic factor (BDNF) receptor TrkB could prevent spontaneous seizures, ameliorate anxiety-like

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behavior, and limit loss of hippocampal neurons when tested weeks to months later (Liu et al., 2013). Furthermore antiepileptogenic drug effects also have been demonstrated by using models in which brain injury has been induced by other methods than SE. In a TBI model in rats, mild passive focal cooling following rostral parasagittal fluid percussion injury (FPI) in rats was associated with potent and persistent prevention and modification of epileptic seizures (D’Ambrosio et al., 2013); in contrast, the experimental ASD carisbamate was without effect on epileptogenesis in this model (Eastman et al., 2011). Antiepileptogenic drug effects also have been demonstrated in genetic animal models of epilepsy and in neonatal models (Löscher and Brandt, 2010a; White and Löscher, 2014). For instance, Koyama et al. (2012) reported that bumetanide prevents the development of epilepsy after febrile seizures in neonatal rats and Lippman-Bell et al. (2013) reported similar findings when administering the AMPA receptor-antagonist NBQX after hypoxia-induced neonatal seizures. However, it has to be kept in mind that none of the models used in the search for antiepileptogenic drugs has been validated as yet (White and Löscher, 2014). To this point, clinical validation will not be provided until that first truly ‘antiepileptic’ or ‘disease modifying’ therapy, identified in a specific animal model, is proven effective in an appropriately designed clinical trial. Having said this, the community should not be discouraged from pursing this approach but should be aware of the limitations of the existing models and employ caution when designing preclinical studies and interpreting the results obtained. An unresolved question is why the same initial brain insult, e.g., a TBI or stroke, does induce epilepsy in some but not the majority of patients (Löscher et al., 2015). Several crucial modifiers are discussed in this respect, including genetic predispositions or susceptibility genes that undoubtedly account for differences in the ability of potential epileptogenic lesions to cause acquired epilepsy from one individual to another, and most likely also determine, to a certain extent, whether epilepsy will be medically refractory (Engel and Dichter, 2014). Identification of these susceptibility genes will be important for further development and validation of accurate animal models of acquired human epilepsy.

11. Multicenter clinical trials of novel drugs in rodent models? A major concern in the development of new therapies is that results from preclinical target validation or drug testing studies are often not replicable in independent studies (Prinz et al., 2011; Landis et al., 2012; O’Brien et al., 2013; Simonato et al., 2013). An industry rule alleges that at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting. Although these issues are not specific to ASDs, the failure of the many new ASDs to demonstrate increased efficacy over the established medications is an important factor that negatively influences the development of new antiepileptic therapies by industry (O’Brien et al., 2013). The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Working Groups joint meeting to optimize preclinical epilepsy therapy discovery (Galanopoulou et al., 2013) proposed a potential solution to address this problem by implementing a “phase II” multicenter preclinical trial paradigm modeled on the methodology used for double-blinded multicenter clinical trials (O’Brien et al., 2013). This idea is not new but was proposed by Treiman almost 15 years ago (Treiman, 2002). Preclinical phase II multicenter trials would follow the current standard proof-of-concept preclinical studies (“phase I” or early “phase II”), which are typically generated by an investigator, group of investigators, or a company, and indicate a potential treatment may possess promising efficacy as an

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anti-seizure or antiepileptogenic therapy. Phase II preclinical trials would be adequately powered and executed to detect clinically relevant differences in efficacy, involving a larger number of animals than can be achieved in single laboratory studies. This approach would reduce biases related to individual laboratory practices and conditions, implement rigorous blinding and statistical design, and incorporate independent monitoring of data collection and analysis (O’Brien et al., 2013).Thus, the organization of multicenter preclinical studies modeled on phase 2 or 3 clinical trials might ease translation and reduce the risk of failure of subsequent clinical studies (Simonato et al., 2014). The successful implementation of this interesting idea would require an alignment and collaboration from government agencies, industry, and academia (O’Brien et al., 2013). Of course, as other strategies, such a new approach would need to be validated. One may wonder whether this approach will be feasible at the end and really resolve the major problems in translating animal data to the clinic. Instead, performing the work at many different sites may only increase the variance of the animal experiments. Typically, preclinical and clinical drug development takes place within the pharmaceutical industry (or in collaboration with contract research organizations with relevant models and, potentially, academic collaborators), and no company would progress to clinical phase I or early phase II proof-of-concept trials in patients without robust reproduction of all preclinical efficacy studies. Furthermore, there are many more problems during development of novel ASDs, including, as with other drugs, the failure of about 50% of drugs in clinical phase III, although these drugs had been found to be effective in phase II (Arrowsmith, 2011; Arrowsmith and Miller, 2013). In fact, failure rates for both phase II and phase III clinical trials have risen, primarily because of poor showing against placebos (Friedman and French, 2012). 12. Emerging models Describing all available animal models of seizures and epilepsy is beyond the scope of this review. However, there are several more recent models that deserve to be mentioned. The following list of models is not thought to be complete but just to highlight interesting examples. 12.1. Models of pediatric epilepsies Most models discussed so far are typically performed in young adult rats or mice. However, the pathophysiology and hence pharmacology of epileptogenesis in the developing brain may differ markedly (Bender and Baram, 2007; Wasterlain et al., 2013). Several epilepsy or seizure models in neonatal rats or mice, including models of febrile seizures and infantile spasms (Table 2), have been described and are increasingly being used for developing new therapies (Auvin et al., 2012; Kandratavicius et al., 2014; White and Löscher, 2014; Galanopoulou and Moshe, 2015). However, continuous video/EEG monitoring in models of neonatal brain injury is technically demanding and associated with limitations and problems (Löscher et al., 2015). 12.2. Genetic animal models of epilepsy Another interesting area is the use of genetic animal models of epilepsy (White and Löscher, 2014). Several genetic rodent models, with known mutations, have emerged that recapitulate many important characteristics of human epilepsy. These models are providing essential insight into the role of a specific mutation in ictogenesis and epileptogenesis. Furthermore, many of these models are emerging as important tools for validating novel targets for the treatment and prevention of epilepsy. One example here is the use of models of channelopathies to predict efficacy

of ASDs in the increasing number of human epilepsies associated with channel dysfunction (Guerrini et al., 2014; White and Löscher, 2014). Furthermore, genetic models are interesting tools to study antiepileptogenic or disease-modifying drug potential as exemplified by interesting data on ethosuximide, levetiracetam, and zonisamide. Thus, in the WAG/Rij rat model of absence epilepsy, early prophylactic treatment with ethosuximide, levetiracetam or zonisamide (but not carbamazepine) before the onset of SWDs in the EEG suppressed the development of such absence-like seizures (Blumenfeld et al., 2008; Russo et al., 2010; Russo et al., 2011). This phenomenon was subsequently also observed in the GAERS model of absence epilepsy (Dezsi et al., 2013). These findings suggest that models are available in which epileptogenesis can be controlled and that early treatment during development may provide a strategy for preventing genetic epilepsy in susceptible individuals. 12.3. Models for infection-induced epilepsy As discussed above, with respect to epilepsies developing after different types of brain injury, it is important to develop models apart from the widely used post-SE models of TLE. Infections and infestations of the brain are among the most common risk factors for seizures and acquired epilepsy, but until recently no animal models for infection-induced epilepsy were available because most infectious agents are associated with high mortality in rodents (Vezzani et al., 2016). One exception is a recently developed mouse model of virus encephalitis that mirrors many of the features of viral encephalitis-induced epilepsy in humans (Libbey and Fujinami, 2011). In this model, in which encephalitis is induced by intracerebral infection with Theiler’s murine encephalomyelitis virus (TMEV) in C57Bl/6 mice, mice survive the infection and about 50% of them develop early and late seizures and hippocampal damage (Libbey and Fujinami, 2011; Vezzani et al., 2016). The late seizures are not persistent effects of the virus, because the TMEV virus is rapidly cleared from the brain of C57BL/6 mice following infection (Libbey and Fujinami, 2011), but this model leads to a permanent epilepsy, as is so often the case in humans surviving virus-induced encephalitis (Vezzani et al., 2016). Given the importance of brain infection and inflammation as cause of seizures (Vezzani et al., 2016), the TMEV model is important to study how targeting the innate immune response may lead to new therapies. 12.4. Models for studying epilepsy-associated cognitive deficits and psychiatric comorbidities Another important aspect is the increasing use of already established rodent models of epilepsy for studying epilepsy-associated cognitive deficits and psychiatric comorbidities and their prevention or treatment (Löscher et al., 2013; Kandratavicius et al., 2014). Most chronic epilepsy models exhibit such comorbidities, including anxiety- or depression-like abnormalities and impaired learning and memory (Löscher and Brandt, 2010a). Thus, such animal models offer the opportunity to explore shared pathophysiological mechanisms, therapeutic options, and consequences of both the epilepsy syndrome and a given comorbidity (Stafstrom, 2014). 12.5. The zebrafish model In addition to the myriad of rodent seizure and epilepsy models, zebrafish are emerging as a potentially important non-rodent platform system for the early evaluation of anti-seizure activity (Baraban and Löscher, 2014). The ease with which seizure activity, both behavioral and electrographic, induced by either

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chemoconvulsants or genetic mutations can be quantitated make the zebrafish an extremely attractive model for the rapid screening of investigational drugs. Because of its genetic tractability, the zebrafish model is also potentially useful for the study of monogenic epilepsy syndromes. For example, Baraban and colleagues are using the zebrafish system to model some of pediatric epilepsies such as Dravet syndrome that results from mutations in Scn1a (Baraban et al., 2013; Baraban and Löscher, 2014). The rapidity with which known human mutations can be expressed in zebrafish may offer a unique mechanism for assessing purported antiepileptogenic drug and genetic therapies. However, as with other animal models, there are limitations and drawbacks in the zebrafish model (Vela et al., 2014). The main one is inherent to its ability as a model: the potential of zebrafish as an efficient preclinical model of epilepsy or of other diseases in humans remains to be proven. Another is the lack of pharmacokinetic control when administering drugs via the fish tank water. Highly hydrophobic compounds, large molecules, and proteins are not absorbed from the water, but rather need to be injected in the animals, which limits large-scale testing. Further efforts are necessary to fully validate the model (Vela et al., 2014). The zebrafish model does not replace any rodent models described here, but provides a unique platform for targeting genetic mutations involved in epilepsy. Recently, Leclercq et al. (2015) studied the pharmacology of seizures induced by allylglycine in mice and larval zebrafish and proposed the use of this convulsant in zebrafish as a convenient and high-thoughput model of treatment-resistant seizures (Table 2). Allylglycine is an inhibitor of the GABA-synthesizing enzyme glutamic acid decarboxylase (GAD) and long known to cause seizures via decreasing synaptic GABA levels (Alberici et al., 1969). Experiments performed in zebrafish larvae revealed behavioral ASD activity profiles against allylglycine-induced seizures highly analogous to those obtained in mice (Leclercq et al., 2015).

12.6. Patient-derived induced pluripotent stem cells to model epilepsies The landmark work on the induced pluripotent stem cell (iPSC) method by Yamanaka and Takahashi in 2006 redefined the field of translational research by providing access to patient-derived cells for clinical disease studies (Ess, 2013; Du and Parent, 2015; Parent and Anderson, 2015). Neurological disease modeling is especially attractive for iPSC applications with the ability to derive patientspecific neurons for in vitro studies (Ess, 2013). How this approach can be used to model epilepsies has recently been described in detail in several excellent reviews (Ess, 2013; Du and Parent, 2015; Parent and Anderson, 2015). iPSCs can be used to generate human neurons in vitro and to ask specific questions about aberrant neuronal differentiation and function that may underlie disorders such as epilepsy, autism and intellectual disabilities. Such cells can also be employed for assays of neuronal function using electrophysiology. In addition, patient-derived iPSC and derivative cells such as neurons can be employed for higher throughput drug screening as they offer the unique opportunity to develop and test therapies on patient-specific neurons. Using patient-derived iPSCs avoid the genetic background issue and other limitations of genetically modified mice, although human iPSC disease models have their own disadvantages as well (Du and Parent, 2015; Parent and Anderson, 2015). Some of these disadvantages may be overcome, at least in part, by grafting iPSC-derived neural progenitor cells into embryonic rodent brains (or large animal models) to allow them to integrate into developing networks and mature in vivo (Du and Parent, 2015; Parent and Anderson, 2015).

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13. Potential pitfalls of animal models in antiepileptic drug discovery 13.1. The “old models identify old drugs” argument What are the reasons for the apparent failure of previous ASD development to discover drugs with higher efficacy in as yet ASDresistant patients? One major reason is certainly the fact that, with few exceptions, all ASDs have been discovered by the same conventional animal models, particularly the MES test in rodents, which served as a critical gatekeeper (Löscher and Schmidt, 2011; Löscher et al., 2013). These tests have led to useful new ASDs, but obviously did not help developing ASDs with higher efficacy in as yet ASD-resistant patients. This concern is not new but, surprisingly, has largely been unappreciated for several decades. A logical consequence would be to include acute and chronic models of ASDresistant seizures as described in Section 3, but this has started only recently (Wilcox et al., 2013). This should not be restricted to one model, such as the 6-Hz test, but rather should include a battery of models of ASD-resistant seizures to concentrate on drugs that exhibit clear advantages in efficacy over established compounds. 13.2. Seizure types used as endpoints for drug testing in animal models Another argument that has been raised is that the seizure types used as endpoints in the MES, kindling and other models included in current ASD screening programs may usually result in the development of new, but redundant drugs that primarily target convulsive (e.g., tonic-clonic) seizures (D’Ambrosio and Miller, 2010). Thus, during screening of potential ASDs, new agents that may control human complex-partial seizures more effectively than existing ASDs might be missed (D’Ambrosio and Miller, 2010). The kindling or TBI models of focal seizures may have advantages here. For instance, as illustrated in Fig. 4 and discussed in Section 3, even without selecting ASD-resistant and −responsive rats, kindled focal seizures are dramatically less responsive to ASDs than generalized seizures in the MES test, reflecting the clinical situation with these types of seizures. Similarly, spontaneous focal seizures following rostral parasagittal FPI in rats, a well characterized model of TBI, are more resistant to treatment than seizures in acute models of motor seizures such as the MES test (Eastman et al., 2010, 2011, 2015). 13.3. Lack of uniform seizure definition A major caveat of many experimental studies on novel epilepsy treatments is the lack of uniform definitions and classifications of seizures, particularly non-convulsive seizures, and the use of arbitrary definition of seizures based on duration and not on clinically established criteria, which confuses results and complicates translation of findings from animal models to the human (D’Ambrosio and Miller, 2010; Löscher et al., 2015; Walker and Kovac, 2015). As a consequence, there is an ongoing debate on whether the EEG events seen after lateral FPI, perinatal hypoxia or intra-hippocampal kainate in rodent models of acquired epilepsy represent non-convulsive focal epileptic seizures or rather other forms of repetitive, synchronous hyperactivity or oscillatory activity that may even occur in “normal” rodents and are distinct from simple or complex partial seizures (D’Ambrosio and Miller, 2010; Dudek and Bertram, 2010; Rakhade et al., 2011; Lippman-Bell et al., 2013; Löscher et al., 2015; D’Ambrosio et al., 2015; Rodgers et al., 2015a,b). For the FPI model of TBI in rats, D’Ambrosio and colleagues (D’Ambrosio et al., 2009; D’Ambrosio and Miller 2010), reported that the non-convulsive seizures occurring in this model fit the definition of clinical seizure proposed by the ILAE (Fisher et al., 2005).

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In apparent contrast, Rodgers et al. (2015a) have recently reported that SWDs, which oscillate in the same frequency range as the FPI-induced focal seizures reported by D’Ambrosio and colleagues (D’Ambrosio et al., 2005), occur equally in uninjured rats and in rats injured with a novel variant of the FPI procedure, which, however, does not induce post-traumatic epilepsy. They surmised that the bilateral SWDs they observed are identical to the nonconvulsive seizures reported after a conventional FPI injury, and suggested that because these SWDs were observed in most of their rats, they may represent normal brain activity. However, the implicit suggestion that EEG patterns observed in controls must be normal is wrong. Age-dependent development of “absence-like” SWDs has been reported in many laboratory rat strains (Kaplan, 1985; Kelly, 2004; Depaulis and Van Luijtelaar, 2006), but such inherent 8–11 Hz SWDs are distinctive, stereotypic generalized discharges and easily distinguished from lesion-induced ictal discharges in rat TBI models such as the controlled cortical impact model (Kelly et al., 2015). It is possible to select strains, sex and age of rats that are virtually free of these SWDs and then use such animals for conducting acquired epilepsy studies. The same is true for mice. Most mouse strains, including the Swiss, NMRI, FVB/N and C57BL/6 strains that have been used for the intrahippocampal kainate model, do not exhibit spontaneous absence-like SWDs, whereas 6–8 Hz SWDs do occur in DBA/2, A/J and some other inbred mouse strains (Letts et al., 2014). However, in rodents, including strains that do not exhibit inherited spontaneous ictal discharges such as SWDs, the lesion produced by a depth EEG electrode, particularly if implanted in limbic regions such as the amygdala or hippocampus, may induce marked functional alterations in the implanted and adjacent regions, which can cause kindling-like or seizure-like events, most likely as a result of blood-brain barrier disruption resulting in extravasation of albumin, local microhemorrhages or inflammation (Blackwood et al., 1982; Löscher et al., 1995, 1999; Niespodziany et al., 1999; Polikov et al., 2005; McConnell et al., 2009; Hirshler et al., 2010; Bankstahl et al., 2014; Klein et al., 2015). Furthermore, craniotomy alone is sufficient to alter experimental seizures in rats (Forcelli et al., 2013). Thus, appropriate surgical controls are essential when interpreting EEG alterations in rodent models of acquired epilepsy. An ILAE/AES translational Task Force is currently working on the harmonization of video-EEG interpretation and analysis in rodents, both sham control and epileptic animals, with the purpose of increasing the translational value of animal models (Galanopoulou et al., 2013).

13.4. The search for broad spectrum ASDs An important aim of previous R&D efforts was to discover novel ASDs that exert a broad spectrum of activity against different seizure types, i.e., a “one for all”, blockbuster concept (Löscher et al., 2013). Thus, ASDs showing anti-seizure efficacy in a wide range of animal models were often preferred vs. drugs with a narrower spectrum. Indeed, some of the most useful drugs in clinical practice are those with broad spectrum. However, none of these broad spectrum drugs, such as valproate or topiramate, are more efficacious for specific seizure types than narrow spectrum drugs, and for new-onset complex partial seizures carbamazepine was found to be more efficacious than valproate (Mattson et al., 1992; Privitera et al., 2003; Löscher and Schmidt, 2011). In view of the different mechanisms and possibly etiologies underlying diverse types of seizures or epilepsies, there is a growing concern that the broad spectrum concept may not be best suited to identify drugs with higher efficacy in difficult-to-treat patient populations (Löscher et al., 2013). This view is supported by the fact that several new ASDs have shown highly selective efficacy, such as stiripentol for Dravet syndrome, vigabatrin for West syndrome or rapamycin for

seizures in tuberous sclerosis complex – all syndromes which are often resistant to broad spectrum ASDs (Löscher et al., 2013). 13.5. Drug potency vs. efficacy Another important point is that the typical approach of ASD testing in animal models primarily focuses on drug potency and not efficacy (Löscher, 2011). Thus, different investigational drugs are compared in terms of their anti-seizure ED50 s, i.e., the dose suppressing seizures in 50% of the animals, which is calculated from dose-response curves, testing one group of animals per dose. The lower the ED50 , the more potent is the drug, and high potency is often an important argument for selecting drugs for further development. However, it is the antiepileptic efficacy which finally determines the clinical usefulness of a new ASD and should be considered during preclinical drug testing. 13.6. Mechanism of action of ASDs Furthermore, although current strategies of ASD development search for drugs that symptomatically suppress seizures by diverse mechanisms, it is unlikely that anti-seizure efficacy can be markedly enhanced by any of the new mechanisms of seizure suppression of the numerous investigational drugs that are currently in the ASD pipeline (Bialer and White, 2010; Bialer et al., 2015). Instead, one may argue that progress in the efficacy of ASDs, particular with regard to pharmacological treatment of drug-resistant epilepsy, will not be made unless and until we develop drugs that specifically target the underlying disease. Indeed, already in 2001, a workshop organized by the NINDS to explore the current problems, needs, and potential usefulness of existing methods of discovery of new therapies to treat epilepsy patients concluded that the epilepsy research community should undergo a conceptual shift to move away from using models that identify therapies for the symptomatic treatment of epilepsy to those that may be useful for identifying therapies that are more effective in the refractory population and that may ultimately lead to an effective cure in susceptible individuals (Stables et al., 2002). To realize the goal of a cure, the molecular mechanisms of the next generation of therapies must necessarily evolve to include targets that contribute to epileptogenesis and pharmacoresistance in relevant epilepsy models (Löscher et al., 2013). The fit-for-purpose concept discussed in this review will be important for any new drug development strategy. Furthermore, back translation will be important to feedback clinical data for further improving and fine-tuning the predictive value of animal models (Fig. 9). 13.7. Gaps and shortcomings in study design Finally, despite the innumerable animal models that exist (Löscher, 1999, 2011; Auvin et al., 2012; Kandratavicius et al., 2014), there are important gaps and shortcomings in study design. For instance, many researchers use only one sex, typically males, which leads to a sex bias in neuroscience and biomedical research (Beery and Zucker, 2011). Epidemiological studies suggest that gender may affect susceptibility to epilepsy and its prognosis (Perucca et al., 2014), so that it is important to include both sexes in preclinical studies. The NIH is now developing policies that require grant applicants to report their plans for the balance of male and female animals in preclinical studies in all future applications (Clayton and Collins, 2014). The belief that non-human female mammals are intrinsically more variable than males and thus too troublesome for routine inclusion in research protocols is without foundation (Beery and Zucker, 2011). Another issue is the specific strain or substrain of mice or rats used. One recent example here are the striking inter-strain differences in the 6-Hz model in mice discussed

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Fig. 9. A summary of the potential translational value of animal models discussed in this review. Also, back-translation of clinical data for further optimization of models is indicated. Of course, there are various other issues not discussed here; e.g. the use of animal models to study mechanisms of ictogenesis and epileptogenesis or identify new potential drug targets (cf., Löscher et al., 2013).

in Section 3.1.1; similar strain or substrain differences have also been described for other models, including post-SE models of TLE and kindling (Schauwecker, 2002; Honndorf et al., 2011; Langer et al., 2011; Schauwecker, 2011; Müller et al., 2009; Bankstahl et al., 2012). In this respect, it is important to note that even the same strain of mice obtained from two different breeding locations (“barriers”) of the same vendor may strikingly differ in its behavior in an epilepsy model. This can significantly affect studies in this model, if the substrain difference is not known or appreciated (Müller et al., 2009). Also the age of the animals is an important variable in drug studies. Other sources of bias or variation among studies include, among others, limitations in equipment (e.g., in case of MES an electroshock stimulator with insufficient power to deliver 50 mA in mice or 150 mA in rats; cf. Löscher, 1999), insufficient training or experience or of experimenters, inaccurate administration of drugs in experiments where plasma exposure levels are not measured, improper termination of SE in post-SE TLE models, insufficient recording and typing of seizures, incorrect definition of “seizure” (cf., Löscher et al., 2015), inadequate controls, and inadequate sample size, randomization and statistics (Galanopoulou et al., 2012). An important example of a gap in the availability of animal models is the need for additional pediatric epilepsy models (Auvin et al., 2012; Guerrini et al., 2014). Although several models are available (see Section 12.1), there are still epilepsy syndromes without any relevant model (e.g., rolandic epilepsy, juvenile myoclonic epilepsy, Lennox-Gastaut syndrome, and Hemiconvulsion-Hemiplegia-Epilepsy) (Auvin et al., 2012). Here, specific animal models are urgently needed to improve our knowledge regarding potential therapeutic targets in both laboratory rodent and human brains across development in order to decrease the gap between preclinical data and successful clinical trials (Auvin et al., 2012). Thus, overall further development is needed to improve and validate models for the diverse areas in epilepsy research where suitable fit for purpose models are urgently required to search for more effective treatments. The information provided in this review may be used as a framework to guide research in this area.

14. Conclusions Animal models have, since 1937, been the foundation on which many new therapies have been identified for the treatment of symptomatic epilepsy (Bialer and White, 2010). The numerous novel ASDs, which have been discovered by testing of large numbers of investigational compounds in animal models over the last 20 years, have undoubtedly expanded the therapeutic options. This has been particularly important for those in need for a change in medical regimen (Bialer and White, 2010), clearly supporting the value of animal models in the early identification of promising new drugs for the patient with epilepsy. The value of animal models in ASD development also demonstrates that animal models resemble human seizures in their response to ASDs, which is a logical prerequisite for any drug development program, but is often dismissed in the clinical arena. Indeed, animal models with a similarly high predictive value do not exist for other CNS disorders, such as bipolar disorders or migraine (Löscher et al., 2013). Unfortunately, despite this success, approximately 30% of patients with epilepsy fail to achieve full seizure control or suffer intolerable adverse events (Löscher and Schmidt, 2011). Furthermore, many of the new drugs are not really much improved in terms of adverse events and side effects, but they often tend to have less efficacy than some of the classical, older drugs, such as carbamazepine in head-to-head comparisons (Löscher and Schmidt, 2011). As such, no one would disagree with the idea that there is a clear need for more effective and better tolerated therapies for the treatment of symptomatic epilepsy. Several interesting concepts how to achieve this goal have been presented in recent years (Löscher, 2011; Löscher et al., 2013; O’Brien et al., 2013; Wilcox et al., 2013; Harward and McNamara, 2014; Simonato et al., 2014), and some of them are discussed in Sections 9 and 11. However, whether any of these concepts will be successful cannot be readily determined. In addition to the development of better therapies for the symptomatic treatment of epilepsy, the availability of a therapy that would prevent or delay the development of epilepsy or the associated cognitive comorbidities would represent a substantial advance in the overall management of epilepsy (Brooks-Kayal et al., 2013; Löscher et al., 2013; Pitkänen et al., 2013; Simonato et al., 2014; White and Löscher, 2014; Löscher et al., 2015). To develop such a

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therapy may even be more ambitious than identifying more effective treatments for as yet ASD-resistant seizures. In the present review I have tried to summarize the current view on preclinical models for the evaluation of epilepsy therapies and advocate a fit-for-purpose application of models. As discussed in this review and summarized in Fig. 9, animal models when carefully selected, designed and conducted are important parts of any translational drug development strategy. The translational value of animal models can be further enhanced when combined with other translational tools such as quantitative systems pharmacology, biomarkers or experimental clinical trials (Denayer et al., 2014). However, it should be kept in mind that an animal model is a simple representation of a complex system. Consequently, an animal model for a human disease is by no means attempting to reproduce the human disease with all its complexities in an animal but rather to model specific aspects of the disease. Whenever using animal models, it is thus of utmost importance to define a specific question and to ensure that the chosen model is fit for purpose (Denayer et al., 2014; Wartha et al., 2014; Willner and Belzung, 2015). As recently outlined by Harward and McNamara (2014), development of novel therapies for the epilepsies requires properly aligning the animal model with the clinical syndrome, necessitating continuous and effective interactions of skilled clinicians and basic scientists. Apart from animal models, the intuition and creativity of experienced scientists is essential to discover novel targets or correctly interpret unexpected findings that may ultimately lead to novel drugs that really make a difference. Previous examples are valproate, which was serendipitously discovered when used as a lipophilic solvent for experimental compounds (Meunier et al., 1963), levetiracetam, which, based on its unconventional preclinical profile at the time, should never have been pursued as an ASD candidate (Klitgaard and Verdru, 2007), and allopregnanolone, which differs from benzodiazepines in its effects on extrasynaptic GABAA receptors, thus providing a new strategy for treating (super)refractory SE (Rogawski et al., 2013). The latter drug is an excellent example to illustrate the usefulness of targetdriven approaches for the discovery of more efficacious treatments (Löscher et al., 2013). Acknowledgements The author thanks Henrik Klitgaard and Rafal Kaminski from UCB Pharma (Brussels, Belgium), Aristea S. Galanopoulou (Albert Einstein College of Medicine, New York, USA), and Raimondo D’Ambrosio (University of Washington, Harborview Medical Center, Seattle, USA) for excellent comments on previous versions of the manuscript. The author’s own studies have been supported by grants from the German Research Foundation (Bonn, Germany), the National Institutes of Health (NIH; Bethesda, MD, USA: grant # R21 NS049592-01), the European Union’s Seventh Framework Programme (FP7) under grant agreements 201380 (EURIPIDES) and 602102 (EPITARGET), and the Niedersachsen-Research Network on Neuroinfectiology (N-RENNT) of the Ministry of Science and Culture of Lower Saxony in Germany. References Abbott, N.J., Khan, E.U., Rollinson, C.M.S., Reichel, A., Janigro, D., Dombrowski, S.M., Dobbie, M.S., Begley, D.J., 2002. Drug resistance in epilepsy: the role of the blood-brain barrier. In: Ling, V. (Ed.), Mechanisms of Drug Resistance in Epilepsy. Lessons from Oncology. Wiley, Chichester, pp. 38–46. Alberici, M., Rodriguez, D.L.A., De Robertis, E., 1969. Glutamic acid decarboxylase inhibition and ultrastructural changes by the convulsant drug allylglycine. Biochem. Pharmacol. 18, 137–143. Arrowsmith, J., Miller, P., 2013. Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12, 569. Arrowsmith, J., 2011. Trial watch: phase III and submission failures: 2007–2010. Nat. Rev. Drug Discov. 10, 87.

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