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In silico antitarget screening
Drug Discovery Today: Technologies
Vol. 1, No. 3 2004
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead optimization
In silico antitarget screening Maurizio Recanatini*, Giovanni Bottegoni, Andrea Cavalli Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
The need to early predict the possible failure of a drug candidate is becoming an absolute requirement in the drug discovery process. For this reason, from the initial phases of lead development, great attention is paid to the ADMET characteristics of the compounds. In this context, the recent discovery that hitting some wellidentified macromolecular targets can induce undesired side effects has led drug designers to apply some classical in silico technologies to the goal of avoiding the interaction of lead candidates with such antitargets. Introduction The biological response to the administration of a drug to an individual is the consequence of a myriad of events, and manifests itself with physiological changes that we simplistically indicate as the therapeutic and toxic effects. In turn, efficacy and safety of a drug derive from the balance between favorable and adverse phenomena occurring in different phases of the action process. By contrast, even if we are not yet able to disentangle the intricacies and the connections of the biochemical pathways at the basis of most diseases, specific macromolecules involved in some of them have often been identified, and, in the classical paradigm of drug discovery, they constitute the primary targets for the identification/optimization of new leads. In past years, in the design of new drug candidates, the greatest attention was paid to the ability of new compounds to hit and bind the specific target, whereas only recently the tendency emerged also to avoid some protein targets, because of their involvement in undesired effects. This aspect is different from the simple search for selectivity (e.g., between receptor subtypes or enzyme isoforms) and considers some proteins, which are *Corresponding author: (M. Recanatini) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.004
Section Editor: Hugo Kubinyi – Germany Drug development is a costly and time-consuming procedure. To reduce the risk of failure in late stages or after market introduction, it is mandatory to investigate all potential side effects of a drug candidate as early as possible. Human ether-a`-go-go-related gene (hERG) channel inhibition has been recognized as the main reason for severe, even fatal, cardiac side effects of many lipophilic compounds. A prominent example is the non-sedative antihistaminic Terfenadine, which had to be withdrawn from the market because of such a side effect. In this review, several in silico techniques for the prediction of hERG channel inhibition are described. Using such models, the risk potential of compounds can be evaluated even before their synthesis.
able to bind a vast array of structurally different drugs, and whose binding by a drug causes unfavorable and sometimes quite dangerous consequences. In this sense, we consider the following as typical antitargets: the human ether-a`-go-gorelated gene (hERG), the pregnane X and the constitutive androstane receptors (PXR and CAR, respectively) and also the P-glycoprotein (P-gp). In a broader sense, however, also some membrane receptors like the adrenergic a1a, the dopaminergic D2, the serotonergic 5-HT2C, and the muscarinic M1 can be considered antitargets, because of their involvement in orthostatic hypotension, extrapyramidal syndrome, obesity, and hallucinations, respectively. The hERG protein complex is a voltage-gated potassium channel that plays a crucial role in the cardiac action potential (Box 1). PXR and CAR are two orphan nuclear receptors, which have been shown to induce the expression of the drug-metabolizing enzymes CYP3A4 and CYP2B [1]. P-gp is one of the ATP-dependent efflux pumps responsible for the decreased accumulation of drugs into target cells [2]. It appears that because of their implications in cardiac toxicity, drug–drug interactions and drug toxification, and drug resistance, respectively, the above antitargets need not be hit, and the concerns of drug designers are nowadays directed at identifying the www.drugdiscoverytoday.com
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Box 1. Drug-induced long QT syndrome and hERG The QT interval of the electrocardiogram (ECG) is defined as the time from Q wave deflection to the end of T wave (Fig. I), and its prolongation is a typical effect of class III antiarrhythmic drugs at the basis of their pharmacological action. In the past decade, evidence has accrued that several classes of drugs used for non-cardiovascular indications might prolong the QT interval as a side effect. The early reports remained mostly confined to the specialized literature, until it was realized that drug-induced QT prolongation could be associated with the occurrence of ventricular tachyarrhythmias, namely torsades de pointes (TdP), that might degenerate into ventricular fibrillation and sudden death. Although several pathophysiological mechanisms can lead to prolongation of the QT interval, the key mechanism for drug-induced QT prolongation is the increased repolarization duration caused by blockade of outward K+ currents. Actually, cardiac action potential duration (APD) is controlled by a fine balance between inward and outward currents,
Figure I. Diagrammatic ECG tracing.
among which the delayed rectifier repolarizing K+ current, IK, plays an important role. Most of the QT prolonging drugs has been shown to block the K+ channels encoded by the human ether-a`-go-go-related gene (hERG), at the basis of the rapid component of IK. Blockade of the hERG K+ channel is therefore the most important mechanism through which QT prolonging drugs increase cardiac APD. Recently, several regulatory interventions have involved drugs, for which the risk for potentially fatal TdP was recognized only after marketing authorization, and, in some cases (Astemizole, Sertindole, Terfenadine, Cisapride, Grepafloxacin; Fig. II), they have implied the withdrawal from the market. Screening methods for the early detection of an effect on the QT interval are therefore required and several in vivo and in vitro methods are currently available. In the perspective of detecting the QT prolongation liability as soon as possible in the drug design and development pipeline, a great deal of attention has been cast at hERG as a genuine antitarget, and in silico approaches appear quite appropriate for an early screening. As a matter of fact, the computational techniques described in this paper can provide the possibility of interpreting the SAR of hERG ligands, which are at the present rather elusive. Despite a seemingly simple pharmacophoric pattern (easily identifiable also from the compounds of Fig. II: long, flexible, rather lipophilic molecules bearing a tertiary nitrogen are potent hERG blockers, grepafloxacin lacking some hydrophobic features is a weak channel blocker), the chemical space covered by hERG ligands is wide, and factors crucial for the binding to this antitarget are still unknown. For more detailed information on drug-induced long QT syndrome and hERG, see [11,23].
structural characteristics associated with the ability of binding to this proteins, to avoid interactions with them. This article will review some applications of in silico technologies to the study of the structure–activity relationship (SAR; see Glossary)
Figure II. Typical hERG blocking drugs.
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of antitarget-binding drugs and to the prediction of the candidate drugs’ potential for binding to these proteins.
Key in silico technologies The techniques used to computationally model the SAR of drugs and drug–target interactions have rapidly developed through the past decades providing researchers with many sophisticated tools mostly based on two broad strategies: ligand-based statistically driven methods and target-based 3D modeling. Of course, it is nonsense to establish precise limits between the two fields, because contaminations and merging of the two approaches are frequently encountered. However, for the sake of simplicity and on the basis of known applications to antitarget studies, we will briefly describe four main technologies, and under a fifth heading will report some considerations on basic in silico technologies of more general impact to the drug design field.
QSAR The quantitative method for the study of the structure–activity relationships (QSAR; see Glossary) of drugs was introduced by Hansch in the early 1960s [3]. Since then, the QSAR acronym has become quite popular and has given rise to a discipline that evolved from the original approach based on the Hammett’s Linear Free-Energy Relationships (LFER) hypothesis [3] to the more advanced cheminformatic tools available today [4]. The idea behind the method is that of looking for a correlation between the variation of biological potency and that of some molecular descriptors for a series of bioactive compounds. If the relationship exists, one attempts to derive an equation describing a quantitative model that accounts for the SAR of the series and, more importantly, allows the biological activity of virtual new molecules to be predicted. Robust predictive models are usually developed by the use of large databases of compounds, high numbers of descriptors, and adequate statistical methods. The next frontier of the classical LFER-based Hansch approach is the Comparative QSAR (BioByte, Claremont, CA., USA, http:// www.biobyte.com) procedure, which allows to extend the meaning of single QSAR equations by taking into consideration related chemical or biological systems [5]. With regards to QSAR applications in the study of antitarget ligands, a paper by Wang et al. [6] reports a classical example of Hansch analysis carried out on sets of substrates and modulators of P-gp by scanning several physicochemical parameters. Typically, the result of this kind of modeling approach is the identification of several physicochemical or structural properties (in this case, lipophilicity, molecular weight, energy of the highest occupied molecular orbital, chain length, presence of a basic tertiary nitrogen) that can be related to the susceptibility of the molecules to interact with the target. In the field of hERG, an article by Roche et al. [7] illustrates the cheminformatic approach to deriving QSAR
Drug Discovery Today: Technologies | Lead optimization
Glossary ADMET: acronym for absorption–distribution–metabolism–elimination–toxicity; it is used to collectively indicate processes and properties crucial for the pharmacokinetics and the safety of a drug. Docking simulation: in silico simulation of the interaction between a target and a ligand; the result is a 3D model of the ligand–target complex (docking complex) showing a hypothetical binding mode. Ligand-based, target-based: it is said of studies based on information and data regarding the ligands (small molecules, usually drugs or drug candidates), or the target (a macromolecule, like an enzyme, a receptor, etc.), respectively; in drug design terminology, the ligand-based approach is defined as indirect, and the target-based one as direct. (Q)SAR: acronym for (quantitative) structure–activity relationships; in the classical medicinal chemistry practice, SAR studies are carried out to optimize the pharmacological properties of a class of compounds, based on the assumption that to a variation of structure might correspond a variation of activity; in the quantitative approach, predictive SAR models are developed.
models. These authors selected a training set of 242 molecules from a collection of compounds synthesized and tested for hERG inhibition at Hoffmann–La Roche (Basel, Switzerland, http://www.roche.com) over three years. Several statistical techniques were employed to identify the appropriate molecular descriptors and to build a predictive model, and a total number of 1258 descriptors was generated and explored by means of those methods. In this study, indications were searched in terms of predictivity, and the best results were obtained through the use of a neural network system that provided a final prediction model able to correctly classify 71% of the hERG blockers and 93% of the non-blockers. If classification was the goal of the work, this can be considered a good result, even though some important hERG blocking drugs were misclassified. Possible reasons proposed by the authors were the incompleteness of the molecular descriptors set, the biased structure of the database of compounds, and the existence of different binding sites on the channel.
3D QSAR The 3D QSAR techniques [8] are clearly based on the QSAR strategy of statistically deriving ligand-based models, and initially they were intended simply as an evolution of the classical QSAR methods aimed at filling a serious gap in the parameterization of the molecular properties: the consideration of the 3D properties of the compounds. This problem was solved with the calculation and use of molecular fields, whereby the steric, electrostatic or lipophilic properties of each molecule were described as interaction energy values calculated by means of probes and distributed at grid points all around the molecule. The most popular software implementation of this approach is the Comparative Molecular Field Analysis (CoMFA1, Tripos Inc, St. Louis, USA, http:// www.tripos.com) method, whose outcomes are QSAR models that describe the correlation between variation of activity and variation of properties in the 3D space around the molecules. www.drugdiscoverytoday.com
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The capability of 3D QSAR methods to deal with the 3D characteristics of molecules makes them well suited to attempt the building of pharmacophore-based models for the interaction with specific (anti)targets, where a pharmacophore can be defined as the ensemble of the molecular properties needed to bind a target, together with their spatial disposition. Pharmacophores [9] are quite useful tools in drug design, because they allow us to schematically describe the salient features of whole classes of compounds interacting with a single protein. So-called quantitative pharmacophores are endowed with predictive properties, because they combine their spatial descriptive nature with a statistically derived QSAR model. The widely used Catalyst1 (Accelrys Inc, San Diego, USA, http://www.accelrys.com) method belongs to this category. Examples of the application of the CoMFA and of the related Comparative Molecular Similarity Indices Analysis (CoMSIA) (Tripos Inc, St. Louis, USA) techniques to the exploration of the 3D QSAR of blockers of the hERG potassium channel recently appeared in the literature [10,11]. These models based on relatively small sets of compounds showed a reasonable predictive ability (tested on external sets of data), and provided a sort of general picture (actually, a pharmacophore) of how hERG blockers are shaped and of which physicochemical characteristics they possess. Several Catalyst models have been published by Ekins and coworkers on almost all of the above-mentioned antitargets: hERG [12], PXR [13], and P-gp [14]. The quantitative pharmacophores developed suggest which functions (like hydrophobic groups, hydrogen-bond donors or acceptors, ionizable moieties, and so on) might be responsible for the binding to the antitarget, and are also able to correctly rank the potencies of test molecules.
Virtual screening The strategy of simulating in silico the testing of high numbers of compounds was developed during the last decade as a way to assist the selection of new candidate leads [15]. Virtual libraries of compounds can now be screened in a reasonable time by means of software tools, which again follow two main approaches: LIGAND-BASED or TARGET-BASED (see Glossary). In this sense, virtual screening (VS; Box 2) basically consists of searching in a database molecules endowed with specific structural and/or physicochemical characteristics that are dictated by a pharmacophore or by the structure of a protein’s binding site. Otherwise, it can be said that the VS procedure works by filtering out from the database molecules that do not obey certain requirements. The latter view of the VS technology highlights a concept crucial for the success of any VS application: the need to filter the results of a ligand- or target-based search to eliminate compounds that satisfy the pharmacophoric or target binding requirements, but which are not ideal as drugs. This is a hot issue in VS applied to lead 212
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Box 2. Virtual screening main steps Filtering The aim for the screening of large molecular databases is to identify suitable lead compounds for actual synthesis in medicinal chemistry. Databases are searched for the presence/absence of a wide range of chemical features defining the ‘drug-likeness’, as earlier introduced by Lipinski in his ‘Rule of Five’ [24]. A preliminary filtering stage eliminates compounds poorly soluble and permeable, compounds with strongly reactive groups, with heteroatoms from the transition series, with long aliphatic chains or with a great deal of normally allowed groups (e.g., hydroxyls). In this stage, specific rules derived from QSAR and 3D QSAR studies can be formulated to exclude molecules that are probable to hit antitargets.
Docking (a) Target-based: Molecular 3D features of database members are explored to identify the best binding mode of each compound within a pre-characterized amino acidic environment. Current computer performances allow ligand flexibility to be explored inside the binding site, so that flexible docking can be accomplished. Because simulation of the protein side chain flexibility is not yet implemented in standard VS protocols, the so-called ‘induced fit’ effect cannot be evaluated. (b) Ligand-based: Ligand-based docking is a very fast filtering technique used to screen molecular databases in terms of overlap with a structural reference. Such a reference might be the structure of an active compound or a 3D pharmacophore obtained from previous studies. Different ligand conformations are generated to verify the matching of the referenceimposed restraints taking into account molecule flexibility.
Scoring A scoring function based on terms like geometry, attractive/repulsive van der Waals and electrostatic forces, desolvation, and entropy is employed to estimate the energy of binding complexes. Molecules forming the most energy-favorable complexes are ranked as top scorers. In the ligandbased approach, scoring is given in terms of similarity. Molecules closest to the reference are ranked as top scorers.
Hit selection Any docking protocol introduces various degrees of approximation, because deep exploration of the conformational space and accurate energy evaluation are not trivial tasks to accomplish in a reasonable amount of computer time. For this reason, at the end of the screening, it is necessary to carry out a step of visual inspection on the top scorers and check the lead candidates. The application of some other techniques like molecular dynamics simulations or cluster analysis might enhance the accuracy of the final results, and can help the selection of the candidates for ‘wet’ experiments (hits).
discovery, because the elimination of compounds that could fail in late phases of development would help to avoid the financial disasters associated with withdrawing a new drug in advanced clinical phases or even after marketing. In this context, pharmacokinetics and toxicity (ADMET; see Glossary) play a crucial role, and the need to early detect the potential for antitarget binding is evident. Despite the recognized necessity to incorporate antitarget filtering in VS methods, to date there is no report in the literature of such applications. One reason probably lies in the lack of a precise determination of the molecular features associated with binding to the antitarget proteins mentioned above. By contrast, in almost all the QSAR and 3D QSAR
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studies cited in the previous section, it is claimed that the aim of the work was to discover the molecular determinants for the affinity to the antitarget in view of their inclusion in a VS procedure [7,10,12]. Perhaps, a further step in this direction was made by Ekins et al., who described a computational method to filter a library of compounds for their liability to act as substrates of CYP2D6 and CYP3A4, two drug-metabolizing enzymes eligible as antitargets [16].
Computational systems biology Systems biology is an integrated approach to the study of the functions of biological systems (from subcellular networks to, ultimately, the human being), and the effects of perturbations (e.g., diseases or drug administration) on such systems [17]. The computational tools developed in this context are aimed at simulating the behavior of the systems, use experimental data usually obtained via high-throughput techniques from genomic, proteomic or metabolomic analyses, and fully exploit the frontier-level developments of hardware and software technologies. The attractiveness of systems biology for the drug discovery community seems evident if one just can imagine the possibility to simulate the response of a cell, an organ or even the whole body to the administration of a drug. Efforts to incorporate computational systems biology tools in the drug discovery practice are already being carried out [18], and the prediction of the adverse effects of candidate drugs might be expected as one of the main goals of such an approach. As a matter of fact, researchers at Physiome Science Inc (Princeton, USA, http://www.physiome.com) recently reported the use of an integrated biological simulation environment to profile the cardiac safety of drug candidates for assisting the drug discovery process [19]. This study was centered on the modeling of hERG block-related effects and took advantage of a simulation system where mathematical descriptions of fundamental biological processes (such as signaling, transport, excitability, cell cycle and metabolism)
were integrated into models of metabolic and signaling pathways in the cardiac cell.
Other key technologies In a paper dealing with in silico methods applied in the field of drug discovery, it is necessary to mention two other technologies that play a basic role herein, in the sense that many of the mentioned approaches rely more or less on their products: protein modeling [20] and bioinformatics [21]. Under these headings, all those methods can be collected that are used to build 3D models of (anti)targets on one side, and to study genes and proteins from the sequence point of view on the other. Having a reliable 3D model of a protein is indispensable for any target-based simulation technique, like building ligandtarget complex (DOCKING; see Glossary) models and performing VS experiments; also, the functioning of a(n) (anti)target, like an enzyme or a membrane channel, can be simulated, and such models can be useful per se or in view of their integration in higher level system models. Similarly, the impact of bioinformatic techniques in modern drug discovery has recently been highlighted by their application to the analysis of genomic sequences aimed at finding genes eventually involved in diseases, and at assisting the process of (anti)target identification and validation.
Comparison of technologies Main advantages and disadvantages of the approaches described above to the in silico antitarget screening are briefly indicated in Table 1. These pros and cons should be taken in a general meaning that refers to the basic characteristics of each method more than to the specific application to the study of antitargets. Details have been missed out in this short article (the interested reader can find them in the references), but anyway it is hard to say which approach is more or less suitable to this kind of application. Indeed, (3D) QSAR technologies
Table 1. Comparison summary table Name of specific type of technology
QSAR
3D QSAR
Virtual screening
Computational systems biology
Names of specific technologies with associated companies and company websites
C QSAR, BioByte, Claremont, CA, USA, http://www.biobyte.com
CoMFA, CoMSIA, Tripos Inc, St. Louis, USA, http://www.tripos.com Catalyst, Accelrys Inc, San Diego, USA, http://www.accelrys.com
Pros
No need for target information
No need for target information
Possibility to search vast libraries
System-level approach
Cons
No consideration of 3D features
Affected by quality of conformational sampling; building of ‘‘local’’ models
Heavily affected by filtering and by treatment of ligand and target flexibility
Heavy hardware and software requirements
References
[5]
[8]
[15]
[18,19]
QSAR, quantitative structure–activity relationships; C QSAR, comparative QSAR; CoMFA, comparative molecular field analysis; CoMSIA, comparative molecular similarity indices analysis. www.drugdiscoverytoday.com
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seem more appropriate for a quick identification of the molecular features associated with the binding to an antitarget, but, by contrast, a 3D ligand–target interaction model gives a more thorough picture of the putative(s) binding mode(s). Ultimately, however, only a simulation based on the consideration of the entire biological system can (in principle) account for all of the interconnected phenomena driving the effects of a drug. From a more pragmatic point of view, if in silico techniques have to be applied to assist the lead selection process, one might propose that QSAR-based methods could be more appropriate to carry out predictive analyses on smallmedium (tenths to hundreds) sets of compounds, whereas efficient VS protocols should be employed to filter extended (thousands to millions) molecular databases. A crucial issue to be considered when applying computational methods to antitarget screening is that in silico methods in drug design were developed to identify molecules endowed with an increased ability to bind a target (or with a higher potency towards a pharmacological end point), not devoid of it. Conversely, in the case of antitargets, the latter is the goal and this change of perspective has neither been translated yet into appropriate software tools, nor even fully appreciated. However, the inclusion of antitarget filtering in VS protocols might be a step in this direction. Finally, it has to be underlined that any attempt to rigorously consider the problem of antitarget binding in a drug discovery project will heavily depend on some other related issues and not only on the selected computational approach (see Outstanding issues). Indeed, in silico technology alone cannot guarantee the identification of new safe and effective lead compounds, but, more realistically, ‘‘future success [will] depend on the proper integration of new promising technologies with the experience and strategies of classical medicinal chemistry’’ [22].
Conclusions In the selection of new drug candidates, many efforts are focused on the early elimination of compounds that might interact with antitargets related to undesired effects, like toxicity, drug–drug interactions or increased metabolism, among others. In silico screening of antitarget liability is going to become a central issue in any rigorous drug discovery
Related articles Jorgensen, W.L. (2004) The many roles of computation in drug discovery. Science 303, 1813–1818 van de Waterbeemd, H. and Gifford, E. (2003) ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192–204 Ekins, S. (2004) Predicting undesirable drug interactions with promiscuous proteins in silico. Drug Discov. Today 9, 276–285 Bugrim, A. et al. (2004) Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov. Today 9, 127–135
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project, and available ligand- or target-based computational technologies allow this aspect to be accounted for, as shown by several reported examples. At the moment, there is not an ideal approach to this problem, but efforts are being made to incorporate antitarget screening in the most advanced in silico techniques for lead identification and optimization.
Outstanding issues Quality of biological data: any computational model reflects the quality of the experimental data on which it is based. This is particularly true when predictions are attempted based on a ‘‘training set’’ of compounds of known activity: highly uncertain and nonhomogeneous (determined in different laboratories or on different biological systems) data can heavily affect the precision and the reliability of the predicted values. Availability of biological data: collections of data on interactions of candidate leads with antitargets probably exist in the files of pharmaceutical companies, but public access is precluded. Putting them together would allow us to build information-rich training sets of compounds, on which to base the development of comprehensive models of ligand–antitarget interactions. Integration of languages and models: biologists, chemists, and computer scientists collaborate in advanced drug discovery projects, and they need to develop a standard platform of languages, concepts, and research strategies. With regards to the developed computational models, ideally they should be integrated by interfacing the statistical (QSAR), target-based, and system-based levels to allow a flow of input and output information at any level of complexity.
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14 Ekins, S. et al. (2002) Application of three-dimensional quantitative structure–activity relationships of P-glycoprotein inhibitors and substrates. Mol. Pharmacol. 61, 974–981 15 Green, D.V. (2003) Virtual screening of virtual libraries. Prog. Med. Chem. 41, 61–97 16 Ekins, S. et al. (2003) Generation and validation of rapid computational filters for cyp2d6 and cyp3a4. Drug Metab. Dispos. 31, 1077–1080 17 Kitano, H. (2002) Computational systems biology. Nature 420, 206–210 18 Davidov, E. et al. (2003) Advancing drug discovery through systems biology. Drug Discov. Today 8, 175–183 19 Levin, J.M. et al. (2002) Using in silico biology to facilitate drug development. Novartis Found. Symp. 247, 222–238 (discussion 238–243, 244–252)
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20 Hoeltje, H-D. and Folkers, G. (1996) Molecular Modeling. Basic Principles and Applications. VCH, Weinheim, Germany 21 Baxevanis, A.D. and Ouellette, B.F.F. (2001) Bioinformatics. A Practical Guide to the Analysis of Genes and Proteins. (2nd edn) John Wiley and Sons, New York, USA 22 Kubinyi, H. (2003) Drug research: myths, hype and reality. Nat. Rev. Drug Discov. 2, 665–668 23 De Ponti, F. et al. (2002) Safety of non-antiarrhythmic drugs that prolong the QT interval or induce torsades de pointes: an overview. Drug Safety 25, 263–286 24 Lipinski, C.A. et al. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25
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Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead optimization
In silico antitarget screening Maurizio Recanatini*, Giovanni Bottegoni, Andrea Cavalli Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
The need to early predict the possible failure of a drug candidate is becoming an absolute requirement in the drug discovery process. For this reason, from the initial phases of lead development, great attention is paid to the ADMET characteristics of the compounds. In this context, the recent discovery that hitting some wellidentified macromolecular targets can induce undesired side effects has led drug designers to apply some classical in silico technologies to the goal of avoiding the interaction of lead candidates with such antitargets. Introduction The biological response to the administration of a drug to an individual is the consequence of a myriad of events, and manifests itself with physiological changes that we simplistically indicate as the therapeutic and toxic effects. In turn, efficacy and safety of a drug derive from the balance between favorable and adverse phenomena occurring in different phases of the action process. By contrast, even if we are not yet able to disentangle the intricacies and the connections of the biochemical pathways at the basis of most diseases, specific macromolecules involved in some of them have often been identified, and, in the classical paradigm of drug discovery, they constitute the primary targets for the identification/optimization of new leads. In past years, in the design of new drug candidates, the greatest attention was paid to the ability of new compounds to hit and bind the specific target, whereas only recently the tendency emerged also to avoid some protein targets, because of their involvement in undesired effects. This aspect is different from the simple search for selectivity (e.g., between receptor subtypes or enzyme isoforms) and considers some proteins, which are *Corresponding author: (M. Recanatini) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.004
Section Editor: Hugo Kubinyi – Germany Drug development is a costly and time-consuming procedure. To reduce the risk of failure in late stages or after market introduction, it is mandatory to investigate all potential side effects of a drug candidate as early as possible. Human ether-a`-go-go-related gene (hERG) channel inhibition has been recognized as the main reason for severe, even fatal, cardiac side effects of many lipophilic compounds. A prominent example is the non-sedative antihistaminic Terfenadine, which had to be withdrawn from the market because of such a side effect. In this review, several in silico techniques for the prediction of hERG channel inhibition are described. Using such models, the risk potential of compounds can be evaluated even before their synthesis.
able to bind a vast array of structurally different drugs, and whose binding by a drug causes unfavorable and sometimes quite dangerous consequences. In this sense, we consider the following as typical antitargets: the human ether-a`-go-gorelated gene (hERG), the pregnane X and the constitutive androstane receptors (PXR and CAR, respectively) and also the P-glycoprotein (P-gp). In a broader sense, however, also some membrane receptors like the adrenergic a1a, the dopaminergic D2, the serotonergic 5-HT2C, and the muscarinic M1 can be considered antitargets, because of their involvement in orthostatic hypotension, extrapyramidal syndrome, obesity, and hallucinations, respectively. The hERG protein complex is a voltage-gated potassium channel that plays a crucial role in the cardiac action potential (Box 1). PXR and CAR are two orphan nuclear receptors, which have been shown to induce the expression of the drug-metabolizing enzymes CYP3A4 and CYP2B [1]. P-gp is one of the ATP-dependent efflux pumps responsible for the decreased accumulation of drugs into target cells [2]. It appears that because of their implications in cardiac toxicity, drug–drug interactions and drug toxification, and drug resistance, respectively, the above antitargets need not be hit, and the concerns of drug designers are nowadays directed at identifying the www.drugdiscoverytoday.com
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Box 1. Drug-induced long QT syndrome and hERG The QT interval of the electrocardiogram (ECG) is defined as the time from Q wave deflection to the end of T wave (Fig. I), and its prolongation is a typical effect of class III antiarrhythmic drugs at the basis of their pharmacological action. In the past decade, evidence has accrued that several classes of drugs used for non-cardiovascular indications might prolong the QT interval as a side effect. The early reports remained mostly confined to the specialized literature, until it was realized that drug-induced QT prolongation could be associated with the occurrence of ventricular tachyarrhythmias, namely torsades de pointes (TdP), that might degenerate into ventricular fibrillation and sudden death. Although several pathophysiological mechanisms can lead to prolongation of the QT interval, the key mechanism for drug-induced QT prolongation is the increased repolarization duration caused by blockade of outward K+ currents. Actually, cardiac action potential duration (APD) is controlled by a fine balance between inward and outward currents,
Figure I. Diagrammatic ECG tracing.
among which the delayed rectifier repolarizing K+ current, IK, plays an important role. Most of the QT prolonging drugs has been shown to block the K+ channels encoded by the human ether-a`-go-go-related gene (hERG), at the basis of the rapid component of IK. Blockade of the hERG K+ channel is therefore the most important mechanism through which QT prolonging drugs increase cardiac APD. Recently, several regulatory interventions have involved drugs, for which the risk for potentially fatal TdP was recognized only after marketing authorization, and, in some cases (Astemizole, Sertindole, Terfenadine, Cisapride, Grepafloxacin; Fig. II), they have implied the withdrawal from the market. Screening methods for the early detection of an effect on the QT interval are therefore required and several in vivo and in vitro methods are currently available. In the perspective of detecting the QT prolongation liability as soon as possible in the drug design and development pipeline, a great deal of attention has been cast at hERG as a genuine antitarget, and in silico approaches appear quite appropriate for an early screening. As a matter of fact, the computational techniques described in this paper can provide the possibility of interpreting the SAR of hERG ligands, which are at the present rather elusive. Despite a seemingly simple pharmacophoric pattern (easily identifiable also from the compounds of Fig. II: long, flexible, rather lipophilic molecules bearing a tertiary nitrogen are potent hERG blockers, grepafloxacin lacking some hydrophobic features is a weak channel blocker), the chemical space covered by hERG ligands is wide, and factors crucial for the binding to this antitarget are still unknown. For more detailed information on drug-induced long QT syndrome and hERG, see [11,23].
structural characteristics associated with the ability of binding to this proteins, to avoid interactions with them. This article will review some applications of in silico technologies to the study of the structure–activity relationship (SAR; see Glossary)
Figure II. Typical hERG blocking drugs.
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of antitarget-binding drugs and to the prediction of the candidate drugs’ potential for binding to these proteins.
Key in silico technologies The techniques used to computationally model the SAR of drugs and drug–target interactions have rapidly developed through the past decades providing researchers with many sophisticated tools mostly based on two broad strategies: ligand-based statistically driven methods and target-based 3D modeling. Of course, it is nonsense to establish precise limits between the two fields, because contaminations and merging of the two approaches are frequently encountered. However, for the sake of simplicity and on the basis of known applications to antitarget studies, we will briefly describe four main technologies, and under a fifth heading will report some considerations on basic in silico technologies of more general impact to the drug design field.
QSAR The quantitative method for the study of the structure–activity relationships (QSAR; see Glossary) of drugs was introduced by Hansch in the early 1960s [3]. Since then, the QSAR acronym has become quite popular and has given rise to a discipline that evolved from the original approach based on the Hammett’s Linear Free-Energy Relationships (LFER) hypothesis [3] to the more advanced cheminformatic tools available today [4]. The idea behind the method is that of looking for a correlation between the variation of biological potency and that of some molecular descriptors for a series of bioactive compounds. If the relationship exists, one attempts to derive an equation describing a quantitative model that accounts for the SAR of the series and, more importantly, allows the biological activity of virtual new molecules to be predicted. Robust predictive models are usually developed by the use of large databases of compounds, high numbers of descriptors, and adequate statistical methods. The next frontier of the classical LFER-based Hansch approach is the Comparative QSAR (BioByte, Claremont, CA., USA, http:// www.biobyte.com) procedure, which allows to extend the meaning of single QSAR equations by taking into consideration related chemical or biological systems [5]. With regards to QSAR applications in the study of antitarget ligands, a paper by Wang et al. [6] reports a classical example of Hansch analysis carried out on sets of substrates and modulators of P-gp by scanning several physicochemical parameters. Typically, the result of this kind of modeling approach is the identification of several physicochemical or structural properties (in this case, lipophilicity, molecular weight, energy of the highest occupied molecular orbital, chain length, presence of a basic tertiary nitrogen) that can be related to the susceptibility of the molecules to interact with the target. In the field of hERG, an article by Roche et al. [7] illustrates the cheminformatic approach to deriving QSAR
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Glossary ADMET: acronym for absorption–distribution–metabolism–elimination–toxicity; it is used to collectively indicate processes and properties crucial for the pharmacokinetics and the safety of a drug. Docking simulation: in silico simulation of the interaction between a target and a ligand; the result is a 3D model of the ligand–target complex (docking complex) showing a hypothetical binding mode. Ligand-based, target-based: it is said of studies based on information and data regarding the ligands (small molecules, usually drugs or drug candidates), or the target (a macromolecule, like an enzyme, a receptor, etc.), respectively; in drug design terminology, the ligand-based approach is defined as indirect, and the target-based one as direct. (Q)SAR: acronym for (quantitative) structure–activity relationships; in the classical medicinal chemistry practice, SAR studies are carried out to optimize the pharmacological properties of a class of compounds, based on the assumption that to a variation of structure might correspond a variation of activity; in the quantitative approach, predictive SAR models are developed.
models. These authors selected a training set of 242 molecules from a collection of compounds synthesized and tested for hERG inhibition at Hoffmann–La Roche (Basel, Switzerland, http://www.roche.com) over three years. Several statistical techniques were employed to identify the appropriate molecular descriptors and to build a predictive model, and a total number of 1258 descriptors was generated and explored by means of those methods. In this study, indications were searched in terms of predictivity, and the best results were obtained through the use of a neural network system that provided a final prediction model able to correctly classify 71% of the hERG blockers and 93% of the non-blockers. If classification was the goal of the work, this can be considered a good result, even though some important hERG blocking drugs were misclassified. Possible reasons proposed by the authors were the incompleteness of the molecular descriptors set, the biased structure of the database of compounds, and the existence of different binding sites on the channel.
3D QSAR The 3D QSAR techniques [8] are clearly based on the QSAR strategy of statistically deriving ligand-based models, and initially they were intended simply as an evolution of the classical QSAR methods aimed at filling a serious gap in the parameterization of the molecular properties: the consideration of the 3D properties of the compounds. This problem was solved with the calculation and use of molecular fields, whereby the steric, electrostatic or lipophilic properties of each molecule were described as interaction energy values calculated by means of probes and distributed at grid points all around the molecule. The most popular software implementation of this approach is the Comparative Molecular Field Analysis (CoMFA1, Tripos Inc, St. Louis, USA, http:// www.tripos.com) method, whose outcomes are QSAR models that describe the correlation between variation of activity and variation of properties in the 3D space around the molecules. www.drugdiscoverytoday.com
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The capability of 3D QSAR methods to deal with the 3D characteristics of molecules makes them well suited to attempt the building of pharmacophore-based models for the interaction with specific (anti)targets, where a pharmacophore can be defined as the ensemble of the molecular properties needed to bind a target, together with their spatial disposition. Pharmacophores [9] are quite useful tools in drug design, because they allow us to schematically describe the salient features of whole classes of compounds interacting with a single protein. So-called quantitative pharmacophores are endowed with predictive properties, because they combine their spatial descriptive nature with a statistically derived QSAR model. The widely used Catalyst1 (Accelrys Inc, San Diego, USA, http://www.accelrys.com) method belongs to this category. Examples of the application of the CoMFA and of the related Comparative Molecular Similarity Indices Analysis (CoMSIA) (Tripos Inc, St. Louis, USA) techniques to the exploration of the 3D QSAR of blockers of the hERG potassium channel recently appeared in the literature [10,11]. These models based on relatively small sets of compounds showed a reasonable predictive ability (tested on external sets of data), and provided a sort of general picture (actually, a pharmacophore) of how hERG blockers are shaped and of which physicochemical characteristics they possess. Several Catalyst models have been published by Ekins and coworkers on almost all of the above-mentioned antitargets: hERG [12], PXR [13], and P-gp [14]. The quantitative pharmacophores developed suggest which functions (like hydrophobic groups, hydrogen-bond donors or acceptors, ionizable moieties, and so on) might be responsible for the binding to the antitarget, and are also able to correctly rank the potencies of test molecules.
Virtual screening The strategy of simulating in silico the testing of high numbers of compounds was developed during the last decade as a way to assist the selection of new candidate leads [15]. Virtual libraries of compounds can now be screened in a reasonable time by means of software tools, which again follow two main approaches: LIGAND-BASED or TARGET-BASED (see Glossary). In this sense, virtual screening (VS; Box 2) basically consists of searching in a database molecules endowed with specific structural and/or physicochemical characteristics that are dictated by a pharmacophore or by the structure of a protein’s binding site. Otherwise, it can be said that the VS procedure works by filtering out from the database molecules that do not obey certain requirements. The latter view of the VS technology highlights a concept crucial for the success of any VS application: the need to filter the results of a ligand- or target-based search to eliminate compounds that satisfy the pharmacophoric or target binding requirements, but which are not ideal as drugs. This is a hot issue in VS applied to lead 212
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Box 2. Virtual screening main steps Filtering The aim for the screening of large molecular databases is to identify suitable lead compounds for actual synthesis in medicinal chemistry. Databases are searched for the presence/absence of a wide range of chemical features defining the ‘drug-likeness’, as earlier introduced by Lipinski in his ‘Rule of Five’ [24]. A preliminary filtering stage eliminates compounds poorly soluble and permeable, compounds with strongly reactive groups, with heteroatoms from the transition series, with long aliphatic chains or with a great deal of normally allowed groups (e.g., hydroxyls). In this stage, specific rules derived from QSAR and 3D QSAR studies can be formulated to exclude molecules that are probable to hit antitargets.
Docking (a) Target-based: Molecular 3D features of database members are explored to identify the best binding mode of each compound within a pre-characterized amino acidic environment. Current computer performances allow ligand flexibility to be explored inside the binding site, so that flexible docking can be accomplished. Because simulation of the protein side chain flexibility is not yet implemented in standard VS protocols, the so-called ‘induced fit’ effect cannot be evaluated. (b) Ligand-based: Ligand-based docking is a very fast filtering technique used to screen molecular databases in terms of overlap with a structural reference. Such a reference might be the structure of an active compound or a 3D pharmacophore obtained from previous studies. Different ligand conformations are generated to verify the matching of the referenceimposed restraints taking into account molecule flexibility.
Scoring A scoring function based on terms like geometry, attractive/repulsive van der Waals and electrostatic forces, desolvation, and entropy is employed to estimate the energy of binding complexes. Molecules forming the most energy-favorable complexes are ranked as top scorers. In the ligandbased approach, scoring is given in terms of similarity. Molecules closest to the reference are ranked as top scorers.
Hit selection Any docking protocol introduces various degrees of approximation, because deep exploration of the conformational space and accurate energy evaluation are not trivial tasks to accomplish in a reasonable amount of computer time. For this reason, at the end of the screening, it is necessary to carry out a step of visual inspection on the top scorers and check the lead candidates. The application of some other techniques like molecular dynamics simulations or cluster analysis might enhance the accuracy of the final results, and can help the selection of the candidates for ‘wet’ experiments (hits).
discovery, because the elimination of compounds that could fail in late phases of development would help to avoid the financial disasters associated with withdrawing a new drug in advanced clinical phases or even after marketing. In this context, pharmacokinetics and toxicity (ADMET; see Glossary) play a crucial role, and the need to early detect the potential for antitarget binding is evident. Despite the recognized necessity to incorporate antitarget filtering in VS methods, to date there is no report in the literature of such applications. One reason probably lies in the lack of a precise determination of the molecular features associated with binding to the antitarget proteins mentioned above. By contrast, in almost all the QSAR and 3D QSAR
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studies cited in the previous section, it is claimed that the aim of the work was to discover the molecular determinants for the affinity to the antitarget in view of their inclusion in a VS procedure [7,10,12]. Perhaps, a further step in this direction was made by Ekins et al., who described a computational method to filter a library of compounds for their liability to act as substrates of CYP2D6 and CYP3A4, two drug-metabolizing enzymes eligible as antitargets [16].
Computational systems biology Systems biology is an integrated approach to the study of the functions of biological systems (from subcellular networks to, ultimately, the human being), and the effects of perturbations (e.g., diseases or drug administration) on such systems [17]. The computational tools developed in this context are aimed at simulating the behavior of the systems, use experimental data usually obtained via high-throughput techniques from genomic, proteomic or metabolomic analyses, and fully exploit the frontier-level developments of hardware and software technologies. The attractiveness of systems biology for the drug discovery community seems evident if one just can imagine the possibility to simulate the response of a cell, an organ or even the whole body to the administration of a drug. Efforts to incorporate computational systems biology tools in the drug discovery practice are already being carried out [18], and the prediction of the adverse effects of candidate drugs might be expected as one of the main goals of such an approach. As a matter of fact, researchers at Physiome Science Inc (Princeton, USA, http://www.physiome.com) recently reported the use of an integrated biological simulation environment to profile the cardiac safety of drug candidates for assisting the drug discovery process [19]. This study was centered on the modeling of hERG block-related effects and took advantage of a simulation system where mathematical descriptions of fundamental biological processes (such as signaling, transport, excitability, cell cycle and metabolism)
were integrated into models of metabolic and signaling pathways in the cardiac cell.
Other key technologies In a paper dealing with in silico methods applied in the field of drug discovery, it is necessary to mention two other technologies that play a basic role herein, in the sense that many of the mentioned approaches rely more or less on their products: protein modeling [20] and bioinformatics [21]. Under these headings, all those methods can be collected that are used to build 3D models of (anti)targets on one side, and to study genes and proteins from the sequence point of view on the other. Having a reliable 3D model of a protein is indispensable for any target-based simulation technique, like building ligandtarget complex (DOCKING; see Glossary) models and performing VS experiments; also, the functioning of a(n) (anti)target, like an enzyme or a membrane channel, can be simulated, and such models can be useful per se or in view of their integration in higher level system models. Similarly, the impact of bioinformatic techniques in modern drug discovery has recently been highlighted by their application to the analysis of genomic sequences aimed at finding genes eventually involved in diseases, and at assisting the process of (anti)target identification and validation.
Comparison of technologies Main advantages and disadvantages of the approaches described above to the in silico antitarget screening are briefly indicated in Table 1. These pros and cons should be taken in a general meaning that refers to the basic characteristics of each method more than to the specific application to the study of antitargets. Details have been missed out in this short article (the interested reader can find them in the references), but anyway it is hard to say which approach is more or less suitable to this kind of application. Indeed, (3D) QSAR technologies
Table 1. Comparison summary table Name of specific type of technology
QSAR
3D QSAR
Virtual screening
Computational systems biology
Names of specific technologies with associated companies and company websites
C QSAR, BioByte, Claremont, CA, USA, http://www.biobyte.com
CoMFA, CoMSIA, Tripos Inc, St. Louis, USA, http://www.tripos.com Catalyst, Accelrys Inc, San Diego, USA, http://www.accelrys.com
Pros
No need for target information
No need for target information
Possibility to search vast libraries
System-level approach
Cons
No consideration of 3D features
Affected by quality of conformational sampling; building of ‘‘local’’ models
Heavily affected by filtering and by treatment of ligand and target flexibility
Heavy hardware and software requirements
References
[5]
[8]
[15]
[18,19]
QSAR, quantitative structure–activity relationships; C QSAR, comparative QSAR; CoMFA, comparative molecular field analysis; CoMSIA, comparative molecular similarity indices analysis. www.drugdiscoverytoday.com
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seem more appropriate for a quick identification of the molecular features associated with the binding to an antitarget, but, by contrast, a 3D ligand–target interaction model gives a more thorough picture of the putative(s) binding mode(s). Ultimately, however, only a simulation based on the consideration of the entire biological system can (in principle) account for all of the interconnected phenomena driving the effects of a drug. From a more pragmatic point of view, if in silico techniques have to be applied to assist the lead selection process, one might propose that QSAR-based methods could be more appropriate to carry out predictive analyses on smallmedium (tenths to hundreds) sets of compounds, whereas efficient VS protocols should be employed to filter extended (thousands to millions) molecular databases. A crucial issue to be considered when applying computational methods to antitarget screening is that in silico methods in drug design were developed to identify molecules endowed with an increased ability to bind a target (or with a higher potency towards a pharmacological end point), not devoid of it. Conversely, in the case of antitargets, the latter is the goal and this change of perspective has neither been translated yet into appropriate software tools, nor even fully appreciated. However, the inclusion of antitarget filtering in VS protocols might be a step in this direction. Finally, it has to be underlined that any attempt to rigorously consider the problem of antitarget binding in a drug discovery project will heavily depend on some other related issues and not only on the selected computational approach (see Outstanding issues). Indeed, in silico technology alone cannot guarantee the identification of new safe and effective lead compounds, but, more realistically, ‘‘future success [will] depend on the proper integration of new promising technologies with the experience and strategies of classical medicinal chemistry’’ [22].
Conclusions In the selection of new drug candidates, many efforts are focused on the early elimination of compounds that might interact with antitargets related to undesired effects, like toxicity, drug–drug interactions or increased metabolism, among others. In silico screening of antitarget liability is going to become a central issue in any rigorous drug discovery
Related articles Jorgensen, W.L. (2004) The many roles of computation in drug discovery. Science 303, 1813–1818 van de Waterbeemd, H. and Gifford, E. (2003) ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192–204 Ekins, S. (2004) Predicting undesirable drug interactions with promiscuous proteins in silico. Drug Discov. Today 9, 276–285 Bugrim, A. et al. (2004) Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov. Today 9, 127–135
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project, and available ligand- or target-based computational technologies allow this aspect to be accounted for, as shown by several reported examples. At the moment, there is not an ideal approach to this problem, but efforts are being made to incorporate antitarget screening in the most advanced in silico techniques for lead identification and optimization.
Outstanding issues Quality of biological data: any computational model reflects the quality of the experimental data on which it is based. This is particularly true when predictions are attempted based on a ‘‘training set’’ of compounds of known activity: highly uncertain and nonhomogeneous (determined in different laboratories or on different biological systems) data can heavily affect the precision and the reliability of the predicted values. Availability of biological data: collections of data on interactions of candidate leads with antitargets probably exist in the files of pharmaceutical companies, but public access is precluded. Putting them together would allow us to build information-rich training sets of compounds, on which to base the development of comprehensive models of ligand–antitarget interactions. Integration of languages and models: biologists, chemists, and computer scientists collaborate in advanced drug discovery projects, and they need to develop a standard platform of languages, concepts, and research strategies. With regards to the developed computational models, ideally they should be integrated by interfacing the statistical (QSAR), target-based, and system-based levels to allow a flow of input and output information at any level of complexity.
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