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In silico prediction of clinical efficacy
In silico prediction of clinical efficacy Seth Michelson, Anil Sehgal and Christina Friedrich Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials. Addresses Entelos, Inc., 110 Marsh Drive, Foster City, CA 94404, USA Corresponding author: Friedrich, Christina ([email protected])
Current Opinion in Biotechnology 2006, 17:666–670 This review comes from a themed issue on Pharmaceutical biotechnology Edited by Gary Woodnutt and Frank S Walsh Available online 12th October 2006 0958-1669/$ – see front matter # 2006 Elsevier Ltd. All rights reserved. DOI 10.1016/j.copbio.2006.09.004
Introduction Drug discovery and development is a complicated and risky process. Approximately 53% of compounds entering phase II clinical trials are likely to fail, resulting in amortized costs of approximately $800 000 000 per final approved drug [1]. If the pharmaceutical industry could accurately predict which compounds will fail in the clinic before their entry into phase II trials, it would streamline the entire development pipeline and lead to obvious savings in time, money and resources. Yet, despite significant investments in numerous technologies by pharmaceutical companies over the past ten years, clinical attrition rates have changed very little. The ability to predict clinical efficacy in silico (‘in a computer’) could save the pharmaceutical industry time and resources, and might ultimately lead to more targeted, personalized therapies. By ‘clinical efficacy’, we mean a compound that exhibits — within the constraints of a controlled clinical trial — a significant improvement over a current standard of care. By ‘in silico’, we refer to any Current Opinion in Biotechnology 2006, 17:666–670
application of computer-based technologies — algorithms, systems and data mining/analysis techniques — for the characterization of clinically relevant design and decision points in the discovery and development pipelines (e.g. target identification and validation, clinical trial design and execution, etc.). Here we review the suite of in silico tools and technologies aimed at predicting clinical efficacy that could have a major impact on reducing the high rates of failure in the clinical trials of new compounds.
In silico technologies Recent guidelines for applying simulation technologies to drug development were published by the Center for Drug Development Science at the University of California, San Francisco [2]. Each step in the process is depicted in (Figure 1). During step 1, the most relevant underlying biology describing the pathophysiology of the disease is characterized, as are the pharmacokinetics (PK) of any candidate compound aimed at its treatment. In step 2, the various clinical subpopulations expected to receive the compound are identified and characterized, including measures of interpatient variability in drug absorption, distribution, metabolism and excretion (ADME), and compound-specific pharmacodynamics (PD) are established. Once steps 1 and 2 are complete, this information is used in step 3 to design the most efficient clinical trial possible.
Step 1: characterize the underlying disease biology and its treatment Predicting clinical efficacy requires the identification and validation of a drug target, which in turn necessitates a detailed understanding of the disease and its pathophysiology. In silico techniques that have been developed to address this challenge and to test hypotheses a priori include physiological modeling and pharmacokinetic modeling. Physiological modeling
Over the past decade, physiology-based mathematical models and biosimulation systems have been applied to both target identification and validation [3–7]. Once a potential target has been identified and validated, knowledge about the disease — its initiation and progression — can be used to optimize the clinical protocols required to ensure the success of a candidate compound. For example, physiological models of cancer growth and therapy have been used to suggest optimal chemotherapeutic regimens in breast cancer [8–10]. Similarly, a model of the heart was developed to characterize the pathophysiology underlying electrocardiographic dysfunction [11,12]. www.sciencedirect.com
Predicting of clinical efficacy Michelson, Sehgal and Friedrich 667
Figure 1
that whenever a new component or connection is discovered, the entire model must be reconfigured [4]. For a review of the progress of each organization, see the article by Ho [5]. Top-down modeling
By contrast, top-down modeling begins with clinical observations and knowledge about the behavior of a system as a whole. For any given disease process, once clinically relevant behaviors have been identified, the modeling process begins by identifying those subsystems required to reproduce them. The physiological and biological components that make up those subsystems are then modeled. This process continues in an iterative fashion, adding greater detail to each subsystem of the model [4]. In this way, each subsystem is constrained by the overall behavior of the entire system. Pharmacokinetic modeling
The application of simulation technologies in the drug development process together with the in silico tools that can be employed at each stage.
Two recent reviews discuss, in detail, how these types of models have been built and exploited for target identification [4,5]. Two dominant strategies have emerged during these analyses: bottom-up and top-down modeling. Bottom-up modeling
In bottom-up modeling, all the known parts of a system (typically thousands of genes and proteins) are assembled and connected, piece by piece, into a formal structure until a model of the system is attained [12]. Linkages between components are typically determined from expression profile data and hypothetical pathway maps. Several organizations, mainly academic, have generated a number of models across a variety of cellular systems; for example, the Alliance for Cellular Signaling (cellular signaling in B-lymphocytes; http://www.signaling-gatway.org), Cell Systems Initiative (predictive models of dendritic cells and T-cells; http://csi.washington.edu), ECell Project (cellular behavior of erythrocytes, Escherichia coli, and neurons; http://www.e-cell.org/), Institute for Systems Biology (perturbation studies in microbes, immune cells and tumor cells; http://www.systemsbiology.org/), and the Molecular Sciences Institute (design and engineering of biological systems in E. coli and yeast; http://www.molsci.org/). One drawback of this approach is www.sciencedirect.com
To predict clinical efficacy, predicting the pharmacokinetics of a compound is crucial. A compound’s pharmacokinetics are described by parameters for curves that represent the time course of a compound’s presence in any of a number of body ‘compartments’. These compartments typically include those that are easily monitored with minimally invasive procedures such as the collection of serum, urine and feces. Estimates of a compound’s PK are achieved by fitting actual data to a theoretical model of compound distribution and excretion. Typically, a standard compartment model is developed and fit to the observed data (either animal or phase I clinical data). And, although there are a multitude of such models to choose from [13], prior knowledge of the drug’s ADME is typically used to guide the fitting process [14]. Then, a rigorous ‘goodness-of-fit’ statistic is generated, and the ‘best’ model is chosen. For example, Norris et al. [15] developed a physiology-based PK model to predict the oral bioavailability of ganciclovir, a well-known antiviral. On the basis of their in silico results, they determined that the poor bioavailability observed in the clinic resulted from compound solubility issues, rather than from low permeability in the gut as originally hypothesized. Sarkar and Lauffenberger [16] modeled the PK of granulocyte colony-stimulating factor (GCSF). As the PK of GCSF depends explicitly on its therapeutic effects, their model represents both normal clearance (renal and hepatic) and localized endocytosis and compound degradation by target cells. Simulation studies in this system suggest that a modified GCSF analog that eliminates renal clearance would increase the therapy’s potential efficacy by markedly improving peripheral neutrophil counts.
Step 2: characterize relevant patient subpopulations To achieve efficient clinical efficacy, the patients in which a compound will work best must be identified. Current Opinion in Biotechnology 2006, 17:666–670
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Three in silico methods to identify potential responder populations are currently in use: the characterization of covariates that drive population PD; pharmacogenomic pattern recognition; and development of virtual patient profiles. Population pharmacodynamics
An in silico technology that addresses patient variability in diverse populations is termed ‘population pharmacodynamics’ [17,18,19]. This strategy employs a suite of statistical algorithms that define and estimate populationwide dose-response curves, while appropriately accounting for statistical noise. These estimates are used in modeling the ultimate clinical trial and its design during step 3 of the drug development process. These algorithms use ‘mixed effects models’ to fit observed data to a well-defined model of a dose response curve. If the model is nonlinear (e.g. the Hill model of dose response), it is fit with an algorithm termed NONMEM (NONlinear Mixed Effect Models). Based on a priori knowledge of the patient populations at hand, one can propose the existence of relevant blocking factors or covariates (e.g. age, weight, gender, race, etc.) and explicitly account for these during the model fitting process. A general review of these modeling and simulation technologies in drug discovery and development has been presented by Chien et al. [17]. It should be noted that these data are all post hoc, and that this technology is inherently deductive. Pharmacogenomics and pharmacogenetics
One of the key characteristics in determining clinical efficacy in a particular subpopulation is how those patients metabolize and clear a compound [20–22]. In the late 1950s and early 1960s, a series of familial studies were conducted to determine if a genetic component could account for the differences observed in the metabolism of several compounds (e.g. succinylcholine, isoniazid and hydralzine). These linkage analyses identified two classes of patients: fast acetylators and slow acetylators. Studies of this type have been termed ‘pharmacogenetic’. With the advent of microarray technologies and highthroughput screening techniques, genome-wide scans of drug responses and metabolism have been generated. Analysis of this data is termed ‘pharmacogenomics’. This approach requires sophisticated multivariate analyses and pattern-recognition technologies. Whether aimed at characterizing toxic potential (toxicogenomics; e.g. see [23]), patient-specific ADME characteristics (see [22]) or other aspects of subpopulation membership, the data are so massive and complex that the only way to exploit them in a predictive setting is to use a family of in silico algorithms for their analysis. These algorithms fall into two main categories: unsupervised and supervised. Current Opinion in Biotechnology 2006, 17:666–670
Unsupervised pharmacogenomic tools typically include techniques developed from classical multivariate statistics (e.g. hierarchical clustering, discriminant function analysis, etc. [24–26]). Supervised pharmacogenomic tools require that guidance be provided to the algorithm before the first analysis (e.g. a training set of data; see [27] for details). These techniques include support vector machines [28], self-organizing maps [29,30], decision trees [31], and K-means clusters (see [32] for a detailed review). Over the past ten years, these tools have been used to characterize several disease-specific patient subpopulations. Chief among these are the staging, treatment, and prognosis of cancer patients (Wigle et al. in lung cancer [32]; Furey et al. in leukemias and colon cancers [28]; Kari et al. in lymphomas [25]). Virtual patient and subpopulation characterization
By explicitly linking a hypothesis of disease pathophysiology to an explicit set of biological lesions, an image of a patient can be constructed in the context of a particular in silico model. These constructs are termed ‘virtual patients’. In a recent article [33], Michelson described how a multivariate response surface and support space can be developed to characterize patient responder types. On the basis of that construct, biomarkers defining the patient subpopulations can be identified and characterized.
Step 3: design clinical trials Once a disease model has been chosen and responder subpopulations identified, an efficient clinical trial can be designed. Several in silico technologies have been developed to enable this process. The two most notable are Monte Carlo simulation systems [34] and physiologybased predictive biosimulation systems [35]. Monte Carlo simulation
In a recent commentary, Bonate [34] provides a general overview of Monte Carlo simulations and their application to drug development and clinical trial optimization. Additionally, Poland and Wada [36] provide insights into how these systems can include aspects of economic modeling to better inform the drug development process. An excellent example of integrating the outputs of physiology-based PK models into population PD models and Monte Carlo clinical trial systems is provided by Gieschke et al. [37]. In their study, the authors evaluated two candidate trial designs for an oral anticancer drug and, on the basis of their studies, selected an optimal dosing scheme for their trial design. Lynd and O’Brien [38] used a Monte Carlo simulator to estimate the risk-benefit trade-off between dosing with unfractionated heparin and the advent of deep vein thrombosis. In a pair of papers, Eddy and Schlessinger [39,40] describe the construction, validation, and application of the Archimedes system — a statistically based clinical Monte Carlo www.sciencedirect.com
Predicting of clinical efficacy Michelson, Sehgal and Friedrich 669
simulator used to design clinical trials and manage healthcare delivery.
best answers the most pressing questions). By driving the collection of the most relevant data, they increase the ultimate success rates for the trials.
Predictive biosimulation systems
Clinical trial simulations can also be run using a mechanistic model of whole-body physiology. These simulations use virtual patients to represent each of the target patient populations [4]. Kansal and Trimmer [35] used this approach to optimize a clinical study design by identifying the most predictive clinical endpoint and optimal inclusion-exclusion criteria and/or covariate biomarkers to characterize the patient subpopulations. Their design resulted in a protocol that yielded the most informative dosing regimen available. Using a similar approach, Trimmer et al. [41] streamlined a phase I trial for efficacy, while controlling for hypoglycemia in the treatment of diabetes (an unwanted adverse effect). Lastly, Skomorovski et al. [42] used a detailed model of thrombopoiesis to identify possible new treatment protocols for thrombopoietin that would both increase efficacy and decrease the risk of potential immunogenic side effects. The protocol was developed and validated in preclinical models (mouse and rhesus monkey) and showed that the model-derived protocols yielded equivalent platelet profiles with significantly reduced immunogenicities.
Conclusions Drug discovery and development is a complicated and risky process, with failure rates of 85% to 90% for compounds entering clinical trials being common [1]. The pharmaceutical industry could save significant time and resources if they were able to effectively predict clinical efficacy in silico. This article reviews several strategies to predict clinical efficacy: physiological modeling of disease and disease processes, analytical tools for characterizing population PD, analysis tools for genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. All the methodologies described above depend upon both the quality and completeness of the data at hand. The ultimate choice of modeling technology employed depends explicitly upon the availability of that data. For example, in the case of Monte Carlo clinical trial simulators, one must provide estimates for the distribution of covariates and covariate-dependent patient responses to the simulation engine before trial design. To derive these estimates and determine their statistical relevance, one can use data from both PK and population PD modeling efforts. Thus, data from steps 1 and 2 of the process guide the trial design processes outlined in Step 3. Additionally, these technologies are inherently iterative, in that they provide not only answers to the questions at hand, but guidance as to what the ‘next best experiment to do’ would be (i.e. they characterize the experiment that www.sciencedirect.com
Although much of the literature regarding clinical trial simulations is focused on the post hoc verification of the systems against extant datasets, there are some notable examples of prospective efforts that have aided clinical development. Three in particular include the successful application of population PK/PD models by Glaxo Wellcome to support a drug labeling effort for the muscle relaxant cisatracurium (reviewed by Bonate [34]; cisatracurium is sold under the trade name NIMBEX1 and is an immediate-onset/immediate-duration neuromuscular blocking agent administered as an adjunct to general anesthesia during surgery), the efforts of Gieschke et al. [37] to determine the optimal dosing regimen for an oral anticancer drug, and the work by Kansal and Trimmer [35] in which an optimized trial protocol was established that markedly reduced the size and duration of a phase I trial for an antidiabetic drug. In this latter study, the information obtained was then used to further refine the design of follow-on phase II studies. Once the pathophysiology of a disease is mechanistically characterized, and a set of patient subpopulations defined, one can use the simulation technologies described in step 3 to pre-test any adaptive trial design available to the clinical researcher. For example, one can use these simulation technologies to address issues surrounding subpopulation prevalence, the cost of biomarker screening (in both money and patient recruitment rates), and so on. Thus, these in silico technologies can be used to minimize the failure rates such trials may accrue. Also, assuming that the primary aim of a pharmaceutical company is compound approval, it is important that these technologies identify both those patients most likely to respond to a given drug and the biomarkers needed to stratify them. This information can then be used to develop the most predictive inclusion-exclusion criteria and population-specific covariates for the trial design.
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
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10. Hart D, Shochat E, Agur Z: The growth law of primary breast cancer as inferred from mammography screening trials data. Br J Cancer 1998, 78:382-387. 11. Noble D, Levin J, Scott W: Biological simulations in drug discovery. Drug Discov Today 1999, 4:10-16. 12. Noble D: Modeling the heart – from genes to cells to the whole organ. Science 2002, 295:1678-1682. 13. Boobis A, Gundert-Remy U, Kremers P, Macheras P, Pelkonen O: In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15. Eur J Pharm Sci 2002, 17:183-193. 14. Hu Y, Akland GG, Pellizzari ED, Berry MR, Melnyk LJ: Use of pharmacokinetic modeling to design studies for pathway specific exposure model evaluation. Environ Health Perspect 2004, 112:1697-1703. 15. Norris DA, Leesman GD, Sinko PJ, Grass GM: Development of predictive pharmacokinetic simulation models for drug discovery. J Control Release 2000, 65:55-62. 16. Sarkar CA, Lauffenburger DA: Cell-level pharmacokinetic model of granulocyte colony-stimulating factor: implications for ligand lifetime and potency in vivo. Mol Pharmacol 2003, 63:147-158. 17. Chien JY, Friedrich S, Heathman MA, de Alwis DP, Sinha V: Pharmacokinetics/pharmacodynamics and the stages of drug development: role of modeling and simulation. AAPS J 2005, 7:E544-E559. A review of the application of modeling and simulation technologies for the estimation and characterization of PK and PD effects in a clinical population. See especially Table 1 in this review for an excellent overview of the drug development pipeline and the role that modeling and simulation can play in each. 18. Ebling WF, Levy G: Population pharmacodynamics: strategies for concentration- and effect-controlled clinical trials. Ann Pharmacother 1996, 30:12-19. 19. Roy A, Ette EI: A pragmatic approach to the design of population pharmacokinetic studies. AAPS J 2005, 7:E408-E420. An excellent overview of the practical requirements to design, implement, and interpret population PK/PD studies. Included is a comparison between two simulation/analysis strategies (D-optimal designs and informative block designs) and their relative efficiencies in the derivation of the PK parameters of interest. 20. Weinshilboum R: Inheritance and drug response. N Engl J Med 2003, 348:529-537. 21. Evans WE, McLeod HL: Pharmacogenomics – drug disposition, drug targets, and side effects. N Engl J Med 2003, 348:538-549. 22. Ekins S, Nikolsky Y, Nikolskaya T: Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol Sci 2005, 26:202-209. 23. Fielden MR, Matthews JB, Fertuck KC, Halgren RG, Zacharewski TR: In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists. Crit Rev Toxicol 2002, 32:67-112. Current Opinion in Biotechnology 2006, 17:666–670
24. Makretsov NA, Huntsman DG, Nielsen TO, Yorida E, Peacock M, Cheang MC, Dunn SE, Hayes M, van de RM, Bajdik C, Gilks CB: Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clin Cancer Res 2004, 10:6143-6151. 25. Kari L, Loboda A, Nebozhyn M, Rook AH, Vonderheid EC, Nichols C, Virok D, Chang C, Horng WH, Johnston J et al.: Classification and prediction of survival in patients with the leukemic phase of cutaneous T cell lymphoma. J Exp Med 2003, 197:1477-1488. 26. Zhao Y, Karypis G: Data clustering in life sciences. Mol Biotechnol 2005, 31:55-80. 27. Ludwig JA, Weinstein JN: Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005, 5:845-856. 28. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906-914. 29. Tamayo P, Slonim D, Meisrov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E, Golub T: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 1999, 96:2907-2912. 30. Wang D, Ressom H, Musavi M, Domnisoru C: Double selforganizing maps to cluster gene expression data. ESANN0 2002 proceedings – European Symposium on Artificial Neural Networks; Bruges, Belgium, 24-26 April 2002, pp. 45-50. 31. Zhang H, Yu C-Y, Singer B: Cell and tumor classification using gene expression data: Construction of forests. Proc Natl Acad Sci USA 2003, 100:4168-4172. 32. Wigle DA, Tsao M, Jurisica I: Making sense of lung cancer gene-expression profiles. Genome Biol 2004, 5:309-311. 33. Michelson S: The impact of systems biology and biosimulation on drug discovery and development. Mol Biosyst 2006, 2:288-291. 34. Bonate PL: Clinical trial simulation in drug development. Pharm Res 2000, 17:252-256. 35. Kansal A, Trimmer J: Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design. IEE Proc-Syst Biol 2006, 152:214-220. 36. Poland B, Wada R: Combining drug-disease and economic modelling to inform drug development decisions. Drug Discov Today 2001, 6:1165-1170. 37. Gieschke R, Reigner BG, Steimer JL: Exploring clinical study design by computer simulation based on pharmacokinetic/ pharmacodynamic modelling. Int J Clin Pharmacol Ther 1997, 35:469-474. 38. Lynd LD, O’Brien BJ: Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis. J Clin Epidemiol 2004, 57:795-803. 39. Eddy DM, Schlessinger L: Archimedes: a trial-validated model of diabetes. Diabetes Care 2003, 26:3093-3101. 40. Eddy DM, Schlessinger L: Validation of the Archimedes diabetes model. Diabetes Care 2003, 26:3102-3110. 41. Trimmer J, McKenna C, Sudbeck B, Ho R: Use of systems biology in clinical development: design and prediction of a type 2 diabetes clinical Trial. PAREXEL Pharmaceutical R&D Sourcebook 2004 p.131. 42. Skomorovski K, Harpak H, Ivanovski A, Vardi M, Visser TP, Hartong S, van Vliet H, Wagemaker G, Agur Z: New TPO treatment schedules of increased safety and efficacy: preclincial validation of a thrombopoiesis simulation model. Br J Haemtol 2003, 123:683-691. www.sciencedirect.com
Current Opinion in Biotechnology 2006, 17:666–670 This review comes from a themed issue on Pharmaceutical biotechnology Edited by Gary Woodnutt and Frank S Walsh Available online 12th October 2006 0958-1669/$ – see front matter # 2006 Elsevier Ltd. All rights reserved. DOI 10.1016/j.copbio.2006.09.004
Introduction Drug discovery and development is a complicated and risky process. Approximately 53% of compounds entering phase II clinical trials are likely to fail, resulting in amortized costs of approximately $800 000 000 per final approved drug [1]. If the pharmaceutical industry could accurately predict which compounds will fail in the clinic before their entry into phase II trials, it would streamline the entire development pipeline and lead to obvious savings in time, money and resources. Yet, despite significant investments in numerous technologies by pharmaceutical companies over the past ten years, clinical attrition rates have changed very little. The ability to predict clinical efficacy in silico (‘in a computer’) could save the pharmaceutical industry time and resources, and might ultimately lead to more targeted, personalized therapies. By ‘clinical efficacy’, we mean a compound that exhibits — within the constraints of a controlled clinical trial — a significant improvement over a current standard of care. By ‘in silico’, we refer to any Current Opinion in Biotechnology 2006, 17:666–670
application of computer-based technologies — algorithms, systems and data mining/analysis techniques — for the characterization of clinically relevant design and decision points in the discovery and development pipelines (e.g. target identification and validation, clinical trial design and execution, etc.). Here we review the suite of in silico tools and technologies aimed at predicting clinical efficacy that could have a major impact on reducing the high rates of failure in the clinical trials of new compounds.
In silico technologies Recent guidelines for applying simulation technologies to drug development were published by the Center for Drug Development Science at the University of California, San Francisco [2]. Each step in the process is depicted in (Figure 1). During step 1, the most relevant underlying biology describing the pathophysiology of the disease is characterized, as are the pharmacokinetics (PK) of any candidate compound aimed at its treatment. In step 2, the various clinical subpopulations expected to receive the compound are identified and characterized, including measures of interpatient variability in drug absorption, distribution, metabolism and excretion (ADME), and compound-specific pharmacodynamics (PD) are established. Once steps 1 and 2 are complete, this information is used in step 3 to design the most efficient clinical trial possible.
Step 1: characterize the underlying disease biology and its treatment Predicting clinical efficacy requires the identification and validation of a drug target, which in turn necessitates a detailed understanding of the disease and its pathophysiology. In silico techniques that have been developed to address this challenge and to test hypotheses a priori include physiological modeling and pharmacokinetic modeling. Physiological modeling
Over the past decade, physiology-based mathematical models and biosimulation systems have been applied to both target identification and validation [3–7]. Once a potential target has been identified and validated, knowledge about the disease — its initiation and progression — can be used to optimize the clinical protocols required to ensure the success of a candidate compound. For example, physiological models of cancer growth and therapy have been used to suggest optimal chemotherapeutic regimens in breast cancer [8–10]. Similarly, a model of the heart was developed to characterize the pathophysiology underlying electrocardiographic dysfunction [11,12]. www.sciencedirect.com
Predicting of clinical efficacy Michelson, Sehgal and Friedrich 667
Figure 1
that whenever a new component or connection is discovered, the entire model must be reconfigured [4]. For a review of the progress of each organization, see the article by Ho [5]. Top-down modeling
By contrast, top-down modeling begins with clinical observations and knowledge about the behavior of a system as a whole. For any given disease process, once clinically relevant behaviors have been identified, the modeling process begins by identifying those subsystems required to reproduce them. The physiological and biological components that make up those subsystems are then modeled. This process continues in an iterative fashion, adding greater detail to each subsystem of the model [4]. In this way, each subsystem is constrained by the overall behavior of the entire system. Pharmacokinetic modeling
The application of simulation technologies in the drug development process together with the in silico tools that can be employed at each stage.
Two recent reviews discuss, in detail, how these types of models have been built and exploited for target identification [4,5]. Two dominant strategies have emerged during these analyses: bottom-up and top-down modeling. Bottom-up modeling
In bottom-up modeling, all the known parts of a system (typically thousands of genes and proteins) are assembled and connected, piece by piece, into a formal structure until a model of the system is attained [12]. Linkages between components are typically determined from expression profile data and hypothetical pathway maps. Several organizations, mainly academic, have generated a number of models across a variety of cellular systems; for example, the Alliance for Cellular Signaling (cellular signaling in B-lymphocytes; http://www.signaling-gatway.org), Cell Systems Initiative (predictive models of dendritic cells and T-cells; http://csi.washington.edu), ECell Project (cellular behavior of erythrocytes, Escherichia coli, and neurons; http://www.e-cell.org/), Institute for Systems Biology (perturbation studies in microbes, immune cells and tumor cells; http://www.systemsbiology.org/), and the Molecular Sciences Institute (design and engineering of biological systems in E. coli and yeast; http://www.molsci.org/). One drawback of this approach is www.sciencedirect.com
To predict clinical efficacy, predicting the pharmacokinetics of a compound is crucial. A compound’s pharmacokinetics are described by parameters for curves that represent the time course of a compound’s presence in any of a number of body ‘compartments’. These compartments typically include those that are easily monitored with minimally invasive procedures such as the collection of serum, urine and feces. Estimates of a compound’s PK are achieved by fitting actual data to a theoretical model of compound distribution and excretion. Typically, a standard compartment model is developed and fit to the observed data (either animal or phase I clinical data). And, although there are a multitude of such models to choose from [13], prior knowledge of the drug’s ADME is typically used to guide the fitting process [14]. Then, a rigorous ‘goodness-of-fit’ statistic is generated, and the ‘best’ model is chosen. For example, Norris et al. [15] developed a physiology-based PK model to predict the oral bioavailability of ganciclovir, a well-known antiviral. On the basis of their in silico results, they determined that the poor bioavailability observed in the clinic resulted from compound solubility issues, rather than from low permeability in the gut as originally hypothesized. Sarkar and Lauffenberger [16] modeled the PK of granulocyte colony-stimulating factor (GCSF). As the PK of GCSF depends explicitly on its therapeutic effects, their model represents both normal clearance (renal and hepatic) and localized endocytosis and compound degradation by target cells. Simulation studies in this system suggest that a modified GCSF analog that eliminates renal clearance would increase the therapy’s potential efficacy by markedly improving peripheral neutrophil counts.
Step 2: characterize relevant patient subpopulations To achieve efficient clinical efficacy, the patients in which a compound will work best must be identified. Current Opinion in Biotechnology 2006, 17:666–670
668 Pharmaceutical biotechnology
Three in silico methods to identify potential responder populations are currently in use: the characterization of covariates that drive population PD; pharmacogenomic pattern recognition; and development of virtual patient profiles. Population pharmacodynamics
An in silico technology that addresses patient variability in diverse populations is termed ‘population pharmacodynamics’ [17,18,19]. This strategy employs a suite of statistical algorithms that define and estimate populationwide dose-response curves, while appropriately accounting for statistical noise. These estimates are used in modeling the ultimate clinical trial and its design during step 3 of the drug development process. These algorithms use ‘mixed effects models’ to fit observed data to a well-defined model of a dose response curve. If the model is nonlinear (e.g. the Hill model of dose response), it is fit with an algorithm termed NONMEM (NONlinear Mixed Effect Models). Based on a priori knowledge of the patient populations at hand, one can propose the existence of relevant blocking factors or covariates (e.g. age, weight, gender, race, etc.) and explicitly account for these during the model fitting process. A general review of these modeling and simulation technologies in drug discovery and development has been presented by Chien et al. [17]. It should be noted that these data are all post hoc, and that this technology is inherently deductive. Pharmacogenomics and pharmacogenetics
One of the key characteristics in determining clinical efficacy in a particular subpopulation is how those patients metabolize and clear a compound [20–22]. In the late 1950s and early 1960s, a series of familial studies were conducted to determine if a genetic component could account for the differences observed in the metabolism of several compounds (e.g. succinylcholine, isoniazid and hydralzine). These linkage analyses identified two classes of patients: fast acetylators and slow acetylators. Studies of this type have been termed ‘pharmacogenetic’. With the advent of microarray technologies and highthroughput screening techniques, genome-wide scans of drug responses and metabolism have been generated. Analysis of this data is termed ‘pharmacogenomics’. This approach requires sophisticated multivariate analyses and pattern-recognition technologies. Whether aimed at characterizing toxic potential (toxicogenomics; e.g. see [23]), patient-specific ADME characteristics (see [22]) or other aspects of subpopulation membership, the data are so massive and complex that the only way to exploit them in a predictive setting is to use a family of in silico algorithms for their analysis. These algorithms fall into two main categories: unsupervised and supervised. Current Opinion in Biotechnology 2006, 17:666–670
Unsupervised pharmacogenomic tools typically include techniques developed from classical multivariate statistics (e.g. hierarchical clustering, discriminant function analysis, etc. [24–26]). Supervised pharmacogenomic tools require that guidance be provided to the algorithm before the first analysis (e.g. a training set of data; see [27] for details). These techniques include support vector machines [28], self-organizing maps [29,30], decision trees [31], and K-means clusters (see [32] for a detailed review). Over the past ten years, these tools have been used to characterize several disease-specific patient subpopulations. Chief among these are the staging, treatment, and prognosis of cancer patients (Wigle et al. in lung cancer [32]; Furey et al. in leukemias and colon cancers [28]; Kari et al. in lymphomas [25]). Virtual patient and subpopulation characterization
By explicitly linking a hypothesis of disease pathophysiology to an explicit set of biological lesions, an image of a patient can be constructed in the context of a particular in silico model. These constructs are termed ‘virtual patients’. In a recent article [33], Michelson described how a multivariate response surface and support space can be developed to characterize patient responder types. On the basis of that construct, biomarkers defining the patient subpopulations can be identified and characterized.
Step 3: design clinical trials Once a disease model has been chosen and responder subpopulations identified, an efficient clinical trial can be designed. Several in silico technologies have been developed to enable this process. The two most notable are Monte Carlo simulation systems [34] and physiologybased predictive biosimulation systems [35]. Monte Carlo simulation
In a recent commentary, Bonate [34] provides a general overview of Monte Carlo simulations and their application to drug development and clinical trial optimization. Additionally, Poland and Wada [36] provide insights into how these systems can include aspects of economic modeling to better inform the drug development process. An excellent example of integrating the outputs of physiology-based PK models into population PD models and Monte Carlo clinical trial systems is provided by Gieschke et al. [37]. In their study, the authors evaluated two candidate trial designs for an oral anticancer drug and, on the basis of their studies, selected an optimal dosing scheme for their trial design. Lynd and O’Brien [38] used a Monte Carlo simulator to estimate the risk-benefit trade-off between dosing with unfractionated heparin and the advent of deep vein thrombosis. In a pair of papers, Eddy and Schlessinger [39,40] describe the construction, validation, and application of the Archimedes system — a statistically based clinical Monte Carlo www.sciencedirect.com
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simulator used to design clinical trials and manage healthcare delivery.
best answers the most pressing questions). By driving the collection of the most relevant data, they increase the ultimate success rates for the trials.
Predictive biosimulation systems
Clinical trial simulations can also be run using a mechanistic model of whole-body physiology. These simulations use virtual patients to represent each of the target patient populations [4]. Kansal and Trimmer [35] used this approach to optimize a clinical study design by identifying the most predictive clinical endpoint and optimal inclusion-exclusion criteria and/or covariate biomarkers to characterize the patient subpopulations. Their design resulted in a protocol that yielded the most informative dosing regimen available. Using a similar approach, Trimmer et al. [41] streamlined a phase I trial for efficacy, while controlling for hypoglycemia in the treatment of diabetes (an unwanted adverse effect). Lastly, Skomorovski et al. [42] used a detailed model of thrombopoiesis to identify possible new treatment protocols for thrombopoietin that would both increase efficacy and decrease the risk of potential immunogenic side effects. The protocol was developed and validated in preclinical models (mouse and rhesus monkey) and showed that the model-derived protocols yielded equivalent platelet profiles with significantly reduced immunogenicities.
Conclusions Drug discovery and development is a complicated and risky process, with failure rates of 85% to 90% for compounds entering clinical trials being common [1]. The pharmaceutical industry could save significant time and resources if they were able to effectively predict clinical efficacy in silico. This article reviews several strategies to predict clinical efficacy: physiological modeling of disease and disease processes, analytical tools for characterizing population PD, analysis tools for genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. All the methodologies described above depend upon both the quality and completeness of the data at hand. The ultimate choice of modeling technology employed depends explicitly upon the availability of that data. For example, in the case of Monte Carlo clinical trial simulators, one must provide estimates for the distribution of covariates and covariate-dependent patient responses to the simulation engine before trial design. To derive these estimates and determine their statistical relevance, one can use data from both PK and population PD modeling efforts. Thus, data from steps 1 and 2 of the process guide the trial design processes outlined in Step 3. Additionally, these technologies are inherently iterative, in that they provide not only answers to the questions at hand, but guidance as to what the ‘next best experiment to do’ would be (i.e. they characterize the experiment that www.sciencedirect.com
Although much of the literature regarding clinical trial simulations is focused on the post hoc verification of the systems against extant datasets, there are some notable examples of prospective efforts that have aided clinical development. Three in particular include the successful application of population PK/PD models by Glaxo Wellcome to support a drug labeling effort for the muscle relaxant cisatracurium (reviewed by Bonate [34]; cisatracurium is sold under the trade name NIMBEX1 and is an immediate-onset/immediate-duration neuromuscular blocking agent administered as an adjunct to general anesthesia during surgery), the efforts of Gieschke et al. [37] to determine the optimal dosing regimen for an oral anticancer drug, and the work by Kansal and Trimmer [35] in which an optimized trial protocol was established that markedly reduced the size and duration of a phase I trial for an antidiabetic drug. In this latter study, the information obtained was then used to further refine the design of follow-on phase II studies. Once the pathophysiology of a disease is mechanistically characterized, and a set of patient subpopulations defined, one can use the simulation technologies described in step 3 to pre-test any adaptive trial design available to the clinical researcher. For example, one can use these simulation technologies to address issues surrounding subpopulation prevalence, the cost of biomarker screening (in both money and patient recruitment rates), and so on. Thus, these in silico technologies can be used to minimize the failure rates such trials may accrue. Also, assuming that the primary aim of a pharmaceutical company is compound approval, it is important that these technologies identify both those patients most likely to respond to a given drug and the biomarkers needed to stratify them. This information can then be used to develop the most predictive inclusion-exclusion criteria and population-specific covariates for the trial design.
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