Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

A methodological framework for drug development in rare diseases. The CRESim program: Epilogue and perspectives

Based on the 'European Child-Rare-Euro-Simulation' (CRESim) project, this article proposes a generalizable strategy utilizing datasets analysis in combination with modeling and simulation, in order to optimize the clinical drug development applied in the field of rare diseases. The global process includes: (i) the simulation of a realistic virtual population of patients (modeled from a real dataset of patients), (ii) the modeling of disease pathophysiological components and of pharmacokinetic-pharmacodynamic relations of the drug(s) of interest, (iii) the modeling of several randomized controlled clinical trials (RCTs) designs and (iv) the analysis of the results (multi-dimensional approach for RCTs durations and precision of the estimation of the treatment effect). However, whereas modeling and numerical simulation may provide supplementary tools for drug development, they cannot be considered as a substitute for RCTs performed in 'real' patients.

Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions

Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A novel quantitative approach follows for optimizing dose titration in clinical trials. A system of pharmacokinetics (PK), pharmacodynamics, efficacy, and toxicity was simulated for scenarios characterized by varying degrees of different types of variability. Receiver operating characteristic (ROC) and clinical trial simulation (CTS) were used to optimize drug titration by maximizing efficacy/safety. The scenarios included were a low-variability base scenario, and high residual (20%), interoccasion (20%), interindividual (40%), and residual plus interindividual variability scenarios, and finally a shallow toxicity slope scenario. The percentage of subjects having toxicity was reduced by 87.4% to 93.5%, and those having efficacy was increased by 52.7% to 243%. Interindividual PK variability may have less impact on optimal cutoff values than other sources of variability. ROC/CTS methods for optimizing dose titration offer an individualized approach that leverages exposure-response relationships.

Population plasma and urine pharmacokinetics of ivabradine and its active metabolite S18982 in healthy Korean volunteers

Ivabradine, a selective inhibitor of the pacemaker current (If ), is used for heart failure and coronary heart disease and is mainly metabolized to S18982. The purpose of this study was to explore the pharmacokinetics (PK) of ivabradine and S18982 in healthy Korean volunteers. Subjects in a phase I study were randomized to receive 2.5, 5, or 10 mg of ivabradine administered every 12 hours for 4.5 days, and serial plasma and urine concentrations of ivabradine and S18982 were measured. The plasma PK of ivabradine was best described by a 2-compartment model with mixed 0- and first-order absorption, linked to a 2-compartment model for S18982. The introduction of interoccasional variabilities and period as covariate into absorption-related parameters improved the model fit. Urine data have been applied to estimate renal and nonrenal clearance, enabling a more detailed description of the elimination process. We developed a population PK model describing the plasma and urine PK of ivabradine and S18982 in healthy Korean adult males. This model might be useful for predicting the plasma and urine PK of ivabradine, potentially helping to identify the optimal dosing regimens in various clinical situations.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

Purpose

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.

Methods

COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.

Results

Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.

Conclusion

Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

Bridging Systems Medicine and Patient Needs

While there is widespread consensus on the need both to change the prevailing research and development (R&D) paradigm and provide the community with an efficient way to personalize medicine, ecosystem stakeholders grapple with divergent conceptions about which quantitative approach should be preferred. The primary purpose of this position paper is to contrast these approaches. The second objective is to introduce a framework to bridge simulation outputs and patient outcomes, thus empowering the implementation of systems medicine.

A methodological framework for drug development in rare diseases

INTRODUCTION

Developing orphan drugs is challenging because of their severity and the requisite for effective drugs. The small number of patients does not allow conducting adequately powered randomized controlled trials (RCTs). There is a need to develop high quality, ethically investigated, and appropriately authorized medicines, without subjecting patients to unnecessary trials.

AIMS AND OBJECTIVES

The main aim is to develop generalizable framework for choosing the best-performing drug/endpoint/design combinations in orphan drug development using an in silico modeling and trial simulation approach. The two main objectives were (i) to provide a global strategy for each disease to identify the most relevant drugs to be evaluated in specific patients during phase III RCTs, (ii) and select the best design for each drug to be used in future RCTs.

METHODOLOGICAL APPROACH

In silico phase III RCT simulation will be used to find the optimal trial design and was carried out in two steps: (i) statistical analysis of available clinical databases and (ii) integrative modeling that combines mathematical models for diseases with pharmacokinetic-pharmacodynamics models for the selected drug candidates.

CONCLUSION

There is a need to speed up the process of orphan drug development, develop new methods for translational research and personalized medicine, and contribute to European Medicines Agency guidelines. The approach presented here offers many perspectives in clinical trial conception.

Effect model law: an approach for the implementation of personalized medicine

The effect model law states that a natural relationship exists between the frequency (observation) or the probability (prediction) of a morbid event without any treatment and the frequency or probability of the same event with a treatment. This relationship is called the effect model. It applies to a single individual, individuals within a population, or groups. In the latter case, frequencies or probabilities are averages of the group. The relationship is specific to a therapy, a disease or an event, and a period of observation. If one single disease is expressed through several distinct events, a treatment will be characterized by as many effect models. Empirical evidence, simulations with models of diseases and therapies and virtual populations, as well as theoretical derivation support the existence of the law. The effect model could be estimated through statistical fitting or mathematical modelling. It enables the prediction of the (absolute) benefit of a treatment for a given patient. It thus constitutes the theoretical basis for the design of practical tools for personalized medicine.

Optimizing Dose Selection with Modeling and Simulation: Application to the Vasopeptidase Inhibitor M100240

Dual inhibition of neutral endopeptidase 24.11 (NEP) and angiotensin-converting enzyme (ACE) has gained increasing interest in the treatment of hypertension, heart failure, and renoprotection. Specifically, M100240, the thioester of the dual ACE/NEP inhibitor MDL100,173, has been evaluated in the management of hypertension. A model-based analysis, including simulations, was employed to characterize the relationship between individual M100240 drug exposure and neurohormonal response and to optimize the dose selection for future clinical studies. Sixty-two healthy subjects and 189 hypertensive patients were studied after oral once-daily administration of 2.5, 5, 10, 25, or 50 mg M100240. Pharmacokinetic-biomarker and blood pressure response models were fitted to the data with the computer program NONMEM. A direct inhibitory E(max) model adequately described the relationship between MDL100,173 concentration and ACE activity. No clear concentration or dose-dependent NEP or blood pressure responses were evident. Given a target 90% ACE inhibition, simulation reveals that (1). 50 mg M100240 once daily produces adequate ACE inhibition 24 hours postdose in only 20% of subjects, and (2). higher and/or more frequent doses on the order of 25 mg three times daily or 50 mg twice daily are required to achieve the target ACE inhibition in at least 50% of patients over 24 hours. Insufficient blood pressure-lowering effects were observed in healthy subjects and hypertensive patients due to inadequate ACE and NEP inhibition with once-daily oral doses of up to 50 mg of M100240. Divided doses might provide target ACE inhibition in more patients.

Relationship between exposure of (-)-N-{2-[(R)-3-(6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)piperidino]ethyl}-4-fluorobenzamide (YM758), a "funny" if current channel inhibitor, and heart rate reduction in tachycardia-induced beagle dogs

(-)-N-{2-[(R)-3-(6,7-Dimethoxy-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)piperidino]ethyl}-4-fluorobenzamide (YM758), a novel "funny" If current channel (If channel) inhibitor, is developed as a treatment for stable angina and atrial fibrillation. In this study, the pharmacokinetic/pharmacodynamic (PK/PD) relationship after intravenous administration of YM758 to tachycardia-induced dogs was investigated and described based on the simplified compartment model. The PK of YM758 in dogs did not differ between the nontreated and tachycardia-induced groups. A drug-induced reduction in heart rate (HR) was clearly observed, and the half-life of the duration of the effect (approximately 4.0 h) was longer than that of the plasma concentration of the unchanged drug. The fitting and simulation procedure from the PK/PD relationship between the time profiles for YM758 plasma concentration and HR reduction had an ECe(50) value (YM758 concentration in the effective compartment resulting in a 50% decrease of the maximum effect) of 6.0 ng/ml, which did not agree with the results of the in vitro experiment using right atria isolated from guinea pigs (EC(30), 70.4 ng/ml). In addition, in the in vitro experiments, YM758 metabolites had a weak inhibitory effect, if any, on the spontaneous beat rate of the right atria from guinea pigs. These data, along with the previous finding that YM758 and its metabolites are eliminated rapidly from rat hearts, indicate that the duration of the pharmacological effect of YM758 (compared with the rapid elimination of the plasma drug concentration) may be the result of strong binding and/or slower dissociation of YM758 in the If channel. Such PK/PD analyses allow the pharmacological profiles of many drugs, especially cardiovascular drugs, to be more readily understood and better predicted during the clinical stages.

New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear

BACKGROUND AND OBJECTIVES

A relation between the size of treatment efficacy and severity of the disease has been postulated and observed as linear for a few therapies. We have called this relation the effect model. Our objectives were to demonstrate that the relation is general and not necessarily linear.

STUDY DESIGN AND SETTING

We extend the number of observed effect model. Then we established three numerical models of treatment activity corresponding to the three modes of action we have identified. Using these models, we simulated the relation.

RESULTS

Empirical evidence confirms the effect model and suggests that it may be linear over a short range of event frequency. However, it provides an incomplete understanding of the phenomenon because of the inescapable limitations of data from randomized clinical trials. Numerical modeling and simulation show that the real effect model is likely to be more complicated. It is probably linear only in rare instances. The effect model is general. It depends on factors related to the individual, disease and outcome.

CONCLUSION

Contrarily to common, assumption, since the effect model is often curvilinear, the relative risk cannot be granted as constant. The effect model should be taken into account when discovering and developing new therapies, when making, health care policy decisions or adjusting clinical decisions to the patient risk profile.

Simulating pharmacokinetic and pharmacodynamic fuzzy-parameterized models: a comparison of numerical methods

Statistical techniques have been traditionally used to deal with parametric variation in pharmacokinetic and pharmacodynamic models, but these require substantial data for estimates of probability distributions. In the presence of limited, inaccurate or imprecise information, simulation with fuzzy numbers represents an alternative tool to handle parametric uncertainty. Existing methods for implementing fuzzy arithmetic may, however, have significant shortcomings in overestimating (e.g., conventional fuzzy arithmetic) and underestimating (e.g., vertex method) the output uncertainty. The purpose of the present study is to apply and compare the applicability of conventional fuzzy arithmetic, vertex method and two recently proposed numerical schemes, namely transformation and optimization methods, for uncertainty modeling in pharmacokinetic and pharmacodynamic fuzzy-parameterized systems. A series of test problems were examined, including empirical pharmacokinetic and pharmacodynamic models, a function non-monotonic in its parameters, and a whole body physiologically based pharmacokinetic model. Our results verified that conventional fuzzy arithmetic can lead to overestimation of response uncertainty and should be avoided. For the monotonic pharmacokinetic and pharmacodynamic models, the vertex method accurately predicted fuzzy-valued output while incurring the least computational cost. It turned out that the choice of a suitable method for fuzzy simulation of the non-monotonic function depended on the required accuracy of the results: the vertex method was capable of eliciting an initial approximate solution with few function evaluations; for more accurate results, the transformation method was the most superior approach in terms of accuracy per unit CPU time.

Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.

Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model

The plasma concentration-time profile of a drug is essential to explain the relationship between the administered dose and the kinetics of drug action. However, in some cases such as in pre-clinical pharmacology or phase-III clinical studies where it is not always possible to collect all the required PK information, this relationship can be difficult to establish. In these circumstances several authors have proposed simple models that can analyse and simulate the kinetics of the drug action in the absence of PK data. The present work further develops and evaluates the performance of such an approach. A virtual compartment representing the biophase in which the concentration is in equilibrium with the observed effect is used to extract the (pharmaco)kinetic component from the pharmacodynamic data alone. Parameters of this model are the elimination rate constant from the virtual compartment (KDE), which describes the equilibrium between the rate of dose administration and the observed effect, and the second parameter, named EDK(50) which is the apparent in vivo potency of the drug at steady state, analogous to the product of EC(50), the pharmacodynamic potency, and clearance, the PK "potency" at steady state. Using population simulation and subsequent (blinded) analysis to evaluate this approach, it is demonstrated that the proposed model usually performs well and can be used for predictive simulations in drug development. However, there are several important limitations to this approach. For example, the investigated doses should extend from those producing responses well below the EC(50) to those producing ones close to the maximum response, optimally reach steady state response and followed until the response returns to baseline. It is shown that large inter-individual variability on PK-PD parameters will produce biases as well as large imprecision on parameter estimates. It is also clear that extrapolations to dosage routes or schedules other than those used to estimate the parameters should be undertaken with great caution (e.g., in case of non-linearity or complex drug distribution). Consequently, it is advised to apply this approach only when the underlying structural PD and PK are well understood. In any case, K-PD model should definitively not be substituted for the gold standard PK-PD model when correct full model can and should be identified.

Simulation of Correlated Continuous and Categorical Variables using a Single Multivariate Distribution

Clinical trial simulations make use of input/output models with covariate effects; the virtual patient population generated for the simulation should therefore display physiologically reasonable covariate distributions. Covariate distribution modeling is one method used to create sets of covariate values (vectors) that characterize individual virtual patients, which should be representative of real subjects participating in clinical trials. Covariates can be continuous (e.g., body weight, age) or categorical (e.g., sex, race). A modeling method commonly used for incorporating both continuous and categorical covariates, the Discrete method, requires the patient population to be divided into subgroups for each unique combination of categorical covariates, with separate multivariate functions for the continuous covariates in each subset. However, when there are multiple categorical covariates this approach can result in subgroups with very few representative patients, and thus, insufficient data to build a model that characterizes these patient groups. To resolve this limitation, an application of a statistical methodology (Continuous method) was conceived to enable sampling of complete covariate vectors, including both continuous and categorical covariates, from a single multivariate function. The Discrete and Continuous methods were compared using both simulated and real data with respect to their ability to generate virtual patient distributions that match a target population. The simulated data sets consisted of one categorical and two correlated continuous covariates. The proportion of patients in each subgroup, correlation between the continuous covariates, and ratio of the means of the continuous covariates in the subgroups were varied. During evaluation, both methods accurately generated the summary statistics and proper proportions of the target population. In general, the Continuous method performed as well as the Discrete method, except when the subgroups, defined by categorical value, had markedly different continuous covariate means, for which, in the authors' experience, there are few clinically relevant examples. The Continuous method allows analysis of the full population instead of multiple subgroups, reducing the number of analyses that must be performed, and thereby increasing efficiency. More importantly, analyzing a larger pool of data increases the precision of the covariance estimates of the covariates, thus improving the accuracy of the description of the covariate distribution in the simulated population.

Population pharmacokinetic studies in pediatrics: issues in design and analysis

The current review addresses the following 3 frequently encountered challenges in the design and analysis of population pharmacokinetic studies in pediatrics: (1) body size adjustments during the development of pharmacostatistical models, (2) design and validation of limited sampling strategies, and (3) the integration of historical priors in data analysis and trial simulation. Size adjustments with empiric approaches based on body weight or body surface area have frequently proven as a pragmatic tool to overcome large size differences in a pediatric study population. Allometric size adjustments, however, provide a more mechanistic, physiologically based approach that, if used a priori, allows delineation of the effect of size from that of other covariates that show a high degree of collinearity. The frequent lack of dense data sets in pediatric clinical pharmacology because of ethical and logistic constraints in study design can be overcome with the application of D-optimality-based limited sampling schemes in combination with Bayesian and nonlinear mixed-effects modeling approaches. Empirically based dose selection and clinical trial designs for pediatric clinical pharmacology studies can be improved by applying clinical trial simulation techniques, especially if they integrate adult and pediatric in vitro and/or in vivo data as historic priors. Although integration of these concepts and techniques in population pharmacokinetic analyses is not only limited to pediatric research, their application allows researchers to overcome some major hurdles frequently encountered in pharmacokinetic studies in pediatrics and, thus, provides the basis for additional clinical pharmacology research in this previously insufficiently studied fraction of the general population.

Clinical trial simulation in drug development

The simulation of therapeutic models and clinical trial simulation have recently attracted attention as emerging techniques for developing new active molecules and the exploration of possible clinical trial results. Such approaches have benefited from fundamental progress in the development of 'in silico' models, as well as progress in nonlinear mixed-effect pharmacokinetic-pharmacodynamic models. Mixing the two approaches allows simulation of 'virtual' patients, who receive virtual treatments or placebo. These have various uses, such as proof of concept, decision analysis or experimental design optimisation. Also, the effect of departures from protocol on clinical trial results can easily be evaluated by the use of simulation. This technique is now implemented by the pharmaceutical industry for optimising phase II and III experimental designs when a good biomarker or a clinical outcome model is available, but the use of an in silico therapeutic model as a proof of concept is only just beginning. In order to see such methodologies used more widely in drug development, multidisciplinary efforts need to be initiated, new modelling and simulation tools developed, and sound modelling and simulation practice documents need to be adopted. A reduction in the number of failed clinical development projects, the number of negative phase II and III clinical trials, or in just their cost and duration, are among the expected benefits of modelling and simulation in clinical drug development.

Predicting the Outcome of Phase III Trials using Phase II Data: A Case Study of Clinical Trial Simulation in Late Stage Drug Development

Maximizing the likelihood of success in Phase III is the ultimate goal of the use of modelling and simulation in the drug development process. The success in Phase III depends primarily on two questions: 1) Is the drug regimen actually efficacious and safe in the targeted patient population?, and 2) Will the planned Phase III clinical trial(s) be successful in demonstrating this? Traditionally, the first question is addressed in a qualitative, overall interpretation of available study results. Integrating this information into a formal statistical model of the action of the drug, allows running simulations to investigate the impact of uncertainties and imprecision in this knowledge. The second question is related to having an adequately designed clinical trial. Clinical trial simulation, using a drug action model, supplemented with appropriate models for disease progression and trial execution, allows assessing the impact of typical design features such as doses, sample size, in-/exclusion criteria, drop-out and trial duration on the trial outcome and thus optimising trial design. In this contribution, the use of modelling and simulation in the Phase II to Phase III transition is illustrated using real data of a drug for symptom relief in a chronic condition. A dose-response model of the clinical response was developed using data from Phase II. Simulations were performed to 1) generate the range of possible outcomes of ongoing Phase III trials and compare these to the blinded data being generated from these trials; 2) assess the robustness of the ongoing Phase III trials with respect to uncertainty of the true dose-response, patient variability in baseline severity and drug-response, and 3) assess the likelihood of achieving a clinically relevant response with a dose lower than those included in the trials.

Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM and NLMIXED

The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.

Modélisation numérique et simulation : nouvelles applications en pharmacologie

The complexity of pathophysiological mechanisms is beyond the capabilities of traditional approaches. Many of the decision-making problems in public health, such as initiating mass screening, are complex. Progress in genomics and proteomics, and the resulting extraordinary increase in knowledge with regard to interactions between gene expression, the environment and behaviour, the customisation of risk factors and the need to combine therapies that individually have minimal though well documented efficacy, has led doctors to raise new questions: how to optimise choice and the application of therapeutic strategies at the individual rather than the group level, while taking into account all the available evidence? This is essentially a problem of complexity with dimensions similar to the previous ones: multiple parameters with nonlinear relationships between them, varying time scales that cannot be ignored etc. Numerical modelling and simulation (in silico investigations) have the potential to meet these challenges. Such approaches are considered in drug innovation and development. They require a multidisciplinary approach, and this will involve modification of the way research in pharmacology is conducted.

Pharmacogenetics and responders to a therapy: theoretical background and practical problems

In a narrow meaning, responders to a therapy are all those who will react as expected following the administration of this therapy. However, a wider definition is worth considering: all those for whom the administration of the therapy will be beneficial. Innovative therapies are increasingly expensive and hazardous, and limiting prescriptions to responders is both economically and ethically compulsory. The theoretical basis for such an approach exists. The process of defining the profile of responders consists of identifying the characteristics of the patients that interact with the size of the effect and integrating them quantitatively in a predictive model. The effect model, which is the relation between the risks of the event with and without the treatment, can be used for the prediction. It can integrate interactions of the efficacy with risk factors and/or genes. The data to be used to achieve both the identification of the interactions and the building of the predictive model are those from the studied population, the set of patients enrolled in clinical trials. Hence, the process of defining the therapy is an extrapolation from the studied population. To carry out the extrapolation process one can use various available techniques, of which none fully fits the purpose. No method is currently both fully adequate and validated. Finally, the predictive models, which we need to identify responders, do not exist in practice. Fortunately, new research approaches have been developed recently.