Role of modelling and simulation: a European regulatory perspective

Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations

The aim of phase I clinical trials is to obtain reliable information on safety, tolerability, pharmacokinetics (PK), and mechanism of action of drugs with the objective of determining the maximum tolerated dose (MTD). In most phase I studies, dose‐finding and PK analysis are done separately and no attempt is made to combine them during dose allocation. In cases such as rare diseases, paediatrics, and studies in a biomarker‐defined subgroup of a defined population, the available population size will limit the number of possible clinical trials that can be conducted. Combining dose‐finding and PK analyses to allow better estimation of the dose‐toxicity curve should then be considered. In this work, we propose, study, and compare methods to incorporate PK measures in the dose allocation process during a phase I clinical trial. These methods do this in different ways, including using PK observations as a covariate, as the dependent variable or in a hierarchical model. We conducted a large simulation study that showed that adding PK measurements as a covariate only does not improve the efficiency of dose‐finding trials either in terms of the number of observed dose limiting toxicities or the probability of correct dose selection. However, incorporating PK measures does allow better estimation of the dose‐toxicity curve while maintaining the performance in terms of MTD selection compared to dose‐finding designs that do not incorporate PK information. In conclusion, using PK information in the dose allocation process enriches the knowledge of the dose‐toxicity relationship, facilitating better dose recommendation for subsequent trials.

Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

AIMS

The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes.

METHODS

Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10-15 kg and 30-40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups.

RESULTS

Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population.

CONCLUSIONS

In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population.

Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives

With the increased emergence of anti-infective resistance in recent years, much focus has recently been drawn to the development of new anti-infectives and the optimization of treatment regimens and combination therapies for established antimicrobials. In this context, the field of pharmacometrics using quantitative numerical modeling and simulation techniques has in recent years emerged as an invaluable tool in the pharmaceutical industry, academia and regulatory agencies to facilitate the integration of preclinical and clinical development data and to provide a scientifically based framework for rational dosage regimen design and treatment optimization. This review highlights the usefulness of pharmacometric analyses in anti-infective drug development and applied pharmacotherapy with select examples.

Modeling and simulation to optimize the design and analysis of confirmatory trials, characterize risk-benefit, and support label claims

The role of modeling and simulation (M&S) in the design and interpretation of phase III studies, from break out session 4 of the European Medicines Agency (EMA)/European Federation of Pharmaceutical Industries and Associations (EFPIA) M&S workshop, was divided into themes illustrated with case studies (Table 1): (1) M&S being conducted to support the design of confirmatory trials; (2) longitudinal model‐based test as primary inferential analysis (biosimilarity and disease progression trials); (3) assessment of benefit–risk ratio, approval and labeling of an unstudied dose or dosing regimen, and development of future regulatory guidance.

CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e27; doi:10.1038/psp.2013.4; advance online publication 27 February 2013

Modeling and simulation in clinical pharmacology and dose finding

The breakout session 2 of the European Medicines Agency/European Federation of Pharmaceutical Industries and Associations Modeling and Simulation (M&S) workshop focused on two topics: when and how M&S should be used and would be accepted by the authorities for the dose-regimen selection; and when and how M&S can be applied to register a dosing regimen without the need for a specific study. Each topic was introduced by an industry and regulatory perspective, followed by case examples for illustration (Table 1).

From preclinical to human--prediction of oral absorption and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

PURPOSE

A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug-drug interaction (DDI) potential.

METHODS

The PBPK model was built in the rat using Gastroplus® to study the absorption characteristics of the compound. Subsequently relevant model parameters were used to predict the non-linear human PK observed during first-in-human study after optimizing the absorption model for colonic absorption, bile micelle solubilization and unbound fraction in gut enterocytes (fu(gut)) using SIMCYP® simulator. The model fitted absorption parameters were then used to assess the drug-drug interaction (DDI) potential of the test compound when administered along with multiple doses of a potent CYP 3A4 inhibitor, ketoconazole. The impact of fu(gut) in the extent of DDI was assessed using parameter sensitivity analysis.

RESULTS AND CONCLUSIONS

After optimizing the preclinical model and taking into consideration bile micelle solubilization and colonic absorption, the non-linear pharmacokinetics of the test compound was satisfactorily predicted in man. Sensitivity analysis performed with the absorption parameter fu(gut) indicated that it could be an important parameter in predicting oral absorption. In addition, DDI simulations using SIMCYP® suggest that C(max) and AUC ratios may also be sensitive to the fu(gut) input in the model. Since fu(gut) cannot be measured experimentally, sensitivity analysis may help in assessing the importance of fu(gut) in human PK and DDI prediction using SIMCYP®.

Predicting feasibility and characterizing performance of extended-release formulations using physiologically based pharmacokinetic modeling

This review presents nine case studies where physiologically based pharmacokinetic modeling has been used in the design and development of extended-release formulations. While the approaches for creating the models were similar, in each case a product-development or drug-delivery problem unique to each compound was solved so that the drug-release rate could be optimized to achieve the best clinical performance. Examples presented include understanding the relationship between colonic absorption and efflux, effect of drug release and gastric emptying on maximum achieved drug concentration in plasma and area under the plasma concentration-time curve for a Biopharmaceutics Classification System class 3 compound, feasibility of an extended-release product for a prodrug, feasibility of an extended-release product for a biopharmaceutics classification system class 4 compound and predicting the pharmacokinetics in humans based on a primate model and coupling the physiologically-based pharmacokinetic model with a pharmacodynamic model so that the clinical efficacy of the formulations could be predicted based on the simulated plasma concentrations. The use of physiologically based pharmacokinetic models in the development of extended-release formulations is rapidly becoming an acceptable part of the knowledge management and design space components of a quality by design approach to product development. As the use of these in silico tools increase and examples become available through scientific presentations and literature, the inclusion of this approach will become a necessary part of the development process rather than the exception.