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Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

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INTRODUCTION: Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development. Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures. Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

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INTRODUCTION: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

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The purpose of this study was to establish a population pharmacokinetic/pharmacodynamic (PK/PD) model linking etoposide free tumor and total plasma concentrations to the inhibition of solid tumor growth in rats. Walker-256 tumor cells were inoculated subcutaneously in the right flank of Wistar rats, which were randomly divided in control and two treated groups that received etoposide 5 or 10mg/kg i.v. bolus every day for 8 and 4days, respectively, and tumor volume was monitored daily for 30days. The plasma and intratumoral concentrations-time profiles were obtained from a previous study and were modeled by a four-compartment population pharmacokinetic (popPK) model. PK/PD analysis was conducted using MONOLIX v.4.3.3 on average data and by mean of a nonlinear mixed-effect model. PK/PD data were analyzed using a modification of Simeoni Tumor Growth Inhibition (TGI) model by introduction of an Emax function to take into account the concentration dependency of k2variable parameter (variable potency). The Simeoni TGI-Emax model was capable to fit schedule-dependent antitumor effects using the tumor growth curves from the control and two different administered schedules. The PK/PD model was capable of describing the tumor growth inhibition using total plasma or free tumor concentrations, resulting in higher k2max (maximal potency) for free concentrations (25.8mL·µg-1·day-1 - intratumoral vs. 12.6mL·µg-1·day-1 total plasma). These findings indicate that the plasma concentration may not be a good surrogate for pharmacologically active free tumor concentrations, emphasizing the importance of knowing drug tumor penetration to choose the best antitumor therapy.

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