Mechanistic modelling of immuno-modulators in oncology drug development

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Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment

Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8–11 days) and confirming that they were systematically observed across the different preclinical experiments available (p0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development

Target-Site Investigation for the Plasma Prolactin Response: Mechanism-Based Pharmacokinetic-Pharmacodynamic Analysis of Risperidone and Paliperidone in the Rat

To understand the drivers in the biological system response to dopamine D2 receptor antagonists, a mechanistic semiphysiologically based (PB) pharmacokinetic-pharmacodymanic (PKPD) model was developed to describe prolactin responses to risperidone (RIS) and its active metabolite paliperidone (PAL). We performed a microdialysis study in rats to obtain detailed plasma, brain extracellular fluid (ECF), and cerebrospinal fluid (CSF) concentrations of PAL and RIS. To assess the impact of P-glycoprotein (P-gp) functioning on brain distribution, we performed experiments in the absence or presence of the P-gp inhibitor tariquidar (TQD). PK and PKPD modeling was performed by nonlinear mixed-effect modeling. Plasma, brain ECF, and CSF PK values of RIS and PAL were well described by a 12-compartmental semi-PBPK model, including metabolic conversion of RIS to PAL. P-gp efflux functionality was identified on brain ECF for RIS and PAL and on CSF only for PAL. In the PKPD analysis, the plasma drug concentrations were more relevant than brain ECF or CSF concentrations to explain the prolactin response; the estimated EC50 was in accordance with reports in the literature for both RIS and PAL. We conclude that for RIS and PAL, the plasma concentrations better explain the prolactin response than do brain ECF or CSF concentrations. This research shows that PKPD modeling is of high value to delineate the target site of drugs.Pharmacolog

Commentary on the MID3 Good Practices Paper

Contains fulltext : 178217.pdf (publisher's version ) (Open Access)During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective