Covariate pharmacokinetic model building in oncology and its potential clinical relevance

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Changes in individual drug-independent system parameters during virtual paediatric pharmacokinetic trials: introducing time-varying physiology into a paediatric PBPK model

Although both POPPK and physiologically based pharmacokinetic (PBPK) models can account for age and other covariates within a paediatric population, they generally do not account for real-time growth and maturation of the individuals through the time course of drug exposure; this may be significant in prolonged neonatal studies. The major objective of this study was to introduce age progression into a paediatric PBPK model, to allow for continuous updating of anatomical, physiological and biological processes in each individual subject over time. The Simcyp paediatric PBPK model simulator system parameters were reanalysed to assess the impact of re-defining the individual over the study period. A schedule for re-defining parameters within the Simcyp paediatric simulator, for each subject, over a prolonged study period, was devised to allow seamless prediction of pharmacokinetics (PK). The model was applied to predict concentration-time data from multiday studies on sildenafil and phenytoin performed in neonates. Among PBPK system parameters, CYP3A4 abundance was one of the fastest changing covariates and a 1-h re-sampling schedule was needed for babies below age 3.5 days in order to seamlessly predict PK (<5% change in abundance) with subject maturation. The re-sampling frequency decreased as age increased, reaching biweekly by 6 months of age. The PK of both sildenafil and phenytoin were predicted better at the end of a prolonged study period using the time varying vs fixed PBPK models. Paediatric PBPK models which account for time-varying system parameters during prolonged studies may provide more mechanistic PK predictions in neonates and infants.

Vertical Integration of Pharmacogenetics in Population PK/PD Modeling: A Novel Information Theoretic Method

To critically evaluate an information-theoretic method for identifying gene–environmental interactions (GEI) associated with pharmacokinetic (PK), pharmacodynamic (PD), and clinical outcomes from genome-wide pharmacogenetic data. Our approach, which is built on the K-way interaction information (KWII) metric, was challenged with simulated data and clinical PK/PD data sets from the International Warfarin Pharmacogenetics Consortium (IWPC) and a gemcitabine clinical trial. The KWII efficiently identified both novel and known interactions for warfarin and gemcitabine. Interactions between herbal supplementation and VKORC1 genotype were associated with warfarin response. For gemcitabine-associated neutropenia, combination treatment with carboplatin and cytidine deaminase (CDA) 208G→A genotypes were identified as risk factors. Gemcitabine disposition was associated with drug metabolism–transporter interactions between deoxycytidine kinase (DCK) and the equilibrative nucleoside transporter (ENT). This novel approach is effective for detecting GEI involved in drug exposure and response and could enable integration of genome-wide pharmacogenetic data into the population PK/PD analysis paradigm.