Role of pharmacokinetic modeling and simulation in precision dosing of anticancer drugs

Abstract

The prospect of precision dosing in oncology is attractive for several reasons. Many anticancer drugs display narrow therapeutic indices, where suboptimal therapy may lead to severe patient outcomes. Clinical study participant recruitment is seldom extended beyond the intended patient population, leading to difficulties in patient recruitment in dedicated clinical trials. The high rate of non-responders and high cost of cancer therapy warrant novel solutions to increase clinical effectiveness and cost-benefit, pharmacokinetic (PK) modeling and model-informed precision dosing (MIPD) can help to maximize these. PK modeling provides a quantitative framework to account for inter-individual variability in drug exposure, the influence of covariates and extrapolation to special populations or drug-drug interactions, using physiologically-based pharmacokinetic (PBPK) modeling. Here we present the current state of PK modeling in precision dosing of anticancer drugs and illustrate its utility, based on an extensive literature review and numerous case examples from both pharmaceutical industry and healthcare focused research. While some great progress has been made in implementing model-informed dosage guidance in the drug label and much research has been carried out to address clinically relevant dosing questions, the uptake of MIPD has been modest in healthcare. The success of PK modeling in industry has been made possible through collaborative efforts between regulators, industry and academia. Collaboration between academia, healthcare and industry, and financial support for research into patient benefit, cost-benefit and clinical effectiveness of these approaches is imperative for wider adaption of PK modeling in precision dosing of anticancer drugs.

Major pitfalls of protein kinase inhibitors prescription: A review of their clinical pharmacology for daily use

“…Precisely assessing AUC for each patient is not feasible in daily routine. Population pharmacokinetics through parametric or non-parametric modelling (Bayesian models) (Darwich et al, 2017) helps extrapolate the pharmacokinetic parameters from 2 measures, using probabilities to assess steady state AUC (Figure 3).…”

Precision dosing in clinical medicine: present and future

“…This approach is more powerful than dose recommendations in PI/DMs because it combines the flexibility of empirical dosing via biomarker feedback whilst accounting for multiple patient covariates that determine variability in drug response [16]. Examples include model-informed precision dosing (MIPD) of antibiotics in the critically ill [17], the use of models to suggest metformin dose in patients with renal impairment [18], and MIPD of immunosuppressants and chemotherapy in serious pediatric illnesses [19,20].…”

Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Drug Exposure

“…9,10 PBPK models may also be valuable for MIPD-a proof-of-concept study for this approach has recently been published using olanzapine as a test case. 11,12 Given the mechanistic basis of PBPK modeling, interrogation of simulation outputs at a physiological and molecular level represents a novel and efficient approach to predict baseline markers that drive BSV in drug exposure. Importantly, the "population variables" contained within PBPK platforms such as Simcyp are based on reported demographic characteristics from broad but defined populations, and are more likely to reflect the "real-world" treatment population compared with a clinical trial cohort.…”

“…Currently, PBPK models are used throughout drug development to support decisions about when and how to conduct clinical PK studies in specific populations and to support dose recommendations . PBPK models may also be valuable for MIPD—a proof‐of‐concept study for this approach has recently been published using olanzapine as a test case . Given the mechanistic basis of PBPK modeling, interrogation of simulation outputs at a physiological and molecular level represents a novel and efficient approach to predict baseline markers that drive BSV in drug exposure.…”