Individual optimal dose of amrubicin to prevent severe neutropenia in Japanese patients with lung cancer

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Individual optimal dose of amrubicin to prevent severe neutropenia in Japanese patients with lung cancer

This study determined individual optimal amrubicin doses for Japanese patients with lung cancer after platinum‐based treatment. We carried out population pharmacokinetic and pharmacodynamic modeling incorporating gene polymorphisms of metabolizing enzymes and transporters. Fifty patients with lung cancer, who were given 35‐40 mg/m² amrubicin on days 1‐3 every 3‐4 weeks, were enrolled. Mechanism‐based modeling described relationships between the pharmacokinetics of amrubicin and absolute neutrophil counts. A population pharmacokinetic and pharmacodynamic model was developed for amrubicin and amrubicinol (active metabolite), connected by a delay compartment. The final model incorporated body surface area as a covariate of amrubicin and amrubicinol clearance and distribution volume. SLC28A3 single nucleotide polymorphism (rs7853758) was also incorporated as a constant covariate of the delay compartment of amrubicinol. Performance status was considered a covariate of pharmacokinetic (amrubicinol clearance) and pharmacodynamic (mean maturation time) parameters. Twenty‐nine patients with grade 4 neutropenia showed higher amrubicinol area under the plasma concentration‐time curve from 0 to 72 hours (AUC_0‐72, P = .01) and shorter overall survival periods than other patients did (P = .01). Using the final population pharmacokinetic and pharmacodynamic model, median optimal dose to prevent grade 4 neutropenia aggravation was estimated at 22 (range, 8−40) mg/m² for these 29 patients. We clarified correlations between area under the plasma concentration‐time curve from 0 to 72 hours of amrubicinol and severity of neutropenia and survival of patients given amrubicin after platinum chemotherapy. This analysis revealed important amrubicin pharmacokinetic‐pharmacodynamic covariates and provided useful information to predict patients who would require prophylactic granulocyte colony stimulating factor.