Dataset Information

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

ABSTRACT: Pharmacokinetic (PK) variability in cancer clinical trials may be due to heterogeneous populations and identifying sources of variability is important. Use of healthy subjects in clinical pharmacology studies together with detailed knowledge of the characteristics of patients with cancer can allow for quick identification and quantification of factors affecting PK variability. PK data and sources of variability of 40 marketed molecularly targeted oncology therapeutics were compiled from regulatory approval documents covering an 18-year period (1999-2017). Variability in PK parameters was compared and contributors to variability were identified. The results show that PK variability was ~ 16% higher for peak plasma concentration (Cmax ) and area under the concentration time curve (AUC) in patients with cancer compared with healthy subjects. Several factors were identified as major contributors to variability including hepatic/renal impairment and cytochrome P450 inhibition/induction. Lower PK variability in healthy subjects may represent an opportunity to perform rapid and robust pharmacological and PK assessments to inform subsequent studies in the development of new cancer therapies.

SUBMITTER: Reyner E

PROVIDER: S-EPMC7070882 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

Reyner Eric E Lum Bert B Jing Jing J Kagedal Matts M Ware Joseph A JA Dickmann Leslie J LJ

Clinical and translational science 20191212 2

Pharmacokinetic (PK) variability in cancer clinical trials may be due to heterogeneous populations and identifying sources of variability is important. Use of healthy subjects in clinical pharmacology studies together with detailed knowledge of the characteristics of patients with cancer can allow for quick identification and quantification of factors affecting PK variability. PK data and sources of variability of 40 marketed molecularly targeted oncology therapeutics were compiled from regulato ...[more]

PMID: 31729137

Similar Datasets

Project description:During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.

Dataset Information

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

Publications

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets