Integrated systems pharmacology analysis of clinical drug-induced peripheral neuropathy.

A systems pharmacology approach was undertaken to define and identify the proteins/genes significantly associated with clinical incidence and severity of drug-induced peripheral neuropathy (DIPN). Pharmacological networks of 234 DIPN drugs, their known targets (both intended and unintended), and the intermediator proteins/genes interacting with these drugs via their known targets were examined. A permutation test identified 230 DIPN-associated intermediators that were enriched with apoptosis and stress response genes. Neuropathy incidence and severity were curated from drug labels and literature and were used to build a predictive model of DIPN using a regression tree algorithm, based on the drug targets and their intermediators. DIPN drugs whose targets interacted with both v-myc avian myelocytomatosis viral oncogene homolog (MYC) and proliferating cell nuclear antigen-associated factor (PAF15) were associated with a neuropathy incidence of 38.1%, whereas drugs interacting only with MYC had an incidence of 2.9%. These results warrant further investigation in order to develop a predictive tool for the DIPN potential of a new drug.

Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver.

Intracellular concentrations of drugs and metabolites are often important determinants of efficacy, toxicity, and drug interactions. Hepatic drug distribution can be affected by many factors, including physicochemical properties, uptake/efflux transporters, protein binding, organelle sequestration, and metabolism. This white paper highlights determinants of hepatocyte drug/metabolite concentrations and provides an update on model systems, methods, and modeling/simulation approaches used to quantitatively assess hepatocellular concentrations of molecules. The critical scientific gaps and future research directions in this field are discussed.

Integration of diverse data sources for prediction of adverse drug events.

The rapid evolution of large biological, pharmacological, and chemical databases has led to optimism that such data resources can be leveraged for prediction of drug action based on molecular descriptors of the drug. Challenges to realize this possibility include organization of each type of database in a manner that allows extraction of information across disparate data sources and the linkage of information across the biological, pharmacological, and chemical domains.