DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform

Abstract

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and inhouse data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/ .

Effect of chronic administration of ostruthin on depression-like behavior in chronically stressed mice

“…Indeed, the blood ostruthin concentration of the 7-day regimen was lower than that of single-shot mice. This finding also suggests a requirement and usefulness for in silico drug metabolism and pharmacokinetics predicting databases, such as DruMAP ( Kawashima et al, 2023 ). Predicting the induction of drug-metabolizing activity and a decrease in the drug concentration could reduce the need for animal experiments.…”

Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study

“…When developing ML ADME models for a project team, one can create a local model trained solely on the program’s measured data, as in traditional QSAR approaches . Alternatively, one might use a global model that has already been built using large external data sets to predict a given property. , An approach that balances these extremes is to train a model that combines nonproject global data with data from the project itself. This can be done by simply including all available data when training a model ,, or by using other more sophisticated fine-tuning approaches .…”

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

“…ADMETlab 2.0, which is a completely redesigned version of the widely used AMDETlab web server, enables the prediction of pharmacokinetic and toxicity properties, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints, and 8 toxicophore rules. The AMED established the iD3-INST with financial support, with the goal of constructing a drug discovery platform with the components of a high-quality open-access database including public data and in silico prediction models for ADME, cardiotoxicity, and a drug-induced liver injury model [11,135].…”