Dataset Information

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

ABSTRACT: In the development of new drugs, the prediction of metabolite-to-parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite-to-parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters needed included intrinsic clearance, fraction of clearance to the metabolite of interest, various binding values, and, in some cases, active transport clearance. The principles are illustrated with four drugs that yield six metabolites, with one in which clearance is dependent on a pathway subject to genetic polymorphism. Overall, the approach yielded metabolite-to-parent ratios within about twofold of the actual values and, thus, can be valuable in decision making in the drug development process.

SUBMITTER: Callegari E

PROVIDER: S-EPMC7214656 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Callegari Ernesto E Varma Manthena V S MVS Obach R Scott RS

Clinical and translational science 20200204 3

In the development of new drugs, the prediction of metabolite-to-parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite-to-parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters nee ...[more]

PMID: 31880865

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Project description:BackgroundTechnological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.ResultsWe applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets.ConclusionsThis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

Dataset Information

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Publications

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Similar Datasets

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