Dataset Information

Translational potential of a mouse in vitro bioassay in predicting gastrointestinal adverse drug reactions in Phase I clinical trials.

ABSTRACT:

Background

Motility-related gastrointestinal (GI) adverse drug reactions (GADRs) such as diarrhea and constipation are a common and deleterious feature associated with drug development. Novel biomarkers of GI function are therefore required to aid decision making on the GI liability of compounds in development.

Methods

Fifteen compounds associated with or without clinical GADRs were used to assess the ability of an in vitro colonic motility bioassay to predict motility-related GADRs. Compounds were examined in a blinded fashion for their effects on mouse colonic peristaltic motor complexes in vitro. For each compound concentration-response relationships were determined and the results compared to clinical data. Compounds were also assessed using GI transit measurements obtained using an in vivo rat charcoal meal model.

Key results

Within a clinically relevant dosing range, the in vitro assay identified five true and three false positives, four true and three false negatives, which gave a predictive capacity of 60%. The in vivo assay detected four true and four false positives, four false and three true negatives, giving rise to a predictive capacity for this model of 47%.

Conclusions & inferences

Overall these results imply that both assays are poor predictors of GADRs. Further analysis would benefit from a larger compound set, but the data show a clear need for improved models for use in safety pharmacology assessment of GI motility.

SUBMITTER: Keating C

PROVIDER: S-EPMC4207192 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Translational potential of a mouse in vitro bioassay in predicting gastrointestinal adverse drug reactions in Phase I clinical trials.

Keating C C Ewart L L Grundy L L Valentin J P JP Grundy D D

Neurogastroenterology and motility 20140511 7

<h4>Background</h4>Motility-related gastrointestinal (GI) adverse drug reactions (GADRs) such as diarrhea and constipation are a common and deleterious feature associated with drug development. Novel biomarkers of GI function are therefore required to aid decision making on the GI liability of compounds in development.<h4>Methods</h4>Fifteen compounds associated with or without clinical GADRs were used to assess the ability of an in vitro colonic motility bioassay to predict motility-related GAD ...[more]

PMID: 24813024

Similar Datasets

Project description:BackgroundEarly and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem.ResultsIn this work, we propose a machine learning method to predict ADRs of combined medication from pharmacologic databases by building up highly-credible negative samples (HCNS-ADR). Specifically, we fuse heterogeneous information from different databases and represent each drug as a multi-dimensional vector according to its chemical substructures, target proteins, substituents, and related pathways first. Then, a drug-pair vector is obtained by appending the vector of one drug to the other. Next, we construct a drug-disease-gene network and devise a scoring method to measure the interaction probability of every drug pair via network analysis. Drug pairs with lower interaction probability are preferentially selected as negative samples. Following that, the validated positive samples and the selected credible negative samples are projected into a lower-dimensional space using the principal component analysis. Finally, a classifier is built for each ADR using its positive and negative samples with reduced dimensions. The performance of the proposed method is evaluated on simulative prediction for 1276 ADRs and 1048 drugs, comparing using four machine learning algorithms and with two baseline approaches. Extensive experiments show that the proposed way to represent drugs characterizes drugs accurately. With highly-credible negative samples selected by HCNS-ADR, the four machine learning algorithms achieve significant performance improvements. HCNS-ADR is also shown to be able to predict both known and novel drug-drug-ADR associations, outperforming two other baseline approaches significantly.ConclusionsThe results demonstrate that integration of different drug properties to represent drugs are valuable for ADR prediction of combined medication and the selection of highly-credible negative samples can significantly improve the prediction performance.

Dataset Information

Translational potential of a mouse in vitro bioassay in predicting gastrointestinal adverse drug reactions in Phase I clinical trials.

Background

Methods

Key results

Conclusions & inferences

Publications

Translational potential of a mouse in vitro bioassay in predicting gastrointestinal adverse drug reactions in Phase I clinical trials.

Similar Datasets

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