Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

29 June 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. Biological activities have never been used as a predictive feature. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the 'Pass' class. 'Pass' refers to one of the two classes (Pass/ Fail) of all clinical trials and the model performed well in predicting the 'Pass' category. An analysis of the features demonstrates that bioactivity plays an important role in predicting the outcome of a clinical trial. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. All code to map these entities is available through this study, and all data are from publicly available sources. While our model identifies low-lying inferences when biological activities are included, the code to integrate biological activity and target information provide researchers with access to deep curated and proprietary clinical trial databases the ability to get deeper insights, better statistical significance, and capabilities to better predict trial failures.

Keywords

bioactivity
clinical trial
data integration
ensemble algorithms
graph database
machine learning

Supplementary materials

Title
Description
Actions
Title
Predicting Clinical Trial Outcomes Using Drug Bioactivities through Graph Database Integration and Machine Learning
Description
Supporting Information - Predicting Clinical Trial Outcomes Using Drug Bioactivities through Graph Database Integration and Machine Learning
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.