Predicting Successes and Failures of Clinical Trials With Outer Product–Based Convolutional Neural Network

Sangwoo Seo,Youngmin Kim,Changha Hwang,Zhen-Yu Hong,Hyo-Jeong Han,Woo Chan Son,Jooyong Shim,Insuk Sohn

doi:10.3389/fphar.2021.670670

Abstract

Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product–based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision–recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.

Highlights

Over the past 30 years, failures at all phases of clinical trials have increased rapidly for safety reasons (Ledford, 2011; Hay et al, 2014; Lysenko et al, 2018; Liu et al, 2021)
To develop the prediction model of the outcomes of clinical trials of drug candidates, we proposed outer product–based convolutional neural network (OPCNN) model that employs the augmented outer product to join effectively chemical features of drugs and target-based features
The proposed OPCNN model was evaluated via 10-fold crossvalidation techniques on dataset used in Gayvert et al (Gayvert et al, 2016), which consists of 757 approved drugs for positive class and 71 failed drugs for negative class

Summary

Introduction

Over the past 30 years, failures at all phases of clinical trials have increased rapidly for safety reasons (Ledford, 2011; Hay et al, 2014; Lysenko et al, 2018; Liu et al, 2021) This phenomenon happens despite significant improvements at all stages of the drug development pipeline (Scannell et al, 2012). Drug-likeness scores are widely utilized as a useful guideline for eliminating toxic molecules during the early stages of drug development. This concept was first introduced by Lipinski’s rule of five (Ro5), which screens molecules with a low probability of useful oral activity due to poor absorption or permeation (Lipinski et al, 1997). The quantitative estimate for druglikeness (QED) was recently proposed as an alternative to rule-based methods (Bickerton et al, 2012)

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