Predicting clinically promising therapeutic hypotheses using tensor factorization

Abstract

Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. We identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a goldstandard data set of 6,140 known clinical outcomes. We used information from Open Targets and others databases that covered 17 different sources of evidence for target-indication association and represented the data as a matrix of 21,437×2,211×17 with over two million non-null values. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, Elasticnet, Random Forest, Tensor Factorization and Gradient Boosting Machine. With ten-fold cross validation, tensor factorization achieved AUROC=0.82±0.02 and AUPRC=0.71±0.03. Across multiple validation schemes, this was comparable or better than other methods. Tensor factorization is a general form of matrix factorization that has been successfully exploited in recommendation systems that suggest items to users based on their existing preference on a small number of items. Our application, using Bayesian probabilistic modelling, extends the capacity of matrix factorization to model multiple relationships between and among targets and indications. We use the model to show that our predicted probabilities of success correlate with clinical phases, and within clinical phase we can predict which trials are most likely to succeed.

Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval

“…In this analysis we depart from the original Nelson et al approach and exclude all drugs assigned an active development phase by Pharmaprojects, as it is unknown whether these development programs will ultimately lead to approval. This decision is consistent with other work estimating clinical success probabilities [14] [34]. We include unapproved drugs with unknown latest historical phase.…”

“…When this is controlled for using logistic regression, GWAS-supported target-indication pairs are more likely to succeed than those without a GWAS-linked gene target. This highlights the need for predictive models including target properties, work that is beginning to emerge [34].…”