PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Abstract

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug’s benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs’ marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

“…For each drug, PathFX analysis yielded interaction networks and a list of phenotypes associated with these networks. For a full list of features and outputs, see 2,9 .…”

“…Yet, curated pathways do not exist for drugs in development and drug pathways do not cleanly align with curated pathways, further necessitating rapid and reliable tools for generating potential pathway mechanisms. To draft these pathways, many have applied network methods for understanding associations between drug target proteins and safety or efficacy phenotypes and extensions of these models have predicted drug repurposing opportunities (1,2) and synergies for drug combinations (3,4). These methods are compelling because a network approach may yield statistically significant associations for a drug's protein targets to many more phenotypic associations than validated evidence exists.…”

“…In an over-simplified view, distance-based methods identify the number of protein-protein interactions to a reach a relevant phenotype association by calibrating to the distance between protein targets of marketed drugs and their intend-to-treat disease genes (1,6,7). Statistical enrichment methods look for enrichment of a network's genes relative to all gene associations across the entire interactome (2) and some rank drug phenotypes based on the connectivity and closeness of phenotypes shared by drug combinations (8). Neural network methods can achieve high accuracy at labeling known drug-drug interactions using protein-protein interaction networks, drug-target binding data, and gene/protein-phenotype data 4 .…”

PathFXweb: a web application for identifying drug safety and efficacy phenotypes

“…Computational tools can improve understanding of drug mechanisms by integrating multiple data sources and leveraging networks of information around drug targets. To this effect, we developed PathFX and demonstrated its ability to identify drug-relevant phenotypes (Wilson et al , 2018). PathFX is designed to look at the pathways-level effects (pathway FX) of a drug intervention.…”

“…However, downloading source code of the application is not ideal for all stakeholders and computational tools should be accessible to non-computational scientists to achieve greatest impact. We were specifically motivated to inform the regulatory review process and collaborated closely with regulators at the US Food and Drug Administration to first design PathFX (Wilson et al , 2018) and second, to design an easy-to-use PathFX web application which allows users to easily search drug networks and associated phenotypes.…”