Kevin Grimes, Postdoctoral Faculty Sponsor
Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug's pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb.https://www.pathfxweb.net/.Supplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btz419
View details for PubMedID 31114840
Clinical trial designs targeting patient subgroups with certain genetic characteristics may enhance the efficiency of developing drugs for cardiovascular disease (CVD). To evaluate the extent to which genetic knowledge translates to the CVD pipeline, we analyzed how genomic biomarkers are utilized in trials. Phase II and III trial protocols for investigational new drugs for CVD and risk factors were evaluated for prospective and exploratory genomic biomarker use; drug targets were evaluated for the presence of evidence that genetic variations can impact CVD risk or drug response. We identified 134 programs (73 unique drug targets) and 147 clinical trials. Less than 1% (n = 1/147) trials used a genomic biomarker prospectively for in-trial enrichment despite 32% (n = 23/73) of the drug targets having evidence of genetic variations. Additionally, 46% (n = 68/147) of the trials specified exploratory biomarker use. The results highlight an opportunity for more targeted CVD drug development by leveraging genomic biomarker knowledge.
View details for DOI 10.1002/cpt.1473
View details for PubMedID 31002380
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.
View details for PubMedID 30532240
IMPACT STATEMENT: Many untreated diseases are not monogenic and are instead caused by multiple genetic defects. Because of this complexity, computational, logical, and systems understanding will be essential to discovering novel therapies. The scientist engineer is uniquely disposed to use this type of understanding to advance therapeutic discovery. This work highlights benefits of the scientist engineer perspective and underscores the potential impact of these approaches for future therapeutic development. By framing the scientist engineer's tool set and increasing awareness about this approach, this article stands to impact future therapeutic development efforts in an age of rising development costs and high drug attrition.
View details for PubMedID 30458646
Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.
View details for PubMedID 29199461