Predictive Systems Biology Approach to Broad-Spectrum, Host-Directed Drug Target Discovery in Infectious Diseases
Authors: Ramon M. Felciano, Sina Bavari, Daniel R. Richards, Jean-noel Billaud, Travis Warren, Rekha Panchal, Andreas Krämer
Computational Drug Repositioning
Abstract: Knowledge of immune system and host-pathogen pathways can inform development of targeted therapies and molecular diagnostics based on a mechanistic understanding of disease pathogenesis and the host response. We investigated the feasibility of rapid target discovery for novel broad-spectrum molecular therapeutics through comprehensive systems biology modeling and analysis of pathogen and host-response pathways and mechanisms. We developed a system to identify and prioritize candidate host targets based on strength of mechanistic evidence characterizing the role of the target in pathogenesis and tractability desiderata that include optimal delivery of new indications through potential repurposing of existing compounds or therapeutics. Empirical validation of predicted targets in cellular and mouse model systems documented an effective target prediction rate of 34%, suggesting that such computational discovery approaches should be part of target discovery efforts in operational clinical or biodefense research initiatives. We describe our target discovery methodology, technical implementation, and experimental results. Our work demonstrates the potential for in silico pathway models to enable rapid, systematic identification and prioritization of novel targets against existing or emerging biological threats, thus accelerating drug discovery and medical countermeasures research.
Predicting drug treatment using semantic technology, scientific knowledge bases (KBs) of mammalian biochemistry, and bioinformatics tools developed by Ingenuity for drug discovery and development
Overview of Ingenuity-USAMRIID predictive systems biology pilot, including knowledge base (KB) construction (A) and host-pathogen pathway model inference (B) for 6 pilot pathogens; multiple rounds (“iterations”) of in silico target prediction (C) based on suite of target ID algorithms (D); expert review and prioritization of targets using our system prototype (E); and final target selections for in vitro and in vivo validation at USAMRIID (F). KBs are updated between each iteration. PIC = pathway intervention candidate, i.e. a proposed target centered around the perturbation of a specific pathway of interest.
Example of target (SERPINE2, blue node) hypothesis identified by the upstream regulators algorithm as playing a common role in pathogenesis of Ebola virus and Marburg virus, and a drug (Drotrecogin Alfa / Xigris™, Elli Lily)) that may be repositioning for this indication. This drug target hypothesis is grounded in signature of host proteins (yellow nodes) that are commonly downregulated by Ebola and Marburg infection. SERPINE2 is further linked to relevant immune functions, including ones found in viral hemorrhagic fever infection (e.g. coagulation pathways). SERPINE2 was validated in vitro and in vivo to have the predicted effect on systems infected by the Ebola and Marburg viruses.