A Predictive Computational Platform for Gut Microbiome Precision Medicine - Project Summary / Abstract
The human gut microbiome has recently become a primary focus of therapeutic research. This trend has been driven by a
growing understanding of the intimate ties between the gut microbiome and various diseases, as well as interactions with
different types of xenobiotics such as drugs. Specifically, evidence for the role of the gut microbiome as a determining
factor in drug pharmacokinetics and therapeutics response has exponentially expanded. The pharmaceutical industry has
become increasingly interested in the use of microbiome therapeutics that target and manipulate the gut microbiota to
improve health conditions or prevent/treat diseases. A major challenge, however, is the high variability of the gut
microbiome composition across individuals. Leading pharmaceutical researchers have begun to recognize that inter-
individual variations should be accounted for in attempts to develop personalized medicine. However, despite extensive
progress in gut microbiome research, no current effort had been able to leverage personalized “omics” data to explore the
dynamics of gut microbiome in interacting with therapeutics.
This Phase I proposal targets an essential need at the intersection of two recent research initiatives announced by the
White House, namely the Precision Medicine Initiative (2015) and the National Microbiome Initiative (2016). This project
seeks to develop a predictive computational platform by integrating novel state-of-the-art computational techniques and
modeling approaches. By simulating the personalized, multi-scale dynamics of the gut microbiome in interaction with
therapeutics, new information can be leveraged to improve the effectiveness of drug design and development. We will
employ complex systems approaches, combined with computational biology techniques, to explicitly model the individual
components of the complex network of gut microbiota as well as the rules governing their behavior (e.g. interactions) and
predict systems-level emergent behavior of the whole system. This multi-scale platform will be validated using targeted in
vitro experiments and in vivo studies.
The outcome of this Phase I project will be a proof-of-concept of an integrated, multi-scale computational platform for
predicting the functional dynamics of gut microbiome in the context of pharmacomicrobiomics, i.e. interactions of the gut
microbiome with therapeutics. This in-silico platform would reduce the cost and timeframe of drug development by
reducing the need for iterative pre-clinical experiments and better directing clinical trials, while increasing the
effectiveness of the therapeutics themselves. Moreover, this platform can help increase the safety of pharmaceuticals by
evaluating the mechanisms of toxicity as they may differ from individual-to-individual. As a result, the pharmaceutical
industry as well as academic researchers in the gut microbiome and precision medicine fields would benefit from the
successful outcome of this project and, in addition, this platform could eventually become an essential tool in
personalizing the prescription of drug types and doses for specific therapeutics.
