Computational prediction of gut microbiome-mediated drug metabolism - Project Summary / Abstract
Notwithstanding pre-clinical experiments and clinical trials performed to identify efficacy, side effects, and
adverse drug reactions (ADRs), only 25-60% of patients respond favorably to prescribed drugs, leading to a
cost of $30-$130 billion in the US annually. ADRs are partially attributed to the gut microbiome, i.e. the
complex and dynamic community of microorganisms residing in gastrointestinal tract. The gut microbiome
interacts with different types of xenobiotics including drugs, resulting in biotransformation of therapeutics into
metabolites with altered disposition, efficacy, and toxicity. Gut microbiome-mediated drug metabolism leads to
non-effective treatments as well as teratogenic, toxic, and lethal effects that in some cases were not
recognized until the drug was on the market. As a result, leading pharmaceutical researchers have begun to
recognize that the role of gut microbiome in drug metabolism should be accounted for in attempts to improve
treatment effectiveness. However, despite extensive progress in gut microbiome research, there is currently no
reliable, cost-effective approach to integrate gut-mediated drug metabolism in drug development pipelines.
This Phase I proposal aims to address this challenge by developing a new computational platform with the
ability to predict microbial metabolism of therapeutic drugs and to leverage that information to enhance drug
design and development. We will employ a range of state-of-the-art computational biology techniques to
reliably screen for microorganisms that may metabolize the target drugs. The novelty of this project lies in the
ability to screen drug-metabolizing enzymes/microorganisms using multiple metrics and methods to increase
the reliability of predictions to achieve the accuracy necessary for clinical and commercial use. This multi-
method platform will be built, integrated, and validated in an iterative fashion using targeted in vitro
experiments on two candidate drugs, i.e. the anti-arrhythmic drug amiodarone and the anti-viral drug
famciclovir. This project is designed to both advance our current understanding of microbiome function in the
context of drug-gut interactions as well as inform strategies to help enhance public health and economic
growth.
The value proposition of this project includes leveraging publicly available bioinformatics databases as well as
advances in computational biology techniques to develop a more precise, reliable, and inexpensive tool for gut
microbiome-mediated metabolism of therapeutic drugs. This in-silico platform could be employed for both
current drugs as well as drugs under development. For current drugs, this platform can help increase the
safety of drugs by predicting the mechanisms of efficacy and toxicity as they may differ from individual-to-
individual. For new drugs, the platform would reduce the cost and timeframe of drug development, while
increasing the effectiveness of the therapeutics themselves.
