MDM-QScreen: A Quantitative Screen for the Assessment of Microbiome Derived Drug Metabolism for Drug Development and Personalized Medicine
Princeton Docket # 20-3681
The human gut microbiome is composed of hundreds of individual species of bacteria and varies greatly between individuals. It has been shown for more than 70 years that bacterial isolates from the gut microbiome can directly metabolize clinically used drugs, with important clinical implications (e.g., effects on toxicity or therapeutic efficacy). Despite this knowledge, the exact contribution of the gut microbiome to drug pharmacokinetics has not been considered in the drug development pipeline.
Researchers at Princeton University’s Department of Molecular Biology have developed a novel quantitative screen that directly measures the ability of the collective human gut microbiome to metabolize any drug of interest: Microbiome-Derived Metabolism Quantitative Screen, or MDM-QScreen. MDM-QScreen does not rely on single isolates of the gut microbiome but instead takes into account the collective contribution of complex microbial communities that are derived from the gut microbiome. Importantly, MDM-QScreen accomplishes this goal in a subject-personalized manner and quantifies the inter-individual variability between subjects with respect to their MDM. By discovering new drug-microbiome interactions, and quantifying their inter-individual variability, MDM-QScreen can be used to explain non-linear pharmacokinetics and toxicity profiles for currently used drugs, inform future drug design and formulation for newly developed drugs, and guide efforts for personalized medicine.
MDM-QScreen can be used to discover, measure, and explain inter-individual variability in drug metabolism that is attributed to the gut microbiome. This information is crucial in explaining the potential toxic effects of administered drugs, as well as variability in response to therapy between individuals. It is also important in understanding the mechanistic basis for how drugs are metabolized in the body, and to inform changes in drug design.
- Assessment of unexplained variability in drug response and toxicity of already used medications for personalized medicine
- Inform drug design, where an undesired effect of the microbiome on the drug under development can be discovered and eliminated early on in the process
- Drug development to aid in the design and interpretation of clinical trials
- Defines a new source of ADME for drugs
- Provides a quantitative measure of inter-individual variability in drug metabolism, regarding drug degradation and metabolites
- The personalized measure of drug metabolism by the gut microbiome, which can be performed prior to or during therapy to determine potential adverse effects or sub-adequate responses mediated by the gut microbiome
Personalized Mapping of Drug Metabolism by the Human Gut Microbiome
Bahar Javdan, Jaime G. Lopez, Pranatchareeya Chankhamjon, Ying-Chiang J. Lee, Raphaella Hull, Qihao Wu, Xiaojuan Wang, Seema Chatterjee, and Mohamed S. Donia, Cell https://doi.org/10.1016/j.cell.2020.05.001
Mohamed Donia, Ph.D. is an associate professor of molecular biology in the Department of Molecular Biology. His research interests are mainly to study the chemical and biological interactions within complex microbial communities (microbe-microbe interactions) and between microbial communities and their multicellular hosts (microbe-host interactions) in the context of the human microbiome. In particular, the Donia lab has a special interest in the impact of the human microbiome on the therapeutic efficacy of administered pharmaceuticals. Dr. Donia is a recipient of the NIH Director's New Innovator and Transformative Research Awards, the Kenneth Rainin Foundation Innovation and Breakthrough Awards, The Pershing Square Sohn Prize for Young Investigators in Cancer Research, The Vilcek Prize for Creative Promise in Biomedical Science, and is named a Pew Biomedical Scholar. Dr. Donia is a member of the Scientific Advisory Board for Deepbiome Therapeutics.
Jaime G Lopez is a graduate student in the Quantitative and Computational Biology Ph.D. program at Princeton University.
Bahar Javdan was a graduate student in the MD/Ph.D. program with Rutgers and Princeton and did her Ph.D. research in the Donia lab.
- Stage of Development
MDM-QScreen has been tested extensively in its current iteration.
- Intellectual Property
Patent protection is pending.
Princeton is currently seeking commercial partners for the further development and commercialization of this opportunity.
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