Notwithstanding pre-clinical experiments and clinical trials performed to identify efficacy, side effects, andadverse drug reactions (ADRs), only 25-60% of patients respond favorably to prescribed drugs, leading to acost of $30-$130 billion in the US annually. ADRs are partially attributed to the gut microbiome, i.e. thecomplex and dynamic community of microorganisms residing in gastrointestinal tract. The gut microbiomeinteracts with different types of xenobiotics including drugs, resulting in biotransformation of therapeutics intometabolites with altered disposition, efficacy, and toxicity. Gut microbiome-mediated drug metabolism leads tonon-effective treatments as well as teratogenic, toxic, and lethal effects that in some cases were notrecognized until the drug was on the market. As a result, leading pharmaceutical researchers have begun torecognize that the role of gut microbiome in drug metabolism should be accounted for in attempts to improvetreatment effectiveness. However, despite extensive progress in gut microbiome research, there is currently noreliable, 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 theability to predict microbial metabolism of therapeutic drugs and to leverage that information to enhance drugdesign and development. We will employ a range of state-of-the-art computational biology techniques toreliably screen for microorganisms that may metabolize the target drugs. The novelty of this project lies in theability to screen drug-metabolizing enzymes/microorganisms using multiple metrics and methods to increasethe 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 vitroexperiments on two candidate drugs, i.e. the anti-arrhythmic drug amiodarone and the anti-viral drugfamciclovir. This project is designed to both advance our current understanding of microbiome function in thecontext of drug-gut interactions as well as inform strategies to help enhance public health and economicgrowth.The value proposition of this project includes leveraging publicly available bioinformatics databases as well asadvances in computational biology techniques to develop a more precise, reliable, and inexpensive tool for gutmicrobiome-mediated metabolism of therapeutic drugs. This in-silico platform could be employed for bothcurrent drugs as well as drugs under development. For current drugs, this platform can help increase thesafety 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, whileincreasing the effectiveness of the therapeutics themselves. Project Narrative Adverse drug reactions (ADRs) place significant clinical and economic burden on patients, their care-givers, and healthcare systems. This project aims to increase the effectiveness of drug development through the development of a novel computational platform to reliably predict gut microbiome-mediated drug metabolism. Achievement ; Achievement Attainment ; Algorithms ; Amiodarone ; Anti-Arrhythmia Agents ; Anti-Arrhythmia Drugs ; Anti-Arrhythmics ; Antiarrhythmia Agents ; Antiarrhythmia Drugs ; Antiarrhythmic Drugs ; antiarrhythmic agent ; arrhythmic agent ; Antiviral Agents ; Antiviral Drugs ; Antivirals ; anti-viral agents ; anti-viral drugs ; anti-virals ; Biotechnology ; Biotech ; Metabolic Biotransformation ; Biotransformation ; California ; Clinical Trials ; Communities ; Digoxin ; Lanoxin ; Drug Design ; Drug Industry ; Pharmaceutic Industry ; Pharmaceutical Industry ; Pharmaceutical Preparations ; Drugs ; Medication ; Pharmaceutic Preparations ; drug/agent ; Enzymes ; Enzyme Gene ; Gastrointestinal tract structure ; Alimentary Canal ; Digestive Tract ; GI Tract ; Gastrointestinal Tract ; alimentary tract ; digestive canal ; Government ; Growth ; Generalized Growth ; Tissue Growth ; ontogeny ; Healthcare Systems ; Health Care Systems ; Human ; Modern Man ; In Vitro ; Industrialization ; Levodopa ; L-Dopa ; Metabolism ; Intermediary Metabolism ; Metabolic Processes ; Methods ; Persons ; Patients ; Drug Prescriptions ; Drug Prescribing ; medication prescription ; prescribed medication ; Public Health ; Research ; Research Personnel ; Investigators ; Researchers ; Role ; social role ; Technology ; Teratogens ; Teratogenic ; Teratogenicity ; Universities ; Xenobiotics ; Caregivers ; Care Givers ; Mediating ; Treatment Effectiveness ; Data Set ; Dataset ; base ; improved ; Clinical ; Phase ; Famciclovir ; Famvir ; Evaluation ; Training ; Individual ; Databases ; Data Bases ; data base ; Drug usage ; drug use ; Letters ; Therapeutic ; tool ; machine learned ; Machine Learning ; computer biology ; Computational Biology ; Complex ; microorganism ; Techniques ; System ; 3-D ; 3D ; three dimensional ; 3-Dimensional ; Molecular Dynamics Simulation ; molecular dynamics ; microbial ; Molecular Modeling Nucleic Acid Biochemistry ; Molecular Modeling Protein/Amino Acid Biochemistry ; Molecular Models ; molecular modeling ; Toxicities ; Toxic effect ; novel ; drug metabolism ; response ; drug development ; High Throughput Assay ; high throughput screening ; Pharmacogenomics ; Bio-Informatics ; Bioinformatics ; Pharmaceutical Agent ; Pharmaceuticals ; Pharmacological Substance ; Pharmacologic Substance ; Effectiveness ; Address ; Data ; Economic Burden ; Resolution ; Clinical Data ; Small Business Technology Transfer Research ; STTR ; Validation ; Molecular ; Docking ; Development ; developmental ; pre-clinical ; preclinical ; microbiome ; cost ; health economics ; design ; designing ; Outcome ; three dimensional structure ; 3-D structure ; 3-dimensional structure ; 3D structure ; cost effective ; clinically relevant ; clinical relevance ; therapeutic effectiveness ; Plug-in ; web based interface ; novel therapeutics ; new drug treatments ; new drugs ; new therapeutics ; new therapy ; next generation therapeutics ; novel drug treatments ; novel drugs ; novel therapy ; prototype ; drug candidate ; gut microbiota ; GI microbiota ; Gastrointestinal microbiota ; enteric microbial community ; enteric microbiota ; gastrointestinal microbial flora ; gut commensal ; gut community ; gut flora ; gut microbe community ; gut microbial community ; gut microbial composition ; gut microbial consortia ; gut microbiotic ; gut microflora ; intestinal flora ; intestinal microbes ; intestinal microbiota ; intestinal microflora ; intestinal tract microflora ; Drug Targeting ; precision medicine ; precision-based medicine ; gut microbiome ; GI microbiome ; digestive tract microbiome ; enteric microbiome ; gastrointestinal microbiome ; gut-associated microbiome ; intestinal biome ; intestinal microbiome ; software as a service ; parallelization ; experimental study ; experiment ; experimental research ; microbiome research ; Microbiomics ; microbiome science ; microbiome studies ; Drug Screening ; deep learning ; Infrastructure ; convolutional neural network ; ConvNet ; convolutional network ; convolutional neural nets ; computational platform ; computing platform ; side effect ; adverse drug reaction ; medication safety ; drug safety ; pharmaceutical safety ; pharmacokinetics and pharmacodynamics ; PK/PD ; Computer Models ; Computerized Models ; computational modeling ; computational models ; computer based models ; computerized modeling ; in silico ;
