1
|
Jackson KD, Achour B, Lee J, Geffert RM, Beers JL, Latham BD. Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine. Drug Metab Dispos 2023; 51:1238-1253. [PMID: 37419681 PMCID: PMC10506699 DOI: 10.1124/dmd.122.001066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023] Open
Abstract
Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.
Collapse
Affiliation(s)
- Klarissa D Jackson
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Brahim Achour
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Jonghwa Lee
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Raeanne M Geffert
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Jessica L Beers
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Bethany D Latham
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| |
Collapse
|
2
|
Terranova N, Venkatakrishnan K, Benincosa LJ. Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities. AAPS JOURNAL 2021; 23:74. [PMID: 34008139 PMCID: PMC8130984 DOI: 10.1208/s12248-021-00593-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 04/08/2021] [Indexed: 02/06/2023]
Abstract
The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.
Collapse
Affiliation(s)
- Nadia Terranova
- Translational Medicine, Merck Institute for Pharmacometrics, Merck Serono S.A., Lausanne, Switzerland
| | - Karthik Venkatakrishnan
- Translational Medicine, EMD Serono Research & Development Institute, Inc., Billerica, Massachusetts, USA
| | - Lisa J Benincosa
- Translational Medicine, EMD Serono Research & Development Institute, Inc., Billerica, Massachusetts, USA.
| |
Collapse
|