1
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Li D, Wu L, Lin YC, Huang HY, Cotton E, Liu Q, Chen R, Huang R, Zhang Y, Xu J. Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1. Exp Biol Med (Maywood) 2024; 249:10393. [PMID: 39691764 PMCID: PMC11650518 DOI: 10.3389/ebm.2024.10393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Accepted: 11/18/2024] [Indexed: 12/19/2024] Open
Abstract
Pharmacogenomics (PGx) holds the promise of personalizing medical treatments based on individual genetic profiles, thereby enhancing drug efficacy and safety. However, the current landscape of PGx research is hindered by fragmented data sources, time-consuming manual data extraction processes, and the need for comprehensive and up-to-date information. This study aims to address these challenges by evaluating the ability of Large Language Models (LLMs), specifically Llama3.1-70B, to automate and improve the accuracy of PGx information extraction from the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling (FDA PGx Biomarker table), which is well-structured with drug names, biomarkers, therapeutic area, and related labeling texts. Our primary goal was to test the feasibility of LLMs in streamlining PGx data extraction, as an alternative to traditional, labor-intensive approaches. Llama3.1-70B achieved 91.4% accuracy in identifying drug-biomarker pairs from single labeling texts and 82% from mixed texts, with over 85% consistency in aligning extracted PGx categories from FDA PGx Biomarker table and relevant scientific abstracts, demonstrating its effectiveness for PGx data extraction. By integrating data from diverse sources, including scientific abstracts, this approach can support pharmacologists, regulatory bodies, and healthcare researchers in updating PGx resources more efficiently, making critical information more accessible for applications in personalized medicine. In addition, this approach shows potential of discovering novel PGx information, particularly of underrepresented minority ethnic groups. This study highlights the ability of LLMs to enhance the efficiency and completeness of PGx research, thus laying a foundation for advancements in personalized medicine by ensuring that drug therapies are tailored to the genetic profiles of diverse populations.
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Affiliation(s)
- Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Ying-Chi Lin
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan
- Master/Doctoral Degree Program in Toxicology, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ho-Yin Huang
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Pharmacy, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ebony Cotton
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Qi Liu
- Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - Ru Chen
- Immediate Office, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Ruihao Huang
- Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - Yifan Zhang
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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Mai CW, Sridhar SB, Karattuthodi MS, Ganesan PM, Shareef J, Lee EL, Armani K. Scoping review of enablers and challenges of implementing pharmacogenomics testing in the primary care settings. BMJ Open 2024; 14:e087064. [PMID: 39500605 PMCID: PMC11552560 DOI: 10.1136/bmjopen-2024-087064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 09/24/2024] [Indexed: 11/13/2024] Open
Abstract
INTRODUCTION Pharmacogenomic testing (PGx) plays a crucial role in improving patient medication safety, yet ethical concerns and limitations impede its clinical implementation in the primary care settings. AIMS To systematically review the current state of PGx in the primary care settings and determine the enablers and challenges of its implementation. DESIGN A scoping review was carried out by adhering to Arksey and O'Malley's 6-stage methodological framework and the 2020 Joanna Briggs Institute and Levac et al. DATA SOURCES: Cochrane Library, EMBASE, Global Health, MEDLINE and PubMed were searched up to 17 July 2023. ELIGIBILITY CRITERIA All peer-reviewed studies in English, reporting the enablers and the challenges of implementing PGx in the primary care settings were included. DATE EXTRACTION AND SYNTHESIS Two independent reviewers extracted the data. Information was synthesised based on the reported enablers and the challenges of implementing PGx testing in the primary care settings. Information was then presented to stakeholders for their inputs. RESULTS 78 studies discussing the implementation of PGx testing are included, of which 57% were published between 2019 and 2023. 68% of the studies discussed PGx testing in the primary care setting as a disease-specific themes. Healthcare professionals were the major stakeholders, with primary care physicians (55%) being the most represented. Enablers encompassed various advantages such as diagnostic and therapeutic benefits, cost reduction and the empowerment of healthcare professionals. Challenges included the absence of sufficient scientific evidence, insufficient training for healthcare professionals, ethical and legal aspects of PGx data, low patient awareness and acceptance and the high costs linked to PGx testing. CONCLUSION PGx testing integration in primary care requires increased consumer awareness, comprehensive healthcare provider training on legal and ethical aspects and global feasibility studies to better understand its implementation challenges. Managing high costs entails streamlining processes, advocating for reimbursement policies and investing in research on innovation and affordability research to improve life expectancy.
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Affiliation(s)
- Chun-Wai Mai
- Faculty of Pharmaceutical Sciences, UCSI University, Kuala Lumpur, Cheras, Malaysia
| | | | - Mohammed Salim Karattuthodi
- Manipal College of Pharmaceutical Sciences Department of Pharmaceutical Biotechnology, Manipal, Karnataka, India
| | | | - Javedh Shareef
- RAK Medical & Health Sciences University, Ras Al Khaimah, UAE
| | - E Lyn Lee
- IMU University, Kuala Lumpur, Malaysia
| | - Keivan Armani
- Department of Primary Care and Public Health, School of Public Health, Imperial College London Faculty of Medicine, London, UK
- UCSI University Faculty of Pharmaceutical Sciences, Cheras, Kuala Lumpur, Malaysia
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Marcu LG, Marcu DC. Pharmacogenomics and Big Data in medical oncology: developments and challenges. Ther Adv Med Oncol 2024; 16:17588359241287658. [PMID: 39483136 PMCID: PMC11526290 DOI: 10.1177/17588359241287658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 09/12/2024] [Indexed: 11/03/2024] Open
Abstract
Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology.
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Affiliation(s)
- Loredana G. Marcu
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia
- Faculty of Informatics and Science, University of Oradea, Oradea 410087, Romania
| | - David C. Marcu
- Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania
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Avci CB, Bagca BG, Shademan B, Takanlou LS, Takanlou MS, Nourazarian A. Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy. Funct Integr Genomics 2024; 24:182. [PMID: 39365298 DOI: 10.1007/s10142-024-01462-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 10/05/2024]
Abstract
This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.
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Affiliation(s)
- Cigir Biray Avci
- Department of Medical Biology, Faculty of Medicine, Ege University, Izmir, Turkey
| | - Bakiye Goker Bagca
- Department of Medical Biology, Faculty of Medicine, Adnan Menderes University, Aydın, Turkey
| | - Behrouz Shademan
- Stem Cell Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | | | - Alireza Nourazarian
- Department of Basic Medical Sciences, Khoy University of Medical Sciences, Khoy, Iran.
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LeBaron von Baeyer S, Crocker RM, Rakotoarivony R, Ranaivoarisoa JF, Spiral GJ, Pascart T, Wheeler V, Mairai T, Gregersen NO, Castel SE, Yerges-Armstrong LM, Fox K, Wasik KA. Nothing about us without us: Sharing results with communities that provide genomic data. Cell 2024; 187:5483-5489. [PMID: 39303717 DOI: 10.1016/j.cell.2024.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 08/12/2024] [Accepted: 08/12/2024] [Indexed: 09/22/2024]
Abstract
Sharing genetic and other study results with the communities who participate in research falls under benefit-sharing and capacity-building initiatives that underpin a more equitable biomedical research relationship. Yet, which results to return and how remain fundamental challenges that persist in the absence of practical guidance and institutional policies. Here, we discuss how the return of results can be implemented across different geographies, study designs, and project budgets.
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Affiliation(s)
| | | | | | | | | | | | - Vehia Wheeler
- Australian National University, Canberra, ACT, Australia; Sustainable Oceania Solutions, Afareaitu, Mo'orea, French Polynesia
| | | | - Noomi O Gregersen
- Faroe Genome Project, Tórshavn, Faroe Islands; University of the Faroe Islands, Tórshavn, Faroe Islands
| | | | | | - Keolu Fox
- Indigenous Futures Institute, University of California, San Diego, La Jolla, CA, USA; J. Craig Venter Research Institute, La Jolla, CA, USA.
| | - Kaja A Wasik
- Variant Bio, Seattle, WA, USA; J. Craig Venter Research Institute, La Jolla, CA, USA.
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6
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Andreoli L, Berca C, Katz S, Korshevniuk M, Head RM, Van Steen K. Bridging the gap in precision medicine: TranSYS training programme for next-generation scientists. Front Med (Lausanne) 2024; 11:1348148. [PMID: 38854671 PMCID: PMC11160483 DOI: 10.3389/fmed.2024.1348148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/26/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction In the evolving healthcare landscape, precision medicine's rise necessitates adaptable doctoral training. The European Union has recognized this and promotes the development of international, training-focused programmes called Innovative Training Networks (ITNs). In this article, we introduce TranSYS, an ITN focused on educating the next generation of precision medicine researchers. In an ambition to go beyond describing the consortium goals, our article explores two key aspects of ITNs: the training and collaboration. Methods Using self-report questionnaires, we evaluate the scientific, professional, and personal growth of ESRs over the duration of the ITN and investigate whether this can be linked to network activities. Results Our quantitative analysis approach reveals substantial improvements in scientific, professional, and social skills among young researchers facilitated by the engagement in this interdisciplinary network. We provide case studies underlining the advantages of collaborative environments, featuring innovative scientific exchange within TranSYS. Discussion While challenging, ITNs foster positive growth in young researchers, yet exhibit weaknesses such as balancing stakeholder interests and partner commitment. We believe this study may benefit a variety of stakeholders, from prospective ITN creators to industry partners, to design better sustainable training networks going forward.
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Affiliation(s)
- Lara Andreoli
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Catalina Berca
- Epithelial Carcinogenesis Group, Molecular Oncology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Sonja Katz
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
- LifeGlimmer GmbH, Berlin, Germany
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Maryna Korshevniuk
- Genetics Department, University Medical Center Groningen, Groningen, Netherlands
| | | | - Kristel Van Steen
- Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
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7
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Wang D, Bolleddula J, Coenen-Stass A, Grombacher T, Dong JQ, Scheuenpflug J, Locatelli G, Feng Z. Implementation of whole-exome sequencing for pharmacogenomics profiling and exploring its potential clinical utilities. Pharmacogenomics 2024; 25:197-206. [PMID: 38511470 DOI: 10.2217/pgs-2023-0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
Whole-exome sequencing (WES) is widely used in clinical settings; however, the exploration of its use in pharmacogenomic analysis remains limited. Our study compared the variant callings for 28 core absorption, distribution, metabolism and elimination genes by WES and array-based technology using clinical trials samples. The results revealed that WES had a positive predictive value of 0.71-0.92 and a sensitivity of single-nucleotide variants between 0.68 and 0.95, compared with array-based technology, for the variants in the commonly targeted regions of the WES and PhamacoScan™ assay. Besides the common variants detected by both assays, WES identified 200-300 exclusive variants per sample, totalling 55 annotated exclusive variants, including important modulators of metabolism such as rs2032582 (ABCB1) and rs72547527 (SULT1A1). This study highlights the potential clinical advantages of using WES to identify a wider range of genetic variations and enabling precision medicine.
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Affiliation(s)
- Danyi Wang
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | - Jayaprakasam Bolleddula
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | | | | | - Jennifer Q Dong
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | | | | | - Zheng Feng
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
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8
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Sobsey CA, Mady N, Richard VR, LeBlanc A, Zakharov T, Borchers CH, Jagoe RT. Measurement of CYP1A2 and CYP3A4 activity by a simplified Geneva cocktail approach in a cohort of free-living individuals: a pilot study. Front Pharmacol 2024; 15:1232595. [PMID: 38370474 PMCID: PMC10869543 DOI: 10.3389/fphar.2024.1232595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 01/18/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: The cytochrome P450 enzyme subfamilies, including CYP3A4 and CYP1A2, have a major role in metabolism of a range of drugs including several anti-cancer treatments. Many factors including environmental exposures, diet, diseaserelated systemic inflammation and certain genetic polymorphisms can impact the activity level of these enzymes. As a result, the net activity of each enzyme subfamily can vary widely between individuals and in the same individual over time. This variability has potential major implications for treatment efficacy and risk of drug toxicity, but currently no assays are available for routine use to guide clinical decision-making. Methods: To address this, a mass spectrometry-based method to measure activities of CYP3A4, CYP1A2 was adapted and tested in free-living participants. The assay results were compared with the predicted activity of each enzyme, based on a self-report tool capturing diet, medication, chronic disease state, and tobacco usage. In addition, a feasibility test was performed using a low-volume dried blood spots (DBS) on two different filter-paper supports, to determine if the same assay could be deployed without the need for repeated standard blood tests. Results: The results confirmed the methodology is safe and feasible to perform in free-living participants using midazolam and caffeine as test substrates for CYP3A4 and CYP1A2 respectively. Furthermore, though similar methods were previously shown to be compatible with the DBS format, the assay can also be performed successfully while incorporating glucuronidase treatment into the DBS approach. The measured CYP3A4 activity score varied 2.6-fold across participants and correlated with predicted activity score obtained with the self-report tool. The measured CYP1A2 activity varied 3.5-fold between participants but no correlation with predicted activity from the self-report tool was found. Discussion: The results confirm the wide variation in CYP activity between individuals and the important role of diet and other exposures in determining CYP3A4 activity. This methodology shows great potential and future cross-sectional and longitudinal studies using DBS are warranted to determine how best to use the assay results to guide drug treatments.
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Affiliation(s)
- Constance A. Sobsey
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Noor Mady
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
| | - Vincent R. Richard
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Andre LeBlanc
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Thomas Zakharov
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
| | - Christoph H. Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - R. Thomas Jagoe
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
- Department of Medicine, Jewish General Hospital, Montreal, QC, Canada
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9
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Lim CX, Bozkurt A, Chen ZY, Hird A, Wickens J, Lazarakis S, Hussainy SY, Alexander M. Healthcare professionals' and consumers' knowledge, attitudes, perspectives, and education needs in oncology pharmacogenomics: A systematic review. Clin Transl Sci 2023; 16:2467-2482. [PMID: 37991131 PMCID: PMC10719462 DOI: 10.1111/cts.13672] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/19/2023] [Accepted: 10/04/2023] [Indexed: 11/23/2023] Open
Abstract
Clinical implementation of pharmacogenomic (PGx)-guided prescribing in oncology lags behind research evidence generation. We aimed to identify healthcare professionals' (HCPs) and consumers' knowledge, attitudes, perspectives, and education needs to inform strategies for implementation of scalable and sustainable oncology PGx programs. Systematic review of original articles indexed in EMBASE, EMCARE, MEDLINE, and PsycInfo from January 2012 until June 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and using the Mixed Methods Appraisal Tool. PROSPERO registration number CRD42022352348. Of 1442 identified studies; 23 met inclusion criteria with 87% assessed high quality. Of these, 52% reported on HCPs, 35% on consumers, and 13% on both HCPs and consumers. Most were conducted in the United States (70%) and included multiple cancer types (74%). Across studies, HCPs and consumers mostly perceived value in PGx, however, both groups reported barriers to utilization, including cost, lack of consistent recommendations across guidelines, and limited knowledge among HCPs; test accuracy, clear testing benefits, and genomic information confidentiality among consumers. HCPs and consumers value and want to engage in PGx strategies in oncology care, however, are inhibited by unmet needs and practice and knowledge gaps. Implementation strategies aimed at addressing these issues may best support increased PGx uptake in oncology practice.
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Affiliation(s)
- Chiao Xin Lim
- Pharmacy, School of Health and Biomedical SciencesRMIT UniversityBundooraVictoriaAustralia
| | - Alistair Bozkurt
- Pharmacy, School of Health and Biomedical SciencesRMIT UniversityBundooraVictoriaAustralia
| | - Zi Yue Chen
- Pharmacy, School of Health and Biomedical SciencesRMIT UniversityBundooraVictoriaAustralia
| | - Abbey Hird
- Pharmacy, School of Health and Biomedical SciencesRMIT UniversityBundooraVictoriaAustralia
| | - Joanne Wickens
- Pharmacy, School of Health and Biomedical SciencesRMIT UniversityBundooraVictoriaAustralia
| | - Smaro Lazarakis
- Health Sciences Library, Royal Melbourne HospitalParkvilleVictoriaAustralia
| | - Safeera Y. Hussainy
- Pharmacy Department, Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyThe University of MelbourneMelbourneVictoriaAustralia
- Department of General Practice, School of Public Health and Preventive MedicineMonash UniversityClaytonVictoriaAustralia
| | - Marliese Alexander
- Pharmacy Department, Peter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyThe University of MelbourneMelbourneVictoriaAustralia
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10
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Zheng P, Mo L, Zhao B, Li L, Cen B, Xu Z, Li Y. Pharmaceutical care model in precision medicine in China. FARMACIA HOSPITALARIA 2023; 47:T218-T223. [PMID: 37598018 DOI: 10.1016/j.farma.2023.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 08/21/2023] Open
Abstract
Pharmacy service is to provide individualized pharmaceutical care for patients, which should follow the current evidence-based pharmacy, and constantly verify the evidence and then produce new evidence. In pharmaceutical care, differences are often found in the efficacy and adverse reactions of drugs among individuals, even within individuals, which are closely related to patients' genetics, liver and kidney functions, disease states, and drug interactions. Back in the 1980s, therapeutic drug monitoring (TDM) has been applied to routinely monitor the blood drug concentration of patients taking antiepileptic drugs or immunosuppressants after transplantation to provide individualized dosage recommendations and accumulate a large amount of pharmacokinetic (PK)/pharmacodynamic (PD) data. As individualized pharmaceutical care proceeds, the concept of precision medicine was introduced into pharmacy services in combination with evidence-based pharmacy, PK/PD theories, and big data to further promote the TDM technology and drugs, and carry out pharmacogenomics analysis. The TDM and pharmacogenomics have been applied gradually to the fields of antimicrobial, antitumor, and antipsychotic drugs and immunosuppressants. Based on the concept of precision pharmacy, we adopted approaches including PK/PD, quantitative pharmacology, population pharmacokinetics, and big data machine learning to provide more personalized pharmacy services, which is mainly for special patients, such as critical patients, patients with interaction risk of multiple drugs, patients with liver and renal insufficiency, pregnant women, children, and elderly patients. As the service pattern of precision pharmacy has been constructed and constantly improved, better evidence in clinical practice will be produced to provide patients with better precision pharmacy service.
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Affiliation(s)
- Ping Zheng
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Liqian Mo
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Boxin Zhao
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Liren Li
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Baihong Cen
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Zhongyuan Xu
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China.
| | - Yilei Li
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
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11
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Zheng P, Mo L, Zhao B, Li L, Cen B, Xu Z, Li Y. Pharmaceutical care model in precision medicine in China. FARMACIA HOSPITALARIA 2023; 47:218-223. [PMID: 37248115 DOI: 10.1016/j.farma.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/31/2023] Open
Abstract
Pharmacy service is to provide individualized pharmaceutical care for patients, which should follow the current evidence-based pharmacy, and constantly verify the evidence and then produce new evidence. In pharmaceutical care, differences are often found in the efficacy and adverse reactions of drugs among individuals, even within individuals, which are closely related to patient's genetics, liver and kidney functions, disease states, and drug interactions. Back in the 1980s, therapeutic drug monitoring (TDM) has been applied to routinely monitor the blood drug concentration of patients taking antiepileptic drugs or immunosuppressants after transplantation to provide individualized dosage recommendations and accumulate a large amount of pharmacokinetic (PK)/pharmacodynamic (PD) data. As individualized pharmaceutical care proceeds, the concept of precision medicine was introduced into pharmacy services in combination with evidence-based pharmacy, PK/PD theories and big data to further promote the TDM technology and drugs, and carry out pharmacogenomics analysis. The TDM and pharmacogenomics have been applied gradually to the fields of antimicrobial, antitumor and antipsychotic drugs and immunosuppressants. Based on the concept of precision pharmacy, we adpoted approaches including PK/PD, quantitative pharmacology, population pharmacokinetics, and big data machine learning to provide more personalized pharmacy services, which is mainly for special patients, such as critical patients, patients with interaction risk of multiple drugs, patients with liver and renal insufficiency, pregnant women, children and elderly patients. As the service pattern of precision pharmacy has been constructed and constantly improved, better evidence in clinical practice will be produced to provide patients with better precision pharmacy service.
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Affiliation(s)
- Ping Zheng
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China
| | - Liqian Mo
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China
| | - Boxin Zhao
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China
| | - Liren Li
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China
| | - Baihong Cen
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China
| | - Zhongyuan Xu
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China.
| | - Yilei Li
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, Guangzhou City, Guangdong Province, China.
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12
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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13
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Zhang H, Mehrotra DV, Shen J. AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS. Bioinformatics 2023; 39:6994182. [PMID: 36661328 PMCID: PMC9885423 DOI: 10.1093/bioinformatics/btac834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/05/2022] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect. RESULTS In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials. AVAILABILITY AND IMPLEMENTATION The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, North Wales, PA 19454, USA
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14
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Biswas M, Jinda P, Sukasem C. Pharmacogenomics in Asians: Differences and similarities with other human populations. Expert Opin Drug Metab Toxicol 2023; 19:27-41. [PMID: 36755439 DOI: 10.1080/17425255.2023.2178895] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/07/2023] [Indexed: 02/10/2023]
Abstract
INTRODUCTION Various pharmacogenomic (PGx) variants differ widely in different ethnicities. and clinical outcomes associated with these variants may also be substantially varied. Literature was searched in different databases, i.e. PubMed, ScienceDirect, Web of Science, and PharmGKB, from inception to 30 June 2022 for this review. AREAS COVERED Certain PGx variants were distinctly varied in Asian populations compared to the other human populations, e.g. CYP2C19*2,*3,*17; CYP2C9*2,*3; CYP2D6*4,*5,*10,*41; UGT1A1*6,*28; HLA-B*15:02, HLA-B*15:21, HLA-B*58:01, and HLA-A*31:01. However, certain other variants do not vary greatly between Asian and other ethnicities, e.g. CYP3A5*3; ABCB1, and SLCO1B1*5. As evident in this review, the risk of major adverse cardiovascular events (MACE) was much stronger in Asian patients taking clopidogrel and who inherited the CYP2C19 loss-of-function alleles, e.g. CYP2C19*2 and*3, when compared to the western/Caucasian patients. Additionally, the risk of carbamazepine-induced severe cutaneous adverse drug reactions (SCARs) for the patients inheriting HLA-B*15:02 and HLA-B*15:21 alleles varied significantly between Asian and other ethnicities. In contrast, both Caucasian and Asian patients inheriting the SLCO1B1*5 variant possessed a similar magnitude of muscle toxicity, i.e. myopathy. EXPERT OPINION Asian countries should take measures toward expanding PGx research, as well as initiatives for the purposes of obtaining clinical benefits from this newly evolving and economically viable treatment model.
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Affiliation(s)
- Mohitosh Biswas
- Department of Pharmacy, University of Rajshahi, 6205, Rajshahi, Bangladesh
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
| | - Pimonpan Jinda
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
- Pharmacogenomics and Precision Medicine Clinic, Bumrungrad Genomic Medicine Institute (BGMI), Bumrungrad International Hospital, 10110, Bangkok, Thailand
- MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 3GL, Liverpool, UK
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15
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Albalwy F, McDermott JH, Newman WG, Brass A, Davies A. A blockchain-based framework to support pharmacogenetic data sharing. THE PHARMACOGENOMICS JOURNAL 2022; 22:264-275. [PMID: 35869255 DOI: 10.1038/s41397-022-00285-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 06/22/2022] [Accepted: 07/01/2022] [Indexed: 12/11/2022]
Abstract
The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain's performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.
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Affiliation(s)
- F Albalwy
- Department of Computer Science, Kilburn Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. .,Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia. .,Division of Informatics, Imaging and Data Sciences, Stopford Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
| | - J H McDermott
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,Division of Evolution Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK
| | - W G Newman
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,Division of Evolution Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK
| | - A Brass
- Department of Computer Science, Kilburn Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.,Division of Informatics, Imaging and Data Sciences, Stopford Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - A Davies
- Division of Informatics, Imaging and Data Sciences, Stopford Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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Patrinos GP, Shuldiner AR. Pharmacogenomics: the low-hanging fruit in the personalized medicine tree. Hum Genet 2022; 141:1109-1111. [PMID: 35482087 DOI: 10.1007/s00439-022-02456-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- George P Patrinos
- Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, University of Patras School of Health Sciences, University Campus, Rion, 265 04, Patras, Greece. .,College of Medicine and Health Sciences, Department of Genetics and Genomics, United Arab Emirates University, Al-Ain, United Arab Emirates. .,Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
| | - Alan R Shuldiner
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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17
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Mendelian randomization in pharmacogenomics: The unforeseen potentials. Biomed Pharmacother 2022; 150:112952. [PMID: 35429744 DOI: 10.1016/j.biopha.2022.112952] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 02/08/2023] Open
Abstract
Mendelian randomization (MR) is an epidemiological method that uses genetic variants to proxy an exposure predicting its causal association with an outcome. It occupies a valuable niche between observational studies and randomized trials. MR applications expanded lately, facilitated by the availability of big data, to include disease risk causation prediction, supporting evidence of prior observational data, identifying new drug targets, and drug repurposing. Concurrently, the last decade witnessed the growth of pharmacogenomics (PGx) research as a cornerstone in precision medicine. PGx research, conducted at discovery and implementation levels, resulted in validated PGx biomarkers and tests. Despite many clinically relevant PGx associations that could be translated into clinical applications, worldwide implementation is lagging far behind. The current review examines the intersection zones between MR and PGx research. MR can provide supporting evidence that allows generalizing PGx findings supporting its implementation. Interchangeability, PGx research can fuel MR studies with libraries of genetic variants of validated biological relevance. Furthermore, PGx and MR exhibit a synergistic relationship in drug discovery that can accelerate identifying new targets and repurposing old drugs. Interdisciplinary research applied by PGx researchers, epidemiologists with MR experience, and data scientists' collaborations can unlock unforeseen opportunities in accelerating precision medicine acquisition.
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Zagotto G, Bortoli M. Drug Design: Where We Are and Future Prospects. Molecules 2021; 26:7061. [PMID: 34834152 PMCID: PMC8622624 DOI: 10.3390/molecules26227061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 11/24/2022] Open
Abstract
Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.
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Affiliation(s)
- Giuseppe Zagotto
- Department of Pharmaceutical Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Marco Bortoli
- Institute of Computational Chemistry and Catalysis (IQCC) and Department of Chemistry, Faculty of Sciences, University of Girona, C/M. A. Capmany 69, 17003 Girona, Spain;
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