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Jamrat S, Sukasem C, Sratthaphut L, Hongkaew Y, Samanchuen T. A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making. Comput Biol Med 2023; 165:107329. [PMID: 37611418 DOI: 10.1016/j.compbiomed.2023.107329] [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: 06/06/2023] [Revised: 07/14/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Screening potential drug-drug interactions, drug-gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making. The approach focuses on raising the performance of four potential interaction screenings in the prescribing process, including drug-drug interactions, drug-gene interactions, drug-herb interactions, drug-social lifestyle interactions, and two potential considerations for patients with liver or renal impairment. The work describes the design of a curated knowledge-based model called the knowledge model for potential interaction and consideration screening, the screening logic for both the detection module and inference module, and the personalized prescribing report. Three case studies have demonstrated the proof-of-concept and effectiveness of this approach. The proposed approach aims to reduce decision-making processes for healthcare professionals, reduce medication-related harm, and enhance treatment effectiveness. Additionally, the recommendation with a semantic network is suggested to assist in risk-benefit analysis when health professionals plan therapeutic interventions with new medicines that have insufficient evidence to establish explicit recommendations. This approach offers a promising solution to implementing precision medicine in clinical practice.
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Affiliation(s)
- Samart Jamrat
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand; Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand; Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center, Ramathibodi Hospital, Bangkok, 10400, Thailand; Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Lawan Sratthaphut
- Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand; Department of Biomedicine and Health Informatics, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Yaowaluck Hongkaew
- Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand; Research and Development Laboratory, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Taweesak Samanchuen
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand.
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2
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Silva P, Janjan N, Ramos KS, Udeani G, Zhong L, Ory MG, Smith ML. External control arms: COVID-19 reveals the merits of using real world evidence in real-time for clinical and public health investigations. Front Med (Lausanne) 2023; 10:1198088. [PMID: 37484840 PMCID: PMC10359981 DOI: 10.3389/fmed.2023.1198088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/31/2023] [Indexed: 07/25/2023] Open
Abstract
Randomized controlled trials are considered the 'gold standard' to reduce bias by randomizing patients to an experimental intervention, versus placebo or standard of care cohort. There are inherent challenges to enrolling a standard of care or cohorts: costs, site engagement logistics, socioeconomic variability, patient willingness, ethics of placebo interventions, cannibalizing the treatment arm population, and extending study duration. The COVID-19 pandemic has magnified aspects of constraints in trial recruitment and logistics, spurring innovative approaches to reducing trial sizes, accelerating trial accrual while preserving statistical rigor. Using data from medical records and databases allows for construction of external control arms that reduce the costs of an external control arm (ECA) randomized to standard of care. Simultaneously examining covariates of the clinical outcomes in ECAs that are being measured in the interventional arm can be particularly useful in phase 2 trials to better understand social and genetic determinants of clinical outcomes that might inform pivotal trial design. The FDA and EMA have promulgated a number of publicly available guidance documents and qualification reports that inform the use of this regulatory science tool to streamline clinical development, of phase 4 surveillance, and policy aspects of clinical outcomes research. Availability and quality of real-world data (RWD) are a prevalent impediment to the use of ECAs given such data is not collected with the rigor and deliberateness that characterizes prospective interventional control arm data. Conversely, in the case of contemporary control arms, a clinical trial outcome can be compared to a contemporary standard of care in cases where the standard of care is evolving at a fast pace, such as the use of checkpoint inhibitors in cancer care. Innovative statistical methods are an essential aspect of an ECA strategy and regulatory paths for these innovative approaches have been navigated, qualified, and in some cases published.
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Affiliation(s)
- Patrick Silva
- Institute of Bioscience and Technology and Department of Translational Medical Sciences, College Station, TX, United States
| | - Nora Janjan
- Center for Community Health and Aging, School of Public Health, Texas A&M University, College Station, TX, United States
| | - Kenneth S. Ramos
- Institute of Bioscience and Technology and Department of Translational Medical Sciences, College Station, TX, United States
| | - George Udeani
- Department of Clinical Pharmacy, School of Pharmacy, Texas A&M University, College Station, TX, United States
| | - Lixian Zhong
- Department of Pharmaceutical Sciences, School of Pharmacy, Texas A&M University, College Station, TX, United States
| | - Marcia G. Ory
- Center for Community Health and Aging, School of Public Health, Texas A&M University, College Station, TX, United States
| | - Matthew Lee Smith
- Center for Community Health and Aging, School of Public Health, Texas A&M University, College Station, TX, United States
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Silva P, Dahlke DV, Smith ML, Charles W, Gomez J, Ory MG, Ramos KS. An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology. J Pers Med 2022; 12:jpm12050713. [PMID: 35629136 PMCID: PMC9144063 DOI: 10.3390/jpm12050713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 11/26/2022] Open
Abstract
Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.
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Affiliation(s)
- Patrick Silva
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
- Correspondence: ; Tel.: +1-979-436-9055
| | - Deborah Vollmer Dahlke
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Matthew Lee Smith
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Wendy Charles
- BurstIQ, 9635 Maroon Circle, #310, Englewood, CO 80112, USA;
| | - Jorge Gomez
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
| | - Marcia G. Ory
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Kenneth S. Ramos
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
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Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021; 41:1123-1133. [PMID: 34950987 PMCID: PMC8702375 DOI: 10.1007/s11596-021-2485-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/03/2021] [Indexed: 12/19/2022]
Abstract
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
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Affiliation(s)
- Yi Xie
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lin Lu
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fei Gao
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuang-Jiang He
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui-Juan Zhao
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ying Fang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Ming Yang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ying An
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430032, China
| | - Zhe-Wei Ye
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhe Dong
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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Pharmacogenomics: From Basic Research to Clinical Implementation. J Pers Med 2021; 11:jpm11080800. [PMID: 34442444 PMCID: PMC8402167 DOI: 10.3390/jpm11080800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 11/17/2022] Open
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7
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Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_340-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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