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Li Z, Qiao S, Ning H, Sun X, Zhang J, Olatosi B, Li X. Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South. AIDS Behav 2023:10.1007/s10461-023-04163-4. [PMID: 37792234 DOI: 10.1007/s10461-023-04163-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2023] [Indexed: 10/05/2023]
Abstract
BACKGROUND The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. OBJECTIVES Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. METHODS We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. RESULTS Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. CONCLUSIONS Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.
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Affiliation(s)
- Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA.
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Huan Ning
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Geography, The Pennsylvania State University, University Park, PA, 16801, USA
| | - Xiaowen Sun
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Bankole Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
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Garett R, Young SD. Potential application of conversational agents in HIV testing uptake among high-risk populations. J Public Health (Oxf) 2023; 45:189-192. [PMID: 35211740 PMCID: PMC9383533 DOI: 10.1093/pubmed/fdac020] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 01/24/2022] [Indexed: 01/12/2023] Open
Abstract
Human Immunodeficiency Virus (HIV) continues to be a significant public health problem, with ~1.2 million Americans living with HIV and ~14% unaware of their infection. The Centers for Disease Control and Prevention recommends that patients 13 to 64 years of age get screened for HIV at least once, and those with higher risk profiles screen at least annually. Unfortunately, screening rates are below recommendations for high-risk populations, leading to problems of delayed diagnosis. Novel technologies have been applied in HIV research to increase prevention, testing and treatment. Conversational agents, with potential for integrating artificial intelligence and natural language processing, may offer an opportunity to improve outreach to these high-risk populations. The feasibility, accessibility and acceptance of using conversational agents for HIV testing outreach is important to evaluate, especially amidst a global coronavirus disease 2019 pandemic when clinical services have been drastically affected. This viewpoint explores the application of a conversational agent in increasing HIV testing among high-risk populations.
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Affiliation(s)
| | - Sean D Young
- Department of Emergency Medicine, University of California, Irvine, Orange, CA 92868, USA
- Institute for Prediction Technology, Department of Informatics, University of California, Irvine, CA 92617, USA
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3
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Kumar K, Kumar P, Deb D, Unguresan ML, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel) 2023; 11:healthcare11020207. [PMID: 36673575 PMCID: PMC9859198 DOI: 10.3390/healthcare11020207] [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: 11/17/2022] [Revised: 01/01/2023] [Accepted: 01/04/2023] [Indexed: 01/13/2023] Open
Abstract
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
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Affiliation(s)
- Kamlesh Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Prince Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Dipankar Deb
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
- Correspondence:
| | | | - Vlad Muresan
- Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
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4
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Blasiak A, Truong ATL, Remus A, Hooi L, Seah SGK, Wang P, Chye DH, Lim APC, Ng KT, Teo ST, Tan YJ, Allen DM, Chai LYA, Chng WJ, Lin RTP, Lye DCB, Wong JEL, Tan GYG, Chan CEZ, Chow EKH, Ho D. The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens. NPJ Digit Med 2022; 5:83. [PMID: 35773329 PMCID: PMC9244889 DOI: 10.1038/s41746-022-00627-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/01/2022] [Indexed: 12/15/2022] Open
Abstract
IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.
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Affiliation(s)
- Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
| | - Anh T L Truong
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Alexandria Remus
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
| | - Shirley Gek Kheng Seah
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Peter Wang
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - De Hoe Chye
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Angeline Pei Chiew Lim
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Kim Tien Ng
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Swee Teng Teo
- Infectious Diseases Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore
| | - Yee-Joo Tan
- Infectious Diseases Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore
- Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore, 138673, Singapore
| | - David Michael Allen
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Division of Infectious Diseases, National University Hospital, Singapore, 119074, Singapore
| | - Louis Yi Ann Chai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Division of Infectious Diseases, National University Hospital, Singapore, 119074, Singapore
| | - Wee Joo Chng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Hospital, Singapore, 119074, Singapore
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
| | - Raymond T P Lin
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, 119074, Singapore
| | - David C B Lye
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - John Eu-Li Wong
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Hospital, Singapore, 119074, Singapore
| | - Gek-Yen Gladys Tan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Conrad En Zuo Chan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore.
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore.
| | - Edward Kai-Hua Chow
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore.
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
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Garett R, Young SD. The importance of diverse key stakeholders in deciding the role of artificial intelligence for HIV research and policy. HEALTH POLICY AND TECHNOLOGY 2022. [DOI: 10.1016/j.hlpt.2022.100599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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6
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Zhang J, Olatosi B, Yang X, Weissman S, Li Z, Hu J, Li X. Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol. BMC Infect Dis 2022; 22:122. [PMID: 35120435 PMCID: PMC8817473 DOI: 10.1186/s12879-022-07047-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support. Methods The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits. Discussion With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
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Affiliation(s)
- Jiajia Zhang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA. .,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA. .,Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.
| | - Xueying Yang
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Sharon Weissman
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Department of Internal Medicine, School of Medicine, University of South Carolina, Columbia, SC, 29208, USA
| | - Zhenlong Li
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Geoinformation and Big Data Research Laboratory, University of South Carolina, Columbia, SC, 29208, USA
| | - Jianjun Hu
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
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7
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 269] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Wang B, Warden AR, Ding X. The optimization of combinatorial drug therapies: Strategies and laboratorial platforms. Drug Discov Today 2021; 26:2646-2659. [PMID: 34332097 DOI: 10.1016/j.drudis.2021.07.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/19/2021] [Accepted: 07/14/2021] [Indexed: 12/26/2022]
Abstract
Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.
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Affiliation(s)
- Boqian Wang
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Antony R Warden
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Xianting Ding
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China.
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9
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Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho CM, Mobley WC, Nolan GP, Rosen ST, Tan P, Yen Y, Zarrinpar A. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol 2020; 38:497-518. [PMID: 31980301 PMCID: PMC7924935 DOI: 10.1016/j.tibtech.2019.12.021] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 02/06/2023]
Abstract
Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.
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Affiliation(s)
- Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore; The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, CA, USA; Department of Applied Physics, Stanford University, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA
| | | | - Wee Joo Chng
- Department of Haematology and Oncology, National University Cancer Institute, National University Health System, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Edward K Chow
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Xianting Ding
- Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bruce D Gelb
- Mindich Child Health and Development Institute, Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Duke University, NC, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, MO, USA; Psychological & Brain Sciences, Washington University in St. Louis, MO, USA
| | - Chih-Ming Ho
- Department of Mechanical Engineering, University of California, Los Angeles, CA, USA
| | - William C Mobley
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University, CA, USA
| | - Steven T Rosen
- Comprehensive Cancer Center and Beckman Research Institute, City of Hope, CA, USA
| | - Patrick Tan
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Yun Yen
- College of Medical Technology, Center of Cancer Translational Research, Taipei Cancer Center of Taipei Medical University, Taipei, Taiwan
| | - Ali Zarrinpar
- Department of Surgery, Division of Transplantation & Hepatobiliary Surgery, University of Florida, FL, USA
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Ho D. Addressing COVID-19 Drug Development with Artificial Intelligence. ADVANCED INTELLIGENT SYSTEMS (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 2:2000070. [PMID: 32838299 PMCID: PMC7235485 DOI: 10.1002/aisy.202000070] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Indexed: 05/04/2023]
Abstract
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that led to the COVID-19 (Coronavirus Disease 2019) pandemic, has resulted in substantial overburdening of healthcare systems as well as an economic crisis on a global scale. This has in turn resulted in widespread efforts to identify suitable therapies to address this aggressive pathogen. Therapeutic antibody and vaccine development are being actively explored, and a phase I clinical trial of mRNA-1273 which is developed in collaboration between the National Institute of Allergy and Infectious Diseases and Moderna, Inc. is currently underway. Timelines for the broad deployment of a vaccine and antibody therapies have been estimated to be 12-18 months or longer. These are promising approaches that may lead to sustained efficacy in treating COVID-19. However, its emergence has also led to a large number of clinical trials evaluating drug combinations composed of repurposed therapies. As study results of these combinations continue to be evaluated, there is a need to move beyond traditional drug screening and repurposing by harnessing artificial intelligence (AI) to optimize combination therapy design. This may lead to the rapid identification of regimens that mediate unexpected and markedly enhanced treatment outcomes.
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Affiliation(s)
- Dean Ho
- The N. 1 Institute for Health (N. 1)National University of SingaporeSingapore117456Singapore
- The Institute for Digital Medicine (WisDM)Yong Loo Lin School of MedicineNational University of SingaporeSingapore117456Singapore
- Department of Biomedical EngineeringNUS EngineeringNational University of SingaporeSingapore117583Singapore
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeSingapore117600Singapore
- Smart Systems InstituteNational University of SingaporeSingapore119613Singapore
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