Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus

Amartya Mukhopadhyay, Jennifer Sumner, Lieng Hsi Ling, Raphael Hao Chong Quek, Andre Teck Huat Tan, Gim Gee Teng, Santhosh Kumar Seetharaman, Satya Pavan Kumar Gollamudi, Dean Ho and Mehul Motani
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Amartya Mukhopadhyay: Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Jennifer Sumner: Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Lieng Hsi Ling: Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Raphael Hao Chong Quek: Department of Electrical & Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Andre Teck Huat Tan: Division of Endocrinology, Department of Medicine, National University Hospital, Singapore 119074, Singapore
Gim Gee Teng: Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Santhosh Kumar Seetharaman: Healthy Ageing Programme, Alexandra Hospital, National University Health System, Singapore 159964, Singapore
Satya Pavan Kumar Gollamudi: FAST Programme, Alexandra Hospital, National University Health System, Singapore 159964, Singapore
Dean Ho: Department of Biomedical Engineering, National University of Singapore, Singapore 119077, Singapore
Mehul Motani: Department of Electrical & Computer Engineering, National University of Singapore, Singapore 117583, Singapore

IJERPH, 2022, vol. 19, issue 15, 1-11

Abstract: Chronic diseases typically require long-term management through healthy lifestyle practices and pharmacological intervention. Although efficacious treatments exist, disease control is often sub-optimal leading to chronic disease-related sequela. Poor disease control can partially be explained by the ‘one size fits all’ pharmacological approach. Precision medicine aims to tailor treatments to the individual. CURATE.AI is a dosing optimisation platform that considers individual factors to improve the precision of drug therapies. CURATE.AI has been validated in other therapeutic areas, such as cancer, but has yet to be applied in chronic disease care. We will evaluate the CURATE.AI system through a single-arm feasibility study ( n = 20 hypertensives and n = 20 type II diabetics). Dosing decisions will be based on CURATE.AI recommendations. We will prospectively collect clinical and qualitative data and report on the clinical effect, implementation challenges, and acceptability of using CURATE.AI. In addition, we will explore how to enhance the algorithm further using retrospective patient data. For example, the inclusion of other variables, the simultaneous optimisation of multiple drugs, and the incorporation of other artificial intelligence algorithms. Overall, this project aims to understand the feasibility of using CURATE.AI in clinical practice. Barriers and enablers to CURATE.AI will be identified to inform the system’s future development.

Keywords: chronic disease management; ambulatory care; self-management; artificial intelligence; personalised medicine (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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