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

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.

N-of-1 medicine

“…CURATE.AI has been trialled for various indications that span oncology to digital therapy, hypertension and transplant. [ 12 18 19 21 22 23 60 81 83 84 85 86 ] It is important to note that the CURATE.AI workflow varies substantially from what one might perceive an AI-driven treatment protocol to entail [ Figure 3 ]. For example, CURATE.AI implementation is not characterised as an AI-driven process that operates independently from the clinical treatment team followed by re-engagement of the clinical team to approve or reject a treatment recommendation.…”

“…Through emerging methodologies of harnessing data to drive healthcare decision-making, the fields of precision and personalised medicine are being increasingly impacted by the fields of digital health and digital medicine. [ 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ] Wearable devices are increasing our access to longitudinal data to drive digital biomarker development, and the rapid rise in actionable data is empowering new ways of driving clinical decision support. [ 26 27 28 29 30 ] Realising clinically actionable strategies to correlate this data, big or small, into optimal and personalised treatment guidance may profoundly impact the ability to sustainably deliver truly personalised N-of-1 medicine at the required scale for all stakeholders involved.…”

A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

“…It will improve the accuracy and effectiveness of the targeted drug delivery and can help doctors tailor treatment plans more effectively by minimizing the likelihood of any side effects. AI algorithms can also be used to optimize drug dosing for individual patients based on factors such as weight, age, and another medical history, and hence, reduce the risks of overdose or underdose and result in improved treatment outcomes [ 251 ]. Recently, AI algorithms have also been adopted to continuously monitor the response of a patient to a particular drug or treatment regimen in real time.…”

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

“…CURATE.AI has potential applications in hypertension, diabetes, and digital therapeutics. The platform harnesses AI to provide tailored dosing recommendations, improving patient care and treatment outcomes [ 53 ].…”