Harnessing CURATE.AI for N‐of‐1 Optimization Analysis of Combination Therapy in Hypertension Patients: A Retrospective Case Series

Hypertension is a global public health challenge that imposes a significant burden on patients and healthcare systems. Conventional treatment involves dose escalation of an antihypertensive drug, and if the desired response is not achieved, patients are prescribed another drug, often in combination with other therapies. Importantly, drug synergy is dose‐, time‐ and patient‐dependent. Coupled with the challenges of intra‐ and inter‐individual variability, standard care can lead to sub‐optimal outcomes, additional visits, and adherence issues. Furthermore, these factors can cause the additional complication of patients being misperceived as refractory to regimens that are sub‐optimally administered. A scalable strategy that can longitudinally optimize patient response would be a powerful advance for chronic disease management. This four‐patient case series reports the application of CURATE.AI as a mechanism‐independent and disease‐agnostic platform for a retrospective N‐of‐1 (personalized) dose optimization using each patient's own data, including drug doses and corresponding changes in blood pressures. This approach may enable the rapid prediction of treatment response and the identification of optimal doses that may yield improved outcomes. CURATE.AI can be implemented in clinical workflows without creating additional burden of extensive data collection. The findings from this study support the prospective validation of CURATE.AI to optimize hypertension management. This study demonstrates the application of CURATE.AI as a clinical decision support tool for retrospective N‐of‐1 dosing optimization for four elderly hypertensive patients. CURATE.AI uses each patient's own data of antihypertensive drug doses and corresponding blood pressure changes to rapidly predict drug responses and identify optimal doses that potentially yield favorable outcomes over the standardofcare approach.