Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Medical Affairs-Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore 159964, Singapore.
Int J Environ Res Public Health. 2022 Jul 23;19(15):8979. doi: 10.3390/ijerph19158979.
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 ( = 20 hypertensives and = 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.
慢性病通常需要通过健康的生活方式实践和药物干预进行长期管理。尽管存在有效的治疗方法，但疾病控制往往不理想，导致与慢性病相关的后遗症。疾病控制不佳的部分原因可以解释为“一刀切”的药物治疗方法。精准医学旨在针对个体定制治疗方法。CURATE.AI 是一个剂量优化平台，它考虑个体因素以提高药物治疗的精确性。CURATE.AI 已经在其他治疗领域（如癌症）得到验证，但尚未在慢性病治疗中应用。我们将通过一项单臂可行性研究（= 20 例高血压患者和= 20 例 2 型糖尿病患者）来评估 CURATE.AI 系统。剂量决策将基于 CURATE.AI 的建议。我们将前瞻性地收集临床和定性数据，并报告使用 CURATE.AI 的临床效果、实施挑战和可接受性。此外，我们将探索如何使用回顾性患者数据进一步增强算法。例如，纳入其他变量、同时优化多种药物以及纳入其他人工智能算法。总的来说，本项目旨在了解在临床实践中使用 CURATE.AI 的可行性。将确定 CURATE.AI 的障碍和促进因素，以为系统的未来发展提供信息。
