Background: Faced with a trade-off between efficacy and toxicity, oncologists have conventionally administered the maximum tolerated doses in chemotherapy on an assumption that higher doses increase efficacy. However, multiple studies have shown that this method of toxicity-guided dosing may result in more frequent toxicities and potentially suboptimal efficacy. With the advent of artificial intelligence (AI), personalized dosing in chemotherapy may be considered to optimize patient care. CURATE.AI is an efficacy-driven, indication-agnostic and mechanism-independent personalized dosing platform that may offer an optimal solution. In contrast to traditional AI approaches based on massive volumes of population data, CURATE.AI requires only the individual patient's medical profile for dose recommendations. Based on the observation that the relationship between a drug dose and a phenotypic response in a human system can be modelled by a dynamic quadratic surface, CURATE.AI continually guides dosing throughout the treatment duration to optimize efficacy. While CURATE.AI has been used in various clinical settings, there are no prior randomized controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumors. Therefore, we are conducting a pilot study to assess the technical and logistical feasibility of an RCT for CURATE.AI-guided solid tumor chemotherapy dosing. We aim to collect exploratory data on efficacy and toxicity, and on the use of longitudinal blood tumor marker measurements, including ctDNA, to inform dose guidance decision.

Methods: PRECISE is an open-label, single-arm, multi-centre, prospective pilot clinical trial on using CURATE.AI to achieve personalized, efficacy-driven and dynamically optimized chemotherapy dosing for solid tumors (NCT04522284). Adults with metastatic solid tumors and raised baseline tumor marker levels who are planned for palliative-intent, capecitabine-based chemotherapy will be recruited. CURATE.AI will guide drug dosing for each participant based only on their own tumor marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. As an exploratory outcome, we will analyze the utility of tumor markers including CEA, CA19-9 and ctDNA in high frequency serial measurements. We aim to initially enroll 10 participants from 2 hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or a RCT based on initial pilot data. Recruitment of patients began in August 2020. As of December 2020, 2 participants have been enrolled with recruitment planned for 1 year.

Citation Format: Chong Boon Teo, Benjamin Kye Jyn Tan, Xavier Tadeo, Siyu Peng, Hazel Pei Lin Soh, Sherry De Xuan Du, Vilianty Wen Ya Luo, Aishwarya Bandla, Raghav Sundar, Dean Ho, Theodore Kee, Agata Blasiak. Personalized, rational, efficacy-driven chemotherapy dosing via an artificial intelligence system (PRECISE): A protocol for the PRECISE CURATE.AI pilot clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT211.