PRECISE CURATE.AI Pilot Clinical Trial

Solid Tumor Clinical Trial

Official title: Personalised, Rational, Efficacy-driven Cancer Drug Dosing Via an Artificial Intelligence SystEm - CURATE.AI (PRECISE CURATE.AI Trial)

Status Recruiting

Clinical Trial Summary

In the current clinical context, drug dosing in oncology is dictated by toxicity. The optimal dosages of drugs in combinatory regimens for solid tumours are not clear, and the typical physician's decision on dose adjustment is a clinical judgement based on the degree of toxicity experienced by the patient. CURATE.AI - a small data, AI-derived technology platform - allows personalised guidance of an individual's dose modulations based only on that individual's data. Additionally, CURATE.AI is mechanism-independent, and dynamically adapts to changes experienced by the subject, providing dynamic dose optimisation throughout the duration of the subject's treatment. This study aims to demonstrate the feasibility of applying CURATE.AI in standard of care settings for treatment of solid tumours. An additional objective is to explore tumour markers in serial measurements at weekly frequency of probing, with modulated doses.

Clinical Trial Description

Cancer patients are given drug combinations that promote cancer cell elimination. The final drug concentration in the body must fall within a narrow range that maximises cancer elimination while minimizing toxic side effects. The complexity of this task increases significantly with the number of drugs given in combination due to increasing parameters and stochastic behaviour of a biological system. Currently, the established approach is to select maximum tolerated doses (MTD) - the highest drug doses that do not cause unacceptable side effects. Treatment efficacy does not guide dose selection. Combined with limited personalisation, this dosing strategy often results in suboptimal outcomes of the treatment.

CURATE.AI is an AI-derived, mechanism-independent, small data technology platform for personalised, dynamic dosing. CURATE.AI uses a quadratic equation to generate individualised CURATE.AI profile and dosing recommendation based on only that individual's medical data: drug doses and the corresponding response marker (e.g. blood tumour markers). Profile recalibration via CURATE.AI facilitates dynamic dosing and personalised care throughout the treatment duration, aimed at achieving the highest efficacy within pre-specified safe dose ranges.

CURATE.AI is an indication-agnostic platform that has already been applied clinically for a range of indications including in oncology. CURATE.AI can be applied to indications that demonstrate regularly measured dose-dependent relationship between the treatment dose and the treatment response (i.e. biomarker level). Currently, the gold standard of monitoring treatment response in solid tumour is via radiology (using criteria such as RECIST), which is usually done at the end of a few cycles of systemic therapy and therefore not suitable to be used as a CURATE.AI input signal for drug dose adjustment between cycles.. Additionally, haematological neoplasms often cannot be monitored with imaging. Waldenström macroglobulinaemia treatment response is assessed using an adaptation of the response criteria from the Eighth International Workshop on Waldenström macroglobulinaemia (IWWM-8) that includes blood markers. Blood-based tumour markers, e.g. carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) and immunoglobulin M (IgM) markers, are more suitable to be implemented into CURATE.AI. Apart from these traditional tumour markers, recent advances in genomic sequencing have led to the application of plasma circulating tumour DNA (ctDNA) level as a novel marker of tumour burden. In addition, the serum free light chain (sFLC) has been widely used to assess treatment response for patients with multiple myeloma and other plasma cell dyscrasias, and has shown potential as a novel marker for disease burden in Waldenström macroglobulinaemia. Therefore, serial ctDNA and sFLC measurements, may be an appropriate input for CURATE.AI.

This Pilot Clinical Trial aims to set foundation to investigate the applicability and feasibility of the CURATE.AI platform within the current clinical setting for guided dosing of various systemic therapies commonly used for solid and haematological neoplasms

Individualised CURATE.AI profiles will be generated based on each participant's response to a set of drug doses. Subsequently, the personalised CURATE.AI profile will be used to recommend the efficacy-driven dose. CURATE.AI will operate only within the safety range for each drug prespecified for each participant.

This Pilot Clinical Trial and feasibility study will inform the investigators on the logistical and practical aspects of performing a large-scale randomised study and on the suitability of CURATE.AI for guided dosing of a wider range of chemotherapy regimens. An additional objective is to explore the utility of CEA, CA19-9, Ig M, ctDNA and sFLC as tumour markers in serial measurements at weekly frequency of probing, with modulated doses. ctDNA and sFLC will be also explored as an input for CURATE.AI to generate dose recommendations, however this analysis will not be used to prospectively guide dosing. ;

Related Conditions & MeSH terms

• Solid Tumor

• NCT number: NCT04522284
• Study type: Interventional
• Source: National University Hospital, Singapore
• Contact: Raghav Sundar
• Phone: +65 6779 5555
• Email: raghav_sundar@nuhs.edu.sg
• Status: Recruiting
• Phase: Phase 1/Phase 2
• Start date: August 20, 2020
• Completion date: August 1, 2023