Several drug combinations and modulation in drug dosing are given to promote cancer cell elimination in cancer patients. While advances in omics tools have led to greater understanding of the complexity of diseases such as cancer, they have also led to the understanding that large networks of molecular interactions contribute to both disease progression and therapeutic resistance. The rational design of drug combinations is a challenge because complex molecular networks contribute to feedback mechanisms of drug resistance and compensatory oncogenic drivers that limit the efficacy of targeted inhibitors. This challenge is compounded by the vast number of available drugs to identify optimal drug combinations from.
In addition to the complexities in identifying optimal drug combinations, optimal dosing remains a challenge as drug synergy is both dose, time- and patient- dependent. 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 sub-optimal outcomes of the treatment.
In this pilot feasibility study, participants will undergo QPOP drug selection, a stage of CURATE.AI profile generation, and a stage of CURATE.AI profile-based, efficacy-driven drug dosing.
As there are no prior clinical trial cohorts using CURATE.AI in participants with solid tumours and there are existing data for breast and gastric cancer for input into QPOP, this feasibility pilot study will focus on the practicality and feasibility of using QPOP and CURATE.AI in this clinical context.
At the end of the participation of the first 10 patients, an interim analysis will be conducted using the data generated from these participants, which will include formal power and statistical sample size calculations. Based on these outcomes, the investigators will consider cohort expansion or an RCT. Specifically, the interim analysis will aid the decisions on whether to proceed with future RCTs; their design (superiority, equivalence or non-inferiority); logistical and practical aspects of running a large-scale RCT; patient population selection for the RCT; and potential applicability of CURATE.AI in a wider range of systemic therapy regimens, response markers and/or expansion of the current cohort to elicit further data on secondary endpoints and/or new randomized cohorts.
Although not standard of care treatment for breast and gastric cancer, azacitidine combination therapy is chosen by the investigators as azacitidine combination therapy as azacitidine is a potent DNA methyltransferase inhibitor (DNMT) that can increase the sensitivity of a range of metastatic or advanced solid tumours, such as breast and gastric cancer, to treatment with docetaxel, paclitaxel, or irinotecan after developing resistance. Studies have also demonstrated the possibility of low dose treatment with chemotherapeutic agents when given together with azacitidine. However, cytotoxicity of azacitidine increases with dose and exposure time, which highlights the need to rapidly identify the optimal azacitidine-containing drug combinations and for personalised dose modulation during treatment. As such, QPOP drug selection and CURATE.AI dose modulation pipeline is in the ideal position to optimise treatment with azacitidine in combination with docetaxel, paclitaxel, or irinotecan via a personalised manner to maximise efficacy while minimising toxicities.
Participants will undergo QPOP drugs selection optimisation, and those participants who are identified via QPOP to potentially benefit from azacitidine in combination with docetaxel, paclitaxel, or irinotecan will transition to the CURATE.AI stage of the trial after treatment fails. Participants who have undergone QPOP drug selection (e.g. under QGAIN (2019/00924) or NGAIN trial (2021/00009)) are allowed to enrol for the CURATE.AI modulation period of this study at the approval of the Principal Investigator and Sponsor.
CURATE.AI will facilitate personalised treatment to each of the participants by recommending optimal doses in a dynamic fashion. In this phase, only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI. Criteria for recruitment allow a high variability in the participant population to reflect a true variability in the cases faced in the clinical practice.
