Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Digestive System Diseases

Breast Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Antineoplastic Agents

All Drugs and Chemicals

Irinotecan

Paclitaxel

Docetaxel

Azacitidine

Albumin-Bound Paclitaxel

Tubulin Modulators

Antimitotic Agents

Topoisomerase I Inhibitors

Enzyme Inhibitors

Antimetabolites

Participants will undergo two study stages: QPOP drug selection and CURATE.AI-guided dosing modulation. In the QPOP drug selection stage, participants will undergo a baseline biopsy for organoid generation and subsequently receive treatment as per SOC. During this time, QPOP will identify optimal drug combinations for the participant based on ex vivo experiments on the participant's organoid. Participants who are identified via QPOP to potentially benefit from azacitidine in combination therapy (azacitidine + docetaxel, azacitidine + paclitaxel or azacitidine + irinotecan) will move on to the CURATE.AI-guided dosing modulation stage with treatment with azacitidine. Azacitidine treatment will begin once disease progression after SOC treatment is determined based on CT scans. Only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI

QPOP is a mechanism-agnostic platform for optimizing drug selection. QPOP uses a quadratic equation to describe the patient-specific drug-drug interaction space based solely on experimentally derived drug-response data on individual patient's tissue sample, from which optimal drug combinations can be identified. Drug selection via QPOP allows for identification of an optimal combination therapy without the need for exhaustive testing of every combination. The first stage of the trial aims to generate a personalised QPOP drugs list for each participant based on experimentally derived data from ex vivo testing in the participant's tissue sample. Optimal drugs from a pre-specified drug pool will be recommended by the QPOP team.

QPOP

CURATE.AI - a small data, AI-derived technology platform based on this discovery - 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 participant, providing dynamic dose optimisation throughout the duration of the participant's treatment.

The second stage of the trial aims to obtain a personalised CURATE.AI profile for each participant, based on their phenotypic response to a set of drug doses from the drug combinations with azacitidine identified and recommended by the QPOP team. The doses will be recommended by the CURATE.AI team, when relevant to the clinical decision-making process. Once an actionable profile is obtained, dose recommendations are based on the profile and aimed to treat the participant.

The maximum period of involvement with this study when azacitidine may be adjusted by CURATE.AI is 18 months.

CURATE.AI

Azacitidine subcutaneous injection Day 1, 2 and Day 8, 9 + 30 mg/m2 docetaxel intravenously Day 1 and 8

Each chemotherapy cycle will be 21 days.

Azacitidine + docetaxel

Azacitidine subcutaneous injection Day 1, 2 and Day 8, 9 + 80 mg/m2 paclitaxel intravenously Day 1 and 8

Each chemotherapy cycle will be 21 days.

Azacitidine + paclitaxel

Azacitidine intravenously subcutaneous injection Day 1, 2 and Day 8, 9 + 100 mg/m2 irinotecan intravenously Day 1 and 8.

Each chemotherapy cycle will be 21 days.

Azacitidine + irinotecan

Wei Peng Yong

Cheng Ean Chee

Raghav Sundar

Hon Lyn Tan

Jingshan Ho

Gloria Chan

Joan Choo

Soo Chin Lee

Boon Cher Goh

Ross Soo

David Tan

Andrea Wong

Joline Lim

Robert Walsh

Jia Li Low

This pilot feasibility study aims to set the foundation to investigate the applicability of QPOP drug selection followed by CURATE.AI-guided dose optimisation of the selected azacitidine combination therapy for solid tumours using CURATE.AI within the current clinical setting.

QPOP will identify drug interactions towards optimal efficacy and cytotoxicity from the pre-specified drug pool based on ex vivo experimental data from the individual participant's tissue sample model. With these drug interactions, QPOP will identify the optimal drugs for the specific participant whose biopsy provided the cells for the ex vivo experimentation. Subsequently, CURATE.AI will be used to guide dosing for the selected combination therapy for that participant.

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 pre-specified for each participant.

This pilot feasibility study will inform the investigators on the logistical and scientific feasibility of performing a large-scale randomised controlled trial (RCT) with the selected azacitidine combination therapy regimens and response markers. A secondary objective is to collect toxicity and efficacy data using established and exploratory response markers within and in-between cycles as exploratory outcomes.

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.

10 participants diagnosed with breast or gastric cancer undergoing or planned for treatment will be enrolled. The participants will undergo QPOP drug 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 dose modulation phase of the trial. Only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI

Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI

Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours Using the Quadratic Phenotypic Optimization Platform (QPOP) and an Artificial Intelligence-based Platform (CURATE.AI)

Data collection and explorative analysis of response marker in higher frequency serial measurements after modulated dosing in relation to standard frequency readings and other efficacy measures, e.g RECIST criteria

Data collection and explorative analysis of ctDNA as:

1. a response marker in serial measurements at given clinical context and after modulated dosing;
2. potential input for CURATE.AI.

A decision on whether we "successfully apply" the QPOP drug selection requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

1. The goodness-of-fit of the QPOP derived equation is acceptable to allow for a reliable list of effective drug combinations;
2. The drugs list is generated sufficiently early for the participant to potentially benefit

A decision on whether we "successfully apply" the CURATE.AI profile requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

1. Error/variance (biological/analytical) is sufficiently small to allow accurate predictions;
2. Profile can be generated sufficiently early for the participant to potentially benefit;
3. Dose-dependent relationship is observed;
4. Profile is actionable (i.e. fulfils the co-investigator's pre-specified safety requirements);
5. Systemic changes in the participant which require profile recalibration are rare or readily assimilated into the CURATE.AI algorithm.

percentage of participants in whom the CURATE.AI-guided cumulative dose is substantially (≥10%) different from the projected SOC cumulative dose, which is defined as the maximum dose of the modulated drug\*, azacitidine, multiplied by the number of completed chemotherapy cycles.

\* The maximum dose of the modulated drug azacitidine is 120mg/m2 once daily given on days 1-2 and days 8-9, every 21 days, in combination with weekly docetaxel, paclitaxel or irinotecan for two weeks followed by one week of rest.

The N.1 Institute for Health (N.1)

Cancer Science Institute of Singapore

National University Hospital, Singapore

Heart and Blood Diseases

Blood and Lymph Conditions

Immune System Diseases

Rare Diseases

Waldenstrom Macroglobulinemia

Amino Acids

Capecitabine

Oxaliplatin

Ibrutinib

Myeloma Proteins

Paraproteins

Tyrosine Kinase Inhibitors

Protein Kinase Inhibitors

Tyrosine

- Cohort 1: Capecitabine in solid tumours. In this cohort, participants will receive the treatment in three-week cycles. Only the dose of capecitabine will be modulated with CURATE.AI, based on measurements of the tumour marker (CEA/CA19-9). CT scans for radiological assessment will be performed according to standard of care, usually after every 2 to 3 cycles of chemotherapy for Cohort 1 only.

Cohort 2: Ibrutinib in Waldenström macroglobulinaemia In this cohort, participants will receive the treatment in four-week cycles. The total cumulative dose of Ibrutinib will be modulated with CURATE.AI, based on measurements of the tumour marker (IgM paraprotein).

Efficacy and toxicity measurements at the end of each dosing cycle, together with an information on given drugs and their doses, and other patient data, will be used by CURATE.AI to recommend the dose of capecitabine and Ibrutinib for the next cycle. The clinical investigators will decide whether or not to administer or prescribe the dose recommendation from CURATE.AI.

Treatment with the selected regimen will take up to a maximum duration of 12 months, in 3-week cycles. CURATE.AI dose recommendations will be generated before each chemotherapy cycle; fixed throughout the cycle; and within 1) the predetermined safety range (50% to 100% of dose used in standard of care treatment) and 2) subject-specific dosing range. Maximum total cumulative dose per cycle of capecitabine in the predetermined safety range is set at 100% of standard starting dose (i.e. , 1250mg/m2 twice daily for single agent capecitabine regimen). Minimum total cumulative dose per cycle of capecitabine in the predetermined safety range is set at 50% of the standard starting dose (i.e. 625mg/m2 twice daily for single agent capecitabine). Subject-specific dosing range may alter those numbers to suit the specific circumstances of the subject, thus giving the subject specific safe dosing range.

Treatment with the selected regimen will take up to a maximum duration of 12 months, in 3-week cycles. CURATE.AI dose recommendations will be generated before each chemotherapy cycle; fixed throughout the cycle; and within 1) the predetermined safety range (50% to 100% of dose used in standard of care treatment) and 2) subject-specific dosing range. Maximum dose of capecitabine in the predetermined safety range is set at 100% of standard starting dose (i.e. 1000 mg/m2 twice daily for XELOX regimen). Minimum dose of capecitabine in the predetermined safety range is set at 50% of the standard starting dose (i.e. 500 mg/m2 twice daily for XELOX regimen). Subject-specific dosing range may alter those numbers to suit the specific circumstances of the subject, thus giving the subject specific safe dosing range. The doses of other drug in the XELOX regimen (oxaliplatin) will be held constant or adjusted at the clinical investigator's discretion, as per standard of care.

XELOX

Treatment with the selected regimen will take up to a maximum duration of 12 months, in 3-week cycles. CURATE.AI dose recommendations will be generated before each chemotherapy cycle; fixed throughout the cycle; and within 1) the predetermined safety range (50% to 100% of dose used in standard of care treatment) and 2) subject-specific dosing range. Maximum dose of capecitabine in the predetermined safety range is set at 100% of standard starting dose (i.e. 1000 mg/m2 twice daily for XELIRI regimen). Minimum dose of capecitabine in the predetermined safety range is set at 50% of the standard starting dose (i.e. 500 mg/m2 twice daily for XELIRI regimen). Subject-specific dosing range may alter those numbers to suit the specific circumstances of the subject, thus giving the subject specific safe dosing range.

The doses of other drug in the XELIRI regimen (irinotecan) will be held constant or adjusted at the clinical investigator's discretion, as per standard of care.

XELIRI

Treatment with the selected regimen will take up to a maximum duration of 12 months, in 4-week cycles. CURATE.AI dose recommendations will be generated before each chemotherapy cycle; fixed throughout the cycle; and within 1) the predetermined safety range (50% to 100% of dose used in standard of care treatment) and 2) subject-specific dosing range.

Maximum total cumulative dose per cycle of Ibrutinib in the predetermined safety range is set at 100% of standard starting dose (i.e. 420 mg once daily for 4 weeks, which constitutes the total of 11760 mg per cycle). Minimum total cumulative dose per cycle of Ibrutinib in the predetermined safety range is set at 50% of the standard starting dose (i.e. 280 mg once daily on alternative days with 140 mg once daily, which constitutes the total of 5880 mg per cycle). The dosing range may be altered to suit the specific circumstances of the participant, thus giving the participant-specific safe dosing range.

Michelle Au

Cheng Ean Chee, Dr

Wei Peng Yong, Dr

Raghav Sundar, Dr

Hon Lyn Tan, Dr

Jingshan Ho, Dr

Gloria Chan, Dr

Joan Choo, Dr

Hui Sin Tan

Yi Wan Lim

Wan Qin Chong

Angela Shien Ling Pang

Samuel Guan Wei Ow

Yvonne Li En Ang

Natalie Yan Li Ngoi

Gloria Hui Jia Chan

Joline Si Jing Lim

Yiqing Huang

Joan Rou-en Choo

Matilda Xinwei Lee

Alvin Seng Cheong Wong

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.

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.

This is a multi-centre, one-arm, prospective pilot study of participants diagnosed with either solid organ or haematological neoplasm.The participants will be enrolled for the duration of their treatment with the selected regimens, XELOX, XELIRI and single agent capecitabine regimens or single-agent ibrutinib . At 12-month mark, the decision on the patient engagement with the study will be made by the principal investigator in consultation with the study sponsors and treating physician.

PRECISE CURATE.AI Pilot Clinical Trial

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

CT scans for radiological assessment will be performed according to standard of care, usually after every 2 to 3 cycles of chemotherapy

Percentage of trial participants with clinical progressive disease(which is defined as the clinical investigator deeming that the patient will not benefit any further from the chemotherapy regimen and considering stopping it) at the time of the first radiological assessment performed as per standard-of-care.

Mandatory blood draws for the measurement of tumour marker(s)as per standard of care.Additional blood draws solely for the purposes of the trial for measurements of tumour markers(s)

Tumour marker measurements at higher frequency will be analysed to inform about temporal dynamics of tumour response to modulated dosing.

For CURATE.AI profile generation, a minimum of 3 dose levels and corresponding tumour marker (CEA/CA19-9/Ig M) readouts are needed for the modulated drug. After obtaining the first tumour marker readout a set of potential doses of capecitabine or Ibrutinib to pick from will be recommended by the CURATE.AI team with calibration-intent, based on the mathematical requirements of the method.

If the modulations do not yield a dose-dependent response (e.g. biomarker readouts were affected by the factors unrelated to capecitabine or Ibrutinib modulation) a new data pair will have to be acquired according to calibration-intent recommendation from the CURATE.AI team.

When dose-dependent response is obtained, the profile will be checked for actionability - an ability to recommend an optimum dose within the pre-specified personal safety range.

Percentage of trial participants with clinically relevant toxicities of grades 3-4 based on CTCAE version 4.0.

Data collection and explorative analysis of the ctDNA and/or sFLC as: (1) a tumour marker in serial measurements at given clinical context and after modulated dosing; (2) potential input for CURATE.AI

Data collection and explorative analysis of CEA , CA19-9 , Ig M and/or other traditional tumour markers in higher frequency serial measurements after modulated dosing in relation to standard frequency readings and other efficacy measures, e.g RECIST criteria or IWWM-8

CURATE.AI applicability: Percentage of participants in whom we successfully apply CURATE.AI profile. Based on percentages, outcomes will be classified as Green (\>70%) / Yellow (10-70%) / Red (\<10%).

A decision on whether we "successfully apply" the CURATE.AI profile requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

1. Error/variance (biological/analytical) is sufficiently small to allow accurate predictions
2. Profile can be generated sufficiently early for the participant to potentially benefit;
3. Dose-dependent relationship is observed;
4. Profile is actionable (i.e. fulfils the clinical investigator's pre-specified safety requirements);
5. Systemic changes in the participant which require profile recalibration are rare or readily assimilated into the CURATE.AI algorithm.

Percentage of participants who always adhered to the prescribed dose whenever they took their medication, as measured by the standardised pharmacovigilance protocol. Based on percentages, outcomes will be classified as Green (\>90%) / Yellow (10-90%) / Red (\<10%).

Percentage of CURATE.AI recommendations provided in time for the next chemotherapy cycle, across all participants and cycles. Based on percentages, outcomes will be classified as Green (100%) / Yellow (10-99%) / Red (\<10%).

Percentage of dosing events across all participants and cycles in which CURATE.AI recommendation is considered in the clinical decision-making process. Based on percentages, outcomes will be classified as Green (100%) / Yellow (10-99%) / Red (\<10%).

Percentage of CURATE.AI recommended doses that were used by the clinical investigator. Based on percentages, outcomes will be classified as Green (\>70%) / Yellow (10-70%) / Red (\<10%).

Percentage of participants in whom the CURATE.AI-guided cumulative dose is substantially (≥10%) different from the projected standard-of-care cumulative dose. Based on percentages, outcomes will be classified as Green (\>20%) / Yellow (1-20%) / Red (0%).

Pembrolizumab

Immunomodulating Agents

Nivolumab

Antineoplastic Agents, Immunological

Immune Checkpoint Inhibitors

Participants will undergo two treatment periods: selection period and CURATE.AI modulation period. During the selection period, baseline ctDNA measurements and CT scans will be performed. Subsequently, participants will receive Standard of Care (SOC) doses of nivolumab/pembrolizumab and have their ctDNA measured at the end of the third cycle together with other SOC monitoring.

After the first ctDNA measurement and CT scan, selected patients who responded to treatment through both ctDNA and CT scan may continue into the CURATE.AI modulation period. Non-responders will receive SOC doses of nivolumab/pembrolizumab for 2 cycles before having their ctDNA measured and CT scan at the end of the 2 cycles. Subsequent responders through both ctDNA and CT scans may then continue into the CURATE.AI modulation period.

Only the dose of nivolumab/pembrolizumab will be modulated with CURATE.AI, based on measurements of the response marker (ctDNA).

Efficacy and toxicity measurements at the end of each two-/three- week dosing cycle, together with an information on given drugs and their doses, and other patient data, will be used by CURATE.AI to recommend the dose of nivolumab/pembrolizumab for the next cycle. The clinical investigators will decide whether or not to administer or prescribe the dose recommendation from CURATE.AI.

Treatment with the selected regimen will take up to a maximum duration of 12 months, in two-/three- week cycles. CURATE.AI dose recommendations will be generated before each chemotherapy cycle; fixed throughout the cycle; Maximum total cumulative dose per cycle of nNivolumab/pembrolizumab in the predetermined safety range is set at 100% of standard starting dose (i.e. , 240 mg/m2 once every 2 weeks for single agent Nivolumab and 200 mg/m2 once every 3 weeks for single agent Pembrolizumab). Minimum total cumulative dose per cycle of nivolumab/pembrolizumab in the predetermined safety range is set at 30% of the standard starting dose (i.e. 8072 mg/m2 once every 2 weeks for single agent Nivolumab and 60 mg/m2 once every 3 weeks for single agent Pembrolizumab). Subject-specific dosing range may alter those numbers to suit the specific circumstances of the subject, thus giving the subject specific safe dosing range.

Nivolumab, Pembrolizumab

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 with Nivolumab. An additional objective is to explore sequential ctDNA measurements as a response marker collected at a higher frequency of probing, with modulated doses.

The first stage of the trial aims to identify patients who are responsive to the given treatment, determined from both SOC - radiology via criteria such as RECIST/immune-related RECIST (irRECIST) as well as the intended response marker for CURATE.AI. These patients will then proceed with the second stage of the trial.

The second stage of the trial aims to obtain a personalised CURATE.AI profile for each participant, based on their phenotypic response to a set of drug doses. The doses will be recommended by the CURATE.AI team, when relevant to the clinical decision-making process. For participants who started on the selected therapy prior to their engagement with the study, the previously recorded data, limited to: administered doses, corresponding phenotypic responses and treatment-related toxicities (optional) may be included in the CURATE.AI process. Once an actionable profile is obtained, dose recommendations are based on the profile and aimed to treat the participant.

Since there are no prior clinical trials using CURATE.AI in participant treated with combinational or single-agent immunotherapy with drugs such as nivolumab or pembrolizumab, this trial will comprise of participants diagnosed with the main tumour types: non-small cell lung cancer (NSCLC), gastric cancer (GC), hepatocellular carcinoma (HCC) or nasopharyngeal carcinoma (NPC), or other eligible tumours undergoing or planned for palliative-intent immune-oncology combinational or single-agent therapy with nivolumab/ pembrolizumab.

Nivolumab and Pembrolizumab are an immune checkpoint inhibitors and a novel therapeutic treatment modalities recently approved by the FDA and are becoming the standard of care treatment for a range of oncological indications, including NSCLC, GC, HCC and NPC. However, they are associated with significant semi-annual costs - approximately $140,000 per patient in the United States. In light of the evidence supporting dose personalization and dose reduction in immune-oncology treatments, patients may stand to benefit from CURATE.AI's personalized dose optimisation in maximizing clinical outcomes at reduced costs. Together with the increasing adoption of circulating tumour DNA (ctDNA) as an effective response marker for tumour burden in NSCLC, GC, HCC and NPC, CURATE.AI is ideally suited for pairing these findings with healthcare economics research towards the development of novel healthcare workflows integrating AI and serial measurements of ctDNA response to sustain the scalable implementation of N-of-1 dosing optimisation in cancer treatments.

This is a multi-centre, one-arm, prospective pilot study of participants diagnosed with solid tumours. The participants will be enrolled for the duration of their treatment with Nivolumab. At 12-month mark, the decision on the patient engagement with the study will be made by the principal investigator in consultation with the study sponsors and treating physician.

Nivolumab and Pembrolizumab Dose Optimisation in Solid Tumours With CURATE.AI Platform and Sequential ctDNA Measurements

CT scans for radiological assessment will be performed according to standard of care, usually after every 2 to 3 cycles of chemotherapy Percentage of trial participants with clinical progressive disease(which is defined as the clinical investigator deeming that the patient will not benefit any further from the chemotherapy regimen and considering stopping it) at the time of the first radiological assessment performed as per standard-of-care.

Mandatory blood draws for the measurement of response marker(s)as per standard of care. Additional blood draws solely for the purposes of the trial for measurements of response markers(s) Tumour marker measurements at higher frequency will be analysed to inform about temporal dynamics of tumour response to modulated dosing.

For CURATE.AI profile generation, a minimum of 3 dose levels and corresponding response marker readouts are needed for the modulated drug. After obtaining the first response marker readout a set of potential doses to pick from will be recommended by the CURATE.AI team with calibration-intent, based on the mathematical requirements of the method.

Data collection and explorative analysis of ctDNA as:

1. As response marker in serial measurements at given clinical context and after modulated dosing in comparison to radiology (via criteria such as RECIST/immune related RECIST (irRECIST) for monitoring treatment response dynamics in the immuno-oncology;
2. As a potential input for CURATE.AI

CURATE.AI applicability: Percentage of participants in whom we successfully apply CURATE.AI profile. Based on percentages, outcomes will be classified as Green (\>70%) / Yellow (10-70%) / Red (\<10%).

A decision on whether we "successfully apply" the CURATE.AI profile requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

Error/variance (biological/analytical) is sufficiently small to allow accurate predictions Profile can be generated sufficiently early for the participant to potentially benefit; Dose-dependent relationship is observed; Profile is actionable (i.e. fulfils the clinical investigator's pre-specified safety requirements); Systemic changes in the participant which require profile recalibration are rare or readily assimilated into the CURATE.AI algorithm

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI	NCT05381038	Solid Tumor Gastrointestina... Breast Cancer	QPOP CURATE.AI Azacitidine + d... Azacitidine + p... Azacitidine + i...	21 Years - 99 Years	National University Hospital, Singapore
PRECISE CURATE.AI Pilot Clinical Trial	NCT04522284	Solid Tumor	CURATE.AI Capecitabine XELOX XELIRI Ibrutinib	21 Years - 99 Years	National University Hospital, Singapore
Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI	NCT05381038	Solid Tumor Gastrointestina... Breast Cancer	QPOP CURATE.AI Azacitidine + d... Azacitidine + p... Azacitidine + i...	21 Years - 99 Years	National University Hospital, Singapore
Nivolumab and Pembrolizumab Dose Optimisation in Solid Tumours With CURATE.AI Platform and Sequential ctDNA Measurements	NCT05175235	Solid Tumor	CURATE.AI Nivolumab, Pemb...	21 Years - 99 Years	National University Hospital, Singapore
Nivolumab and Pembrolizumab Dose Optimisation in Solid Tumours With CURATE.AI Platform and Sequential ctDNA Measurements	NCT05175235	Solid Tumor	CURATE.AI Nivolumab, Pemb...	21 Years - 99 Years	National University Hospital, Singapore

Spots Global Cancer Trial Database for curate.ai

Every month we try and update this database with for curate.ai cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for curate.ai

Every month we try and update this database with for curate.ai cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for curate.ai cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.