Dataset Information

Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus.

ABSTRACT: 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 (n = 20 hypertensives and n = 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.

SUBMITTER: Mukhopadhyay A

PROVIDER: S-EPMC9332044 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus.

Mukhopadhyay Amartya A Sumner Jennifer J Ling Lieng Hsi LH Quek Raphael Hao Chong RHC Tan Andre Teck Huat ATH Teng Gim Gee GG Seetharaman Santhosh Kumar SK Gollamudi Satya Pavan Kumar SPK Ho Dean D Motani Mehul M

International journal of environmental research and public health 20220723 15

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 ...[more]

PMID: 35897349

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Project description:Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations. Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. 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. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology. Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals. Trial registration number: NCT04522284.

Project description:Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug-target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant-protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.

Project description:IntroductionFor people with type 2 diabetes mellitus (T2DM) who require an antidiabetic drug as an add-on to metformin, there is controversy about whether newer drug classes such as dipeptidyl peptidase-4 inhibitors (DPP4i) or sodium-glucose co-transporter-2 inhibitors (SGLT2i) reduce the risk of long-term complications compared with sulfonylureas (SU). There is widespread variation across National Health Service Clinical Commissioning Groups (CCGs) in drug choice for second-line treatment in part because National Institute for Health and Care Excellence guidelines do not specify a single preferred drug class, either overall or within specific patient subgroups. This study will evaluate the relative effectiveness of the three most common second-line treatments in the UK (SU, DPP4i and SGLT2i as add-ons to metformin) and help target treatments according to individual risk profiles.Methods and analysisThe study includes people with T2DM prescribed one of the second-line treatments-of-interest between 2014 and 2020 within the UK Clinical Practice Research Datalink linked with Hospital Episode Statistics and Office of National Statistics. We will use an instrumental variable (IV) method to estimate short-term and long-term relative effectiveness of second-line treatments according to individuals' risk profiles. This method minimises bias from unmeasured confounders by exploiting the natural variation in second-line prescribing across CCGs as an IV for the choice of prescribed treatment. The primary outcome to assess short-term effectiveness will be change in haemoglobin A1c (%) 12 months after treatment initiation. Outcome measures to assess longer-term effectiveness (maximum ~6 years) will include microvascular and macrovascular complications, all-cause mortality and hospital admissions during follow-up.Ethics and disseminationThis study was approved by the Independent Scientific Advisory Committee (20-064) and the London School of Hygiene & Tropical Medicine Research Ethics Committee (21395). Results, codelists and other analysis code will be made available to patients, clinicians, policy-makers and researchers.

Project description:BackgroundCardiovascular disease is the most prevalent cause of morbidity and mortality in diabetic patients. Hypertension (HTN) has been confirmed as a major risk factor for cardiovascular disease, which is frequently associated with diabetes mellitus (DM). Therefore, the detection and management of elevated blood pressure (BP) is a critical component of the comprehensive clinical management of diabetics. Since the rates of hypertension in diabetics are lacking in Afghanistan, this study aimed to evaluate the prevalence of elevated blood pressure in type-2 diabetic patients.Materials and methodsThis is a descriptive cross-sectional study, which included 321 type -2 diabetic patients (119 males, 202 females) with a mean age of 53.86 ± 11.54 years who were presented to the Noble OPD center from November 2019 to January 2020.ResultsThe elevated blood pressure was detected in 70.5% of the patients. It was more prevalent in women than men (76.8% and 59.7% respectively). The mean systolic blood pressure was 146.94 ± 23.19 mmHg and mean diastolic blood pressure was 89.61 ± 11.59 mmHg. The mean pulse pressure was 57.32 ± 15.58 mmHg and the mean arterial pressure was 48.98 ± 7.73 mmHg. The mean body weight was 73.09 ± 13.75 Kg. The mean duration of diabetes mellitus was 7.08 ± 5.95 years with the average HbA1c of 9.27 ± 2.41%. The mean body mass index (BMI) of the patients was noted 28.77 ± 5.58 kg/m2.ConclusionThis study showed high prevalence of hypertension in type-2 diabetes patients with a significant difference in the rate of elevated blood pressure between males and females (it was higher in females than males). The systolic blood pressure had a positive correlation with age. However, it was insignificant for diastolic blood pressure. Healthcare providers and other health sector should work in collaboration for designing appropriate preventive strategies targeting the modifiable risk factors associated with hypertension.

Dataset Information

Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus.

Publications

Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus.

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