Lead Investigator: Rohan Khera, Yale School of Medicine
Title of Proposal Research: A computational phenomapping approach to personalize the cardiometabolic benefits of novel glucagon-like peptide-1 receptor and glucose-dependent insulinotropic polypeptide agonists in patients with type 2 diabetes mellitus.
Vivli Data Request: 9767
Funding Source: None
Potential Conflicts of Interest: Dr. Khera reports: R.K. is a co-inventor of the U.S. Patent Applications 63/508,315 & 63/177,117 (both filed by Yale University) which describe machine learning phenomapping methods for individualized treatment effect assessment in clinical trials, and co-founder of Evidence2Health, a health analytics company with the goal to improve evidence-based cardiovascular care. The methods described in these patent applications have been previously used by the applicants to perform post-hoc analyses of randomized controlled trials across a range of cardiovascular and cardiometabolic conditions, which have been published in peer-reviewed, academic journals (PMID: 36307193; 35120199; 33881513). Outside the scope of this research, Dr Khera is an Associate Editor of JAMA. He receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He also receives research support, through Yale, from Bristol-Myers Squibb and Novo Nordisk. Dr Khera is also a co-founder of Ensight AI, outside the scope of this work. For all co-authors and investigators in this work, all related and unrelated conflicts will be declared in any future presentations and publications.
Dr. Oikonomou reports: E.K.O. is a co-inventor of the U.S. Patent Applications 63/508,315 & 63/177,117 (both filed by Yale University) which describe machine learning phenomapping methods for individualized treatment effect assessment in clinical trials, and a co-founder of Evidence2Health, a health analytics company with the goal to improve evidence-based cardiovascular care. The methods described in these patent applications have been previously used by the applicants to perform post-hoc analyses of randomized controlled trials across a range of cardiovascular and cardiometabolic conditions, which have been published in peer-reviewed, academic journals (PMID: 36307193; 35120199; 33881513). For all co-authors and investigators in this project, all related and unrelated conflicts will be declared in any future presentations and publications.
Summary of the Proposed Research:
According to the World Health Organization (WHO) 422 million people worldwide have diabetes, which is a direct or indirect cause of 1.5 million deaths annually. The most common type of diabetes is type 2 diabetes mellitus, where the blood sugar (glucose) levels are too high due to the body not making enough of a hormone called insulin, or not using it properly.
Agonists (drugs or substances that bind to a receptor inside a cell or on its surface and causes the same action as the substance that normally binds to the receptor) of glucagon-like peptide-1 (GLP-1) receptor and glucose-dependent insulinotropic polypeptide (GIP) are a new group of glucose-lowering medications used to treat diabetes, which have been shown to improve several aspects of cardiovascular (heart and blood system) health and prevent complications related to diabetes.
A series of randomized clinical trials (RCTs) have demonstrated the efficacy and safety profile of tirzepatide, a novel dual GIP and GLP-1 agonist, across a range of type 2 diabetes mellitus patient profiles highlighting marked effects on several metrics of glucose control and weight. However, the cardiometabolic benefit (i.e., the benefit relating to the chemical processes affecting the cardiovascular system and metabolic health) may vary across different patient populations and for different parameters. For instance, in a clinical trial of patients with obesity, tirzepatide provided substantial and sustained reductions in body weight when compared to placebo (sugar pill). On the other hand, a recent analysis of 7,215 patient data from seven RCTs with at least 26 weeks of treatment demonstrated no significant difference between patients receiving tirzepatide and those receiving placebo when assessing events such as a non-fatal heart attack, non-fatal stroke, death due to heart failure or hospitalization for unstable angina (chest pain caused by reduced blood flow to the heart muscle).
Given the known heterogeneity (difference) of patients with type 2 diabetes mellitus, understanding the links between unique sets of characteristics (signature phenotypes) and the predicted benefit of blood sugar levels and weight loss response is important to better inform treatment practices to ensure that patients receive individualized treatment programs that are safe and efficacious.
We hereby propose a study with the aim to provide personalized estimates of treatment efficacy and safety for GLP-1/GIP agonists. Our approach is based on our machine learning method, called ‘computational trial phenomapping’, that has been previously employed across a range of cardiometabolic trials and has detected heterogeneous treatment effects, including among patients with type 2 diabetes treated with newer antihyperglycemic therapies (glucose-lowering medication), such as sodium-glucose cotransporter-2 (SGLT2) inhibitors, a class of drugs that lower blood sugar levels by preventing the kidneys from reabsorbing sugar that is created by the body.
Aims/Objectives and Hypotheses
Objective 1: To define an algorithm that integrates each patient’s unique phenotypic signature and provides personalized treatment effect estimates on weight loss for use of tirzepatide versus placebo or traditional, non-GLP-1-agonist, antihyperglycemic therapies. In this aim, we hypothesize that the relative weight loss benefit of tirzepatide is dependent on each patient’s baseline phenotypic profile, defined as a constellation of the patient’s age, sex, baseline adiposity, metabolic profile, comorbidities and concomitant medications. We further hypothesize that a phenomapping method that accounts for complex interrelationships among these features can provide a data-driven method to quantify individualized effect estimates that can guide clinical decision-making.
Objective 2: To define an algorithm that integrates each patient’s unique phenotypic signature and provides personalized cardiovascular effectiveness estimates for treatment with tirzepatide versus placebo or traditional antihyperglycemic therapies. In this aim, we hypothesize that the cardiovascular profile of tirzepatide is dependent on each patient’s baseline phenotypic profile and that a phenomapping method that accounts for complex interrelationships among these features can provide a data-driven method to maximize the tolerability and continued use of such agents.
Requested Studies:
A Phase 3, Randomized, Open-Label Trial Comparing Efficacy and Safety of Tirzepatide Versus Semaglutide Once Weekly as Add-on Therapy to Metformin in Patients With Type 2 Diabetes
Data Contributor: Lilly
Study ID: NCT03987919
Sponsor ID: 17001
Efficacy and Safety of LY3298176 Once Weekly Versus Insulin Glargine in Patients With Type 2 Diabetes and Increased Cardiovascular Risk (SURPASS-4)
Data Contributor: Lilly
Study ID: NCT03730662
Sponsor ID: 17072
A Phase 3, Long-Term Safety Study of Tirzepatide in Combination With Monotherapy of Oral Antihyperglycemic Medications in Patients With Type 2 Diabetes Mellitus (SURPASS J-combo)
Data Contributor: Lilly
Study ID: NCT03861039
Sponsor ID: 17078
A Randomized, Double-blind, Placebo-Controlled Trial Comparing the Efficacy and Safety of Three Tirzepatide Doses Versus Placebo in Patients With Type 2 Diabetes, Inadequately Controlled With Diet and Exercise Alone
Data Contributor: Lilly
Study ID: NCT03954834
Sponsor ID: 17000
Randomized, Phase 3, Double-blind Trial Comparing the Effect of the Addition of Tirzepatide Versus Placebo in Patients With Type 2 Diabetes Inadequately Controlled on Insulin Glargine With or Without Metformin
Data Contributor: Lilly
Study ID: NCT04039503
Sponsor ID: 16998
A Randomized, Phase 3, Open-Label Trial Comparing the Effect of LY3298176 Versus Titrated Insulin Degludec on Glycemic Control in Patients With Type 2 Diabetes
Data Contributor: Lilly
Study ID: NCT03882970
Sponsor ID: 16997
A Phase 3 Study of Tirzepatide Monotherapy Compared to Dulaglutide 0.75 mg in Patients With Type 2 Diabetes Mellitus
Data Contributor: Lilly
Study ID: NCT03861052
Sponsor ID: 17077
A Randomized, Phase 3, Open-label Trial Comparing the Effect of Tirzepatide Once Weekly Versus Titrated Insulin Glargine on Glycemic Control in Patients With Type 2 Diabetes on Metformin With or Without a Sulfonylurea
Data Contributor: Lilly
Study ID: NCT04093752
Sponsor ID: 17210
A Phase 2, Double-Blind, Placebo-Controlled, 3-Month Trial of LY3298176 Versus Placebo in Patients With Type 2 Diabetes Mellitus
Data Contributor: Lilly
Study ID: NCT03311724
Sponsor ID: 16860
A Phase 2 Study of Once-Weekly LY3298176 Compared With Placebo and Dulaglutide in Patients With Type 2 Diabetes Mellitus
Data Contributor: Lilly
Study ID: NCT03131687
Sponsor ID: 16335