Completed Diabetes, Hormones & Metabolism Heart, Stroke & Blood

MRC APBI STratification and Extreme Response Mechanism IN Diabetes - MASTERMIND

In plain English

AI plain-English summary

Doctors currently prescribe type 2 diabetes drugs based largely on cost, even though patients vary enormously in how they respond. This project aims to stop that guesswork by figuring out why some patients have extreme responses—good or bad—to the same drug. The problem is that current guidelines treat all patients as if they respond identically, ignoring the fact that many therapies work well for some people and fail for others. Without understanding the biology behind these differences, patients may take ineffective drugs for months, risking side effects and worsening blood sugar control. The researchers will define the clinical and biological markers that predict whether a patient will respond to a specific drug, and whether non-responders simply fail to take their medication or truly cannot benefit from that class of drug. They will also test how consistent a person’s response is by briefly stopping treatment and measuring the resulting blood sugar rise. If this succeeds, doctors could match each patient to the drug most likely to work for them—improving blood sugar control, cutting unnecessary pills, and reducing side effects. For the pharmaceutical industry, it would enable targeted drug development for patients who currently have few options. The work is applied, not fundamental science, and aims directly at changing clinical practice within the next decade.

View original technical description
Context The present clinical guidelines for the treatment of type 2 diabetes propose that treatment given to patients is primarily determined by the cost of the therapy and assumes that all patients respond similarly to treatment. This ignores the fact that for many therapies there is enormous variation in response between individuals with type 2 diabetes. If it was possible to understand the reasons why patients responded differently to therapy then it would be possible to choose the therapy most likely to be effective for an individual patient thus maximising the benefit and minimising the risk of a particular treatment. Aims and Objectives The aim of this research is to develop a scientific framework which will be used to develop the stratification of treatment in Type 2 Diabetes; that is individualising treatment for a patient or subgroups of patients with the aim of giving the right drug to the right patient at the right time. In Strand 1, the main aim is to define the biological mechanisms involved in patients' extreme response to second and third line treatment in type 2 diabetes. The objectives are to define exactly why some patients respond very differently to the same drug. We will define the clinical characteristics which relate to whether patients are more or less likely to respond to a drug, including whether the patients who do not respond are just those that do not take their tablets. We will also define those characteristics related to rapid deterioration of the blood glucose. We will determine whether if a person does not respond to one type of drug they are likely to not respond to other drugs or whether that person simply does not respond to all diabetes treatment. We will determine how consistent someone's response is by asking patients to stop their drug treatment briefly; someone who is a consistent good responder to the drug will have a rapid in rise in blood sugar when the drug treatment is stopped. Finally, we will set up a resource to enable future genetic and non genetic markers of drug response to be developed. In Strand 2 we will develop critical information that is required before an approach using stratification can come into clinical practice. We will develop a model which allows us to predict a patient's likely response to a particular therapy. We will then work out in theory when it would be both effective and cost effective to use treatment stratification in type 2 diabetes. Potential applications and benefits There are enormous potential benefits to giving drugs to patients who are likely to respond to them and not to patients who are unlikely to respond. This would have considerable benefits in improving the patient's blood sugar control and hence reducing their risk of complications, cutting down on the number of tablets that they need to take (hence saving money on unnecessary therapy) and reducing the risk of side effects to therapies that were ineffective. For the pharmaceutical industry it would enable targeted drug development for patients where other therapy was ineffective and also to define patient subgroups that were most likely to benefit from new drug development. In addition this new understanding about why patients responded very well to drugs already developed would aid in the future modification of therapy to give improved patient outcome.

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Researchers

Alastair Gray (Co-Investigator)Andrew Farmer (Co-Investigator)Andrew Hattersley (Principal Investigator)Andrew Morris (Co-Investigator)Beverley Shields (Co-Investigator)Christopher Hyde (Co-Investigator)Christopher Jennison (Co-Investigator)Colin Palmer (Co-Investigator)Ewan Pearson (Co-Investigator)Frank Sullivan (Co-Investigator)Mark Maccarthy (Co-Investigator)Mark Walker (Co-Investigator)Michael Weedon (Co-Investigator)Naveed Sattar (Co-Investigator)Rury Holman (Co-Investigator)Stephanie Amiel (Co-Investigator)Stephen Gough (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

MICA: MRC APBI STratification and Extreme Response Mechanism IN Diabetes - MASTERMIND
Precision medicine in diabetes: Pharmacogenetic studies of large randomised controlled trials of diabetes therapies
PERsonalised Medicine for Intensification of Treatment (PERMIT): the case of type 2 Diabetes Mellitus
Modelling Treatment Mechanism for Stratified Medicine in Randomised Clinical Trials
Developing a decision support tool to enable precision treatment of type 2 diabetes

Original classification

Research Grant

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