Treatments for type 2 diabetes (T2D) are approved based on clinical trial data. Favourable and unfavourable drug effects are weighed on a population level, and if the first outweigh the latter a positive ratio of benefit and risk can be assumed and the drug is approved. In diabetes, drugs are approved based on its effects on a validated surrogate endpoint, the biomarker glycosylated haemoglobin: HbA1c, and when there is sufficient reassurance the drugs are not harmful (cardiovascular safety). A thorough assessment of other, on- and off-target, effects, extensive subgroup analyses and available non clinical data further shapes the conditions – labeling and postmarketing surveillance – under which the drug is to be used in daily practice. Regulators translate this drug knowledge to healthcare providers (HCPs) and patients primarily through the Summary of Product Characteristics (SmPC) respectively Patient Information Leaflet (PIL). HCPs then have to translate this population level information to an individual patient. Importantly, reimbursement authorities, HCPs and patients may take different views when compared to regulators and may value drug effects and the ratio of benefits and risks differently.
Various classes of antidiabetes drugs are currently available that have differential drug effects. Current clinical T2D guidelines favour drugs with demonstrated clinical benefit and well known safety profiles, but do allow some degree of personalized treatment. The main goal of antidiabetic treatment is to achieve normo-glycemia without serious high or low blood glucose levels to prevent or delay the onset and progression of diabetic complications. Factors to be considered include efficacy, age, potential side effects, weight gain, comorbidities, hypoglycemia risk, costs and patient preferences (see also ProminenT Denig program). Further, recent research shows that collecting evidence-based information based on individual patient preferences in the context of favourable and unfavourable effects is feasible and useful and could guide to a more patient-centered value judgement of pharmacological agents and thus contribute to a more transparent communication on how the patient views have been incorporated in the regulatory decision making. However, these findings have not been systematically evaluated and translated back into the drug approval process. Moreover, while regulators evaluate carefully drugs’ various effects these are not quantitatively weighted. Finally, how evidence on individualized / personalized treatments should translate back into the regulatory decision-making is largely unknown.
This project aims to study, from a regulatory perspective, how elicited patient preference information can be combined with clinical trial data to estimate the acceptability of various classes of antidiabetes drugs currently available that have differential drug effects. Questions may include:
To improve regulatory decision-making for glucose-regulating medicines incorporating patient preferences and individual patient needs.
This project includes four main parts:
For part 1: review regulator’s drug dossiers /systematic review of public literature on patient preferences for glucose-regulating medication
For part 2: a list of candidate attributes will be determined by reviewing existing commonly reported study outcomes, patient reported outcome questionnaires, preference literature concerning type 2 DM and the patient information leaflets.
For part 3: SMAA based MCDA will be applied on glucose-regulating effects and patient preferences
For part 4: develop drug facts boxes [Shwartz&Woloshin] for glucose-regulating medicines
For part 5: organize stakeholder conference with regulators (and payers) on how to collect and weigh and drug effects information relevant to individual patients
This project will give insight in the current regulatory decision-making for diabetes medication and aims to more clearly display differential drug effects important to individual patients. It will feedback to regulators (and HTAs) how information relevant for personalized treatment should be weighted and disclosed to a wider patient and HCP audience.
Important links can be made within the Drug Regulation Domain and the Drug Application Domain. This may lead to expanding the patient-level factors that will be studied; developing tools together that can be used for assessing patient preferences; developing algorithms together that can be used to support healthcare professionals in making personalized decisions.
