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There are personalised therapies for cancer – but what about for the treatment of high blood pressure, diabetes or obesity? We talked about this with Michael Stumvoll, professor of endocrinology at Leipzig University and director of the Department of Endocrinology, Nephrology, Rheumatology at the University of Leipzig Medical Center.

What exactly is personalised medicine?

“Personalised medicine” has been a buzzword for several years now. It reflects the wishful thinking that in the future every patient will receive their own personalised medicine – that is, treatment for a specific disease. The term comes from oncology, where a tumour or leukaemia cell can be directly examined, cancer cells can be precisely analysed at the molecular level and even the disease-causing gene or mutations can be identified. Healthcare providers can then turn to well-established and highly complex treatment algorithms and based on the specific pathology choose the optimal treatment for the patient, such as antibodies, cytostatics or cell therapy.

And will it also be used for widespread diseases such as diabetes, obesity, high blood pressure and cardiovascular diseases?

We don’t have approaches like this for widespread diseases. One reason for this is that individual disease-causing genes are rarely found here. Second, clinical trials for new drugs are not designed to predict response to treatment. Instead, all patients who are suitable in terms of age, weight, disease duration and the most important contraindications are included in order to reach the high number of cases required for approval (and subsequent reimbursement).

One single treatment alone doesn’t work for everyone and certainly not in the same way. Is this taken into account?

A priori knowledge of “non-responders” would complicate and increase the cost of studies and could significantly reduce the market after approval – and is therefore undesirable. If we knew in advance that a patient would not respond to a drug developed for the “average” person, or would only respond with significant side effects, we would choose a different drug. Subgroups – even just women versus men – are not defined in advance, but are instead analysed post hoc, and have at best a rough significance. The resulting therapeutic inertia – try it first to see if it helps – increases the burden of disease (severity times duration) and leads to irreversible organ damage. On the other hand, it would take an astronomical number of cases to represent all possible subgroups in endpoint studies (e.g. heart attack and death).

So are more precise studies for widespread diseases “science fiction”?

Real science can already do a lot and can also help with tailored study designs. Large datasets on disease risk and progression are being generated worldwide. The pharmaceutical industry supports observational studies for newly approved drugs, which could provide initial information on response rates and side effects with little additional effort. Biomarkers (i.e. individual gene, serum, stool and imaging data) could be used to model a priori subgroups that are likely to initially benefit more from a particular drug than others. Similarly, clinical monitoring could be set up in such a way that very hard endpoints could be dispensed with in order to increase knowledge. Feasible pilot studies in appropriate subgroups could then be established. The pharmaceutical industry and academia must jointly pursue these approaches and bring them to the approval stage if personalised medicine is to be successful in the fight against widespread diseases.

This interview was first published on 21 September 2023 in ZEIT für X.


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