Medical transformation: precision medicine

Individualized treatment options in different patient groups, made possible by more complex drugs with a wide range of side effects. Pharmaceutical manufacturers developing new, ever more precise drugs for this purpose at a rapid pace – and doctors who need to know their patients better and better in order to prescribe the best possible therapies. The keyword is precision medicine, and as one component of the transformation in medicine, it raises many new questions.

One of them concerns the level of evidence available for new drugs. Due to the continued use of randomized controlled trials (RCTs), the level of evidence, i.e., the predictive accuracy for individual drugs, remains at the same level as before. And this despite the fact that evidence is one of the most important decision-making aids for doctors when choosing a therapy. That doesn't add up! To enable conventional medicine to evolve toward precision medicine, precise and reliable evidence is needed.

To be able to treat patients ever more precisely, the level of evidence must be raised in order to improve the predictive accuracy regarding treatment efficacy and safety.^[1] Adapting the evidence to the medical progress of precision medicine therefore seems unavoidable.

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What is precision medicine?

The Institute for Precision Medicine (IPM) defines precision medicine as a novel medical approach in which medical therapies are to be tailored to individual patients. To this end, factors such as the patient's genetic makeup and living circumstances are to be given particular consideration.^[2]

To be able to carry out such individualized therapies, the treating doctors' knowledge of the causal relationships of the drugs used, i.e., the exact cause and the physiological response of the (side) effect, plays a central role.

Although such medical approaches are already feasible today, the large amounts of data required mean that they can rarely be implemented systematically. This is referred to as a high required level of evidence. Precision medicine therefore goes hand in hand with extensive data collection and is part of the hotly debated field of big data.^[3]

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Precision medicine, RCTs and side effects

Like all other drugs, drugs newly developed for precision medicine must demonstrate their efficacy and effectiveness before marketing authorization. For this purpose, the “gold standard,” i.e., a randomized controlled trial (RCT), is usually conducted, because, compared with other study designs, it supposedly allows causal relationships to be inferred.^[4] In the course of the RCTs, correlations, i.e., dependencies between taking the drug and its effect, can be calculated. This step ultimately provides initial indications of whether or not a drug works as expected. However, these RCTs often have strict inclusion criteria, so new drugs are usually tested on people who do not fully match the patients who will later be treated. For this reason, the responder behavior, i.e., the way the drug works or the side effects it causes in the trial participant, sometimes differs greatly from that of real patients.

Another problem with RCTs is randomization, i.e., the fact that chance (randomization) decides which trial participant receives the drug and which receives a placebo. Randomization impairs causality because any prior knowledge, for example regarding responder behavior, has to be ignored for the purpose of randomization.^[1] This can lead to bias in the results. Nevertheless, of all available study designs, RCTs are the only ones that can provide any indication of causality at all.^[5] So while the argument of reduced causality due to randomization is valid, it is not necessarily relevant.

Based on the evidence obtained in this way, doctors later decide which patient receives which drug. Studies have shown that the predictive accuracy (level of evidence) of the ten most frequently prescribed drugs is only 5–33%.^[1] Seen in this light, this would correspond to a one-size-fits-most approach that has little to do with precision medicine.^[1]

To make precision medicine truly market-ready and universally available, the level of evidence must therefore be improved, thereby increasing predictive accuracy.

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What role does real-world evidence play?

Real cases of real patients with real conditions need to be observed – ideally in real-world care. This is possible with real-world evidence (RWE), which is generated by observing real cases. Accordingly, RWE would have to be incorporated into clinical trials in the future in order to consolidate the evidence. Predictive accuracy would increase enormously, paving the way for precision medicine.

According to the Deutsches Ärzteblatt, a reliable agreement on when and how RWE is used in registration trials would have several advantages:^[1]

• Drug development times would be shortened
• Trial costs would fall
• Predictive accuracy would increase
• A more precise description of target groups would be possible

Doctors would have greater clarity about the most promising treatment in each case and could prescribe these therapies in a targeted manner. Thanks to the greatly improved evidence, therapies would thus achieve a higher degree of effectiveness. Put simply: there would be more health for every euro spent.^[1]

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XO Life's contribution

For every patient to receive optimal, individually tailored medical care in the future, and thus for a shift toward precision medicine to take place, the use of RWE is enormously important. This requires a system that can capture and process all the necessary information from real-world care.

For this purpose, XO Life has developed the ImpactMonitor, which can be used to collect and evaluate not only general medically relevant information but also individual information directly from patients (PROMs). This promises a consolidation of the evidence and thus a higher level of evidence and greater predictive accuracy. Doctors can rely on more precise evidence, and patients receive individually tailored therapies. It is a cycle that continues in this way and ultimately makes medicine ever more precise and individualized.

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Sources:

^[1] Dtsch Arztebl 2019; 116 (39): A 1708–12. https://www.aerzteblatt.de/archiv/210058/Klinische-Studien-Genauere-Evidenz-fuer-Praezisionsmedizin-notwendig  Last accessed: February 23, 2021

^[2] Institute for Precision Medicine. Definition of Precision Medicine. http://ipm.pitt.edu/definition-precision-medicine Last accessed: February 23, 2021

^[3]Dtsch Arztebl 2020; 117(22-23): A-1184 / B-1000 https://www.baek.de/praezisionsmedizin2020 Last accessed: February 23, 2021

^[4] Cartwright N, Deaton A. 2018. Social Science & Medicine. Understanding and misunderstanding randomized controlled trials. Vol 210. p  2-21.

^[5] Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen. 2020. Allgemeine Methoden. Vol 6.  p 10.