Medical change: precision medicine

Individualized treatment options, through more complex drugs with a wide range of side effects, in different patient groups. Pharmaceutical manufacturers who are rapidly developing new, ever more precise drugs — 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 a component of medical change, raises many new questions.

One of them concerns the level of available evidence for new drugs. Thanks to the continuous use of randomized controlled trials (RCTs), the level of evidence, i.e. the predictive accuracy of individual drugs, remains at the same level. And this is despite the fact that the evidence is one of the most important decision-making tools for doctors to find treatment. It doesn't go together! Accurate and reliable evidence is needed to enable regular medicine to develop into precision medicine.

In order to be able to treat more and more precisely, the level of evidence must be increased in order to improve the predictive accuracy of treatment effectiveness and safety.^[1] Adapting the evidence to the medical progress of precision medicine therefore seems inevitable.

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What is precision medicine?

The Institute of Precision Medicine (IPM) defines precision medicine as a novel medical approach in which medical therapies are to be individually tailored to patients. To this end, particular attention should be paid to several factors, such as the genetic makeup of the patient and living conditions.^[2]

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

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

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Precision Medicine, RCTs, and Side Effects

Like all other drugs, newly developed drugs for precision medicine must prove their effectiveness and effectiveness before marketing approval. For this purpose, the “gold standard”, i.e. a randomized controlled trial (RCT), is usually carried out because this supposedly allows causalities to be derived compared to other study designs.^[4] In the course of RCTs, correlations, i.e. dependencies between drug use and effect, can be calculated. This step finally provides initial clues as to whether a drug is working as expected or not. However, these RCTs often have high inclusion criteria, meaning that new drugs are usually tested on people who do not 100% match the patients to be treated later. For this reason, responder behavior, i.e. the mode of action or side effect of the drug on the study participant, is sometimes very different from the real patient.

Another problem with RCTs is randomization, i.e. that it is decided randomly (randomization) which study participant receives the drug and which participant receives a placebo. Randomization affects causality because you have to ignore any previous knowledge, such as responder behavior, for the purpose of randomization.^[1] This can lead to distortions in the results. Nevertheless, among all available study designs, RCTs are the ones that can provide clues about causality at all.^[5] The argument of reduced causality due to randomization is therefore valid but not necessarily relevant.

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

In order to make precision medicine truly market-ready and ubiquitously available, the level of evidence must be improved and thus the predictive accuracy increased.

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What role does real-world evidence play?

Real cases, of real patients, with real ailments must be observed — ideally in real care. That is through Real-world evidence (RWE), which is generated by observing real cases, is possible. In future, RWE would have to be incorporated into clinical studies to consolidate the evidence. Predictive accuracy would increase enormously and pave the way to precision medicine.

According to the German Medical Journal, reliable agreements as to when and how RWE is used in approval studies would have several advantages:^[1]

• Drug development time would be reduced
• Tuition costs would fall
• Predictive accuracy would increase
• A more detailed description of target groups would be possible

Doctors would have more clarity about the most promising treatment and could prescribe these therapies in a targeted manner. As a result of the significantly improved evidence, a higher effectiveness of the therapies would be achieved. Put simply, there would be more health for every euro spent.^[1]

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XO Life's contribution

The use of RWE is extremely important so that every patient receives optimal, individually tailored medical care in the future and thus a transition to precision medicine can take place. This requires a system that can receive and process all the required information from real care.

For this purpose, XO Life has developed the ImpactMonitor ImpactMonitor, which, in addition to general medically relevant information, can also be used to collect and evaluate individual information directly from the patient (PROMs). This promises a consolidation of evidence and thus a higher level of evidence and a higher level of predictive accuracy. Physicians can rely on more precise evidence and patients receive individually tailored therapies. A cycle that continues and ultimately makes medicine ever more precise and individual.

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sources:

^[1] German doctor's office 2019; 116 (39): A 1708—12. https://www.aerzteblatt.de/archiv/210058/Klinische-Studien-Genauere-Evidenz-fuer-Praezisionsmedizin-notwendig Last accessed: 23.02.2021

^[2] Institute for Precision Medicine. Definition of Precision Medicine. http://ipm.pitt.edu/definition-precision-medicine Last accessed: 23.02.2021

^[3]German Medical Center 2020; 117 (22-23): A-1184/B-1000 https://www.baek.de/praezisionsmedizin2020 Last accessed: 23.02.2021

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

^[5] Institute for Quality and Efficiency in Health Care. 2020. General methods. Vol 6. p 10.