In the last few decades, several billion euros or dollars have been plowed into the development of new therapies and drugs. But there is still no easy answer to the problem of discovering innovative approaches. Huge costs, lengthy lead times, scientific progress and hurdles imposed by methodologies increase the complexity for all players in the health sector. They have a considerable, frequently negative, effect on medical innovations or at least on the scientific and financial risk to which pioneers are exposed. Paradigm shifts such as real-world evidence, for example, in early or retrospective benefit assessments and precision medicine approaches also play a part.

Bild: Darmbakterien

A real decade of genomics seemed to be imminent after the first complete sequencing of the human genome in 2003. Buzzwords such as personalized or individualized medicine started doing the rounds, holding out hopes of cures. Today, roughly one and half decades later, the old promises are being relativized by the discovery of the significance of new types and classes of data. Besides genetics, other factors are appearing on the scene such as:

  • epigenetics
  • transcriptome
  • biometric data from daily work in hospitals (real-world evidence) which are accessible in many different ways (radiology images, histological sections, data from electronic case files, laboratory information systems – LIMS, etc.)
  • metabolomics
  • proteome
  • immunology
  • environment and lifestyle indicated, for example, via the gut microbiome.

The thorough analysis of these classes of data is summed up in the generic term of precision medicine. Precision medicine is therefore increasingly replacing the old buzzwords of personalized medicine and individualized medicine. The new complexity is at once a boon and a curse. On the one hand, it decreases the prospects of developing blockbusters (one size fits all) and hampers the productivity of the R&D process, already somewhat impaired.
On the other hand, the combination of omics-based data with clinical real-world evidence parameters and new bioinformational approaches (machine learning, deep learning) is opening up completely new opportunities for all players in the health system: be they patients, consultant doctors, hospitals or the pharmaceutical industry.

Bild: Non-Responder

Precision medicine promises answers to many old and new questions:

  • What do we know about non-responders?
  • Are all the patients eligible for a therapy or type of medication actually being identified?
  • What role do gene regulation, the immune system and interactions with other drugs play besides genetics?
  • What is the connection between the bioavailability of a drug and the gut microbiome?
  • Can drugs which are already certified also be used for other indications?
  • How similar are test groups and comparative groups in clinical studies in reality? Is the stratification correct?
  • What patient groups do new drugs encounter in the course of daily treatment? How far does clinical routine differ from the test subjects in clinical studies? Will this affect an early or longer-term benefit assessment?

Any look at these questions and the accessibility of the new data classes leads to the following insight. Clinical routine is a crucial location for gaining insights into all these questions. The best prospects are most likely to be offered by a site that is able to link a large number of inpatients with acute symptoms to an even larger number of pre-acute and post-acute outpatients and those at the rehabilitation stage. The bioinformatically processed combination of modern clinical registries with corresponding data from multi-omic biobanks is leading to a new quality of cohort.

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Rudi Schmidt

Rudi Schmidt

Managing Director