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Review
. 2021 Jun 5;19(1):245.
doi: 10.1186/s12967-021-02910-6.

Translational precision medicine: an industry perspective

Dominik Hartl  1   2 , Valeria de Luca  3 , Anna Kostikova  3 , Jason Laramie  4 , Scott Kennedy  4 , Enrico Ferrero  3 , Richard Siegel  3 , Martin Fink  3 , Sohail Ahmed  5 , John Millholland  6 , Alexander Schuhmacher  7 , Markus Hinder  3 , Luca Piali  8 , Adrian Roth  8
Affiliations
Review

Translational precision medicine: an industry perspective

Dominik Hartl et al. J Transl Med. .

Abstract

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Keywords: Artificial intelligence; Biomarkers; Companion diagnostics; Digital biomarkers; Drug development; Modeling; Multi-omics; Pharmaceutical industry; Precision medicine; Translational medicine.

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Conflict of interest statement

The authors declare that there are no competing interests. DH, VdL, AK, JL, SC, EF, RS, MF and MH work for Novartis, LP and AR work for Roche.

Figures

Fig. 1
Fig. 1
Interface position of Translational Precision Medicine in bridging translational medicine (early development) and precision medicine (late development). Disease models, multi-omics and molecular biomarkers are used to define disease endotypes. Real-world evidence, multi-omics, biomarkers (digital and molecular) and companion diagnostics are instrumental for the implementation of precision medicine. Model-based data integration, biomarker-guided trial designs and artificial intelligence are key data-driven tools for the integration of mechanism-centric translational medicine and patient-centric precision medicine
Fig. 2
Fig. 2
Flow of clinical trials (interventional or non-interventional) integrating multi-omics approaches to identify disease endotypes, which enables biomarker-guided trials designs (adaptive or non-adaptive) and paves the way towards precision medicine approaches (tailoring treatments for personalised healthcare)
See this image and copyright information in PMC

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