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.
Highlights
Drug development in large pharmaceutical companies is regarded as a conservative and riskaverse discipline with highly regulated processes and slow adaptation to external innovation
Translational Precision Medicine comes with a paradigm shift from a one-size-fits-all to a biomarker-guided patient-centric medicine
The rise of datadriven and algorithm-based research and development (R&D) necessitates the establishment of a new mindset of how data mining and Artificial Intelligence (AI) tools can be used effectively to discover and develop new drugs [111]
Summary
Drug development in large pharmaceutical companies is regarded as a conservative and riskaverse discipline with highly regulated processes and slow adaptation to external innovation. The near future will show whether and how these emerging AI algorithms will help scientists to (i) identify novel targets or new indications for existing drugs, (ii) uncover latent factors that can inform on disease pathogenesis or drug response, (iii) discover predictive biomarkers enabling patient stratification strategies that can optimize clinical trial designs, and (iv) impact the drug development value chain.
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