A recent study has explored the concept of a modern and collaborative approach to data-driven drug development called Translational Precision Medicine.
Historically, drug development in large pharmaceutical companies was regarded as a conservative discipline. It mainly consisted of highly regulated processes and slow adaptation to external innovation. Cut to the present day, and state-of-the-art digital technologies are booming. Also, COVID-19 has substantially disrupted the traditional methods of pharmaceutical R&D. Therefore, typical approaches used in drug discovery and development are rapidly shifting.
Translational medicine is a cross-disciplinary field that involves researchers, clinicians and patients. It aims to translate advances from the research into the human clinical development stage.
Precision medicine is a field that considers the individual differences in a person’s genes, environment and lifestyle. This personalised insight provides medical professionals with the resources they require to offer tailored treatments for diseases, depending on specific circumstances.
What is Translational Precision Medicine?
Translational medicine and precision medicine overlap a great deal in drug development. An emerging discipline, called ‘Translational Precision Medicine’, has recently been coined. This modern concept integrates core components from both translational medicine and precision medicine into an end-to-end biomarker-guided drug development cycle.
Key components of Translational Precision Medicine are as follows:
Multi-omics profiling: Multi-omics profiling platforms integrate several biological layers, such as, genomics, epigenomics, transcriptomics, proteomics, lipidomics, metabolomics and microbiomics. This coordination allows researchers to acquire a more comprehensive view of the molecular patterns that underpin complex diseases.
Biomarker-guided trial designs: Biomarkers are defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”. The two most applied biomarker-guided trial designs are stratification and enrichment.
Model-based data integration: Model-based data integration is used to describe the time-course of pharmacokinetic and pharmacodynamic results. This improves our understanding of the pharmacology and helps to predict future experimental outcomes.
Artificial Intelligence (AI): The amount of data generated and collected in pharmaceutical R&D is increasing rapidly. High-dimensional multi-omics datasets, derived from large longitudinal clinical studies, provide an idyllic opportunity for the application of machine learning and AI.
Digital Biomarkers: Digital biomarkers are physiological and behavioural measures, collected via digital devices, that influence or predict health-related outcomes. This allows objective data to be collected in real-life settings, in an un-biased way and on a continuous basis. In clinical trials, this permits for a lower sample size, a shorter study duration and real-time feedback for early decision-making.
Patient engagement: Particularly over the last decade, pharmaceutical companies have realised that patient engagement is not an additional burden, but instead, that it can yield improvement for both patients and the industry. Increased patient involvement ensures that the industry remains focussed on the real medical needs of people. Pharmaceutical companies also benefit by accomplishing a quicker path to market and an overall higher credibility.
The future of Translational Precision Medicine
Translational Precision Medicine is an example of a shift from a ‘one size fits all’ approach to a biomarker-guided, patient-centric medicine. Essentially, the discipline has the potential to deliver the right medicine, for the right patient, at the right dose and at the right time.
It is now critical that pharmaceutical organisations continue to engage with regulatory authorities on innovative ways to design upcoming clinical trials. A more widespread use of AI technologies in drug development will be one of the most important factors for the success of Translational Precision Medicine. How data-driven and algorithm-based R&D can use AI tools to discover and develop new drugs most effectively now needs exploring.
In the future, the main challenge for Translational Precision Medicine will be to embrace these new molecular and digital technologies in a manner that is both feasible for large-scale trials and is also accepted by patients. Nevertheless, focussing on patient-centric real-world evidence (RWE) tools, electronic health records, multi-omics profiling, digital biomarkers and AI-based data analysis will only deepen our understanding of the novel discipline, and ultimately enhance patient outcomes.
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