Health Sciences Review (Mar 2023)
Optimizing drug discovery: An opportunity and application with reverse translational research
Abstract
Background: Reverse translational research (RTR) has pertinent potential outside the scope of traditional disease-based research, to affect longevity and health span. Reverse translational has multiple applications for the discovery of novel applications wherein the steps of the research initiates by accounting and examining the actual and real-life subjects and their consequential observations in the clinic. RTR aims to develop actionable ideas for identifying disease mechanisms and treatment response for enabling and identifying known targets and new targets respectively along with implementing applications of precision medicine (PM) techniques. Objective: PM has transformed translational research by supporting a patient-specific strategy based on the discovery of predictive biomarkers particularly identified for obtaining response and eliminating futile medications along with minimising side effects of the treatment regime. The deployment using sensing devices and expanding ''multi-omics'' analysis, as well as the skill for collecting medical, scientific, clinical, behavioural, and ecological data on a broad scale intended for ensuring the digitalization of every subject's state of health. The development of a global health management system able to provide real-time data and individualised preventative and therapeutic alternatives for each individual. Real-world data-driven translational applications may therefore be a viable alternative to conventional evidence-based medicine methodologies that rely on randomised trials to validate a theory. This article examines reverse translational research from the perspective of drug discovery and development. Conclusion: The write-up highlights the enormous prospects for optimizing drug discovery through reverse translational approach. It emphasizes the medication repurposing and precision medicine for improving people's health and the challenges of the new era of big data in medicine.
