Translational Science

Abstract

Translational science transforms basic, clinical and population-based research into new therapies, bringing community level health benefits. The success factors for drug discovery are based on high quality translational science. The first success factor is target identification, which is the major focus in the pre-clinical phase of translational research. Deep understanding of the disease biology and identification of the disease driver as a drug's target increases the probability of success. The second success factor is the identification of patients who respond to the treatment. Patient selection biomarkers, genetic, biological or digital, provide a link between the target and disease subtype. Finally, understanding of unmet clinical needs in patients, and the benefits and risks of the intervention at community level, needs to be clear from the early project to the post-marketing stage. The accomplishments of translational science are determined by the accuracy of the data collected and the disease models used, i.e. translatability of the models into human disease. During the past decade translational science has advanced greatly due to the emergence of new, disruptive technologies. Digital technologies and innovative data analysis techniques are also transforming translational research. The use of data science and artificial intelligence for the analysis of real-world data from population-based studies or clinical trials opens new avenues for target ideas, biomarkers and patient stratification.