With most pharmaceutical companies’ research and development (R&D) department struggling at the time, 90% of drugs that make it to the clinical testing stage are failing. The inefficiencies of the R&D process have also extended the average time to market for new medicines to anywhere between 10 and 15 years.
Faced with these pressing challenges, pharma organizations are looking to revamp their research value chain expediting product innovation and strengthening molecule pipelines at the discovery stage.
One approach pharma companies could take to boost their R&D function is to work with contract research organizations, government entities and other stakeholders for drug discovery. Not only will precompetitive collaboration of this nature bring down product manufacturing costs and shorten development timelines, but it will also help drug manufacturers reduce the likelihood of clinical failures.
Traditionally, precompetitive collaboration in the pharma sector revolved around the identification and corroboration of predictive biomarkers, creation of tools for target validation, and preclinical safety and toxicology. Now, however, the optimization of compounds during the initial stages of the discovery phase has become a prime focus area.
The industry as a whole is also directing its efforts towards improving the efficiency of clinical trial processes. Much of these efforts go in vain, however, due to the inefficient end-to-end management of research data. Emanating from the inadequacy of IT infrastructure, pharma companies are having a tough time aggregating, storing, annotating, and sharing data.
Quite clearly, the need of the hour is for pharma companies to enhance technologies driving their research value chain. For a start, they could leverage cloud computing, as it could offer a collaboration platform that facilitates quicker product innovation.
Instead of implementing siloed IT systems, pharma companies could then invest in plug-and-play platforms with open standards to integrate their IT capabilities. With the aid of this platform, stakeholders across the research value chain can effectively share each other’s R&D capabilities and assets in the areas of target identification and validation, screening, drug design and synthesis, among others.
To make the most of such collaboration, pharma companies must, nevertheless, define standards for data management. Only then can they make informed decisions and create reusable models to strengthen their pipelines.
To read the full article by Abhishek Shankar, click here