In a world where every pharma and biotech company is expected to be data-driven, the generation of value from data needs to happen faster. Companies must find ways to shorten cycle times because the cost and time to get a drug into clinical testing have increased dramatically due to regulations and scientific challenges. One way we can do this is by enabling better decisions in the pre-clinical phase.
The combination of data-driven science and large amounts of data will cause this process to accelerate exponentially in coming years, opening all kinds of possibilities for earlier interventions. The ability to quickly identify the most promising molecules for development based on early-stage data is critical for reducing time to market and increasing value generation from drug discovery and development. This is one of the key challenges that we need to solve: the ability to source, curate and integrate data from many sources, while identifying high-quality targets for new drug discovery and development and delivering these insights in a fast and meaningful way. It is critical that we break down existing barriers between these various stakeholders (drug companies, academic researchers, contract research organizations) to facilitate the rapid generation of value from data.
The pharmaceutical industry is placing more emphasis on analytics solutions that can provide scientific insights into drug discovery and development. The ability to generate these insights faster will not only improve decision-making across R&D, but also increase productivity by making better, faster decisions based on quantitative data. It will also help increase the probability of success and reduce risks, which is a major priority for all stakeholders involved in drug discovery and development.
In the past five years, we’ve seen an evolution in drug discovery and development processes related to better use of technology for data generation, management and analysis. In preclinical research especially, there is a need to integrate different types of big data – from imaging, molecular biology and genetics – into a single platform that can generate new insights across various modalities. There is also a need for fast data generation and integration, so that insights can be delivered rapidly to decision makers across the entire drug discovery and development lifecycle.
Three key components of using data science to discover drugs include: generating high quality data and making it available in a structured and central repository; implementing reference data models to ensure interoperability of the source data; generating insights from these datasets using advanced analytics. The latter can be implemented via self-service analytics, which enable multiple collaborators to have access to the same data at their fingertips to generate new biological insights.
PureSoftware has been working with the best in the pharmaceutical industry and helping them translate value from data. We have worked on more than 300 clinical databases. Our PureClinical framework has helped leading Pharma companies in creating an integrated data ecosystem to accelerate decision making across early discovery, clinical research, and the regulatory process. To learn more about PureClinical and our clinical data analytics framework. Please write to firstname.lastname@example.org