The drug development process has become increasingly expensive and focused on improved returns on investment. With average development taking 12 – 15 years, only one in 25 drug programs are released to the commercial market. Yet, after all this time and investment, the patent expires after only 20 years. Meanwhile, the time needed for drug discovery is lengthening and becoming more costly as well. Companies are facing high operational costs and high development failure, leading to fewer, lesser quality candidates entering clinical phases.
Another challenge is the move towards ‘personalized medicine’, as research indicates these therapies are more effective. This is forcing a shift from blockbuster drugs to targeted solutions for smaller populations. Pharmaceutical companies have opportunity here, but must also find a way to deliver new types of therapeutics while reducing costs and time-to-market.
New drug development requires a combination of innovation and operational efficiency. To achieve new levels of productivity, Life Science companies must learn to capture and build-upon their existing knowledge base by harvesting and sharing the data that already exists within their organizations.
Putting Big Data to Work
As in all industries, there has been a vast increase in data generated by Life Sciences organizations, which will help the industry become more efficient and effective. If data was aggregated and disseminated to its potential, predictive modeling of biological processes and drugs could become more sophisticated and commonplace. This would enable better identification of probable candidate molecules that could be developed into a successful drug. The wealth of new data and improved analytical techniques will enhance future innovation and fuel the drug-development pipeline.
Tools for analyzing and interpreting this data have not been developed and implemented at the same rate, and data without analysis are worthless. Pharmaceutical companies must find ways to mine, integrate and gain knowledge from all this data to improve analytical capabilities.
Eliminating the Silos
Leveraging the existing information that is created, and then stored during drug development, requires the ability for contributors across the enterprise to collaborate and have access to common knowledge. Departmental silos of data must be eliminated so that digital data is captured and shared between functions. This requires a flexible process and system that can capture, manage and document all the data. This level of visibility and collaboration sets the foundation for predictive analytics, which can provide the insight needed to accelerate and improve innovation.
Optimizing Therapeutic Development
Dassault Systèmes Designed to Cure Industry Solution Experience provides a business and scientific platform that can deliver collaborative virtual design, knowledge-driven innovation, as well as the predictive analytics needed to address current industry challenges.
Based on the unique BIOVIA portfolio, the solution integrates the diversity of science, experimental processes and information requirements across R&D, QC and manufacturing. This solutions supports data-driven insight that is key to accelerating and improving innovation. Utilizing a highly integrated, streamlined information gathering and processing system, multi-disciplinary teams can connect to the high quality information at any time, from any location. By unifying siloed applications and enabling seamless data management, Designed to Cure enables scientists within a collaborative global ecosystem to achieve better insights and deliver safe and efficacious drug candidates faster.