Bring Higher Quality Therapeutic Candidates To Market Faster By Accelerating Knowledge-Driven Innovation
The Life Sciences industry faces a ‘new normal’ on the path to successful product development. With increasing operational costs and high development failure rates resulting in fewer, lesser quality candidates entering clinical phases, the pressure is on to deliver better results that can drive down costs while reducing time-to-market. The vast increase in data generated by Life Sciences organizations should help address these challenges; however, tools for analyzing and interpreting this data have not been implemented at the same rate. Using collaboration, common knowledge, and virtual design to drive therapeutic solutions make it possible to ‘fail’ unsuitable candidates, enabling higher quality candidates to get to market faster. Dassault Systèmes Designed to Cure Industry Solution Experience delivers collaborative, knowledge-driven innovation, and predictive analytics to address these challenges.
Integrate Data And Technology To Optimize Collaborative, Multi-Disciplinary Therapeutics Discovery
The time needed for drug discovery is lengthening and becoming more costly. The trend towards ‘personalized medicine’ is shifting the focus from blockbuster drugs to targeted therapies for smaller populations, which reduces the market potential for each drug. This puts even more pressure on pharmaceutical companies to successfully deliver new therapeutics while reducing costs and time-to-market. Dassault Systèmes Designed to Cure* Industry Solution Experience supports data-driven insight that is key to accelerating and improving innovation. Utilizing a highly integrated, streamlined information gathering and processing system, multi-disciplinary project teams can connect to the highest quality information available, 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 sooner and reduce typical wet-lab experimentation.