On a global level, the CoE PASS is an active contributor and participant in several international consortia that aim to define a cross-industry standard on new stability approaches that are acceptable to regulatory authorities. The team has a long-standing tradition, and a track record of successful collaborations with universities and research institutes worldwide on fundamental research in the area of stability and predictive stability.
Furthermore, the CoE PASS is now exploring opportunities to expand its scope and develop new predictive models that could accelerate the speed of drug development and improve the overall quality of the final drug.
Position summary:
This position frames in the ongoing virtualization of parts of the development process. The successful candidate will contribute to current stability prediction efforts in the NME selection process. (S)he will use the current computational methodology to support new and ongoing projects. Furthermore, (s)he will be looking into expanding this methodology to predict more chemical degradation pathways, leveraging current experimental and in-silico data. (S)he will develop a semi-automated computing platform to support the discovery portfolio. Furthermore, the successful candidate will be looking into expanding the calculations to predict molecules' behaviours in more complex matrixes. S(he) will explore ways to predict excipient compatibility for a new drug formulation and explore physical stability predictions.
The scientist will have his/her lines of research and collaborate with pioneer external labs. Moreover, the scientist will translate the evolving field into concrete and actionable recommendations for the development portfolio. (S)he will interact and collaborate with other scientists in the developmental organization, R&D IT, statistics, and computational chemistry colleagues to create new solutions for the future's predictive analytics.