Scientist, Computational Chemistry - Center of Excellence for Predictive Analytics and Stability Sciences - Janssen Pharmaceuticals - Beerse

Johnson & Johnson Family of Companies

Région

Beerse
Johnson & Johnson is the world's most comprehensive and broadly-based manufacturer of health care products, pharmaceuticals, and medical devices. The Janssen Pharmaceutical Companies of Johnson & Johnson conduct research and development in a variety of therapeutic areas to discover novel therapeutic approaches to address unmet medical conditions. Janssen has a rich portfolio of small and large molecules, as well as vaccines, driven by strong science and innovation.

The Janssen campus in Beerse (Belgium) hosts 5,000 employees. It has a unique ecosystem covering the complete drug development life cycle, with all capabilities from basic science to market access on one campus. The integrated environment of the campus provides employees a unique chance to experience many different aspects of drug development throughout their careers. It has a successful track record of over sixty years of drug discovery and development, and it is one of the most important innovation engines of the Janssen group worldwide.

The Center of Excellence for Predictive Analytics and Stability Sciences (CoE PASS) is a team of scientists within the Analytical Development organization of Janssen Pharmaceutica R&D that focuses on the use of accelerated studies and predictive models to assess drug stability.
Within Janssen Pharmaceutica, the expertise of the CoE PASS is drawn upon throughout the various stages of the drug lifecycle, ranging from the late discovery phase, over the full drug development cycle, and all the way up to the commercialization stage of the product. In the discovery stage, the CoE PASS supports the selection process of a New Molecular Entity (NME) by providing early hydrolytic and oxidative stability warnings by combining experimental design and calculations. During the early drug development stage, the CoE PASS conducts forced degradation studies, excipient compatibility studies, and mechanistic investigations to determine the drug chemistry of the product and to assess the physical and chemical stability of a drug substance in combination with its excipients. To support the formulation selection during development, short-term accelerated chemical and physical stability studies are performed, and for the final formulation, the CoE PASS applies predictive models to assess the long-term stability profile of a drug, using risk-based predictive stability approaches.

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.


Qualifications
Your Profile:

Overall Qualifications

  • Ph.D. in computational chemistry with master level training in organic chemistry or biochemistry or equivalent interdisciplinary training in related fields
  • Experience in modeling and statistics

Experience and Skills

  • Exposure to one or more other disciplines of relevance to drug discovery, such as synthetic organic chemistry, pharmacology, toxicology, formulation, biochemistry, physical chemistry or analytical chemistry
  • Firm knowledge of QM modelling methods such as DFT, HF, SE and molecular mechanics.
  • Experience with programming languages such as Python, Javascript and/or Java, Fortran (or any other established programming language)
  • Experience with common analysis tools like R, Octave or Matlab
  • Knowledge of machine learning frameworks is a plus
  • Demonstrated creativity and innovation in problem solving
  • Preferably experienced in working in matrix teams (ideally in an industrial or semi-industrial setting)
  • Excellent communication, reporting, planning and team interaction skills, self-motivation, proactivity and the ability to work independently
  • Fluent in English both verbal and in writing
  • Ability to build bridges between colleagues, disciplines, departments and collaborators
  • Willing to challenge the status quo in a constructive manner suggesting alternative solutions for existing problems

Our offer

  • An exciting position in an international and dynamic environment with continuous learning and growth opportunities
  • An attractive remuneration package with additional benefit packages (for example hospital insurance, and other health care incentives if applicable)
  • An inclusive team environment where diversity and different opinions are respected and valued, and the importance of a good work-life balance is recognized



Primary Location
Belgium-Antwerp-Beerse-
Organization
Janssen Pharmaceutica N.V. (7555)
Job Function
R&D
Requisition ID
2005836882W
Johnson & Johnson Family of Companies

Société

Johnson & Johnson Family of Companies