I had the honor of serving as the DSSS team's subject matter expert for Rave EDC and its integrations in the past. However, I now have the privilege of leading a diverse group of talented senior developers and user access management professionals within the DSSS team. By working together, we aim to deliver innovative solutions that optimize clinical trial performance and drive positive outcomes for our study teams.

I work in the System Managers team and currently support the reporting application CART. I am also responsible for the reporting part of Gemstone and currently working on the reports needed for MVP-2. I am located in the UK and have worked at Bayer for 15 years.

Over 15+ years of professional experience as a Product Owner/Software Business Analyst and Process Owner in Pharmaceuticals, Banking, and Health care domains. Working from India for Bayer pharmaceuticals since 2018.

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The R&D Digital Fluency Program (DFP) is supporting you with learning offerings to build up competencies and further skillsets around digital technologies to enable a competitive and digitally literate organization. At the Digital Fluency Program, we set ourselves apart by offering learning opportunities created by your peers who understand the unique challenges you face in your job. Our tailored approach focuses on real-life use cases, taking our training far from generic and providing you relevant and impactful skill development. In 2020 we, a small team of four people, started our journey with the DFP as part of the R&D Digital Roadmap. Since technologies are ever evolving and impacting our work environment it is more important than ever to foster a culture of continuous learning and enable future opportunities. Thus the DFP learning portfolio includes something for everyone, regardless of your level of knowledge: It offers general awareness programs to enhance basic digital literacy, self-learning pathways on data analysis & visualization and the ability to access online courses like Coursera via the Digital Curriculum. The team ensured the learning objective was met by piloting newly developed learning programs, making use of cross-functional development, and incorporated R&D specific components prior to a broader roll-out in the organization.

Given the considerable amount of data that is collected throughout clinical trials, it can be asked if this data can be used to improve our understanding of the trial, regardless of outcome. The radiomics pilot seeks to provide a toolkit that is capable of analyzing the different modalities of information found in clinical trials (genome, gene expression, biomarker, clinical information, and imaging information) to derive insights into why a patient may be responding, or why a clinical trial was unsuccessful. The current efforts have focused on clinical trials in oncology, while collaborating with the RED-ONC function. Overall, the project is in its pilot state, and demonstrating a proof of concept, while documenting many challenges and learnings with the development of such a toolkit. Potential directions that could expand this toolbox include deriving the factors that best predict why a patient would stay in a clinical trial, and providing assistance with patient selection of clinical trials. Other potential directions involve suggesting why a particular trial may have been unsuccessful and providing that information back to the drug development team.

With the new head of R&D Community Engagement, RDCE, a new set-up has also been introduced, which newly regulates the tasks and responsibilities of the business partners assigned to the functions, SPOCs, which stands for Single Point of Contact. What is now in scope and what is not? The poster provides information on this.

Professional with extensive experience in analyzing, designing, and implementing novel solutions through the use of Machine Learning for its applications in Research and Development for the Healthcare and Pharmaceutical Industry. I employ my interpersonal communication, business, and technology integration talents to set product strategy and create product roadmaps in an agile environment. • Talent Management • Team-Player • Effective Communicator • Detail Oriented • Analytical Mindset • Computer Vision • Machine Learning • Natural Language Processing • Data Analysis • Statistical Modeling

The Data Assets and Insights Solutions subfunction, DAIS, consists of a team of dedicated computer and data scientists which combine deep R&D process and domain expertise (biology, chemistry, pharmacy, etc) with advanced computational expertise (software engineering, natural language processing, scalable architectures, bio- & chem-informatics, data integration & visualization, etc). We integrate, enrich and harmonize large scientific datasets to build foundational data assets which are used by the R&D organization and our team to build insights solutions. They address key R&D challenges to improve speed, quality and probability of success of our R&D pipeline. Selected business critical tasks within R&D we are focusing on: - Target identification including holistic assessment - Disease understanding including precision medicine - Pipeline evaluation (therapeutic potential and risks) - Modality selection including identification of tool compounds - Identification of collaboration and in-licensing opportunities For more information contact: Wolfgang Thielemann. Artwork: Licensed pictures.