I am part of the Product Owner Hub helping develop and execute the Digital Strategy for Pharma Clinical.

Process Manager for Data Sciences Services & Solutions

The Applied Machine Learning Group (AML) strives for the application of a data-driven approach to drug research and development by developing and utilizing best-in-class machine learning (ML) models to optimize R&D processes. The implementation of ML algorithms accelerates drug discovery and a platform to monitor and self-train models enables portfolio view of our capabilities, allowing us to become more focused. We envision to work in a fast-paced setting, we expect to fail quickly and adapt swiftly, all while expanding your ML capabilities. We also believe in making the right choices not only for our immediate team but decisions to build solutions catered to broader R&D. The success at AML will be measured by bucketing the benefit of the developed capability into one or more of these three buckets, (1) Savings in time (2) Savings in cost (3) Generation of new insights

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.

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.

Our vision at Data Science Services & Solutions, DS3, is to Enable, Scale, and Operationalize R&D Data Science & AI capabilities to achieve Clinical Research and Development Speed, Efficiency, and Optimization. Learn more about our areas of responsibility, capabilities, strategic priorities, and collaborations. For more information, contact DS3 Head: Abi Velurethu. Artwork created using Stable Diffusion.

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.