Position: Sr. Manager, Value & Insights Team: Value, Engagement & Training (VET) Dep.: Product Owner Hub (POH) Location: Wuppertal, Germany
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.
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.
Understanding how compounds affect the disease biology of patients, and which potential side effects could be expected, is one of the core challenges in drug discovery. Transcriptomics (RNA-seq) allows us to measure these compound effects via an unbiased readout of gene expression changes. While „conventional“ analysis of RNA-seq data yields insights into individual compound effects and similarities, deeper connections between principles of compound structure and transcriptome effect elude us. Insights into this connection would help us support project teams with data-driven decision making on compounds. In this pilot, we are bringing together a cross-functional team of colleagues from LST, CMD, SyMOL and DS&AI to tackle this challenge and build a toolkit to enable understanding of how compound structure relates to complex gene expression patterns and to explore common themes amongst structures or shared off-target/general perturbation effects. This toolbox will become the starting point for other efforts aimed at leveraging multi-omics data in understanding compound mechanism of actions. Artwork created using Stable Diffusion.
At Language of Life, LoL, we combine state-of-the-art machine learning models, bioinformatics and expert knowledge to understand and design large biomolecules (RNA, DNA, Proteins), helping deliver the best drug, faster and cheaper, to address our patients’ needs. Our objectives are, 1) Drug the undruggable 2) Target any modality 3) Efficient Dry lab – Wet lab loops 4) Understand Patient & Disease
