Digital Innovation in Biopharma R&D

Research grants for Digital Innovation leveraging technological solutions such as Digital Pathology for advanced cell simulation and Big Data and X-Omics for Single-Cell RNASeq Analysis

Call for research proposals

We are offering several grants in the field of Digital Innovation in Biopharma R&D. Proposals will be considered that leverage technological solutions in Digital Pathology for advanced cell simulation, Big Data and X-Omics for Single-Cell RNASeq Analysis as well as other applications of AI in R&D as described further in this webpage.  

  • Subtopic Digital Image Analysis Pipeline - 1 grant comprising 100,000 € for 1 year with the option of extension
  • Subtopic Big Data and X-Omics: Single-Cell RNASeq Analysis - 1 grant comprising 50,000 € for 1 year with the option of extension
  • Subtopic AI Use Cases - 1 grant comprising 40,000 € for 1 year with the option of extension

The Digital Innovation subtopics are subject to the Terms and Conditions of the 2021 Research grants which can be downloaded from our websites here.

We have a pipeline of innovative tumor cell targeting therapies. Clinical patient selection requires robust and accurate interpretation of immune reactivity for effective response prediction. Furthermore, Transferability of pre-clinical data e.g. derived from PDX, xenograft models to a clinical setting, is of high importance. We are seeking proposals for a Digital Image Analysis pipeline for advanced cell simulation to improve quantification of DAB immune-reactivity in FFPE stained tumor tissue. This may include a regions-of-interest module as well as a main module for advanced cell segmentation and shal include a setup for IHC quantification in membrane, cytoplasm and nuclei of tumor cells. Tools should be customizable and transferable.

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Single-cell RNASeq offers unprecedented opportunities to understand drug action. By this grant, we aim to support development of computational tools to analyze the effect of drugs on pathway activity (especially immune system-related) in single cells based on scRNASeq data. We are interested in scalable approaches that do not only model treated and untreated conditions, but also take different concentrations and/or time points of measurement into account. Our aim is to better understand the modes of action of our drugs and targets, both on the levels of single genes and pathways. We encourage individuals or teams to apply with their approaches to this problem that comprise innovative methods and visualization by an R Shiny App.

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As a leading science and technology company, we strategically drive the development and use of artificial intelligence (AI) in optimizing operational efficiency.  Our Healthcare R&D generates high quality data along the research & development value chain; these data are already being collected and analyzed to support business processes.  In order to maximize the full potential of these existing data, our Healthcare R&D wants to address of the challenge of identifying the most beneficial application of AI in R&D, and to identify and close any gaps so that additional beneficial, relevant insights using AI can be obtained.

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