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Data science tools and applications on the way to Pharma 4.0.
Valentin Steinwandter1, Daniel Borchert1, Christoph Herwig2
1Exputec GmbH, Mariahilferstr. 147/2/2D, Vienna, Austria.
Drug Discovery Today
|June 18, 2019
Summary
Digital transformation in biopharma faces hurdles. Data science tools for Pharma 4.0 show promise but require DevOps to overcome deployment challenges and enhance bioprocess efficiency.
Area of Science:
- Biopharmaceutical Industry
- Digital Transformation
- Data Science Applications
Background:
- Biopharmaceutical industry faces significant obstacles in digital transformation.
- Pharma 4.0 concepts, including cyberphysical systems and dark factories, leverage data science.
- Current data science tools are crucial for advancing bioprocesses.
Purpose of the Study:
- Review current data science applications across the bioprocess life cycle.
- Identify limitations in the scope and usefulness of existing data science tools.
- Propose solutions to enhance the development and deployment of these tools.
Main Methods:
- Literature review of data science applications in biopharma.
- Analysis of data sources and scopes for various bioprocess stages.
- Evaluation of technical and nontechnical challenges in tool implementation.
Main Results:
- Data science tools are applied at multiple stages of the bioprocess life cycle.
- The effectiveness of current tools is constrained by development and deployment issues.
- Specific examples of data science applications and their limitations are discussed.
Conclusions:
- DevOps methodologies and toolsets can address current limitations.
- Implementing DevOps is essential for the advancement of Pharma 4.0.
- Improved data science tool integration will accelerate biopharmaceutical innovation.
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