Realise the true power of your online, atline and offline biologics data

Supported by:

20 May 2021

Supported by:

20 May 2021

Realise the true power of your online, atline and offline biologics data


Eliminate the data analysis bottleneck: Polar High-Throughput Process Development (HTPD) finally brings high-throughput data management and analysis to biopharma development.

Managing bioprocess data is a complex challenge even if you run just a few experiments per month. With HTPD, this can quickly scale up to hundreds of automated, parallelised experiments.

If your company is struggling to co-ordinate data from different laboratory software and instrument systems, then you may see your high hopes in high-throughput fade away. Watch this on-demand webinar to discover why HTPD can offer a solution to these challenges. Used effectively as part of a BioPharma Lifecycle Management strategy, the data generated from HTPD can accelerate development by supporting better decision making and reducing timelines.

Learning outcomes of this webinar:

  • Learn how to harvest the true power of your mini bioreactors
  • Discover how to maximise the productivity of your cell cultures
  • Hear about faster progression through traceable data
  • Enable better trend analysis and insight.


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    Matt CliffordMatt Clifford, Senior Product Manager, IDBS

    Matthew Clifford is a product manager at IDBS with a particular focus on pre-clinical solutions. With over 19 years’ experience working within and alongside the pharma industry, including eight years in informatics roles in a top 20 pharma business supporting discovery and pre-clinical organisations, he has a proven background in industry trends and solutions. At IDBS, Matt leads the development of solutions to support all aspects of R&D across pharma from bench to clinic. Matthew has a degree in Chemistry from the University of Newcastle upon Tyne.

    One response to “Realise the true power of your online, atline and offline biologics data”

    1. Isatou Sarr says:


      how would you advise to handle(export/store/analyse) research data so as to exhaustively link clinical indicators to experimental results?

      Thank you,

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