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Beneficiaries

A novel self-learning, small-scale, high-throughput bioprocess platform for fast and optimal design of bioprocesses

Run2Run
m2p-labs-2019-05_03b

Challenge

Producing drugs/vaccines at scale requires manufacturing processes to be highly reliable & economically efficient. Yet process development is cost & time intensive, as a plethora of engineering possibilities require experimental investigation and optimization.

Solution

This project seeks to establish a self-learning, small-scale, high-throughput bioprocess platform for fast and optimal design of bioprocesses. It is based on m2p’s miniaturized high-throughput bioreactor device, the BioLector® (BL), and DataHow’s suite of unique knowledge centred machine-learning algorithms, which are being fully integrated.

Achievements

High-throughput experimental Data can be transferred from the BioLector® (BL) to DataHow’s software suite, where they can be directly analyzed and the processes modeled. Based on this insight, the next round of BioLector® (BL) experiments can then be automatically designed such as to achieve the desired criteria.

Impacts

The self-learning platform that we seek to build is the first of its kind & hence highly competitive to the existing offering. The platform can change how early biopharmaceutical process development is done, significantly lowering costs by millions € per drug.

Partners

DataHow AG

www.datahow.ch

Switzerland

m2p-labs

www.m2p-labs.com

Germany

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