At the dawn of bioprocess development, biopharmaceutical manufacturing, upstream and downstream primary processing were worlds apart.
This is because, in the pre-biotech days of industrial bioprocessing, the professionals in upstream, usually biologists, microbiologists, biochemists, and chemists, worked independently from the downstream professionals who were mostly chemical engineers working on unit processes for product recovery.
Recently, these separate streams have started to integrate thanks to end-to-end biopharmaceutical process control and the FDA’s support of process analytical technologies (PAT).
Economic drivers also play a part. In the past, pharmaceutical companies didn’t have to optimize manufacturing, but now they do, and they are utilizing more advanced analytics and control processes to help reduce costs.
Despite much progress, challenges remain. Biopharmaceutical production creates a lot of data. As a result, the industry faces the challenge of moving from a data-heavy environment to one where that data is actually translated into information that leads to knowledge.
If this knowledge is effectively deployed it can advance production developments, and the benefits may spill into other areas too, such as financial savings and improving the environmental agenda of a company.
The big data revolution is changing drug development by helping companies enter into smarter research partnerships and develop better drugs than ever before – and match those drugs to patients who will most benefit from them.
In the future, the most successful pharmaceutical companies will be those that can take advantage of data analytics and use them to stay ahead of the curve – and at IDBS, we can help. You can learn more about our bioprocessing software solutions here.
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