Manufacturing

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Review of SUT Adoption in Biopharma Manufacturing

The evolution of therapeutic modalities drives the adoption of single-use technologies.

Review of SUT Adoption in Biopharma Manufacturing

Why Are Pharmaceutical Companies Reluctant to Adopt Cloud Technologies?

Despite its understandable hesitancy, the pharma industry is facing a need for more widespread adoption of cloud-based solutions.

Why Are Pharmaceutical Companies Reluctant to Adopt Cloud Technologies?

Automating the Future of Fill/Finish

Given the criticality of fill/finish processes, it is clear that automation is the next technological step.

 Automating the Future of Fill/Finish

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Women, scientist and discussion with tablet by glass, reading and review with results at pharma company. People, mentor and feedback with notes, application and medical research for mpox vaccine | Image Credit: ©peopleimages.com -stock.adobe.com

Pharmaceutical Technology®’s quality experts, Susan J. Schniepp, distinguished fellow at Regulatory Compliance Associates, a Nelson Labs company, and Siegfried Schmitt, PhD, vice president, Technical at Parexel discuss how pharmaceutical manufacturing training has evolved over the years and how the influx of new facilities is demanding a skilled workforce.

Pharmacist uses barcode reader to identify and sell a drug | Image Credit: ©alphaspirit -stock.adobe.com

Life sciences is a decade behind other industries in its optimization and strategic exploitation of data. This is perplexing, given how much companies profess their ambitions to exploit AI. An industry podcast brought together life sciences thought leaders to debate the subject. The panelists noted that really, by now, standardized data should be yielding greater intelligence, and powering pharma’s future, accelerated by AI. If only companies could find new momentum to finally sort out their underlying data. This article sets out some of the key points that arose from the panel.

Large-language models are excellent for general-use AI systems, but they don’t understand pharmaceutical companies’ proprietary documentation—the validated procedures and quality protocols that ensure drug safety. Smaller, domain-specific language models give companies more control and efficiency in their AI use.