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The second installment of the monthly BioPharma By the Numbers infographic series details the hiring and retention challenges faced by US biopharma.

AI and innovative technology platforms are revolutionizing biopharma R&D and manufacturing, but success requires high data quality and next-gen solutions.

Codexis and Nitto Denko Avecia aim to advance next-gen siRNA production methods for improving scalability of RNA therapeutics.

In the conclusion of a two-part conversation, Viktoria Enkmann of RNAnalytics says she had a hard time understanding the United States’ mRNA decision in August 2025.

Digital platforms, automation, and new tech are driving efficiency, innovation, and advanced skill building in biomanufacturing, according to Sigma Mostafa of KBI Biopharma.

Toni Manzano, PhD, discusses his CPHI Europe presentation, stating that AI is essential for managing complex drug and biologics manufacturing.

Biopharma innovators were recognized at CPHI 2025 for advances in drug delivery, continuous bioprocessing, and supply chain capabilities shaping global therapies.

Toni Manzano, PhD, Azion, says AI integration in drug manufacturing necessitates regulatory risk assessment, cloud infrastructure for big data, and strict data integrity compliance.

Toni Manzano, PhD, Aizon, says AI adoption in biopharma, supported by new GXP guidelines, is challenged by poor data quality and the complexity of industrializing many specific models.

Silvia Scaglione of React4Life explores how organ-on-chip systems, AI, and multi-organ models are revolutionizing preclinical testing and advancing personalized therapeutics.

In the first of two parts, Viktoria Enkmann addresses problems with quality control for personalized medicines in the context of lipid nanoparticles and RNA therapeutics overall.

Enzyme engineering holds promise, but Marina Cañellas of Zymvol Biomodeling says the enzymes themselves need to be improved through increased discovery and investigation to be able to achieve a greater amount of chemical reactions.