The Process Development Blog

APC & VLE Therapeutics is looking forward to seeing you March 18th-19th, 2025 in Barcelona!

APC & VLE Therapeutics is looking forward to seeing you at booth #105 March 18th-19th, 2025 in London!

APC & VLE Therapeutics is looking forward to seeing you, March 17th-19th, 2025 in Milan!

Bayesian Optimization provides a more efficient alternative to traditional Design of Experiments (DoE) for process development, particularly when navigating multiple factors and levels. This case study highlights an innovative approach involving the exploration of areas characterized by high uncertainty and high potential yield. This focus enabled the identification of optimal operating ranges for critical factors, ultimately maximizing reaction yield.

CEO & Co-founder Mark Barrett reveals how The Medicine Accelerator is transforming medicine development in 2025 with €100M campus expansion, AI-driven iAchieve®, and deep-tech innovation.

Avoid FDA Complete Response Letters (CRLs) by focusing on a strong CMC strategy, deep process understanding, and scientific excellence to ensure smoother drug approvals.

The Medicine Accelerator proudly announces its triple win at the 2024 Pharma Industry Awards, securing Biotech Company of the Year, Pharma Company of the Year - SME, and Pharma Industry Company of the Year. Shortlisted in eight categories, APC and VLE stood out for their excellence in scientific innovation, operational performance, sustainability, and talent development.

Decoupling process development from manufacturing enhances control, flexibility and innovation - accelerating time to market and reducing risks in pharmaceutical development.

We’ve curated a list of key crystallization publications from the past 25 years, showcasing the research and breakthroughs that have defined our approach and expertise. These publications represent the culmination of decades of innovative work by APC’s crystallization experts, illustrating our ongoing impact in this field.

This study introduces a hybrid method combining the COSMO-RS model with machine learning to predict aqueous solubility at room temperature.