Hovione's $35M CDC Flex line links spray drying to tablets, designed to eliminate scale-up under FDA Q13 guidance.
|Articles|March 2, 2019
Eppendorf
Eppendorf is a leading life-science company that develops and sells instruments, consumables, and services for liquid-, sample-, and cell handling.
Eppendorf is a leading life-science company that develops and sells instruments, consumables, and services for liquid-, sample-, and cell handling. Its portfolio includes pipettes and automated pipetting systems, mixers, spectrometers, and DNA amplification equipment, bioreactors, CO2 incubators, shakers, and cell manipulation systems. Consumables such as pipette tips, plates, and disposable bioreactor vessels complement the range of premium products.
102 Motor Parkway,
Hauppauge, NY 11788
custserv@eppendorf.com
tel. 800.645.3050
INTERPHEX Booth Number: 3647
Related to this article
This week on PharmTech, Merck diversified its pipeline, USP emphasizes supply chain security, AI systems are examined, and speakers preview their presentations at CPHI Milan and AAPS PharmSci 360.
Capsugel's Bram Baert and Sandrine Picco argue that chasing zero nitrite in pharmaceutical excipients is often impractical and costly. They recommend setting scientifically justified, risk-based limits and consistently staying below them.
Ron Scarboro, Azurity Pharmaceuticals, claims drug reformulation that removes patient barriers must preserve efficacy, urging early cross-functional team alignment.
PharmTech AI Pulse Check episode 2 covers AI data governance, API-gated journals, watermarking limits, and why attribution beats detection in GxP.
The FDA opened an expedited IND pilot, tightened scrutiny of foreign trial data, and finalized a rule recognizing non-animal testing methods. In Europe, the EMA advanced pharmaceutical legislation reform, restricted 2 legacy products, and opened an injectable-iron safety review.
Novartis licenses Abogen's mRNA-encoded T-cell engager for autoimmune disease, raising scale-up questions for mRNA-lipid nanoparticle manufacturing.
Pharma manufacturing spent 90 years learning that producing correct outputs isn't the same as catching your own errors. AI has only learned the first.