Vibha Jawa, PhD, chief scientific officer at EpiVax, explains why the need for animal studies varies by modality and what it takes for organ-on-chip and other human-relevant models to replace these studies in IND-enabling toxicology work.
|Articles|October 2, 2012
- Pharmaceutical Technology-10-02-2012
- Volume 36
- Issue 10
Pharmaceutical Technology, October 2012 Issue (PDF)
Combination Drugs - Adding Up the Opportunities in Solid Dosage
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Articles in this issue
about 14 years ago
Integration of Less-Formal Risk Assessment Tools into Change Controlabout 14 years ago
Technical Forum: Disposable Chromatographyabout 14 years ago
Uniformity of Dosage Units Using Large Sample Sizesabout 14 years ago
A World without R&Dabout 14 years ago
Supplier Qualificationabout 14 years ago
Determining Potency of Preclinical Dose Formulationsabout 14 years ago
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Sanjay Konagurthu, PhD, senior director of Science and Innovation at Thermo Fisher Scientific, explains what teams need to do to ensure the usefulness of AI and what strides have been made with AI in regulatory submissions.
Pauric Bannigan, Intrepid Labs, explains where AI speeds formulation decisions, why data is the bottleneck, and how active learning trains models with far less data.
The new initiative aims to apply traceable AI and science to expand care worldwide by translating discovery into tools that accelerate drug development.
Sanjay Konagurthu, PhD, senior director, Science and Innovation at Thermo Fisher Scientific, provides an update on the progress of AI and machine learning used in drug delivery.
Stephanie Johnson, Pfizer, explains how off-platform triggers, single-owner risk registers, and traceable prior knowledge keep protein formulation programs on track.
Stephanie Johnson, Pfizer, discusses how platform formulations moved 2 Pfizer protein programs to IND in 8 months by redistributing, not removing risk.
Shubhajit Paul, a senior research fellow in Boehringer Ingelheim’s Department of Material Analytical Sciences, spoke with PharmTech at AAPS PharmSci 360 to explain how tabletability prediction can be improved for roller-compacted formulations.
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