Innovative Methods to Optimize Prediction of Oral Drug Bioavailability: More than a Gut Feeling
A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".
Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 12541
Special Issue Editor
Special Issue Information
Dear Colleagues,
The need to improve predictability and efficiency along the critical path from pre-clinical discovery to the commercialization of therapeutic drugs using relevant animal models and in vitro systems has been strongly emphasized by the U.S. FDA in its White Paper on New Medical Technologies. Specifically, the optimization of in vitro and in silico methods for studying drug intestinal transport and metabolism is critical for predicting the oral bioavailability of therapeutic drugs. A promising development in this regard derives from recent findings in the literature demonstrating that 3D intestinal organoids can be maintained long-term in vitro and develop into fully differentiated epithelial cells. Data generated in these systems could help in refining existing in silico models for optimizing the prediction of oral permeability, while assessing the impact of dosage forms, drug–drug interactions, and pharmacogenetic variability on drug–exposure–response relationships.
This Special Issue of Pharmaceutics invites review articles and original research papers focusing on the development of novel 3D models (either static systems such as organoids, or dynamic microfluidic organ-on-a-chip technologies) to improve the prediction of oral drug bioavailability for small molecules and biologics. This Issue will also welcome research articles discussing spontaneous animal models to characterize the impact of disease on drug absorption, as well as innovative computational models for in vitro to in vivo extrapolations.
Dr. Jonathan Paul Mochel
Guest Editor
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Keywords
- oral bioavailability
- pharmacokinetics
- organoids
- organ-on-a-chip
- in silico models
- one health
