» Articles » PMID: 17803196

PAMPA--critical Factors for Better Predictions of Absorption

Overview
Journal J Pharm Sci
Publisher Elsevier
Specialties Pharmacology
Pharmacy
Date 2007 Sep 7
PMID 17803196
Citations 45
Authors
Affiliations
Soon will be listed here.
Abstract

PAMPA, log P(OCT), and Caco-2 are useful tools in drug discovery for the prediction of oral absorption, brain penetration and for the development of structure-permeability relationships. Each approach has its advantages and limitations. Selection criteria for methods are based on many different factors: predictability, throughput, cost and personal preferences (people factor). The PAMPA concerns raised by Galinis-Luciani et al. (Galinis-Luciani et al., 2007, J Pharm Sci, this issue) are answered by experienced PAMPA practitioners, inventors and developers from diverse research organizations. Guidelines on how to use PAMPA are discussed. PAMPA and PAMPA-BBB have much better predictivity for oral absorption and brain penetration than log P(OCT) for real-world drug discovery compounds. PAMPA and Caco-2 have similar predictivity for passive oral absorption. However, it is not advisable to use PAMPA to predict absorption involving transporter-mediated processes, such as active uptake or efflux. Measurement of PAMPA is much more rapid and cost effective than Caco-2 and log P(OCT). PAMPA assay conditions are critical in order to generate high quality and relevant data, including permeation time, assay pH, stirring, use of cosolvents and selection of detection techniques. The success of using PAMPA in drug discovery depends on careful data interpretation, use of optimal assay conditions, implementation and integration strategies, and education of users.

Citing Articles

ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.

Wang D, Jin J, Shi G, Bao J, Wang Z, Li S J Cheminform. 2025; 17(1):3.

PMID: 39794857 PMC: 11724520. DOI: 10.1186/s13321-025-00947-z.


Design of an innovative nanovehicle to enhance brain permeability of a novel 5-HT6 receptor antagonist.

Alvarez-Figueroa M, Nunez-Navarro F, Recabarren-Gajardo G, Gonzalez-Aramundiz J Int J Pharm X. 2024; 8:100296.

PMID: 39525530 PMC: 11550157. DOI: 10.1016/j.ijpx.2024.100296.


Bioequivalence Analysis of Terazosin Hydrochloride Tablets Based on Parallel Artificial Membrane Permeability Analysis.

Niu J, Huang H, Ji M, Zhang W, Huang Y, Ma L Pharmaceuticals (Basel). 2024; 17(8).

PMID: 39204129 PMC: 11357005. DOI: 10.3390/ph17081024.


Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration.

Djuris J, Cvijic S, Djekic L Pharmaceuticals (Basel). 2024; 17(2).

PMID: 38399392 PMC: 10892858. DOI: 10.3390/ph17020177.


Membrane Permeability in a Large Macrocyclic Peptide Driven by a Saddle-Shaped Conformation.

Faris J, Adaligil E, Popovych N, Ono S, Takahashi M, Nguyen H J Am Chem Soc. 2024; 146(7):4582-4591.

PMID: 38330910 PMC: 10885153. DOI: 10.1021/jacs.3c10949.