May 21, 2007

We have just received news from the Editor of The Journal of Pharmaceutical Sciences, that our major collaboration paper, noted below, has been accepted for publication.

PAMPA - Critical Factors for Better Predictions of Absorption, authored by Alex Avdeef (pION), Stefanie Bendels (Roche), Li Di (Wyeth), Bernard Faller (Novartis), Manfred Kansy (Roche), Kiyohiko Sugano (Pfizer), Yukinori Yamauchi (Pfizer) (list of authors is alphabetical)

This had been a rare opportunity to assemble the team of authors who have been the earliest investigators in the rapidly emerging new field of Parallel Artificial Membrane Permeability Assays, better known as PAMPA, and who account for about 95% of all the papers published in this area.

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. are answered by experienced PAMPA practitioners, inventors, and developers from diverse research organizations. Guidelines on how to best 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. Proper 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.