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Millipore Technical Publications


AN1728EN00.pdf
AN- AN1728EN00

MultiScreen Filter Plates for PAMPA

Evaluation of the reproducibility of Parallel Artificial Membrane Permeation Assays (PAMPA)

Lit No:AN1728EN00
Year:2003


Abstract
The reproducibility and precision of an automation compatible, high throughput method for predicting passive, intestinal absorption was assessed. The Parallel Artificial Membrane Permeation Assay (PAMPA) has been previously reported,1, 2 and is based on the use of a 96-well membrane filter based plate. The permeability of six drugs (propranolol, methotrexate, warfarin, carbamazepine, furosemide, and testosterone) was measured on different days using three different lots of plates. In addition, several protocol variations (volume of lipid solution, time between lipid application and drug addition, etc.) were tested to simulate the analytical variability that might be encountered from lab to lab and from operator to operator. The results presented in this Application Note demonstrate that under typical conditions, the PAMPA assay is robust and generates reproducible data. Small protocol variations can have an effect on the apparent permeability rates of some drugs, but in general, the rank order by log Pe (log of the effective permeability) of the compounds is unaffected. As would be expected, variation in the lipid content can significantly alter log Pe results for some compounds. This observation supports the use of the PAMPA method to measure permeability through lipid formulations that simulate different membrane types (e.g., blood–brain barrier, mitochondrial membrane, etc.).

1. Kansy, M.; Senner, F.; Gubernator, K. Physicochemical High Throughput Screening: Parallel Artificial Membrane Permeation Assay in the Description of Passive Absorption Processes, J. Med. Chem., 1998; 41, p. 1007–1010.

2. Kansy, M.; Fischer, H.; Kratzat, K.; Senner, F.; Wagner, B.; Parrilla, I. High-Throughput Artificial Membrane Permeability Studies in Early Lead Discovery and Development, Pharmacokinetic Optimization in Drug Research, 2001; XII, p. 448–464.

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