Application of PAMPA-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters.

This study examines whether algorithms to predict brain penetration of 88 drug candidates could benefit from inclusion of PAMPA data such as P(eff), flux and membrane retention. Specifically the ability to fit experimentally derived LogBB data with PAMPA information and compound related physicochemical and structural parameters was assessed. Collected data were analyzed by partial least square analysis and various regression models for LogBB. Four PAMPA methodologies were evaluated in this study including: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (double sink) model, (3) a PAMPA-BBB (blood-brain barrier) model and (4) a PAMPA-BBB-UWL (unstirred water layer). Additionally, plasma protein binding (PPB) experiments and a Caco-2 assay were performed to determine the unbound fraction in plasma and the efflux ratio, respectively, for subsets of the selected compounds. This information was combined with the obtained PAMPA data in an effort to improve the predictions of LogBB. Taken in aggregate, the results presented, suggest that the PAMPA-BLM parameters are the most important contributors to predict the LogBB. The optimized multiple linear regression (MLR) relationship including the PAMPA-BLM properties demonstrated a slightly improved prediction compared to the model without the PAMPA-BLM parameters. Including the plasma protein binding of 15 compounds resulted in a significantly improved PAMPA-BLM prediction of LogBB, while integrating the efflux ratio with PAMPA-BLM or PAMPA-BBB P(eff) values, resulted in improved classification of brain permeable [BBB+(LogBB>/=0)] and impermeable [BBB-(LogBB<0)] compounds.

Application of PAMPA-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters.

This study examines whether algorithms to predict brain penetration of 88 drug candidates could benefit from inclusion of PAMPA data such as Peff, flux and membrane retention. Specifically the ability to fit experimentally derived LogBB data with PAMPA information and compound related physicochemical and structural parameters was assessed. Collected data were analyzed by partial least square analysis and various regression models for LogBB. Four PAMPA methodologies were evaluated in this study including: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (double sink) model, (3) a PAMPA-BBB (blood-brain barrier) model and (4) a PAMPA-BBB-UWL (unstirred water layer). Additionally, plasma protein binding (PPB) experiments and a Caco-2 assay were performed to determine the unbound fraction in plasma and the efflux ratio, respectively, for subsets of the selected compounds. This information was combined with the obtained PAMPA data in an effort to improve the predictions of LogBB. Taken in aggregate, the results presented, suggest that the PAMPA-BLM parameters are the most important contributors to predict the LogBB. The optimized multiple linear regression (MLR) relationship including the PAMPA-BLM properties demonstrated a slightly improved prediction compared to the model without the PAMPA-BLM parameters. Including the plasma protein binding of 15 compounds resulted in a significantly improved PAMPA-BLM prediction of LogBB, while integrating the efflux ratio with PAMPA-BLM or PAMPA-BBB Peff values, resulted in improved classification of brain permeable [BBB + (LogBB >or= 0)] and impermeable [BBB--(LogBB < 0)] compounds.

Evaluation of various PAMPA models to identify the most discriminating method for the prediction of BBB permeability.

The Parallel Artificial Membrane Permeability Assay (PAMPA) has been successfully introduced into the pharmaceutical industry to allow useful predictions of passive oral absorption. Over the last 5 years, researchers have modified the PAMPA such that it can also evaluate passive blood-brain barrier (BBB) permeability. This paper compares the permeability of 19 structurally diverse, commercially available drugs assessed in four different PAMPA models: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (Double Sink) model, (3) a PAMPA-BBB model and (4) a PAMPA-BBB-UWL (unstirred water layer) model in order to find the most discriminating method for the prediction of BBB permeability. Both the PAMPA-BBB model and the PAMPA-BLM model accurately identified compounds which pass the BBB (BBB+) and those which poorly penetrate the BBB (BBB-). For these models, BBB+ and BBB- classification ranges, in terms of permeability values, could be defined, offering the opportunity to validate the paradigm with in vivo data. The PAMPA models were subsequently applied to a set of 14 structurally diverse internal J&J candidates with known log (brain/blood concentration) (LogBB) values. Based on these LogBB values, BBB classifications were established (BBB+: LogBB0 >or=; BBB-: LogBB<0). PAMPA-BLM resulted in three false positive identifications, while PAMPA-BBB misclassified only one compound. Additionally, a Caco-2 assay was performed to determine the efflux ratio of all compounds in the test set. The false positive that occurred in both models was shown to be related to an increased efflux ratio. Both the PAMPA-BLM and the PAMPA-BBB models can be used to predict BBB permeability of compounds in combination with an assay that provides p-gp efflux data, such as the Caco-2 assay.

Novel generic UPLC/MS/MS method for high throughput analysis applied to permeability assessment in early Drug Discovery.

A novel generic ultra performance liquid chromatography-tandem mass spectrometric (UPLC/MS/MS) method for the high throughput quantification of samples generated during permeability assessment (PAMPA) has been developed and validated. The novel UPLC/MS/MS methodology consists of two stages. Firstly, running a 1.5min isocratic method, compound-specific multiple reaction monitoring (MRM) methods were automatically prepared. In a second stage, samples were analyzed by a 1.5min generic gradient UPLC method on a BEH C18 column (50mmx2.1mm). Compounds were detected with a Waters Micromass Quattro Premier mass spectrometer operating in positive electrospray ionization using the compound-specific MRM methods. The linearity for the validation compounds (caffeine, propranolol, ampicillin, atenolol, griseofulvin and carbamazepine) typically ranges from 3.05nM to 12,500nM and the limits of detection for all generically developed methods are in the range between 0.61nM and 12nM in an aqueous buffer. The novel generic methodology was successfully introduced within early Drug Discovery and resulted in a four-fold increase of throughput as well as a significant increase in sensitivity compared to other in-house generic LC/MS methods.