PAMPA and Caco-2 cell models are often used in combination to evaluate the permeability properties of a large number of compounds at the early drug discovery stage. PAMPA model has been demonstrated in recent years to be an efficient, economical and high-throughput model. Caco-2 cell model, on the other hand, has been the gold standard model for over a decade for permeability screening in drug discovery phase. PAMPA captures the transcellular passive permeability across lipoidal membrane barrier without the contribution from pores or drug transporters. Caco-2 cell model is capable of incorporating not only the transcel-lular passive permeability but also the transporter mediated (efflux and influx) and the paracellular components of transport. Figure 5.2 demonstrates that a reasonably good prediction of the extent of absorption in humans can be obtained for ~22 marketed drugs by both Caco-2 cell and PAMPA models. Table 5.3 lists the permeability values in these two models and the fraction absorbed in humans for the validation set. These drugs were selected from different therapeutic areas and they represent diverse structures and physicochemical properties. Also, they are well known to be passively absorbed with no known major transporter involvement. It is evident that for passively absorbed drugs, both permeability models showed similar correlation. The two models also shared several other characteristics that are quite common. The dynamic range of permeability values (i.e., the fold increase in permeability value for highly absorbed drugs compared to a poorly absorbed drugs) was close to 2-orders of magnitude in both models with similar slope. The steepness of the slopes may reflect the predictability of the model for drugs that are moderately absorbed, and it appears that the two models have similar predictability for drugs with moderate permeability. The figure also demonstrates that it is very difficult to differentiate drugs in the mid range because of a large variability in the mid range of the curve. However, both models may work very well in binning the drug candidates into broad categories (high, medium, low) based on their permeability values, but might lack the sensitivity to accurately predict small differences in permeability values. The two models also demonstrate similar variability of data (20-30% coefficient of variation) and mass balance/recovery issues (many compounds had incomplete recovery).
Figure 5.3 represents the correlation observed between the two permeability models for ~100 internal research compounds from various drug discovery programs. The compounds were carefully selected from several research programs and they reflected a wide diversity of chemical space and physicochemical properties. The permeability values for these compounds were determined in both models using standard experimental conditions described above. Next, the compounds were classified into "low" and "high" permeability bins based on internal calibration data. Permeability value of ~100nm/s was selected as a cut-off for both models. A cursory glance at the correlation figure might suggest a lack of linear correlation between the two models in terms of their permeability values. However, incorporation of the binning strategy (using 100nm/s as the cutoff value) suggests that the two models demonstrate an acceptable agreement. Around 80% of the compounds tested were assigned to the same bin (i.e., the results were in agreement) by both the models. These compounds are represented in quadrants 2 and 3 in the figure. Almost half of those compounds were classified as "low" permeability by both methods (quadrant 3) and the other half
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