Immobilized Artificial Membrane HPLC Derived Parameters vs PAMPA-BBB Data in Estimating in Situ Measured Blood–Brain Barrier Permeation of Drugs

Abstract

The affinity indexes for phospholipids (log kW(IAM)) for 42 compounds were measured by high performance liquid chromatography (HPLC) on two different phospholipid-based stationary phases (immobilized artificial membrane, IAM), i.e., IAM.PC.MG and IAM.PC.DD2. The polar/electrostatic interaction forces between analytes and membrane phospholipids (Δlog kW(IAM)) were calculated as the differences between the experimental values of log kW(IAM) and those expected for isolipophilic neutral compounds having polar surface area (PSA) = 0. The values of passage through a porcine brain lipid extract (PBLE) artificial membrane for 36 out of the 42 compounds considered, measured by the so-called PAMPA-BBB technique, were taken from the literature (P0(PAMPA-BBB)). The values of blood-brain barrier (BBB) passage measured in situ, P0(in situ), for 38 out of the 42 compounds considered, taken from the literature, represented the permeability of the neutral forms on "efflux minimized" rodent models. The present work was aimed at verifying the soundness of Δlog kW(IAM) at describing the potential of passage through the BBB as compared to data achieved by the PAMPA-BBB technique. In a first instance, the values of log P0(PAMPA-BBB) (32 data points) were found significantly related to the n-octanol lipophilicity values of the neutral forms (log P(N)) (r(2) = 0.782) whereas no significant relationship (r(2) = 0.246) was found with lipophilicity values of the mixtures of ionized and neutral forms existing at the experimental pH 7.4 (log D(7.4)) as well as with either log kW(IAM) or Δlog kW(IAM) values. log P0(PAMPA-BBB) related moderately to log P0(in situ) values (r(2) = 0.604). The latter did not relate with either n-octanol lipophilicity indexes (log P(N) and log D(7.4)) or phospholipid affinity indexes (log kW(IAM)). In contrast, significant inverse linear relationships were observed between log P0(in situ) (38 data points) and Δlog kW(IAM) values for all the compounds but ibuprofen and chlorpromazine, which behaved as moderate outliers (r(2) = 0.656 and r(2) = 0.757 for values achieved on IAM.PC.MG and IAM.PC.DD2, respectively). Since log P0(in situ) refer to the "intrinsic permeability" of the analytes regardless their ionization degree, no correction for ionization of Δlog kW(IAM) values was needed. Furthermore, log P0(in situ) were found roughly linearly related to log BB values (i.e., the logarithm of the ratio brain concentration/blood concentration measured in vivo) for all the analytes but those predominantly present at the experimental pH 7.4 as anions. These results suggest that, at least for the data set considered, Δlog kW(IAM) parameters are more effective than log P0(PAMPA-BBB) at predicting log P0(in situ) values for all the analytes. Furthermore, ionization appears to affect differently, and much more markedly, BBB passage of acids (yielding anions) than that of the other ionizable compounds.

Revisiting the application of Immobilized Artificial Membrane (IAM) chromatography to estimate in vivo distribution properties of drug discovery compounds based on the model of marketed drugs

“…The brain tissue binding can be estimated from the sum of the albumin and the phospholipid binding as described by equation 8. The model was built on 135 drug discovery compounds from several neuroscience projects using rat brain tissue binding data obtained by equilibrium dialysis and measured HSA and IAM binding data as described in reference [12]. The plot of the estimated and measured clinical volumes of distribution using the new batch R20511-014-3 IAM.PC.DD2 column versus the reference log V dss [13].…”

“…The plot of the estimated brain tissue binding (%BTB) obtained using the IAM data obtained from the reference [13] and that obtained using IAM.PC.DD2 column P20511-014-3 Figure 11. A plot of the estimated unbound volume of distribution using the IAM data from the reference (reference [12]) and on the IAM.PC.DD2 R20511-014-3 column…”

“…The molecular factors that influence IAM retention have also been discussed [9,10]. It has been found that IAM columns retain strongly positively charged compounds as the IAM columns are negatively charged on the surface similar to cell membranes [11][12][13]. Several applications of IAM chromatography have also published [14] and this is summarized below.…”

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach

“…The calculation of drug-specific parameters including the aqueous diffusivity coefficient and BBB transmembrane permeability of the compound was performed as described previously (Yamamoto et al, 2017b) and the details for the calculation are described in Supplementary material S1. In short, the aqueous diffusivity coefficient was calculated using the molecular weight of each compound (Avdeef et al, 2004), and transmembrane permeability was calculated using the log P of each compound (Grumetto et al, 2016). The influence of the net effect of active transporters on the drug exchange at the BBB and BCSFB was incorporated into the model using three asymmetry factors (AFin1-3 or AFout1-3, which can be calculated from Kp,uu values (unbound brain/CSF-to-plasma concentration ratio), such that they produced the same Kp,uu values within the model).…”

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

“…As discussed earlier, BBB permeability, which is the rate of BBB transport, was often confused with the extent of brain distribution, which confounded the search for good predictors of brain exposure. Other in silico approaches have focused on quantitative structure-property relationships (QSPR), using the physicochemical properties of a drug as predictors of the rate and extent of BBB transport and brain distribution [25][26][27][28][29].…”