The adequate distribution of STI-571 (Gleevec) to the central nervous system (CNS) is critical for its effective use in CNS tumors. P-glycoprotein-mediated efflux in the blood-brain barrier may play a role in the CNS delivery of this drug. Whether STI-571 is a substrate of P-glycoprotein was determined by examining the directional flux of [14 C]STI-571 in parental and MDR1-transfected Madin-Darby canine kidney (MDCK) II epithelial cell monolayers. The basolateral-to-apical flux of STI-571 was 39-fold greater than the apical-to-basolateral flux in the MDR1-transfected cells and 8-fold greater in the parental cell monolayers. This difference in directional flux was significantly reduced by a specific P-glycoprotein inhibitor (2R)-anti-5-{3-[4-(10,11-difluoromethanodibenzo-suber-5-yl)piperazin-1-yl]-2-hydroxypropoxy}quinoline trihydrochloride (LY335979). The role of P-glycoprotein in the CNS distribution of STI-571 was examined in vivo, using wild-type and mdr1a/b (Ϫ/Ϫ) knockout mice that were orally administered 25 mg/kg [ 14 C]STI-571. In the wild-type mice, the brain-to-plasma STI-571 concentration ratio at all time points was low (1-3%); however, there was an 11-fold greater brain partitioning of STI-571 at 1 h postdose in the mdr1a/b (Ϫ/Ϫ) mice compared with the wild-type mice. When 12.5 mg/kg STI-571 was given intravenously, the brain-to-plasma ratio of STI-571 in the mdr1a/b (Ϫ/Ϫ) mice was approximately 7-fold greater than that of wild-type mice up to 120 min postdose. These data indicate that STI-571 is a substrate of P-glycoprotein, and that the inhibition of P-glycoprotein affects the transport of STI-571 across MDCKII monolayers. Moreover, P-glycoprotein plays an important role in limiting the distribution of STI-571 to the CNS.

The distribution of cyclosporin A between plasma, leucocytes and erythrocytes was studied in vitro by means of sedimentation in Ficoll-Paque and dextran. The uptake by erythrocytes was found to be about 50% and the fraction of cyclosporin A bound to leucocytes amounted to 15%. Fractionation of plasma by ultracentrifugation also showed that two thirds of the drug were associated with lipoproteins whereas binding studies with isolated lipoproteins and plasma also indicated that lipoproteins were the major complexing constituents for cyclosporin A in plasma. The binding of cyclosporin A to erythrocytes and lipoproteins seems to be a linear process. The binding to the leucocytes may be a saturable process, however it is of minor importance in terms of the overall binding capacity in the blood.

Imatinib, a protein tyrosine kinase inhibitor, may prevent the growth of glioblastoma cells. Unfortunately, its brain distribution is restricted by p-glycoprotein (p-gp or multidrug resistance protein Mdr1a), and probably by breast cancer resistance protein (Bcrp1), two efflux pumps expressed at the blood-brain barrier (BBB). We have used in situ brain perfusion to investigate the mechanisms of imatinib transport across the mouse BBB. The brain uptake of imatinib in wildtype mice was limited by saturable efflux processes. The inhibition of p-gp, by valspodar and zosuquidar, increased imatinib uptake (2.5-fold), as did the deficiency of p-gp in Mdr1a/1b()/)) mice (5.5-fold). Perfusing imatinib with the p-gp/Bcrp1 inhibitor, elacridar, enhanced the brain uptake of imatinib in wild-type (4.1-fold) and Mdr1a/1b()/)) mice (1.2-fold). However, the brain uptake of imatinib was similar in wild-type and Bcrp1()/)) mice when it was perfused at a nonsaturating concentration. The brain uptake of CGP74588, an active metabolite of imatinib, was low. It was increased by perfusion with elacridar (twofold), but not with valspodar and zosuquidar. CGP74588 uptake was 1.5 times greater in Bcrp1()/)) mice than in wild-type mice. These data suggest that imatinib transport at the mouse BBB is limited by p-gp and probably by Bcrp1, and that CGP74588 transport is restricted by Bcrp1.

The immunosuppressant, SDZ IMM 125 (IMM), is a derivative of cyclosporin A (CyA). The disposition kinetics of IMM in plasma, blood cells, and various tissues of the rat was characterized by a physiologically based pharmacokinetic (PBPK) model; the model was then applied to predict the disposition kinetics in dog and human. Accumulation of IMM in blood cell is high (equilibrium blood cell/plasma ratio = 8), although the kinetics of drug transference between plasma and blood cell is moderately slow, taking approximately 10 min to reach equilibrium, implying a membrane-limited distribution into blood cells. A local PBPK model, assuming blood-flow limited distribution and tissue/blood partition coefficient (KP) data, failed to adequately describe the observed kinetics of distribution, which were slower than predicted. A membrane transport limitation is therefore needed to model dynamic tissue distribution data. Moreover, a slowly interacting intracellular pool was also necessary to adequately describe the kinetics of distribution in some organs. Three elimination pathways (metabolism, biliary secretion, and glomerular filtration) of IMM were assessed at steady state in vivo and characterized independently by the corresponding clearance terms. A whole-body PBPK model was developed according to these findings, which described closely the IMM concentration-time profiles in arterial blood as well as 14 organs/tissues of the rat after intravenous administration. The model was then scaled up to larger mammals by modifying physiological parameters, tissue distribution and elimination clearances; in vivo enzymatic activity was considered in the scale-up of metabolic clearance. The simulations agreed well with the experimental measurements in dog and human, despite the large interspecies difference in the metabolic clearance, which does not follow the usual allometric relationship. In addition, the nonlinear increase in maximum blood concentration and AUC with increasing dose, observed in healthy volunteers after intravenous administration, was accommodated quantitatively by incorporating the known saturation of specific binding of IMM to blood cells. Overall, the PBPK model provides a promising tool to quantitatively link preclinical and clinical data.

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