Michel Lemaire

Dose-dependent brain penetration of SDZ PSC 833, a novel multidrug resistance-reversing cyclosporin, in rats

Abstract This study quantitatively assessed the brain penetration of a potent P-glycoprotein inhibitor, SDZ PSC 833, and its effect on the blood-brain barrier (BBB) permeability (PS) of an anticancer agent, vincristine. At lower doses of SDZ PSC 833 the brain penetration, defined as the brain-to-blood partition coefficient (Kp), was very low in spite of the high lipophilicity of this compound. At higher doses, however, the brain penetration of SDZ PSC 833 was markedly increased. Since the blood pharmacokinetics of SDZ PSC 833 proved to be linear in the dose range studied, these results demonstrated a dose-dependent brain passage of SDZ PSC 833. The brain passage of cyclosporin A was also found to be dose-dependent. However, the potency of SDZ PSC 833 in inhibiting the efflux mechanism at the BBB was higher than that of cyclosporin A since 10 times higher doses of cyclosporin A were required to obtain the same Kp values recorded for SDZ PSC 833. Moreover, the coadministration of SDZ PSC 833 increased the brain penetration of cyclosporin A, whereas the latter did not modify that of SDZ PSC 833. The increase in SDZ PSC 833 and vincristine PS values observed at high blood levels of SDZ PSC 833 are consistent with the hypothesis of a saturation of the P-glycoprotein pump present at the BBB. The involvement of P-glycoprotein in the brain passage of SDZ PSC 833 could be of great significance for clinical application of the drug in the treatment of brain cancers when it is given in combination with anticancer agents.

Physiologically based pharmacokinetic modeling as a tool for drug development

Since the pioneering work of Haggard and Teorell in the first half of the 20th century, and of Bischoff and Dedrick in the late 1960s, physiologically based pharmacokinetic (PBPK) modeling has gone through cycles of general acceptance, and of healthy skepticism. Recently, however, the trend in the pharmaceuticals industry has been away from PBPK models. This is understandable when one considers the time and effort necessary to develop, test, and implement a typical PBPK model, and the fact that in the present-day environment for drug development, efficacy and safety must be demonstrated and drugs brought to market more rapidly. Although there are many modeling tools available to the pharmacokineticist today, many of which are preferable to PBPK modeling in most circumstances, there are several situations in which PBPK modeling provides distinct benefits that outweigh the drawbacks of increased time and effort for implementation. In this Commentary, we draw on our experience with this modeling technique in an industry setting to provide guidelines on when PBPK modeling techniques could be applied in an industrial setting to satisfy the needs of regulatory customers. We hope these guidelines will assist researchers in deciding when to apply PBPK modeling techniques. It is our contention that PBPK modeling should be viewed as one of many modeling tools for drug development.