Elucidation of the Impact of P-glycoprotein and Breast Cancer Resistance Protein on the Brain Distribution of Catechol-O-Methyltransferase Inhibitors

Abstract

P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) are clinically important efflux transporters that act cooperatively at the blood-brain barrier, limiting the entry of several drugs into the central nervous system (CNS) and affecting their pharmacokinetics, therapeutic efficacy, and safety. In the present study, the interactions of catechol--methyltransferase (COMT) inhibitors (BIA 9-1059, BIA 9-1079, entacapone, nebicapone, opicapone, and tolcapone) with P-gp and BCRP were investigated to determine the contribution of these transporters in their access to the brain. In vitro cellular accumulation and bidirectional transport assays were conducted in Madin-Darby canine kidney (MDCK) II, MDCK-MDR1, and MDCK-BCRP cells. In vivo pharmacokinetic studies were carried out for tolcapone and BIA 9-1079 in rats, with and without elacridar, a well-known P-gp and BCRP modulator. The results suggest that BIA 9-1079, nebicapone, and tolcapone inhibit BCRP in a concentration-dependent manner. Moreover, with net flux ratios higher than 2 and decreased over 50% in the presence of verapamil or Ko143, BIA 9-1079 was identified as a P-gp substrate while BIA 9-1059, entacapone, opicapone, and nebicapone were revealed to be BCRP substrates. In vivo, brain exposure was limited for tolcapone and BIA 9-1079, although tolcapone crossed the blood-brain barrier at a greater rate and to a greater extent than BIA 9-1079. The extent of brain distribution of both compounds was significantly increased in the presence of elacridar, attesting to the involvement of efflux transporters. These findings provide relevant information and improve the understanding of the mechanisms that govern the access of these COMT inhibitors to the CNS.

The hepatocyte export carrier inhibition assay improves the separation of hepatotoxic from non-hepatotoxic compounds

“…This does not invalidate the concept of the CMFDA assay but complicates the mechanistic explanation of toxicity for a certain compound if non bile acid efflux transporters are involved. For example, entacapone may be a more potent inhibitor of BCRP [35] than BSEP. On the other hand, the present CMFDA assay is performed in human hepatocytes with drug metabolizing capacity that would not be provided by the vesicle assay.…”

Elucidation of the Differences in Cinobufotalin’s Pharmacokinetics Between Normal and Diethylnitrosamine-Injured Rats: The Role of P-Glycoprotein

“…Then we employed MDCK II and MDCK-MDR1 cell models to estimate the role of P-gp involvement into cinobufotalin transport. The efflux ratio (ER) value was higher than 2.0 in MDCK-MDR1 cell monolayers, indicated that P-gp mediate the active transport of cinobufotalin (Bicker et al, 2017). Furthermore, cinobufotalin was, for the first time, identified as the substrate of P-gp with the net flux ratio was higher than 2.0 (Bicker et al, 2017; Liu L. et al, 2018).…”

“…The efflux ratio (ER) value was higher than 2.0 in MDCK-MDR1 cell monolayers, indicated that P-gp mediate the active transport of cinobufotalin (Bicker et al, 2017). Furthermore, cinobufotalin was, for the first time, identified as the substrate of P-gp with the net flux ratio was higher than 2.0 (Bicker et al, 2017; Liu L. et al, 2018). After preincubation with verapamil, the noticeably reduction of net flux ratio ascertain cinobufotalin was the substrate of P-gp.…”

“…Wild type MDCK II or MDCK-MDR1 cells were seeded into six-well plates at the destiny of 2 × 10 5 cells/well and maintained at 37°C under 5% CO 2 in DMEM with 10% FBS. The well-known P-gp inhibitor, verapamil (100 μM), was used as the positive control to evaluate the effect of P-gp on cinobufotalin uptake (Bicker et al, 2017).…”

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

“…Furthermore, when biomedical outcomes change over time, machine learning methods integrating the outcomes need to account for these dynamics. For example, cancer cells, bacteria, and viruses evolve rapidly to gain drug resistance [24], and ignoring the dynamics of drug response can lead to poor performance in predicting drug efficacy and toxicity.…”