Survey of Pharmaceutical Industry's Best Practices around In Vitro Transporter Assessment and Implications for Drug Development: Considerations from the International Consortium for Innovation and Quality for Pharmaceutical Development Transporter Working Group.

The International Consortium for Innovation and Quality in Pharmaceutical Development Transporter Working Group had a rare opportunity to analyze a crosspharma collation of in vitro data and assay methods for the evaluation of drug transporter substrate and inhibitor potential. Experiments were generally performed in accordance with regulatory guidelines. Discrepancies, such as not considering the impact of preincubation for inhibition and free or measured in vitro drug concentrations, may be due to the retrospective nature of the dataset and analysis. Lipophilicity was a frequent indicator of crosstransport inhibition (P-gp, BCRP, OATP1B, and OCT1), with high molecular weight (MW ≥500 Da) also common for OATP1B and BCRP inhibitors. A high level of overlap in in vitro inhibition across transporters was identified for BCRP, OATP1B1, and MATE1, suggesting that prediction of DDIs for these transporters will be common. In contrast, inhibition of OAT1 did not coincide with inhibition of any other transporter. Neutrals, bases, and compounds with intermediate-high lipophilicity tended to be P-gp and/or BCRP substrates, whereas compounds with MW <500 Da tended to be OAT3 substrates. Interestingly, the majority of in vitro inhibitors were not reported to be followed up with a clinical study by the submitting company, whereas those compounds identified as substrates generally were. Approaches to metabolite testing were generally found to be similar to parent testing, with metabolites generally being equally or less potent than parent compounds. However, examples where metabolites inhibited transporters in vitro were identified, supporting the regulatory requirement for in vitro testing of metabolites to enable integrated clinical DDI risk assessment. SIGNIFICANCE STATEMENT: A diverse dataset showed that transporter inhibition often correlated with lipophilicity and molecular weight (>500 Da). Overlapping transporter inhibition was identified, particularly that inhibition of BCRP, OATP1B1, and MATE1 was frequent if the compound inhibited other transporters. In contrast, inhibition of OAT1 did not correlate with the other drug transporters tested.

Genetic Diversity in Drug Transporters: Impact in African Populations.

Polymorphisms in drug transporters, like the adenosine triposphate-binding cassette (ABC) and solute carrier (SLC) superfamilies, may contribute to the observed diversity in drug response in African patients. This review aims to provide a comprehensive summary and analysis of the frequencies and distributions in African populations of ABC and SLC variants that affect drug pharmacokinetics (PK) and pharmacodynamics (PD). Of polymorphisms evaluated in African populations, SLCO1B1 rs4149056 and SLC22A6 rs1158626 were found at markedly higher frequencies than in non-African populations. SLCO1B1 rs4149056 was associated with reduction in rifampin exposure, which has implications for dosing this important anti-tuberculosis therapy. SLC22A6 rs1158626 was associated with increased affinity for antiretroviral drugs. Genetic diversity in SLC and ABC transporters in African populations has implications for conventional therapies, notably in tuberculosis and HIV. More PK and PD data in African populations are needed to assess potential for a different response to drugs compared with other global populations.

Assessment of pharmacokinetic drug-drug interaction between pradigastat and atazanavir or probenecid.

Pradigastat, a novel diacylglycerol acyltransferase-1 inhibitor, has activity in common metabolic diseases associated with abnormal accumulation of triglycerides. In vitro studies suggest that glucuronidation is the predominant metabolism pathway for elimination of pradigastat in humans and confirmed the role of uridine 5'-diphosphoglucuronosyltransferase (UGT) enzymes, UGT1A1, -1A3, and -2B7. The in vitro studies using atazanavir as a selective inhibitor of UGT1A1 and -1A3 indicated that these enzymes contribute ∼55% toward the overall glucuronidation pathway. Therefore, a clinical study was conducted to assess the potential for drug interaction between pradigastat and probenecid (purported general UGT inhibitor) or atazanavir (selective UGT1A1, -1A3 inhibitor). The study included 2 parallel cohorts, each with 3 sequential treatment periods and 22 healthy subjects per cohort. The 90%CI of the geometric mean ratios for Cmax,ss and AUCτ,ss of pradigastat were within 0.80-1.25 when administered in combination with probenecid. However, the Cmax,ss and AUCτ,ss of pradigastat decreased by 31% (90%CI: 0.62-0.78) and 26% (0.67-0.82), respectively, when administered in combination with atazanavir. This magnitude of decrease in pradigastat steady-state exposure is not considered clinically relevant. Pradigastat was well tolerated by all subjects, either alone or in combination with atazanavir or probenecid.

Variability in P-glycoprotein inhibitory potency (IC50) using various in vitro experimental systems: implications for universal digoxin drug-drug interaction risk assessment decision criteria.

A P-glycoprotein (P-gp) IC50 working group was established with 23 participating pharmaceutical and contract research laboratories and one academic institution to assess interlaboratory variability in P-gp IC50 determinations. Each laboratory followed its in-house protocol to determine in vitro IC50 values for 16 inhibitors using four different test systems: human colon adenocarcinoma cells (Caco-2; eleven laboratories), Madin-Darby canine kidney cells transfected with MDR1 cDNA (MDCKII-MDR1; six laboratories), and Lilly Laboratories Cells--Porcine Kidney Nr. 1 cells transfected with MDR1 cDNA (LLC-PK1-MDR1; four laboratories), and membrane vesicles containing human P-glycoprotein (P-gp; five laboratories). For cell models, various equations to calculate remaining transport activity (e.g., efflux ratio, unidirectional flux, net-secretory-flux) were also evaluated. The difference in IC50 values for each of the inhibitors across all test systems and equations ranged from a minimum of 20- and 24-fold between lowest and highest IC50 values for sertraline and isradipine, to a maximum of 407- and 796-fold for telmisartan and verapamil, respectively. For telmisartan and verapamil, variability was greatly influenced by data from one laboratory in each case. Excluding these two data sets brings the range in IC50 values for telmisartan and verapamil down to 69- and 159-fold. The efflux ratio-based equation generally resulted in severalfold lower IC50 values compared with unidirectional or net-secretory-flux equations. Statistical analysis indicated that variability in IC50 values was mainly due to interlaboratory variability, rather than an implicit systematic difference between test systems. Potential reasons for variability are discussed and the simplest, most robust experimental design for P-gp IC50 determination proposed. The impact of these findings on drug-drug interaction risk assessment is discussed in the companion article (Ellens et al., 2013) and recommendations are provided.