Transporter-Mediated Disposition, Clinical Pharmacokinetics and Cholestatic Potential of Glyburide and Its Primary Active Metabolites

Successful Prediction of Fetal Exposure to Dual BCRP/P-gp Drug Substrates Using the Efflux Ratio-Relative Expression Factor Approach and PBPK M&S

To inform fetal drug safety, it is important to determine or predict fetal drug exposure throughout pregnancy. The former is not possible in the first or second trimester. In contrast, at the time of birth, fetal drug exposure, relative to maternal exposure, can be estimated as Kp,uu (unbound fetal umbilical venous (UV) plasma area under the curve (AUC)/unbound maternal plasma (MP) AUC), provided the observed UV/MP values, spanning the dosing interval, are available from multiple maternal-fetal dyads. However, this fetal Kp,uu cannot be extrapolated to other drugs. To overcome the above limitations, we have used an efflux ratio-relative expression factor (ER-REF) approach to successfully predict the fetal Kp,uu of P-gp substrates. Because many drugs taken by pregnant people are also BCRP substrates, here, we extend this approach to drugs that are effluxed by both placental BCRP and P-gp or P-gp alone. To verify our predictions, we chose drugs for which UV/MP data were available at term: glyburide and imatinib (both BCRP and P-gp substrates) and nelfinavir (only P-gp substrate). First, the ER of the drugs was determined using Transwells and MDCKII cells expressing either BCRP or P-gp. Then, the ER was scaled using the proteomics-informed REF value to predict the fetal Kp,uu of the drug at term. The ER-REF predicted fetal Kp,uu of glyburide (0.43), imatinib (0.42), and nelfinavir (0.40) fell within two-fold of the corresponding in vivo fetal Kp,uu (0.44, 0.37, and 0.46, respectively). These data confirm that the ER-REF approach can successfully predict fetal drug exposure to BCRP/P-gp and P-gp substrates, at term.

Physiologically based pharmacokinetic model outputs depend on dissolution data and their input: Case examples glibenclamide and dipyridamole

A plethora of dissolution tests exists for oral dosage forms, with variations in selection of the dissolution medium, the hydrodynamics and the dissolution equipment. This work aimed at determining the influence of media composition, the type of dissolution test and the method for entering the data into a PBPK model on the ability to simulate the in vivo plasma profile of an immediate release formulation. Using two rDCS IIa substances, glibenclamide and dipyridamole, housed in immediate-release formulations as model dosage forms, dissolution tests were performed in USP apparatus II with the biorelevant media FaSSGF, FaSSIF V1, V2 and V3 using both single-stage and two-stage test designs. The results were then integrated into the PBPK software Simcyp^Ⓡ either as the observed release profile (dissolution rate model, DRM) or using a semi-mechanistic model (diffusion layer model, DLM) and compared with in vivo plasma profiles. The selection of the FaSSIF version did not appear to have any relevant influence on the dissolution of the weakly basic dipyridamole, while the weakly acidic glibenclamide was sensitive to the difference in pH between FaSSIF V1, V2 and FaSSIF V3. Since both compounds have pKa values close to the pH of biorelevant media representing conditions in the small intestine, these results may be specific to compounds with similar ionization behavior. Single-stage and two-stage testing led to equivalent simulations for glibenclamide. Only results from the single-stage test in FaSSGF led to a close simulation of the pharmacokinetic profile of dipyridamole when data were inputted using the DRM, while simulations from two-stage testing were most similar to the observed pharmacokinetic profile when DLM with selection of a dynamic pH profile in the small intestine was selected as the data input method. These results emphasize the importance of data input to the simulation results.

Glibenclamide transfer across the perfused human placenta is determined by albumin binding not transporter activity

The placenta mediates the transfer of maternal nutrients into the fetal circulation while removing fetal waste products, drugs and environmental toxins that may otherwise be detrimental to fetal development. This study investigated the role of drug transporters and protein binding in the transfer of the antidiabetic drug glibenclamide across the human placental syncytiotrophoblast using placental perfusion experiments and computational modeling. In the absence of albumin, placental glibenclamide uptake from the fetal circulation was not affected by competitive inhibition with bromosulphothalein (BSP), indicating that OATP2B1 does not mediate placental glibenclamide uptake from the fetus. In the presence of maternal and fetal albumin, BSP increased placental glibenclamide uptake from the fetal circulation by displacing glibenclamide from BSA, increasing the free fraction of glibenclamide driving diffusive transport. The P-gp and BCRP inhibitor GF120918 did not affect placental glibenclamide uptake from the maternal circulation and as such this study did not find any evidence for the apical efflux transporters in placental glibenclamide transfer. Computational modeling confirmed that albumin binding and not transporter activity, is the dominant factor in the transfer of glibenclamide across the human placenta. The effect of BSP binding to albumin on promoting the diffusive transfer of glibenclamide highlights the importance of drug-protein binding interactions and their interpretation using computational modeling.

Estimation of the Effect of OAT2-Mediated Active Uptake on Meloxicam Exposure in the Human Liver

Active uptake mediated by organic anion transporter 2 (OAT2) has been previously hypothesized as a key player in hepatic disposition of its substrates. Previous studies have shown that another hepatic uptake transporter, organic anion transporting polypeptides (OATP) 1B1, significantly elevates liver concentrations of drugs transported by it. As tissue concentration typically governs pharmacodynamics, drug-drug interactions, and toxicity in the liver, it is important to understand if OAT2 functions similarly to OATP1B1 in raising liver exposure. Since this is a research problem that cannot be easily assessed in clinical studies at this time, here we estimated human liver exposure of an OAT2 substrate meloxicam using a deduction method based on physiologically based pharmacokinetic (PBPK) modeling of clinical systemic exposure data. Although in vitro data suggest that OAT2-mediated active uptake is involved in meloxicam disposition, the modeling result concludes that its unbound liver exposure is unlikely significantly different from its unbound systemic exposure. This conclusion is further supported by data and modeling from a terminal monkey study and in vitro hepatocyte studies with bovine serum albumin. Overall, based on currently available data, we do not expect that OAT2 has a strong impact on the liver exposure of meloxicam.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Hepatobiliary Clearance Prediction: Species Scaling From Monkey, Dog, and Rat, and In Vitro-In Vivo Extrapolation of Sandwich-Cultured Human Hepatocytes Using 17 Drugs

Hepatobiliary elimination can be a major clearance pathway dictating the pharmacokinetics of drugs. Here, we first compared the dose eliminated in bile in preclinical species (monkey, dog, and rat) with that in human and further evaluated single-species scaling (SSS) to predict human hepatobiliary clearance. Six compounds dosed in bile duct-cannulated (BDC) monkeys showed biliary excretion comparable to human; and the SSS of hepatobiliary clearance with plasma fraction unbound correction yielded reasonable predictions (within 3-fold). Although dog SSS also showed reasonable predictions, rat overpredicted hepatobiliary clearance for 13 of 24 compounds. Second, we evaluated the translatability of in vitro sandwich-cultured human hepatocytes (SCHHs) to predict human hepatobiliary clearance for 17 drugs. For drugs with no significant active uptake in SCHH studies (i.e., with or without rifamycin SV), measured intrinsic biliary clearance was directly scalable with good predictability (absolute average fold error [AAFE] = 1.6). Drugs showing significant active uptake in SCHH, however, showed improved predictability when scaled based on extended clearance term (AAFE = 2.0), which incorporated sinusoidal uptake along with a global scaling factor for active uptake and the canalicular efflux clearance. In conclusion, SCHH is a useful tool to predict human hepatobiliary clearance, whereas BDC monkey model may provide further confidence in the prospective predictions.