Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling. ) † Venkatesh Pilla Reddy and Kunal S. Taskar equally contributed to this article and are joint first authors. REVIEW(1)) CL H,int = (PS inf ,act + PS inf,pas ) * (CL int,met + CL int,sec ) PS ef f,act + PS ef f,pas + CL int,met + CL int,sec (3) CL H,int = PS inf * REVIEW

Pemetrexed, an anionic anticancer drug with a narrow therapeutic index, is eliminated mainly by active renal tubular secretion. The in vitro to in vivo extrapolation approach used in this work was developed to predict possible drug-drug interactions (DDIs) that may occur after coadministration of pemetrexed and nonsteroidal anti-inflammatory drugs (NSAIDs), and it included in vitro assays, risk assessment models, and physiologically based pharmacokinetic (PBPK) models. The pemetrexed transport and its inhibition parameters by several NSAIDs were quantified using HEK-PEAK cells expressing organic anion transporter (OAT) 3 or OAT4. The NSAIDs were ranked according to their DDI index, calculated as the ratio of their maximum unbound concentration in plasma over the concentration inhibiting 50% (IC 50 ) of active pemetrexed transport. A PBPK model for ibuprofen, the NSAID with the highest DDI index, was built incorporating active renal secretion in Simcyp Simulator. The bottom-up model for pemetrexed underpredicted the clearance by 2-fold. The model we built using a scaling factor of 5.3 for the maximal uptake rate (V max ) of OAT3, which estimated using plasma concentration profiles from patients given a 10-minute infusion of 500 mg/m 2 of pemetrexed supplemented with folic acid and vitamin B 12 , recovered the clinical data adequately. The observed/predicted increases in C max and the area under the plasma-concentration time curve (AUC 0-inf ) of pemetrexed when ibuprofen was coadministered were 1.1 and 1.0, respectively. The coadministration of all other NSAIDs was predicted to have no significant impact on the AUC 0-inf based on their DDI indexes. The PBPK model reasonably reproduced pemetrexed concentration time profiles in cancer patients and its interaction with ibuprofen.

ABSTRACT.The programme for the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25-29 October 2015) included a sunrise session presenting an overview of the state-of-the-art tools for in vitro-in vivo extrapolation (IVIVE) and mechanistic prediction of renal drug disposition. These concepts are based on approaches developed for prediction of hepatic clearance, with consideration of scaling factors physiologically relevant to kidney and the unique and complex structural organisation of this organ. Physiologically relevant kidney models require a number of parameters for mechanistic description of processes, supported by quantitative information on renal physiology (system parameters) and in vitro/in silico drug-related data. This review expands upon the themes raised during the session and highlights the importance of high quality in vitro drug data generated in appropriate experimental setup and robust system-related information for successful IVIVE of renal drug disposition. The different in vitro systems available for studying renal drug metabolism and transport are summarised and recent developments involving state-of-the-art technologies highlighted. Current gaps and uncertainties associated with system parameters related to human kidney for the development of physiologically based pharmacokinetic (PBPK) model and quantitative prediction of renal drug disposition, excretion, and/or metabolism are identified.

Baricitinib, an oral selective Janus kinase 1 and 2 inhibitor, undergoes active renal tubular secretion. Baricitinib was not predicted to inhibit hepatic and renal uptake and efflux drug transporters, based on the ratio of the unbound maximum eliminatingorgan inlet concentration and the in vitro half-maximal inhibitory concentrations (IC 50 ). In vitro, baricitinib was a substrate for organic anion transporter (OAT)3, multidrug and toxin extrusion protein (MATE)2-K, P-glycoprotein (P-gp), and breast cancer resistance protein (

Cellular membranes are a component of the doxorubicin efflux mechanism in K562 cells. Dominant-negative GFP chimeric mutants can be used to elucidate the role of specific membrane trafficking pathways in subcellular drug transport routes.

Abemaciclib, a selective inhibitor of cyclin‐dependent kinases 4 and 6, is metabolized mainly by cytochrome P450 (CYP)3A4. Clinical studies were performed to assess the impact of strong inhibitor (clarithromycin) and inducer (rifampin) on the exposure of abemaciclib and active metabolites. A physiologically based pharmacokinetic (PBPK) model incorporating the metabolites was developed to predict the effect of other strong and moderate CYP3A4 inhibitors and inducers. Clarithromycin increased the area under the plasma concentration‐time curve (AUC) of abemaciclib and potency‐adjusted unbound active species 3.4‐fold and 2.5‐fold, respectively. Rifampin decreased corresponding exposures 95% and 77%, respectively. These changes influenced the fraction metabolized via CYP3A4 in the model. An absolute bioavailability study informed the hepatic and gastric availability. In vitro data and a human radiolabel study determined the fraction and rate of formation of the active metabolites as well as absorption‐related parameters. The predicted AUC ratios of potency‐adjusted unbound active species with rifampin and clarithromycin were within 0.7‐ and 1.25‐fold of those observed. The PBPK model predicted 3.78‐ and 7.15‐fold increases in the AUC of the potency‐adjusted unbound active species with strong CYP3A4 inhibitors itraconazole and ketoconazole, respectively; and 1.62‐ and 2.37‐fold increases with the concomitant use of moderate CYP3A4 inhibitors verapamil and diltiazem, respectively. The model predicted modafinil, bosentan, and efavirenz would decrease the AUC of the potency‐adjusted unbound active species by 29%, 42%, and 52%, respectively. The current PBPK model, which considers changes in unbound potency‐adjusted active species, can be used to inform dosing recommendations when abemaciclib is coadministered with CYP3A4 perpetrators.

Abstract.It is envisaged that application of mechanistic models will improve prediction of changes in renal disposition due to drug-drug interactions, genetic polymorphism in enzymes and transporters and/or renal impairment. However, developing and validating mechanistic kidney models is challenging due to the number of processes that may occur (filtration, secretion, reabsorption and metabolism) in this complex organ. Prediction of human renal drug disposition from preclinical species may be hampered by species differences in the expression and activity of drug metabolising enzymes and transporters. A proposed solution is bottom-up prediction of pharmacokinetic parameters based on in vitro-in vivo extrapolation (IVIVE), mediated by recent advances in in vitro experimental techniques and application of relevant scaling factors. This review is a follow-up to the Part I of the report from the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25th-29th October 2015) which focuses on IVIVE and mechanistic prediction of renal drug disposition. It describes the various mechanistic kidney models that may be used to investigate renal drug disposition. Particular attention is given to efforts that have attempted to incorporate elements of IVIVE. In addition, the use of mechanistic models in prediction of renal drugdrug interactions and potential for application in determining suitable adjustment of dose in kidney disease are discussed. The need for suitable clinical pharmacokinetics data for the purposes of delineating mechanistic aspects of kidney models in various scenarios is highlighted.KEY WORDS: active renal excretion; human kidney transporters; human renal drug clearance; kidney disease; non-hepatic drug metabolism.

Purpose To determine the contribution of intestinal PepT1 on the permeability and oral absorption of the β-lactam antibiotic drug cefadroxil. Methods The effective permeability (Peff) of cefadroxil was evaluated in wild-type and PepT1 knockout mice following in situ single-pass intestinal perfusions. The plasma concentration-time profiles of cefadroxil were also examined after oral gavage. Results The Peff (cm/s) of cefadroxil in wild-type mice was 0.49×10-4 in duodenum, 0.80×10-4 in jejunum, 0.88×10-4 in ileum and 0.064x10-4 in colon. The Peff (cm/s) in PepT1 knockout mice was significantly reduced in small intestine, but not in colon, as shown by values of 0.003×10-4, 0.090×10-4, 0.042×10-4 and 0.032×10-4, respectively. Jejunal uptake of cefadroxil was saturable (Km=2-4 mM) and significantly attenuated by the sodium-proton exchange inhibitor 5-(N,N-dimethyl)amiloride. Jejunal permeability of cefadroxil was not affected by L-histidine, glycine, cephalothin, p-aminohippurate or N-methylnicotinamide. In contrast, cefadroxil permeability was significantly reduced by glycylproline, glycylsarcosine, or cephalexin. Finally, PepT1 ablation resulted in 23-fold reductions in peak plasma concentrations and 14-fold reductions in systemic exposure of cefadroxil after oral dosing. Conclusions The findings are definitive in demonstrating that PepT1 is the major transporter responsible for the small intestinal permeability of cefadroxil as well as its enhanced oral drug performance.