Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling

Ball, Kathryn; Jamier, Tanguy; Parmentier, Yannick; Denizot, Claire; Mallier, Agnes; Chenel, Marylore

doi:10.1016/j.ejps.2017.05.055

Cited by 20 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a PBPK model for TFV and PRO was published as an OAT1 and OAT3 substrate and inhibitor, respectively, and studied for DDIs with a new molecule in drug development. 23 The in vitro parameters for TFV were similar to ours, but PRO in vitro inhibitory constants varied, and the model underpredicted PRO effects on the new molecule in vivo. 23 In our work, the inhibition model for PRO was optimized from its effect on other molecules with similar pharmacokinetics.…”

Section: Discussionsupporting

confidence: 58%

“…23 The in vitro parameters for TFV were similar to ours, but PRO in vitro inhibitory constants varied, and the model underpredicted PRO effects on the new molecule in vivo. 23 In our work, the inhibition model for PRO was optimized from its effect on other molecules with similar pharmacokinetics. 20 PRO has been studied previously in conjunction with other antiinfective agents, particularly those with a high propensity for renal secretion.…”

Section: Discussionsupporting

confidence: 58%

See 1 more Smart Citation

Probenecid‐Boosted Tenofovir: A Physiologically‐Based Pharmacokinetic Model‐Informed Strategy for On‐Demand HIV Preexposure Prophylaxis

Liu

Desta

Gufford

2019

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Multiple doses of tenofovir disoproxil fumarate (TDF) together with emtricitabine is effective for HIV preexposure prophylaxis (PrEP). TDF is converted to tenofovir (TFV) in circulation, which is subsequently cleared via tubular secretion by organic ion transporters (OATs; OAT1 and OAT3). Using in vitro kinetic parameters for TFV and the OAT1 and OAT3 inhibitor probenecid, a bottom-up physiologically-based pharmacokinetic model was successfully developed for the first time that accurately describes the probenecid-TFV interaction. This model predicted an increase in TFV plasma exposure by 60%, which was within 15% of the observed clinical pharmacokinetic data, and a threefold decrease in renal cells exposure following coadministration of a 600 mg TDF dose with 2 g probenecid. When compared with multiple-dose regimens, a singledose probenecid-boosted TDF regimen may be effective for HIV PrEP and improve adherence and safety by minimizing TFV-induced nephrotoxicity by reducing TFV accumulation in renal cells. www.psp-journal.comProbenecid-Tenofovir PBPK Model for HIV Prevention Liu et al.

show abstract

Section: Discussionsupporting

confidence: 58%

Section: Discussionsupporting

confidence: 58%

Probenecid‐Boosted Tenofovir: A Physiologically‐Based Pharmacokinetic Model‐Informed Strategy for On‐Demand HIV Preexposure Prophylaxis

Liu

Desta

Gufford

2019

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

show abstract

“…The overall aim of this study was to assess the accuracy of a mechanistic kidney model for simulation of CL R and intratubular concentrations under perturbed conditions, particularly changes in urine flow and urine pH, when only effects relating to passive permeability were considered. Mechanistic description of active processes was not addressed; readers interested in this topic are directed elsewhere (Hsu et al, 2014;Posada et al, 2015;Ball et al, 2017). The accuracy of IVIVE-based predictions of both CL R and fold changes in CL R from urine flow or pH changes was evaluated for caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole, and theophylline.…”

Section: Introductionmentioning

confidence: 99%

Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance

Matsuzaki¹,

Scotcher²,

Darwich³

et al. 2018

J Pharmacol Exp Ther

View full text Add to dashboard Cite

In vitro-in vivo extrapolation (IVIVE) of renal excretory clearance (CL R ) using the physiologically based kidney models can provide mechanistic insight into the interplay of multiple processes occurring in the renal tubule; however, the ability of these models to capture quantitatively the impact of perturbed conditions (e.g., urine flow, urine pH changes) on CL R has not been fully evaluated. In this work, we aimed to assess the predictability of the effect of urine flow and urine pH on CL R and tubular drug concentrations (selected examples). Passive diffusion clearance across the nephron tubule membrane was scaled from in vitro human epithelial cell line Caco-2 permeability data by nephron tubular surface area to predict the fraction reabsorbed and the CL R of caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole, and theophylline. CL R values predicted using mechanistic kidney model at a urinary pH of 6.2 and 7.4 resulted in prediction bias of 2.87-and 3.62-fold, respectively. Model simulations captured urine flow-dependent CL R , albeit with minor underprediction of the observed magnitude of change. The relationship between drug solubility, urine flow, and urine pH, illustrated in simulated intratubular concentrations of acyclovir and sulfamethoxazole, agreed with clinical data on tubular precipitation and crystal-induced acute kidney injury. This study represents the first systematic evaluation of the ability of the mechanistic kidney model to capture the impact of urine flow and urine pH on CL R and drug tubular concentrations with the aim of facilitating refinement of IVIVE-based mechanistic prediction of renal excretion.

show abstract

“…The rivaroxaban model employed a mechanistic kidney model to evaluate the effects of inhibitors/inducers on OAT3 and P‐gp‐mediated secretory clearance. Several PBPK studies investigated renal transporter‐mediated DDIs, although most of the studies were for the inhibition of basal membrane transporters, such as OAT1 and OAT3 34,35 . The inhibitory effects of probenecid, an OAT1 and OAT3 inhibitor, were investigated, and PBPK accurately predicted probenecid’s effect on S44121 34 and baricitinib 35 plasma exposures.…”

Section: Discussionmentioning

confidence: 99%

Physiologically‐Based Pharmacokinetic Modeling for the Prediction of a Drug–Drug Interaction of Combined Effects on P‐glycoprotein and Cytochrome P450 3A

Otsuka

Choules

Bonate

et al. 2020

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Direct oral anticoagulants, like apixaban and rivaroxaban, are important for the treatment and prophylaxis of venous thromboembolism and to reduce the risk of stroke and systemic embolism in patients with nonvalvular atrial fibrillation. Since apixaban and rivaroxaban are predominantly eliminated by CYP3A and P-glycoprotein (P-gp), concomitant use of combined P-gp and strong CYP3A4 inhibitors and inducers should be avoided. Physiologically-based pharmacokinetic (PBPK) models for apixaban and rivaroxaban were developed to estimate the net effect of CYP3A induction, P-gp inhibition and P-gp induction by rifampicin. The disposition of rivaroxaban is more complex compared to apixaban since both hepatic and renal P-gp is considered to contribute to rivaroxaban elimination. Furthermore, organic anion transporter-3 (OAT3), a renal uptake transporter, may also contribute the elimination of rivaroxaban from systemic circulation. The models were verified with observed clinical drug-drug interactions with CYP3A and P-gp inhibitors. With the developed models, the predicted AUC and C max ratios were 0.43 and 0.48, respectively, for apixaban, and 0.50 to 0.52 and 0.72 to 0.73, respectively, for rivaroxaban when co-administered with 600 mg multiple dose of rifampicin and which were very close to observed data. The impact of each of the elimination pathways was assessed for rivaroxaban and inhibition of CYP3A led to a larger impact over intestinal and hepatic P-gp. Inhibition of renal OAT3 or P-gp led to an overall modest interaction. The developed apixaban and rivaroxaban models can be further applied to the investigation of interaction with other P-gp and/or CYP3A4 inhibitors and inducers.

show abstract

Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling

Cited by 20 publications

References 29 publications

Probenecid‐Boosted Tenofovir: A Physiologically‐Based Pharmacokinetic Model‐Informed Strategy for On‐Demand HIV Preexposure Prophylaxis

Probenecid‐Boosted Tenofovir: A Physiologically‐Based Pharmacokinetic Model‐Informed Strategy for On‐Demand HIV Preexposure Prophylaxis

Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance

Physiologically‐Based Pharmacokinetic Modeling for the Prediction of a Drug–Drug Interaction of Combined Effects on P‐glycoprotein and Cytochrome P450 3A

Contact Info

Product

Resources

About