scholarly journals Evaluation for Potential Drug–Drug Interaction of MT921 Using In Vitro Studies and Physiologically–Based Pharmacokinetic Models

2021 ◽  
Vol 14 (7) ◽  
pp. 654
Author(s):  
Hyo-jeong Ryu ◽  
Hyun-ki Moon ◽  
Junho Lee ◽  
Gi-hyeok Yang ◽  
Sung-yoon Yang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Bile Acid ◽  
Pbpk Model ◽  
Organic Anion ◽  
Organic Anion Transporter ◽  
Physiologically Based Pharmacokinetic ◽  
Anion Transporter ◽  
Physiologically Based ◽  
Drug Drug Interaction

MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could be administered with multiple medications, such as those for hypertension, diabetes, and hyperlipidemia, the pharmacokinetic drug–drug interaction (DDI) has not been investigated yet. Therefore, we studied in vitro against drug-metabolizing enzymes and transporters. Moreover, we predicted the potential DDI between MT921 and drugs for chronic diseases using physiologically-based pharmacokinetic (PBPK) modeling and simulation. The magnitude of DDI was found to be negligible in in vitro inhibition and induction of cytochrome P450s and UDP-glucuronosyltransferases. Organic anion transporting polypeptide (OATP)1B3, organic anion transporter (OAT)3, Na+-taurocholate cotransporting polypeptide (NTCP), and apical sodium-dependent bile acid transporter (ASBT) are mainly involved in MT921 transport. Based on the result of in vitro experiments, the PBPK model of MT921 was developed and evaluated by clinical data. Furthermore, the PBPK model of amlodipine was developed and evaluated. PBPK DDI simulation results indicated that the pharmacokinetics of MT921 was not affected by the perpetrator drugs. In conclusion, MT921 could be administered without a DDI risk based on in vitro study and related in silico simulation. Further clinical studies are needed to validate this finding.

scholarly journals Application of a PBPK Model of Rivaroxaban to Prospective Simulations of Drug-Drug-Disease Interactions with Protein Kinase Inhibitors in CA-VTE

2021 ◽  
Author(s):  
Eleanor Jing Yi Cheong ◽  
Daniel Zhi Wei Ng ◽  
Sheng Yuan Chin ◽  
Ziteng Wang ◽  
Eric Chun Yong Chan
Keyword(s):  
Protein Kinase ◽  
Kinase Inhibitors ◽  
Pbpk Model ◽  
Organic Anion ◽  
Protein Kinase Inhibitors ◽  
Organic Anion Transporter ◽  
Pbpk Modelling ◽  
Anion Transporter

Background and Purpose Rivaroxaban is emerging as a viable anticoagulant for the pharmacological management of cancer associated venous thromboembolism (CA-VTE). Being eliminated via CYP3A4/2J2-mediated metabolism and organic anion transporter 3 (OAT3)/P-glycoprotein-mediated renal secretion, rivaroxaban is susceptible to drug-drug interactions (DDIs) with protein kinase inhibitors (PKIs), erlotinib and nilotinib. Physiologically based pharmacokinetic (PBPK) modelling was applied to interrogate the DDIs for dose adjustment of rivaroxaban in CA-VTE. Experimental Approach The inhibitory potencies of erlotinib and nilotinib on CYP3A4/2J2-mediated metabolism of rivaroxaban were characterized. Using prototypical OAT3 inhibitor ketoconazole, in vitro OAT3 inhibition assays were optimized to ascertain the in vivo relevance of derived inhibitory constants (K). DDIs between rivaroxaban and erlotinib or nilotinib were investigated using iteratively verified PBPK model. Key Results Mechanism-based inactivation (MBI) of CYP3A4-mediated rivaroxaban metabolism by both PKIs and MBI of CYP2J2 by erlotinib were established. The importance of substrate specificity and nonspecific binding to derive OAT3-inhibitory K values of ketoconazole and nilotinib for the accurate prediction of DDIs was illustrated. When simulated rivaroxaban exposure variations with concomitant erlotinib and nilotinib therapy were evaluated using published dose-exposure equivalence metrics and bleeding risk analyses, dose reductions from 20 mg to 15 mg and 10 mg in normal and mild renal dysfunction, respectively, were warranted. Conclusion and Implications We established the PBPK-DDI platform to prospectively interrogate and manage clinically relevant interactions between rivaroxaban and PKIs in patients with underlying renal impairment. Rational dose adjustments were proposed, attesting to the capacity of PBPK modelling in facilitating precision medicine.

Renal Organic Anion Transporter-Mediated Drug-Drug Interaction between Gemcabene and Quinapril

2009 ◽  
Vol 330 (1) ◽  
pp. 191-197 ◽  
Author(s):  
Haodan Yuan ◽  
Bo Feng ◽  
Ying Yu ◽  
Jonathan Chupka ◽  
Jenny Y. Zheng ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Organic Anion ◽  
Organic Anion Transporter ◽  
Anion Transporter ◽  
Drug Drug Interaction

scholarly journals Prediction of Drug–Drug Interaction Potential of Tegoprazan Using Physiologically Based Pharmacokinetic Modeling and Simulation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1489
Author(s):  
Deok Yong Yoon ◽  
SeungHwan Lee ◽  
In-Jin Jang ◽  
Myeongjoong Kim ◽  
Heechan Lee ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Pbpk Model ◽  
Phase 1 ◽  
Dose Range ◽  
Systemic Exposure ◽  
Multiple Dosing ◽  
Suppression Effect ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of tegoprazan and to predict the drug–drug interaction (DDI) potential between tegoprazan and cytochrome P450 (CYP) 3A4 perpetrators. The PBPK model of tegoprazan was developed using SimCYP Simulator® and verified by comparing the model-predicted pharmacokinetics (PKs) of tegoprazan with the observed data from phase 1 clinical studies, including DDI studies. DDIs between tegoprazan and three CYP3A4 perpetrators were predicted by simulating the difference in tegoprazan exposure with and without perpetrators, after multiple dosing for a clinically used dose range. The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed PK parameters were between 0.5 and 2.0. In DDI simulation, systemic exposure to tegoprazan was expected to increase about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when rifampicin was co-administered. Based on the simulation by the PBPK model, it is suggested that the DDI potential be considered when tegoprazan is used with CYP3A4 perpetrator, as the acid suppression effect of tegoprazan is known to be associated with systemic exposure.

scholarly journals A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies

Pharmaceutics ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 556 ◽  
Author(s):  
Nina Hanke ◽  
Denise Türk ◽  
Dominik Selzer ◽  
Sabrina Wiebe ◽  
Éric Fernandez ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Pharmacokinetic Model ◽  
Competitive Inhibition ◽  
Open Systems ◽  
Whole Body ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction ◽  
Enantioselective Metabolism

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or Ctrough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

scholarly journals Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes

2020 ◽  
Vol 21 (19) ◽  
pp. 7023
Author(s):  
Yiting Yang ◽  
Ping Li ◽  
Zexin Zhang ◽  
Zhongjian Wang ◽  
Li Liu ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Pbpk Model ◽  
Drug Disposition ◽  
Organic Anion ◽  
Whole Body ◽  
Efflux Transporters ◽  
Cytochrome P450 Enzymes ◽  
Control Drug ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Uptake transporter organic anion transporting polypeptides (OATPs), efflux transporters (P-gp, BCRP and MRP2) and cytochrome P450 enzymes (CYP450s) are widely expressed in the liver, intestine or kidney. They coordinately work to control drug disposition, termed as “interplay of transporters and enzymes”. Cyclosporine A (CsA) is an inhibitor of OATPs, P-gp, MRP2, BCRP and CYP3As. Drug–drug interaction (DDI) of CsA with victim drugs occurs via disordering interplay of transporters and enzymes. We aimed to establish a whole-body physiologically-based pharmacokinetic (PBPK) model which predicts disposition of CsA and nine victim drugs including atorvastatin, cerivastatin, pravastatin, rosuvastatin, fluvastatin, simvastatin, lovastatin, repaglinide and bosentan, as well as drug–drug interactions (DDIs) of CsA with nine victim drugs to investigate the integrated effect of enzymes and transporters in liver, intestinal and kidney on drug disposition. Predictions were compared with observations. Most of the predictions were within 0.5–2.0 folds of observations. Atorvastatin was represented to investigate individual contributions of transporters and CYP3As to atorvastatin disposition and their integrated effect. The contributions to atorvastatin disposition were hepatic OATPs >> hepatic CYP3A > intestinal CYP3As ≈ efflux transporters (P-gp/BCRP/MRP2). The results got the conclusion that the developed PBPK model characterizing the interplay of enzymes and transporters was successfully applied to predict the pharmacokinetics of 10 OATP substrates and DDIs of CsA with 9 victim drugs.

scholarly journals In vitro and physiologically‐based pharmacokinetic based assessment of drug–drug interaction potential of canagliflozin

2016 ◽  
Vol 83 (5) ◽  
pp. 1082-1096 ◽  
Author(s):  
Rao N. V. S. Mamidi ◽  
Shannon Dallas ◽  
Carlo Sensenhauser ◽  
Heng Keang Lim ◽  
Ellen Scheers ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Interaction Potential ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction

scholarly journals Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib

2020 ◽  
Vol 86 (4) ◽  
pp. 461-473
Author(s):  
Fan Wu ◽  
Gopal Krishna ◽  
Sekhar Surapaneni
Keyword(s):  
Drug Interaction ◽  
Pbpk Model ◽  
The United States ◽  
Janus Kinase ◽  
Cyp3a4 Inhibitor ◽  
Drug Label ◽  
Cyp Enzymes ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction

Abstract Purpose Fedratinib (INREBIC®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios. Methods The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. Results The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. Conclusions The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

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