scholarly journals Physiologically Based Pharmacokinetic Modeling of Transdermal Selegiline and Its Metabolites for the Evaluation of Disposition Differences between Healthy and Special Populations

Pharmaceutics ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 942
Author(s):  
Santosh Kumar Puttrevu ◽  
Sumit Arora ◽  
Sebastian Polak ◽  
Nikunj Kumar Patel
Keyword(s):  
Pbpk Model ◽  
Systemic Exposure ◽  
Special Populations ◽  
Physiologically Based Pharmacokinetic Modeling ◽  
Primary Metabolites ◽  
Physiologically Based Pharmacokinetic ◽  
Significant Difference ◽  
Model Predictions ◽  
Physiologically Based ◽  
Simulation Results

A physiologically based pharmacokinetic (PBPK) model of selegiline (SEL), and its metabolites, was developed in silico to evaluate the disposition differences between healthy and special populations. SEL is metabolized to methamphetamine (MAP) and desmethyl selegiline (DMS) by several CYP enzymes. CYP2D6 metabolizes the conversion of MAP to amphetamine (AMP), while CYP2B6 and CYP3A4 predominantly mediate the conversion of DMS to AMP. The overall prediction error in simulated PK, using the developed PBPK model, was within 0.5–1.5-fold after intravenous and transdermal dosing in healthy and elderly populations. Simulation results generated in the special populations demonstrated that a decrease in cardiac output is a potential covariate that affects the SEL exposure in renally impaired (RI) and hepatic impaired (HI) subjects. A decrease in CYP2D6 levels increased the systemic exposure of MAP. DMS exposure increased due to a reduction in the abundance of CYP2B6 and CYP3A4 in RI and HI subjects. In addition, an increase in the exposure of the primary metabolites decreased the exposure of AMP. No significant difference between the adult and adolescent populations, in terms of PK, were observed. The current PBPK model predictions indicate that subjects with HI or RI may require closer clinical monitoring to identify any untoward effects associated with the administration of transdermal SEL patch.

scholarly journals Investigation of the Impact of CYP3A5 and CYP2C19 Polymorphisms on Drug-Drug Interactions between Tacrolimus and Voriconazole Based on Physiologically-Based Pharmacokinetic Modeling

2021 ◽  
Author(s):  
Fei Gong ◽  
Ying Ouyang ◽  
Zhengzheng Liao ◽  
Ying Kong ◽  
Qingxian Li ◽  
...  
Keyword(s):  
Pbpk Model ◽  
Model Development ◽  
Pbpk Modeling ◽  
Physiologically Based Pharmacokinetic Modeling ◽  
Final Model ◽  
Physiologically Based Pharmacokinetic ◽  
Significant Difference ◽  
Physiologically Based ◽  
The Impact ◽  
Cyp2c19 Polymorphisms

ABSTRACT Aims: This study aimed to develop a PBPK model for tacrolimus incorporating CYP3A5 and CYP2C19 polymorphisms to predict the DDIs between tacrolimus and voriconazole. Methods: Pharmacokinetic (PK) data in rats and healthy subjects receiving tacrolimus with and without voriconazole were used for model development and evaluation. Then, we used the final model to simultaneously investigate the effect of CYP3A5 and CYP2C19 polymorphisms on the PK data of tacrolimus when combined with voriconazole. Results: The final results showed that the predicted Cmax in CYP3A5 nonexpressers was 1.5-fold higher than expressers, and the predicted AUC0-∞ was 1.92 to 1.96-fold higher in nonexpressers. However, the Cmax and AUC0-∞ of tacrolimus both have no significant difference between different CYP2C19 metabolizers. Conclusions: A physiologically-based pharmacokinetic (PBPK) model for tacrolimus integrated with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole in patients with different CYP3A5 and CYP2C19 genotypes. Furthermore, this study highlights the feasibility of PBPK modeling to predict DDIs between these two drugs and the need to include CYP3A5 polymorphisms but not CYP2C19 polymorphisms.

scholarly journals 1315. No Dose Adjustment of Metformin with Fostemsavir Coadministration Based on Mechanistic Static and Physiologically Based Pharmacokinetic Models

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S669-S669
Author(s):  
Dung N Nguyen ◽  
Xiusheng Miao ◽  
Mindy Magee ◽  
Guoying Tai ◽  
Peter D Gorycki ◽  
...  
Keyword(s):  
Dose Adjustment ◽  
Pbpk Model ◽  
Systemic Exposure ◽  
Multidrug Resistant ◽  
Pbpk Modeling ◽  
Repeat Dose ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Abstract Background Fostemsavir (FTR) is an oral prodrug of the first-in-class attachment inhibitor temsavir (TMR) which is being evaluated in patients with multidrug resistant HIV-1 infection. In vitro studies indicated that TMR and its 2 major metabolites are inhibitors of organic cation transporters (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs). To assess the clinical relevance, of OCT and MATE inhibition, mechanistic static DDI prediction with calculated Imax,u/IC50 ratios was below the cut-off limits for a DDI flag based on FDA guidelines and above the cut-off limits for MATEs based on EMA guidelines. Methods Metformin is a commonly used probe substrate for OCT1, OCT2 and MATEs. To predict the potential for a drug interaction between TMR and metformin, a physiologically based pharmacokinetic (PBPK) model for TMR was developed based on its physicochemical properties, in vitro and in vivo data. The model was verified and validated through comparison with clinical data. The TMR PBPK model accurately described AUC and Cmax within 30% of the observed data for single and repeat dose studies with or without food. The SimCYP models for metformin and ritonavir were qualified using literature data before applications of DDI prediction for TMR Results TMR was simulated at steady state concentrations after repeated oral doses of FTR 600 mg twice daily which allowed assessment of the potential OCT1, OCT2, and MATEs inhibition by TMR and metabolites. No significant increase in metformin systemic exposure (AUC or Cmax) was predicted with FTR co-administration. In addition, a sensitivity analysis was conducted for either hepatic OCT1 Ki, or renal OCT2 and MATEs Ki values. The model output indicated that, a 10-fold more potent Ki value for TMR would be required to have a ~15% increase in metformin exposure Conclusion Based on mechanistic static models and PBPK modeling and simulation, the OCT1/2 and MATEs inhibition potential of TMR and its metabolites on metformin pharmacokinetics is not clinically significant. No dose adjustment of metformin is necessary when co-administered with FTR Disclosures Xiusheng Miao, PhD, GlaxoSmithKline (Employee) Mindy Magee, Doctor of Pharmacy, GlaxoSmithKline (Employee, Shareholder) Peter D. Gorycki, BEChe, MSc, PhD, GSK (Employee, Shareholder) Katy P. Moore, PharmD, RPh, ViiV Healthcare (Employee)

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 Terbinafine in Combination with Other Antifungal Agents for Treatment of Resistant or Refractory Mycoses: Investigating Optimal Dosing Regimens Using a Physiologically Based Pharmacokinetic Model

2013 ◽  
Vol 58 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Michael J. Dolton ◽  
Vidya Perera ◽  
Lisa G. Pont ◽  
Andrew J. McLachlan
Keyword(s):  
Dose Regimen ◽  
Antifungal Agents ◽  
Pbpk Model ◽  
Standard Dose ◽  
Systemic Exposure ◽  
High Dose ◽  
Unbound Fraction ◽  
Minimum Concentration ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

ABSTRACTTerbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergisticin vitroantifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens.

scholarly journals Physiologically-Based Pharmacokinetic Modeling for Drug-Drug Interactions of Procainamide and N-Acetylprocainamide with Cimetidine, an Inhibitor of rOCT2 and rMATE1, in Rats

Pharmaceutics ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 108 ◽  
Author(s):  
Yoo-Seong Jeong ◽  
Anusha Balla ◽  
Kwang-Hoon Chun ◽  
Suk-Jae Chung ◽  
Han-Joo Maeng
Keyword(s):  
Urinary Excretion ◽  
Drug Interactions ◽  
Pbpk Model ◽  
Systemic Exposure ◽  
Renal Elimination ◽  
Physiologically Based Pharmacokinetic ◽  
Pharmacokinetic Interactions ◽  
Physiologically Based

Previous observations demonstrated that cimetidine decreased the clearance of procainamide (PA) and/or N-acetylprocainamide (NAPA; the primary metabolite of PA) resulting in the increased systemic exposure and the decrease of urinary excretion. Despite an abundance of in vitro and in vivo data regarding pharmacokinetic interactions between PA/NAPA and cimetidine, however, a mechanistic approach to elucidate these interactions has not been reported yet. The primary objective of this study was to construct a physiological model that describes pharmacokinetic interactions between PA/NAPA and cimetidine, an inhibitor of rat organic cation transporter 2 (rOCT2) and rat multidrug and toxin extrusion proteins (rMATE1), by performing extensive in vivo and in vitro pharmacokinetic studies for PA and NAPA performed in the absence or presence of cimetidine in rats. When a single intravenous injection of PA HCl (10 mg/kg) was administered to rats, co-administration of cimetidine (100 mg/kg) significantly increased systemic exposure and decreased the systemic (CL) and renal (CLR) clearance of PA, and reduced its tissue distribution. Similarly, cimetidine significantly decreased the CLR of NAPA formed by the metabolism of PA and increased the AUC of NAPA. Considering that these drugs could share similar renal secretory pathways (e.g., via rOCT2 and rMATE1), a physiologically-based pharmacokinetic (PBPK) model incorporating semi-mechanistic kidney compartments was devised to predict drug-drug interactions (DDIs). Using our proposed PBPK model, DDIs between PA/NAPA and cimetidine were successfully predicted for the plasma concentrations and urinary excretion profiles of PA and NAPA observed in rats. Moreover, sensitivity analyses of the pharmacokinetics of PA and NAPA showed the inhibitory effects of cimetidine via rMATE1 were probably important for the renal elimination of PA and NAPA in rats. The proposed PBPK model may be useful for understanding the mechanisms of interactions between PA/NAPA and cimetidine in vivo.

scholarly journals Prediction of survival after hepatectomy using a physiologically based pharmacokinetic model of indocyanine green liver function tests

2021 ◽  
Author(s):  
Adrian Köller ◽  
Jan Grzegorzewski ◽  
Michael Tautenhahn ◽  
Matthias König
Keyword(s):  
Liver Cirrhosis ◽  
Liver Resection ◽  
Indocyanine Green ◽  
Pbpk Model ◽  
Hepatic Function ◽  
Pharmacokinetic Data ◽  
Functional Evaluation ◽  
Physiologically Based Pharmacokinetic ◽  
Model Predictions ◽  
Physiologically Based

The evaluation of hepatic function and functional capacity of the liver are essential tasks in hepatology, especially in the context of liver surgery. Indocyanine Green (ICG) is a widely applied test compound that is used in clinical routine to evaluate hepatic function. Important questions for the functional evaluation with ICG in the context of hepatectomy are how liver disease such as cirrhosis alters ICG elimination, and if postoperative survival can be predicted from preoperative ICG measurements. Within this work a physiologically based pharmacokinetic (PBPK) model of ICG pharmacokinetics was developed and applied to the prediction of liver resection under various degrees of cirrhosis. For the parametrization of the computational model and validation of model predictions a database of ICG pharmacokinetic data was established. The model was applied (i) to study the effect of liver cirrhosis and hepatectomy on ICG pharmacokinetics; and (ii) to evaluate model-based prediction of postoperative ICG-R15 as a measure for postoperative outcome. Key results were that the model is able to accurately predict changes in ICG pharmacokinetics caused by liver cirrhosis and postoperative changes of ICG-elimination after liver resection, as validated with a wide range of data sets. Based on the PBPK model predictions a classifier allowed to predict survival after hepatectomy, demonstrating its potential value as a clinical tool.

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