Can a monthly exenatide extended release regimen provide a therapeutic and cost benefit?

Exenatide is used to treat type 2 diabetes mellitus. The current regimen is a 2 mg extended release (ER) weekly injection. The aim of our study was to prove the efficacy of exenatide ER if administered once-monthly. The proposed monthly dose was based on an Excel simulation using pharmacokinetic parameters extracted using Plot Digitizer® (version 2.6.8) from Cirincione et al. (2017), as well as accounting for the exenatide ER formulation characteristics, in vivo and in vitro exenatide stability. A PBPK model of exenatide molecule was developed using (Simcyp® version 19) based on data from in vitro and clinical PK studies. The model was used to confirm the Excel simulation findings of the effectiveness of exenatide ER monthly in maintaining the plasma level above the minimum effective concentration (MEC). Our simulation from Excel and Simcyp® showed that the drug plasma levels of the once monthly ER dose maintained a steady state concentration (Css ) above the MEC. The simulated Excel plasma level ranged from Cmin to Cmax of 60-130ng/L, respectively. The exenatide compound was successfully modeled and used to predict the Css of the ER monthly dose. The Simcyp® simulated Css of the ER was 117 ng/L. A monthly exenatide ER dose provides a plasma level within the therapeutic range. This new proposed dose has a significant pharmacoeconomic benefit and could well improve patient adherence.

Biopharmaceutic In Vitro In Vivo Extrapolation (IVIV_E) Informed Physiologically-Based Pharmacokinetic Model of Ritonavir Norvir Tablet Absorption in Humans Under Fasted and Fed State Conditions.

Ritonavir is a well-known CYP3A4 and CYP2D6 enzyme inhibitor, frequently used to assess the drug-drug interaction (DDI) liability of susceptible drugs. It is also used as a pharmacokinetic booster to increase exposure to CYP3A4 substrates. This study aimed to develop a mechanistic absorption and disposition model to describe exposure to ritonavir following oral dosing of the commercial amorphous solid dispersion tablet, Norvir, under fasted and fed conditions. A mechanistic description of ritonavir absorption from Norvir tablets may help to improve the design of DDI studies. Key parameters of amorphous ritonavir including free base solubility (solubility of the unbound, un-ionized species), bile micelle partition coefficients, formulation wetting/disintegration, and in vivo precipitation parameters were either obtained from the literature or estimated by modeling in vitro biopharmaceutic experiments. Based on variety of in vitro evidence, a main assumption of the model is that ritonavir does not form a crystalline precipitate while resident in the gastrointestinal tract. In the model, if simulated luminal concentration exceeds the amorphous solubility limit, then precipitation to an amorphous form is immediate. Simulated and observed Cmax and AUC0-t parameters were well captured (within 1.5-fold) for both fasted and fed states in healthy volunteers. By accounting for luminal fluid viscosity differences in the different prandial states (affecting drug diffusivity) as well as the effect of drug free fraction on gut wall permeation rates, it was possible to explain the negative food effect observed for Norvir tablets in humans. In summary, a biopharmaceutic in vitro in vivo extrapolation approach provides confidence in (verification of) key input parameters of the physiologically-based pharmacokinetic ritonavir model which resulted in successful simulation of observed plasma profiles.

Application of a physiologically based pharmacokinetic model for the prediction of mirabegron plasma concentrations in a population with severe renal impairment.

We previously verified a physiologically based pharmacokinetic (PBPK) model for mirabegron in healthy subjects using the Simcyp Simulator by incorporating data on the inhibitory effect on cytochrome P450 (CYP) 2D6 and a multi-elimination pathway mediated by CYP3A4, uridine 5'-diphosphate-glucuronosyltransferase (UGT) 2B7 and butyrylcholinesterase (BChE). The aim of this study was to use this PBPK model to assess the magnitude of drug-drug interactions (DDIs) in an elderly population with severe renal impairment (sRI), which has not been evaluated in clinical trials. We first determined the system parameters, and meta-analyses of literature data suggested that the abundance of UGT2B7 and the BChE activity in an elderly population with sRI was almost equivalent to and 20% lower than that in healthy young subjects, respectively. Other parameters, such as the CYP3A4 abundance, for an sRI population were used according to those built into the Simcyp Simulator. Second, we confirmed that the PBPK model reproduced the plasma concentration-time profile for mirabegron in an sRI population (simulated area under the plasma concentration-time curve (AUC) was within 1.5-times that of the observed value). Finally, we applied the PBPK model to simulate DDIs in an sRI population. The PBPK model predicted that the AUC for mirabegron with itraconazole (a CYP3A4 inhibitor) was 4.12-times that in healthy elderly subjects administered mirabegron alone, and predicted that the proportional change in AUC for desipramine (a CYP2D6 substrate) with mirabegron was greater than that in healthy subjects. In conclusion, the PBPK model was verified for the purpose of DDI assessment in an elderly population with sRI.

Single-Center Pharmacokinetic Study and Simulation of a Low Meropenem Concentration in Brain-Dead Organ Donors.

Meropenem is an ultrabroad-spectrum antibiotic of the carbapenem family. In brain-dead organ donors, administration of standard meropenem dosages does not reach therapeutic levels. Our objectives were to determine the plasma concentration of meropenem after the administration of standard meropenem dose and to estimate an improved dosage regimen for these patients. One gram of meropenem was administered as a 1-h infusion every 8 h for 1 to 3 days, and blood samples were collected. The plasma concentration of meropenem was measured and subjected to pharmacokinetic analysis. Simcyp simulation was performed to predict the optimum plasma levels and dosage based on the patients' individual pharmacokinetic parameters. The maximum plasma concentration of meropenem was 3.29 µg/ml, which was lower than four times the MIC of 8 µg/ml. Although the mean creatinine clearance of patients was moderately low (67.5 ml/min), the apparent volume of distribution at steady state (Vss) and time-averaged total body clearance (CL) of meropenem were markedly elevated (4.97 liters/kg and 2.06 liters/h/kg, respectively), owing to massive fluid loading to decrease the high sodium levels and to treat shock or dehydration. The simulation revealed that dose and infusion time of meropenem should be increased based on patients' Vss and CL, and a loading dose is recommended to reach rapidly the target concentration. In conclusion, a standard meropenem regimen is insufficient to achieve optimal drug levels in brain-dead patients, and an increase in dose and extended or continuous infusion with intravenous bolus administration of a loading dose are recommended for these patients.

In vitro studies and in silico predictions of fluconazole and CYP2C9 genetic polymorphism impact on siponimod metabolism and pharmacokinetics.

PURPOSE: The purpose of the study is to investigate the enzyme(s) responsible for siponimod metabolism and to predict the inhibitory effects of fluconazole as well as the impact of cytochrome P450 (CYP) 2C9 genetic polymorphism on siponimod pharmacokinetics (PK) and metabolism. METHODS: In vitro metabolism studies were conducted using human liver microsomes (HLM), and enzyme phenotyping was assessed using a correlation analysis method. SimCYP, a physiologically based PK model, was developed and used to predict the effects of fluconazole and CYP2C9 genetic polymorphism on siponimod metabolism. Primary PK parameters were generated using the SimCYP and WinNonlin software. RESULTS: Correlation analysis suggested that CYP2C9 is the main enzyme responsible for siponimod metabolism in humans. Compared with the CYP2C9*1/*1 genotype, HLM incubations from CYP2C9*3/*3 and CYP2C9*2/*2 donors showed ~ 10- and 3-fold decrease in siponimod metabolism, respectively. Simulations of enzyme contribution predicted that in the CYP2C9*1/*1 genotype, CYP2C9 is predominantly responsible for siponimod metabolism (~ 81%), whereas in the CYP2C9*3/*3 genotype, its contribution is reduced to 11%. The predicted exposure increase of siponimod with fluconazole 200 mg was 2.0-2.4-fold for CYP2C9*1/*1 genotype. In context of single dosing, the predicted mean area under the curve (AUC) is 2.7-, 3.0- and 4.5-fold higher in the CYP2C9*2/*2, CYP2C9*2/*3 and CYP2C9*3/*3 genotypes, respectively, compared with the CYP2C9*1/*1 genotype. CONCLUSION: .Enzyme phenotyping with correlation analysis confirmed the predominant role of CYP2C9 in the biotransformation of siponimod and demonstrated the functional consequence of CYP2C9 genetic polymorphism on siponimod metabolism. Simulation of fluconazole inhibition closely predicted a 2-fold AUC change (ratio within ~ 20% deviation) to the observed value. In silico simulation predicted a significant reduction in siponimod clearance in the CYP2C9*2/*2 and CYP2C9*3/*3 genotypes based on the in vitro metabolism data; the predicted exposure was close (within 30%) to the observed results for the CYP2C9*2/*3 and CYP2C9*3/*3 genotypes.

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development.

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.

Prediction of drug-drug interactions using physiologically-based pharmacokinetic models of CYP450 modulators included in Simcyp software.

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio-pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug-drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism-based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism-based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp® software. The aim of this report was to establish confidence in each CYP-specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.

Utilizing postmortem drug concentrations in mechanistic modeling and simulation of cardiac effects: a proof of concept study with methadone.

Methadone-related poisoning has been found to be the leading and increasing cause of death among intoxication cases in several countries. Aside from respiratory depression, methadone is known to cause QT-prolongation, which may lead to sudden cardiac death. Concentrations in heart tissue should be more accurate for estimating cardiotoxic effects. The aim of this study was to investigate whether the effect of methadone on the QT-interval could be simulated and whether the concentrations in heart tissues allowed for better prediction of the Bazett corrected QT-interval (QTcB). A predictive performance study was conducted using the simulation platform Cardiac Safety Simulator to mimic five literature studies using their described study conditions. Both free and total plasma and heart concentrations were investigated using two different in silico models: the O'Hara-Rudy (ORD) model and the 10 Tusscher (TNNP) model. The results showed that the QTcB of methadone was best predicted either with total plasma using the TNNP model or with free plasma using the ORD model. The ORD model was highly sensitive to the total heart concentrations, resulting in overprediction of the QTcB. The TNNP model also overpredicted the QTcB, but to a lesser degree than the ORD model. Furthermore, due to a low baseline QTcB, the ORD model underpredicted the QTcB for both the free plasma and free heart concentrations. In conclusion, it is possible to simulate the cardiac effects of methadone, yet several elements influence the approach uncertainty including but not limited to biophysically details model of cardiac electrophysiology, exposure data, and input parameters.

Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA.

Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) is used for detailed characterization of pathology of lesions sites, such as brain tumors, by quantitative analysis of tracer's data through the use of pharmacokinetic (PK) models. A key component for PK models in DCE-MRI is the estimation of the concentration-time profile of the tracer in a nearby vessel, referred as Arterial Input Function (AIF). The aim of this work was to assess through full body physiologically-based pharmacokinetic (PBPK) model approaches the PK profile of gadoteric acid (Gd-DOTA) and explore potential application for parameter estimation in DCE-MRI based on PBPK-derived AIFs. The PBPK simulations were generated through Simcyp(®) platform and the predicted PK parameters for Gd-DOTA were compared with available clinical data regarding healthy volunteers and renal impairment patients. The assessment of DCE-MRI parameters was implemented by utilizing similar virtual profiles based on gender, age and weight to clinical profiles of patients diagnosed with glioblastoma multiforme. The PBPK-derived AIFs were then used to compute DCE-MRI parameters through the Extended Tofts Model and compared with the corresponding ones derived from image-based AIF computation. The comparison involved: (i) image measured AIF of patients vs AIF of in silico profile, and, (ii) population average AIF vs in silico mean AIFs. The results indicate that PBPK-derived AIFs allowed the estimation of comparable imaging biomarkers with those calculated from typical DCE-MRI image analysis. The incorporation of PBPK models and potential utilization of in silico profiles to real patient data, can provide new perspectives in DCE-MRI parameter estimation and data analysis.

Evaluation of the likelihood of a selective CHK1 inhibitor (LY2603618) to inhibit CYP2D6 with desipramine as a probe substrate in cancer patients.

LY2603618 is a selective inhibitor of deoxyribonucleic acid damage checkpoint kinase 1 (CHK1) and has been in development for the enhancement of chemotherapeutic agents. The study described was to assess the potential interaction between LY2603618 and cytochrome P450 isoform 2D6 (CYP2D6) substrate desipramine in patients with cancer. Before clinical investigation, in silico simulations (using Simcyp®) were conducted. An open-label, two-period, fixed-sequence study was planned in 30 patients with advanced or metastatic cancers, in which a 50 mg oral dose of desipramine was administered alone and in combination with 275 mg of LY2603618 (i.v. infusion). An interim analysis was planned after 15 patients completed both periods. Ratios of geometric least squares means (LSMs) of primary pharmacokinetic (PK) parameters and 90% repeated confidence intervals (RCIs) between desipramine plus LY2603618 and desipramine alone were calculated. Lack of an interaction was declared if the 90% RCI fell between 0.8 and 1.25. The LSM ratios (90% RCI) for areas under the plasma concentration-time curve from time zero to tlast (AUC[0-tlast]) and to infinity (AUC[0-∞]) and maximum plasma concentration (Cmax) were 1.14 (1.04, 1.25), 1.09 (0.99, 1.21) and 1.16 (1.05, 1.29). In silico simulations were predictive of clinical results. Single doses of 275 mg LY2603618 administered with 50 mg desipramine were generally well tolerated. In conclusion, no clinically significant interaction was observed between LY2603618 and desipramine in patients with cancer. In silico predictions of clinical results demonstrated that mechanistic and physiologically based PK approaches may inform clinical study design in cancer patients.

Advancements in physiologically based pharmacokinetic modeling for fedratinib: updating dose guidance in the presence of a dual inhibitor of CYP3A4 and CYP2C19.

PURPOSE: A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance. METHODS: The original minimal PBPK model was developed using Simcyp® Simulator v17. The model was updated by substituting a single distribution rate (Qsac) with 2 separate rates (CLin/CLout) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole). RESULTS: Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400 mg once daily along with fluconazole 200 mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state. CONCLUSIONS: The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors.

Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole.

Albendazole (ABZ) is a weakly basic drug that undergoes extensive presystemic metabolism after oral administration and converts to its active form albendazole sulfoxide (ABZ_SO). The absorption of albendazole is limited by poor aqueous solubility, and dissolution is the rate-limiting step in the overall exposure of ABZ_SO. In this study, PBPK modeling was used to identify formulation-specific parameters that impact the oral bioavailability of ABZ_SO. In vitro experiments were carried out to determine pH solubility, precipitation kinetics, particle size distribution, and biorelevant solubility. A transfer experiment was conducted to determine the precipitation kinetics. A PBPK model for ABZ and ABZ_SO was developed using the Simcyp™ Simulator based on parameter estimates from in vitro experiments. Sensitivity analyses were performed to assess the impact of physiological parameters and formulation-related parameters on the systemic exposure of ABZ_SO. Model simulations predicted that increased gastric pH significantly reduced ABZ absorption and, subsequently, ABZ_SO systemic exposure. Reducing the particle size below 50 µm did not improve the bioavailability of ABZ. Modeling results illustrated that systemic exposure of ABZ_SO was enhanced by increasing solubility or supersaturation and decreasing the drug precipitation of ABZ at the intestinal pH level. These results were used to identify potential formulation strategies to enhance the oral bioavailability of ABZ_SO.

Use of In Vitro Dynamic Colon Model (DCM) to Inform a Physiologically Based Biopharmaceutic Model (PBBM) to Predict the In Vivo Performance of a Modified-Release Formulation of Theophylline.

A physiologically based biopharmaceutic model (PBBM) of a modified-release formulation of theophylline (Uniphyllin Continus® 200 mg tablet) was developed and implemented to predict the pharmacokinetic (PK) data of healthy male volunteers by integrating dissolution profiles measured in a biorelevant in vitro model: the Dynamic Colon Model (DCM). The superiority of the DCM over the United States Pharmacopeia (USP) Apparatus II (USP II) was demonstrated by the superior predictions for the 200 mg tablet (average absolute fold error (AAFE): 1.1-1.3 (DCM) vs. 1.3-1.5 (USP II). The best predictions were obtained using the three motility patterns (antegrade and retrograde propagating waves, baseline) in the DCM, which produced similar PK profiles. However, extensive erosion of the tablet occurred at all agitation speeds used in USP II (25, 50 and 100 rpm), resulting in an increased drug release rate in vitro and overpredicted PK data. The PK data of the Uniphyllin Continus® 400 mg tablet could not be predicted with the same accuracy using dissolution profiles from the DCM, which might be explained by differences in upper gastrointestinal (GI) tract residence times between the 200 and 400 mg tablets. Thus, it is recommended that the DCM be used for dosage forms in which the main release phenomena take place in the distal GI tract. However, the DCM again showed a better performance based on the overall AAFE compared to the USP II. Regional dissolution profiles within the DCM cannot currently be integrated into Simcyp®, which might limit the predictivity of the DCM. Thus, further compartmentalization of the colon within PBBM platforms is required to account for observed intra-regional differences in drug distribution.

Integrating In Vitro Biopharmaceutics into Physiologically Based Biopharmaceutic Model (PBBM) to Predict Food Effect of BCS IV Zwitterionic Drug (GSK3640254).

A strategy followed to integrate in vitro solubility and permeability data into a PBBM model to predict the food effect of a BCS IV zwitterionic drug (GSK3640254) observed in clinical studies is described. The PBBM model was developed, qualified and verified using clinical data of an immediate release (IR)-tablet (10-320 mg) obtained in healthy volunteers under fasted and fed conditions. The solubility of GSK3640254 was a function of its ionization state, the media composition and pH, whereas its permeability determined using MDCK cell lines was enhanced by the presence of mixed micelles. In vitro data alongside PBBM modelling suggested that the positive food effect observed in the clinical studies was attributed to micelle-mediated enhanced solubility and permeability. The biorelevant media containing oleic acid and cholesterol in fasted and fed levels enabled the model to appropriately capture the magnitude of the food effect. Thus, by using Simcyp® v20 software, the PBBM model accurately predicted the results of the food effect and predicted data were within a two-fold error with 70% being within 1.25-fold. The developed model strategy can be effectively adopted to increase the confidence of using PBBM models to predict the food effect of BCS class IV drugs.

Predicting the systemic exposure and lung concentration of nafamostat using physiologically-based pharmacokinetic modeling.

Nafamostat has been actively studied for its neuroprotective activity and effect on various indications, such as coronavirus disease 2019 (COVID-19). Nafamostat has low water solubility at a specific pH and is rapidly metabolized in the blood. Therefore, it is administered only intravenously, and its distribution is not well known. The main purposes of this study are to predict and evaluate the pharmacokinetic (PK) profiles of nafamostat in a virtual healthy population under various dosing regimens. The most important parameters were assessed using a physiologically based pharmacokinetic (PBPK) approach and global sensitivity analysis with the Sobol sensitivity analysis. A PBPK model was constructed using the SimCYP® simulator. Data regarding the in vitro metabolism and clinical studies were extracted from the literature to assess the predicted results. The model was verified using the arithmetic mean maximum concentration (Cmax), the area under the curve from 0 to the last time point (AUC0-t), and AUC from 0 to infinity (AUC0-∞) ratio (predicted/observed), which were included in the 2-fold range. The simulation results suggested that the 2 dosing regimens for the treatment of COVID-19 used in the case reports could maintain the proposed effective concentration for inhibiting severe acute respiratory syndrome coronavirus 2 entry into the plasma and lung tissue. Global sensitivity analysis indicated that hematocrit, plasma half-life, and microsomal protein levels significantly influenced the systematic exposure prediction of nafamostat. Therefore, the PBPK modeling approach is valuable in predicting the PK profile and designing an appropriate dosage regimen.

Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations.

Malaria is a severe parasite infectious disease with high fatality. As one of the approved treatments of this disease, hydroxychloroquine (HCQ) lacks clinical administration guidelines for patients with special health conditions and co-morbidities. This may result in improper dosing for different populations and lead them to suffer from severe side effects. One of the most important toxicities of HCQ overdose is cardiotoxicity. In this study, we built and validated a physiologically based pharmacokinetic modeling (PBPK) model for HCQ. With the full-PBPK model, we predicted the pharmacokinetic (PK) profile for malaria patients without other co-morbidities under the HCQ dosing regimen suggested by Food and Drug Administration (FDA) guidance. The PK profiles for different special populations were also predicted and compared to the normal population. Moreover, we proposed a series of adjusted dosing regimens for different populations with special health conditions and predicted the concentration-time (C-T) curve of the drug plasma concentration in these populations which include the pregnant population, elderly population, RA patients, and renal impairment populations. The recommended special population-dependent dosage regimens can maintain the similar drug levels observed in the virtual healthy population under the original dosing regimen provided by FDA. Last, we developed mathematic formulas for predicting dosage based on a patient's body measurements and two indexes of renal function (glomerular filtration rate and serum creatine level) for the pediatric and morbidly obese populations. Those formulas can facilitate personalized treatment of this disease. We hope to provide some advice to clinical practice when taking HCQ as a treatment for malaria patients with special health conditions or co-morbidities so that they will not suffer from severe side effects due to higher drug plasma concentration, especially cardiotoxicity.

Performance Verification of CYP2C19 Enzyme Abundance Polymorphism Settings within the Simcyp Simulator v21.

Physiologically based pharmacokinetic (PBPK) modeling has a number of applications, including assessing drug−drug interactions (DDIs) in polymorphic populations, and should be iteratively refined as science progresses. The Simcyp Simulator is annually updated and version 21 included updates to hepatic and intestinal CYP2C19 enzyme abundance, including addition of intermediate and rapid metabolizer phenotypes and changes to the ultra-rapid metabolizer enzyme abundance, with implications for population clearance and DDI predictions. This work details verification of the updates with sensitive CYP2C19 substrates, omeprazole and lansoprazole, using available clinical data from literature. Multiple assessments were performed, including recovery of areas under the concentration-time curve (AUC) and Cmax from compiled datasets for each drug, recovery of victim DDI ratios with CYP2C19 and/or CYP3A4 inhibition and recovery of relative exposure between phenotypes. Simulated data were within respective acceptance criteria for >80% of omeprazole AUC values, >70% of lansoprazole AUC and Cmax, >60% of AUC and Cmax DDI ratios and >80% of exposure ratios between different phenotypes. Recovery of omeprazole Cmax was lower (>50−70% within 2-fold) and possibly attributed to the variety of formulations used in the clinical dataset. Overall, the results demonstrated that the updated data used to parameterize CYP2C19 phenotypes reasonably described the pharmacokinetics of omeprazole and lansoprazole in genotyped or phenotyped individuals.

Extrapolation of physiologically based pharmacokinetic model for tacrolimus from renal to liver transplant patients.

Physiologically based pharmacokinetic (PBPK) modeling is useful for evaluating differences in drug exposure among special populations, but it has not yet been employed to evaluate the absorption process of tacrolimus. In this study, we developed a minimal PBPK model with a compartmental absorption and transit model for renal transplant patients using available data in the literature and clinical data from our hospital. The effective permeability value of tacrolimus absorption and parameters for the single adjusting compartment were optimized via sensitivity analyses, generating a PBPK model of tacrolimus for renal transplant patients with good predictability. Next, we extrapolated the pharmacokinetics of tacrolimus for liver transplant patients by changing the population demographic parameters of the model. When the physiological parameters of a population with normal liver function were changed to those of a population with impaired hepatic function (Child-Pugh class A) in the constructed renal transplant PBPK model, the predicted tacrolimus concentrations were consistent with the observed concentrations in liver transplant patients. In conclusion, the constructed tacrolimus PBPK model for renal transplant patients could predict the pharmacokinetics in liver transplant patients by slightly reducing the hepatic function, even at three weeks post-transplantation.

Prediction of drug-drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling.

PURPOSE: Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess drug-drug interaction (DDI) potential between dasatinib and known substrates for these transporters in a virtual population. METHODS: The dasatinib PBPK model was constructed using Simcyp® Simulator by combining its physicochemical properties, in vitro data, in silico predictions, and pharmacokinetic (PK) results from clinical studies. Model validation against three independent clinical trials not used for model development included dasatinib DDI studies with ketoconazole, rifampin, and simvastatin. The validated model was used to simulate DDIs of dasatinib and known substrates for OCT2 and MATEs (metformin) and OATP1B1/1B3 (pravastatin and rosuvastatin). RESULTS: Simulations of metformin PK in the presence and absence of dasatinib, using inhibitor constant (Ki) values measured in vitro, produced estimated geometric mean ratios (GMRs) of the maximum observed concentration (Cmax) and area under the concentration-time curve (AUC) of 1.05 and 1.06, respectively. Sensitivity analysis showed metformin exposure increased < 30% in both AUC and Cmax when dasatinib Ki was reduced by tenfold for OCT2 and MATEs simultaneously, and < 40% with a 20-fold Ki reduction. The estimated GMRs of Cmax and AUC for pravastatin and rosuvastatin with co-administration of dasatinib were unity (1.00). CONCLUSIONS: This PBPK model accurately described the observed PK profiles of dasatinib. The validated PBPK model predicts low risk of clinically significant DDIs between dasatinib and metformin, pravastatin, or rosuvastatin.

Physiologically Based Pharmacokinetic Modelling and Simulation to Predict the Plasma Concentration Profile of Doxorubicin.

Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved in PK and pharmacodynamics (PD). By utilizing existing published data and by analysing two clinical case studies we attempt to create physiologically based pharmacokinetic (PBPK) models for DOX using widely accepted methodologies. Based on two different approaches on three different key points we derived eight plausible models. The validation of the models provides evidence that is all performing as designed and opens the way for further exploitation by integrating metabolites and pharmacogenomic information.