Salivary Therapeutic Monitoring of Buprenorphine in Neonates After Maternal Sublingual Dosing Guided by Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Opioid use disorder (OUD) during pregnancy is associated with high mortality rates and neonatal opioid withdrawal syndrome (NOWS). Buprenorphine, an opioid, is used to treat OUD and NOWS. Buprenorphine active metabolite (norbuprenorphine) can cross the placenta and cause neonatal respiratory depression (EC 50 = 35 ng/mL) at high brain extracellular fluid (bECF) levels. Neonatal therapeutic drug monitoring using saliva decreases the likelihood of distress and infections associated with frequent blood sampling.

METHODS

An adult physiologically based pharmacokinetic model for buprenorphine and norbuprenorphine after intravenous and sublingual administration was constructed, vetted, and scaled to newborn and pregnant populations. The pregnancy model predicted that buprenorphine and norbuprenorphine doses would be transplacentally transferred to the newborns. The newborn physiologically based pharmacokinetic model was used to estimate the buprenorphine and norbuprenorphine levels in newborn plasma, bECF, and saliva after these doses.

RESULTS

After maternal sublingual administration of buprenorphine (4 mg/d), the estimated plasma concentrations of buprenorphine and norbuprenorphine in newborns exceeded the toxicity thresholds for 8 and 24 hours, respectively. However, the norbuprenorphine bECF levels were lower than the respiratory depression threshold. Furthermore, the salivary buprenorphine threshold levels in newborns for buprenorphine analgesia, norbuprenorphine analgesia, and norbuprenorphine hypoventilation were observed to be 22, 2, and 162 ng/mL.

CONCLUSIONS

Using neonatal saliva for buprenorphine therapeutic drug monitoring can facilitate newborn safety during the maternal treatment of OUD using sublingual buprenorphine. Nevertheless, the suitability of using adult values of respiratory depression EC 50 for newborns must be confirmed.

Integrating Clopidogrel's First-Pass Effect in a Joint Semi-Physiological Population Pharmacokinetic Model of the Drug and Its Inactive Carboxylic Acid Metabolite

Clopidogrel (CLO), a pro-drug for preventing thrombotic events, undergoes rapid absorption and extensive metabolism, with approximately 85-90% converted to an inactive carboxylic acid metabolite (CLO-CA) and the remaining to an active thiol (CLO-TH). Few pharmacokinetic models for the drug and its metabolites exist, with most focusing on CLO-TH. Although CLO-CA is inactive, its predominant (compared to its parent drug and metabolites) presence in plasma underscores the importance of characterizing its formation and pharmacokinetic profile. This study aimed to characterize the process of the absorption of CLO and its conversion to CLO-CA via developing a population pharmacokinetic model. Individual participants' data from two bioequivalence studies were utilized. Extensive blood samples were collected at predetermined intervals, including 841 concentrations of CLO and 1149 of CLO-CA. A nonlinear, mixed-effects modelling approach using NONMEM® software (v 7.5) was applied. A one-compartment model was chosen for CLO, while a two-compartment proved optimal for CLO-CA. Absorption from the depot compartment was modeled via two transit compartments, incorporating transit rate constants (Ktr). A semi-physiological model explained the first-pass effect of CLO, integrating a liver compartment. The estimated mean transit times (MTTs) for the studies were 0.470 and 0.410 h, respectively. The relative bioavailability for each study's generic medicine compared to the reference were 1.08 and 0.960, respectively. Based on the estimated parameters, the fractions metabolized to inactive metabolites (FiaM_st1 and FiaM_st2) were determined to be 87.27% and 86.87% for the two studies, respectively. The appropriateness of the final model was confirmed. Our model offers a robust framework for elucidating the pharmacokinetic profiles of CLO and CLO-CA.

Development and verification of a physiologically based pharmacokinetic model of dronedarone and its active metabolite N-desbutyldronedarone: Application to prospective simulation of complex drug-drug interaction with rivaroxaban

AIMS

Despite potential enzyme- and transporter-mediated drug-drug interactions (DDIs) between dronedarone and rivaroxaban in atrial fibrillation (AF) patients, pharmacokinetic/pharmacodynamic data remain limited to guide clinical practice. We aimed to develop, verify and validate a physiologically based pharmacokinetic (PBPK) model of dronedarone and its major metabolite, N-desbutyldronedarone (NDBD), to prospectively interrogate this clinically relevant DDI in healthy and mild renal impairment populations.

METHODS

The middle-out development of our PBPK model combined literature-derived or in-house in vitro data, predicted in silico data and in vivo clinical data. Model verification was performed for intravenous and oral (single and multiple) dosing regimens. Model validation for the accurate prediction of cytochrome P450 (CYP)3A4- and P-glycoprotein-mediated DDI utilized simvastatin and digoxin as respective victim drugs. Rivaroxaban-specific inhibitory parameters of dronedarone and/or NDBD against CYP3A4, CYP2J2, OAT3 and P-glycoprotein were incorporated into the PBPK-DDI model for prospective dronedarone-rivaroxaban DDI simulation.

RESULTS

Dronedarone and NDBD PK following clinically relevant doses of 400 mg dronedarone across single and multiple oral dosing were accurately simulated by incorporating effect of auto-inactivation on dose nonlinearities. Following successful model validation, nondose-adjusted rivaroxaban-dronedarone DDI in healthy and mild renal impairment populations revealed simulated rivaroxaban area under the plasma concentration-time curve up to 24 h fold change greater than dose exposure equivalence (0.70-1.43) at 1.65 and 1.84, respectively. Correspondingly, respective major bleeding risk was 4.24 and 4.70% compared with threshold of 4.5% representing contraindicated rivaroxaban-ketoconazole DDI.

CONCLUSION

Our PBPK-DDI model predicted clinically significant dronedarone-rivaroxaban DDI in both healthy and mild renal impairment subjects. Greater benefit vs. risk could be achieved with rivaroxaban dose reductions to at least 15 mg in mild renal impairment subjects on concomitant dronedarone and rivaroxaban.

Influence of genetic polymorphisms in P2Y12 receptor signaling pathway on antiplatelet response to clopidogrel in coronary heart disease

BACKGROUNDS

Remarkable interindividual variability in clopidogrel response is observed, genetic polymorphisms in P2RY12 and its signal pathway is supposed to affect clopidogrel response in CHD patients.

METHODS

539 CHD patients treated with clopidogrel were recruited. The platelet reaction index (PRI) indicated by VASP-P level were detected in 12-24 h after clopidogrel loading dose or within 5-7 days after initiation of maintain dose clopidogrel. A total of 13 SNPs in relevant genes were genotyped in sample A (239 CHD patients). The SNPs which have significant differences in PRI will be validated in another sample (sample B, 300 CHD patients).

RESULTS

CYP2C19*2 increased the risk of clopidogrel resistance significantly. When CYP2C19*2 and CYP2C19*3 were considered, CYP2C19 loss of function (LOF) alleles were associated with more obviously increased the risk of clopidogrel resistance; P2RY12 rs6809699C > A polymorphism was also associated with increased risk of clopidogrel resistance (AA vs CC: P = 0.0398). This difference still existed after stratification by CYP2C19 genotypes. It was also validated in sample B. The association was also still significant even in the case of stratification by CYP2C19 genotypes in all patients (sample A + B).

CONCLUSION

Our data suggest that P2RY12 rs6809699 is associated with clopidogrel resistance in CHD patients. Meanwhile, the rs6809699 AA genotype can increase on-treatment platelet activity independent of CYP2C19 LOF polymorphisms.

Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration

BACKGROUND

The concurrent administration of dronedarone and oral anti-coagulants is common because both are used in managing atrial fibrillation (AF). Dronedarone is a moderate inhibitor of the cytochrome P450 3A4 (CYP3A4) enzyme and P-glycoprotein (P-gp). Apixaban and rivaroxaban are P-gp and CYP3A4 substrates. This study aims to investigate the impact of exposure and bleeding risk of apixaban or rivaroxaban when co-administered with dronedarone using physiologically based pharmacokinetic/pharmacodynamic analysis.

METHODS

Modeling and simulation were conducted using Simcyp® Simulator. The parameters required for dronedarone modeling were collected from the literature. The developed dronedarone physiologically based pharmacokinetic (PBPK) model was verified using reported drug-drug interactions (DDIs) between dronedarone and CYP3A4 and P-gp substrates. The model was applied to evaluate the DDI potential of dronedarone on the exposure of apixaban 5 mg every 12 h or rivaroxaban 20 mg every 24 h in geriatric and renally impaired populations. DDIs precipitating major bleeding risks were assessed using exposure-response analyses derived from literature.

RESULTS

The model accurately described the pharmacokinetics of orally administered dronedarone in healthy subjects and accurately predicted DDIs between dronedarone and four CYP3A4 and P-gp substrates with fold errors <1.5. Dronedarone co-administration led to a 1.29 (90 % confidence interval (CI): 1.14-1.50) to 1.31 (90 % CI: 1.12-1.46)-fold increase in the area under concentration-time curve for rivaroxaban and 1.33 (90 % CI: 1.15-1.68) to 1.46 (90 % CI: 1.24-1.92)-fold increase for apixaban. The PD model indicated that dronedarone co-administration might potentiate the mean major bleeding risk of apixaban with a 1.45 to 1.95-fold increase. However, the mean major bleeding risk of rivaroxaban was increased by <1.5-fold in patients with normal or impaired renal function.

CONCLUSIONS

Dronedarone co-administration increased the exposure of rivaroxaban and apixaban and might potentiate major bleeding risks. Reduced apixaban and rivaroxaban dosing regimens are recommended when dronedarone is co-administered to patients with AF.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions

The antiplatelet agent clopidogrel is listed by the FDA as a strong clinical index inhibitor of cytochrome P450 (CYP) 2C8 and weak clinical inhibitor of CYP2B6. Moreover, clopidogrel is a substrate of—among others—CYP2C19 and CYP3A4. This work presents the development of a whole-body physiologically based pharmacokinetic (PBPK) model of clopidogrel including the relevant metabolites, clopidogrel carboxylic acid, clopidogrel acyl glucuronide, 2-oxo-clopidogrel, and the active thiol metabolite, with subsequent application for drug–gene interaction (DGI) and drug–drug interaction (DDI) predictions. Model building was performed in PK-Sim^® using 66 plasma concentration-time profiles of clopidogrel and its metabolites. The comprehensive parent-metabolite model covers biotransformation via carboxylesterase (CES) 1, CES2, CYP2C19, CYP3A4, and uridine 5′-diphospho-glucuronosyltransferase 2B7. Moreover, CYP2C19 was incorporated for normal, intermediate, and poor metabolizer phenotypes. Good predictive performance of the model was demonstrated for the DGI involving CYP2C19, with 17/19 predicted DGI AUC_last and 19/19 predicted DGI C_max ratios within 2-fold of their observed values. Furthermore, DDIs involving bupropion, omeprazole, montelukast, pioglitazone, repaglinide, and rifampicin showed 13/13 predicted DDI AUC_last and 13/13 predicted DDI C_max ratios within 2-fold of their observed ratios. After publication, the model will be made publicly accessible in the Open Systems Pharmacology repository.

A physiologically based pharmacokinetic model of clopidogrel in populations of European and Japanese ancestry: An evaluation of CYP2C19 activity

Treatment response to clopidogrel is associated with CYP2C19 activity through the formation of the active H4 metabolite. The aims of this study were to develop a physiologically based pharmacokinetic (PBPK) model of clopidogrel and its metabolites for populations of European ancestry, to predict the pharmacokinetics in the Japanese population by CYP2C19 phenotype, and to investigate the effect of clinical and demographic factors. A PBPK model was developed and verified to describe the two metabolic pathways of clopidogrel (H4 metabolite, acyl glucuronide metabolite) for a population of European ancestry using plasma data from published studies. Subsequently, model predictions in the Japanese population were evaluated. The effects of CYP2C19 activity, fluvoxamine coadministration (CYP2C19 inhibitor), and population-specific factors (age, sex, BMI, body weight, cancer, hepatic, and renal dysfunction) on the pharmacokinetics of clopidogrel and its metabolites were then characterized. The predicted/observed ratios for clopidogrel and metabolite exposure parameters were acceptable (twofold acceptance criteria). For all CYP2C19 phenotypes, steady-state AUC0-τ of the H4 metabolite was lower for the Japanese (e.g., EM, 7.69 [6.26-9.45] ng·h/ml; geometric mean [95% CI]) than European (EM, 24.8 [20.4-30.1] ng·h/ml, p < .001) population. In addition to CYP2C19-poor metabolizer phenotype, fluvoxamine coadministration, hepatic, and renal dysfunction were found to reduce H4 metabolite but not acyl glucuronide metabolite concentrations. This is the first PBPK model describing the two major metabolic pathways of clopidogrel, which can be applied to populations of European and Japanese ancestry by CYP2C19 phenotype. The differences between the two populations appear to be determined primarily by the effect of varying CYP2C19 liver activity.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of co-medicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A4 leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and microsome (HLM) to predict clinically relevant DDIs was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were pre-incubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition (TDI) was detected in HLM for four moderate/strong inhibitors suggesting that some optimization of incubation conditions (i.e. lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters k_inact and K_I into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models (MSM), which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within 2-fold of the observed value. Significance Statement Collectively, the data demonstrate that coupling time-dependent inactivation parameters derived from pooled human hepatocytes and HLM with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical DDI risk from in vitro data. Weak and moderate inhibitors did not show TDI under standard incubation conditions using HLM and optimization of incubation conditions is warranted. Recommendations are made with respect to input parameters for IVIVE of TDI with non-CYP3A enzymes using available data from HLM and HHEPs.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.

Physiologically based pharmacokinetic (PBPK) modeling for prediction of celecoxib pharmacokinetics according to CYP2C9 genetic polymorphism

Celecoxib is a non-steroidal anti-inflammatory drug (NSAID) and a representative selective cyclooxygenase (COX)-2 inhibitor, which is commonly prescribed for osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, acute pain, and primary dysmenorrhea. It is mainly metabolized by CYP2C9 and partly by CYP3A4 after oral administration. Many studies reported that CYP2C9 genetic polymorphism has significant effects on the pharmacokinetics of celecoxib and the occurrence of adverse drug reactions. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of celecoxib according to CYP2C9 genetic polymorphism for personalized pharmacotherapy. Initially, a clinical pharmacokinetic study was conducted where a single dose (200 mg) of celecoxib was administered to 39 healthy Korean subjects with CYP2C9*1/*1 or CYP2C9*1/*3 genotypes to obtain data for PBPK development. Based on the conducted pharmacokinetic study and a previous pharmacokinetic study involving subjects with CYP2C9*1/*13 and CYP2C9*3/*3 genotype, PBPK model for celecoxib was developed. A PBPK model for CYP2C9*1/*1 genotype group was developed and then scaled to other genotype groups (CYP2C9*1/*3, CYP2C9*1/*13 and CYP2C9*3/*3). After model development, model validation was performed with comparison of five pharmacokinetic studies. As a result, the developed PBPK model of celecoxib successfully described the pharmacokinetics of each CYP2C9 genotype group and its predicted values were within the acceptance criterion. Additionally, all the predicted values were within two-fold error range in comparison to the previous pharmacokinetic studies. This study demonstrates the possibility of determining the appropriate dosage of celecoxib for each individual through the PBPK modeling with CYP2C9 genomic information. This approach could contribute to the reduction of adverse drug reactions of celecoxib and enable precision medicine.

The application of precision dosing in the use of sertraline throughout pregnancy for poor and ultrarapid metabolizer CYP 2C19 subjects: A virtual clinical trial pharmacokinetics study

Sertraline is known to undergo changes in pharmacokinetics during pregnancy. CYP 2C19 has been implicated in the interindividual variation in clinical effect associated with sertraline activity. However, knowledge of suitable dose titrations during pregnancy and within CYP 2C19 phenotypes is lacking. A pharmacokinetic modeling virtual clinical trials approach was implemented to: (i) assess gestational changes in sertraline trough plasma concentrations for CYP 2C19 phenotypes, and (ii) identify appropriate dose titration strategies to stabilize sertraline levels within a defined therapeutic range throughout gestation. Sertraline trough plasma concentrations decreased throughout gestation, with maternal volume expansion and reduction in plasma albumin being identified as possible causative reasons. All CYP 2C19 phenotypes required a dose increase throughout gestation. For extensive metabolizer (EM) and ultrarapid metabolizer (UM) phenotypes, doses of 100-150 mg daily are required throughout gestation. For poor metabolizers (PM), 50 mg daily during trimester 1 followed by a dose of 100 mg daily in trimesters 2 and 3 are required.

Investigation of the Impact of CYP3A5 Polymorphism on Drug-Drug Interaction between Tacrolimus and Schisantherin A/Schisandrin A Based on Physiologically-Based Pharmacokinetic Modeling

Wuzhi capsule (WZC) is commonly prescribed with tacrolimus in China to ease drug-induced hepatotoxicity. Two abundant active ingredients, schisantherin A (STA) and schisandrin A (SIA) are known to inhibit CYP3A enzymes and increase tacrolimus’s exposure. Our previous study has quantitatively demonstrated the contribution of STA and SIA to tacrolimus pharmacokinetics based on physiologically-based pharmacokinetic (PBPK) modeling. In the current work, we performed reversible inhibition (RI) and time-dependent inhibition (TDI) assays with CYP3A5 genotyped human liver microsomes (HLMs), and further integrated the acquired parameters into the PBPK model to predict the drug–drug interaction (DDI) in patients with different CYP3A5 alleles. The results indicated STA was a time-dependent and reversible inhibitor of CYP3A4 while only a reversible inhibitor of CYP3A5; SIA inhibited CYP3A4 and 3A5 in a time-dependent manner but also reversibly inhibited CYP3A5. The predicted fold-increases of tacrolimus exposure were 2.70 and 2.41, respectively, after the multidose simulations of STA. SIA also increased tacrolimus’s exposure but to a smaller extent compared to STA. An optimized physiologically-based pharmacokinetic (PBPK) model integrated with CYP3A5 polymorphism was successfully established, providing more insights regarding the long-term DDI between tacrolimus and Wuzhi capsules in patients with different CYP3A5 genotypes.

Physiologically-Based Pharmacokinetic-Pharmacodynamics Model Characterizing CYP2C19 Polymorphisms to Predict Clopidogrel Pharmacokinetics and Its Anti-Platelet Aggregation Effect Following Oral Administration to Coronary Artery Disease Patients With or Without Diabetes

Background and Objective: Clopidogrel (CLOP) is commonly used in coronary artery disease (CAD) patients with or without diabetes (DM), but these patients often suffer CLOP resistance, especially those with diabetes. This study was aimed to develop a physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and pharmacodynamics of clopidogrel active metabolite (CLOP-AM) in CAD patients with or without DM.

Methods: The PBPK-PD model was first established and validated in healthy subjects and then in CAD patients with or without DM. The influences of CYP2C19, CYP2C9, CYP3A4, carboxylesterase 1 (CES1), gastrointestinal transit rates (K_t,i) and platelets response to CLOP-AM (k_irre) on predicted pharmacokinetics and pharmacodynamics were investigated, followed with their individual and integrated effects on CLOP-AM pharmacokinetics due to changes in DM status.

Results: Most predictions fell within 0.5–2.0 folds of observations, indicating successful predictions. Sensitivity analysis showed that contributions of interested factors to pharmacodynamics were CES1> k_irre> K_t,i> CYP2C19 > CYP3A4> CYP2C9. Mimicked analysis showed that the decreased exposure of CLOP-AM by DM was mainly attributed to increased CES1 activity, followed by decreased CYP2C19 activity.

Conclusion: The pharmacokinetics and pharmacodynamics of CLOP-AM were successfully predicted using the developed PBPK-PD model. Clopidogrel resistance by DM was the integrated effects of altered K_t,i, CYP2C19, CYP3A4, CES1 and k_irre.

Predicting the Effects of CYP2C19 and Carboxylesterases on Vicagrel, a Novel P2Y12 Antagonist, by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Approach

Vicagrel, a novel acetate derivative of clopidogrel, exhibits a favorable safety profile and excellent antiplatelet activity. Studies aim at identifying genetic and non-genetic factors affecting vicagrel metabolic enzymes Cytochrome P450 2C19 (CYP2C19), Carboxylesterase (CES) 1 and 2 (CES1 and CES2), which may potentially lead to altered pharmacokinetics and pharmacodynamics, are warranted. A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model incorporating vicagrel and its metabolites was constructed, verified and validated in our study, which could simultaneously characterize its sequential two step metabolism and clinical response. Simulations were then performed to evaluate the effects of CYP2C19, CES1 and CES2 genetic polymorphisms as well as inhibitors of these enzymes on vicagrel pharmacokinetics and antiplatelet effects. Results suggested vicagrel was less influenced by CYP2C19 metabolic phenotypes and CES1 428 G > A variation, in comparison to clopidogrel. No pharmacokinetic difference in the active metabolite was also noted for volunteers carrying different CES2 genotypes. Omeprazole, a CYP2C19 inhibitor, and simvastatin, a CES1 and CES2 inhibitor, showed weak impact on the pharmacokinetics and pharmacodynamics of vicagrel. This is the first study proposing a dynamic PBPK/PD model of vicagrel able to capture its pharmacokinetic and pharmacodynamic profiles simultaneously. Simulations indicated that genetic polymorphisms and drug-drug interactions showed no clinical relevance for vicagrel, suggesting its potential advantages over clopidogrel for treatment of cardiovascular diseases. Our model can be utilized to support further clinical trial design aiming at exploring the effects of genetic polymorphisms and drug-drug interactions on PK and PD of this novel antiplatelet agent.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.

Towards Personalized Antithrombotic Treatments: Focus on P2Y12 Inhibitors and Direct Oral Anticoagulants

Oral anticoagulants and antiplatelet drugs are commonly prescribed to lower the risk of cardiovascular diseases, such as venous and arterial thrombosis, which represent the leading causes of mortality worldwide. A significant percentage of patients taking antithrombotics will nevertheless experience bleeding or recurrent ischemic events, and this represents a major public health issue. Cardiovascular medicine is now questioning the one-size-fits-all policy, and more personalized approaches are increasingly being considered. However, the available tools are currently limited and they are only moderately able to predict clinical events or have a significant impact on clinical outcomes. Predicting concentrations of antithrombotics in blood could be an effective means of personalization as they have been associated with bleeding and recurrent ischemia. Target concentration interventions could take advantage of physiologically based pharmacokinetic (PBPK) and population-based pharmacokinetic (POPPK) models, which are increasingly used in clinical settings and have attracted the interest of governmental regulatory agencies, to propose dosages adapted to specific population characteristics. These models have the benefit of combining parameters from different sources, such as experimental in vitro data and patients' demographic, genetic, and physiological in vivo data, to characterize the dose-concentration relationships of compounds of interest. As such, they can be used to predict individual drug exposure. In the near future, these models could therefore be a valuable means of predicting personalized antithrombotic blood concentrations and, hopefully, of preventing clinical non-response or bleeding in a given patient. Existing approaches for personalization of antithrombotic prescriptions will be reviewed using practical examples for P2Y₁₂ inhibitors and direct oral anticoagulants. The review will additionally focus on the existing PBPK and POPPK models for these two categories of drugs. Lastly, we address potential scenarios for their implementation in clinics, along with the main limitations and challenges.

CYP2C19 and CYP3A4 activity and ADP-induced platelet reactivity in prasugrel- or ticagrelor-treated STEMI patients: monocentric study in PRAGUE-18 trial participants

We assessed the contribution of CYP2C19 and CYP3A4 metabolic activity to the ADP-induced platelet aggregation 1h and 24h after a loading dose of 60 mg prasugrel or 180 mg ticagrelor in patients with ST-elevation myocardial infarction (STEMI). Further, we assessed the contribution of CYP2C19 polymorphisms and medication to the CYP enzymatic activity.Patients with STEMI were randomly assigned to the treatment with prasugrel (n = 51) or ticagrelor (n = 46). Metabolic activity of CYP2C19 and CYP3A4 was assessed by the rate of 5-hydroxylation and sulfoxidation of lansoprazole. Further, patients were genotyped for CYP2C19 *2 and *17 alleles.In prasugrel-treated patients, high ADP-induced platelet reactivity 1h after the loading dose positively correlated with 5OH-lansoprazole/lansoprazole ratio (r = 0.44, p = 0.002), a marker of CYP2C19 metabolic activity, and negatively with lansoprazole-sulfone/lansoprazole ratio, which reflects CYP3A4 metabolic activity (r = -0.35, p = 0.018).CYP2C19 poor metabolizers had lower 5OH-lansoprazole/lansoprazole ratio and higher lansoprazole-sulfone/lansoprazole ratio, but without any effect on the ADP-induced platelet reactivity. The treatment with amiodarone, a CYP3A4 inhibitor, influenced neither the metabolic ratios nor the ADP-induced platelet reactivity.The CYP3A4 and CYP2C19 metabolic activity is associated with ADP-induced platelet reactivity in prasugrel-treated, but not ticagrelor-treated patients with STEMI.

Physiologically based pharmacokinetic modelling of oxycodone drug-drug interactions

Oxycodone is an opioid analgesic with several pharmacologically active metabolites and relatively narrow therapeutic index. Cytochrome P450 (CYP) 3A4 and CYP2D6 play major roles in the metabolism of oxycodone and its metabolites. Thus, inhibition and induction of these enzymes may result in substantial changes in the exposure of both oxycodone and its metabolites. In this study, a physiologically based pharmacokinetic (PBPK) model was built using GastroPlus™ software for oxycodone, two primary metabolites (noroxycodone, oxymorphone) and one secondary metabolite (noroxymorphone). The model was built based on literature and in house in vitro and in silico data. The model was refined and verified against literature clinical data after oxycodone administration in the absence of drug-drug interactions (DDI). The model was further challenged with simulations of oxycodone DDI with CYP3A4 inhibitors ketoconazole and itraconazole, CYP3A4 inducer rifampicin and CYP2D6 inhibitor quinidine. The magnitude of DDI (AUC ratio) was predicted within 1.5-fold error for oxycodone, within 1.8-fold and 1.3-4.5-fold error for the primary metabolites noroxycodone and oxymorphone, respectively, and within 1.4-4.5-fold error for the secondary metabolite noroxymorphone, when compared to the mean observed AUC ratios. This work demonstrated the capability of PBPK model to simulate DDI of the administered compounds and the formed metabolites of both DDI victim and perpetrator. However, the predictions for the formed metabolites tend to be associated with higher uncertainty than the predictions for the administered compound. The oxycodone model provides a tool for forecasting oxycodone DDI with other CYP3A4 and CYP2D6 DDI perpetrators that may be co-administered with oxycodone.

Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation

Comparisons between observed data and model simulations represent a critical component for establishing confidence in population physiologically-based pharmacokinetic (Pop-PBPK) models. Numerical predictive checks (NPC) that assess the proportion of observed data that correspond to Pop-PBPK model prediction intervals (PIs) are frequently used to qualify such models. We evaluated the effects of three components on the performance of NPC for qualifying Pop-PBPK model concentration-time predictions: (1) correlations (multiple samples per subject), (2) residual error, and (3) discrepancies in the distribution of demographics between observed and virtual subjects. Using a simulation-based study design, we artificially created observed pharmacokinetic (PK) datasets and compared them to model simulations generated under the same Pop-PBPK model. Observed datasets containing uncorrelated and correlated observations (± residual error) were formulated using different random-sampling techniques. In addition, we created observed datasets where the distribution of subject body weights differed from that of the virtual population used to generate model simulations. NPC for each observed dataset were computed based on the Pop-PBPK model's 90% PI. NPC were associated with inflated type-I-error rates (> 0.10) for observed datasets that contained correlated observations, residual error, or both. Additionally, the performance of NPC were sensitive to the demographic distribution of observed subjects. Acceptable use of NPC was only demonstrated for the idealistic case where observed data were uncorrelated, free of residual error, and the demographic distribution of virtual subjects matched that of observed subjects. Considering the restricted applicability of NPC for Pop-PBPK model evaluation, their use in this context should be interpreted with caution.

Antithrombotic P2Y12 receptor antagonists: recent developments in drug discovery

The P2Y₁₂ receptor is one of eight known P2Y receptor subtypes, and belongs to the G-protein-coupled receptor (GPCR) family. The P2Y₁₂ receptor is highly expressed on blood platelets and in the brain. Potent, selective, peripherally acting antagonists for the P2Y₁₂ receptor are used clinically as antithrombotic drugs. Several different scaffolds have been identified as P2Y₁₂ receptor antagonists, including irreversibly acting thienotetrahydropyridines (prodrugs), and reversible competitive antagonists, including adenine nucleotide analogs, piperazinyl-glutamate-quinolines, -pyridines, and -pyrimidines, and anthraquinone derivatives. Here, we provide an overview of the different scaffolds that have been developed as P2Y₁₂ receptor antagonists, some of which have become important therapeutics.

Clopidogrel Pharmacokinetics in Malaysian Population Groups: The Impact of Inter-Ethnic Variability

Malaysia is a multi-ethnic society whereby the impact of pharmacogenetic differences between ethnic groups may contribute significantly to variability in clinical therapy. One of the leading causes of mortality in Malaysia is cardiovascular disease (CVD), which accounts for up to 26% of all hospital deaths annually. Clopidogrel is used as an adjunct treatment in the secondary prevention of cardiovascular events. CYP2C19 plays an integral part in the metabolism of clopidogrel to the active metabolite clopi-H4. However, CYP2C19 genetic polymorphism, prominent in Malaysians, could influence target clopi-H4 plasma concentrations for clinical efficacy. This study addresses how inter-ethnicity variability within the Malaysian population impacts the attainment of clopi-H4 target plasma concentration under different CYP2C19 polymorphisms through pharmacokinetic (PK) modelling. We illustrated a statistically significant difference (P < 0.001) in the clopi-H4 C_max between the extensive metabolisers (EM) and poor metabolisers (PM) phenotypes with either Malay or Malaysian Chinese population groups. Furthermore, the number of PM individuals with peak clopi-H4 concentrations below the minimum therapeutic level was partially recovered using a high-dose strategy (600 mg loading dose followed by a 150 mg maintenance dose), which resulted in an approximate 50% increase in subjects attaining the minimum clopi-H4 plasma concentration for a therapeutic effect.

Evaluation of Tolerability, Pharmacokinetics and Pharmacodynamics of Vicagrel, a Novel P2Y12 Antagonist, in Healthy Chinese Volunteers

Background: Vicagrel is a novel anti-platelet drug and hydrolyzed to the same intermediate as clopidogrel via esterase, instead of CYP2C19. Here we report the first clinical trial on the tolerability, pharmacokinetics and pharmacodynamics of different doses of vicagrel, and comparison with clopidogrel in healthy Chinese volunteers. Methods: This study was conducted in two parts. Study I was a dose-escalating (5-15 mg) study. For each dose, 15 participants were randomized into three groups (total n = 45); nine participants were given vicagrel, three were given clopidogrel, and three were given a placebo. Study II was conducted to assess interactions between vicagrel and aspirin in 15 healthy participants. The plasma concentrations of the metabolites of vicagrel and clopidogrel were determined using a LC-MS/MS method. Platelet aggregation was assessed using the VerifyNow-P2Y12 assay. Results: Vicagrel (5-15 mg per day) dosing for 10 days or addition of aspirin was well tolerated in healthy volunteers. The exposure of the active metabolite increased proportionally across the dose range and was higher (~10-fold) than clopidogrel. The levels of IPA dosing 75 mg clopidogrel were between the responses of 5 mg and 10 mg vicagrel. After a single loading dose of vicagrel (30 mg) and a once-daily maintenance dose (7.5 mg) for 8 days, the maximum inhibition of platelet aggregation was similar to that seen with the combined use of vicagrel and aspirin (100 mg/day). Conclusion: Oral vicagrel demonstrated a favorable safety profile and excellent anti-platelet activity, which could be a promising P2Y12 antagonist as anti-platelet drug and can be further developed in phase II/III studies, and marketing for the unmet medical needs of cardiovascular diseases. The study was registered at http://www.chictr.org.cn (ChiCTR-IIR-16009260).

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

Pharmacogenomic Impact of CYP2C19 Variation on Clopidogrel Therapy in Precision Cardiovascular Medicine

Variability in response to antiplatelet therapy can be explained in part by pharmacogenomics, particularly of the CYP450 enzyme encoded by CYP2C19. Loss-of-function and gain-of-function variants help explain these interindividual differences. Individuals may carry multiple variants, with linkage disequilibrium noted among some alleles. In the current pharmacogenomics era, genomic variation in CYP2C19 has led to the definition of pharmacokinetic phenotypes for response to antiplatelet therapy, in particular, clopidogrel. Individuals may be classified as poor, intermediate, extensive, or ultrarapid metabolizers, based on whether they carry wild type or polymorphic CYP2C19 alleles. Variant alleles differentially impact platelet reactivity, concentration of plasma clopidogrel metabolites, and clinical outcomes. Interestingly, response to clopidogrel appears to be modulated by additional factors, such as sociodemographic characteristics, risk factors for ischemic heart disease, and drug-drug interactions. Furthermore, systems medicine studies suggest that a broader approach may be required to adequately assess, predict, preempt, and manage variation in antiplatelet response. Transcriptomics, epigenomics, exposomics, miRNAomics, proteomics, metabolomics, microbiomics, and mathematical, computational, and molecular modeling should be integrated with pharmacogenomics for enhanced prediction and individualized care. In this review of pharmacogenomic variation of CYP450, a systems medicine approach is described for tailoring antiplatelet therapy in clinical practice of precision cardiovascular medicine.

Influence of genetic co-factors on the population pharmacokinetic model for clopidogrel and its active thiol metabolite

PURPOSE

A high interindividual variability is observed in the pharmacokinetics of clopidogrel, a widely used antiplatelet drug. In the present study, a joint parent-metabolite population pharmacokinetic model was developed to adequately describe observed concentrations of clopidogrel and its active thiol metabolite (H4).

METHODS

The study included 63 patients undergoing elective coronarography or percutaneous coronary intervention. The population pharmacokinetic model was developed in the NONMEM 7.3 software, and first-order conditional estimation method with interaction was applied. Also, the influence of covariates was evaluated (age, weight, body mass index (BMI), obesity defined as BMI ≥ 30 kg/m2, sex, diabetes mellitus, co-administration of PPI or statins, presence of CYP2C19*2, CYP2C19*17, CYP3A4*1G alleles, and ABCB1 3435 TT genotype).

RESULTS

It was found that the only significant covariate was the presence of CYP2C19*2 allele, which had an impact on lower conversion of clopidogrel to H4. As a result, predicted area under the time-concentration curve values was lower in carriers of this allele, with median 5.94 ng h/ml (interquartile range 3.92-12.51 [ng∙h/ml]) vs. 12.70 ng h/ml in non-carriers (interquartile range, 7.00-19.39 [ng∙h/ml]), respectively (p = 0.004).

CONCLUSIONS

Developed model predicts that the only significant covariate influencing the observed concentrations and therefore the exposure to the active H4 metabolite is the presence of CYP2C19*2 allele.

Pharmacokinetic interactions and dosing rationale for antiepileptic drugs in adults and children

AIMS

Population pharmacokinetic modelling has been widely used across many therapeutic areas to identify sources of variability, which are incorporated into models as covariate factors. Despite numerous publications on pharmacokinetic drug-drug interactions (DDIs) between antiepileptic drugs (AEDs), such data are not used to support the dose rationale for polytherapy in the treatment of epileptic seizures. Here we assess the impact of DDIs on plasma concentrations and evaluate the need for AED dose adjustment.

METHODS

Models describing the pharmacokinetics of carbamazepine, clobazam, clonazepam, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, topiramate, valproic acid and zonisamide in adult and paediatric patients were collected from the published literature and implemented in NONMEM v7.2. Taking current clinical practice into account, we explore simulation scenarios to characterize AED exposure in virtual patients receiving mono- and polytherapy. Steady-state, maximum and minimum concentrations were selected as parameters of interest for this analysis.

RESULTS

Our simulations show that DDIs can cause major changes in AED concentrations both in adults and children. When more than one AED is used, even larger changes are observed in the concentrations of the primary drug, leading to significant differences in steady-state concentration between mono- and polytherapy for most AEDs. These results suggest that currently recommended dosing algorithms and titration procedures do not ensure attainment of appropriate therapeutic concentrations.

CONCLUSIONS

The effect of DDIs on AED exposure cannot be overlooked. Clinical guidelines must consider such covariate effects and ensure appropriate dosing recommendations for adult and paediatric patients who require combination therapy.

Pharmacokinetic and Pharmacodynamic Responses to Clopidogrel: Evidences and Perspectives

Clopidogrel has significantly reduced the incidence of recurrent atherothrombotic events in patients with acute coronary syndrome (ACS) and in those undergoing percutaneous coronary intervention (PCI). However, recurrence events still remain, which may be partly due to inadequate platelet inhibition by standard clopidogrel therapy. Genetic polymorphisms involved in clopidogrel’s absorption, metabolism, and the P2Y12 receptor may interfere with its antiplatelet activity. Recent evidence indicated that epigenetic modification may also affect clopidogrel response. In addition, non-genetic factors such as demographics, disease complications, and drug-drug interactions can impair the antiplatelet effect of clopidogrel. The identification of factors contributing to the variation in clopidogrel response is needed to improve platelet inhibition and to reduce risk for cardiovascular events. This review encompasses the most recent updates on factors influencing pharmacokinetic and pharmacodynamic responses to clopidogrel.

Development of a physiology-directed population pharmacokinetic and pharmacodynamic model for characterizing the impact of genetic and demographic factors on clopidogrel response in healthy adults

Clopidogrel (Plavix®), is a widely used antiplatelet agent, which shows high inter-individual variability in treatment response in patients following the standard dosing regimen. In this study, a physiology-directed population pharmacokinetic/pharmacodynamic (PK/PD) model was developed based on clopidogrel and clopidogrel active metabolite (clop-AM) data from the PAPI and the PGXB2B studies using a step-wise approach in NONMEM (version 7.2). The developed model characterized the in vivo disposition of clopidogrel, its bioactivation into clop-AM in the liver and subsequent platelet aggregation inhibition in the systemic circulation reasonably well. It further allowed the identification of covariates that significantly impact clopidogrel's dose-concentration-response relationship. In particular, CYP2C19 intermediate and poor metabolizers converted 26.2% and 39.5% less clopidogrel to clop-AM, respectively, compared to extensive metabolizers. In addition, CES1 G143E mutation carriers have a reduced CES1 activity (82.9%) compared to wild-type subjects, which results in a significant increase in clop-AM formation. An increase in BMI was found to significantly decrease clopidogrel's bioactivation, whereas increased age was associated with increased platelet reactivity. Our PK/PD model analysis suggests that, in order to optimize clopidogrel dosing on a patient-by-patient basis, all of these factors have to be considered simultaneously, e.g. by using quantitative clinical pharmacology tools.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.