Prediction of in vivo clearance and associated variability of CYP2C19 substrates by genotypes in populations utilizing a pharmacogenetics-based mechanistic model

Physiologically-based pharmacokinetic modeling of pantoprazole to evaluate the role of CYP2C19 genetic variation and obesity in the pediatric population

Pantoprazole is a proton pump inhibitor indicated for the treatment of gastroesophageal reflux disease, a condition that disproportionately affects children with obesity. Appropriately dosing pantoprazole in children with obesity requires understanding the body size metric that best guides dosing, but pharmacokinetic (PK) trials using traditional techniques are limited by the need for larger sample sizes and frequent blood sampling. Physiologically-based PK (PBPK) models are an attractive alternative that can account for physiologic-, genetic-, and drug-specific changes without the need for extensive clinical trial data. In this study, we explored the effect of obesity on pantoprazole PK and evaluated label-suggested dosing in this population. An adult PBPK model for pantoprazole was developed using data from the literature and accounting for genetic variation in CYP2C19. The adult PBPK model was scaled to children without obesity using age-associated changes in anatomical and physiological parameters. Lastly, the pediatric PBPK model was expanded to children with obesity. Three pantoprazole dosing strategies were evaluated: 1 mg/kg total body weight, 1.2 mg/kg lean body weight, and US Food and Drug Administration-recommended weight-tiered dosing. Simulated concentration-time profiles from our model were compared with data from a prospective cohort study (PAN01; NCT02186652). Weight-tiered dosing resulted in the most (>90%) children with pantoprazole exposures in the reference range, regardless of obesity status or CYP2C19 phenotype, confirming results from previously published population PK models. PBPK models may allow for the efficient study of physiologic and developmental effects of obesity on PK in special populations where clinical trial data may be limited.

Impact of CYP2C:TG Haplotype on CYP2C19 Substrates Clearance In Vivo, Protein Content, and In Vitro Activity

A novel haplotype composed of two non-coding variants, CYP2C18 NM_000772.3:c.*31T (rs2860840) and NM_000772.2:c.819+2182G (rs11188059), referred to as "CYP2C:TG," was recently associated with ultrarapid metabolism of various CYP2C19 substrates. As the underlying mechanism and clinical relevance of this effect remain uncertain, we analyzed existing in vivo and in vitro data to determine the magnitude of the CYP2C:TG haplotype effect. We assessed variability in pharmacokinetics of CYP2C19 substrates, including citalopram, sertraline, voriconazole, omeprazole, pantoprazole, and rabeprazole in 222 healthy volunteers receiving one of these six drugs. We also determined its impact on CYP2C8, CYP2C9, CYP2C18, and CYP2C19 protein abundance in 135 human liver tissue samples, and on CYP2C18/CYP2C19 activity in vitro using N-desmethyl atomoxetine formation. No effects were observed according to CYP2C:TG haplotype or to CYP2C19*1+TG alleles (i.e., CYP2C19 alleles containing the CYP2C:TG haplotype). In contrast, CYP2C19 intermediate (e.g., CYP2C19*1/*2) and poor metabolizers (e.g., CYP2C19*2/*2) showed significantly higher exposure in vivo, lower CYP2C19 protein abundance in human liver microsomes, and lower activity in vitro compared with normal, rapid (i.e., CYP2C19*1/*17), and ultrarapid metabolizers (i.e., CYP2C19*17/*17). Moreover, a tendency toward lower exposure was observed in ultrarapid metabolizers compared with rapid metabolizers and normal metabolizers. Furthermore, when the CYP2C19*17 allele was present, CYP2C18 protein abundance was increased suggesting that genetic variation in CYP2C19 may be relevant to the overall metabolism of certain drugs by regulating not only its expression levels, but also those of CYP2C18. Considering all available data, we conclude that there is insufficient evidence supporting clinical CYP2C:TG testing to inform drug therapy.

Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms

PURPOSE

To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.

METHODS

A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.

RESULTS

Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5-2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were Vmax,2C19 ≈ Papp > Vmax,3A4 > Kti, and contributions to its pharmacodynamic were Vmax,2C19 > kome > kms > Papp > Vmax,3A4. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.

CONCLUSIONS

The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.

Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax

PURPOSE

Venetoclax (VEN), an anti-tumor drug that is a substrate of cytochrome P450 3A enzyme (CYP3A4), is used to treat leukemia. Voriconazole (VCZ) is an antifungal medication that inhibits CYP3A4. The goal of this study is to predict the effect of VCZ on VEN exposure.

METHOD

Two physiological based pharmacokinetics (PBPK) models were developed for VCZ and VEN using the bottom-up and top-down method. VCZ model was also developed to describe the effect of CYP2C19 polymorphism on its pharmacokinetics (PK). The reversible inhibition constant (Ki) of VCZ for CYP3A4 was calibrated using drug-drug interaction (DDI) data of midazolam and VCZ. The clinical verified VCZ and VEN model were used to predict the DDI of VCZ and VEN at clinical dosing scenario.

RESULT

VCZ model predicted VCZ exposure in the subjects of different CYP2C19 genotype and DDI related fold changes of sensitive CYP3A substrate with acceptable prediction error. VEN model can capture PK of VEN with acceptable prediction error. The DDI PBPK model predicted that VCZ increased the exposure of VEN by 4.5-9.6 fold. The increase in VEN exposure by VCZ was influenced by subject's CYP2C19 genotype. According to the therapeutic window, VEN dose should be reduced to 100 mg when co-administered with VCZ.

CONCLUSION

The PBPK model developed here could support individual dose adjustment of VEN and DDI risk assessment. Predictions using the robust PBPK model confirmed that the 100 mg dose adjustment is still applicable in the presence of VCZ with high inter-individual viability.

Selective Serotonin Reuptake Inhibitor Pharmacokinetics During Pregnancy: Clinical and Research Implications

Pregnancy and associated physiologic changes affect the pharmacokinetics of many medications, including selective serotonin reuptake inhibitors—the first-line pharmacologic interventions for depressive and anxiety disorders. During pregnancy, SSRIs exhibit extensive pharmacokinetic variability that may influence their tolerability and efficacy. Specifically, compared to non-pregnant women, the activity of cytochrome P450 (CYP) enzymes that metabolize SSRIs drastically changes (e.g., decreased CYP2C19 activity and increased CYP2D6 activity). This perspective examines the impact of pharmacokinetic genes—related to CYP activity on SSRI pharmacokinetics during pregnancy. Through a simulation-based approach, plasma concentrations for SSRIs metabolized primarily by CYP2C19 (e.g., escitalopram) and CYP2D6 (e.g., fluoxetine) are examined and the implications for dosing and future research are discussed.

Pharmacogenetically Guided Escitalopram Treatment for Pediatric Anxiety Disorders: Protocol for a Double-Blind Randomized Trial

Current pharmacologic treatments for pediatric anxiety disorders (e.g., selective serotonin reuptake inhibitors (SSRIs)) frequently use "one size fits all" dosing strategies based on average responses in clinical trials. However, for some SSRIs, including escitalopram, variation in CYP2C19 activity produces substantial variation in medication exposure (i.e., blood medication concentrations). This raises an important question: would refining current SSRI dosing strategies based on CYP2C19 phenotypes increase response and reduce side effect burden? To answer this question, we designed a randomized, double-blind trial of adolescents 12-17 years of age with generalized, separation, and/or social anxiety disorders (N = 132). Patients are randomized (1:1) to standard escitalopram dosing or dosing based on validated CYP2C19 phenotypes for escitalopram metabolism. Using this approach, we will determine whether pharmacogenetically-guided treatment-compared to standard dosing-produces faster and greater reduction in anxiety symptoms (i.e., response) and improves tolerability (e.g., decreased risk of treatment-related activation and weight gain). Secondarily, we will examine pharmacodynamic variants associated with treatment outcomes, thus enhancing clinicians' ability to predict response and tolerability. Ultimately, developing a strategy to optimize dosing for individual patients could accelerate response while decreasing side effects-an immediate benefit to patients and their families. ClinicalTrials.gov Identifier: NCT04623099.

Application of a Physiologically Based Pharmacokinetic Model to Characterize Time-dependent Metabolism of Voriconazole in Children and Support Dose Optimization

Background: Voriconazole is a potent antifungal drug with complex pharmacokinetics caused by time-dependent inhibition and polymorphisms of metabolizing enzymes. It also exhibits different pharmacokinetic characteristics between adults and children. An understanding of these alterations in pharmacokinetics is essential for pediatric dose optimization. Objective: To determine voriconazole plasma exposure in the pediatric population and further investigate optimal dosage regimens. Methods: An adult and pediatric physiologically based pharmacokinetic (PBPK) model of voriconazole, integrating auto-inhibition of cytochrome P450 3A4 (CYP3A4) and CYP2C19 gene polymorphisms, was developed. The model was evaluated with visual predictive checks and quantitative measures of the predicted/observed ratio of the area under the plasma concentration-time curve (AUC) and maximum concentration (Cmax). The validated pediatric PBPK model was used in simulations to optimize pediatric dosage regimens. The probability of reaching a ratio of free drug (unbound drug concentration) AUC during a 24-h period to minimum inhibitory concentration greater than or equal to 25 (fAUC24h/MIC ≥ 25) was assessed as the pharmacokinetic/pharmacodynamic index. Results: The developed PBPK model well represented voriconazole's pharmacokinetic characteristics in adults; 78% of predicted/observed AUC ratios and 85% of Cmax ratios were within the 1.25-fold range. The model maintained satisfactory prediction performance for intravenous administration in pediatric populations after incorporating developmental changes in anatomy/physiology and metabolic enzymes, with all predicted AUC values within 2-fold and 73% of the predicted Cmax within 1.25-fold of the observed values. The simulation results of the PBPK model suggested that different dosage regimens should be administered to children according to their age, CYP2C19 genotype, and infectious fungal genera. Conclusion: The PBPK model integrating CYP3A4 auto-inhibition and CYP2C19 gene polymorphisms successfully predicted voriconazole pharmacokinetics during intravenous administration in children and could further be used to optimize dose strategies. The infectious fungal genera should be considered in clinical settings, and further research with large sample sizes is required to confirm the current findings.

Escitalopram in Adolescents With Generalized Anxiety Disorder: A Double-Blind, Randomized, Placebo-Controlled Study

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are commonly used to treat pediatric anxiety disorders, including generalized anxiety disorder (GAD); however, their efficacy and tolerability are difficult to predict. This study evaluated the efficacy and tolerability of escitalopram in adolescents with GAD (DSM-IV-TR) and the impact of variants in HTR2A and serotonin transporter (SLC6A4) genes and cytochrome P450 2C19 (CYP2C19) phenotypes on response as well as CYP2C19 phenotype on escitalopram pharmacokinetics from February 2015 through November 2018.

METHODS

Patients were treated with escitalopram (forced titration to 15 mg/d, then flexible titration to 20 mg/d) (n = 26, mean ± SD age: 14.8 ± 1.7 years) or placebo (n = 25, mean ± SD age: 14.9 ± 1.6 years) for 8 weeks. Outcomes were the change in scores on the Pediatric Anxiety Rating Scale (PARS) and Clinical Global Impressions (CGI) scales as well as vital signs and adverse events. Plasma escitalopram and desmethylcitalopram area under the curve during 24 hours (AUC0-24) and maximum concentration (Cmax) were determined and compared across CYP2C19 phenotypes.

RESULTS

Escitalopram was superior to placebo for mean ± SD baseline-to-endpoint change in PARS (-8.65 ± 1.3 vs -3.52 ± 1.1, P = .005) and CGI scores, and increasing CYP2C19 metabolism was associated with decreases in escitalopram Cmax (P = .07) and AUC0-24 (P < .05). Vital signs, corrected QT interval, and adverse events were similar in patients who received escitalopram and placebo.

CONCLUSIONS

Escitalopram reduces anxiety symptoms, and pharmacogenetics variables influence the trajectory and magnitude of improvement. Variation in CYP2C19 metabolism accounts for significant differences in escitalopram pharmacokinetics, raising the possibility that CYP2C19 phenotype should be considered when prescribing escitalopram.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02818751.

Quantitative Prediction of CYP3A4- and CYP3A5-Mediated Drug Interactions

We verified a physiologically-based pharmacokinetic (PBPK) model to predict cytochrome P450 3A4/5-mediated drug-drug interactions (DDIs). A midazolam (MDZ)-ketoconazole (KTZ) interaction study in 24 subjects selected by CYP3A5 genotype, and liquid chromatography and mass spectroscopy quantification of CYP3A4/5 abundance from independently acquired and genotyped human liver (n = 136) and small intestinal (N = 12) samples, were conducted. The observed CYP3A5 genetic effect on MDZ systemic and oral clearance was successfully replicated by a mechanistic framework incorporating the proteomics-informed CYP3A abundance and optimized small intestinal CYP3A4 abundance based on MDZ intestinal availability (F_G ) of 0.44. Furthermore, combined with a modified KTZ PBPK model, this framework recapitulated the observed geometric mean ratio of MDZ area under the curve (AUCR) following 200 or 400 mg KTZ, which was, respectively, 2.7-3.4 and 3.9-4.7-fold in intravenous administration and 11.4-13.4 and 17.0-19.7-fold in oral administration, with AUCR numerically lower (P > 0.05) in CYP3A5 expressers than nonexpressers. In conclusion, the developed mechanistic framework supports dynamic prediction of CYP3A-mediated DDIs in study planning by bridging DDIs between CYP3A5 expressers and nonexpressers.

CYP2C19-Guided Escitalopram and Sertraline Dosing in Pediatric Patients: A Pharmacokinetic Modeling Study

Objective: Cytochrome P4502C19 (CYP2C19) is a highly polymorphic gene that encodes an enzyme that metabolizes escitalopram and sertraline, two selective serotonin reuptake inhibitors (SSRIs) that are FDA approved for pediatric use and commonly used to treat anxiety and depressive disorders in youth. Using pharmacokinetic (PK) models in adolescents, we sought to (1) model SSRI dosing across CYP2C19 phenotypes to compare SSRI exposure (area under curve, AUC) and maximum concentration (Cmax), (2) evaluate the impact of b.i.d. dosing (in rapid metabolizers [RM] and ultrarapid metabolizers [UM]) on SSRI exposure and Cmax, and (3) determine pharmacogenomically-informed dosing strategies to provide similar exposure across CYP2C19 phenotypes in adolescents. Methods: Using PK parameters in CYP2C19 phenotype groups and previously reported pediatric PK data for escitalopram and sertraline, we modeled exposure (AUC0-24) and Cmax and determined CYP2C19-guided dosing strategies. Results: Compared with normal CYP2C19 metabolizers treated with either escitalopram or sertraline, Cmax and AUC0-24 were higher in slower metabolizers and lower in patients with increased CYP2C19 activity, although the magnitude of these differences was more pronounced for escitalopram than for sertraline. For escitalopram, poor metabolizers (PMs) require 10 mg/day and UMs require 30 mg/day to achieve an exposure that is equivalent to 20 mg/day in a normal metabolizer (NM). For sertraline, to achieve AUC0-24 and Cmax similar to NMs receiving 150 mg/day, PMs require 100 mg/day, whereas a dose of 200 mg/day was required in rapid and UMs. For UMs, b.i.d. escitalopram dosing was necessary to achieve comparable trough levels and exposure to NMs. Conclusions: This simulation study raises the possibility that achieving similar escitalopram and sertraline plasma concentrations could require dose adjustments in CYP2C19 poor metabolizers and UMs, although the magnitude of these differences were more pronounced for escitalopram than for sertraline. However, prospective trials of pharmacogenomically guided dosing in the pediatric population are needed to extend the findings of these modeling studies.

Content and Activities of UGT2B7 in Human Liver In Vitro and Predicted In Vivo: A Bottom-Up Approach

UDP-glucuronosyltransferase 2B7 (UGT2B7) is one of the most significant isoforms of UGTs in human liver. This research measured UGT2B7 protein content and activities, including maximum velocity (V_max) and intrinsic clearance (CL_int), in human liver at isoform, microsomal, liver tissue, and liver levels and identified the factors that influence expression. We determined absolute protein content by liquid chromatography-tandem mass spectroscopy and activities using the probe drug zidovudine in 82 normal human liver microsomes. Using a bottom-up method for derivation, we showed UGT2B7 content at the microsomal, liver tissue, and liver levels, as well as activities at the isoform, microsomal, liver tissue, and liver levels in vitro, and predicted hepatic clearance in vivo, with median, range, variation, and 95% and 50% prediction intervals. With regard to the intrinsic activities, the maximum velocity (V_max) had a median (range) of 7.5 (2-24) pmol/min per picomole of 2B7, and the CL_int was 0.08 (0.02-0.31) μl/min per picomole of 2B7. Determinations at liver level showed larger variations than at microsomal level, so it was more suitable for evaluating individual differences. By analyzing factors that affect UGT2B7, we found that: 1) The content at the liver tissue and liver levels correlated positively with activities; 2) the mutant heterozygotes of -327G>A, -900A>G, -161C>T may lead to decreased protein content and increased intrinsic CL_int; and 3) the transcription factor pregnane X receptor mRNA expression level was positively associated with the measured protein content. In all, we showed that protein content and activities at different levels and the factors that influence content provide valuable information for UGT2B7 research and clinically individualized medication.

Physiologically-based pharmacokinetic modelling of a CYP2C19 substrate, BMS-823778, utilizing pharmacogenetic data

AIMS

Previous studies demonstrated direct correlation between CYP2C19 genotype and BMS-823778 clearance in healthy volunteers. The objective of the present study was to develop a physiologically-based pharmacokinetic (PBPK) model for BMS-823778 and use the model to predict PK and drug-drug interaction (DDI) in virtual populations with multiple polymorphic genes.

METHODS

The PBPK model was built and verified using existing clinical data. The verified model was simulated to predict PK of BMS-823778 and significance of DDI with a strong CYP3A4 inhibitor in subjects with various CYP2C19 and UGT1A4 genotypes.

RESULTS

The verified PBPK model of BMS-823778 accurately recovered observed PK in different populations. In addition, the model was able to capture the exposure differences between subjects with different CYP2C19 genotypes. PK simulation indicated higher exposures of BMS-823778 in CYP2C19 poor metabolizers who were also devoid of UGT1A4 activity, compared to those with normal UGT1A4 functionality. Moderate DDI with itraconazole was predicted in subjects with wild-type CYP2C19 or UGT1A4. However, in subjects without CYP2C19 or UGT1A4 functionality, significant DDI was predicted when BMS-823778 was coadministered with itraconazole.

CONCLUSIONS

A PBPK model was developed using clinical data that accurately predicted human PK in different population with various CYP2C19 phenotypes. Simulations with the verified PBPK model indicated that UGT1A4 was probably an important clearance pathway in CYP2C19 poor metabolizers. DDI with itraconazole is likely to be dependent on the genotypes of CYP2C19 and UGT1A4.

Pharmacodynamic studies of voriconazole: informing the clinical management of invasive fungal infections

INTRODUCTION

Voriconazole is a broad-spectrum antifungal agent commonly used to treat invasive fungal infections (IFI), including aspergillosis, candidiasis, Scedosporium infection, and Fusarium infection. IFI often occur in immunocompromised patients, leading to increased morbidity and mortality.

AREAS COVERED

The objective of this review is to summarize the pharmacodynamic properties of voriconazole and to provide considerations for potential optimal dosing strategies. Studies have demonstrated superior clinical response when an AUC/MIC >25 or Cmin/MIC >1 is attained in adult patients, correlating to a trough concentration range as narrow as 2-4.5 mg/L; however, these targets are poorly established in the pediatric population. Topics in this discussion include voriconazole use in multiple age groups, predisposing patient factors for IFI, and considerations for clinicians managing IFI. Expert commentary: The relationship between voriconazole dosing and exposure is not well defined due to the large inter- and intra-subject variability. Development of comprehensive decision support tools for individualizing dosing, particularly in children who require higher dosing, will help to increase the probability of achieving therapeutic efficacy and decrease sub-therapeutic dosing and adverse events.

IV fosphenytoin in obese patients: Dosing strategies, safety, and efficacy

Background

Previous studies evaluated the disposition of IV phenytoin loading doses and found that obese patients had increased drug distribution into excess body weight, larger volumes of distribution, and longer half-lives when compared to their nonobese counterparts. We assess the safety and efficacy of fosphenytoin loading doses in patients with different body mass indices (BMIs).

Methods

A retrospective chart review was conducted in 410 patients who received fosphenytoin. Patients were divided into 2 groups: BMI <30 (nonobese) and BMI ≥30 (obese). Patient demographics, fosphenytoin dose administered in mg/kg body weight, renal and liver function tests, fosphenytoin drug levels, and pre- and post-fosphenytoin administration vital signs were collected to assess for adverse events. Necessity of additional antiepileptic loading doses was used as a surrogate for clinical efficacy.

Results

The median dose of fosphenytoin administered was 19 mg/kg (interquartile range 15-20). The most frequently encountered adverse event was hypotension, which occurred in 39% of the cohort. Using a Bonferroni adjustment for multiple comparisons, there were no differences in adverse events between the 2 groups. The need for additional antiepileptic loading doses was not different between the 2 groups (p = 0.07).

Conclusions

The incidence of adverse events and the need for repeat loading antiepileptic medications was similar between the 2 groups. From our findings, the patients in our study did not receive empiric loading dose adjustments and the current method of loading fosphenytoin achieves similar outcomes, regardless of the patient's BMI.

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.