Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for valproic acid and divalproex

Conventional vs Mechanistic IVIVC: A Comparative Study in Establishing Dissolution Safe Space for Extended Release Formulations

The use of in vitro-in vivo correlation (IVIVC) for extended release oral dosage forms is an important technique that can avoid potential clinical studies. IVIVC has been a topic of discussion over the past two decades since the inception of USFDA guidance. It has been routinely used for biowaivers, establishment of dissolution safe space and clinically relevant dissolution specifications, for supporting site transfers, scale-up and post approval changes. Although conventional or mathematical IVIVC is routinely used, other approach such as mechanistic IVIVC can be of attractive choice as it integrates all the physiological aspects. In the present study, we have performed comparative evaluation of mechanistic and conventional IVIVC for establishment of dissolution safe space using divalproex sodium and tofacitinib extended release formulations as case examples. Conventional IVIVC was established using Phoenix and mechanistic IVIVC was set up using Gastroplus physiologically based biopharmaceutics model (PBBM). Virtual dissolution profiles with varying release rates were constructed around target dissolution profile using Weibull function. After internal and external validation, the virtual dissolution profiles were integrated into mechanistic and conventional IVIVC and safe space was established by absolute error and T/R ratio's methods. The results suggest that mechanistic IVIVC yielded wider safe space as compared to conventional IVIVC. The results suggest that a mechanistic approach of establishing IVIVC may be a flexible approach as it integrates physiological aspects. These findings suggest that mechanistic IVIVC has wider potential as compared to conventional IVIVC to gain wider dissolution safe space and thus can avoid potential clinical studies.

Factors Influencing Plasma Concentrations of Valproic Acid in Pediatric Patients with Epilepsy and the Clinical Significance of CYP2C9 Genotypes in Personalized Valproic Acid Therapy

BACKGROUND

The aim of this study was to investigate the factors affecting plasma valproic acid (VPA) concentration in pediatric patients with epilepsy and the clinical significance of CYP2C9 gene polymorphisms in personalized dosing using therapeutic drug monitoring and pharmacogenetic testing.

METHODS

The medical records of children with epilepsy who underwent therapeutic drug monitoring at our institution between July 2022 and July 2023 and met the inclusion criteria were reviewed. Statistical analysis was performed to determine whether age, sex, blood ammonia, liver function, kidney function, and other characteristics affected the concentration-to-dose ratio of VPA (CDRV) in these patients. To investigate the effect of CYP2C9 polymorphisms on CDRV, DNA samples were collected from patients and the CYP2C9 genotypes were identified using real-time quantitative PCR.

RESULTS

The mean age of 208 pediatric patients with epilepsy was 5.50 ± 3.50 years. Among these patients, 182 had the CYP2C9 *1/*1 genotype, with a mean CDRV (mcg.kg/mL.mg) of 2.64 ± 1.46, 24 had the CYP2C9 *1/*3 genotype, with a mean CDRV of 3.28 ± 1.74, and 2 had the CYP2C9 *3/*3 genotype, with a mean CDRV of 6.46 ± 3.33. There were statistical differences among these 3 genotypes (P < 0.05). The CDRV in these patients were significantly influenced by age, aspartate aminotransferase, total bilirubin, direct bilirubin, globulin, albumin/globulin ratio, prealbumin, creatinine, and CYP2C9 polymorphisms. In addition, multivariate linear regression analysis identified total bilirubin, direct bilirubin, and CYP2C9 polymorphisms as independent risk factors for high CDRV.

CONCLUSIONS

Liver problems and mutations in the CYP2C9 gene increase VPA levels. This underscores the importance of considering these factors when prescribing VPA to children with epilepsy, thereby enhancing the safety and efficacy of the therapy.

Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®

BACKGROUND AND OBJECTIVE

In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software.

METHODS

A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included.

RESULTS

The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range.

CONCLUSIONS

Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.

Applied physiologically-based pharmacokinetic modeling to assess uridine diphosphate-glucuronosyltransferase-mediated drug-drug interactions for Vericiguat

Vericiguat (Verquvo; US: Merck, other countries: Bayer) is a novel drug for the treatment of chronic heart failure. Preclinical studies have demonstrated that the primary route of metabolism for vericiguat is glucuronidation, mainly catalyzed by uridine diphosphate-glucuronosyltransferase (UGT)1A9 and to a lesser extent UGT1A1. Whereas a drug-drug interaction (DDI) study of the UGT1A9 inhibitor mefenamic acid showed a 20% exposure increase, the effect of UGT1A1 inhibitors has not been assessed clinically. This modeling study describes a physiologically-based pharmacokinetic (PBPK) approach to complement the clinical DDI liability assessment and support prescription labeling. A PBPK model of vericiguat was developed based on in vitro and clinical data, verified against data from the mefenamic acid DDI study, and applied to assess the UGT1A1 DDI liability by running an in silico DDI study with the UGT1A1 inhibitor atazanavir. A minor effect with an area under the plasma concentration-time curve (AUC) ratio of 1.12 and a peak plasma concentration ratio of 1.04 was predicted, which indicates that there is no clinically relevant DDI interaction anticipated. Additionally, the effect of potential genetic polymorphisms of UGT1A1 and UGT1A9 was evaluated, which showed that an average modest increase of up to 1.7-fold in AUC may be expected in the case of concomitantly reduced UGT1A1 and UGT1A9 activity for subpopulations expressing non-wild-type variants for both isoforms. This study is a first cornerstone to qualify the PK-Sim platform for use of UGT-mediated DDI predictions, including PBPK models of perpetrators, such as mefenamic acid and atazanavir, and sensitive UGT substrates, such as dapagliflozin and raltegravir.

Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.

Physiologically based mechanistic insight into differential risk of valproate hepatotoxicity between children and adults: A focus on ontogeny impact

The anticonvulsant valproic acid (VPA) despite complex pharmacokinetics has been in clinical use for nearly 6 decades. Previous reports indicated neonates, infants, and toddlers/preschoolers had higher risk of valproate hepatotoxicity than adults. However, dosing recommendations for those less than 10 years of age are lacking. To decipher clinical puzzles, physiologically-based pharmacokinetic (PBPK) models of VPA and its hepatotoxic metabolite 4-ene-VPA were constructed and simulated with particularly integrated information of drug-metabolizing enzyme ontogeny. Adult and pediatric PK data of VPA (n = 143 subjects) and 4-ene-VPA (n = 8 subjects) collected from previous reports were used for model development and validation. Sensitivity analyses were performed to characterize ontogeny impacts of CYP2C9 and UGT2B7 on dispositions of VPA and 4-ene-VPA across age groups. Optimal VPA dosing for each pediatric age group was also predicted and objectively judged by ensuring VPA efficacy and avoiding 4-ene-VPA hepatotoxicity. The study revealed UGT2B7 ontogeny was quite influential on VPA clearance even in neonates and small children. Intrinsic clearance of CYP2C9 was the most prominent determinant for areas under the concentration-time curve of VPA and 4-ene-VPA in infants, and toddlers/preschoolers, reflecting higher hepatotoxicity risk due to noxious 4-ene-VPA accumulation in these groups. The ontogeny-based PBPK approach complements conventional allometric methods in dosing estimation for the young by providing more mechanistic insight of the processes changing with age. The established ontogeny-based PBPK approach for VPA therapy deserves further corroboration by real-world therapeutic data to affirm its clinical applicability.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy

We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Tolerability of Antiseizure Medications in Individuals With Newly Diagnosed Epilepsy

Importance

Tolerability is a key determinant of the effectiveness of epilepsy treatment. It is important to evaluate whether the overall tolerability has improved.

Objective

To identify factors associated with poor tolerability of antiseizure medications (ASMs) and examine temporal changes in tolerability.

Design, Setting, and Participants

This was a longitudinal cohort study at a specialist clinic in Glasgow, Scotland. Patients with newly diagnosed and treated epilepsy between July 1982 and October 2012 were included from 2282 eligible individuals. They were followed up until April 2016 or death. Data analysis was completed in August 2019.

Exposures

Antiseizure medications.

Main Outcomes and Measures

Univariable and multivariable survival analyses were performed to examine associations between potential risk factors and development of intolerable adverse effects (AEs). Intolerable AE rates of the ASMs as the initial monotherapy were compared between 3 epochs (July 1982-June 1992, July 1992-June 2002, and July 2002-April 2016).

Results

Of 1795 patients, 969 (54.0%) were male, and the median (interquartile range) age was 33 (21-50) years. A total of 3241 ASMs were prescribed during the period, of which 504 (15.6%) were discontinued within 6 months owing to intolerable AEs. Children younger than 18 years had lower intolerable AE rates than adults (vs aged 18-64 years: adjusted hazard ratio [aHR], 1.58; 95% CI, 1.07-2.32; vs aged ≥65 years: aHR, 1.90; 95% CI, 1.19-3.02) while female individuals (aHR, 1.60; 95% CI, 1.30-1.96) and those who had more than 5 pretreatment seizures (aHR, 1.24; 95% CI, 1.03-1.49) were associated with having higher risk. For each ASM trial, the risk of intolerable AEs increased with the number of previous drug withdrawals due to AEs (aHR, 1.18; 95% CI, 1.09-1.28) and the number of concomitant ASMs (aHR, 1.31; 95% CI, 1.04-1.64). The proportion of second-generation ASMs prescribed as the initial monotherapy increased from 22.3% (33 of 148) in the first epoch to 68.7% (645 of 939) in the last (P < .001). Although differences in intolerable AE rates and types of AEs were found between the ASMs, there was no difference in the overall intolerable AEs rates to the initial monotherapy across the 3 epochs (first: 10.1% [15 of 148]; second: 13.8% [98 of 708]; third: 14.0% [131 of 939]; P = .41).

Conclusions and Relevance

In this cohort study, the increased use of the second-generation ASMs had not improved overall treatment tolerability. Greater effort to improve tolerability in ASM development is needed.

What has been the impact of new drug treatments on epilepsy?

PURPOSE OF REVIEW

Nearly two dozen antiseizure medications (ASMs) with different mechanisms of action have been introduced over the past three decades with the aim of providing better efficacy or safety profile than the previous drugs. Several new ASMs with improvement on a classic drug family or have novel mechanisms of action have been recently approved for epilepsy. The present review explored recent studies or guidelines on new agents and discussed the potential impact of these novel treatments on epilepsy management and future directions of research.

RECENT FINDINGS

Long-term cohort studies showed that, collectively, the second-generation did not improve the overall prognosis of epilepsy. Individual monotherapy studies showed similar efficacy of second-generation (levetiracetam and zonisamide) and third-generation (eslicarbazepine acetate and lacosamide) ASMs compared to controlled-release carbamazepine for the treatment of focal epilepsy. However, there appears to be no evidence to support any second-generation or third-generation ASMs to be as efficacious as valproate monotherapy for generalized and unclassified epilepsies. Cannabidiol adjunctive treatments were found to be efficacious for Dravet syndrome and Lennox-Gastaut syndrome. Although most newer generation ASMs are less prone to drug-drug interactions, stiripentol and cannabidiol can elevate the plasma concentration of N-desmethylclobazam, the active metabolite of clobazam. Generally speaking, the second-generation ASMs have lower teratogenic risk than the older drugs but there is scant study on neurodevelopmental effect of third-generation ASMs.

SUMMARY

Although the newer generation ASMs may not have improved the overall seizure control they have advantages in terms of drug-drug interactions and teratogenicity, and thus offer valuable individualized options in the treatment of epilepsy.

Physiologically-Based Pharmacokinetic Modeling of the Drug-Drug Interaction of the UGT Substrate Ertugliflozin Following Co-Administration with the UGT Inhibitor Mefenamic Acid

The sodium-glucose cotransporter 2 inhibitor ertugliflozin is metabolized by the uridine 5'-diphospho-glucuronosyltransferase (UGT) isozymes UGT1A9 and UGT2B4/2B7. This analysis evaluated the drug-drug interaction (DDI) following co-administration of ertugliflozin with the UGT inhibitor mefenamic acid (MFA) using physiologically-based pharmacokinetic (PBPK) modeling. The ertugliflozin modeling assumptions and parameters were verified using clinical data from single-dose and multiple-dose studies of ertugliflozin in healthy volunteers, and the PBPK fraction metabolized assignments were consistent with human absorption, distribution, metabolism, and excretion results. The model for MFA was developed using clinical data, and in vivo UGT inhibitory constant values were estimated using the results from a clinical DDI study with MFA and dapagliflozin, a UGT1A9 and UGT2B4/2B7 substrate in the same chemical class as ertugliflozin. Using the verified compound files, PBPK modeling predicted an ertugliflozin ratio of area under the plasma concentration-time curves (AUC_R ) of 1.51 when co-administered with MFA. ClinicalTrials.gov identifier: NCT00989079.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.