Journal of Clinical Pharmacology最新文献

Oxcarbazepine (OXC) is a second-generation antiseizure medication, effective through its active metabolite, 10-mono-hydroxy derivative (MHD). OXC is used as adjunctive therapy for focal-onset and primary generalized tonic-clonic seizures, with recommended dosing based on age and body weight. This study uses physiologically based pharmacokinetic (PBPK) modeling and leverages pharmacokinetic (PK) data acquired from children enrolled in pragmatic trials to understand dosing and subsequent exposure requirements in children with obesity. Drug concentrations of OXC and MHD (n = 148 each) from children with (n = 31) and without (n = 10) obesity, aged 2-20 years, were collected from two clinical trials (NCT01431326 and NCT02993861) and used for external evaluation of a previously developed PBPK model of OXC using PK-Sim. We used a previously published virtual population that accounts for the obesity-related changes in physiology (e.g., liver size and glomerular filtration rate) in children for PK simulations in children with obesity. Model evaluation showed that ≥80% of MHD concentrations contributed by about two thirds of study subjects (26 out of 41) fell within the 90% prediction interval. The PBPK model showed that children with obesity had lower median (interquartile range) simulated weight-normalized clearance (0.060 L/h/kg [0.048-0.076 L/h/kg]) than children without obesity (0.067 L/h/kg [0.060-0.077 L/h/kg]). Simulations revealed that the recommended pediatric dosing regimen produced comparable MHD exposure between children with and without obesity at steady state, supporting its applicability regardless of obesity status. This PBPK-based dosing aligns with product label recommendations and demonstrates the potential of PBPK modeling for dosing other drugs in children with obesity.

We evaluated the influence of clinical, biochemical, and genetic factors on response in 142 patients diagnosed with rheumatoid arthritis, of whom 87 patients were treated with tocilizumab (61.26%) and 55 patients were treated with rituximab (38.7%;) according to the variables European League Against Rheumatism (EULAR) response, remission, low disease activity, and improvement in Disease Activity Score, 28 joints (DAS28) at 6, 12, and 18 months. A retrospective prospective cohort study was conducted. Patients carrying the FCGR3A rs396991-TT genotype treated with tocilizumab showed higher EULAR response (OR, 5.075; 95%CI, 1.20-21.33; P = .027) at 12 months, those who were naive for biological disease-modifying antirheumatic drugs (bDMARDs) at the beginning of treatment showed satisfactory EULAR response, higher remission, and greater improvement in DAS28 at 6 months. Younger age at start of tocilizumab treatment was associated with satisfactory EULAR response at 18 months and greater remission at 6 and 18 months. Subcutaneous tocilizumab administration was associated with higher remission at 6 months and improved low disease activity rate at 12 months. In patients treated with rituximab, carriers of the FCGR2A rs1801274-TT genotype had higher EULAR response at 6 months (OR, 4.861; 95%CI, 1.11-21.12; P = .035), 12 months (OR, 4.667; p = 0.066, 95%CI, 0.90-24.12; P = .066), and 18 months (OR, 2.487; 95%CI, 0.35-17.31; P = .357), higher remission (OR: 10.625; p = 0.044, CI_95% : 1.07, 105.47) at 6 months, and greater improvement in DAS28 at 12 months (B = 0.782; 95%CI, -0.15 to 1.71; P = .098) and 18 months (B = 1.414; 95%CI, 0.19-2.63; P = .025). The FCGR3A rs396991-G allele was associated with improved low disease activity rate (OR, 4.904; 95%CI, 0.84-28.48; P = .077) and greater improvement in DAS28 (B = -1.083; 95%CI, -1.98 to -0.18; P = .021) at 18 months. Patients with a lower number of previous biological therapies had higher remission at 12 months. We suggest that the FCGR3A rs396991-TT genotype, higher baseline value of DAS28, subcutaneous tocilizumab administration, younger age at the beginning of treatment, and being bDMARD naive are associated with better response to tocilizumab. In patients treated with rituximab, we found better response in those patients with the FCGR2A rs1801274-TT genotype, the FCGR3A rs396991-G allele, and lower number of previous biological therapies.

Evaluation of the effect of food on the pharmacokinetics of oral oncology drugs is critical to drug development, as food can mitigate or exacerbate toxicities and alter systemic exposure. Our aim is to expand on current US Food and Drug Administration (FDA) guidance and provide data-driven food-effect study design recommendations specific to the oncology therapeutic area. Data for recently approved small-molecule oncology drugs was extracted from the clinical pharmacology review in the sponsor's FDA submission package. Information on subject selection, meal types, timing of the study relative to the pivotal trial, and study outcomes was analyzed. The number of subjects enrolled ranged from 12 to 60, and the majority of studies (19 of 29) were conducted in healthy volunteers. Using AstraZeneca cost data, healthy volunteer studies were estimated to cost 10-fold less than cancer patient studies. Nine of 29 (31%) studies included meals with multiple levels of fat content. Analysis of a subset of 16 drugs revealed that final results for the food-effect study were available before the start of the pivotal trial for only 2 drugs. Conducting small food-effect studies powered to estimate effect, rather than confirm no effect, with only a standardized high-fat meal according to FDA guidance may eliminate unnecessary studies, reduce cost, and improve efficiency in oncology drug development. Starting food-effect studies as early as possible is key to inform dosing in pivotal trials.

A pediatric population pharmacokinetic model including covariate effects was developed using data from 2 clinical trials in pediatric patients with epilepsy (SP0847 and SP1047). Lacosamide plasma concentration-time data (n = 402) were available from 79 children with body weights ranging from 6 to 76 kg, and a balanced age distribution (6 months to <2 years: n = 14; 2 to <6 years: n = 22; 6 to <12 years: n = 25; 12 to <18 years: n = 18). A single-compartment population pharmacokinetic model with first-order absorption and elimination described the data adequately. Plasma clearance was modeled using allometric scaling on body weight with a freely estimated allometric exponent, while volume of distribution used a fixed theoretical allometric exponent. Covariate search identified a significant effect of enzyme-inducing antiepileptic drugs resulting in a 35% decrease in lacosamide average plasma concentration. No additional effects on clearance could be attributed to race, sex, age, or renal function. Different dosing adaptation schemes by body weight bands were simulated to approximate, in pediatric patients aged 4 to 17 years, the same average plasma concentration as in adult patients receiving the maximum recommended lacosamide daily dose.

Population pharmacokinetics (PK) and exposure-response (E-R) analyses were conducted to compare the PK and E-R relationships of golimumab between children and adults with ulcerative colitis. PK data following subcutaneous golimumab administration to children with ulcerative colitis (6-17 years) in the PURSUIT-PEDS-PK study, adults with ulcerative colitis in the PURSUIT study, and children with pediatric polyarticular juvenile idiopathic arthritis (2-17 years) in the GO-KIDS study, were included in the population PK analysis. E-R analysis was conducted using logistic regression to link serum golimumab concentration and Mayo score-based efficacy outcomes in pediatric and adult ulcerative colitis. Golimumab PK was adequately described by a 1-compartment model with first-order absorption and elimination. Golimumab apparent clearance and volume of distribution increased with body weight. Golimumab apparent clearance was higher in patients with lower serum albumin, no methotrexate use, and positive antibodies to golimumab; age was not an influential factor after accounting for body weight. Model-estimated terminal half-life (9.2 days in children; 9.5 days in adults) and other PK parameters suggest that golimumab PK properties are generally comparable between children and adults with ulcerative colitis. Simulations suggest that a higher induction dose than that tested in PURSUIT-PEDS-PK may be needed for children ≤45 kg to achieve exposures comparable to adults. Comparable E-R relationships between children and adults with ulcerative colitis were observed, although children appeared to be more responsive for the more stringent remission end point. The overall comparable PK and E-R relationships between children and adults support the extrapolation of golimumab efficacy from the adult to the pediatric ulcerative colitis population.

Rolapitant (Varubi) is a neurokinin-1 receptor antagonist approved for the prevention of chemotherapy-induced nausea and vomiting. Rolapitant is primarily metabolized by the cytochrome P450 3A4 (CYP3A4) enzyme. Unlike other neurokinin-1 receptor antagonists, rolapitant is neither an inhibitor nor an inducer of CYP3A4 in vitro. The objective of this analysis was to examine the pharmacokinetics of rolapitant in healthy subjects and assess drug-drug interactions between rolapitant and midazolam (a CYP3A substrate), ketoconazole (a CYP3A inhibitor), or rifampin (a CYP3A4 inducer). Three phase 1, open-label, drug-drug interaction studies were conducted to examine the pharmacokinetic interactions of orally administered rolapitant with midazolam, rolapitant with ketoconazole, and rolapitant with rifampin. The pharmacokinetic profiles of midazolam and 1-hydroxy midazolam metabolites were essentially unchanged when coadministered with rolapitant, indicating the lack of a clinically relevant inhibition or induction of CYP3A by rolapitant. Coadministration of ketoconazole with rolapitant had no effects on rolapitant maximum concentration and resulted in an approximately 20% increase in the area under the concentration-time curve of rolapitant, suggesting that strong CYP3A inhibitors have minimal inhibitory effects on rolapitant exposure. Repeated administrations of rifampin appeared to reduce rolapitant exposure, resulting in a 33% decrease in maximum concentration and 87% decrease in area under the concentration-time curve from time zero to infinity. Coadministration of rolapitant did not affect the exposure of midazolam. Rifampin coadministration resulted in lower concentrations of rolapitant, and ketoconazole coadministration had no or minimal effects on rolapitant exposure. Rolapitant was safe and well tolerated when coadministered with ketoconazole, rifampin, or midazolam. No new safety signals were reported compared with previous studies of rolapitant.

Although current quetiapine labeling recommends that its dosage should be lowered 6-fold when coadministered with strong cytochrome P450 (CYP)3A inhibitors, a reported case of coma in a patient receiving quetiapine with lopinavir and ritonavir prompted the reevaluation of labeling recommendations for the dosing of quetiapine when coadministered with human immunodeficiency virus (HIV) protease inhibitors. Literature and database (FDA Adverse Event Reporting System and United States Symphony Health Solutions' Integrated Dataverse Database) searches allowed us to identify cases of coma and related adverse events involving the coadministration of quetiapine and HIV protease inhibitors and to estimate the frequency of concomitant use. Literature review and physiologically based pharmacokinetic modeling allowed us to estimate the potential for CYP3A inhibition to contribute to adverse events related to HIV protease inhibitor-quetiapine coadministration. We identified excess sedation following coadministration of quetiapine and an HIV protease inhibitor in 3 reports without obvious confounders. In prescription claims data, 0.4% of quetiapine patients were dispensed a concurrent ritonavir prescription. The quetiapine dose was not reduced on ritonavir initiation in 90% of therapy episodes. Available data indicate to us that all HIV protease inhibitors combined with ritonavir are likely to be strong CYP3A inhibitors. We predicted that ritonavir would increase quetiapine exposure comparable to the strong CYP3A inhibitor ketoconazole. The current dosing recommendations for use of quetiapine with strong CYP3A inhibitors (ie, 6-fold lower quetiapine dose) are appropriate and should be followed when quetiapine is coadministered with HIV protease inhibitors.

NEPA is the first fixed-combination antiemetic composed of the neurokinin-1 receptor antagonist netupitant (netupitant; 300 mg) and the 5-hydroxytryptamine-3 receptor antagonist palonosetron (palonosetron; 0.50 mg). This study evaluated the pharmacokinetic profiles of netupitant and palonosetron. The pharmacokinetic profiles of both drugs were summarized using data from phase 1-3 clinical trials. netupitant and palonosetron have high absolute bioavailability (63%-87% and 97%, respectively). Their overall systemic exposures and maximum plasma concentrations are similar under fed and fasting conditions. netupitant binds to plasma proteins in a high degree (>99%), whereas palonosetron binds to a low extent (62%). Both drugs have large volumes of distribution (cancer patients: 1656-2257 L and 483-679 L, respectively). netupitant is metabolized by cytochrome P450 3A4 to 3 major pharmacologically active metabolites (M1, M2, and M3). palonosetron is metabolized by cytochrome P450 2D6 to 2 major substantially inactive metabolites (M4 and M9). Both drugs have similar intermediate-to-low systemic clearances and long half-lives (cancer patients: netupitant, 19.5-20.8 L/h and 56.0-93.8 hours; palonosetron: 7.0-11.3 L/h and 43.8-65.7 hours, respectively). netupitant and its metabolites are eliminated via the hepatic/biliary route (87% of the administered dose), whereas palonosetron and its metabolites are mainly eliminated via the kidneys (85%-93%). Altogether, these data explain the lack of pharmacokinetic interactions between netupitant and palonosetron at absorption, binding, metabolic, or excretory level, thus highlighting their compatibility as the oral fixed combination NEPA, with administration convenience that may reduce dosing mistakes and increase treatment compliance.

High-dose methotrexate (>0.5 g/m² ) is among the first-line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high-dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model-based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician-friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl-speaks-NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7-12.8 seconds to 0.4-1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model-based, convenient, and efficient tool for high-dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose.

Amitriptyline is a tricyclic antidepressant that is metabolized mainly by CYP2C19 and CYP2D6 enzymes. Higher plasma levels of amitriptyline and its active metabolite, nortriptyline, are associated with an increased risk of adverse events including anticholinergic effects. The aim of this study was to evaluate the effects of CYP2C19 and CYP2D6 genetic polymorphisms on amitriptyline and nortriptyline pharmacokinetics. Twenty-four Korean healthy adult male volunteers were enrolled in the study after stratification by their CYP2C19 and CYP2D6 genotypes. Serial blood draws for pharmacokinetic analysis were made after a single oral 25-mg dose of amitriptyline was administered. Plasma amitriptyline and nortriptyline concentrations were measured by a validated liquid chromatography with tandem mass spectrometry. Population pharmacokinetic modeling analysis was conducted using NONMEM, which evaluated the effects of CYP2C19 and CYP2D6 genotypes on amitriptyline and nortriptyline pharmacokinetics. The biotransformation of amitriptyline into nortriptyline was significantly different between subjects with the CYP2C19*2/*2, *2/*3, and *3/*3 genotypes and those with the other genotypes, with an estimated metabolic clearance of 17 and 61.5 L/h, respectively. Clearance of amitriptyline through pathways other than biotransformation into nortriptyline was estimated as 18.8 and 30.6 L/h for subjects with the CYP2D6*10/*10 and *10/*5 genotypes and those with the other genotypes, respectively. This study demonstrated a quantitative effect of the CYP2C19 and CYP2D6 genotypes on amitriptyline and nortriptyline pharmacokinetics. Production of nortriptyline from amitriptyline was associated with CYP2C19 genotypes, and clearance of amitriptyline through pathways other than biotransformation into nortriptyline was associated with CYP2D6 genotypes. These observations may be useful in developing individualized, optimal therapy with amitriptyline.