Journal of Clinical Pharmacology最新文献

Model-informed drug development (MIDD) has traditionally been guided by hypothesis-driven modeling, in which models are constructed based on established biological mechanisms, physicochemical principles, and experimental hypotheses. More recently, the rise of artificial intelligence has enabled data-driven modeling approaches that often bypass explicit mechanistic assumptions. The potential synergy between these two paradigms is reshaping strategies for implementing MIDD. This study aims to compare deep-learning-driven and hypothesis-driven approaches, highlighting their respective strengths, limitations, and opportunities for integration. A case study is presented with the re-analysis of warfarin PK and PK/PD using both modeling paradigms. The comparative PK analysis indicated that the deep-learning model achieved predictive performance comparable to, and numerically slightly better than, hypothesis-driven modeling based on a one-compartment PK model. The hypothesis-based PK/PD analysis was conducted using three model structures: direct effect, effect compartment, and indirect response. The results of these analyses, including the estimated clinical dose, were compared with those obtained from the deep-learning approach. The deep-learning model demonstrated predictive performance similar to that of the effect compartment and indirect response models and yielded a comparable estimate of the effective dose across the two modeling approaches. A simulation-based sensitivity analysis was conducted to evaluate the robustness of the dose selection derived from the deep-learning-based modeling approach. The outcomes of the analysis suggest that effective implementation of the MIDD paradigm may be enhanced through the complementary use of deep-learning-based approaches alongside established hypothesis-driven, mechanistic models, thereby supporting evidence-based drug development and regulatory decision-making.

Accurate characterization of time-varying clearance (CL) is critical during early drug development, as it informs clinical study design and dosing strategies for future trials. However, detecting and modeling time-varying clearance is challenging due to limited data availability in early clinical development. Using pharmacokinetic data from a well-controlled first-in-human (FIH) study with single and multiple ascending doses, this study explored time-varying clearance, dose proportionality, and meal effects on pharmacokinetics (PK) of utreloxastat. Initial data analysis revealed time-varying systemic clearance, more-than-dose-proportional kinetics, and meal-dependent PK alterations, which informed the development of a population pharmacokinetic (PopPK) model. A systematic evaluation of twelve distinct time-varying clearance models identified an exponential time-varying clearance model within a two-compartment framework, incorporating first-order absorption with eight transit compartments, as the best fit to the data. Covariate analysis confirmed that sex, age, and body weight were not significant predictors of variability. This study is the first to showcase a comparative evaluation of time-varying clearance for a small molecule. It highlights the innovative application of time-varying clearance modeling during the FIH study as a pivotal step in optimizing model-informed dosing strategies. This approach underscores its importance in addressing non-linear pharmacokinetics and enhancing drug development practices for future clinical trials.

Malnutrition occurs at higher rates in children with complex medical conditions and can independently influence drug disposition and action. Yet FDA-approved product labels rarely address dosing in malnutrition. This study explores the extent to which malnourished children are expressly excluded from clinical trials. Industry-sponsored, pediatric, phase I-III studies deposited in ClinicalTrials.Gov through December 2024 with a full study protocol were reviewed. Protocols were evaluated for inclusion and exclusion (I/E) criteria related to anthropometric and clinical indicators of malnutrition. I/E criteria were fully characterized along with the study phase, intervention type, and treatment indication. 9882 studies were identified, 1759 with an uploaded protocol. 616 studies (35%) contained 777 distinct I/E criteria related to malnutrition (1-6 per study). Across all protocols, 71% exclusively restricted participation of children with evidence of undernutrition, 9% with overnutrition, and 20% with both. There were no statistical differences observed based on intervention type, though differences by study phase were observed. Restrictions were seen most frequently for respiratory, mental/behavioral, obstetric/perinatal, and emergency use indications and least frequently for dermatologic, oncologic, and eyes, ears, nose, and throat disorders. Non-specific I/E criteria suggest that these findings likely underestimate the extent of malnutrition-based exclusions. Despite growing attention paid to obesity, pediatric clinical trials are far more likely to restrict the participation of undernourished children. Though unrealistic to relax malnutrition related I/E criteria for all studies, consideration should be given for conditions where high rates of malnutrition are expected to avoid trial populations that do not reflect clinical practice.

The poly(ADP-ribose) polymerase inhibitor talazoparib, combined with the androgen receptor inhibitor enzalutamide is approved for patients with homologous recombination repair (HRR) gene-mutated metastatic castration-resistant prostate cancer (mCRPC) in the US and with mCRPC in whom chemotherapy is not clinically indicated in Europe. We provide a population pharmacokinetic model for this combination in patients with mCRPC unselected for HRR deficiencies from TALAPRO-2 (NCT03395197). The pooled dataset included 811 patients treated with enzalutamide plus either talazoparib or placebo. The final enzalutamide model was a two-compartment model with first-order absorption and inter-individual variability (IIV) on apparent clearance (CL_e/F_e) and apparent central volume of distribution (Vc_e/F_e) and included effects of baseline body weight and age on CL_e/F_e and Vc_e/F_e. For the active metabolite N-desmethyl enzalutamide, a two-compartment model with IIV on CL_n and Vc_n adequately described the observed data and included the effect of body weight on CL_n and Vc_n. The final talazoparib model was well characterized by a two-compartment model with first-order absorption and IIV on talazoparib apparent base clearance (CL_t0/F_t) and Vc_t/F_t. The effect of enzalutamide and N-desmethyl enzalutamide on CL_t/F_t of talazoparib was modeled through a linear relationship. The single covariate effect of baseline creatinine clearance on CL_t0/F_t showed that relative to the reference value for normal renal function, CL_t0/F_t decreased by 8% for mild, 27% for moderate, and 46.7% for severe renal impairment. Simulations showed that a dose reduction of enzalutamide does not require talazoparib dose modification since the magnitude of exposure reduction for talazoparib was not considered clinically significant.

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.