CPT: Pharmacometrics & Systems Pharmacology最新文献

RLYB212 is a human monoclonal anti-human platelet antigen (HPA)-1a immunoglobulin gamma 1 in clinical development as a subcutaneous injection for the prevention of maternal alloimmunization to fetal HPA-1a leading to fetal and neonatal alloimmune thrombocytopenia (FNAIT). This analysis developed a target-mediated drug disposition (TMDD) model to simultaneously characterize RLYB212 pharmacokinetics (PK) and HPA-1a-positive platelet dynamics in HPA-1b/b (HPA-1a-negative) volunteers. The model was then used to perform simulations to inform a dosing regimen in a phase II clinical study in pregnant women, where simulations accounted for physiological changes throughout pregnancy. Allometric scaling (0.75) for clearance and intercompartment transfer rate and volume (1) was included in the base model to account for variations in body weight. A 0.06 mg RLYB212 dose with a loading dose of 0.12 mg was identified as the optimal dosing regimen of RLYB212, which maintained exposures below the target upper boundary of ~10 ng/mL throughout pregnancy. This work presents an application of the TMDD model that advances the quantitative clinical pharmacology toolkit to understand monoclonal antibody PK in pregnancy.

Zavegepant (ZAVZPRET™) is a high-affinity, selective, small-molecule calcitonin gene-related peptide receptor antagonist available for acute treatment of migraine in adults. A population pharmacokinetic analysis was performed to describe zavegepant plasma concentration-time course, characterize bioavailability, and identify covariates affecting zavegepant exposure. The model was developed and validated using data from 10 phase I clinical studies, wherein zavegepant was administered intravenously, intranasally, or orally to healthy adults and patients with migraine. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling. A three-compartment model with first-order elimination from the central compartment, and sequential zero- and first-order absorption best described the observed plasma concentration-time course of zavegepant. Bioavailability was 5.1% and 0.65% for intranasal and oral treatment, respectively; absorption rate constants were 5.8 and 0.8 h^-1, respectively. Body weight-based empirical allometric scaling was applied using standard exponents (0.75 for clearance and 1 for volume of distribution). Age (range 18-71 years), race, ethnicity, sex, renal function, and co-administration of oral contraceptives or sumatriptan did not significantly change zavegepant pharmacokinetics. Moderate hepatic impairment (Child-Pugh score 7-9) or co-administration of rifampin decreased elimination clearance of oral zavegepant by ~40%. The zavegepant population pharmacokinetic model adequately characterized zavegepant concentration-time profiles, the bioavailability of intranasal and oral zavegepant, as well as the effect of intrinsic and extrinsic factors on zavegepant pharmacokinetics.

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically-based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non-pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates. The model was then applied to predict the impact of UGT1A1 phenotypes in extensive (EM), poor (PM), and ultra-rapid metabolizers (UM). The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 PMs was 1.6-fold higher than in EMs. The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 UMs mothers was 1.3-fold lower than in EMs. The predicted mean systemic and umbilical vein concentrations were in excess of the dolutegravir IC₉₀ at 17, 28, and 40 gestational weeks, regardless of UGT1A1 phenotype, indicating that the standard dose of dolutegravir (50 mg q.d., fed state) is generally appropriate in late pregnancy, across UGT1A1 phenotypes. Applying the model in breastfed infants, a 1.5-, 1.7-, and 2.2-fold higher exposure in 2-day-old neonates, 10-day-old neonates, and infants who were UGT1A1 PMs, respectively, compared with EMs of the same age. However, it should be noted that the exposure in breastfed infants who were UGT1A1 PMs was still an order of magnitude lower than maternal exposure with a relative infant daily dose of <2%, suggesting safe use of dolutegravir in breastfeeding women.

Uremia, a condition characterized by the retention of uremic toxins due to impaired renal function, may affect drug metabolism mediated by CYP3A4 enzymes. Evogliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor diabetic drug that is primarily metabolized by CYP3A4. This study aimed to construct a population pharmacokinetic (PK) and pharmacodynamic (PD) model for evogliptin in patients with varying degrees of renal disease, including end-stage renal disease on hemodialysis. A total of 688 evogliptin concentration and 598 DPP-4 activity data were available from 46 subjects. PK and PD data analyses were performed using a nonlinear mixed-effects model. The PK of evogliptin was optimally described by a two-compartment model with first-order absorption. The significant covariates in the final model included blood amylase and triglyceride on F1 (relative bioavailability). The simulation findings, together with previously reported PK data, provided evidence of a significant inhibition of the first-pass effect of evogliptin in patients with renal impairment. A direct link sigmoidal E_max model was developed to describe the relationship between evogliptin concentration and DPP-4 inhibition. The PD model predicted significant inhibition of DPP-4 at maximum effect (E_max: 88.9%) and a low EC₅₀ value (1.08 μg/L), indicating the high potency and efficacy of evogliptin. The developed PK/PD model accurately predicted exposure and the resulting DPP-4 activity of evogliptin in renal impairment. The findings of this study suggest that renal impairment and associated biochemical changes may impact the bioavailability of CYP3A4-metabolized drugs.

Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.

Breastfeeding is important in childhood development, and medications are often necessary for lactating individuals, yet information on the potential risk of infant drug exposure through human milk is limited. Establishing a lactation modeling framework can advance our understanding of this topic and potentiate clinical decision making. We expanded the modeling framework previously developed for sotalol using pregabalin as a second prototypical probe compound with similar absorption, distribution, metabolism, and elimination (ADME) properties. Adult oral models were developed in PK-Sim® and used to build a lactation model in MoBi® to simulate drug transfer into human milk. The adult model was applied to breastfeeding pediatrics (ages 1 to 23 months) and subsequently integrated with the lactation model to simulate infant drug exposure according to age, size, and breastfeeding frequency. Physiologically based pharmacokinetic (PBPK) model simulations captured the data used for verification both in adults and pediatrics. Lactation simulations captured observed milk and plasma data corresponding to doses of 150 mg administered twice daily to lactating individuals, and estimated a relative infant dose (RID) of approximately 7% of the maternal dose. The infant drug exposure simulations showed peak plasma concentrations of 0.44 μg/mL occurring within the first 2 weeks of life, followed by gradual decline with age after week four. The modeling framework performs well for this second prototypical drug and warrants expansion to other drugs for further validation. PBPK modeling and simulation approaches together with clinical lactation data could ultimately help inform infant drug exposure risk assessments to guide clinical decision making.

The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed-effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed-effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed-effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed-effects model. As an example, we use a simulated clinical trial with within-subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained.

Aldehyde oxidase (AO) contributes to the clearance of many approved and investigational small molecule drugs, which are often dual substrates of AO and drug-metabolizing enzymes such as cytochrome P450s (CYPs). As such, the lack of established framework for quantitative translation of the clinical pharmacologic correlates of AO-mediated clearance represents an unmet need. This study aimed to evaluate the utility of physiologically based pharmacokinetic (PBPK) modeling in the development of AO and dual AO-CYP substrates. PBPK models were developed for capmatinib, idelalisib, lenvatinib, zaleplon, ziprasidone, and zoniporide, incorporating in vitro functional data from human liver subcellular fractions and human hepatocytes. Prediction of metabolic elimination with/without the additional empirical scaling factors (ESFs) was assessed. Clinical pharmacokinetics, human mass balance, and drug-drug interaction (DDI) studies with CYP3A4 modulators, where available, were used to refine/verify the models. Due to the lack of clinically significant AO-DDIs with known AO inhibitors, the fraction metabolized by AO (fm_AO) was verified indirectly. Clearance predictions were improved by using ESFs (GMFE ≤1.4-fold versus up to fivefold with physiologically-based scaling only). Observed fm_i from mass balance studies were crucial for model verification/refinement, as illustrated by capmatinib, where the fm_AO (40%) was otherwise underpredicted up to fourfold. Subsequently, independent DDI studies with ketoconazole, itraconazole, rifampicin, and carbamazepine verified the fm_CYP3A4, with predicted ratios of the area under the concentration-time curve (AUCR) within 1.5-fold of the observations. In conclusion, this study provides a novel PBPK-based framework for predicting AO-mediated pharmacokinetics and quantitative assessment of clinical DDI risks for dual AO-CYP substrates within a totality-of-evidence approach.

The number of quantitative systems pharmacology (QSP) submissions to the U.S. Food and Drug Administration has continued to increase over the past decade. This report summarizes the landscape of QSP submissions as of December 2023. QSP was used to inform drug development across various therapeutic areas and throughout the drug development process of small molecular drugs and biologics and has facilitated dose finding, dose ranging, and dose optimization studies. Though the majority of QSP submissions (>66%) focused on drug effectiveness, QSP was also utilized to simulate drug safety including liver toxicity, risk of cytokine release syndrome (CRS), bone density, and others. This report also includes individual contexts of use from a handful of new drug applications (NDAs) and biologics license applications where QSP modeling was used to demonstrate the utility of QSP modeling in regulatory drug development. According to the models submitted in QSP submissions, an anonymous case was utilized to illustrate how QSP informed development of a bispecific monoclonal antibody with respect to CRS risk. QSP submissions for informing pediatric drug development were summarized along with highlights of a case in inborn errors of metabolism. Furthermore, simulations of response variability with QSP were described. In summary, QSP continues to play a role in informing drug development.