CPT: Pharmacometrics & Systems Pharmacology最新文献

Myelodysplastic syndromes (MDS) represent a group of bone marrow disorders involving cytopenias, hypercellular bone marrow, and dysplastic hematopoietic progenitors. MDS remains a challenge to treat due to the complex interplay between disease-induced and treatment-related cytopenias. Venetoclax, a selective BCL-2 inhibitor, in combination with azacitidine, a hypomethylating agent, is currently being investigated in patients with previously untreated higher-risk MDS. We present an integrated semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model developed using preliminary clinical data from an ongoing Phase 1b study evaluating the safety and efficacy of venetoclax in combination with azacitidine in treatment-naïve patients with higher-risk MDS. Longitudinal data from 57 patients were used to develop the model, which accounted for venetoclax PK and azacitidine treatment to describe time dynamics of bone marrow blasts, neutrophils, red blood cells, and platelets. The proliferation and maturation of progenitor cells in the bone marrow to peripheral cells is described via three parallel connected transit models including feedback terms. The model also accounted for bone marrow crowding and its impact on hematopoiesis. Model validation demonstrated adequate goodness-of-fit, visual and numerical predictive checks. Model predicted complete remission (CR) rates and marrow complete remission (mCR) rates closely matched observed rates in the clinical study, and simulated efficacy (recovery of blast count, CR, and mCR rates) and safety (neutropenia and thrombocytopenia) endpoints aligned with expected outcomes from various dosing regimens. Importantly, the semi-mechanistic model may aid understanding and discriminating between disease-driven and drug-induced cytopenias.

The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3. QSP modeling has proven successful in metabolic and cardiovascular diseases and has expanded into oncology, immunotherapy, and infectious diseases. Despite its benefits, QSP model validation through clinical trial simulations using virtual patients (VPs) is challenging because of parameter variability and high computational costs. To address these challenges, this study proposes a hybrid method that combines Bayesian optimization with machine learning for efficient parameter screening. Our approach achieved an acceptance rate of 27.5% in QSP simulations, which is in sharp contrast with the 2.5% rate of conventional random search methods, indicating more than 10-fold improvement in efficiency. By facilitating faster and more diverse VPs generation, this method promises to advance clinical trial simulations and accelerate drug development in pharmaceutical research.

Krzyzanski W, Stockard B, Gaedigk A, et al. Developmental pharmacokinetics of indomethacin in preterm neonates: severely decreased drug clearance in the first week of life. CPT Pharmacometrics Syst Pharmacol. 2023;12:110–121. doi:10.1002/psp4.12881

In the published version of the above article, the equation reported to convert dried blood spot (DBS) indomethacin concentrations to plasma concentrations is incorrect. Rather than “plasma[IND] = DBS[IND]/(1 – hematocrit) * 1.608,” the equation should be “C(plasma) = 1.837(C(DBS)/(1 – Hct/100)) – 236.6.” There is also an inaccurate statement in the Bioanalytical methods section: “A correction factor (1.608, mean of the ratio of plasma:DBS concentrations) was used to calculate the theoretical plasma concentrations from the hematocrit-corrected DBS concentration,” which does not align with the data analysis that was performed.

This author error in reporting does not affect the results or conclusions of the paper as the correct equation (more accurate and appropriate) was used to convert DBS to plasma concentrations for data analysis, and the wrong equation was reported in the manuscript.

We apologize for this error.

This study aimed at leveraging data from phase I/II clinical trials to build a nonlinear joint model of serum M-protein kinetics and progression-free survival (PFS) accounting for the effects of isatuximab (Isa), pomalidomide (Pom), and dexamethasone (Dex) in patients with relapsed and/or refractory multiple myeloma. Serum M-protein levels and PFS data from 203 evaluable patients, included either in a phase I/II study (n = 173) or in a phase I study (n = 30), were used to build the model. First, we independently developed a longitudinal model and a PFS model. Then, we linked them in a nonlinear joint model by selecting the link function that best captured the association between serum M-protein kinetics and PFS. A Claret tumor growth-inhibition model accounting for the additive effects of Isa, with an E_max function, Pom, and Dex on serum M-protein elimination was selected to describe serum M-protein kinetics. PFS was best described with a log-logistic model and associations with baseline beta-2 microglobulin level, age, and coadministration of Dex were identified. The instantaneous change in serum M-protein level was found to be associated with PFS in the final joint model. Using model simulations, we retrospectively supported the Isa 10 mg/kg weekly for 4 weeks, then biweekly (QW/Q2W) dosing regimen of the ICARIA-MM phase III pivotal study, and validated it using the same phase III pivotal study data.

Duchenne muscular dystrophy (DMD), a rare pediatric disease, presents numerous challenges when designing clinical trials, mainly due to the scarcity of available trial participants and the heterogeneity of disease progression. A quantitative clinical trial simulator (CTS) has been developed based on previously published five disease progression models describing each of the longitudinal changes in the velocity at which individuals can complete specified timed functional tests, frequently used as clinical trial efficacy endpoints (supine-stand, 4-stair climb, and 10 m walk/run test or 30-foot walk/run test), as well as each of the longitudinal changes in forced vital capacity and North Star Ambulatory Assessment total score. The model-based CTS allows researchers to optimize the selection of numerous trial parameters for designing trials for the five functional measures commonly used as endpoints in DMD clinical trials. This case report serves as a demonstration of the tool's functionality while providing an easy-to-follow guide for users to reference when preparing simulations of their own design. Two case studies, using input selection based on previous DMD clinical trials, provide realistic examples of how the tool can help optimize clinical trial design without the risk of decreasing statistical significance. This optimization allows researchers to mitigate the risk of designing trials that may be longer, larger, or more inclusive/exclusive than necessary.

Midomafetamine (3,4-methylenedioxymethamphetamine [MDMA]) is under the U.S. Food and Drug Administration review for treatment of post-traumatic stress disorder in adults. MDMA is metabolized by CYP2D6 and is a strong inhibitor of CYP2D6, as well as a weak inhibitor of renal transporters MATE1, OCT1, and OCT2. A pharmacokinetic phase I study was conducted to evaluate the effects of food on MDMA pharmacokinetics. The results of this study, previously published pharmacokinetic data, and in vitro data were combined to develop and verify MDMA population pharmacokinetic and physiologically based pharmacokinetic models. The food effect study demonstrated that a high-fat/high-calorie meal did not alter MDMA plasma concentrations, but delayed T_max. The population pharmacokinetic model did not identify any clinically meaningful covariates, including age, weight, sex, race, and fed status. The physiologically based pharmacokinetic model simulated pharmacokinetics for the proposed 120 and 180 mg MDMA HCl clinical doses under single- and split-dose (2 h apart) conditions, indicating minor differences in overall exposure, but lower AUC within the first 4 h and delayed T_max when administered as a split dose compared to a single dose. The physiologically based pharmacokinetic model also investigated the drug–drug interaction magnitude by varying the fraction metabolized by a representative CYP2D6 substrate (atomoxetine) and evaluated inhibition of renal transporters. The simulations confirm MDMA is a potent CYP2D6 inhibitor, but likely has no meaningful impact on the pharmacokinetics of drugs sensitive to renal transport. This model-informed drug development approach was employed to inform drug–drug interaction potential and predict pharmacokinetics of clinically relevant dosing regimens of MDMA.

Vancomycin is an antibiotic used for severe infections. To ensure microbiological efficacy, a ratio of AUC/MIC ≥400 is recommended. However, there is significant interindividual variability in its pharmacokinetic parameters, necessitating therapeutic drug monitoring to adjust dosing regimens and ensure efficacy while avoiding toxicity. Population pharmacokinetic (PopPK) models enable dose personalization, but the challenge lies in the choice of the model to use among the multitude of models in the literature. We compared 18 PopPK models created from populations with the same sociodemographic and clinicobiological characteristics. Simulations were performed for a 47 years old man, weighing 70 kg, with an albumin level of 35.5 g/L, a creatinine clearance of 100 mL/min, an eGFR of 106 mL/min/1.73 m², and receiving an intravenous infusion of 1 g × 2/day of VCM over 1 h for 48 h. Simulations of time–concentration profiles revealed differences, leading us to determine the probability of achieving microbiological efficacy (AUC/MIC ≥ 400) with each model. Depending on some models, a dose of 1 g × 2/day is required to ensure microbiological efficacy in over 90% of the population, while with the same dose other models do not exceed 10% of the population. To ensure that 90% of the patients are correctly exposed, a dose of vancomycin ranging from 0.9 g × 2/day to 2.2 g × 2/day is necessary a priori depending on the chosen model. These differences raise an issue in choosing a model for performing therapeutic drug monitoring using a PopPK model with or without Bayesian approach. Thus, it is fundamental to evaluate the impact of these differences on both efficacy/toxicity.

Batch-to-batch variability in inhalation powder has been identified as a potential challenge in the development of generic versions. This study explored the impact of batch-to-batch variability on the probability of establishing pharmacokinetic (PK) bioequivalence (BE) in a two-sequence, two-period (2 × 2) crossover study. A model-based parametric simulation approach was employed, incorporating batch-to-batch variability through the relative bioavailability (RBA) ratio. In the absence of batch variability, recruiting a total of 48 subjects in a 2 × 2 crossover study with the reference formulation resulted in a 95% probability of concluding BE. However, this probability decreased to 80% with a 5% batch difference in RBA and further declined to 30% with a 10% batch difference. With a 10% batch difference, the required number of subjects to achieve an 80% probability of concluding BE increased to 84. When considering product differences between the reference and the test formulations, an additional 10% batch difference reduced the study power from 97% to 30% for a T/R bioavailability ratio of 100% in a 2 × 2 crossover study with 48 subjects. As a result, the substantial impact of batch-to-batch variability on the study power and type I error of the PK BE study may pose significant challenges for the development of generic Advair Diskus due to its degree of PK batch-to-batch variability. Therefore, alternative PK BE study designs and guidelines are needed to adequately address the influence of batch-to-batch variability in products like Advair Diskus.

Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.

Selexipag is an oral selective prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension (PAH) in adults. To date, no treatment targeting the prostacyclin pathway is approved for pediatric patients. Our goal is to identify a pediatric dose regimen that results in comparable exposures to selexipag and its active metabolite JNJ-68006861 as those shown to be efficacious in adult PAH patients. Extrapolation from the population pharmacokinetic (PK) model developed in adults (GRIPHON study; NCT01106014) resulted in the definition of three different pediatric body weight groups (≥9 to <25 kg, ≥25 to <50 kg, and ≥50 kg) with corresponding starting doses (100, 150, and 200 μg twice daily) and maximum allowed doses (800, 1200, and 1600 μg twice daily). The proposed pediatric dose regimen was subsequently tested in a clinical study (NCT03492177), including 63 pediatric PAH patients ≥2 to <18 years of age and a body weight range of 9.9–93.5 kg. The body weight-adjusted dose regimen for selexipag resulted in comparable systemic exposures to selexipag and its active metabolite in pediatric patients as previously observed in adult PAH patients. Updating the adult selexipag population PK model provided overall consistent parameters and confirmed that the PK characteristics of selexipag and its active metabolite were comparable between pediatric and adult patients. The presented selexipag dose regimen for pediatric PAH patients is considered appropriate for continuing the clinical evaluation of the safety and efficacy of selexipag in pediatric patients ≥2 years of age.