CPT: Pharmacometrics & Systems Pharmacology最新文献

Bispecific antibodies (bsAbs), for which each arm binds a distinct molecular target, are developed to engage soluble and cell surface targets in different therapeutic indications. Three key examples of mechanisms of action (MoA) for bsAbs are (1) immune cell engagers that foster immune cell interactions with target cells, (2) bispecifics that use one arm to increase specificity/localization to desired tissues to reduce on-target off-tissue toxicity, and (3) bispecifics that use two different arms to neutralize different disease targets. Understanding the pharmacokinetic (PK) profiles and target engagement of bsAbs poses unique challenges due to the existence of multiple targets with distinct biological properties. Here, we present a generalized minimal physiologically based pharmacokinetic (mPBPK) model to capture and predict the PK and target engagement of bsAbs across multiple tissues. First, we model the clinical PK and pharmacodynamic (PD) data for an anti-IL-13/IL-17 bsAb to capture and explain the PD response in the soluble cytokine target levels. Second, we model and simulate the PK-PD of the anti-CD20/CD3 T cell engaging antibody, mosunetuzumab, which acts via trans-binding between cell targets on B- and T-lymphocytes. Third, we use the model to explore case studies for other bsAb approaches to demonstrate the impact of binding affinity and avidity on both PK and target engagement and to provide insights into drug design. Overall, our work yields a model with example applications to advance the use of mechanistic modeling for early PK and target engagement predictions as well as for optimization of bsAb design.

Talazoparib is a poly(ADP-ribose) polymerase inhibitor approved for the treatment of breast and prostate cancer. Commercialization of a soft gelatin capsule (SGC) formulation developed post-approval required a bioequivalence (BE) and food effect (FE) study to bridge SGC with the initial commercial hard capsule (HC) formulation. Study execution and meeting BE criteria are challenging due to high variability in C_max, potentially higher C_max for SGC based on dissolution data, and the need to perform BE/FE assessment at steady state in cancer patients. Model-informed drug development (MIDD) was used to facilitate an efficient/feasible study design. Semi-mechanistic pharmacokinetic (PK)/pharmacodynamic (PD) modeling and simulations showed that AUC, instead of C_max, drove hematologic events, the main side effects of talazoparib. This supported a BE study powered for AUC equivalence only. Population PK simulation showed that following a 28-day treatment in the first period, 14 days in subsequent periods is sufficient for steady-state BE/FE assessments. Study results showed AUC met BE criteria while C_max was 37% higher for SGC relative to HC, which was deemed not clinically significant based on the PK/PD model. FE on SGC formulation was consistent with FE on HC formulation reported previously. The safety profile of the two formulations was generally consistent with the known safety profile. The totality of data (AUC equivalence, lack of impact of C_max on safety, observed safety data) supported bridging of the two formulations although C_max failed to meet BE criteria. MIDD was critical in study design optimization and supported approval of the SGC formulation.

Trial Registration: ClinicalTrials.gov Identifier: NCT04672460

Nintedanib reduces the rate of decline in forced vital capacity (FVC) in adult patients with idiopathic pulmonary fibrosis (IPF), chronic progressive-fibrosing interstitial lung diseases (ILDs) and systemic sclerosis-associated ILD (SSc-ILD). A pediatric Phase 3 trial (InPedILD) has been conducted in children 6–17 years, and partial extrapolation from adults to pediatrics was performed to support dose selection and benefit–risk assessment in pediatric patients with progressive-fibrosing ILDs. Previously developed population pharmacokinetic (popPK) and efficacy exposure-response (ER) meta-models across all non-oncologic pulmonary indications of nintedanib in adults were used as a basis for partial extrapolation to pediatric patients. Data from InPedILD and its open-label extension trial (InPedILD-ON) were incorporated and a Bayesian approach was utilized to extrapolate PK and FVC-based efficacy endpoints. PK of nintedanib was adequately described using a similar structure as the adult popPK model. The weight-based dosing scheme applied in InPedILD resulted in exposures similar to those in adult patients receiving the approved dose of nintedanib 150 mg twice daily. The ER models for FVC %predicted and FVC Z-score were similar to those developed in adults. Compared to adults, pediatric patients showed a slower estimated rate of decline for both endpoints. The estimated EC50 and Emax values in children and adolescents were comparable to those in adults. Partial extrapolation from adult to pediatric patients showed that the pre-defined pediatric dosing regimen resulted in similar nintedanib exposures compared to the efficacious exposures in adults. ER models suggested a similar beneficial treatment effect of nintedanib on FVC in children and adults.

Non-Hodgkin lymphoma (NHL) is the fifth most common malignancy and accounts for 5% of all cancers in the US, with the largest proportion being B-cell CD20 positive NHL. Odronextamab is a CD20xCD3 IgG4 bispecific T-cell-engaging monoclonal antibody under development for the treatment of relapsed or refractory (R/R) B-NHL. The objectives of this analysis were to characterize the pharmacokinetics (PK) of odronextamab in adult patients, and elucidate sources and correlates of variability. PK data of 507 patients with R/R B-NHL from ELM-1 (NCT02290951, Phase I; n = 167) and ELM-2 (NCT03888105, Phase II; n = 340) were analyzed. Odronextamab concentration–time profiles following intravenous administration of 0.03 mg to 320 mg doses were described by a bi-exponential decline with parallel linear (first-order) and non-linear (Michaelis–Menten) elimination processes. The modified Michaelis–Menten or target-mediated elimination was not only concentration–dependent but also time-dependent. A reduction in target-mediated clearance over time suggests a reduction in target abundance to a larger extent than associated with concentration alone, which is consistent with the treatment-induced depletion of the B cells observed in patients who underwent assessment. Linear clearance (CL) and steady-state volume of distribution were 0.189 L/day and 9.41 L, respectively. Target-mediated clearance was ~5 L/day at baseline, with an asymptote of ~0.03 L/day at steady state. With the largest covariate effect on odronextamab exposure, baseline body weight was directly correlated with CL and volume of distribution, albumin was inversely correlated with CL and volume of distribution, and baseline interleukin-10 was inversely correlated with CL.

Model-based meta-analysis (MBMA) informs key drug development decisions by integrating data, published or unpublished, from multiple studies. Due to these various sources of information and the use of summary-level data (e.g., mean responses over treatment arms or percent responders), MBMA models require careful implementation. This tutorial provides a comprehensive guide for conducting an MBMA with MonolixSuite, focusing on longitudinal continuous and categorical data. Two case studies are presented: the first examining naproxen in osteoarthritis and the second evaluating canakinumab compared to existing treatments in rheumatoid arthritis. The tutorial explains the process of model building and handling study heterogeneity in Monolix, including how to include between-study variability and between-treatment-arm variability. It also shows how to apply appropriate weighting due to the use of summary-level data. For model evaluation, the tutorial demonstrates the use of automatically generated diagnostic plots, statistical tests, and convergence assessment tools. Furthermore, it illustrates how to use the model in Simulx to support the decision-making process, such as by simulating clinical trials. This step-by-step guidance offers practical insights for leveraging MBMA in model-informed drug development.

We present a framework for maximum likelihood analysis on count observations that begin high and quickly drop to zero, for example, from hollow fiber drug comparison studies. This simulation study focuses on treating observed counts as Poisson or normally distributed for the purpose of estimating infection rebound after effective treatment. CFU profiles were simulated from inoculation to 96 h post-treatment. The PK-PD link was an Emax inhibitory model. Random parameters were pathogen growth and natural decay rates, drug concentration for half-maximal effect, and drug pathogen kill rate. Other parameters, including PK, were fixed. Parameters were adjusted to attain 67% efficacy at 24 h. Random parameter values were optimized for profiles observed at 24, 48, 72, and 96 h assuming each of four probability assumptions: (1) all CFU measurements were Poisson distributed (truth); (2) CFU < 128 were Poisson, higher values were normally distributed; (3) all observations were normally distributed; and (4) observations were normally distributed but CFU < 10 were censored. CFU-time profiles were re-simulated using the optimized parameter densities. Rebound percentage (CFU ≥ 10 at 24 h post-treatment) was best predicted using strategy 2, above. For limited periodically collected time series count data that quickly fall to 0, the true proportion reaching 0 (lack of rebound) was best modeled by assuming Poisson distribution at low counts. At higher counts (≥ 128), assuming normality is reasonable. Censoring observations leads to biased models.

Apixaban, a factor Xa inhibitor, is a direct oral anticoagulant with a well-balanced elimination; it is eliminated evenly via feces, urine (with no active secretion), and as metabolites after oral administration. The common understanding is that biliary secretion and enterohepatic circulation (EHC) of apixaban are limited in humans, and that fecal excretion may be attributable to intestinal secretion. However, a decrease in apixaban blood concentration with activated charcoal coadministration in humans suggests possible involvement of EHC. This study aimed to evaluate the contribution of biliary excretion, EHC, and intestinal secretion to apixaban pharmacokinetics (PK) using a physiologically-based pharmacokinetic (PBPK) model. A top-down analysis was performed using blood concentration and mass balance data from healthy volunteers. Model parameters were optimized using the Cluster-Gauss Newton method (CGNM), followed by the bootstrap method. The model accurately described observed data and indicated moderate to high biliary secretion relative to metabolic clearance. Simulated biliary secretion into the duodenum well predicted the biliary secretion data in humans (< 1% of dose at 8 h post-dose). Virtual knockout of EHC resulted in a shortened half-life from 8.7 to 2.9 h, and 17% and 55% decrease in area under the concentration curve (AUC) and fecal excretion after intravenous dosing, respectively, confirming the significant contribution of biliary excretion and EHC. The model also accurately described apixaban PK with activated charcoal coadministration at 2 or 6 h post-dose. Although further experimental validation (e.g., sandwich-cultured hepatocytes) would strengthen these findings, our study demonstrates that biliary secretion and EHC play a substantial role in apixaban elimination and disposition in humans.

A twice-daily administration of oral selumetinib (SLT) in the fasted state is the only approved pharmaceutical option for treating inoperable neurofibromatosis type I (NF-1) and plexiform neurofibromas (PN). In children, exposure to SLT is highly variable, and fasting presents a substantial burden. Therapeutic drug monitoring and pharmacokinetic modeling can support individualized therapy accompanied by a more rational alimentary routine. Twenty-eight children diagnosed with inoperable NF-1 or PN were recruited at a major pediatric oncological center. Twenty-two patients donated 156 blood samples in steady state for nonparametric population pharmacokinetic modeling. An equation was developed experimentally for estimating model error. Eleven three-compartment models were compared in terms of statistical performance. Monte Carlo simulations were performed to validate a limited external model using six additional patients and to compare the trough-to-peak SLT concentration ratios simulated for various dosing regimens to develop better control over exposure. A pharmacokinetic model that included total body weight as a covariate provided the best fit between predicted and observed concentrations (r = 0.994) and the best performance statistics. In the first Monte Carlo simulation, measured concentrations fell within the 0.01%–95% (median: 19.7%) quantiles of the simulated ranges. The second simulation revealed that 6-h (q6h), 8-h (q8h), and 12-h (q12h) dosing intervals would yield comparable trough-to-peak concentration ratios, with medians of 0.126 (range: 0.001–0.335), 0.104 (0.000–0.306), and 0.065 (0.000–0.279), respectively. The nonparametric population model provides efficient priors for making individual predictions of SLT concentrations. The simulation did not reveal any disadvantages of q6h or q8h dosing.

Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) may exhibit altered pharmacokinetics (PK) and pharmacodynamics (PD) of drugs compared with healthy populations. However, no physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model has been specifically developed for MASLD. Acetaminophen (APAP), a widely used analgesic, was selected to develop a PBPK/PD model predicting PK/PD changes of APAP and its metabolites in MASLD-related populations. Based on a comprehensive review of published APAP PK studies and examination of existing PBPK models, a simultaneous parent-metabolite PBPK model for APAP was developed and optimized in healthy people. The model simulated the dynamics of APAP and its five major metabolites: APAP-glucuronide (APAP-glu), APAP-sulfate (APAP-sul), N-acetyl-p-benzoquinone imine (NAPQI), APAP-cysteine (APAP-cys), and APAP-mercapturate (APAP-merc). The validated model was expanded to MASLD-related populations, including overweight, obese, nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), and cirrhosis with different severities. Finally, a PD model was integrated to correlate APAP's PK with pain relief scores. The PBPK model reproduced published clinical PK data for APAP and its metabolites in healthy and MASLD-related populations. At therapeutic doses, the toxic NAPQI remained at very low levels. APAP's pain relief efficacy was retained, but onset time may change in MASLD-related populations. This PBPK/PD approach provides a strategy for projecting drug exposure in MASLD-related populations, even without specific PK or PD data. It highlights modeling's utility for personalized medicine in MASLD patients and MASLD treatment drug development.

Rituximab (RTX), an anti-CD20 monoclonal antibody, has been used to treat autoimmune diseases such as rheumatoid arthritis (RA). However, variability in therapeutic response to RTX remains a challenge. Here, a systems model is developed to mimic B cell differentiation leading to antibody-secreting cells (ASCs), including plasmablasts (PBs) and plasma cells (PCs). The model features the localization of B cell subsets in the bone marrow and secondary lymphoid organs and incorporates the internalization process of the CD20–RTX complex. To reproduce clinical data from patients with RA receiving RTX and glucocorticoids, pharmacokinetic models for the drugs were built and respective pharmacodynamic profiles of CD19⁺ and CD20⁺ cells and PBs were well captured by optimizing model parameters, which were estimated with good precision. As ASCs are the primary source of pathogenic autoantibodies in RA, the extent and duration of ASC depletion were hypothesized as drivers of therapeutic response to RTX. Global sensitivity analyses identified the CD20–RTX binding affinity and elimination rate constant (i.e., Fcγ-mediated degradation, internalization) as major determinants of both CD19⁺ cells and ASCs. The influence of baseline PBs and PCs on ASCs was also suggested, providing potential mechanisms underlying responder and non-responder variability. The model accurately reproduced the temporal changes in CD19⁺ cells after combination treatment with RTX and glucocorticoids suggesting successful model validation. This study provides a mechanistic framework and insights into key drivers of responses to CD20-depletion treatment using B cell dynamics as an indirect biomarker of clinical endpoints, which might ultimately improve therapeutic outcomes.