CPT: Pharmacometrics & Systems Pharmacology最新文献

Valproic acid (VPA), a widely prescribed short-chain fatty acid for managing epilepsy, psychiatric conditions, and migraines, offers significant therapeutic benefits despite its concerning toxicity profile. This study extends our previously developed Quantitative Systems Pharmacology (QSP) model by incorporating age, sex, and formulation-related covariates to characterize VPA-induced toxicity across diverse populations. We developed virtual populations representing four demographic groups: toddlers (0-2 years), children (2-14 years), women (14-40 years), and men (14-40 years). Age-appropriate dosing regimens were simulated: 35 mg/kg/day for toddlers, 25 mg/kg/day for children, and 15 mg/kg/day for adults. The model successfully predicted overall incidences of hyperammonemia (29%), hyperlipidemia (54%), and hepatotoxicity (2%), aligning with previously reported clinical data. Notably, our model revealed distinct age-dependent toxicity patterns, with significantly lower incidences in toddlers compared to similar profiles observed in children and adult women. Formulation comparison demonstrated that extended-release formulations showed consistent directional trends toward lower adverse effect incidences compared to delayed-release formulations across all endpoints. The model also quantitatively assessed L-carnitine supplementation (CS) benefits, suggesting that administering L-carnitine at twice the VPA dose (in mg) effectively prevents hyperammonemia and maintains physiological fatty acid levels. This work advances our understanding of VPA-induced toxicity mechanisms across populations and provides evidence-based recommendations for optimizing formulation selection and CS in both pediatric and adult patients receiving VPA therapy.

This study introduces a novel drug-disease modeling framework designed to assess the benefit-risk balance of antibody-drug conjugates (ADC) in oncology. The framework integrates dose levels, pharmacokinetics, tumor growth dynamics, progression-free survival (PFS), and dose-adjusted adverse events. We demonstrated this through its application to tusamitamab ravtansine (Tusa), an ADC targeting Carcinoembryonic Antigen-Related Cell Adhesion Molecule 5 in non-squamous non-small cell lung cancer (nsq NSCLC). We developed our model using phase I trial safety data from 254 patients (doses: 5-190 mg/m²) and efficacy data from 88 nsq NSCLC patients (dose 100 mg/m²). This model accurately predicted phase III outcomes for the Tusa arm via an iterative simulation. Using phase III baseline characteristics, simulations of Tusa doses comparing three dose levels (80, 100, and 120 mg/m² every 2 weeks) revealed a critical trade-off: while higher doses increased response rates, they also substantially increased corneal toxicity without improving survival. These findings demonstrate how early-phase data can inform optimal dose selection by quantifying benefit-risk. This robust framework and methodology is generalizable beyond Tusa, offering value to support dose selection and trial decision-making in oncology drug development.

Model-informed drug development (MIDD) has emerged as a cornerstone paradigm in global pharmaceutical innovation. Historically underutilized in China, MIDD methodologies gained momentum following the National Medical Products Administration's (NMPA) 2020 release of the Model-Informed Drug Development Technical Guideline, which was subsequently augmented by supplementary technical guidelines to systematically promote and institutionalize MIDD adoption. This study conducts a longitudinal analysis of MIDD implementation in China-approved innovative drugs from 2018 to 2024, spanning pre- and post-guideline eras.

Exposure-response analyses are central to dose selection in drug development. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose-exposure-response analyses. For simulated example studies inspired by real-world scenarios, we define dose-response estimands of clinical interest. The estimands are formalized using the potential outcome notation. Assumptions on the setup of the studies and the relation between treatment, exposure and response are expressed as a directed acyclic graph (DAG). The estimand is transformed using the assumption into expressions to identify the estimand based on the observed data. Three types of expressions are obtained. First, a pooled dose-exposure-response (DER) analysis that corresponds to a standard DER analysis as executed for many projects. Second, a pooled, covariate adjusted dose-response (DR) analysis, and third summaries of the outcomes in each randomized cohort. In our example, DER provides more precise estimates than DR as judged by the mean square error (MSE) of repeated simulation estimation. This work advances methodological rigor in DER analyses by integrating with causal inference methodologies and the estimand framework, enabling clearer interpretation of modeling assumptions and results. This has important concrete advantages. We obtain different estimation methods for the same estimand that may be compared to validate them. The potential for bias in the different estimation methods can be formally assessed. The proposed approach provides a generalizable strategy to improve exposure-response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

Oncolytic viruses, specifically Sindbis virus (SINV), combined with cytokines show promising results in slowing glioma progression, but a quantitative understanding of their effects remains limited. In this study, we use an ordinary differential equation (ODE) model to examine the effect of adding cytokines to oncolytic SINV therapy. We fit the mathematical model to data extracted from published tumor growth curves to estimate key model parameters. We find that there are statistically significant differences between the infection rates of SINV and cytokine-bearing SINV, as well as differences in the cytokine's ability to reduce viral production. Model simulations show that the addition of cytokines causes an almost immediate reduction in the tumor size caused by the increased viral infection rate. The simultaneous reduction in viral production caused by the cytokines results in oscillations in virus, cytokines, and tumor volume. By providing parameter estimates for key biological processes, our model can help optimize treatment strategies and guide future research in oncolytic virotherapy.

The investigational antigen-specific immunotherapy mRNA-4359 is a lipid nanoparticle-encapsulated mRNA-based immunotherapy that encodes for the immunogenic indoleamine 2,3-dioxygenase (IDO) and programmed death-ligand 1 (PD-L1) antigens. An ongoing first-in-human (FIH) phase 1/2 clinical trial (NCT05533697) will evaluate the safety and antitumor activity of mRNA-4359 when administered alone and in combination with the anti-programmed death-1 agent pembrolizumab in participants with advanced solid tumors. The current analysis applied a novel immunostimulatory/immunodynamic (IS/ID) modeling approach to determine a plausible starting dose of mRNA-4359 for the FIH trial. The model used for the FIH dose prediction was calibrated to previously published clinical trial data obtained for an immunomodulatory peptide-based vaccine activating IDO- and PD-L1-specific T cells in patients with metastatic melanoma. The analysis found that a 180 μg dose of mRNA-4359 would possibly elicit a T-cell response similar to a 200 μg dose of the peptide-based vaccine with a range of 45-360 μg, assuming a potential 4-fold higher to 2-fold lower efficiency (the ability to elicit IFN-γ secreting T cells, indicative of cytotoxic potential). Model simulations further predicted that a 15-cycle every 3 weeks regimen of mRNA-4359 could be expected to provide longer responses than other feasible simulated regimens. Finally, the IS/ID modeling analysis determined that a 100 μg dose of mRNA-4359 would be the most appropriate starting dose for FIH trials. The described approach represents a unique application of IS/ID modeling to determine a therapeutically relevant FIH starting dose in the absence of supporting preclinical animal data.

Pediatric physiologically-based pharmacokinetic (PBPK) modelling plays an increasing role in selecting doses in children and addressing clinical pharmacology questions. Ethical concerns often limit clinical pharmacology studies that have no direct therapeutic benefit in children, highlighting the value of PBPK model predictions. However, regulatory acceptance of pediatric PBPK models remains limited because of uncertainties in system-specific information and inadequate model qualification. Ambiguous ontogeny data of drug metabolizing enzymes (DME) and transporters are recognized as significant obstacles to the accurate pharmacokinetics (PK) prediction in children and the leading cause of insufficient pediatric PBPK model qualification. To address this challenge, a population PBPK modeling approach is proposed. This method is analogous to whole-body PBPK modeling and allows the estimation of DME/transporter ontogenies using sparse PK data collected from children and adults by nonlinear mixed-effect modeling. Well-characterized ontogeny functions of key DME/transporters enhance the extrapolation ability of PBPK models and facilitate model-informed drug development (MIDD) in children. This article proposes a strategy for pediatric PK extrapolation using population PBPK modeling, illustrated through the case example of risdiplam, approved for the treatment of spinal muscular atrophy. The ontogeny modeling, extrapolations of PK to unstudied pediatric populations, and drug-drug interaction (DDI) risk assessment are also discussed. The population PBPK modeling approach is intended to address the inconsistencies in ontogeny data and augment PBPK modeling for quantitative clinical pharmacology assessments in children. It will accelerate optimal dose finding and provide guidance for adequate use of drugs in pediatric patients, which is especially important for developing treatments for progressive pediatric rare diseases.

We introduce the Regularized Horseshoe (RHS) in the context of covariate selection for population PK/PD models. Unlike stepwise approaches which are commonly used in this context, the RHS can simultaneously assess all possible parameter-covariate relationships in a single model fit by leveraging the fact that such relationships are usually sparse in practice. Furthermore, the RHS avoids the over-estimation of effect sizes that commonly occurs with stepwise approaches and avoids overfitting by averaging over the posterior uncertainty of possible parameter-covariate relationships. This leads to improved predictive performance on held-out data. We first give an overview of common covariate selection methods for population PK/PD modeling, then we define the RHS and provide intuition for how the method works. We then provide Stan code and a set of hyperparameters applicable to general population PK/PD models that can readily be applied by practitioners. Using an extensive simulation study, the beneficial properties of the RHS are illustrated and compared to popular covariate selection methods that are commonly used on population PK/PD models. Lastly, we compare the RHS to other commonly used methods on four real-world PK/PD datasets and illustrate its superior predictive performance on held-out data.

This work investigates how correlations between covariates influence the estimation of their effects in pharmacometric models. The focus is on quantifying the impact on conditional and unconditional covariate effect estimates and assessing the consequences for model interpretation, communication, and dosing recommendations. A theoretical framework was used to describe the mathematical relationship between conditional and unconditional coefficients. This was verified by simulations across a wide range of covariate correlation strengths and relative covariate effect sizes. The practical consequences of misinterpreting conditional effects were evaluated in the context of dose selection and a priori dose individualization. As predicted by theory, covariate correlation had a substantial effect on the conditional covariate coefficient estimates, while unconditional estimates remained stable. Interpreting conditional covariate effects in isolation led to incorrect conclusions about dosing needs and introduced bias and imprecision in individual dose predictions. In contrast, both the complete conditional model and the unconditional model gave accurate predictions when applied appropriately. Unconditional covariate effects offer greater interpretability, making them more suitable for communicating individual covariate impacts in drug labels, publications, and forest plots. We demonstrate that conditional effects are highly sensitive to model context and covariate correlation, making them poor proxies for the unconditional effect, which is often the quantity of interest for dosing and communication. To minimize misinterpretation, unconditional effects should be reported when describing the influence of individual covariates, while the complete conditional model should be used for simulations and exposure predictions. This dual approach can improve clarity and reduce the risk of misunderstanding in model-informed decision-making.

Checkpoint inhibitors that target PD-1 or PD-L1 have had a profound effect in a variety of cancers, both as a single therapy and in combinations. Meta-analyses suggest that monoclonal antibodies (mAbs) targeting PD-1 may yield better survival outcomes compared to anti-PD-L1 mAbs, however these conclusions are limited by a lack of direct clinical comparisons between the two classes. There is a shared hypothesis for the mechanism of action of these drugs: inhibition of the PD-1:PD-L1 signaling pathway through binding to either target. Using a Quantitative Systems Pharmacology (QSP) model-based analysis, we test whether differential inhibition of PD-1:PD-L1 complex formation (a surrogate for inhibition of the signaling pathway) is sufficient to explain the efficacy difference between anti-PD-1 and anti-PD-L1 mAbs observed in clinical meta-analyses. The model predicts that high levels of PD-1:PD-L1 complex inhibition are achieved by all the considered mAbs at their clinical dosing regimens, but it does not indicate that anti-PD-1 mAbs yield higher inhibition over anti-PD-L1s, in contrast to the meta-analyses. Significant model parameter variability and a bootstrap sampling analysis mirroring the comparison from Duan et al. (2020) do not change this conclusion. This suggests that anti-PD-1 and anti-PD-L1 mAbs are not differentiable based on PD-1:PD-L1 complex inhibition alone, and that the hypothesized shared mechanism of action of the two classes of drugs is incomplete.