CPT: Pharmacometrics & Systems Pharmacology最新文献

Statins are frequently prescribed for hyperlipidemia, a common comorbidity in patients with obesity and/or metabolic dysfunction-associated steatohepatitis (MASH). However, limited knowledge exists on how MASH may alter statin disposition within hepatocytes where the statin target, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, is located. This study used a physiologically based pharmacokinetic (PBPK)/permeability-limited multicompartment liver (PerMCL) framework, incorporating zonal transporter and drug-metabolizing enzyme data. Systemic and hepatocellular concentrations of pravastatin, rosuvastatin, and atorvastatin were simulated in Healthy Volunteers (HV), Obese, Morbidly Obese, and MASH virtual populations with the Simcyp Simulator. A pharmacodynamic model in Simcyp Designer was then used to simulate alterations in rosuvastatin cholesterol-lowering efficacy between these populations. Hepatic transport and metabolism pathways were verified against clinical data. Organic anion transporting polypeptide (OATP)1B model uptake pathways were verified using genotype and drug–drug interaction data. Atorvastatin metabolism pathways were verified using metabolite data. Steady-state plasma and zonal hepatocellular concentration–time profiles for each statin were simulated across virtual populations of 100 individuals aged 40–65 years. Simulations predicted > 70% increases in maximal total plasma concentrations and area under the curve for pravastatin and rosuvastatin in MASH compared to HV, with changes in these parameters for atorvastatin simulated to increase > 250%. In MASH, unbound hepatocellular exposure increased by up to 127% in the periportal region for atorvastatin and decreased by up to 55% in the pericentral region for rosuvastatin. The pharmacodynamic model simulated decreased rosuvastatin cholesterol-lowering efficacy in MASH compared with Obese, which could be compensated for with a 50% increase in dose according to exploratory simulations.

Asundexian is a potent, selective, and reversible inhibitor of activated clotting Factor XI currently under development for secondary prevention of recurrent ischemic stroke in the ongoing Phase III OCEANIC-STROKE study (NCT05686070). Here, we report the development of a population pharmacokinetic (popPK) model for asundexian. Plasma concentration data were available from 2914 participants enrolled in nine Phase I and II studies of asundexian. The pharmacokinetics (PK) of asundexian were well described by the popPK model. Within the investigated dose range of asundexian 10–100 mg once daily, the PK of asundexian was dose-proportional. The systemic apparent clearance (CL/F) of asundexian was estimated to be 2.25 L/h and the central volume of distribution (V_C/F) was 35.3 L. Body weight, age, sex, concomitant administration of cytochrome P450 3A4 (CYP3A4) inhibitors, and renal function were identified as statistically significant covariates influencing the PK of asundexian. After accounting for differences in the distribution of these covariates, the PK of asundexian was comparable in healthy participants and participants at risk for thromboembolic/cardiovascular events. Similarly, no significant differences in PK were noted among participants with atrial fibrillation, ischemic stroke, or acute myocardial infarction. No clinically relevant covariates were identified that would warrant dose adjustments in various special populations of interest, including those defined by body weight, age, sex, and renal function, for the prevention of secondary ischemic strokes.

Quantitative Systems Pharmacology (QSP) is increasingly utilized to support the design and translation of gene therapies. This perspective outlines the application of QSP modeling across three domains of gene therapy: mRNA-based therapeutics, adeno-associated virus (AAV) vectors, and genome editing systems. We highlight opportunities for dose optimization, biomarker interpretation, and mechanistic understanding, while addressing current limitations in model generalizability, data sparsity, and translational relevance. Examples include QSP platforms for lipid nanoparticle (LNP)-delivered mRNA, physiologically based pharmacokinetics (PBPK)-informed AAV biodistribution models, and CRISPR-Cas9-based editing systems. These case studies demonstrate QSP's value in de-risking development and personalizing therapies for rare and complex diseases.

This study demonstrates the application of a model based meta analysis (MBMA) framework to characterize the safety and efficacy profiles of therapies in relapsed and refractory multiple myeloma (RRMM). Published clinical trial data were analyzed to evaluate the incidence of Grade ≥ 3 neutropenia and overall response rate (ORR), providing a quantitative foundation for model-informed drug development. The final model incorporated trial- and treatment-level covariates and was evaluated using visual predictive checks and predictive simulations. Results revealed increased neutropenia risk associated with alkylating agents and higher ORR in regimens with background corticosteroids and in patients with only one prior line of therapy. MBMA-derived estimates facilitated systematic comparisons across regimens, accounting for heterogeneity in trial design and populations. The MBMA estimates can also support benchmarking of internal regimens against current standards. A quantitative systems pharmacology (QSP) model, developed in parallel, was also used to simulate patient responses across a broad array of RRMM treatments, including novel combinations involving T-cell engagers (TCEs) and CELMoD agents. Trial-calibrated virtual patients and a classifier for prior therapy exposure enabled the prediction of regimen-specific ORR across different treatment histories. Together, the MBMA-informed and QSP-supported modeling strategy enabled a comprehensive benefit–risk assessment by combining statistical estimation with mechanistic simulation. This coordinated approach enhances clinical decision-making by enabling comparison of novel or investigational therapies to the evolving treatment landscape, particularly in the absence of head-to-head trials.

Multiple sclerosis (MS) is a chronic disorder that typically shows accumulation of disability, affecting the ability to work. The disease severity is usually graded by physicians with the expanded disability status scale (EDSS) in the clinic, but patient-reported outcome questionnaires are also available, like the Multiple Sclerosis Impact Scale (MSIS-29) or Fatigue Scale for Motor and Cognitive Functions (FSMC). The aim of this work was to investigate the quantitative link between disease severity and the number of days with MS-related sickness benefits from registry data (the Swedish MS registry and the Swedish Social Insurance Agency's Micro Data for Analyzes of Social Insurance registry). An item response theory model for the disability was built, linking the EDSS, MSIS-29, and FSMC to the same underlying disease construct through five correlated latent variables. A Markov state model for the level of sickness benefits was also developed, in which the disease severities from the disability model were tested as covariates, on top of age. The latent variable for EDSS was the most important predictor of work ability. Patients with low disability (EDSS < 3) hardly had any sickness benefit days, while patients with severe disability (EDSS ≥ 6) were found to spend over 50% of their time with sickness benefits. Physical aspects of the disease were found to be more important than psychological aspects in predicting work ability. This underlines the patient-specific nature of MS, and the need for predictive models such as these to evaluate treatment effects, make risk assessments, and calculate societal and individual costs.

Generative Artificial Intelligence (AI) frameworks, such as Variational Autoencoders (VAEs), have proven powerful in learning structured representations from complex, high-dimensional data. In pharmacometrics (PMX), nonlinear mixed effects (NLME) modeling is widely used to capture inter-individual variability and link covariates to characterize parameters with the goal of informing key decisions in drug research and development. This research combines the strengths of both approaches by introducing a VAE framework specifically designed for NLME modeling. The proposed method integrates the flexibility of generative AI with the interpretability and robustness of mechanism-based PMX modeling. To advance covariate selection in PMX, we replace the Evidence Lower Bound objective in VAEs with an objective function based on the corrected Bayesian information criterion. This enables the simultaneous evaluation of all potential covariate-parameter combinations, thereby allowing for automated and joint estimation of population parameters and covariate selection within a single run. Manual selection and repeated model fitting across covariate combinations are no longer required. We demonstrate the effectiveness of this combined AI-PMX approach with two representative cases. As the first generative AI-based optimization method for NLME modeling, the VAE achieves high-quality results in a single run, outperforming traditional stepwise procedures in terms of efficiency. As such, the presented approach facilitates automated model development, advancing PMX and its applications in model-informed drug development.

Artificial intelligence (AI) is increasingly being explored as a tool to support pharmacometric modeling, particularly in addressing the coding challenges associated with NONMEM. In this study, we evaluated the ability of seven Large Language Models (LLMs) to generate NONMEM codes across 13 pharmacometrics tasks, including a range of population pharmacokinetic (PK) and pharmacodynamic (PD) models. We further developed a standardized scoring rubric to assess code accuracy and created an optimized prompt to improve LLM performance. Our results showed that the OpenAI o1 and gpt-4.1 models achieved the best performance, both generating codes with great accuracy for all tasks when using our optimized prompt. Overall, LLMs performed well in writing basic NONMEM model structures, providing a useful foundation for pharmacometrics model coding. However, user review and refinement remain essential, especially for complex models with special dataset alignment or advanced coding techniques. We also discussed the applications of AI in pharmacometrics education, particularly strategies to prevent overreliance on AI for coding. This work provides a benchmark for current LLMs' performance in NONMEM coding and introduces a practical prompt that can facilitate more accurate and efficient use of AI in pharmacometrics research and education.

Pirtobrutinib is a reversible Bruton tyrosine kinase (BTK) inhibitor. In vitro, pirtobrutinib is metabolized by cytochrome P450 (CYP) 3A4 and uridine 5′-diphosphoglucuronosyl transferases (UGTs) and causes reversible and time-dependent inhibition and induction of CYP3A4. Coadministration of itraconazole, a strong CYP3A4 inhibitor, with pirtobrutinib in healthy human subjects, resulted in a pirtobrutinib area under the plasma concentration-time curve (AUC) ratio of 1.49, while rifampin, a strong CYP3A4 inducer, decreased pirtobrutinib AUC by 71%. Oral administration of pirtobrutinib 200 mg once daily (QD) increased the AUC of oral and intravenous midazolam by 1.70- and 1.12-fold, respectively. A physiologically based pharmacokinetic (PBPK) model was developed for pirtobrutinib using physicochemical properties, in vitro data, and clinical pharmacology study results. The PBPK model captured the clinically observed interactions for itraconazole, rifampin, and midazolam, with predicted pirtobrutinib and midazolam AUC ratios within 0.91- to 1.16-fold of observed. The model predicted 1.20- to 1.73-fold increases in the pirtobrutinib AUC with strong and moderate CYP3A4 inhibitors. Furthermore, the predicted pirtobrutinib AUC ratios were within 0.51–0.86 with moderate and weak CYP3A4 inducers. The predicted effects of CYP3A4 modulators on pirtobrutinib pharmacokinetics, together with the known exposure-response relationships for safety and efficacy in patients with hematological malignancies, were used for recommending appropriate dosing regimens during coadministration.

Quantitative Systems Pharmacology (QSP) is a powerful approach to provide decision-making support throughout the drug development process. QSP comes with many challenges in model development, validation, and applications. Traditional QSP workflows are limited by slow knowledge integration, labor-intensive model construction, inconsistent validation practices, and restricted scalability. In this work, we introduce QSP-Copilot, the first end-to-end AI-augmented solution designed to improve QSP modeling workflows by integrating a multi-agent system utilizing large language models (LLMs). QSP-Copilot provides modular support from project scoping and model structuring to model evaluation and reporting. Through the automation of routine tasks, QSP-Copilot reduces model development time by approximately 40% and improves methodological transparency through systematic documentation of literature sources and modeling assumptions. We demonstrate QSP-Copilot's application for two rare diseases of blood coagulation and Gaucher disease. In the blood coagulation case, automated extraction from ten peer-reviewed articles yielded 179 biological entity interaction pairs; out of these, only 105 unique mechanisms were retained after standardization. For Gaucher disease, screening nine articles produced 151 pairs, which were consolidated into 68 distinct biological interactions following the same post-processing workflow. The extraction precision for blood coagulation and Gaucher disease is 99.1% and 100.0%, respectively. QSP-Copilot extractions can be incorporated into effect diagrams with minimal expert filtering, significantly reducing the manual curation burden. The integration of AI-augmented workflows like QSP-Copilot represents a pivotal shift toward enhanced scalability and impact for QSP across the drug development pipelines, especially in disease areas where biological knowledge is sparse, such as rare diseases.