CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

Accurate prediction of human pharmacokinetics (PK) for lead compounds is one of the critical determinants of successful drug development. Traditional methods for PK parameter prediction, such as in vitro to in vivo extrapolation and physiologically based pharmacokinetic modeling, often require extensive experimental data and time-consuming calibration of parameters. Machine learning (ML) has been widely applied to predict ADME and physicochemical properties (ADMEP descriptors), but studies focusing on concentration-time (C-t) profile prediction remain limited. In this study, we developed a Long Short-Term Memory (LSTM) based ML framework to predict C-t profiles following intravenous (IV) bolus drug administration in humans. The model used ADMEP descriptors generated by ADMETlab 3.0 and dose information as input. A total of 40 drugs were used for training and 18 for testing, with concentration data simulated from published PK models. Our approach achieved R² of 0.75 across all C-t profiles, and 77.8% of C_max, 55.6% of clearance, and 61.1% of volume of distribution predictions within a 2-fold error range, demonstrating predictive performance comparable to previously published ML methods. Furthermore, model performance was found to be associated with the input dose level and ADMEP descriptors, suggesting the accuracy and confidence of the prediction may be expected in advance via these descriptors. This LSTM-based framework using a small number of compounds enables efficient prediction of human PK profiles with IV dosing, offering a practical alternative to traditional PK prediction models. It holds promise for improving early-phase prioritizing lead compounds and reducing reliance on animals in drug development.

In vitro–in vivo correlation/relationship (IVIVC/R) models such as physiologically based biopharmaceutics modeling (PBBM) are crucial tools that link biopharmaceutical properties to clinical performance. They accelerate development, reduce costly experimental studies and clinical trials, and justify regulatory decisions for drug formulation related questions of interest (QOI). This paper consolidates insights from academia, industry, and service providers, exploring future opportunities, organizational challenges, regulatory perspectives, and competency gaps for further enhanced application in pharmaceutical development and regulatory decision-making.

The objectives of this study were to quantify the relationship between vaccine-induced immunogenicity responses and the protection against respiratory syncytial virus (RSV) infection-related clinical outcomes, and to evaluate immunogenicity as a surrogate marker for vaccine efficacy (VE) to accelerate RSV vaccine development. Serum neutralizing activity (SNA) and cell-mediated immunity (CMI) may serve as surrogate markers for the protection against RSV infection and are evaluated as immunogenicity endpoints in clinical trials of RSV vaccine candidates. Two meta-analytical approaches were applied to data from seven randomized placebo-controlled clinical trials that investigated RSV vaccines in older adults. The primary analysis examined the relationship between SNA and VE across three different clinical severity levels: (1) acute respiratory infection, (2) RSV lower respiratory tract disease (LRTD) with ≥ 2 clinical symptoms, and (3) RSV LRTD with ≥ 3 clinical symptoms (LRTD 3+). Furthermore, the additional contribution of CMI to VE, after accounting for the effect of SNA, was explored in a secondary analysis. The results demonstrated a positive correlation between SNA and VE across three clinical severity levels. Higher CMI was associated with higher VE specifically for RSV LRTD 3+, the most severe clinical level, suggesting that CMI may be correlated with additional clinical benefits in mitigating the severity of RSV infection. These findings provided preliminary evidence for immune correlates of protection against RSV infection and may aid in accelerating the development of new RSV vaccines.

Classic and hybrid pharmacometric-machine learning models (hPMxML) are gaining momentum for applications in clinical drug development and precision medicine, especially within the oncology therapeutic area. However, standardized workflows are needed to ensure transparency, rigor, and effective communication for broader adoption. In this tutorial, we review pharmacometric (PMx) and machine learning (ML) reporting standards and evaluate them against hPMxML works in oncology contexts as a motivational example to identify current deficiencies and propose mitigation strategies for future efforts. The revealed gaps include insufficient benchmarking, absence of error propagation, feature stability assessments, and ablation studies, limited focus on external validation and final parametrization, and discrepancies between the performance metrics chosen and the original clinical questions. To address these, we propose a checklist for hPMxML model development and reporting, consisting of steps for estimand definition, data curation, covariate selection, hyperparameter tuning, convergence assessment, model explainability, diagnostics, uncertainty quantification, validation and verification with sensitivity analyses. This standardized approach is expected to enhance the reliability and reproducibility of hPMxML outputs, enabling their confident application in oncology clinical drug development, while fostering trust among all stakeholders.

Isavuconazole is a broad-spectrum triazole approved for the treatment of invasive aspergillosis or mucormycosis in adults and children aged ≥ 1 year. Current prescribing information lacks guidance regarding the co-administration of isavuconazole with anticancer drugs–limited by the availability of clinical drug–drug interaction (DDI) data in the patient population. This study utilized physiologically-based pharmacokinetic (PBPK) modeling to evaluate the DDI risk of isavuconazole compared with other azoles and provide dosing recommendations when co-administered with anticancer drugs (ibrutinib, venetoclax, and midostaurin). PBPK models were developed in the Simcyp simulator using physiochemical properties, in vitro, and clinical pharmacokinetic data. The model well-predicted isavuconazole pharmacokinetic changes with cytochrome-P450 3A (CYP3A) modulators (itraconazole and rifampicin), and recovered midazolam DDI with isavuconazole as a CYP3A inhibitor. PBPK models for ibrutinib, venetoclax, and midostaurin were developed and validated by comparing simulated and observed pharmacokinetic parameters with and without the CYP3A inhibitor, ketoconazole. The PBPK model predicted area under the plasma concentration–time curve ratios of 2.1, 1.1, and 2.1 for ibrutinib, venetoclax, and midostaurin, respectively, when co-administered with isavuconazole at clinically relevant doses. The findings suggest that isavuconazole can be safely co-administered following appropriate dose adjustments with ibrutinib (50% of normal dose), venetoclax (50–100% of normal dose), or midostaurin (50% of normal dose). Other azoles, posaconazole and voriconazole, showed larger CYP3A-mediated DDIs and consequently require 3–6-fold lower doses of the substrate drugs. In conclusion, this model-informed PK-based dose optimization can enable treatment management in these untested scenarios.

‘Drug loss’ in Japan refers to drugs that have been approved and marketed overseas but haven't been developed, submitted, or approved in Japan. A comprehensive Model-Informed Drug Development (MIDD) strategy, enhanced with artificial intelligence/machine learning, can minimize drug loss. Continued pharmaceutical industry and regulatory commitment and collaboration in applying MIDD will facilitate Japanese patients' access to essential medicines and solidify Japan's role in global pharmaceutical advancement.