CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

In silico clinical trials (ISCT) are computational frameworks that employ mathematical models to generate virtual patients and simulate their responses to new treatments, treatment regimens, or medical devices via simulations mirroring real-world clinical trials. ISCTs are an important component of the model-informed drug development (MIDD) framework for optimizing therapies, treatment personalization, informing regulatory decisions, and accelerating overall drug development by enhancing R&D productivity. However, the emergence of complex models, such as quantitative systems pharmacology (QSP) models, presents significant challenges for their effective implementation. Guidelines for conducting ISCTs have been published to address these challenges, focusing on algorithms and credibility frameworks for generating plausible virtual patients and calibrating virtual populations. However, it is not straightforward to apply existing workflows to models where parameter distributions and correlations are estimated using nonlinear mixed effects (NLME) population fitting approaches, a common practice in the pharmaceutical industry when individual-patient-level data is available. Here, we illustrate a modeling workflow for conducting ISCTs with NLME models, detailing key considerations, methods, and challenges at each step. We demonstrate the practical implementation of this workflow through two examples to showcase its broad applicability: (1) a simple model predicting tumor growth in response to chemotherapy and (2) a more complex mechanistic QSP model of hepatitis B virus infection that captures the physiological mechanisms underlying treatment response with standard-of-care therapies.

Ibrutinib, a Bruton's tyrosine kinase (Btk) inhibitor, is a key therapy for chronic lymphocytic leukemia (CLL). In clinical practice, adverse events, such as hypertension, frequently necessitate dose reductions or treatment discontinuation. Emerging evidence suggests that reduced doses may retain clinical efficacy while mitigating toxicity. The synergistic ibrutinib–venetoclax combination remains understudied at low doses, particularly for ibrutinib. This study aimed to explore dose optimization strategies, with/without venetoclax, in treatment-naïve (TN) and relapsed/refractory (R/R) CLL using mechanism-based, model-informed approaches to characterize the relationship between systemic ibrutinib exposure and efficacy and safety biomarkers. We leveraged data from phase 1b/2 and 3 studies, including plasma concentrations, leukocyte and lymphocyte counts, lymph node and spleen size measurements, and blood pressure. A previously developed semi-mechanistic population pharmacokinetic-pharmacodynamic (PKPD) framework was re-evaluated, extended by integrating additional biomarkers and identifying differences between TN and R/R patients, and used to simulate alternative dosing strategies. The model successfully captured the temporal dynamics of all biomarkers simultaneously. We quantified a 76% longer phospho-Btk half-life and a 43% shorter peripheral CLL cell half-life in TN versus R/R patients, with no evidence of ibrutinib resistance in TN patients. Dose reductions based on response depth or toxicity preserved comparable response rates and progression-free survival to standard dosing. Ibrutinib de-escalation schedules with venetoclax resulted in a ≤ 5% reduction in peripheral blood measurable residual disease compared to standard dosing at 2 years. This PKPD framework supports dose individualization to improve tolerability without sacrificing treatment outcomes, offering a path toward more personalized, effective CLL management.

Assessing the risk or benefit of an inhaled substance is challenging due to the variety of forms it can take (gas, vapor, particle, or droplet) and the complex biological processes involved in its uptake and retention. Physiologically based kinetic (PBK) models offer an alternative to in vivo experiments. However, PBK models for inhalational uptake are to date either designed for gases/vapors or airborne particulates, often with only low regional compartmentalization. The here-presented, newly developed model combines both applications. Its mechanisms are an amalgamation of PBK and non-PBK models integrated into a multicompartmental description of the human lung to include the relevant uptake and clearance processes in the different lung regions, of which macrophage-mediated dissolution is novel to PBK modeling. The model was designed to use a minimal number of substance-specific input parameters, which can be derived from in vitro assays or in silico methods. Model predictions for hypothetical substances with varying physicochemical properties were performed, along with rudimentary sensitivity analyses to identify the most important parameters for gases/vapors and particles. This novel PBK model combines all these aspects and provides in silico predictions of systemic and local lung concentrations, reducing uncertainty in risk assessments and supporting drug development. It serves as a valuable tool to translate nominal ambient air doses into effective localized doses within the lung.

Amlitelimab is a fully human, nondepleting, anti-OX40 ligand monoclonal antibody being investigated for the treatment of moderate-to-severe atopic dermatitis (AD) in adults and adolescents. Population pharmacokinetic (PopPK) and pharmacokinetic/pharmacodynamic-Eczema Area and Severity Index (PopPK/PD-EASI) models were used to inform dosing regimen selection for amlitelimab phase 3 trials. The PopPK model was developed using phase 1 (healthy volunteers) and phase 2 (participants with AD) trial data, including individual exposure variables from the STREAM-AD phase 2b trial following subcutaneous amlitelimab doses ranging from 62.5 to 250 mg given every 4 weeks (Q4W). The PopPK model was used to compute exposures for an extended dosing regimen of 250 mg Q12W (with 500 mg loading dose [+LD]). The PopPK/PD-EASI model was developed from phase 2 trials to predict treatment responses (EASI values) with selected dosing scenarios. Finally, the dose for individuals with lower body weight (i.e., < 40 kg) was determined. Utilizing the PopPK model, the amlitelimab 250 mg Q12W + LD computed exposures were between the exposures of 62.5 mg Q4W and 250 mg Q4W + LD efficacious doses in the STREAM-AD trial. Using the PopPK/PD-EASI model, the simulated efficacy for dosing scenarios of 250 mg Q12W + LD regimen from initiation or 250 mg Q4W + LD for 24 weeks followed by Q12W to Week 60 was similar to continuous 250 mg Q4W. Simulations identified that a twofold dose reduction would allow participants < 40 kg to achieve amlitelimab exposures within the range observed in participants ≥ 40 kg on 250 mg Q4W or Q12W. These results support evaluation of a Q12W dosing regimen for adults and adolescents in phase 3 trials.