CPT: Pharmacometrics & Systems Pharmacology最新文献

The field of Quantitative Systems Pharmacology continues to innovate new methods to derive insights from clinical data. In the past few years, Digital Twins have emerged as a "new" way of deriving patient-specific model parameterizations to inform possible outcomes for novel scenarios using limited clinical data. Here, we explore the meaning of Digital Twins for QSP, its relationship with definitions in other contexts, and principles guiding their efficient and correct generation using patient data.

Exposure-response (ER) analyses of repeated time-to-event (RTTE) data can be confounded when treatment modifications occur due to the event of interest. One particularly challenging scenario is when an event is preceded by an undocumented clinical worsening leading to treatment discontinuation shortly before the event. Event-related treatment modifications introduce potential reversed causality in the ER analysis and bias, as events may predict exposure, rather than exposure predicting events. We systematically evaluated three analysis approaches in multiple simulated placebo-controlled studies to assess their capacity to estimate unbiased ER relationships: (1) average exposure (2) time-varying exposure (3) average exposure + percentile-rank of the average amount of blinded study treatment received. The novel Approach 3 was designed to remove confounding from event-related treatment modifications by leveraging information on blinded study treatment received (including in the placebo treatment arm). Approach 1 was biased in all scenarios with event-related treatment discontinuations. Approach 2 generally resulted in unbiased estimates, except in the scenarios in which events were sometimes preceded by a treatment discontinuation. In these scenarios, only Approach 3 provided unbiased estimates with linear ER, though it showed slight bias with pronounced nonlinear relationships. In conclusion, treatment modifications associated with events warrant careful consideration in ER analyses of RTTE data. Using a ranked measure of the average amount of blinded study treatment received may be used to lower the risk of confounding due to treatment modifications.

Hyperbilirubinemia, characterized by elevated total blood bilirubin levels including both unconjugated and conjugated forms, serves as a diagnostic marker for drug-induced liver toxicity associated with a wide range of medications. This study aimed to develop a mechanistic model for assessing hyperbilirubinemia risk using genetic markers. We developed an ordinary differential equation (ODE)-based mechanistic model of human bilirubin metabolism, incorporating key processes such as unconjugated bilirubin synthesis, hepatic uptake, conjugation to form conjugated bilirubin, and elimination via hepatic and renal pathways. The model includes key transporters and enzymes like OATP1B1, MRP2, MRP3, and UGT1A1 involved in bilirubin metabolism. The model was parametrized using in vitro and published human data, validated in healthy subjects and genetic disease cases, and assessed for genetic mutations' impact on bilirubin levels. A 90% reduction in OATP1B1 enzymatic activity increased predicted unconjugated and conjugated bilirubin concentrations (1.58-fold and 2.2-fold, respectively), mimicking data from individuals with mutations in OATP1B1. Sensitivity analysis of OATP1B1, MRP2, and UGT1A1 revealed increased OATP1B1 sensitivity in the presence of low UGT1A1 activity. Model simulations linked nilotinib-induced hyperbilirubinemia to UGT1A1 mutations, and simulations were used to assess the risk of hyperbilirubinemia associated with varying doses of nelfinavir, atazanavir, and TAK-875, based on their off-target effects on transporters. Results demonstrated that uncertainty in free drug tissue concentration may be crucial in hyperbilirubinemia, especially for highly protein-bound drugs. This approach may help assess hyperbilirubinemia risk using a drug's inhibitory in vitro data coupled with patient pharmacogenetic data for OATP1B1, UGT1A1, and MRP2 mutations.

Despite the fact that modeling and simulation are now recognized as promising innovative methodologies, their use in the context of development of drugs for sickle cell disease and Thalassemia has not yet been reviewed. Considering the challenges of conducting clinical trials for hemoglobinopathies, our work aims at exploring the current status of use of modeling and simulation by drug developers and their endorsement by regulators, based on European Medicines Agency scientific advices given to industry by the Committee for Medicinal Products for Human Use from January 2000 to December 2024. The present study describes different aspects of how modeling and simulation methods have been used and assessed. Our results highlight the need for an openly accessible structured framework that details the standards/criteria that make a method acceptable for regulators. The use of the ICH M15 credibility assessment framework is suggested for this purpose. Bearing in mind the question-centric approach, a repository of drug development questions and associated methods and data was built from 45 and 31 scientific advices for sickle cell disease and Thalassemia, respectively. The classification of the questions, methods and data based on the question-centric approach enabled modeling and simulation methods to be easily identified, objectively assessed and benchmarked against alternative methods used to address the same questions to evaluate their performance for characterizing benefit/risk of the different drugs.

Systemic sclerosis (SSc) is a complex autoimmune disorder characterized by extensive fibrosis, vascular abnormalities, and immune dysregulation, affecting clinical outcomes such as skin thickness and pulmonary function with high mortality rates. B cells play a pivotal role in the pathogenesis of SSc. This study aimed to develop a systems model for B cell differentiation and tissue distribution to characterize the therapeutic responses to CD19⁺ (inebilizumab) and CD20⁺ cell depletion (rituximab) in patients with SSc. We integrated real-world data (RWD) of B cell subsets from 61 patients with untreated SSc using this model. Upon successful model validation, virtual clinical simulations revealed uniform dynamics of CD19 cells but distinct patterns of antibody-secreting cells (ASCs) among patients, with significant variabilities due to CD20 treatment. The ratio of plasma cells to plasmablasts (PC/PB) was identified as a crucial factor, with a high ratio correlating with a poor response to CD20 treatment but stable depletion by CD19 treatment. Furthermore, the CD20-binding affinity of rituximab and its elimination rate constant were also suggested to contribute to the therapeutic variabilities of CD20 treatment. This study addressed ASC responses as a marker of a proof-of-mechanism; nonetheless, the model must be extended to further address the aforementioned clinical outcomes. Overall, the systems model provided mechanistic insights into the contrasting responses of ASCs depending on the study drugs and identified potential predictors of treatment efficacy. By integrating RWD, our study provides a mechanistic framework to optimize dosing strategies and guide personalized treatment approaches to refine B-cell depletion therapies for SSc.

Paracetamol (PCM) is extensively metabolized in the liver via glucuronidation, sulfation, and oxidation. Although oral and intravenous PCM are commonly used interchangeably, a comprehensive evaluation of PCM metabolism across both routes is lacking. This study aimed to characterize the full pharmacokinetic (PK) profiles of PCM and its metabolites following oral and intravenous administration, accounting for presystemic and systemic metabolism. Concentrations of PCM, PCM-glucuronide (PCM-GLU), PCM-sulfate (PCM-SUL), PCM-cysteine (PCM-CYS), and PCM-mercapturate (PCM-MER) were pooled from three clinical studies, involving 53 adults with obesity and 16 adults without obesity (18–65 years, 53–198 kg). A semi-physiological population PK model was developed with nonlinear mixed-effects modeling, incorporating intestinal and liver compartments, and enterohepatic recirculation. A semi-physiological PK model incorporating presystemic and systemic hepatic metabolism captured PK of PCM and PCM-SUL following oral and intravenous administration. Intestinal oxidative metabolism for PCM-CYS and PCM-MER and enterohepatic recirculation for PCM-GLU were added to capture their full PK profiles. The estimated fraction of PCM absorbed was 0.745 (95% CI 0.699–0.792), with extensive first-pass metabolism and faster metabolite formation after oral than after intravenous administration. This semi-physiological population PK model identified both hepatic and intestinal presystemic metabolism of PCM, as well as enterohepatic recirculation of PCM-GLU. Oral administration of PCM results in faster metabolite formation than intravenous dosing, mainly for the oxidative metabolites. This modeling approach may support the quantification of presystemic and systemic metabolism, which can be relevant for oral and intravenous dosing of drugs metabolized by multiple pathways.

Developed at Bayer Technology Services, PK-Sim and MoBi transitioned into the Open Systems Pharmacology (OSP) Suite, released as free open-source software in 2017. An active community with stakeholders from academia, industries, and regulators contributes to the continuous improvement of open-source model-informed drug development (MIDD). This perspective summarizes the latest advancements presented at the second OSP Community Conference (OSP-CC) hosted from 29 to 30th of September 2025 at Sanofi Paris, that gathered over 100 attendees from more than 40 institutions.

Liver cirrhosis is accompanied by pathophysiological changes. Due to multiple absorption, distribution, metabolism and excretion (ADME)-related pathophysiological alterations, the estimation of the net pharmacokinetics (PK) change in cirrhotic patients is complex. Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic modeling technique that combines knowledge of physiological and drug-related properties and, thereby, allows the prediction of organism-specific drug PK. For the integration of pathophysiological changes into a PBPK model, such changes need to be quantified appropriately. To date, published liver cirrhosis pathophysiology repositories contain only average changes for three distinct disease stages limiting clinical applicability. Therefore, the aim of this study was the development of a repository that (1) describes physiological alterations throughout the body during cirrhosis progression, (2) quantifies both mean changes and population variability, and (3) adds parameters of not yet included changes. For this purpose, data was gathered and processed using a Markov-Chain-Monte-Carlo (MCMC)-based approach that allowed the handling of heterogenous data and information on population variability. The resulting repository, based on 216,609 data points from 68 literature studies and 208,851 patients from the IBM Explorys electronic health records database, encompasses 30 physiological parameters. Integration into a PBPK modeling framework revealed good predictive performance with 96% of all data points for predicted PK parameter ratios lying within a twofold prediction range. In summary, the presented approach provides an advancement in the field of PK modeling in liver cirrhosis patients, possibly facilitating the planning and analysis of clinical studies in these patients and moving towards virtual studies.

The purpose was to evaluate retrieval-augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug information and adherence to best practices in clinical trial protocols. Integrated systems containing RAG and large language model (LLM) components were employed to evaluate drug information and clinical trial protocols. The drug information for adalimumab, insulin glargine, atorvastatin calcium, sertraline, and alprazolam was evaluated for compliance with Food and Drug Administration (FDA) clinical pharmacology guidance for indications, use in specific populations, and warnings and precautions. The reasons for the withdrawal of rofecoxib, valdecoxib, and troglitazone were elicited. The clinical trial protocol evaluation system was used to assess a Phase-2a clinical trial protocol of Rifafour in tuberculosis with the FDA E9 and E9 (R1) guidance documents. The RAG system correctly identified the indications, use in specific populations, and warnings and precautions for adalimumab, insulin glargine, atorvastatin calcium, sertraline, and alprazolam. The drug information was evaluated against the requirements in the guidance documents, confirming compliance when present and providing explanations for deficiencies. The causes underlying the withdrawal of rofecoxib, valdecoxib, and troglitazone were explained. The clinical protocol summary included study design, population definitions, treatments, dose levels, and route of administration. The summary of the statistical analysis plan included primary/secondary endpoints, statistical tests, pharmacokinetic parameters, and handling of missing data and outliers. The findings aligned with manual protocol reviews. RAG-based AI methods can improve the usefulness of LLMs in document-restricted settings and are a promising approach for evaluating the compliance of clinical pharmacology documents.

Programmed cell death (PD) protein (ligand [L])-1 inhibitors are established treatments for metastatic non-small cell lung cancer (mNSCLC). In oncology, progression-free survival (PFS) and objective response rate (ORR) are often used as surrogates for overall survival (OS) to inform clinical development; however, there remains uncertainty in the concordance between these endpoints. This study evaluated the impact of a broad set of PD-(L)1 inhibitors on efficacy, explored the relationship between ORR and survival endpoints, and compared PD-1 and PD-L1 treatments for mNSCLC. A dataset of 114 studies was used to conduct a sequential two-stage model-based meta-analysis (MBMA). Firstly, an MBMA with mixed-effects logistic regression was applied to evaluate treatment-specific and clinical covariate effects on ORR. Secondly, MBMAs for OS and PFS were conducted with a mixed-effects semi-parametric proportional hazard approach using digitized Kaplan–Meier curves, with the treatment type, covariates, and ORR as inputs. ORR was demonstrated to be a significant predictor of OS and PFS. Simulations of head-to-head comparisons of treatment types were conducted using these models. Trends in predicted outcomes numerically favored PD-1 over PD-L1 treatments, but differences were not statistically significant. These findings support evidence-based decision-making for late-stage trial designs using ORR data from earlier phase trials, enabling benchmarking of emerging data by adjusting for explained and unexplained sources of variability in existing and emerging data.