Search Results (305)

Sodium tanshinone IIA sulfonate (STS) injection is clinically used to protect against cerebral ischemia–reperfusion injury (CIRI). This study aimed to establish physiologically based pharmacokinetic–pharmacodynamic (PBPK–PD) models for normal and CIRI rats and to quantitatively characterize the time–concentration–effect relationship, as well as disease-specific mechanistic differences. A middle cerebral artery occlusion rat model was established. STS was administered via the tail vein, and blood samples were collected at serial time points. High-performance liquid chromatography and enzyme-linked immunosorbent assay were used to quantify plasma STS concentrations and inflammatory markers, respectively, whereas equilibrium dialysis was performed to determine protein binding. PK-Sim and Python were used to establish a PBPK model, which was subsequently extrapolated to humans to construct PBPK–PD models. The results showed that plasma STS concentrations were consistently higher in the model rats than in normal rats. STS significantly reduced inflammatory levels in model rats, with a delayed onset of pharmacological effect. Human PBPK model simulations indicated that STS is rapidly eliminated in healthy individuals, while its elimination is reduced under pathological conditions. This study provides a robust modeling framework and methodological reference for dose optimization and prediction of clinical efficacy of STS in the treatment of CIRI. Full article

Objective: Posaconazole, a second-generation triazole antifungal used for the prevention or treatment of invasive fungal infections, has been shown to markedly increase tacrolimus exposure in vivo when co-administered, potentially leading to clinically significant adverse events. A physiologically based pharmacokinetic (PBPK) model was developed to predict tacrolimus–posaconazole interactions in renal transplant recipients with different CYP3A5 genotypes, to inform tacrolimus dose adjustment in clinical practice. Methods: First, to obtain the critical inhibition parameters, in vitro enzyme kinetic studies were conducted. Based on these data, a whole-body physiologically based pharmacokinetic (PBPK) model for TAC was developed and validated in PK-Sim. A published, validated posaconazole PBPK model was applied concurrently. Model performance was evaluated against published pharmacokinetic data in healthy volunteers receiving tacrolimus with posaconazole. A virtual Chinese renal transplant recipient was generated by incorporating population-specific physiological parameters, including CYP3A5 genotype-dependent enzyme expression. Results: In vitro experimental results demonstrated that POSA acts as a potent reversible competitive inhibitor of CYP3A4/5-mediated TAC metabolism. The tacrolimus PBPK model adequately captured pharmacokinetics across CYP3A5 genotypes, and tacrolimus pharmacokinetics during co-administration with posaconazole were also predicted. Compared with CYP3A5 expressers, nonexpressers showed greater variability in tacrolimus whole-blood concentrations and greater susceptibility to posaconazole-mediated interactions. The CYP3A5*3*3 genotype was associated with higher C_max and AUC. Dose optimization simulations predicted that after 6–7 days of posaconazole co-administration, nonexpressers would require the reduction of tacrolimus dosing frequency from every 12 h to every 24 h to maintain trough concentrations within 8–15 ng/mL, whereas a 50% dose reduction was predicted to be optimal for expressers. Conclusions: A tacrolimus–posaconazole PBPK drug–drug interaction model was developed for the population of renal transplant recipients and used to simulate tacrolimus trough concentrations across CYP3A5 genotypes and dosing regimens, supporting genotype-informed co-administration in clinical practice. Full article

Background/Objectives: Propranolol (PROP) is a non-selective β-blocker widely prescribed for cardiovascular and neurological disorders. Its pharmacokinetics (PK) are highly variable, and co-administration with omeprazole (OME), a CYP2C19 substrate and inhibitor, may alter systemic exposure. Herein, this study aimed to investigate factors influencing PROP PK variability and evaluate the effect of OME coadministration using physiologically based pharmacokinetic (PBPK) modeling and population PK (popPK) analysis. Methods: PBPK models for PROP and OME were developed and validated against published data. DDI simulations were conducted across clinically relevant dosing regimens. A two-period fixed-sequence virtual trial of 125 subjects was simulated with PROP alone and PROP combined with OME. Population PK (popPK) analysis was performed on simulated plasma concentration data to identify covariates affecting PROP disposition and quantify DDI magnitude. Results: PBPK models were successfully developed and validated. PROP disposition was best described by a two-compartment model with linear elimination. Health status was found to influence clearance, and body surface area (BSA) affected the central volume of distribution. Co-administration with OME increased PROP exposure, with larger effects in patients with renal impairment. Simulated plasma concentrations remained below established toxicity thresholds. Conclusions: Virtual clinical trials integrating PBPK and popPK modeling provide a robust approach to identifying key determinants of PK variability and DDI risk. Although these findings were not directly translated to clinical observations, this helps identify sources of PK variability in PROP treatment settings and factors that may intensify its interaction with OME, thereby supporting model-informed precision dosing to enhance safety and efficacy. Full article

Background/Objective: Childhood obesity induces physiological changes that alter drug distribution and clearance; however, these patients are often excluded from clinical trials, creating a critical safety gap for high-alert medications (HAM). The Objective was to evaluate HAM dosing strategies and pharmacokinetic (PK) alterations in overweight and obese pediatric patients. Methods: A systematic review was conducted and registered in PROSPERO (CRD42023452126). A search of MEDLINE, EMBASE, Web of Science, and Cochrane CENTRAL (1990–March 2026) identified studies reporting dosing strategies or PK of HAM in obese or overweight pediatric patients. Studies were included if they reported dosing recommendations or PK parameters. Eligible designs comprised prospective and retrospective, randomized and non-randomized, observational (cohort, case-control, and cross-sectional), case series, case reports, and narrative and systematic reviews. Study selection, data extraction, and quality assessment were conducted independently by two reviewers. Methodological quality was assessed using validated tools, and results were synthesized qualitatively. Results: Of 5801 records, 91 studies were included, providing evidence for only 27% of the evaluated HAM. Total body weight (TBW) appeared to be appropriate for insulin and vancomycin, although close monitoring was required. TBW-based dosing was associated with approximately 20% overexposure for enoxaparin, supporting the use of fat-free mass (FFM) or reduced dosing strategies. Increased clearance may justify higher doses for amlodipine and consideration of adult-equivalent dosing for metformin in adolescents. For gentamicin, FFM appeared to be the most appropriate descriptor, while adjusted body weight was used for valproic acid. In anesthetics and sedatives, reduced TBW-based dosing may be considered for propofol, whereas ideal body weight (IBW) or FFM were generally preferred for ketamine and dexmedetomidine. Analgesics such as fentanyl and morphine may require IBW- or FFM-based dosing, and maintenance dosing of paracetamol may require adjustment. Conclusions: Evidence remains limited and heterogeneous, with no standardized dosing approach. Model-informed strategies—such as population PK (PopPK) and physiologically based PK model (PBPK) approaches—may be useful for hypothesis generation and exploring PK variability; however, their clinical applicability is constrained by the limited and heterogeneous evidence base, and they should be considered exploratory. Full article

Artificial intelligence has advanced from merely predicting static protein structures to modeling equilibrium conformational ensembles. It now concurrently forecasts structure and binding affinity and actively participates in candidate selection during the initial stages of drug discovery. Foundation models introduced between 2024 and 2026, including BioEmu, AlphaFlow, DiG, Boltz-2, Chai-1, NeuralPLexer, and explicit-solvent prediction systems such as SuperWater, have begun to address issues previously identified as fundamental concerns in earlier critiques of AI in drug discovery. Nevertheless, many of these models are presently accessible only as preprints and require validation through independent peer review. Evidence indicates a shift in the primary bottleneck from representation challenges to validation difficulties. However, this transition remains incomplete and heavily dependent on context. The risks associated with AI-enabled drug discovery are increasingly not solely about the models’ capacity to accurately represent ensembles, but also about whether the evidentiary standards used to validate AI-derived predictions keep pace with the rapidity with which these predictions are generated and employed. This article introduces a four-tier validation framework designed to align the extent of computational and experimental evidence with the translational and regulatory risks associated with various artificial intelligence (AI) applications within the molecular sciences. These applications include machine learning (ML) models that analyze sequences, structures, conformational ensembles, protein–ligand complexes, and molecular dynamics trajectories. Tier 1 addresses the internal reproducibility of ML inference when applied to molecular inputs; Tier 2 pertains to the robustness of molecular-science benchmarks such as CASP, CASF-2016, PoseBusters, and OpenFE; Tier 3 involves prospective experimental validation against biophysical and biochemical measurements; and Tier 4 encompasses clinical and translational calibration within physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) frameworks. This validation hierarchy functions as an explicit conceptual guide, serving as a framework rather than a regulatory requirement. It is firmly grounded in established principles derived from ICH Q8/Q9/Q10, the FDA model-informed drug development (MIDD) approach, the EMA reflection paper on AI in the medicinal product lifecycle, and the EU AI Act. The manuscript further incorporates recent evidence from ensemble-aware AI, prospective docking, free-energy campaigns, and clinical-stage AI-derived candidates. It concludes with specific recommendations pertaining to lifecycle governance, uncertainty reporting, and the adoption of harmonized evidentiary templates for AI/ML applications in the molecular sciences. Full article

Background/Objectives: The rapid expansion of New Approach Methodologies (NAMs) is transforming oral biopharmaceutics by offering mechanistically rich, human-relevant tools that can reduce reliance on animal testing while improving translational confidence. Regulatory agencies, including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are increasingly open to NAM-generated evidence, provided that methods are fit-for-purpose and scientifically justified. This review synthesizes current advances and evaluates how NAMs can be integrated across drug-development stages to enhance the prediction of oral absorption, formulation performance, and regulatory decision-making. Methods: A comprehensive literature review was conducted across classical and emerging methodologies, including in vitro permeability and solubility models, organoids, organ-on-a-chip (OoC) systems, machine learning frameworks, and mechanistic approaches such as the physiologically based pharmacokinetic (PBPK) and biopharmaceutics (PBBM) models. Emphasis was placed on physiological relevance, predictive performance, validation status, and regulatory applicability. Results: Classical tools remain essential for the Biopharmaceutics Classification System (BCS)-based biowaivers and risk-based assessments, yet they often lack physiological fidelity. NAMs provide enhanced representation of intestinal architecture, hydrodynamics, transporter activity, and metabolism. Organoids and microphysiological systems generate high-quality permeability and metabolic data, while computational NAMs enable scalable prediction of ADME properties and formulation behavior. When integrated into PBPK/PBBM models, these methods have great potential in predicting in vivo performance in humans. Evidence demonstrates that NAMs can refine, reduce, and, in specific contexts, replace animal studies without compromising scientific rigor. Conclusions: NAMs complement, rather than displace, classical biopharmaceutic tools, enabling a more mechanistic, human-centered, and ethically responsible framework for drug development. Their effective implementation will depend on continued validation, standardization, and regulatory harmonization as the field transitions toward fully NAM-supported biopharmaceutical assessment. Full article

Target-controlled infusion (TCI) enables the precise delivery of intravenous anesthetics based on pharmacokinetic–pharmacodynamic (PK–PD) models and represents a key component of total intravenous anesthesia (TIVA). However, its use in obstetric anesthesia remains limited, as current TCI algorithms are derived from non-pregnant populations and do not account for pregnancy-related physiological changes or maternal–fetal drug distribution. This narrative review examines the clinical application of TIVA-TCI in caesarean delivery under general anesthesia, summarizing evidence from recent observational studies and national audits, which suggest feasibility but limited adoption in routine obstetric practice. Pregnancy induces significant alterations in drug distribution, protein binding, metabolism, and clearance, which may affect anesthetic pharmacokinetics and fetal exposure. Physiologically based pharmacokinetic (PBPK) modeling is explored as a complementary approach that may improve understanding of maternal–fetal drug disposition by integrating physiological and drug-specific parameters. Although promising, these model-based strategies require further validation before clinical implementation. Overall, current evidence supports the cautious use of TIVA-TCI in selected obstetric settings while highlighting the need for pregnancy-specific pharmacokinetic models and prospective clinical studies. Full article

Bioequivalence (BE) studies are essential to file an abbreviated new drug application (ANDA) against an innovator drug product. Conventional BE studies can be complex, time-consuming, and operationally challenging, particularly for products with long half-life drugs, high variability, or formulation complexity. Advances in quantitative modeling and simulation have expanded the role of model-generated information in generic drug development from a supportive role toward providing critical regulatory evidence. Model-Integrated Bioequivalence (MIBE) represents a focused application of this paradigm in which mechanistic or empirical models are used to directly support BE determination. While physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) approaches have been widely discussed in the literature, increasing attention is being directed toward population pharmacokinetic (POP-PK) modeling for MIBE implementation, particularly when mechanistic assumptions are uncertain or extensive in vitro characterization is impractical. This review provides a contemporary overview of MIBE in generic drug development, with a specific emphasis on POP-PK-based approaches. Key quantitative modeling frameworks are discussed along with evolving regulatory perspectives that support the integration of model-based evidence for BE assessment. We illustrate six diverse hypothetical case examples covering different formulations, a variety of BE scenarios and using MIBE to answer specific regulatory questions on BE. Collectively, this manuscript addresses an important topic of MIBE for complex and non-complex generic formulations and may provoke thinking among the generic companies to use such approaches in the regulatory context to enable faster and timely approval to bring the necessary medicines to the market at a rapid pace. Full article

Objectives: The co-administration of Bruton’s tyrosine kinase (BTK) inhibitors with antiretroviral drugs is challenging due to potential drug–drug interactions (DDIs). However, clinical trials specifically assessing such DDIs are lacking. We aimed to evaluate DDIs between the BTK inhibitors ibrutinib, zanubrutinib and acalabrutinib with representative antiretroviral drugs and to provide dose adjustment strategies using physiologically based pharmacokinetic (PBPK) models. Methods: PBPK models were developed in PK-Sim software. Model performance was verified by comparing simulated pharmacokinetic parameters and DDI magnitudes with probe drugs (midazolam or maraviroc) with reported clinical data. The validated models were subsequently applied to assess DDIs and explore dose adjustment strategies. Results: The developed PBPK model accurately describes the pharmacokinetics of each drug. Darunavir/ritonavir substantially increased the maximum plasma concentration (C_max) of ibrutinib, zanubrutinib, and acalabrutinib by 496%, 312%, and 160%, respectively. In contrast, efavirenz reduced C_max by 43%, 33%, and 37%, respectively, while etravirine caused smaller decreases of 5%, 0%, and 10%. Based on these predictions, recommended dose adjustment strategies include ibrutinib 105 mg once daily, zanubrutinib 40 mg twice daily, and acalabrutinib 50 mg twice daily when co-administered with darunavir/ritonavir or ibrutinib 980 mg once daily, zanubrutinib 240 mg twice daily, and acalabrutinib 150 mg twice daily when co-administered with efavirenz. No dose adjustment is required with etravirine. Conclusions: The PBPK models accurately predicted the in vivo pharmacokinetics of ibrutinib, zanubrutinib, acalabrutinib, and those of the antiretrovirals darunavir/ritonavir, efavirenz, and etravirine, and the DDIs between them. The dose adjustment strategies provided information valuable to the optimization of antineoplastic therapy in HIV-related lymphoma (HRL) patients. Full article

Background/Objectives: Tafamidis, a transthyretin kinetic stabilizer, increases circulating transthyretin levels in treated patients. While this effect is well documented, its underlying mechanism remains incompletely understood. This study aimed to evaluate the performance of physiologically based pharmacokinetic (PBPK) model performance and to calibrate a hypothesis-consistent quantitative systems pharmacology (QSP) model of tafamidis and transthyretin dynamics to explore mechanistic hypotheses underlying the clinically observed increase in circulating transthyretin and the associated dose–response relationship. The PBPK model constitutes the primary framework, while the coupled QSP component illustrates how tafamidis exposure predictions can be used to evaluate mechanistic hypotheses of TTR turnover. Methods: A PBPK–QSP model was constructed in Simcyp (V23) using LUA-based modules. The PBPK part was parameterized from the literature and validated against data from therapeutic single-dose, therapeutic multiple-dose, and supratherapeutic dose clinical studies. The QSP part of the model describes tafamidis–TTR binding kinetics, stabilization, and clearance of bound complexes. Simulations were performed in thirty virtual healthy male subjects aged 30–40 years, incorporating physiological variability in baseline TTR concentrations. Results: Mean predicted versus observed ratios of tafamidis AUC and $C_{max}$ values were within a 1.3-fold range across validation studies. The integrated model reproduced the clinically reported 33% increase in TTR concentration through a calibrated clearance-scaling factor. It supports the hypothesis that reduced clearance of tafamidis-bound TTR may explain the observed effect without modifying TTR synthesis. Dose-sensitivity simulations indicated that patients with low baseline TTR may achieve adequate stabilization at reduced doses, while those with higher baseline TTR concentration may require higher doses. Conclusions: The developed PBPK–QSP model not only reproduces tafamidis pharmacokinetics and TTR responses but also proposes a plausible mechanistic hypothesis consistent with clearance modulation of stabilized TTR contributing to the clinical effect. Full article

Background: Ribociclib, initially approved for HR+/HER2− advanced breast cancer (ABC) at a 600 mg dose, was recently approved for HR+/HER2− early breast cancer (EBC) at a 400 mg dose based on the NATALEE trial. Differences in dose and patient population warrant reassessment of ribociclib drug–drug interactions (DDIs) and the impact of hepatic or renal impairment (HI/RI) in EBC patients to guide co-medication management and subpopulation dose recommendations. Methods: Physiologically-based pharmacokinetic (PBPK) modeling based on a healthy volunteer population was conducted to assess ribociclib DDIs with CYP3A4 substrates/modulators in patients with EBC. Subgroup analysis from NATALEE assessed HI/RI impact on ribociclib PK in EBC patients. Existing data from ABC/advanced cancer patients and non-cancer subjects were also integrated to inform dose recommendations for EBC subpopulations. Results: PBPK modeling predicted that ritonavir or erythromycin (strong and moderate CYP3A4 inhibitors) would increase ribociclib steady-state area under the concentration–time curve (AUC) by 1.84-fold or show no meaningful impact, respectively. Steady-state ribociclib AUC was estimated to decrease by 83% and 74% with rifampicin and efavirenz, strong and moderate CYP3A4 inducers, respectively. Ribociclib was estimated to increase CYP3A4 substrate midazolam exposure by 280%. Mild HI or mild/moderate RI did not show an apparent impact on ribociclib PK. Conclusions: Using relevant data and methodology for EBC patients, this analysis informed the approved ribociclib label of no dose adjustment for EBC patients with concomitant use of a moderate CYP3A inhibitor, any degree of HI, or mild/moderate RI, and a reduced 200 mg dose for patients with concomitant use of a strong CYP3A inhibitor or severe RI. Full article

Background: Remimazolam tosilate is a novel, ultra-short-acting benzodiazepine. To address the unmet clinical need for safe and controllable general anesthetic options in children and adolescents, both top-down (i.e., population pharmacokinetics—PopPK) and bottom-up (i.e., physiologically based PK—PBPK) modeling approaches were combined to leverage their respective strengths for dose selection in children and adolescents aged 3–18 years. Methods: Pooled PK data from adult studies were used to develop and verify the adult PopPK and PBPK models. The PopPK model included allometric scaling to describe body weight effects, while the PBPK modeling incorporated the age-dependent physiological and metabolic ontogeny. Potential covariates and intrinsic factors influencing remimazolam exposure were assessed. Both models were then applied to simulate PK and derive exposure metrics in 3–18-year-old children and adolescents. The predictions from both approaches were used to support pediatric dose selection using an adult-matching exposure approach. Results: The PopPK and PBPK model simulations yielded consistent exposure predictions and converged on the same recommended dosing regimens for the pediatric population, providing mutual confirmation of model reliability. Both models indicated that the proposed regimens of remimazolam would achieve systemic exposures in children and adolescents (3–18 years) comparable to those in adults receiving an induction dose of 0.3 mg/kg followed by maintenance infusions of 1.0 or 3.0 mg/kg/h. Two pediatric dosing regimens were recommended: 1. Lower dose group: induction 0.2 mg/kg, initial maintenance 1.0 mg/kg/h, titratable as needed, with a maximum rate of 3.0 mg/kg/h (up to 4.0 mg/kg/h for individuals ≤ 30 kg). 2. Higher dose group: induction 0.3 mg/kg, initial maintenance 2.0 mg/kg/h, titratable as needed, with a maximum rate of 3.0 mg/kg/h (up to 4.0 mg/kg/h for individuals ≤ 30 kg). The model-informed dosing regimens have received regulatory approval from the Center for Drug Evaluation (CDE) in China and are currently being evaluated in an ongoing clinical trial. Conclusions: The integrated PopPK–PBPK approach supports evidence-based dosing recommendations of remimazolam for general anesthesia in children and adolescents aged 3–18 years and provides a reference for dose selection in future clinical studies. Full article

Background/Objectives: The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model to predict gentamicin therapeutic protocols for dogs with varying degrees of renal function impairment, considering the minimum inhibitory concentrations (MICs) of the infecting bacteria. Methods: The PBPK model was built using PK-Sim^® software (OPEN SYSTEMS PHARMACOLOGY), based on pharmacokinetic data available in the literature and information on the physicochemical properties of the drug. Model evaluation included the calculation of the geometric mean fold error (GMFE), weighted and percentage residuals were calculated, as well as the following measures: AFE, AWRi, MWRi, MAWRi, APE%, MPE%, MAPE%, MdPE%, and MdAPE%. Therapeutic efficacy was assessed according to the Probability of Target Attainment (PTA), considering an MIC distribution of 0.25 to 8 μg/mL for different doses (2, 4, 6, 8, and 10 mg/kg) using the PK/PD indices C_max/MIC ≥ 10, AUC/MIC ≥ 50, and AUC/MIC ≥ 110. To compare the pharmacokinetics of gentamicin between healthy dogs and those with decreased renal function, different GFR values corresponding to stages of renal impairment were used, as determined by clinical biomarkers (microalbuminuria, UPC ≥ 2, sCr ≥ 1.2 mg/dL, sCr ≥ 2.4 mg/dL, and sCr ≥ 5 mg/dL). The risk of toxicity was assessed according to AUC_24h ≥ 700 mg·h/L and C_min ≥ 0.5. Results: The model demonstrated good predictive performance, with a GMFE value of 1.13 meeting the double error criterion, and weighted residuals randomly distributed around 0 (p = 0.3792). Through the calculation of PTA, it was observed that efficacy varied according to the PK/PD index used, but values greater than 90% were obtained for MICs up to 4 μg/mL. The model allowed the estimation of protocols for each stage of renal impairment, considering the GFR of each group and the risk of nephrotoxicity, in association with the optimal dose to ensure therapeutic efficacy. Conclusions: These findings make it possible to propose a dose for the treatment of an infection, considering the MIC and the patient’s GFR stage, thereby reducing the risk of adverse effects without compromising treatment efficacy. Full article

Background/Objectives: Dapagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus. The drug lowers blood glucose levels by increasing urinary glucose excretion (UGE). Despite established efficacy, dapagliflozin demonstrates significant inter-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD), with potential impact on treatment outcomes. Methods: To evaluate the sources of variability and to support patient stratification and model-informed individualized therapy, we developed a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of dapagliflozin using curated data from 28 clinical studies. This framework integrates absorption, distribution, metabolism, excretion, and pharmacodynamics, and accounts for key determinants of variability including renal and hepatic function, and food effects. Results: The simulations reproduced dose-dependent pharmacokinetics with predicted C_max and AUC values typically within 10–15% of observed data. Renal impairment reduced UGE by 40–60% despite modest changes in plasma exposure, while hepatic impairment produced only small shifts in PK and PD. The model also reproduced the fed-state reduction of peak concentrations, consistent with the 30–50% decrease reported clinically. Conclusions: All model files, code, and curated datasets are openly available in line with FAIR standards and Open Science practices, enabling transparent and reproducible analyses and providing a mechanistic basis for individualized therapy in type 2 diabetes. Full article

Background/Objectives: The external oblique intercostal (EOI) plane block shows promise for postoperative analgesia after open liver surgery. Pharmacokinetic profiles of ropivacaine after EOI plane block remain unclear. Meanwhile the pharmacokinetic data informs safety assessment, guides post-block monitoring duration, and predicts blockade duration. This study aimed to characterize ropivacaine pharmacokinetics and propose a safe dosing regimen. Methods: In this prospective study, patients undergoing open liver surgery received a unilateral single-shot ultrasound-guided EOI plane block with 30 mL of 0.375% ropivacaine. Plasma ropivacaine concentrations were measured to define pharmacokinetics, identify influencing factors, and develop physiology-based pharmacokinetic (PBPK) models for dose optimization. Results: Twenty-eight patients (Child-Pugh A, ≤3 liver segments resected) were included. Peak plasma ropivacaine concentration occurred at 10 min and remained below the toxic threshold in all patients. No adverse events were observed. Demographic and surgical factors did not significantly affect pharmacokinetics. The PBPK model-predicted safe doses of ropivacaine were comparable across age groups and relatively high. Conclusions: A single-shot EOI plane block with ropivacaine is safe for patients undergoing open liver surgery (Child-Pugh A) with limited resection (≤3 segments). This study provides critical pharmacokinetic data and validated PBPK model, guiding safe dosing to reduce toxicity risks. Full article