Search Results (281)

Antibody-drug conjugates (ADCs) have risen in prominence over the past 15 years, with numerous regulatory approvals in oncology. A complicating factor in the development of ADCs is the presence of numerous analytes with unique pharmacologic properties. Following administration, ADCs are present in the body as the intact ADC, unconjugated antibody, and liberated payload. Due to heterogeneity in conjugation and in vivo deconjugation rates, the drug-to-antibody ratio (DAR) changes with time. Each of these molecular species has unique pharmacokinetic (PK) and pharmacodynamic (PD) properties that should be understood and characterized. One approach that is frequently applied is the development of in silico mathematical models to characterize and predict the PK/PD of ADCs. In this review, we summarize key mechanisms controlling the PK/PD of ADCs. This provides context for a detailed discussion of the array of PK/PD models that have been applied for ADCs, ranging from empirical compartmental models all the way through system-level models, such as physiologically based pharmacokinetics (PBPK) and cell-level PK/PD models. We provide a critical discussion of the strengths, weaknesses, and utility of each of these model structures. Full article

The progress of contemporary pharmacology is deeply linked to pharmacokinetics (PK) and its quantitative exploration through PK modeling. By offering a robust mathematical framework to describe and predict drug absorption, distribution, metabolism, and excretion (ADME), PK modeling is essential for designing and optimizing safe and effective dosing regimens and for advancing personalized medicine and model-informed drug development (MIDD). The reliability of population PK (popPK) and physiologically based PK (PBPK) models depends on high-quality experimental data to estimate PK parameters. Traditional PK data sources include clinical studies, preclinical animal models, and human-derived cell lines. Although considered gold standards, these sources have significant drawbacks. Clinical trials are often restricted by ethical, logistical, and financial challenges and often include homogenous populations that fail to reflect real-world interindividual variability. Similarly, animal and cell-based models lack the physiological complexity of humans, leading to discrepancies between preclinical predictions and clinical outcomes. These constraints have stimulated interest in alternative platforms that more faithfully recapitulate human physiology and interindividual diversity. This review explores the potential of organoids as a novel or complementary source of PK-relevant data. Organoids, three-dimensional (3D) stem cell-derived structures, mimic the cellular architecture, functional heterogeneity, and physiological responses of human tissues. In particular, intestinal, liver, and kidney organoids preserve essential cellular features of ADME processes, positioning them as promising tools for integration into popPK and PBPK modeling frameworks. Full article

Background/Objectives: Accurate absorbed dose estimation is essential for optimising targeted radionuclide therapy (TRT) in metastatic castration-resistant prostate cancer, where kidney toxicity is dose-limiting. [¹⁷⁷Lu]Lu-rhPSMA-10.1 is a novel PSMA-targeted radioligand with favourable tumour-to-kidney uptake ratios; however, inter-patient pharmacokinetic variability can lead to differences in organ and tumour absorbed doses under fixed-activity administration. Personalised dosimetry offers a means to address this variability. This work aims to create mouse PBPK model-based digital twins for [¹⁷⁷Lu]Lu-rhPSMA-10.1 to test the model’s resistance to noise and evaluate its impact on accuracy and absorbed dose calculations. Methods: Five CB-17 SCID mice bearing LNCaP tumour xenografts received 2.6–3.1 MBq [¹⁷⁷Lu]Lu-rhPSMA-10.1 intravenously. Biodistribution was assessed 24 h post-injection by organ weighing and gamma counting. The PBPK model, implemented in MATLAB SimBiology (R2023a), was fitted to individual biodistribution data using mouse-specific physiological parameters. Digital twins—combining the model with fitted parameters—were used to generate time–activity curves (TACs) for kidneys, tumours, and the whole body. Gaussian noise ($\sigma$ = 0–0.35) was added to TACs to simulate measurement error. The model was refitted, and absorbed doses from time-integrated activities (TIAs) were compared to digital twin references. Results: The digital twin approach reproduced experimental data with physiologically plausible parameters. Absorbed dose estimates remained consistent and robust, deviating by <2.3% in kidneys and <1.0% in tumours. Conclusions: PBPK-based digital twins enable reliable, individualised dosimetry, even under substantial measurement uncertainty. Full article

Background/Objectives: Cirrhosis significantly alters physiological function and drug metabolism, particularly for medications primarily metabolized by CYP2C19 and CYP3A4. This study aims to establish a physiologically based pharmacokinetic (PBPK) modelling framework capable of predicting pharmacokinetic changes across different stages of cirrhosis, and to determine optimal dosing regimens that achieve drug exposure levels comparable to those in healthy individuals. Methods: We constructed a physiologically based pharmacokinetic (PBPK) model that incorporates six drugs, including omeprazole, lansoprazole, midazolam, ondansetron, verapamil, and alfentanil, which are metabolized primarily by CYP2C19 or CYP3A4. The pharmacokinetics of these drugs following oral or injectable administration were simulated in 1000 virtual healthy subjects, and the PBPK model was validated using clinical data. The model was further adapted to account for physiological changes in cirrhotic patients, extending its application to a population of 1000 virtual patients with liver cirrhosis. Results: Most observed data fell within the 5th and 95th percentiles of the virtual patient simulation results. Additionally, for most simulations, the area under the concentration-time curve (AUC) and peak concentration (C_max) were within 0.5- to 2-fold of the observed values. Sensitivity analysis indicated that the reduced expression of metabolizing enzymes increased plasma concentrations of drugs, which was a major factor contributing to the elevated drug exposure in patients with cirrhosis. The clinical dosing regimens of the CYP2C19 substrate omeprazole and the CYP3A4 substrate ondansetron were optimized for use in cirrhotic patients. Conclusions: The developed PBPK model successfully predicted the pharmacokinetics of CYP2C19 and CYP3A4 substrates in both healthy individuals and cirrhotic patients and can be effectively used for dose optimization in cirrhotic populations. Full article

Background: Meropenem (MEM) is frequently prescribed for the empirical management of severe infections in the pediatric intensive care unit (PICU). Critically ill children exhibit substantial pharmacokinetic (PK) variability, and current dosing strategies remain inadequately evaluated, particularly in neonates, infants, and those with altered renal function. Methods: This study employed a dual modeling approach integrating population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) methodologies. Clinical data from two PICUs were utilized for PopPK model development and PBPK model evaluation. Both models were rigorously assessed using goodness-of-fit plots and prediction-based metrics. Monte Carlo simulations were subsequently conducted to calculate the probability of target attainment (PTA) for multiple dosing regimens across MICs of 0.25–16 mg/L. The pharmacodynamic target (PDT) was defined as maintaining unbound plasma concentrations above the MIC for 100% of the dosing interval (100% ƒT _{> MIC}), and dosing regimens were considered acceptable if the PTA exceeded 90% for efficacy while avoiding potential toxicity (Css ≥ 50 mg/L). Results: A total of 202 MEM plasma concentrations from 101 pediatric patients were analyzed. Marked inter-individual variability in MEM pharmacokinetics and pharmacodynamics was observed. Augmented renal clearance (ARC) was frequently identified in PICU patients. We simultaneously developed a two-compartment population pharmacokinetic model incorporating body weight and estimated glomerular filtration rate, and a whole-body physiologically based pharmacokinetic model scaled from adults with adjustments for transporter ontogeny and renal function. The PopPK model, by incorporating interindividual variability on clearance and volume of distribution, captured a wider range of drug exposures and demonstrated superior predictive performance, particularly in subgroups with high eGFR. The PBPK model showed higher precision in the low eGFR subgroup but slightly lower overall predictive accuracy. Both models identified ARC as a key driver of subtherapeutic exposure. Standard regimens were insufficient for preterm neonates when the MIC was ≥4 mg/L, and even the maximum label-recommended dose failed to achieve the pharmacodynamic target for infants older than 1 month when the MIC was ≥2 mg/L. Conclusions: Both PBPK and PopPK frameworks reliably predicted MEM pharmacokinetics in critically ill pediatric patients, with complementary strengths across renal function strata. Model-informed simulations highlighted the inadequacy of standard dosing under conditions of ARC or elevated MIC, supporting individualized, precision-guided dosing strategies based on age, eGFR, and pathogen MIC. Full article

Cytotoxicity testing remains a cornerstone of modern toxicology, providing critical insight into how chemicals and drugs affect cell viability and function. Classical colorimetric assays such as MTT, LDH release, and neutral red uptake established the methodological basis of in vitro toxicology and continue to serve as regulatory benchmarks. However, their limited mechanistic depth and physiological relevance have prompted the field to evolve towards more predictive and human-centred approaches. Recent advances in high-content imaging, flow cytometry, and real-time impedance analysis have transformed cytotoxicity testing into a multiparametric discipline capable of detecting adaptive and sub-lethal cellular responses. Parallel progress in computational toxicology has introduced in silico models—QSAR, machine learning, and physiologically based pharmacokinetic (PBPK) modelling—that enable quantitative in vitro–in vivo extrapolation (QIVIVE). The integration of these computational tools with 3D organoids, organ-on-chip systems, and stem cell-based models allows for cross-validation between predictive simulations and experimental evidence, enhancing mechanistic interpretation and translational accuracy. Together, these developments underpin New Approach Methodologies (NAMs) and Integrated Approaches to Testing and Assessment (IATA), marking the transition from descriptive assays to predictive, mechanism-anchored frameworks that bridge in silico prediction with in vitro and in vivo validation—advancing both biomedical research and regulatory toxicology. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is applied to address clinical pharmacology issues including dose selection and exposure assessments for special populations (e.g., pediatrics, and renally or hepatically impaired patients). The objective of this study was to evaluate the predictive performance of a PBPK model for dosing assessment of intravenous immunoglobulin (IVIG) and anti-D immunoglobulin (anti-D Ig) products in pregnant women. Methods: A minimal PBPK (mPBPK) model that incorporates pregnancy-specific physiological parameters and allometric scaling approaches was developed and evaluated for predicting the exposure of IVIG and anti-D Ig in pregnant women. The concentration versus time data were obtained from the published literature. Results: The IVIG (n = 22) and anti-D Ig (n = 29) concentrations were predicted using the mPBPK model with an average fold error of 1.17 and 1.22, respectively. A total of 100% and 95% of IVIG concentrations were predicted within the 0.5–2-fold and 0.5–1.5-fold prediction error ranges, respectively. For anti-D Ig, predictions fell within the 0.5–2-fold and 0.5–1.5-fold ranges for 93% and 76% concentrations, respectively. A mPBPK model-based simulation following administration of 0.5 g/kg IVIG in 100 virtual nonpregnant and pregnant subjects revealed that the maximum plasma concentration (Cmax) was 15% lower and trough concentration (Ctrough) was 8% lower during the third trimester of pregnancy compared to nonpregnant subjects. In contrast, with flat dosing, Cmax and Ctrough were 32% and 26% lower in pregnant subjects, respectively. Overall, the model demonstrated reasonable predictive performance, and bodyweight-based dosing regimen is an acceptable approach that results in minimal change in exposure of IVIG in pregnant women. Full article

Introduction: Diltiazem hydrochloride (DH) is a calcium channel blocker used in the treatment of hypertension, angina pectoris, and arrhythmias. Its short half-life and frequent dosing requirements limit patient adherence and cause plasma concentration fluctuations. Objective: This review critically examines recent pharmaceutical technologies and formulation strategies for modified-release dosage forms (MRDFs) of diltiazem hydrochloride, emphasizing their impact on pharmacokinetics, clinical performance, and regulatory aspects. Methodology: A structured literature review (2010–2025) was conducted using databases such as PubMed, ScienceDirect, MDPI, and ACS Publications. Studies were selected based on relevance to solid oral MRDFs of DH and their associated manufacturing techniques. Results: Techniques including direct compression, granulation, extrusion–spheronization, spray drying, solvent evaporation, and ionotropic gelation have enabled the development of hydrophilic matrices, coated pellets, microspheres, and osmotic systems. Functional polymers such as HPMC, Eudragit^®, and ethylcellulose play a central role in modulating release kinetics and improving bioavailability. Conclusions: This review not only synthesizes current formulation strategies but also explores reverse engineering of ideal release profiles and the integration of advanced modeling tools such as physiologically based pharmacokinetic (PBPK) modeling and in vitro–in vivo correlation (IVIVC). These approaches support the rational design of personalized, regulatory-compliant DH therapies. Full article

Background: Dravet syndrome, a severe early-onset epileptic encephalopathy, is treated with multiple antiepileptic drugs such as clobazam (CLB) and stiripentol (STP), increasing the risk of drug–drug interactions (DDIs). Given the limited pediatric pharmacokinetic data, this study developed physiologically based pharmacokinetic (PBPK) models for CLB and STP to optimize dosing and assess DDI risk across pediatric age groups. Methods: We developed PBPK models for CLB, its active metabolite, N-desmethylclobazam (N-CLB), and STP in healthy adults and pediatric patients with Dravet syndrome aged two years and older. We evaluated the inhibitory effect of STP on CLB and N-CLB metabolism, accounting for CYP2C19 phenotypes. The model was extrapolated to predict drug exposure in pediatric patients under two years of age. Results: PBPK models for CLB, N-CLB, and STP successfully recapitulated observed pharmacokinetics in healthy adults and pediatric patients older than two years. Model verification against clinical DDI data showed that co-administration of STP with CLB resulted in a clinically insignificant increase in CLB exposure (C_min ratio = 1.77). In contrast, N-CLB exposure increased approximately 7-fold in CYP2C19 extensive metabolizers (C_min ratio ≈ 7) and slightly decreased in poor metabolizers (C_min ratio = 0.9), consistent with the CYP2C19-dependent metabolism of N-CLB. Extrapolation to pediatric patients under two years of age predicted CLB, N-CLB, and STP exposures that were comparable to older children and remained within their reported efficacy and safety margins, suggesting no major ontogeny-related effect on exposure. Conclusions: The PBPK model supports the safe extrapolation of CLB and STP co-administration to pediatric Dravet syndrome patients as young as six months. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp^® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article

Although traditionally contraindicated, the coadministration of tamoxifen and estradiol may hold clinical relevance in specific contexts, particularly in breast cancer survivors with premature menopause and a high risk of osteoporosis, thereby justifying the need to re-evaluate this therapeutic combination. This study presents an innovative physiologically based pharmacokinetic (PBPK) modeling approach to evaluate the coadministration of tamoxifen and estradiol in women with breast cancer and a high risk of osteoporosis. Using GastroPlus^® software, PBPK models were developed and validated for both drugs, based on physicochemical and kinetic data obtained from the literature and, where necessary, supplemented by estimates generated in ADMET Predictor^®. The simulations considered different hormonal profiles (pre and postmenopausal) and therapeutic regimens, evaluating potential interactions mediated by the CYP3A4 enzyme. Analysis of the pharmacokinetic parameters (F, C_max, T_max and AUC) revealed strong agreement between the simulated and experimental values, with prediction errors of less than twofold. The drug interaction studies, carried out in dynamic and stationary modes, indicated that estradiol does not significantly alter the pharmacokinetics of tamoxifen, even at increasing doses or in enlarged virtual populations. These results represent the first in silico evidence that, under certain conditions, the concomitant use of estradiol does not compromise the pharmacokinetic efficacy of tamoxifen. Although the study is computational, it provides a solid scientific basis for re-evaluating this therapeutic combination and proposes a pioneering model for personalized strategies in complex oncological contexts. All simulations assumed average enzyme abundance/activity without CYP polymorphism parameterization; findings are restricted to parent-tamoxifen pharmacokinetics and do not infer metabolite (e.g., endoxifen) exposure or phenotype effects. Full article

Background: Heart failure (HF), as the end stage of various cardiac diseases, alters blood flow to key organs responsible for drug clearance. This can lead to unpredictable and often suboptimal drug exposure, creating a critical need for quantitative tools to guide precise dosing in this vulnerable population. Methods: This study aimed to establish a whole-body physiologically based pharmacokinetic (PBPK) model for characterizing drug pharmacokinetics in both healthy subjects and patients across the HF severity spectrum. Eight commonly used drugs (digoxin, furosemide, bumetanide, torasemide, captopril, valsartan, felodipine and midazolam) for treating HF and its comorbidities were selected. Following successful validation against clinical data from healthy subjects, the PBPK model was extrapolated to HF patients. Pharmacokinetics of the eight drugs in 1000 virtual HF patients were simulated by replacing tissue blood flows and compared using clinical observations. Results: Most of the observed concentrations were encompassed within the 5th–95th percentiles of simulated values from 1000 virtual HF patients. Predicted area under the concentration–time curve and maximum plasma concentration fell within the 0.5~2.0-fold range relative to clinical observations. Sensitivity analysis demonstrated that intrinsic renal clearance, unbound fraction in blood, muscular blood flow, and effective permeability coefficient significantly impact plasma exposure of digoxin at a steady state. Oral digoxin dosing regimens for HF patients were optimized via the validated PBPK model to ensure that steady-state plasma concentrations in all HF patients remain below the toxicity threshold (2.0 ng/mL). Conclusions: A PBPK model was successfully developed to predict the plasma concentration–time profiles of the eight tested drugs in both healthy subjects and HF patients. Furthermore, this model may also be applied to guide digoxin dose optimization for HF patients. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifactorial condition requiring multi-target therapeutic strategies beyond traditional single-marker approaches. In this work, we present a fully in silico nutraceutical screening pipeline that integrates molecular prediction, systemic aggregation, and technological design. A curated panel of ten MASLD-relevant targets, spanning nuclear receptors (FXR, PPAR-α/γ, THR-β), lipogenic and cholesterogenic enzymes (ACC1, FASN, DGAT2, HMGCR), and transport/regulatory proteins (LIPG, FABP4), was assembled from proteomic evidence. Bioactivity records were extracted from ChEMBL, structurally standardized, and converted into RDKit descriptors. Predictive modeling employed a stacked ensemble of Random Forest, XGBoost, and CatBoost with isotonic calibration, yielding robust performance (mean cross-validated ROC-AUC 0.834; independent test ROC-AUC 0.840). Calibrated probabilities were aggregated into total activity (TA) and weighted TA metrics, combined with structural clustering (six structural clusters, twelve MOA clusters) to ensure chemical diversity. We used physiologically based pharmacokinetic (PBPK) modeling to translate probabilistic profiles into minimum simulated doses (MSDs) and chrono-specific exposure (%T>IC50) for three prototype concepts: HepatoBlend (morning powder), LiverGuard Tea (evening aqueous form), and HDL-Chews (postprandial chew). Integration of physicochemical descriptors (MW, logP, TPSA) guided carrier and encapsulation choices, addressing stability and sensory constraints. The results demonstrate that a computationally integrated pipeline can rationally generate multi-target nutraceutical formulations, linking molecular predictions with systemic coverage and practical formulation specifications, and thus provides a transferable framework for MASLD and related metabolic conditions. Full article

Background/Objectives: Escitalopram, a first-line antidepressant, is primarily metabolized by CYP2C19. Its pharmacokinetics are altered during pregnancy. This study aims to predict maternal and fetal exposure to escitalopram during pregnancy and to propose safe and effective dosing strategies using physiologically based pharmacokinetic (PBPK) modeling. Methods: Predictive PBPK models for escitalopram were developed in nonpregnant women, pregnant women, and the fetoplacental unit using the Simcyp^® simulator. Additional models incorporating CYP2C19 phenotypes were constructed. Model performance was evaluated using visual predictive checks and by comparing predicted-to-observed ratios for the maximum plasma concentration (C_max) and the area under the curve (AUC), within an acceptance criterion of 0.7–1.3. Results: Escitalopram concentrations at doses of 10–20 mg declined with advancing gestation. The cord-to-maternal concentration ratio was approximately 0.70 for both doses. Simulations of maternal and fetoplacental PBPK models across CYP2C19 phenotypes showed that most observed concentrations fell within the 95% confidence intervals of the predictions. Based on the therapeutic range attained and the maintenance of steady-state exposure, a once-daily 20 mg escitalopram dose was predicted to be appropriate during pregnancy. Conclusions: These findings suggest that a once-daily 20 mg dose appears optimal during pregnancy, highlighting the need to consider the gestational stage and CYP2C19 phenotype in dose optimization. Full article

Background/Objectives: Tuberculosis (TB) remains a global health challenge due to long treatment durations, poor adherence, and growing drug resistance. Inhalable co-amorphous systems (COAMS) offer a promising strategy for targeted pulmonary delivery of fixed-dose combinations, improving efficacy and reducing systemic side effects. Methods: Our in-house-developed machine learning (ML) tool identified two promising API-API combinations for TB therapy, rifampicin (RIF)–moxifloxacin (MOX) and RIF–ethambutol (ETH). Physiologically based pharmacokinetic (PBPK) modeling was used to estimate therapeutic lung doses of RIF, ETH, and MOX following oral administration. Predicted lung doses were translated into molar ratios, and COAMS of RIF-ETH and RIF-MOX at both model-predicted (1:1) and PBPK-informed ratios were prepared by spray drying and co-milling, followed by comprehensive physicochemical and aerodynamic characterization. Results: RIF-MOX COAMS could be prepared in all molar ratios tested, whereas RIF-ETH failed to result in COAMS for therapeutically relevant molar ratios. Spray drying and ball milling successfully produced stable RIF-MOX formulations, with spray drying showing superior behavior in terms of morphology (narrow particle size distribution; lower Sauter mean diameter), aerosolization performance (fine particle fraction above 74% for RIF and MOX), and dissolution. Conclusions: This study demonstrated that PBPK modeling and ML are useful tools to develop COAMS for pulmonary delivery of active pharmaceutical ingredients (APIs) routinely applied through the oral route. It was also observed that COAMS may be less effective when the therapeutic lung dose ratio significantly deviates from the predicted 1:1 molar ratio. This suggests the need for alternative delivery strategies in such cases. Full article