Search Results (379)

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

Digital twins (DTs), virtual replicas that integrate mechanistic modeling with real-time clinical data, are emerging as powerful tools in healthcare with particular promise in pediatrics, where age-dependent physiology and ethical considerations complicate infectious disease management. This narrative review examines current and potential applications of DTs across antimicrobial stewardship (AMS), diagnostics, vaccine personalization, respiratory support, and system-level preparedness. Evidence indicates that DTs can optimize antimicrobial therapy by simulating pharmacokinetics and pharmacodynamics to support individualized dosing, enable Bayesian therapeutic drug monitoring, and facilitate timely de-escalation. They also help guide intravenous-to-oral switches and treatment durations by integrating host-response markers and microbiological data, reducing unnecessary antibiotic exposure. Diagnostic applications include simulating host–pathogen interactions to improve accuracy, forecasting clinical deterioration to aid in early sepsis recognition, and differentiating between viral and bacterial illness. Immune DTs hold potential for tailoring vaccination schedules and prophylaxis to a child’s unique immune profile, while hospital- and system-level DTs can simulate outbreaks, optimize patient flow, and strengthen surge preparedness. Despite these advances, implementation in routine pediatric care remains limited by challenges such as scarce pediatric datasets, fragmented data infrastructures, complex developmental physiology, ethical concerns, and uncertain regulatory frameworks. Addressing these barriers will require prospective validation, interoperable data systems, and equitable design to ensure fairness and inclusivity. If developed responsibly, DTs could redefine pediatric infectious disease management by shifting practice from reactive and population-based toward proactive, predictive, and personalized care, ultimately improving outcomes while supporting AMS and health system resilience. 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

Background/Objectives: Physiologically based kinetics (PBK) modelling provides (internal) exposure concentrations. We used a PBK model parameterized exclusively with in silico and in vitro data in a bottom-up approach to predict the pharmacokinetics of oxybenzone, a UV filter, present in two formulations (for which dose-normalized C_max and AUC from clinical studies were different). Methods: Skin absorption data were used to refine chemical-specific dermal absorption parameters for oxybenzone in a lotion and spray. The Transdermal Compartmental Absorption and Transit (TCAT) model in GastroPlus^® 9.9 was used to estimate vehicle and skin layer diffusion and partitioning and then used to simulate systemic exposure. The model was validated according to the OECD 331 guideline. Results: PK profiles simulated for both formulations after single and repeated applications correlated with clinical data profiles (used only to validate our approach), with a deviation from the C_max and AUC of <2-fold. Sensitivity and uncertainty analyses indicated that most input parameters had a medium to high reliability, whereas only a few parameters related to dermal delivery had a low reliability: the partition coefficient between vehicle and water for spray and the diffusion coefficient in stratum corneum for lotion. In vitro skin absorption results suggested that absorption kinetics were not statistically different between the formulations; however, parameters such as vehicle evaporation time were different. The fine-tuned TCAT model containing the absorption data suggested that the variability in clinical data might be due to other factors, e.g., the small number of subjects. Conclusions: These results demonstrate how formulation-dependent absorption kinetics improve confidence in estimated exposure, thanks to the PBK model with its bottom-up approach for nonanimal-based safety assessments. 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

Palmitoylethanolamide (PEA) is a naturally occurring fatty acid amide and an endocannabinoid-related lipid that has been extensively studied for its analgesic, immunomodulatory, antimicrobial, and anti-inflammatory properties. It has demonstrated efficacy in various applications and is currently utilized as a nutraceutical for its antinociceptive, neuroprotective, and immunomodulatory effects, particularly in supporting brain and joint health and in mitigating inflammatory processes. Background/Objectives: Despite its significant therapeutic potential, the clinical effectiveness of PEA is limited by its poor water solubility and, consequently, low oral bioavailability. Additionally, degradation in the acidic gastrointestinal environment further compromises its absorption. To address these challenges, several technological strategies have been explored to improve its pharmacokinetic profile, including conventional micronization and ultra-micronization techniques. The objective of this study was to characterize a novel liposomal formulation based on PEA and evaluate its intestinal permeation and absorption. Methods: Comparative permeation studies of PEA were conducted using ex vivo models to evaluate its absorption characteristics across gastrointestinal mucosae. The experiments were performed in a Franz diffusion cell system using a porcine colon mucosa in two physiologically relevant media: Simulated Gastric Fluid (SGF) and Fasted State Simulated Intestinal Fluid (FaSSIF). Results: Liposomal PEA showed a more efficient and continuous release over time, reaching higher concentrations of PEA permeated through the membrane. Conclusions: Our findings demonstrate a significant improvement in PEA’s permeability and absorption in an ex vivo simulated gastrointestinal environment. Liposomal PEA appears to be more affine to biological membranes. These results suggest that liposomal PEA may represent a promising therapeutic strategy for managing chronic pain and inflammatory conditions such as chronic pelvic pain. Full article

Background/Objectives: Fixed-dose combinations (FDCs) hold significant clinical value for the management of hypertension, diabetes and other chronic diseases. However, since the complexity of formulations, generic compounds require both in vitro pharmaceutical equivalence and in vivo bioequivalence (BE) for each active pharmaceutical ingredient (API). Physiologically based biopharmaceutics modeling (PBBM) not only bridges in vitro drug properties to in vivo pharmacokinetics but effectively assesses the impact of formulations on systemic exposure. This study was aimed at developing a PBBM for metformin–glyburide FDC and investigating its clinically relevant quality specifications. Methods: PK-Sim^® software (Version 11.3) was used to establish a PBBM for a metformin–glyburide FDC. Sensitivity analysis identified critical parameters and guided design of virtual populations. Subsequently, virtual bioequivalence (VBE) was assessed between both reference and test formulations, and BE-ensuring dissolution space was explored by the change in dissolution characteristics. Results: The in vivo behavior of products was successfully captured by the developed model. Sensitivity analysis indicated that systemic exposure was primarily sensitive to gastrointestinal (GI) pH and transit times. VBE analysis confirmed BE between the reference and test formulations. The dissolution safe space for the FDC was defined as the concurrent achievement of ≥ 50% dissolution within 25 min for metformin and between 35 and 170 min for glyburide, which constituted equivalent specification. Conclusions: The PBBM developed in this study systematically evaluated the VBE of metformin–glyburide FDC, optimized the acceptance criteria for traditional in vitro dissolution testing, and thereby explored its clinically relevant quality specification. 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

Physiologically based pharmacokinetic (PBPK) models allow to simulate the behaviour of compounds in diverse physiological populations. However, the categorization of individuals into distinct populations raises questions regarding the classification criteria. In previous research, simulations of the pharmacokinetics of the mycotoxin aflatoxin B1 (AFB1), were performed in the black South African population, using PBPK modeling. This study investigates the prevalence of clinical CYP450 phenotypes (CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5) across Sub-Saharan Africa (SSA), to determine the feasibility of defining SSA as a single population. SSA was subdivided into Central, East, South and West Africa. The phenotype data were assigned to the different regions and a fifth SSA group was composed of all regions’ weighted means. Available data from literature only covered 7.30% of Central, 56.9% of East, 38.9% of South and 62.9% of West Africa, clearly indicating critical data gaps. A pairwise proportion test was performed between the regions on enzyme phenotype data. When achieving statistical significance (p < 0.05), a Cohen’s d-test was performed to determine the degree of the difference. Next, per region populations were built using SimCYP starting from the available SSA based SouthAfrican_Population FW_Custom population, supplemented with the phenotype data from literature. Simulations were performed using CYP probe substrates in all populations, and derived PK parameters (C_max, T_max, AUC_ss and CL) were plotted in bar charts. Significant differences between the African regions regarding CYP450 phenotype frequencies were shown for CYP2B6, CYP2C19 and CYP2D6. Limited regional data challenge the representation of SSA populations in these models. The scarce availability of in vivo data for SSA regions restricted the ability to fully validate the developed PBPK populations. However, observed literature data from specific SSA regions provided partial validation, indicating that SSA populations should ideally be modelled at a regional level rather than as a single entity. The findings, emerging from the initial AFB1-focused PBPK work, underscore the need for more extensive and region-specific data to enhance model accuracy and predictive value across SSA. Full article

Background: Asciminib (Scemblix^®) is approved for the first-line treatment of adult patients with chronic myeloid leukemia in the chronic phase at 40 mg twice daily (BID) and 80 mg once daily (QD) or 200 mg BID for patients harboring the T315I mutation. Objectives: (1) Extrapolate the DDI magnitude as the perpetrator or victim of other drugs and the effect of organ impairment to untested doses; (2) Predict clinically untested DDI scenarios. Methods: Asciminib is primarily cleared by cytochrome P450 (CYP)3A4, UDP-glucuronosyltransferases (UGT)2B7, UGT2B17, UGT1A3/4, and the breast-cancer-resistance protein (BCRP). In vitro asciminib is an inhibitor of several CYP, UGT enzymes, and transporters and is an inducer of CYP1A2 and CYP3A4. Clinical DDI studies assessed asciminib 40 mg BID as a perpetrator on CYP-sensitive substrates. Additional studies evaluated the impact of strong CYP3A4 perpetrators and imatinib on a single 40 mg dose of asciminib. Hepatic and renal impairment studies were also conducted at the 40 mg dose. A nonlinear whole-body physiologically based pharmacokinetic (PBPK) model was developed and verified for asciminib as a CYP3A4, UGT, and BCRP substrate and a perpetrator of several CYP and UGT enzymes. Results: This PBPK model was applied in lieu of clinical pharmacology studies to support the new drug application of Scemblix^® and to bridge data from 40 mg BID to the 80 mg QD and 200 mg BID dose regimens. Conclusions: The PBPK predictions informed the drug product label and are estimated to have replaced at least 10 clinical studies. Full article

Background: Following vitamin D₃ oral administration, attained serum concentrations of its metabolite 25-hydroxyvitamin D₃ (25(OH)D₃) are variable among children. Methods: We developed physiologically based pharmacokinetic (PBPK) modelling using annually measured serum 25(OH)D₃ concentrations in 77 Cape Town schoolchildren aged 6–11 years who received weekly oral doses of 10,000 IU vitamin D₃ for 3 years during a clinical trial (Δ25(OH)D = 32.2 nmol/L, 95% CI: [−3.2, 65.8] nmol/L). Simulations were performed to test the model on 463 other participants in the same trial, and in a cohort of 1756 Mongolian schoolchildren aged 6–11 years who received weekly oral doses of 14,000 IU vitamin D₃ for 3 years in another trial. Results: The best model attributed most of the variability in post-supplementation 25(OH)D₃ concentrations to hepatic clearance and covariates including weight (ΔAIC = −21) and ZBMI (body mass index Z-score, ΔAIC = −34). For 463 other children from the Cape Town trial (Δ25(OH)D = 25.8 nmol/L, 95% CI: [8.3, 47.2] nmol/L), mean estimation error was 5.3 nmol/L, and 76.7% of observations were within the 95% prediction intervals. Our simulation supported the previous proposal that serum 25(OH)D₃ should exceed 50 nmol/L among 97.5% of European children at 24.4 μg/day vitamin D₃ dosing. At a higher weekly dose (14,000 IU), the Mongolian children demonstrated a higher average increase in serum 25(OH)D₃ (40.6 [−2.9, 88.9] nmol/L) but were overestimated by the model. Conclusion: We developed the first PBPK model to successfully predict the long-term serum 25(OH)D₃ increases in healthy schoolchildren in Cape Town who received orally administered vitamin D₃ and exhibited higher relative increases than Mongolian children. Full article

Background/Objectives: Despite the recognized importance and distinctive characteristics of the intestinal lymphatic pathway in drug absorption, its pharmacokinetic modeling remains largely unexplored. This study aimed to address this gap by developing a physiologically based pharmacokinetic model (PBPK) to represent the oral lymphatic uptake of halofantrine following a fatty meal. Methods: Using GastroPlus™ 9.8.3 and published literature data, halofantrine absorption, distribution, metabolism, and elimination in both fasting and fed states were modeled. As the used software does not directly simulate intestinal lymphatic transport, lymphatic involvement in the fed state was examined indirectly through parameter adjustments such as first-pass metabolism, pKa-driven solubility changes, and bile-salt-mediated solubilization, with the aid of molecular dynamics simulations under post-prandial pH. Results: The pharmacokinetic models revealed a reduction in the first-pass effect of halofantrine in the fed state compared to that in the fasting state. While adjustments in metabolism kinetics sufficed for constructing a representative PBPK model in the fasting state, capturing the fed-state profile required both modifications to metabolism kinetics and other parameters related to the structural rearrangements of halofantrine driven by the changes in intestinal pH following food intake. These changes were confirmed using molecular dynamics simulations of halofantrine in pHs reflecting the post-prandial conditions. Conclusions: This study underscores the need for further exploration and direct modeling of intestinal lymphatic uptake via PBPK models, highlighting its underexplored status in simulation algorithms. Moreover, the importance of integrating representative physicochemical factors for drugs, particularly in post-prandial conditions or lipid formulations, is evident. Overall, these findings contribute to advancing predictive regulatory and developmental considerations in drug development using post hoc analyses. Full article

Background: In the last several years, “physiologically based pharmacokinetic (PBPK) modeling” has gathered significant emphasis in the modeling of drug absorption, disposition, and metabolism. This research study aims to elaborate the plasma/serum concentration–time profiles and pharmacokinetics (PK) of ofloxacin by establishing a PBPK model in healthy subjects and those suffering from renal impairment (RI). Methods: A comprehensive literature analysis was conducted to screen out all the systemic PK profiles and parameters specific to ofloxacin, followed by their implementation in PK-Sim^® version 12 software. This model-driven approach begins by developing the model in healthy populations using both intravenous (IV) and per-oral (PO) routes and then extrapolating it to the diseased population. The model evaluation was then strengthened for different PK variables such as the maximal plasma/serum concentration (C_max), the area under the curve from 0 to t (AUC_0–t), and plasma/serum clearance (CL) by employing various metrics such as predicted/observed ratios (R_pre/obs), visual predictive checks, the average fold error (AFE), root mean squared error (RMSE), and mean absolute error (MAE). Results: The AFE, RSME, and MAE for C_max in RI were 1.10, 0.22, and 0.16, respectively, which fell within the acceptable simulated error range. Furthermore, dosage adjustments for individuals with mild, moderate, and severe RI were presented by box-whisker plots to compare their systemic exposure with that of the healthy population. Conclusions: These model predictions have confirmed the PK variations in ofloxacin, which may assist the clinicians in optimizing dosage schedules in healthy and various categories of RI populations. Full article