Search Results (320)

Objective: This study aims to evaluate free vancomycin concentrations in tissues of septic patients that received empirical doses. Methods: A PBPK model was built in PK-Sim to simulate vancomycin concentrations in healthy volunteers and septic patients. Literature data were used to validate the model. A strain of MRSA (methicillin-resistant Staphylococcus aureus) was evaluated through time-kill curves. Based on the information obtained from the time-kill study, a PD model, including adaptive resistance, was developed using NONMEM. The PBPK and PD models were combined to evaluate the vancomycin effect in plasma and tissues against MRSA. Results: A PBPK model was successfully built for both healthy volunteers and septic patients. The tissue concentrations were found to be significantly lower than plasma concentrations. The studied strain of MRSA was found to have an MIC of 2 µg/mL, and the PD model described the EC50 as 1.05 µg/mL. The PBPK and PD models were successfully combined, and septic patients infected with MRSA strains with MIC of 2 µg/mL had effective treatment response. However, septic patients infected with MRSA strains with MICs of 4 µg/mL and 8 µg/mL did not have adequate response to vancomycin treatment. Conclusions: In septic patients, response was limited against resistant MRSA strains. These findings should be considered hypothesis-generating and interpreted with caution, underscoring the need for individualized approaches and rigorous monitoring. Full article

Background/Objectives: Adapalene is a synthetic retinoid used as a treatment for acne vulgaris. In this study, we attempted to evaluate the dermal pharmacokinetics of adapalene utilizing experimental and in silico tools. Methods: We utilized three over the counter (OTC) adapalene gels to evaluate local dermal pharmacokinetics. A data-driven, robust, mechanistic dermal physiologically based pharmacokinetic (PBPK) model was developed by integrating the physicochemical properties of adapalene, the formulation attributes of the gels, and the biophysical aspects of dermal absorption. The dermal PBPK model was validated against experimental data using in vitro release studies and in vitro permeation studies with human cadaver skin. A clinical study was performed to evaluate the effects of adapalene from the three gel formulations. The impact of adapalene delivery from three gels on the stratum corneum (SC) thickness, pilosebaceous unit area, keratinocyte number, and epidermal thickness was captured using a non-invasive technique, line-field confocal optical coherence tomography (LC–OCT). These responses were evaluated using an Emax model. Results: The dermal PBPK model has successfully predicted adapalene penetration profiles across different gel formulations. The model accuracy, in predicting drug release and permeation characteristics, was confirmed using the experimental data. Clinical evaluation revealed formulation-dependent differences in adapalene’s effects on measured skin parameters, with distinct pharmacodynamic profiles observed for each gel formulation. Conclusions: The overall study gave us a detailed insight into potential effects of formulation on the dermal pharmacokinetics and pharmacodynamics of adapalene using three marketed gels. Full article

Background: Accurate assessment of CYP2C induction-mediated drug–drug interactions (DDIs) remains a challenge, despite the importance of CYP2C enzymes in drug metabolism. Limitations in available models and scarce clinical induction data have hampered quantitative preclinical DDI risk evaluation. Methods: In this study, the authors utilized an all-human hepatocyte triculture system to capture CYP2C induction using the perpetrators rifampicin, efavirenz, carbamazepine, and apalutamide. In vitro induction parameters were quantified by measuring changes in both mRNA and enzyme activities for CYP2C8, CYP2C9, and CYP2C19. These induction parameters, along with CYP-specific intrinsic clearance (CL_int) for the victim compounds, were incorporated into a physiologically based pharmacokinetic (PBPK) model, and pharmacokinetics (PK) of known CYP2C substrates were predicted with and without co-administration of perpetrator compounds using clinical dosing regimens. The results were quantitatively compared with the currently utilized mechanistic static modeling (MSM) approach and the reported clinical DDI outcomes. Results: By incorporating the measured f_m of CYP2C substrates into PBPK modeling, we observed a lower propensity to over- or underpredict the exposure of these substrates as victims of CYP2C induction-based DDIs when co-administered with known perpetrators, which resulted in an excellent correlation to observed clinical outcomes. The MSM approach predicted the CYP3A4 induction-based DDI risk accurately but could not capture CYP2C induction with similar precision. Conclusions: Overall, this is the first study that demonstrates the utility of PBPK modeling as a complementary approach to MSM for CYP2C induction-based DDI risk assessment. Full article

Background/Objective: Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy medication burden and is thereby at high risk for drug–drug–disease interactions (DDDIs). Furthermore, the pronounced inter-individual variability in PK observed in ESRD patients highlights the urgent need for individualized PBPK models. Methods: In this study, we developed a PBPK population model for ESRD patients, incorporating functional changes in key drug-metabolizing enzymes and transporters (DMETs), including CYP3A4, OATP1B1/3, P-gp, and BCRP. The model was initially constructed using the recalibrated demographic and physiological parameters of ESRD patients. Then, we used five well-validated substrates (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) and their corresponding PK profiles from ESRD patients taking a microdose cocktail regimen to simultaneously estimate the abundance of all these DMETs. Lastly, machine learning was employed to identify potential factors influencing individual clearance. Results: Our study suggested a significant reduction in hepatic OATP1B1/3 (75%) and intestinal P-gp abundance (34%) in ESRD patients. Ileum BCRP abundance was estimated to increase by 100%, while change in hepatic CYP3A4 abundance is minimal. Notably, simulations of drug combinations revealed potential DDDI risks that were not observed in healthy volunteers. Machine learning further identified Clostridium XVIII and Escherichia genus abundances as significant factors influencing dabigatran clearance. For rosuvastatin, aspartate aminotransferase, total bilirubin, Bacteroides, and Megamonas genus abundances were key influencers. No significant factors were identified for midazolam, pitavastatin, or atorvastatin. Conclusions: Our study proposes a feasible strategy for individualized PK prediction by integrating PBPK modeling with machine learning to support the development and precise use of the aforementioned DMET substrates in ESRD patients. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses can be predicted using physiologically based pharmacokinetic (PBPK) modeling. High-resolution computational models of the eye are desirable to improve simulations of drug delivery; however, these approaches can have long run times. In this study, a fast-running computational quasi-3D (Q3D) model of the human tear film was developed to account for absorption, blinking, drainage, and evaporation. Visualization of blinking mechanics and flow distributions throughout the tear film were enabled using this Q3D approach. Average drug absorption throughout the tear film subregions was quantified using a high-resolution compartment model based on a system of ordinary differential equations (ODEs). Simulations were validated by comparing them with experimental data from topical administration of 0.1% dexamethasone suspension in the tear film (R² = 0.76, RMSE = 8.7, AARD = 28.8%). Overall, the Q3D tear film model accounts for critical mechanistic factors (e.g., blinking and drainage) not previously included in fast-running models. Further, this work demonstrated methods toward improved computational efficiency, where central processing unit (CPU) time was decreased while maintaining accuracy. Building upon this work, this Q3D approach applied to the tear film will allow for more seamless integration into full-body models, which will be an extremely valuable tool in the development of treatments for ocular conditions. Full article

Background/Objectives: Tofacitinib is a Janus kinase 1 and 3 inhibitor that was developed to treat rheumatoid arthritis. Accordingly, this study aimed to predict plasma tofacitinib concentrations and pharmacokinetic parameters in patients with renal failure through physiologically based pharmacokinetic (PBPK) simulations. Methods: PK-Sim and Simcyp simulators were used, as well as conventional Dedrick plot analysis, employing a single animal extrapolation method. The predictions were compared with previously published data. Results: PBPK simulations of tofacitinib in patients with renal failure closely matched the observed plasma concentration profiles and pharmacokinetic results, including the area under the plasma concentration–time curve (AUC), maximum plasma concentration (C_max), and time to reach C_max (T_max). The ratios of the simulated to observed plasma concentrations and pharmacokinetic parameters for tofacitinib were within a 0.5–2.0-fold error range. Although the results from the Dedrick plot were reasonably good, they were less accurate than those of the PBPK simulations. This was because the Dedrick plot relied solely on preclinical plasma concentration data without incorporating drug physicochemical properties, in vitro data, or physiological and pathophysiological variables. Conclusions: The findings suggest that PBPK simulations using single-species extrapolation effectively provide preliminary estimates of plasma tofacitinib concentration profiles and pharmacokinetic parameters in humans under specific conditions, including renal failure. Furthermore, the results provide a foundation for adjusting tofacitinib dosage and dosing schedules to maintain effective plasma concentrations by considering the pathophysiological characteristics of patients according to their specific diseases. Full article

Sulfotransferase (SULT) enzymes contribute significantly to drug metabolism in pediatric patients. The purpose of this study was to develop a PBPK model for acetaminophen (APAP) in pediatric populations that accounts for the ontogeny of SULT isozymes that play a critical role in APAP metabolism. PBPK modeling and simulation were performed using the Simcyp^® Simulator. The model incorporated the developmental ontogeny of three key hepatic SULT enzymes: SULT1A1, SULT1A3, and SULT2A1 using “best-fit” ontogeny equations for each isozyme as determined by nonlinear regression analysis of enzyme abundance versus age. PBPK model-simulated pharmacokinetic profiles for APAP captured observed clinical data for systemic exposure (Cmax, AUC) in neonates, infants, and children. SULTS accounted for ~60% APAP metabolism in neonates, with decreased contributions to infants and children. Model sensitivity analysis highlighted the potential for APAP metabolic DDIs, primarily through SULT1A1. The study demonstrates that the impact of SULT enzymes on drug metabolism is significant in neonates, which is an important clinical consideration for APAP. A PBPK model that incorporates SULT ontogeny has the potential to help inform dosing decisions in this special patient population. Full article

Background: Understanding interactions between cytokine antagonists and drugs is essential for effective medication management in inflammatory conditions. Recent regulatory authority guidelines emphasise a systematic, risk-based approach to evaluating these interactions, underscoring the need for mechanistic insight. Proinflammatory cytokines, such as interleukin-6 (IL-6), modulate cytochrome P450 (CYP) enzymes, reducing the metabolism of CYP substrates. Cytokine antagonists (such as IL-6 receptor antagonists) can counteract this effect, restoring CYP activity and increasing drug clearance. However, quantitative prediction of cytokine-mediated drug interactions remains challenging, as existing models often lack the mechanistic detail needed to capture the dynamic relationship between cytokine signalling, receptor engagement, and downstream modulation of drug metabolism. Methods: A physiologically based pharmacokinetic (PBPK) framework incorporating cytokine–receptor binding, subsequent downregulation of CYP expression, and blockade of the cytokine signalling by a therapeutic protein antagonist was developed to simulate and investigate cytokine antagonist-drug interactions. Tocilizumab, a humanised IL-6 receptor antagonist used to treat several inflammatory conditions associated with elevated IL-6 levels, was selected as a model drug to demonstrate the utility of the framework. Results: The developed PBPK model accurately predicted the pharmacokinetics profiles of tocilizumab and captured clinically observed dynamic changes in simvastatin exposure before and after tocilizumab treatment in rheumatoid arthritis (RA) patients. Simulated IL-6 dynamics aligned with observed clinical profiles, showing transient elevation following receptor blockade and associated restoration of CYP3A4 activity. Prospective simulations with commonly co-administered CYP substrates (celecoxib, chloroquine, cyclosporine, ibuprofen, prednisone, simvastatin, and theophylline) in RA patients revealed dose regimen- and drug-dependent differences in interaction magnitude. Conclusions: This study demonstrated the utility of PBPK models in providing a mechanistic understanding of cytokine antagonist-drug interactions, supporting enhanced therapeutic decision-making and optimising patient care in inflammatory conditions. Full article

Bariatric surgery involves major changes in the anatomy and physiology of the gastrointestinal tract, which may alter oral drug bioavailability and efficacy. Phosphodiesterase-5 inhibitor (PDE5i) drugs are the first-line treatment of erectile dysfunction, a condition associated with a higher BMI. In this paper, we examine the PDE5i vardenafil for possible post-bariatric changes in solubility/dissolution and absorption. Vardenafil solubility was determined in vitro, as well as ex vivo using aspirated gastric contents from patients prior to vs. following bariatric procedures. Dissolution was tested in vitro under unoperated stomach vs. post-gastric sleeve/bypass conditions. Lastly, the gathered solubility/dissolution data were used to produce an in silico physiologically based pharmacokinetic (PBPK) model (GastroPlus^®), where gastric volume, pH, and transit time, as well as proximal GI bypass (when relevant) were all adjusted for, evaluating vardenafil dissolution, gastrointestinal compartmental absorption, and pharmacokinetics before vs. after different bariatric procedures. pH-dependent solubility was demonstrated for vardenafil with low (pH 7) vs. high solubility (pH 1–5), which was confirmed ex vivo. The impaired dissolution of all vardenafil doses under post-gastric bypass conditions was demonstrated, contrary to complete (100%) dissolution under pre-surgery and post-sleeve gastrectomy conditions. Compared to unoperated individuals, PBPK simulations revealed altered pharmacokinetics post-gastric bypass (but not after sleeve gastrectomy), with 30% lower peak plasma concentration (C_max) and 40% longer time to C_max (T_max). Complete absorption after gastric bypass is predicted for vardenafil, which is attributable to significant absorption from the large intestine. The biopharmaceutics and PBPK analysis indicate that vardenafil may be similarly effective after sleeve gastrectomy as before the procedure. However, results after gastric bypass question the effectiveness of this PDE5i. Specifically, vardenafil’s onset of action might be delayed and unpredictable, negatively affecting the practicality of the intended use. Full article

Background: Accurately determining pediatric dosing is essential prior to initiating clinical trials or administering medications in routine clinical settings. In children, ethical considerations demand careful evaluation of both safety and effectiveness. Typically, dosing recommendations for therapeutic proteins, such as monoclonal antibodies (mAbs), are derived from adult dosages using body weight as a scaling factor. However, this method overlooks key physiological and biochemical distinctions between pediatric and adult patients. Therefore, this could lead to the underexposure of mAbs, limiting their efficacy in this population. Additional methods are necessary to predict pediatric doses mechanistically. For small molecules, physiologically based pharmacokinetic (PBPK) models have been extensively used to predict pediatric doses based on physiological age-related changes and enzymes/transporters ontogeny. This study aims to evaluate the ability of PBPK models to predict mAbs’ pediatric exposure. Methods: Three mAbs were used for model development and validation: bevacizumab, infliximab, and atezolizumab. The PBPK models were built using GastroPlus^© Biologics module. For each mAb, the PBPK model was developed based on observed data in healthy and/or patient adults. Then, the physiological parameters were scaled to describe the pediatric physiology to predict exposure to the pediatric populations. Predicted plasma concentration–time courses were overlaid with reported observed data to assess the ability of the PBPK model to predict pediatric exposure. Results: Results showed that PBPK models accurately predicted pediatric pharmacokinetics for mAbs. Conclusions: This research marks a significant step in validating mechanistic extrapolation methods for biologics exposure prediction in children using PBPK models. Full article

Background: Biomimetic strategies have gained increasing attention for their ability to enhance the delivery, stability, and functionality of nutraceuticals by emulating natural biological systems. However, the literature remains fragmented, often focusing on isolated technologies without integrating regulatory, predictive, or translational perspectives. Objective: This review aims to provide a comprehensive and multidisciplinary synthesis of biomimetic and bio-inspired nanocarrier strategies for nutraceutical delivery, while identifying critical gaps in standardization, scalability, and clinical translation. Results: We present a structured classification matrix that maps biomimetic delivery systems by material type, target site, and bioactive compound class. In addition, we analyze predictive design tools (e.g., PBPK modeling and AI-based formulation), regulatory frameworks (e.g., EFSA, FDA, and GSRS), and risk-driven strategies as underexplored levers to accelerate innovation. The review also integrates ethical and environmental considerations, and highlights emerging trends such as multifunctional hybrid systems and green synthesis routes. Conclusions: By bridging scientific, technological, and regulatory domains, this review offers a novel conceptual and translational roadmap to guide the next generation of biomimetic nutraceutical delivery systems. It addresses key bottlenecks and proposes integrative strategies to enhance design precision, safety, and scalability. Full article

Background: Glucuronide recycling in the gut and liver profoundly affects the systemic and/or local exposure of drugs and their glucuronide metabolites, impacting both clinical efficacy and toxicity. This recycling also alters drug exposure in the colon, making it critical to establish local concentration for drugs targeting colon (e.g., drugs for colon cancer and inflammatory bowel disease). Methods: In this study, a parent–metabolite middle-out physiologically based pharmacokinetic (PBPK) model was built for genistein and its glucuronide metabolite to estimate the systemic and local exposure of the glucuronide and its corresponding aglycone in rats by incorporating UDP-glucuronosyltransferase (UGT)-mediated metabolism and transporter-dependent glucuronide disposition in the liver and intestine, as well as gut microbial-mediated deglucuronidation that enables the recycling of the parent compound. Results: This parent–metabolite middle-out rat PBPK model utilized in vitro-to-in vivo extrapolated (IVIVE) metabolic and transporter clearance values based on in vitro kinetic parameters from surrogate species, the rat tissue abundance of relevant proteins, and saturable Michaelis–Menten mechanisms. Inter-system extrapolation factors (ISEFs) were used to account for transporter protein abundance differences between in vitro systems and tissues and between rats and surrogate species. Model performance was evaluated at multiple dose levels for genistein and its glucuronide. Model sensitivity analyses demonstrated the impact of key parameters on the plasma concentrations and local exposure of genistein and its glucuronide. Our model was applied to simulate the quantitative impact of glucuronide recycling on the pharmacokinetic profiles in both plasma and colonocytes. Conclusions: Our study underlines the importance of glucuronide recycling in determining local drug concentrations in the intestine and provides a preliminary modeling tool to assess the influence of transporter-mediated drug–drug interactions on glucuronide recycling and local drug exposure, which are often misrepresented by systemic plasma concentrations. Full article

Background/Objectives: As a monocyclic β-lactam antibiotic, aztreonam has regained attention recently because combining it with β-lactamase inhibitors helps fight drug-resistant bacteria. This study aimed to systematically characterize the plasma and tissue concentration-time profiles of aztreonam in rats, mice, dogs, monkeys, and humans by developing a multi-species, physiologically based pharmacokinetic (PBPK) model. Methods: A rat PBPK model was optimized and validated using plasma concentration-time curves determined by liquid chromatography–tandem mass spectrometry (LC-MS/MS) following intravenous administration, with reliability confirmed through another dose experiment. The rat model characteristics, modeling experience, ADMET Predictor (11.0) software prediction results, and allometric scaling were used to extrapolate to mouse, human, dog, and monkey models. The tissue-to-plasma partition coefficients (K_p values) were predicted using GastroPlus (9.0) software, and the sensitivity analyses of key parameters were evaluated. Finally, the cross-species validation was performed using the average fold error (AFE) and absolute relative error (ARE). Results: The cross-species validation showed that the model predictions were highly consistent with the experimental data (AFE < 2, ARE < 30%), but the deviation of the volume of distribution (V_ss) in dogs and monkeys suggested the need to supplement the species-specific parameters to optimize the prediction accuracy. The K_p values revealed a high distribution of aztreonam in the kidneys (K_p = 2.0–3.0), which was consistent with its clearance mechanism dominated by renal excretion. Conclusions: The PBPK model developed in this study can be used to predict aztreonam pharmacokinetics across species, elucidating its renal-targeted distribution and providing key theoretical support for the clinical dose optimization of aztreonam, the assessment of target tissue exposure in drug-resistant bacterial infections, and the development of combination therapy strategies. Full article

The characterization of a drug’s ADME (absorption, distribution, metabolism, and excretion) profile is crucial for accurately determining its safety and efficacy. The rising prevalence of polypharmacy has significantly increased the risk of drug-drug interactions (DDIs). These interactions can lead to altered drug exposure, potentially compromising efficacy or increasing the risk of adverse drug reactions (ADRs), thereby posing significant clinical and regulatory concerns. Traditional methods for assessing potential DDIs rely heavily on in vitro models, including enzymatic assays and transporter studies. While indispensable, these approaches have inherent limitations in scalability, cost, and ability to predict complex interactions. Recent advancements in analytical technologies, particularly the development of more sophisticated cellular models and computational modeling, have paved the way for more accurate and efficient DDI assessments. Emerging methodologies, such as organoids, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI), demonstrate significant potential in this field. A powerful and increasingly adopted approach is the integration of in vitro data with in silico modeling, which can lead to better in vitro-in vivo extrapolation (IVIVE). This review provides a comprehensive overview of both conventional and novel strategies for DDI predictions, highlighting their strengths and limitations. Equipping researchers with a structured framework for selecting optimal methodologies improves safety and efficacy evaluation and regulatory decision-making and deepens the understanding of DDIs. Full article