Search Results (256)

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: 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: 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

Cytochrome P450 (CYP450) enzymes are pivotal in the metabolism of numerous anticancer agents, with CYP3A4 being the predominant isoform involved. Inhibition of CYP450 enzymes is a major mechanism underlying clinically significant drug-drug interactions (DDIs), particularly in oncology, where polypharmacy is frequent. This review aims to provide a comprehensive and critical overview of CYP450 enzyme inhibition, focusing specifically on the impact of kinase inhibitors (KIs) and poly adenosine diphosphate-ribose polymerase (PARP) inhibitors. A systematic review of the current literature was conducted, focusing on the molecular mechanisms of CYP450 inhibition, including reversible, time-dependent, mechanism-based, and pseudo-irreversible inhibition. Specific attention was given to the inhibitory profiles of clinically relevant KIs and PARP inhibitors, with analysis of pharmacokinetic consequences and regulatory considerations. Many KIs, such as abemaciclib and ibrutinib, demonstrate time-dependent or quasi-irreversible inhibition of CYP3A4, while PARP inhibitors like olaparib and rucaparib exhibit moderate reversible and time-dependent CYP3A4 inhibition. These inhibitory activities can significantly alter the pharmacokinetics of co-administered drugs, leading to increased risk of toxicity or therapeutic failure. Regulatory guidelines now recommend early identification of time-dependent and mechanism-based inhibition using physiologically based pharmacokinetic) (PBPK) modeling. CYP450 inhibition by KIs and PARP inhibitors represents a critical but often underappreciated challenge in oncology pharmacotherapy. Understanding the mechanistic basis of these interactions is essential for optimizing treatment regimens, improving patient safety, and supporting personalized oncology care. Greater clinical vigilance and the integration of predictive modeling tools are necessary to mitigate the risks associated with CYP-mediated DDIs. Full article

The appropriate use of antibiotics is crucial and involves selecting an optimal dosing regimen based on pharmacokinetic (PK) and pharmacodynamic (PD) indicators. Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool that integrates drugs’ physicochemical properties with anatomical and physiological data to predict PK behavior. In pediatric populations, PBPK modeling accounts for developmental changes in organ function, making it particularly useful for optimizing antibiotic dosing across different age groups, from neonates to adolescents. In recent decades, PBPK modeling has been widely applied to predict antibiotic disposition in pediatric patients for various clinical and research purposes. Model-informed precision dosing (MIPD) is an evolving approach that enhances traditional therapeutic drug monitoring by integrating multiple information sources into a mathematical framework. By incorporating PBPK modeling, MIPD could offer a more optimized antibiotic dosing that accounts for PK/PD parameters at the site of infection, improving therapeutic outcomes while minimizing toxicity. This review summarizes currently published pediatric PBPK modeling studies on antibiotics, covering various objectives such as evaluating drug–drug interactions, PK/PD analyses in targeted tissues, predicting PK in specific populations (e.g., maternal/fetal, renal impairment, obesity), and PK predictions for preterm neonates. Based on these reports, the review discusses the implications of PBPK modeling for MIPD in pediatric antibiotic therapy. Full article

Background: Physiologically based biopharmaceutics modeling (PBBM) models can help to predict drug release and in vivo absorption behaviors. Colon drug delivery systems have gained interest over the past few years due to the advantages they provide in treating certain diseases in a local way. The objectives of this work were to simulate the biopharmaceutical and pharmacokinetic behavior of metronidazole hydrophilic matrices coated with different enteric polymers and to highlight the factors with a significant impact on the simulated pharmacokinetic parameters. Methods: Physicochemical properties of metronidazole were introduced into Simcyp^® simulator platform, and the Advanced Dissolution Absorption Model (ADAM) was employed to simulate the in vivo intestinal absorption and colonic concentrations of metronidazole using a PBBM model. A Kruskal–Wallis test was carried out in order to determine which one of the factors studied has a statistically significant impact on the pharmacokinetic parameters (AUC, C_max, and T_max) simulated. Results: Enteric-coated matrix tablets are capable of avoiding metronidazole absorption in the small intestine and releasing it in the colonic region. The release and absorption rates of metronidazole depend largely on the percentage of weight gain of the coating and also on the coating agent. Coated tablets with a time-dependent coating show less variability. Conclusions: PBBM models can help predict the release from drug delivery systems and the pharmacokinetics in vivo of metronidazole from data obtained in vitro, although complementary in vivo studies should be needed. Full article

Physiologically based pharmacokinetic (PBPK) modelling for biologics, such as monoclonal antibodies and therapeutic proteins, involves capturing complex processes, including target-mediated drug disposition (TMDD), FcRn-mediated recycling, and tissue-specific distribution. The Simcyp Designer Biologics PBPK Platform Model offers an intuitive and efficient platform for constructing mechanistic PBPK models with pre-defined templates and automated model assembly, reducing manual input and improving reproducibility. This tutorial provides a step-by-step guide to using the platform, highlighting features such as cross-species scaling, population variability simulations, and flexibility for model customization. Practical case studies demonstrate the platform’s capability to streamline workflows, enabling rapid, mechanistic model development to address key questions in biologics drug development. By automating critical processes, this tool enhances decision-making in translational research, optimizing the modelling and simulation of large molecules across discovery and clinical stages. Full article

Background/Objectives: 5-fluorouracil (5-FU) is used in the treatment of solid cancer types. Because of its narrow therapeutic window, drug monitoring is recommended. We were confronted to answer a question on the relevance of concentration as opposed to the area under the plasma concentration–time curve (AUC) to predict toxicity when we had to assess the case of a patient who died after an erroneously high infusion rate. Methods: We used physiologically-based pharmacokinetic modeling (PBPK) to simulate the concentration–time profile of 5-FU data on doses, dosing schedules and life-threatening toxicity for both the patient in question as well as data from the literature. Furthermore, steady-state 5-FU concentrations were calculated from an additional set of data found in the literature on AUCs and non-life-threatening toxicity. Results: The model predictions matched well with experimental data, confirming the suitability of the model. Life-threatening toxicity was related to a concentration above 6 mg/L, whereas non-life-threatening toxicity was low at concentrations less than 3 mg/L but steeply increased between 3 and 4 mg/L. Data analysis supported by a decision algorithm suggests that the 5-FU steady-state plasma concentration is a better toxicity predictor than the AUC. Conclusions: We recommend monitoring the concentration one hour after infusion starts when about 50% of the steady state is reached in patients for whom higher doses are clinically considered relevant. Monitoring the concentration one hour after starting the infusion has the advantage that dose correction could be made early before toxicity can be observed. Full article

Background: Lamotrigine plays a crucial role in the treatment of epilepsy and bipolar disorder in adults and children. However, its pharmacokinetic (PK) behavior in first or long-term treatment in pediatric patients and the changes in drug exposure in patients with renal impairment are not well characterized. The purpose of the research was to build a robust physiologically based pharmacokinetic (PBPK) model of lamotrigine for the prediction of drug exposure in diverse populations to facilitate therapeutic drug monitoring (TDM) and guide dosing regimens. Methods: The physicochemical parameter values of lamotrigine were integrated to establish and validate the model in an adult population in PK-sim. This adult PBPK model can be extrapolated to children and patients with renal impairment to predict PK changes. Results: Most of the observed data were within the 5th and 95th percentile intervals of the variability around the predicted plasma concentrations. The model predicted pharmacokinetic thresholds and exposure values for clinically safe and effective doses recommended by the FDA for initial and long-term treatment of epilepsy in adults and children aged 2–12 years. Notably, patients with severe renal impairment and end-stage renal disease experienced an average increase in the area under the curve of 1.51 folds and 1.62 folds, respectively. This scenario necessitates further lamotrigine dose adjustments. Conclusions: The developed lamotrigine PBPK model offers a strategy for assisting clinicians in TDM and dose adjustment for special populations, thereby offering a reference (PK parameters, as well as peak and valley concentrations to reach a steady state) for a safer administration regimen in clinical treatment. Full article

Background/Objectives: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in biologic drug development. However, a standardized modeling strategy is currently missing. A cross-industry collaboration developed PBPK models for seven case studies, including monoclonal antibodies, antibody–drug conjugates, and bispecific T-cell engagers, to identify key parameters and establish a workflow to simulate biologic drugs in monkeys and in humans. Methods: PBPK models were developed in the monkey with limited data, including the molecular weight, the binding affinity to FcRn, and the additional systemic clearance of IgG, which is 20% of the total clearance. The binding affinity was only available for human FcRn and corrected for the known species-dependent differences in IgG binding. The strategy of monkey simulations was evaluated with an additional 14 studies published in the literature. Three different scenarios were simulated in humans afterwards: without, with allometrically scaled, and with optimized additional systemic clearance. Results: The plasma peak concentration and the area under the curve were predicted within 50% of the observed data for all studied case examples in the monkey, which demonstrates that sparse input parameters are sufficient for successful predictions in the monkey. Simulations in humans demonstrated the need for additional systemic clearance, because drug exposure was highly overpredicted without an additional systemic clearance term. Allometric scaling improved the predictions, but optimization led to the best fit, which is currently a limitation in the translation from animals to humans. Conclusions: This work highlights the importance of understanding the general mechanisms of drug uptake in different tissue types and cells in both target-dependent and -independent processes. Full article

Background/Objective: Cabotegravir (CAB), available as an oral tablet and as a long-acting (LA) nanosuspension for intramuscular injection, is approved as a combination therapy for the treatment, and as a monotherapy for the prevention, of HIV-1 infection. People living with HIV may receive multiple concomitant medications, with the associated risk of drug–drug interactions (DDIs). CAB is an inhibitor of OAT1/OAT3 renal transporters and a substrate of the UDP-glucuronosyltransferase enzymes UGT1A1 and 1A9, in vitro. While the effect of induction of UGT1A1/UGT1A9 on CAB exposure had been investigated in the clinic, the effect of the risk of DDIs with CAB via inhibition of these enzymes, or as an inhibitor of OAT1/OAT3 transporters, had not been evaluated. Methods: A physiologically-based pharmacokinetic (PBPK) model was developed and verified for orally dosed CAB to investigate the DDI risks associated with CAB, using a matrix approach to extensively qualify the PBPK platform and the substrates and/or inhibitors of either OAT1/OAT3 or UGT1A1/UGT1A9. The effect of uncertainties in in vitro inhibition values for OAT1/OAT3 was assessed via sensitivity analysis. Results: A mean increase of less than 25% in systemic exposure for OAT1/OAT3 substrates was predicted, with the potential for an increase of up to 80% based on the sensitivity analysis. On co-dosing with UGT1A1/UGT1A9 inhibitors, the predicted mean increase in CAB exposure was within 11%. Conclusions: PBPK modelling indicated that clinically relevant DDIs are not anticipated with OAT1/3 substrates or UGT1A1/1A9 inhibitors and CAB. With maximal exposure of the LA formulation of CAB being lower than the oral, the results of these simulations can be extrapolated to LA injectable dosing. Full article