Search Results (82)

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

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

Vancomycin, a cornerstone antibiotic against severe Gram-positive infections, is increasingly challenged by resistance in Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin Enterococcus spp. (VRE), necessitating the development of novel therapeutic strategies. This review examines how structural modifications to vancomycin can enhance its antibacterial activity and explores the critical role of computational approaches in designing the next generation of analogs. By analyzing the existing literature, we highlight how strategic alterations, such as the introduction of lipophilic side chains, substitutions on the sugar moieties, and modifications to the aglycone core, have yielded derivatives with improved antibacterial potency. Notably, certain analogs (e.g., Vanc-83, Dipi-Van-Zn) have demonstrated expanded activity against Gram-negative bacteria and exhibited enhanced pharmacokinetic profiles, including prolonged half-lives and improved tissue penetration, crucial for effective treatment. Semisynthetic glycopeptides like telavancin, dalbavancin, and oritavancin exemplify successful translation of structural modifications, offering sustained plasma concentrations and simplified dosing regimens that improve patient compliance. Complementing these experimental efforts, computational methods, including molecular docking and molecular dynamics simulations, provide valuable insights into drug–target interactions, guiding the rational design of more effective analogs. Furthermore, physiologically based pharmacokinetic modeling aids in predicting the in vivo behavior and optimizing the pharmacokinetic properties of these novel compounds. This review highlights a critical path forward in the fight against multidrug-resistant infections. By meticulously examining the previously carried out structural refinement of vancomycin, guided by computational predictions and validated through rigorous experimental testing, we underscore its immense potential. 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/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

The objective of this study was to develop a multicompartmental physiologically based pharmacokinetic (PBPK) model that allows for the reproduction of the function of the gastrointestinal system in silico. Based on the typical tools of chemical engineering, transport phenomena, and human physiological and anatomical knowledge, the developed model is not limited to representing the transport of drugs and their interactions with ingested foods but also describes several physiological aspects that quantitatively regulate the distribution, absorption, and elimination of drugs. Nevertheless, the model only contains a limited number of parameters: the permeability constants of jejunum, ileum, and colon membranes and the drug removal rates in both the blood and cellular compartments. Therefore, it can be used for a preliminary drug bioavailability assessment in the early stages of drug development when limited experimental data are available. The model was tested on two drugs, Ketoprofen and Ibuprofen, which yielded satisfactory results in accordance with the existing literature. Full article

Background: Pharmacotherapy of chronic heart failure (CHF) with a reduced ejection fraction includes a combination of drugs. Often, different groups of drugs are added together for the treatment of concomitant conditions, such as statins, anticoagulants, antiplatelet agents, and cardiac glycosides, which have a narrow therapeutic window. Increased medication intake is a prerequisite for the increased risk of potential adverse drug–drug interactions (DDI), especially those occurring at the pharmacokinetic level. The main objectives of this study are to identify the most common potential pharmacokinetic drug–drug interactions (pPKDDIs), to explore more complex cases, and to simulate and analyze them with appropriate software. Methods: The data selected for the simulations were collected over a two-year period from January 2014 to December 2015. Identification of the pPKDDIs was carried out using Lexicomp Drug interaction, while simulations were performed with Simcyp software (V20, R1). Results: The most common pharmacokinetic mechanisms responsible for the occurrence of drug–drug interactions in the selected drugs with narrow therapeutic windows are those featuring the cytochrome isoforms CYP3A4 and 2C9 and the efflux pump—P-glycoprotein (P-gp). Simulations with the available data in Simcyp software showed possibilities to analyze and evaluate pPKDDIs, which would be difficult to assess without appropriate software, as well as ways to manage them. Conclusions: The frequency and complexity of pPKDDIs in patients with cardiovascular disease are high. Therefore, such patients require a specific approach to reduce these risks as well as to optimize the therapy. An appropriate PBPK software with the necessary database would be suitable in these cases. Full article

Background: Tofacitinib is mainly used in the adult population for immune-mediated inflammatory diseases. There is little information available on the pharmacokinetics of tofacitinib in pediatric patients, populations with hepatic impairment and renal impairment, and patients with drug–drug interactions (DDIs). This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of tofacitinib in the populations mentioned above. Methods: We developed the PBPK models in PK-Sim^® and evaluated the models with observed clinical PK data. The Monte Carlo algorithm was used for parameter identification. Results: The adult PBPK model accurately simulated the pharmacokinetic profiles of all administration scenarios. The geometric mean fold errors for the predicted/observed maximum concentration and area under the curve are 1.17 and 1.16, respectively. The extrapolated models accurately simulated the pharmacokinetic characteristics of tofacitinib. The pediatric patients aged 12-to-<18 years and 2-to-<6 years need to adjust the dose to 4 mg BID and 1.7 mg BID, respectively, to achieve comparable steady-state exposures to 5 mg BID in adults. The populations with moderate hepatic impairment and severe renal impairment need to reduce the dose to 50% and 75% of the original dose, respectively. Tofacitinib should be reduced to 50% and 65% of the original dose for concomitant use with fluconazole and ketoconazole, respectively, and increased to 150% of the original dose for concomitant use with rifampicin. Conclusions: We developed a tofacitinib PBPK model and extrapolated it to special populations and DDIs. The predictive results of the models can help the rational use of tofacitinib in these populations. Full article

Background/Objectives: Omeprazole is widely used for managing gastrointestinal disorders like GERD, ulcers, and H. pylori infections. However, its use in pediatrics presents challenges due to drug interactions (DDIs), metabolic variability, and safety concerns. Omeprazole’s pharmacokinetics (PK), primarily influenced by CYP2C19 metabolism, is affected by ontogenetic changes in enzyme expression, complicating dosing in children. Methods: This study aimed to develop and validate a physiologically based pharmacokinetic (PBPK) model for omeprazole and its metabolites to predict age-related variations in metabolism and response. Results: The PBPK model successfully predicted exposure to parent and metabolites in adults and pediatrics, incorporating competitive and mechanism-based inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites. By accounting for age-dependent metabolic pathways, the model enabled priori predictions of omeprazole exposure in different age groups. Linking PK to the pharmacodynamics (PD) model, we described the impact of age-related physiological changes on intragastric pH, the primary outcome for proton pump inhibitors efficacy. Conclusions: The PBPK-PD model allowed for the virtual testing of dosing scenarios, providing an alternative to clinical studies in pediatrics where traditional DDI studies are challenging. This approach offers valuable insights for accurate dosing recommendations in pediatrics, accounting for age-dependent variability in metabolism, and underscores the potential of PBPK modeling in guiding pediatric drug development. Full article

Background/Objectives: The increasing use of statins in people with cystic fibrosis (CF) necessitates the investigation of potential drug–drug interactions (DDI) of statins with cystic fibrosis transmembrane conductance regulator (CFTR) modulators, including elexacaftor, tezacaftor, and ivacaftor (ETI). The interactions may involve the potential inhibition of cytochrome P450 isoenzymes (CYPs), organic anion-transporting polypeptides (OATPs), and Breast Cancer Resistance Protein (BCRP) by ETI. This presents a therapeutic challenge in CF due to the potential for elevated statin levels, consequently heightening the risk of myopathy. This study aimed to predict potential DDIs between statins and ETI using a physiologically based pharmacokinetic (PBPK) modeling approach. Methods: We performed in vitro assays to measure the inhibitory potency of ETI against OATPs and CYP2C9 and incorporated these data into our PBPK models alongside published inhibitory parameters for BCRP and CYP3A4. Results: The PBPK simulation showed that atorvastatin had the highest predicted AUC ratio (3.27), followed by pravastatin (2.27), pitavastatin (2.24), and rosuvastatin (1.83). Conclusions: Based on these findings, rosuvastatin appears to exhibit a weak interaction with ETI, whereas other statins exhibited a moderate interaction, potentially requiring appropriate dose reductions. These data indicate potential clinically significant DDIs between ETI and certain statins, which warrants a clinical study to validate these findings. Full article

Background: The drug candidate BI 730357 is a Biopharmaceutics Classification System (BCS) Class II compound showing atypical absorption after oral administration in fasted and fed healthy individuals, for which conventional traditional deconvolution methods could not explain formulation dependencies. Methods: A physiologically based pharmacokinetic (PBPK) model of BI 730357 was developed using the Open Systems Pharmacology (OSP) PBPK software tool PK-Sim^®, which could describe the pharmacokinetics in fasted and fed subjects after single and multiple doses. A Weibull function was used to describe the in vivo formulation kinetics, whereas colonic absorption was adopted as the main driver to describe the late phases of observed pharmacokinetics after oral administration. The food effect was applied using the implemented feature PK-Sim^®. Results: The model accurately predicted an observed itraconazole drug–drug interaction (DDI) in fasted subjects and was used to explore the effects of the strong CYP3A4 inducer rifampicin on the pharmacokinetics of BI 730357 after administration in fed subjects. Conclusions: The combined results suggest that the BI 730357 PBPK model with semi-mechanistic absorption can prospectively explore the effects of CYP3A4 inhibitors and inducers on the pharmacokinetics after administration in fed or fasted subjects within the given dose range. Full article

The perioperative period, encompassing preoperative, intraoperative, and postoperative phases, is crucial for comprehensive patient care. During this time, the use of opioids and other drugs can lead to drug–drug interactions (DDIs), potentially resulting in adverse drug reactions (ADRs) that increase morbidity, mortality, and healthcare costs. This study investigates the drug–drug interactions (DDIs) between rocuronium, propofol, paracetamol, and lidocaine, focusing on the CYP-mediated metabolism of these drugs in the perioperative context, where these drugs are frequently co-administered. Using physiologically based pharmacokinetic (PBPK) modeling through the GastroPlus™ software and in vitro experiments with Hep G2 cells, we aimed to assess potential toxicities and pharmacokinetic interactions. Cellular viability assays revealed significant toxicity when lidocaine was combined with propofol and rocuronium, while paracetamol exhibited no considerable impact on viability. PBPK simulations confirmed moderate interactions with rocuronium and weak interactions with propofol but no relevant interactions with paracetamol. These findings emphasize the need for dose adjustments in perioperative settings to enhance patient safety, particularly with propofol and rocuronium, while supporting the co-administration of lidocaine and paracetamol. These findings show the importance of moving towards a personalized medicine model, adjusting the clinical use of lidocaine according to individual patient needs, thus promoting safer and more effective perioperative care and moving beyond the “one-size-fits-all” approach in anesthetic management. Full article

Background: Morphine is a commonly prescribed opioid analgesic used to treat chronic pain. Morphine undergoes glucuronidation by UDP-glucuronosyltransferase (UGT) 2B7 to form morphine-3-glucuronide and morphine-6-glucuronide. Morphine is the gold standard for chronic pain management and has a narrow therapeutic index. Reports have shown that chronic pain patients have increasingly used other supplements to treat their chronic pain, including cannabidiol (CBD). Up to 50% of chronic pain patients report that they co-use cannabis with their prescribed opioid for pain management, including morphine. Previous work has shown that cannabidiol is a potent inhibitor of UGT2B7, including morphine-mediated metabolism. Co-use of morphine and CBD may result in unwanted drug–drug interactions (DDIs). Methods: Using available physiochemical and clinical parameters, morphine and CBD physiologically based pharmacokinetic (PBPK) models were developed and validated in both healthy and cirrhotic populations. Models for the two populations were then combined to predict the severity and clinical relevance of the potential DDIs during coadministration of both morphine and CBD in both healthy and hepatic-impaired virtual populations. Results: The predictive DDI model suggests that a ~5% increase in morphine exposure is to be expected in healthy populations. A similar increase in exposure of morphine is predicted in severe hepatic-impaired populations with an increase of ~10. Conclusions: While these predicted increases in morphine exposure are below the Food and Drug Administration’s cutoff (1.25-fold increase), morphine has a narrow therapeutic index and a 5–10% increase in exposure may be clinically relevant. Future clinical studies are needed to fully characterize the clinical relevance of morphine-related DDIs. Full article