Drug Transporters in Drug Disposition - Highlights from the Year 2023.

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all co-authors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each co-author independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: 1) transporter structure and in vitro evaluation, 2) novel in vitro/ex vivo models, 3) endogenous biomarkers, and 4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Physiologically based pharmacokinetic modeling in obesity: applications and challenges.

INTRODUCTION: Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity. AREAS COVERED: Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity. EXPERT OPINION: Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

PBPK model of pegylated liposomal doxorubicin to simultaneously predict the concentration-time profile of encapsulated and free doxorubicin in tissues.

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict the concentrations of encapsulated and free doxorubicin in plasma and tissues in mice after intravenous injection of PEGylated liposomes (Doxil®). The PBPK model used in this study contains liposomes and free doxorubicin disposition components. The free doxorubicin disposition component was used to simulate the disposition of free doxorubicin produced by mononuclear phagocyte system (MPS)-degrading liposomes. The liver, spleen, kidneys, and lungs contain an additional MPS subcompartment. These compartments are interconnected through blood and lymphatic circulation. The model was validated strictly by four doses of external observed plasma and tissue concentration-time profiles. The fold error (FE) values were almost all within threefold. The sensitivity analysis revealed that the MPS-related parameters greatly influenced the model. The predicted in vivo distribution characteristics of the doxorubicin liposomes and doxorubicin solution were consistent with the observed values. The PBPK model was established based on the physiological mechanism and parameters of practical significance that can be measured in vitro. Thus, it can be used to study the pharmacokinetic properties of liposomes. This study also provides a reference for the establishment of liposome PBPK model.

Predict the Drug-Drug Interaction of a Novel PI3Kα/δ Inhibitor, TQ-B3525, and Its Two Metabolites Using Physiologically Based Pharmacokinetic Modeling.

A novel dual PI3K α/δ inhibitor, TQ-B3525, has been developed for the targeted treatment of lymphoma and solid tumors. TQ-B3525 is primarily metabolized by CYP3A4 and FOM3, while also serving as a substrate for the P-glycoprotein transporter. The aim of this study was to anticipate the drug-drug interaction (DDI) of TQ-B3525 and its two metabolites with CYP3A4 enzyme potent inducer (rifampicin) and CYP3A4/P-gp inhibitor (itraconazole) utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Clinical data from healthy and cancer patient adults were employed to construct and evaluate the PBPK model for TQ-B3525, M3, and M8-3. Models involving rifampicin combined with midazolam, itraconazole combined with midazolam or digoxin were utilized to showcase the robustness of evaluating DDI effects. The simulated drug exposure of TQ-B3525, M3, and M8-3 in healthy and patient adults were consistent with clinical data, and the mean fold error values were within the acceptable ranges. The simulated results of positive substrates correspond to those reported in the literature. Co-administration with rifampicin reduces Cmax and AUC of TQ-B3525 to 76.1% and 46.0%, while increasing the levels of M3 and M8-3. With itraconazole, Cmax and AUC of TQ-B3525 rise to 131% and 204%, but decrease substantially for M3 and M8-3. PBPK model simulation results showed that the systemic exposure of TQ-B3525 was significantly affected when co-administered with CYP3A4/P-gp inducers and inhibitors. This indicates that the combination with strong inducers and inhibitors should be carefully avoided or adjust the dosage of TQ-B3525 in clinic.

When to consider intra-target microdosing: physiologically based pharmacokinetic modeling approach to quantitatively identify key factors for observing target engagement.

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM.

Bottom-up PBPK modeling of phenytoin brain disposition in postpartum newborns after intrauterine dosing.

OBJECTIVES: The antiepileptic phenytoin has a narrow therapeutic window, nonlinear pharmacokinetics, and can cross the placenta causing apathy and jitteriness in postpartum newborns. Further, the sudden decay of phenytoin concentration can cause withdrawal seizures. This work aimed to assess the brain toxic exposure to phenytoin in newborns after transplacental transfer using neonatal saliva-brain correlations. METHODS: The phenytoin dose that the newborn receives transplacentally at birth was estimated using verified physiologically based pharmacokinetic (PBPK) model simulations in third-trimester pregnancy (pregnancy T3). Such doses were used as an input to the newborn PBPK model to estimate the neonatal levels of phenytoin and their correlations in brain extracellular fluid (bECF), plasma, and saliva. RESULTS: The PBPK model-estimated neonatal plasma and bECF concentrations of phenytoin were below the necessary thresholds for anticonvulsant and toxic effects. The neonatal salivary thresholds for phenytoin anticonvulsant and toxic effects were estimated to be 1.3 and 2.5 mg/L, respectively using the plasma-saliva-bECF correlations established herein. CONCLUSIONS: The salivary TDM of phenytoin can be a more convenient option for avoiding phenytoin brain toxicity in newborns of epileptic mothers. Still, the appropriateness of using the same adult values of phenytoin anticonvulsant and toxic effects for infants needs investigation.

Advancements in physiologically based pharmacokinetic modeling for fedratinib: updating dose guidance in the presence of a dual inhibitor of CYP3A4 and CYP2C19.

PURPOSE: A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance. METHODS: The original minimal PBPK model was developed using Simcyp® Simulator v17. The model was updated by substituting a single distribution rate (Qsac) with 2 separate rates (CLin/CLout) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole). RESULTS: Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400 mg once daily along with fluconazole 200 mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state. CONCLUSIONS: The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors.

Advancing ecotoxicological studies: Utilizing new approach methodologies to enable cross-species extrapolation and reduce avian testing.

Ecological risk assessments of agrochemicals have traditionally depended on in vivo guideline tests using northern bobwhite and mallard to provide relevant endpoints for avian species. However, these studies have limitations, including animal welfare concerns, the time and cost involved, limited potential for extrapolation to more realistic exposure conditions, and the lack of mechanistic understanding. The proof-of-concept work presented a case study for thiamethoxam in three avian species, demonstrating the potential of physiologically based kinetic (PBK) modeling to enable dosimetry extrapolations that inform hazard characterization in risk assessment, and reduce the use of avian testing. The model structure for northern bobwhite and mallard contained ten compartments, while an additional ovulation model was included for chicken in the physiological state of egg-laying. The model was first parameterized and evaluated for chicken and northern bobwhite using in vitro kinetic measurements and in vivo toxicokinetic (TK) data. The chicken model was then extrapolated to mallard based on allometric scaling. The models were then used to map the TK profiles across species by simulating internal dose metrics in different avian toxicology studies. These metrics, including peak blood concentrations (Cmax) and area under the curve (AUC) for blood concentration, were determined for acute, subacute, or chronic toxicity endpoints for mallard and northern bobwhite, enabling a quantitative cross-species and cross-route comparison of dosimetry. The results suggested that the chronic toxicological response of birds exposed to thiamethoxam is highly dependent on internal exposure, while mallard appeared to be more dynamically sensitive to thiamethoxam on an acute oral exposure basis. The case study increases the confidence in using new approach methodologies (NAMs) for interpreting avian toxicity studies and facilitating in vitro-in silico-based ecological risk assessments of agrochemicals.

Physiologically-based pharmacokinetic/pharmacodynamic modeling of meropenem in critically ill patients.

This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD) of meropenem for critically ill patients. A PBPK model of meropenem in healthy adults was established using PK-Sim software and subsequently extrapolated to critically ill patients based on anatomic and physiological parameters. The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between predicted and observed values of pharmacokinetic parameters Cmax, AUC0-∞, and CL to evaluate the accuracy of the PBPK model. The model was verified using meropenem plasma samples obtained from Intensive Care Unit (ICU) patients, which were determined by HPLC-MS/MS. After that, the PBPK model was combined with a PKPD model, which was developed based on f%T > MIC. Monte Carlo simulation was utilized to calculate the probability of target attainment (PTA) in patients. The developed PBPK model successfully predicted the meropenem disposition in critically ill patients, wherein the MFE average and GMFE of all predicted PK parameters were within the 1.25-fold error range. The therapeutic drug monitoring (TDM) of meropenem was conducted with 92 blood samples from 31 ICU patients, of which 71 (77.17%) blood samples were consistent with the simulated value. The TDM results showed that meropenem PBPK modeling is well simulated in critically ill patients. Monte Carlo simulations showed that extended infusion and frequent administration were necessary to achieve curative effect for critically ill patients, whereas excessive infusion time (> 4 h) was unnecessary. The PBPK/PD modeling incorporating literature and prospective study data can predict meropenem pharmacokinetics in critically ill patients correctly. Our study provides a reference for dose adjustment in critically ill patients.

PBPK model-based gender-specific risk assessment of N-nitrosodimethylamine (NDMA) using human biomonitoring data.

Despite several screening levels for NDMA reported in water, soil, air, and drugs, the human risk assessment using biomonitoring concentrations has not been performed. In this study, gender-specific exposure guidance values were determined in humans, then biomonitoring measurements in healthy Korean subjects (32 men and 40 women) were compared to the exposure guidance values to evaluate the current exposure level to NDMA. For the human risk assessment of NDMA, the gender-specific physiologically based pharmacokinetic (PBPK) model was developed in humans using proper physiological parameters, partition coefficients, and biochemical parameters. Using the PBPK model, a Monte Carlo simulation was performed to describe the magnitudes of inter-individual variability and uncertainty on the single model predictions. The PBPK modeling and Monte Carlo simulation allowed the estimation of the relationship between external dose and blood concentration for the risk assessment. The procedure for the human risk assessment was summarized as follows: (1) estimating a steady-state blood concentration (Cavg) corresponding to the daily no observed adverse effect level (NOAEL) administration in rats; (2) applying uncertainty factors (UFs) for deriving the human Cavg; (3) determining the exposure guidance values as screening criteria; (4) interpreting the human biomonitoring measurements by forward and reverse dosimetry approaches. Using the biomonitoring concentrations, current daily exposures to NDMA were estimated to be 3.95 µg/day/kg for men and 10.60 µg/day/kg for women, respectively. The result of the study could be used as a basis for implementing further risk management and regulatory decision-making for NDMA.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models.

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

A Physiologically-Based Pharmacokinetic Simulation to Evaluate Approaches to Mitigate Efavirenz-Induced Decrease in Levonorgestrel Exposure with a Contraceptive Implant.

Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based pharmacokinetic (PBPK) model for levonorgestrel that we developed within the Simcyp® program, we evaluated a higher dose of levonorgestrel implant, a lower dose of efavirenz, and the combination of both, as possible methods to mitigate the interaction. In addition, we investigated the impact on levonorgestrel total and unbound concentrations of other events likely to be associated with efavirenz coadministration: changes in plasma protein binding of levonorgestrel (as with displacement) and high variability of efavirenz exposure (as with genetic polymorphism of its metabolism). The range of fraction unbound tested was 0.6% to 2.6%, and the range of efavirenz exposure ranged from the equivalent of 200 mg to 4800 mg doses. Results: Levonorgestrel plasma concentrations at any given time with a standard 150 mg implant dose are predicted to be approximately 68% of those of control when given with efavirenz 600 mg and 72% of control with efavirenz 400 mg. With double-dose levonorgestrel, the predictions are 136% and 145% of control, respectively. A decrease in levonorgestrel plasma protein binding is predicted to primarily decrease total levonorgestrel plasma concentrations, whereas higher efavirenz exposure is predicted to decrease total and unbound concentrations. Conclusions: Simulations suggest that doubling the dose of levonorgestrel, particularly in combination with 400 mg daily efavirenz, may mitigate the drug interaction. Changes in levonorgestrel plasma protein binding and efavirenz genetic polymorphism may help explain differences between model predictions and clinical data but need to be studied further.

Mathematical Modeling of the Gastrointestinal System for Preliminary Drug Absorption Assessment.

The objective of this study is to demonstrate the potential of a multicompartmental mathematical model to simulate the activity of the gastrointestinal system after the intake of drugs, with a limited number of parameters. The gastrointestinal system is divided into five compartments, modeled as both continuous systems with discrete events (stomach and duodenum) and systems with delay (jejunum, ileum, and colon). The dissolution of the drug tablet occurs in the stomach and is described through the Noyes-Whitney equation, with pH dependence expressed through the Henderson-Hasselbach relationship. The boluses resulting from duodenal activity enter the jejunum, ileum, and colon compartments, where drug absorption takes place as blood flows countercurrent. The model includes only three parameters with assigned physiological meanings. It was tested and validated using data from in vivo experiments. Specifically, the model was tested with the concentration profiles of nine different drugs and validated using data from two drugs with varying initial concentrations. Overall, the outputs of the model are in good agreement with experimental data, particularly with regard to the time of peak concentration. The primary sources of discrepancy were identified in the concentration decay. The model's main strength is its relatively low computational cost, making it a potentially excellent tool for in silico assessment and prediction of drug adsorption in the intestine.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research.

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.

Optimization of wet granulation process for manufacturing Rivaroxban generic immediate-release tablets using PBPK modeling and simulations.

Granulation is the critical process for the pharmaceutical development of poorly water-soluble drug products. Poorly formulated products have challenges in dissolution and bioequivalence studies. Rivaroxaban (RXB) is a poorly soluble drug and has 66% fasting bioavailability at a high strength of 20 mg. Establishing the bioequivalence between test and reference products for high strength requires comparative dissolution profiles and bioequivalence. Improper granulation products and the rest of the batches failed in virtual bioequivalence. The present study provided insight into the optimization of the wet granulation process for manufacturing RXB generic immediate-release tablets using PBPK modeling and simulations. Furthermore, PBPK models are not only useful for formulation optimization but also for process optimization during pharmaceutical product development. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00249-6.

Toward Personalized Salbutamol Therapy: Validating Virtual Patient-Derived Population Pharmacokinetic Model with Real-World Data.

Interindividual variability, influenced by patient-specific factors including age, weight, gender, race, and genetics, among others, contributes to variations in therapeutic response. Population pharmacokinetic (popPK) modeling is an essential tool for pinpointing measurable factors affecting dose-concentration relationships and tailoring dosage regimens to individual patients. Herein, we developed a popPK model for salbutamol, a short-acting ß2-agonist (SABA) used in asthma treatment, to identify key patient characteristics that influence treatment response. To do so, synthetic data from physiologically-based pharmacokinetic (PBPK) models was employed, followed by an external validation using real patient data derived from an equivalent study. Thirty-two virtual patients were included in this study. A two-compartment model, with first-order absorption (no delay), and linear elimination best fitted our data, according to diagnostic plots and selection criteria. External validation demonstrated a strong agreement between individual predicted and observed values. The incorporation of covariates into the basic structural model identified a significant impact of age on clearance (Cl) and intercompartmental clearance (Q); gender on Cl and the constant rate of absorption (ka); race on Cl; and weight on Cl in the volume of distribution of the peripheral compartment (V2). This study addresses critical challenges in popPK modeling, particularly data scarcity, incompleteness, and homogeneity, in traditional clinical trials, by leveraging synthetic data from PBPK modeling. Significant associations between individual characteristics and salbutamol's PK parameters, here uncovered, highlight the importance of personalized therapeutic regimens for optimal treatment outcomes.

Physiologically-Based Pharmacokinetic Modeling and In Vitro-In Vivo Correlation of TV-46000 (Risperidone LAI): Prediction from Dog to Human.

The interest in the development and therapeutic application of long-acting injectable products for chronic or long-term treatments has experienced exponential growth in recent decades. TV-46000 (Uzedy, Teva) is a long-acting subcutaneous (sc) injectable formulation of risperidone, approved for the treatment of schizophrenia in adults. Following sc injection, the copolymers together with risperidone precipitate to form a sc depot under the skin to deliver therapeutic levels of risperidone over a prolonged period of either 1 month or 2 months, depending upon the dose. This work presents the strategy and the results of the physiologically-based pharmacokinetic (PBPK) modeling and establishing of in vitro-in vivo correlation (IVIVC) for the prediction of TV-46000 pharmacokinetic profile in humans, using in vitro release, intravenous (iv), and sc single-dose pharmacokinetic data in beagle dogs. The resulting simulated TV-46000 PK profile in humans showed that the shape of the predicted risperidone and its active metabolite 9-OH-risperidone PK profiles was different from the observed one, thus suggesting that the TV-46000 release profile was species-dependent and cannot be directly extrapolated from dog to human. In conclusion, while level A IVIVC cannot be claimed, this work combining PBPK and IVIVC modeling represents an interesting alternative approach for complex injectable formulations where classical methods are not applicable.

Clinical Ocular Exposure Extrapolation for a Complex Ophthalmic Suspension Using Physiologically Based Pharmacokinetic Modeling and Simulation.

The development of generic ophthalmic drug products with complex formulations is challenging due to the complexity of the ocular system and a lack of sensitive testing to evaluate the interplay of its physiology with ophthalmic drugs. New methods are needed to facilitate the development of ophthalmic generic drug products. Ocular physiologically based pharmacokinetic (O-PBPK) models can provide insight into drug partitioning in eye tissues that are usually not accessible and/or are challenging to sample in humans. This study aims to demonstrate the utility of an ocular PBPK model to predict human exposure following the administration of ophthalmic suspension. Besifloxacin (Bes) suspension is presented as a case study. The O-PBPK model for Bes ophthalmic suspension (Besivance® 0.6%) accounts for nasolacrimal drainage, suspended particle dissolution in the tears, ocular absorption, and distribution in the rabbit eye. A topical controlled release formulation was used to integrate the effect of Durasite® on Bes ocular retention. The model was subsequently used to predict Bes exposure after its topical administration in humans. Drug-specific parameters were used as validated for rabbits. The physiological parameters were adjusted to match human ocular physiology. Simulated human ocular pharmacokinetic profiles were compared with the observed ocular tissue concentration data to assess the OCAT models' ability to predict human ocular exposure. The O-PBPK model simulations adequately described the observed concentrations in the eye tissues following the topical administration of Bes suspension in rabbits. After adjustment of physiological parameters to represent the human eye, the extrapolation of clinical ocular exposure following a single ocular administration of Bes suspension was successful.

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron.

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.