Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Extreme phenotype sampling and next generation sequencing to identify genetic variants associated with tacrolimus in African American kidney transplant recipients

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n = 58) and low (n = 60) TAC troughs (N = 515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12 mg, respectively. Of 34,542 identified variants across 99 genes, 1406 variants were suggestively associated with TAC troughs in univariate models (p-value < 0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome.

CYP2J2-mediated metabolism of arachidonic acid in heart: A review of its kinetics, inhibition and role in heart rhythm control

Cytochrome P450 2 J2 (CYP2J2) is primarily expressed extrahepatically and is the predominant epoxygenase in human cardiac tissues. This highlights its key role in the metabolism of endogenous substrates. Significant scientific interest lies in cardiac CYP2J2 metabolism of arachidonic acid (AA), an omega-6 polyunsaturated fatty acid, to regioisomeric bioactive epoxyeicosatrienoic acid (EET) metabolites that show cardioprotective effects including regulation of cardiac electrophysiology. From an in vitro perspective, the accurate characterization of the kinetics of CYP2J2 metabolism of AA including its inhibition and inactivation by drugs could be useful in facilitating in vitro-in vivo extrapolations to predict drug-AA interactions in drug discovery and development. In this review, background information on the structure, regulation and expression of CYP2J2 in human heart is presented alongside AA and EETs as its endogenous substrate and metabolites. The in vitro and in vivo implications of the kinetics of this endogenous metabolic pathway as well as its perturbation via inhibition and inactivation by drugs are elaborated. Additionally, the role of CYP2J2-mediated metabolism of AA to EETs in cardiac electrophysiology will be expounded.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Progressive tools and critical strategies for development of best fit PBPK model aiming better in vitro-in vivo correlation

Nowadays, conducting discriminative dissolution experiments employing physiologically based pharmacokinetic modeling (PBPK) or physiologically based biopharmaceutical modeling (PBBM) is gaining significant importance in quantitatively predicting oral absorption of drugs. Mechanistic understanding of each process involved in drug absorption and its impact on the performance greatly facilitates designing a formulation with high confidence. Unfortunately, the biggest challenge scientists are facing in current days is the lack of standardized protocol for integrating dissolution experiment data during PBPK modeling. However, in vitro-in vivo drug release interrelation can be improved with the consideration and development of appropriate biorelevant dissolution media that closely mimic physiological conditions. Multiple reported dissolution models have described nature and functionality of different regions of the gastrointestinal tract (GI) to more accurately design discriminative dissolution media. Dissolution experiment data can be integrated either mechanistically or without a mechanism depending primarily on the formulation type, biopharmaceutics classification system (BCS) class and particle size of the drug substance. All such parameters are required to be considered for selecting the appropriate functions during PBPK modeling to produce a best fit model. The primary focus of this review is to critically discuss various progressive dissolution models and tools, existing challenges and approaches for establishing best fit PBPK model aiming better in vitro-in vivo correlation (IVIVC). Strategies for proper selection of dissolution models as an input function in PBPK/PBBM modeling have also been critically discussed. Logical and scientific pathway for selection of different type of functions and integration events in the commercially available in silico software has been described through case studies.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms

PURPOSE

To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.

METHODS

A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.

RESULTS

Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5-2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were Vmax,2C19 ≈ Papp > Vmax,3A4 > Kti, and contributions to its pharmacodynamic were Vmax,2C19 > kome > kms > Papp > Vmax,3A4. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.

CONCLUSIONS

The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.

Mechanistic Middle-Out Physiologically Based Toxicokinetic Modeling of Transporter-Dependent Disposition of Perfluorooctanoic Acid in Humans

Perfluorooctanoic acid (PFOA) is an environmental toxicant exhibiting a years-long biological half-life (t_1/2) in humans and is linked with adverse health effects. However, limited understanding of its toxicokinetics (TK) has obstructed the necessary risk assessment. Here, we constructed the first middle-out physiologically based toxicokinetic (PBTK) model to mechanistically explain the persistence of PFOA in humans. In vitro transporter kinetics were thoroughly characterized and scaled up to in vivo clearances using quantitative proteomics-based in vitro-to-in vivo extrapolation. These data and physicochemical parameters of PFOA were used to parameterize our model. We uncovered a novel uptake transporter for PFOA, highly likely to be monocarboxylate transporter 1 which is ubiquitously expressed in body tissues and may mediate broad tissue penetration. Our model was able to recapitulate clinical data from a phase I dose-escalation trial and divergent half-lives from clinical trial and biomonitoring studies. Simulations and sensitivity analyses confirmed the importance of renal transporters in driving extensive PFOA reabsorption, reducing its clearance and augmenting its t_1/2. Crucially, the inclusion of a hypothetical, saturable renal basolateral efflux transporter provided the first unified explanation for the divergent t_1/2 of PFOA reported in clinical (116 days) versus biomonitoring studies (1.3-3.9 years). Efforts are underway to build PBTK models for other perfluoroalkyl substances using similar workflows to assess their TK profiles and facilitate risk assessments.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole

Albendazole (ABZ) is a weakly basic drug that undergoes extensive presystemic metabolism after oral administration and converts to its active form albendazole sulfoxide (ABZ_SO). The absorption of albendazole is limited by poor aqueous solubility, and dissolution is the rate-limiting step in the overall exposure of ABZ_SO. In this study, PBPK modeling was used to identify formulation-specific parameters that impact the oral bioavailability of ABZ_SO. In vitro experiments were carried out to determine pH solubility, precipitation kinetics, particle size distribution, and biorelevant solubility. A transfer experiment was conducted to determine the precipitation kinetics. A PBPK model for ABZ and ABZ_SO was developed using the Simcyp™ Simulator based on parameter estimates from in vitro experiments. Sensitivity analyses were performed to assess the impact of physiological parameters and formulation-related parameters on the systemic exposure of ABZ_SO. Model simulations predicted that increased gastric pH significantly reduced ABZ absorption and, subsequently, ABZ_SO systemic exposure. Reducing the particle size below 50 µm did not improve the bioavailability of ABZ. Modeling results illustrated that systemic exposure of ABZ_SO was enhanced by increasing solubility or supersaturation and decreasing the drug precipitation of ABZ at the intestinal pH level. These results were used to identify potential formulation strategies to enhance the oral bioavailability of ABZ_SO.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Integrating Forward and Reverse Translation in PBPK Modeling to Predict Food Effect on Oral Absorption of Weakly Basic Drugs

INTRODUCTION

Ketoconazole and posaconazole are two weakly basic broad-spectrum antifungals classified as Biopharmaceutics Classification System class II drugs, indicating that they are highly permeable, but exhibit poor solubility. As a result, oral bioavailability and clinical efficacy can be impacted by the formulation performance in the gastrointestinal system. In this work, we have leveraged in vitro biopharmaceutics and clinical data available in the literature to build physiologically based pharmacokinetic (PBPK) models for ketoconazole and posaconazole, to determine the suitability of forward in vitro-in vivo translation for characterization of in vivo drug precipitation, and to predict food effect.

METHODS

A stepwise modeling approach was utilized to derive key parameters related to absorption, such as drug solubility, dissolution, and precipitation kinetics from in vitro data. These parameters were then integrated into PBPK models for the simulation of ketoconazole and posaconazole plasma concentrations in the fasted and fed states.

RESULTS

Forward in vitro-in vivo translation of intestinal precipitation kinetics for both model drugs resulted in poor predictions of PK profiles. Therefore, a reverse translation approach was applied, based on limited fitting of precipitation-related parameters to clinical data. Subsequent simulations for ketoconazole and posaconazole demonstrated that fasted and fed state PK profiles for both drugs were adequately recapitulated.

CONCLUSION

The two examples presented in this paper show how middle-out modeling approaches can be used to predict the magnitude and direction of food effects provided the model is verified on fasted state PK data.

Pharmacokinetics of Novel Furoxan/Coumarin Hybrids in Rats Using LC-MS/MS Method and Physiologically Based Pharmacokinetic Model

Novel furoxan/coumarin hybrids were synthesized, and pharmacologic studies showed that the compounds displayed potent antiproliferation activities via downregulating both the phosphatidylinositide 3-kinase (PI3K) pathway and the mitogen-activated protein kinase (MAPK) pathway. To investigate the preclinical pharmacokinetic (PK) properties of three candidate compounds (CY-14S-4A83, CY-16S-4A43, and CY-16S-4A93), liquid chromatography, in tandem with the mass spectrometry LC-MS/MS method, was developed and validated for the simultaneous determination of these compounds. The absorption, distribution, metabolism, and excretion (ADME) properties were investigated in in vitro studies and in rats. Meanwhile, physiologically based pharmacokinetic (PBPK) models were constructed using only in vitro data to obtain detailed PK information. Good linearity was observed over the concentration range of 0.01−1.0 μg/mL. The free drug fraction (fu) values of the compounds were less than 3%, and the clearance (CL) values were 414.5 ± 145.7 mL/h/kg, 2624.6 ± 648.4 mL/h/kg, and 500.6 ± 195.2 mL/h/kg, respectively. The predicted peak plasma concentration (Cmax) and the area under the concentration-time curve (AUC) were overestimated for the CY-16S-4A43 PBPK model compared with the experimental ones (fold error > 2), suggesting that tissue accumulation and additional elimination pathways may exist. In conclusion, the LC-MS/MS method was successively applied in the preclinical PK studies, and the detailed information from PBPK modeling may improve decision-making in subsequent new drug development.

Human pharmacokinetics prediction with an in vitro-in vivo correction factor approach and in vitro drug-drug interaction profile of bictegravir, a potent integrase-strand transfer inhibitor component in approved biktarvy® for the treatment of HIV-1 infection

Bictegravir (BIC) is a potent small-molecule integrase strand-transfer inhibitor (INSTI) and a component of Biktarvy^®, a single-tablet combination regimen that is currently approved for the treatment of human immunodeficiency virus type 1 (HIV-1) infection. The in vitro properties, pharmacokinetics (PK), and drug-drug interaction (DDI) profile of BIC were characterised in vitro and in vivo.BIC is a weakly acidic, ionisable, lipophilic, highly plasma protein-bound BCS class 2 molecule, which makes it difficult to predict human PK using standard methods. Its systemic plasma clearance is low, and the volume of distribution is approximately the volume of extracellular water in nonclinical species. BIC metabolism is predominantly mediated by cytochrome P450 enzyme (CYP) 3A and UDP-glucuronosyltransferase 1A1. BIC shows a low potential to perpetrate clinically meaningful DDIs via known drug metabolising enzymes or transporters.The human PK of BIC was predicted using a combination of bioavailability and volume of distribution scaled from nonclinical species and a modified in vitro-in vivo correlation (IVIVC) correction for clearance. Phase 1 studies in healthy subjects largely bore out the prediction and supported the methods used. The approach presented herein could be useful for other drug molecules where standard projections are not sufficiently accurate. .

Assessment of Aging-Related Function Variations of P-gp Transporter in Old-Elderly Chinese CHF Patients Based on Modeling and Simulation

BACKGROUND AND OBJECTIVES

P-glycoprotein (P-gp) is one of the most intensely studied transporters owing to its broad tissue distribution and substrate specificity. Existing research suggests that the risk of systemic exposure to dabigatran etexilate (DABE) and digoxin, two P-gp probe substrates in vivo, has significantly increased in elderly patients. We applied a model-based quantitative pharmacological approach to assess aging-related P-gp changes in the Chinese old-elderly population.

METHODS

Population pharmacokinetic (PopPK) modeling was first performed using clinical pharmacokinetic data to explore the effect of age on the pharmacokinetic characteristics of dabigatran (DAB, the active principle of DABE) and digoxin in elderly Chinese patients. Corresponding physiologically based pharmacokinetic (PBPK) models were established to further explain the elevated systemic exposure to these two drugs. Eventually, standard dosing regimens of DABE and digoxin were assessed in Chinese old-elderly patients with chronic heart failure (CHF) with different stages of renal impairment.

RESULTS

PopPK analysis suggested that age as a covariate had an additional effect on the apparent clearance of these two drugs after correcting for creatinine clearance. PBPK simulation results suggested that disease-specific pathophysiological changes could explain DAB exposure in the young elderly. In the elderly population, 17.1% of elevated DAB exposure remained unexplained, and 25.5% of the reduced P-gp function associated with aging was ultimately obtained using sensitivity analysis. This value was further validated using digoxin data obtained by PBPK modeling. The simulation results suggest that CHF patients with advanced age and moderate-to-severe renal impairment require heightened vigilance for elevated exposure risk during the use of DABE and digoxin.

CONCLUSIONS

Aging might be a significant risk factor for elevated systemic exposure to DAB and digoxin by reducing P-gp-mediated efflux in the Chinese old elderly population.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration-time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Integration of a Physiologically Based Pharmacokinetic and Pharmacodynamic Model for Tegoprazan and Its Metabolite: Application for Predicting Food Effect and Intragastric pH Alterations

A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model for tegoprazan and its major metabolite M1 was developed to predict PK and PD profiles under various scenarios. The PBPK model for tegoprazan and M1 was developed and predicted using the SimCYP^® simulator and verified using clinical study data obtained after a single administration of tegoprazan. The established PBPK/PD model was used to predict PK profiles after repeated administrations of tegoprazan, postprandial PK profiles, and intragastric pH changes. The predicted tegoprazan and M1 concentration-time profiles fit the observed profiles well. The arithmetic mean ratios (95% confidence intervals) of the predicted to observed values for the area under the curve (AUC_{0-24 h}), maximum plasma drug concentration (C_max), and clearance (CL) for tegoprazan and M1 were within a 30% interval. Delayed time of maximum concentration (T_max) and decreased C_max were predicted in the postprandial PK profiles compared with the fasted state. This PBPK/PD model may be used to predict PK profiles after repeated tegoprazan administrations and to predict differences in physiological factors in the gastrointestinal tract or changes in gastric acid pH after tegoprazan administration.

Chemical and biological assessments of environmental mixtures: A review of current trends, advances, and future perspectives

Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture.

Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes

Metoprolol, a selective β₁-adrenoreceptor blocking agent used in the treatment of hypertension, angina, and heart failure, is primarily metabolized by the CYP2D6 enzyme, which catalyzes α-hydroxylation and O-desmethylation. As CYP2D6 is genetically highly polymorphic and the enzymatic activity differs greatly depending on the presence of the mutant allele(s), the pharmacokinetic profile of metoprolol is highly variable depending on the genotype of CYP2D6. The aim of study was to develop the physiologically based pharmacokinetic (PBPK) model of metoprolol related to CYP2D6 genetic polymorphism for personalized therapy with metoprolol. For PBPK modelling, our previous pharmacogenomic data were used. To obtain kinetic parameters (K_m, V_max, and CL_int) of each genotype, the recombinant CYP enzyme of each genotype was incubated with metoprolol and metabolic rates were assayed. Based on these data, the PBPK model of metoprolol was developed and validated in different CYP2D6 genotypes using PK-Sim^® software. As a result, the input values for various parameters for the PBPK model were presented and the PBPK model successfully described the pharmacokinetics of metoprolol in each genotype group. The simulated values were within the acceptance criterion (99.998% confidence intervals) compared with observed values. The PBPK model developed in this study can be used for personalized pharmacotherapy with metoprolol in individuals of various races, ages, and CYP2D6 genotypes.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs

Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.

Towards the Elucidation of the Pharmacokinetics of Voriconazole: A Quantitative Characterization of Its Metabolism

The small-molecule drug voriconazole (VRC) shows a complex and not yet fully understood metabolism. Consequently, its in vivo pharmacokinetics are challenging to predict, leading to therapy failures or adverse events. Thus, a quantitative in vitro characterization of the metabolism and inhibition properties of VRC for human CYP enzymes was aimed for. The Michaelis-Menten kinetics of voriconazole N-oxide (NO) formation, the major circulating metabolite, by CYP2C19, CYP2C9 and CYP3A4, was determined in incubations of human recombinant CYP enzymes and liver and intestine microsomes. The contribution of the individual enzymes to NO formation was 63.1% CYP2C19, 13.4% CYP2C9 and 29.5% CYP3A4 as determined by specific CYP inhibition in microsomes and intersystem extrapolation factors. The type of inhibition and inhibitory potential of VRC, NO and hydroxyvoriconazole (OH-VRC), emerging to be formed independently of CYP enzymes, were evaluated by their effects on CYP marker reactions. Time-independent inhibition by VRC, NO and OH-VRC was observed on all three enzymes with NO being the weakest and VRC and OH-VRC being comparably strong inhibitors of CYP2C9 and CYP3A4. CYP2C19 was significantly inhibited by VRC only. Overall, the quantitative in vitro evaluations of the metabolism contributed to the elucidation of the pharmacokinetics of VRC and provided a basis for physiologically-based pharmacokinetic modeling and thus VRC treatment optimization.

Development of Physiologically Based Pharmacokinetic Model for Pregabalin to Predict the Pharmacokinetics in Pediatric Patients with Renal Impairment and Adjust Dosage Regimens: PBPK Model of Pregabalin in Pediatric Patients with Renal Impairment

Pregabalin (PGB) is widely used clinically; however, its pharmacokinetics (PK) has not been studied in pediatric patients with renal impairment (RI). To design optimized PGB regimens for pediatric patients with varying degrees of RI and predict exposure to PGB, physiologically based pharmacokinetic (PBPK) models of PGB were developed and verified, and its disposition was simulated in the healthy population and adults with RI. The simulated results from the PBPK models after single-dose and multi-dose administrations of PGB were consistent with the corresponding observed data based on the fold error values of less than 2. The area under curve ratios were 1.23 ± 0.06, 2.02 ± 0.10, 3.86 ± 0.21, and 9.92 ± 0.79 in pediatric patients with mild, moderate, severe, and end-stage RI, respectively. Based on the predictions for pediatric patients with moderate, severe, and end-stage RI, the maximum dose should not exceed 7, 3.5, and 1.4 mg/kg/day, respectively, among those weighing < 30 kg, and it should not exceed 5, 2.5, and 1 mg/kg/day, respectively, among those weighing > 30 kg. In conclusion, the developed PBPK model is a valuable tool for predicting PGB dosage for pediatric patients with RI.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Prediction of Fluoxetine and Norfluoxetine Pharmacokinetic Profiles Using Physiologically Based Pharmacokinetic Modeling

Fluoxetine is a selective serotonin reuptake inhibitor that is metabolized to norfluoxetine by cytochrome P450 (CYP) 2D6, CYP2C19, and CYP3A4. A physiologically based pharmacokinetic model for fluoxetine and norfluoxetine metabolism was developed to predict and investigate changes in concentration-time profiles according to fluoxetine dosage in the Korean population. The model was developed based on the Certara repository model and information gleaned from the literature. Digitally extracted clinical study data were used to develop and verify the model. Simulations for plasma concentrations of fluoxetine and norfluoxetine after a single dose of 60 or 80 mg fluoxetine were made based on 1000 virtual healthy Korean individuals using the SimCYP version 19 simulator. The mean ratios (simulated/observed) after a single administration of 80 mg fluoxetine for maximum plasma concentration, area under the plasma concentration-time curve, and apparent clearance were 1.12, 1.08, and 0.93 for fluoxetine; the ratios of maximum plasma concentration and area under the plasma concentration-time curve were 1.08 and 1.08, respectively, for norfluoxetine, indicating that the simulated concentration-time profiles of fluoxetine and norfluoxetine fitted the observed profiles well. The developed model was used to predict plasma fluoxetine and norfluoxetine concentration-time profiles after repeated administrations of fluoxetine in Korean volunteers. This physiologically based pharmacokinetic model could provide basic understanding of the pharmacokinetic profiles of fluoxetine and its metabolite under various situations.

In vitro and In silico biopharmaceutic regulatory guidelines for generic bioequivalence for oral products: Comparison among various regulatory agencies

Generic drug development is a complex process that involves development of formulation similar to reference product. Because of the complexity associated with generic drug development, many regulatory agencies have come up with various guidelines. Out of many guidelines, the biopharmaceutics classification system that was introduced in 1995 based on aqueous solubility and permeability helped many pharmaceutical scientists across the globe to utilize the tool for formulation development, waiver of in vivo studies. Later on in vitro guidelines based on dissolution and in vitro in vivo correlation were introduced by many regulatory agencies with an intent to reduce number of in vivo human testing thereby facilitating shorter development time and faster approvals and launch. Most recently, understanding the importance in silico approaches such as physiologically based pharmacokinetic modelling, regulatory agencies such as United States Food and Drug Administration (USFDA) and European Middle East and Africa (EMA) came up with modelling guidance documents. Even though consensus exists between guidance documents from various regulatory agencies, still there are many minor to major differences exists between these guidance documents that needs to be considered while submitting a generic drug application. This review aims to compare all the in vitro and in silico guidance documents from major regulatory agencies with emphasis on latest trends and technologies combined with regulatory acceptability with an intention to harmonize regulations. Guidance documents from major regulatory agencies such as USFDA, EMA, World Health Organization, International Council for Harmonization and other emerging markets were compared. Similarities &differences among these guidance documents are critically reviewed to provide the reader a detailed overview of these guidance documents at one place.

Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery

The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed.

Ontogeny of Hepatic Transporters and Drug-Metabolizing Enzymes in Humans and in Nonclinical Species

The liver represents a major eliminating and detoxifying organ, determining exposure to endogenous compounds, drugs, and other xenobiotics. Drug transporters (DTs) and drug-metabolizing enzymes (DMEs) are key determinants of disposition, efficacy, and toxicity of drugs. Changes in their mRNA and protein expression levels and associated functional activity between the perinatal period until adulthood impact drug disposition. However, high-resolution ontogeny profiles for hepatic DTs and DMEs in nonclinical species and humans are lacking. Meanwhile, increasing use of physiologically based pharmacokinetic (PBPK) models necessitates availability of underlying ontogeny profiles to reliably predict drug exposure in children. In addition, understanding of species similarities and differences in DT/DME ontogeny is crucial for selecting the most appropriate animal species when studying the impact of development on pharmacokinetics. Cross-species ontogeny mapping is also required for adequate translation of drug disposition data in developing nonclinical species to humans. This review presents a quantitative cross-species compilation of the ontogeny of DTs and DMEs relevant to hepatic drug disposition. A comprehensive literature search was conducted on PubMed Central: Tables and graphs (often after digitization) in original manuscripts were used to extract ontogeny data. Data from independent studies were standardized and normalized before being compiled in graphs and tables for further interpretation. New insights gained from these high-resolution ontogeny profiles will be indispensable to understand cross-species differences in maturation of hepatic DTs and DMEs. Integration of these ontogeny data into PBPK models will support improved predictions of pediatric hepatic drug disposition processes. SIGNIFICANCE STATEMENT: Hepatic drug transporters (DTs) and drug-metabolizing enzymes (DMEs) play pivotal roles in hepatic drug disposition. Developmental changes in expression levels and activities of these proteins drive age-dependent pharmacokinetics. This review compiles the currently available ontogeny profiles of DTs and DMEs expressed in livers of humans and nonclinical species, enabling robust interpretation of age-related changes in drug disposition and ultimately optimization of pediatric drug therapy.

Examination of Physiologically-Based Pharmacokinetic Models of Rosuvastatin

Physiologically‐based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug–drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter‐mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter‐mediated drug disposition, and increase confidence in tDDI prediction.

Physiologically Based Pharmacokinetic Modeling of Transdermal Selegiline and Its Metabolites for the Evaluation of Disposition Differences between Healthy and Special Populations

A physiologically based pharmacokinetic (PBPK) model of selegiline (SEL), and its metabolites, was developed in silico to evaluate the disposition differences between healthy and special populations. SEL is metabolized to methamphetamine (MAP) and desmethyl selegiline (DMS) by several CYP enzymes. CYP2D6 metabolizes the conversion of MAP to amphetamine (AMP), while CYP2B6 and CYP3A4 predominantly mediate the conversion of DMS to AMP. The overall prediction error in simulated PK, using the developed PBPK model, was within 0.5-1.5-fold after intravenous and transdermal dosing in healthy and elderly populations. Simulation results generated in the special populations demonstrated that a decrease in cardiac output is a potential covariate that affects the SEL exposure in renally impaired (RI) and hepatic impaired (HI) subjects. A decrease in CYP2D6 levels increased the systemic exposure of MAP. DMS exposure increased due to a reduction in the abundance of CYP2B6 and CYP3A4 in RI and HI subjects. In addition, an increase in the exposure of the primary metabolites decreased the exposure of AMP. No significant difference between the adult and adolescent populations, in terms of PK, were observed. The current PBPK model predictions indicate that subjects with HI or RI may require closer clinical monitoring to identify any untoward effects associated with the administration of transdermal SEL patch.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips

Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans-using computationally scaled data from multiple fluidically linked two-channel organ chips-predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Quantitative Analysis of Tozadenant Using Liquid Chromatography-Mass Spectrometric Method in Rat Plasma and Its Human Pharmacokinetics Prediction Using Physiologically Based Pharmacokinetic Modeling

Tozadenant is one of the selective adenosine A2a receptor antagonists with a potential to be a new Parkinson's disease (PD) therapeutic drug. In this study, a liquid chromatography-mass spectrometry based bioanalytical method was qualified and applied for the quantitative analysis of tozadenant in rat plasma. A good calibration curve was observed in the range from 1.01 to 2200 ng/mL for tozadenant using a quadratic regression. In vitro and preclinical in vivo pharmacokinetic (PK) properties of tozadenant were studied through the developed bioanalytical methods, and human PK profiles were predicted using physiologically based pharmacokinetic (PBPK) modeling based on these values. The PBPK model was initially optimized using in vitro and in vivo PK data obtained by intravenous administration at a dose of 1 mg/kg in rats. Other in vivo PK data in rats were used to validate the PBPK model. The human PK of tozadenant after oral administration at a dose of 240 mg was simulated by using an optimized and validated PBPK model. The predicted human PK parameters and profiles were similar to the observed clinical data. As a result, optimized PBPK model could reasonably predict the PK in human.

Human tissue-engineered skeletal muscle: a novel 3D in vitro model for drug disposition and toxicity after intramuscular injection

The development of laboratory-grown tissues, referred to as organoids, bio-artificial tissue or tissue-engineered constructs, is clearly expanding. We describe for the first time how engineered human muscles can be applied as a pre- or non-clinical model for intramuscular drug injection to further decrease and complement the use of in vivo animal studies. The human bio-artificial muscle (BAM) is formed in a seven day tissue engineering procedure during which human myoblasts fuse and differentiate to aligned myofibers in an extracellular matrix. The dimensions of the BAM constructs allow for injection and follow-up during several days after injection. A stereotactic setup allows controllable injection at multiple sites in the BAM. We injected several compounds; a dye, a hydrolysable compound, a reducible substrate and a wasp venom toxin. Afterwards, direct reflux, release and metabolism were assessed in the BAM constructs in comparison to 2D cell culture and isolated human muscle strips. Spectrophotometry and luminescence allowed to measure the release of the injected compounds and their metabolites over time. A release profile over 40 hours was observed in the BAM model in contrast to 2D cell culture, showing the capacity of the BAM model to function as a drug depot. We also determined compound toxicity on the BAMs by measuring creatine kinase release in the medium, which increased with increasing toxic insult. Taken together, we show that the BAM is an injectable human 3D cell culture model that can be used to measure release and metabolism of injected compounds in vitro.

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures

BACKGROUND

A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals.

RESULTS

We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data.

CONCLUSIONS

The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

Clopidogrel Pharmacokinetics in Malaysian Population Groups: The Impact of Inter-Ethnic Variability

Malaysia is a multi-ethnic society whereby the impact of pharmacogenetic differences between ethnic groups may contribute significantly to variability in clinical therapy. One of the leading causes of mortality in Malaysia is cardiovascular disease (CVD), which accounts for up to 26% of all hospital deaths annually. Clopidogrel is used as an adjunct treatment in the secondary prevention of cardiovascular events. CYP2C19 plays an integral part in the metabolism of clopidogrel to the active metabolite clopi-H4. However, CYP2C19 genetic polymorphism, prominent in Malaysians, could influence target clopi-H4 plasma concentrations for clinical efficacy. This study addresses how inter-ethnicity variability within the Malaysian population impacts the attainment of clopi-H4 target plasma concentration under different CYP2C19 polymorphisms through pharmacokinetic (PK) modelling. We illustrated a statistically significant difference (P < 0.001) in the clopi-H4 C_max between the extensive metabolisers (EM) and poor metabolisers (PM) phenotypes with either Malay or Malaysian Chinese population groups. Furthermore, the number of PM individuals with peak clopi-H4 concentrations below the minimum therapeutic level was partially recovered using a high-dose strategy (600 mg loading dose followed by a 150 mg maintenance dose), which resulted in an approximate 50% increase in subjects attaining the minimum clopi-H4 plasma concentration for a therapeutic effect.

Recellularized Native Kidney Scaffolds as a Novel Tool in Nephrotoxicity Screening

Drug-induced kidney injury in medicinal compound development accounts for over 20% of clinical trial failures and involves damage to different nephron segments, mostly the proximal tubule. Yet, currently applied cell models fail to reliably predict nephrotoxicity; neither are such models easy to establish. Here, we developed a novel three-dimensional (3D) nephrotoxicity platform on the basis of decellularized rat kidney scaffolds (DS) recellularized with conditionally immortalized human renal proximal tubule epithelial cells overexpressing the organic anion transporter 1 (ciPTEC-OAT1). A 5-day SDS-based decellularization protocol was used to generate DS, of which 100-μm slices were cut and used for cell seeding. After 8 days of culturing, recellularized scaffolds (RS) demonstrated 3D-tubule formation along with tubular epithelial characteristics, including drug transporter function. Exposure of RS to cisplatin (CDDP), tenofovir (TFV), or cyclosporin A (CsA) as prototypical nephrotoxic drugs revealed concentration-dependent reduction in cell viability, as assessed by PrestoBlue and Live/Dead staining assays. This was most probably attributable to specific uptake of CDDP by the organic cation transporter 2 (OCT2), TFV through organic anion transporter 1 (OAT1), and CsA competing for P-glycoprotein-mediated efflux. Compared with 2D cultures, RS showed an increased sensitivity to cisplatin and tenofovir toxicity after 24-hour exposure (9 and 2.2 fold, respectively). In conclusion, we developed a physiologically relevant 3D nephrotoxicity screening platform that could be a novel tool in drug development.

Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in C_max of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.

Clinical Exposure Boost Predictions by Integrating Cytochrome P450 3A4-Humanized Mouse Studies With PBPK Modeling

NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (fmCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate fmCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human fmCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results

Orally administered drugs are subject to a number of barriers impacting bioavailability (F_oral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, F_oral and relative AUC (F_rel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.