A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery

Effect of Hepatic Impairment on Trofinetide Exposures Using an In Silico Physiologically Based Pharmacokinetic Model

INTRODUCTION

Trofinetide is the first drug to be approved for the treatment of Rett syndrome. Hepatic impairment is not expected to affect the pharmacokinetic (PK) profile of trofinetide because of predominant renal excretion. This study was conducted to help understand the potential impact of any hepatic impairment on trofinetide PK.

METHODS

This study used physiologically based PK modeling to estimate trofinetide exposure (maximum drug concentration and area under the concentration-time curve from time zero to infinity) in virtual patients with mild, moderate, and severe hepatic impairment (per Child-Pugh classification) compared with virtual healthy subjects following a 12 g oral trofinetide dose.

RESULTS

In individual deterministic simulations for matched individuals and stochastic simulations at the population level (100 virtual individuals simulated per population), as anticipated, predicted plasma exposures were similar for healthy subjects and for patients with mild, moderate, and severe hepatic impairment. However, predicted blood concentration exposures slightly increased with increasing severity of hepatic impairment because of change in hematocrit levels.

CONCLUSION

This study indicates that hepatic impairment is not expected to have a clinically relevant effect on exposure to trofinetide.

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure-driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

Interspecies scaling of toxicity reference values in human health versus ecological risk assessments: A critical review

Risk assessments that focus on anthropogenic chemicals in environmental media-whether considering human health or ecological effects-often rely on toxicity data from experimentally studied species to estimate safe exposures for species that lack similar data. Current default extrapolation approaches used in both human health risk assessments and ecological risk assessments (ERAs) account for differences in body weight between the test organisms and the species of interest, but the two default approaches differ in important ways. Human health risk assessments currently employ a default based on body weight raised to the three-quarters power. Ecological risk assessments for wildlife (i.e., mammals and birds) are typically based directly on body weight, as measured in the test organism and receptor species. This review describes differences in the experimental data underlying these default practices and discusses the many factors that affect interspecies variability in chemical exposures. The interplay of these different factors can lead to substantial departures from default expectations. Alternative methodologies for conducting more accurate interspecies extrapolations in ERAs for wildlife are discussed, including tissue-based toxicity reference values, physiologically based toxicokinetic and/or toxicodynamic modeling, chemical read-across, and a system of categorical defaults based on route of exposure and toxic mode of action. Integr Environ Assess Manag 2024;20:749-764. © 2023 SETAC.

Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery

A mathematical equation model was developed by building the relationship between the fu,b/fu,p ratio and the computed physicochemical properties of candidate compounds, thereby predicting Kp,uu,brain based on a single experimentally measured Kp,brain value. A total of 256 compounds and 36 marketed published drugs including acidic, basic, neutral, zwitterionic, CNS-penetrant, and non-CNS penetrant compounds with diverse structures and physicochemical properties were involved in this study. A strong correlation was demonstrated between the fu,b/fu,p ratio and physicochemical parameters (CLogP and ionized fraction). The model showed good performance in both internal and external validations. The percentages of compounds with Kp,uu,brain predictions within 2-fold variability were 80.0 %-83.3 %, and more than 90 % were within a 3-fold variability. Meanwhile, "black box" QSAR models constructed by machine learning approaches for predicting fu,b/fu,p ratio based on the chemical descriptors are also presented, and the ANN model displayed the highest accuracy with an RMSE value of 0.27 and 86.7 % of the test set drugs fell within a 2-fold window of linear regression. These models demonstrated strong predictive power and could be helpful tools for evaluating the Kp,uu,brain by a single measurement parameter of Kp,brain during lead optimization for CNS penetration evaluation and ranking CNS drug candidate molecules in the early stages of CNS drug discovery.

Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors

BACKGROUND

The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (K_p) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the K_p value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma.

OBJECTIVE

Instead of experiments, many researchers have sought in silico methods. Today, most of the models for K_p prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for K_p using physicochemical descriptors instead of in vivo experimental data as explanatory variables.

METHODS

We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1-14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for K_p value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model.

RESULTS

Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)].

CONCLUSION

We could develop a 2D-QSAR model for K_p value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.

Pharmacokinetic Profile Evaluation of Novel Combretastatin Derivative, LASSBio-1920, as a Promising Colorectal Anticancer Agent

LASSBio-1920 was synthesized due to the poor solubility of its natural precursor, combretastatin A4 (CA4). The cytotoxic potential of the compound against human colorectal cancer cells (HCT-116) and non-small cell lung cancer cells (PC-9) was evaluated, yielding IC₅₀ values of 0.06 and 0.07 μM, respectively. Its mechanism of action was analyzed by microscopy and flow cytometry, where LASSBio-1920 was found to induce apoptosis. Molecular docking simulations and the enzymatic inhibition study with wild-type (wt) EGFR indicated enzyme-substrate interactions similar to other tyrosine kinase inhibitors. We suggest that LASSBio-1920 is metabolized by O-demethylation and NADPH generation. LASSBio-1920 demonstrated excellent absorption in the gastrointestinal tract and high central nervous system (CNS) permeability. The pharmacokinetic parameters obtained by predictions indicated that the compound presents zero-order kinetics and, in a human module simulation, accumulates in the liver, heart, gut, and spleen. The pharmacokinetic parameters obtained will serve as the basis to initiate in vivo studies regarding LASSBio-1920's antitumor potential.

Investigating the bioaccumulation potential of anionic organic compounds using a permanent rainbow trout liver cell line

Permanent rainbow trout (Oncorhynchus mykiss) cell lines represent potential in vitro alternatives to experiments with fish. We here developed a method to assess the bioaccumulation potential of anionic organic compounds in fish, using the rainbow trout liver-derived RTL-W1 cell line. Based on the availability of high quality in vivo bioconcentration (BCF) and biomagnification (BMF) data and the substances' charge state at physiological pH, four anionic compounds were selected: pentachlorophenol (PCP), diclofenac (DCF), tecloftalam (TT) and benzotriazol-tert-butyl-hydroxyl-phenyl propanoic acid (BHPP). The fish cell line acute toxicity assay (OECD TG249) was used to derive effective concentrations 50 % and non-toxic exposure concentrations to determine exposure concentrations for bioaccumulation experiments. Bioaccumulation experiments were performed over 48 h with a total of six time points, at which cell, medium and plastic fractions were sampled and measured using high resolution tandem mass spectrometry after online solid phase extraction. Observed cell internal concentrations were over-predicted by KOW-derived predictions while pH-dependent octanol-water partitioning (DOW) and membrane lipid-water partitioning (DMLW) gave better predictions of cell internal concentrations. Measured medium and cell internal concentrations at steady state were used to calculate RTL-W1-based BCF, which were compared to DOW- or DMLW-based model approaches and in vivo data. With the exception of PCP, the cell-derived BCF best compared to DOW-based model predictions, which were higher than predictions based on DMLW. All methods predicted the in vivo BCF for diclofenac well. For PCP, the cell-derived BCF was lowest although all BCF predictions underestimated the in vivo BCF by ≥ 1 order of magnitude. The RTL-W1 cells, and all other prediction methods, largely overestimated in vivo BMF, which were available for PCP, TT and BHPP. We conclude that the RTL-W1 cell line can supplement BCF predictions for anionic compounds. For BMF estimations, however, in vitro-in vivo extrapolations need adaptation or a multiple cell line approach.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Prediction of herb-drug interactions involving consumption of furanocoumarin-mixtures and cytochrome P450 1A2-mediated caffeine metabolism inhibition in humans

Herb-drug interactions (HDI) has become important due to the increasing popularity of natural health product consumption worldwide. HDI is difficult to predict as botanical drugs usually contain complex phytochemical-mixtures, which interact with drug metabolism. Currently, there is no specific pharmacological tool to predict HDI since almost all in vitro-in vivo-extrapolation (IVIVE) Drug-Drug Interaction (DDI) models deal with one inhibitor-drug and one victim-drug. The objectives were to modify-two IVIVE models for the prediction of in vivo interaction between caffeine and furanocoumarin-containing herbs, and to confirm model predictions by comparing the DDI predictive results with actual human data. The models were modified to predict in vivo herb-caffeine interaction using the same set of inhibition constants but different integrated dose/concentration of furanocoumarin mixtures in the liver. Different hepatic inlet inhibitor concentration ([I]_H) surrogates were used for each furanocoumarin. In the first (hybrid) model, the [I]_H was predicted using the concentration-addition model for chemical-mixtures. In the second model, the [I]_H was calculated by adding individual furanocoumarins together. Once [I]_H values were determined, the models predicted an area-under-curve-ratio (AUCR) value of each interaction. The results indicate that both models were able to predict the experimental AUCR of herbal products reasonably well. The DDI model approaches described in this study may be applicable to health supplements and functional foods also.

A generic avian physiologically-based kinetic (PBK) model and its application in three bird species

Physiologically-based kinetic (PBK) models are effective tools for designing toxicological studies and conducting extrapolations to inform hazard characterization in risk assessment by filling data gaps and defining safe levels of chemicals. In the present work, a generic avian PBK model for male and female birds was developed using PK-Sim and MoBi from the Open Systems Pharmacology Suite (OSPS). The PBK model includes an ovulation model (egg development) to predict concentrations of chemicals in eggs from dietary exposure. The model was parametrized for chicken (Gallus gallus), bobwhite quail (Colinus virginianus) and mallard duck (Anas platyrhynchos) and was tested with nine chemicals for which in vivo studies were available. Time-concentration profiles of chemicals reaching tissues and egg compartment were simulated and compared to in vivo data. The overall accuracy of the PBK model predictions across the analyzed chemicals was good. Model simulations were found to be in the range of 22-79% within a 3-fold and 41-89% were within 10- fold deviation of the in vivo observed data. However, for some compounds scarcity of in-vivo data and inconsistencies between published studies allowed only a limited goodness of fit evaluation. The generic avian PBK model was developed following a "best practice" workflow describing how to build a PBK model for novel species. The credibility and reproducibility of the avian PBK models were scored by evaluation according to the available guidance documents from WHO (2010), and OECD (2021), to increase applicability, confidence and acceptance of these in silico models in chemical risk assessment.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (V_ss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of V_ss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CL_tot) and steady-state Vd (Vd_ss) (CL_tot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vd_ss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

Mechanistic Modeling of Placental Drug Transfer in Humans: How Do Differences in Maternal/Fetal Fraction of Unbound Drug and Placental Influx/Efflux Transfer Rates Affect Fetal Pharmacokinetics?

Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for. Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics. Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts. Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron. Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.

Mechanistic Coupling of a Novel in silico Cotyledon Perfusion Model and a Physiologically Based Pharmacokinetic Model to Predict Fetal Acetaminophen Pharmacokinetics at Delivery

Little is known about placental drug transfer and fetal pharmacokinetics despite increasing drug use in pregnant women. While physiologically based pharmacokinetic (PBPK) models can help in some cases to shed light on this knowledge gap, adequate parameterization of placental drug transfer remains challenging. A novel in silico model with seven compartments representing the ex vivo cotyledon perfusion assay was developed and used to describe placental transfer and fetal pharmacokinetics of acetaminophen. Unknown parameters were optimized using observed data. Thereafter, values of relevant model parameters were copied to a maternal-fetal PBPK model and acetaminophen pharmacokinetics were predicted at delivery after oral administration of 1,000 mg. Predictions in the umbilical vein were evaluated with data from two clinical studies. Simulations from the in silico cotyledon perfusion model indicated that acetaminophen accumulates in the trophoblasts; simulated steady state concentrations in the trophoblasts were 4.31-fold higher than those in the perfusate. The whole-body PBPK model predicted umbilical vein concentrations with a mean prediction error of 24.7%. Of the 62 concentration values reported in the clinical studies, 50 values (81%) were predicted within a 2-fold error range. In conclusion, this study presents a novel in silico cotyledon perfusion model that is structurally congruent with the placenta implemented in our maternal-fetal PBPK model. This allows transferring parameters from the former model into our PBPK model for mechanistically exploring whole-body pharmacokinetics and concentration-effect relationships in the placental tissue. Further studies should investigate acetaminophen accumulation and metabolism in the placenta as the former might potentially affect placental prostaglandin synthesis and subsequent fetal exposure.

Predicting the in vivo developmental toxicity of benzo[a]pyrene (BaP) in rats by an in vitro-in silico approach

Developmental toxicity testing is an animal-intensive endpoints in toxicity testing and calls for animal-free alternatives. Previous studies showed the applicability of an in vitro–in silico approach for predicting developmental toxicity of a range of compounds, based on data from the mouse embryonic stem cell test (EST) combined with physiologically based kinetic (PBK) modelling facilitated reverse dosimetry. In the current study, the use of this approach for predicting developmental toxicity of polycyclic aromatic hydrocarbons (PAHs) was evaluated, using benzo[a]pyrene (BaP) as a model compound. A rat PBK model of BaP was developed to simulate the kinetics of its main metabolite 3-hydroxybenzo[a]pyrene (3-OHBaP), shown previously to be responsible for the developmental toxicity of BaP. Comparison to in vivo kinetic data showed that the model adequately predicted BaP and 3-OHBaP blood concentrations in the rat. Using this PBK model and reverse dosimetry, a concentration–response curve for 3-OHBaP obtained in the EST was translated into an in vivo dose–response curve for developmental toxicity of BaP in rats upon single or repeated dose exposure. The predicted half maximal effect doses (ED₅₀) amounted to 67 and 45 mg/kg bw being comparable to the ED₅₀ derived from the in vivo dose–response data reported for BaP in the literature, of 29 mg/kg bw. The present study provides a proof of principle of applying this in vitro–in silico approach for evaluating developmental toxicity of BaP and may provide a promising strategy for predicting the developmental toxicity of related PAHs, without the need for extensive animal testing.

Integration of a physiologically-based pharmacokinetic model with a whole-body, organ-resolved genome-scale model for characterization of ethanol and acetaldehyde metabolism

Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol's primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.

Crosstalk of physiological pH and chemical pKa under the umbrella of physiologically based pharmacokinetic modeling of drug absorption, distribution, metabolism, excretion, and toxicity

Introduction: Physiological pH and chemical pKa are two sides of the same coin in defining the ionization of a drug in the human body. The Henderson-Hasselbalch equation and pH-partition hypothesis form the theoretical base to define the impact of pH-pKa crosstalk on drug ionization and thence its absorption, distribution, metabolism, excretion, and toxicity (ADMET).Areas covered: Human physiological pH is not constant, but a diverse, dynamic state regulated by various biological mechanisms, while the chemical pKa is generally a constant defining the acidic dissociation of the drug at various environmental pH. Works on pH-pKa crosstalk are scattered in the literature, despite its significant contributions to drug pharmacokinetics, pharmacodynamics, safety, and toxicity. In particular, its impacts on drug ADMET have not been effectively linked to the physiologically based pharmacokinetic (PBPK) modeling and simulation, a powerful tool increasingly used in model-informed drug development (MIDD).Expert opinion: Lacking a full consideration of the interactions of physiological pH and chemical pKa in a PBPK model limits scientists' capability in mechanistically describing the drug ADMET. This mini-review compiled literature knowledge on pH-pKa crosstalk and its impacts on drug ADMET, from the viewpoint of PBPK modeling, to pave the way to a systematic incorporation of pH-pKa crosstalk into PBPK modeling and simulation.

Physiologically based pharmacokinetic (PBPK) modeling for prediction of celecoxib pharmacokinetics according to CYP2C9 genetic polymorphism

Celecoxib is a non-steroidal anti-inflammatory drug (NSAID) and a representative selective cyclooxygenase (COX)-2 inhibitor, which is commonly prescribed for osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, acute pain, and primary dysmenorrhea. It is mainly metabolized by CYP2C9 and partly by CYP3A4 after oral administration. Many studies reported that CYP2C9 genetic polymorphism has significant effects on the pharmacokinetics of celecoxib and the occurrence of adverse drug reactions. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of celecoxib according to CYP2C9 genetic polymorphism for personalized pharmacotherapy. Initially, a clinical pharmacokinetic study was conducted where a single dose (200 mg) of celecoxib was administered to 39 healthy Korean subjects with CYP2C9*1/*1 or CYP2C9*1/*3 genotypes to obtain data for PBPK development. Based on the conducted pharmacokinetic study and a previous pharmacokinetic study involving subjects with CYP2C9*1/*13 and CYP2C9*3/*3 genotype, PBPK model for celecoxib was developed. A PBPK model for CYP2C9*1/*1 genotype group was developed and then scaled to other genotype groups (CYP2C9*1/*3, CYP2C9*1/*13 and CYP2C9*3/*3). After model development, model validation was performed with comparison of five pharmacokinetic studies. As a result, the developed PBPK model of celecoxib successfully described the pharmacokinetics of each CYP2C9 genotype group and its predicted values were within the acceptance criterion. Additionally, all the predicted values were within two-fold error range in comparison to the previous pharmacokinetic studies. This study demonstrates the possibility of determining the appropriate dosage of celecoxib for each individual through the PBPK modeling with CYP2C9 genomic information. This approach could contribute to the reduction of adverse drug reactions of celecoxib and enable precision medicine.

An Integrated Biophysical Model for Predicting the Clinical Pharmacokinetics of Transdermally Delivered Compounds

The delivery of therapeutic drugs through the skin is a promising alternative to oral or parenteral delivery routes because dermal drug delivery systems (D³S) offer unique advantages such as controlled drug release over sustained periods and a significant reduction in first-pass effects, thus reducing the required dosing frequency and level of patient noncompliance. Furthermore, D³S find applications in multiple therapeutic areas, including drug repurposing. This article presents an integrated biophysical model of dermal absorption for simulating the permeation and absorption of compounds delivered transdermally. The biophysical model is physiologically/biologically inspired and combines a holistic model of healthy skin with whole-body physiology-based pharmacokinetics through dermis microcirculation. The model also includes the effects of chemical penetration enhancers and hair follicles on transdermal transport. The model-predicted permeation and pharmacokinetics of select compounds were validated using in vivo data reported in the literature. We conjecture that the integrated model can be used to gather insights into the permeation and systemic absorption of transdermal formulations (including cosmetic products) released from novel depots and optimize delivery systems. Furthermore, the model can be adapted to diseased skin with parametrization and structural adjustments specific to skin diseases.

PBPK Modeling Approach to Predict the Behavior of Drugs Cleared by Kidney in Pregnant Subjects and Fetus

The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model predicting the pharmacokinetics (PK) of different compounds in pregnant subjects. This model considers the differences in tissue sizes, blood flow rates, enzyme expression levels, glomerular filtration rates, plasma protein binding, and other factors affected during pregnancy in both the maternal and fetal models. The PBPKPlus™ module in GastroPlus^® was used to model the PK of cefuroxime and cefazolin. For both compounds, the model was first validated against PK data in healthy non-pregnant volunteers and then applied to predict pregnant groups PK. The model accurately described the PK in both non-pregnant and pregnant groups and explained well differences in the plasma concentration due to pregnancy. The fetal plasma and amniotic fluid concentrations were also predicted reasonably well at different stages of pregnancy. This work describes the use of a PBPK approach for drug development and demonstrates the ability to predict differences in PK in pregnant subjects and fetal exposure for compounds excreted renally. The prediction for pregnant groups is also improved when the model is calibrated with postpartum or non-pregnant female group if such data are available.

Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations

Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug's physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.

A minimal physiologically based pharmacokinetic model for high-dose methotrexate

Purpose

High-dose methotrexate (HDMTX) is administered for the treatment of a variety of malignant tumors. Wide intra- and inter-individual variabilities characterize the pharmacokinetics of MTX, which is mostly excreted renally. HDMTX dosages are prescribed as a function of body surface area whereas dose adjustments depending on renal function are not well defined. We develop a population pharmacokinetic model with a physiological description of renal excretion as the basis for clinical tools able to suggest model-informed dosages and support therapeutic monitoring.

Methods

This article presents a minimal physiologically based pharmacokinetic (PBPK) model for HDMTX, which specifically accounts for individual characteristics such as body weight, height, gender, age, hematocrit, and serum creatinine to provide individualized predictions. The model supplies a detailed and mechanistic description of capillary and cellular exchanges between plasma, interstitial fluid, and intracellular fluid compartments, and focuses on an individualized description of renal excretion.

Results

The minimal PBPK model is identified and validated with a literature dataset based on Chinese patients suffering from primary central nervous system lymphoma. A comparison with a pharmacokinetic model from the literature suggests that the proposed model provides improved predictions. Remarkably, the model does not present any significant bias in a wide range of degrees of renal function.

Conclusion

Results show that model predictions can capture the wide intra- and inter-individual variability of HDMTX, and highlight the role played by the individual degree of renal function. The proposed model can be the basis for the development of clinical decision-support systems for individualized dosages and therapeutic monitoring.

Metabolic Basis for Nonlinearity in 1,3-Dichloropropene Toxicokinetics and Use in Setting a Kinetically-derived Maximum Inhalation Exposure Concentration in Mice

1,3-Dichloropropene (1,3-D) showed a statistically increased incidence of bronchioloalveolar adenomas in male B6C3F1 mice at 60 ppm air concentration during previous chronic inhalation testing. No tumors were observed in female mice, nor in either sex of F344 rats up to 60 ppm, the highest dose tested. Therefore, to understand if lung tumors observed in high dose male mice are due to saturation of metabolic clearance, the linearity of 1,3-D concentrations in mouse blood was investigated on day 15 of repeated nose-only inhalation exposure to 0, 10, 20, 40, 60, 90, and 120 ppm (6 h/d, 7 d/week). Additional groups were included at 20, 60, and 120 ppm for blood collection at 1.5 and 3 h of exposure and up to 25 or 40 min post-exposure to determine area-under-the-curve. The data provide multiple lines of evidence that systemic exposures to 1,3-D in the mouse become nonlinear at inhalation exposure levels of 30 ppm or above. A reduction in minute volume occurred at the highest exposure concentration. The glutathione (GSH)-dependent metabolism of 1,3-D results in significant depletion of GSH at repeated exposure levels of 30 ppm and above. This loss of GSH results in decreased metabolic clearance of this test material, with a concomitant increase of the 1,3-D isomers in circulating blood at exposure concentrations ≥30 ppm. Shifts in the ratio of cis- and trans-1,3-D also support nonlinear toxicokinetics well below 60 ppm. Based on this data, a kinetically derived maximum dose for 1,3-D in mice for repeated exposures should be at or below 30 ppm. These results support non-relevance of 1,3-D-induced benign pulmonary tumorigenicity in mice for human health risk assessment.

Physiologically based modelling of tranexamic acid pharmacokinetics following intravenous, intramuscular, sub-cutaneous and oral administration in healthy volunteers

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Tranexamic acid (TXA) as a treatment for post-partum haemorrhage (PPH) depends on early intervention and rapid systemic exposure.

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This study uses PBPK modelling to evaluate the pharmacokinetics of TXA given by different routes of administration; intravenous, intramuscular, sub-cutaneous and oral.

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Intramuscular administration of 1000 mg TXA is predicted to achieve >15 mg/L in plasma in <15 min and exceed this level for approximately 3 h post dose.

Background

: Tranexamic acid (TXA) is an antifibrinolytic drug that reduces surgical blood loss and death due to bleeding after trauma and post-partum haemorrhage. Treatment success is dependant on early intervention and rapid systemic exposure to TXA. The requirement for intravenous (IV) administration can in some situations limit accessibility to TXA therapy. Here we employ physiologically based pharmacokinetic modelling (PBPK) to evaluate if adequate TXA exposure maybe achieved when given via different routes of administration.

Methods

: A commercially available PBPK software (GastroPlus®) was used to model published TXA pharmacokinetics. IV, oral and intramuscular (IM) models were developed using healthy volunteer PK data from twelve different single dose regimens (n = 48 participants). The model was verified using separate IV and oral validation datasets (n = 26 participants). Oral, IM and sub-cutaneous (SQ) dose finding simulations were performed.

Results

: Across the different TXA regimens evaluated TXA plasma concentrations varied from 0.1 to 94.0 µg/mL. Estimates of the total plasma clearance of TXA ranged from 0.091 to 0.104 L/h/kg, oral bioavailability from 36 to 67% and Tmax from 2.6 to 3.2 and 0.4 to 1.0 h following oral and intramuscular administration respectively. Variability in the observed TXA PK could be captured through predictable demographic effects on clearance, combined with intestinal permeability and stomach transit time following oral administration and muscle blood flow and muscle/plasma partition coefficients following intra-muscular dosing.

Conclusions

: This study indicates that intramuscular administration is the non-intravenous route of administration with the most potential for achieving targeted TXA exposures. Plasma levels following an IM dose of 1000 mg TXA are predicted to exceed 15 mg/mL in < 15 min and be maintained above this level for approximately 3 h, achieving systemic exposure (AUC0–6) of 99 to 105 µg*hr/mL after a single dose. Well-designed clinical trials to verify these predictions and confirm the utility of intramuscular TXA are recommended.

Physiologically Based Pharmacokinetic Modelling to Describe the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone According to Cytochrome P450 2D6 Phenotypes

BACKGROUND AND OBJECTIVES

The genetic polymorphism of cytochrome P450 (CYP) 2D6 is characterized by an excessive impact on positive and adverse drug reactions to antipsychotics, such as risperidone. Consequently, the pharmacokinetics of the drug and metabolite can be substantially altered and exhibit a high variability between the different phenotypes. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model considering the CYP2D6 genetic polymorphism for risperidone and 9-hydroxyrisperidone (9-OH-RIS) taking CYP3A4 into account. Additionally, risperidone dose adjustments, which would compensate for genetically caused differences in the plasma concentrations of the active moiety (sum of risperidone and 9-OH-RIS) were calculated.

METHODS

Based on available knowledge about risperidone, 9-OH-RIS, and relevant physiological changes according to different CYP2D6 phenotypes, several PBPK models were built. In addition, an initial model was further evaluated based on the plasma concentrations of risperidone and 9-OH-RIS from a single-dose study including 71 genotyped healthy volunteers treated with 1 mg of oral risperidone.

RESULTS

PBPK models were able to accurately describe risperidone exposure after single-dose administration, especially in the concentration range ≥ 1 µg/L, illustrated by a minimal bias and a good precision. About 90.3% of all weighted residuals versus observed plasma concentrations ≥ 1 µg/L were in the ± 30% range. The risperidone/9-OH-RIS ratio increased progressively according to reduced CYP2D6 activity, resulting in a mean ratio of 4.96 for poor metabolizers. Simulations demonstrate that dose adjustment of the drug by - 25% for poor metabolizers and by - 10% for intermediate metabolizers results in a similar exposure to that of extensive metabolizers. Conversely, the risperidone/9-OH-RIS ratio can be used to determine the phenotype of individuals.

CONCLUSION

PBPK modelling can provide a valuable tool to predict the pharmacokinetics of risperidone and 9-OH-RIS in healthy volunteers, according to the different CYP2D6 phenotypes taking CYP3A4 into account. These models are able to ultimately support decision-making regarding dose-optimization strategies, especially for subjects showing lower CYP2D6 activity.

Next generation risk assessment (NGRA): Bridging in vitro points-of-departure to human safety assessment using physiologically-based kinetic (PBK) modelling - A case study of doxorubicin with dose metrics considerations

Using the chemical doxorubicin (DOX), the objective of the present study was to evaluate the impact of dose metrics selection in the new approach method of integrating physiologically-based kinetic (PBK) modelling and relevant human cell-based assays to inform a priori the point of departure for human health risk. We reviewed the literature on the clinical consequences of DOX treatment to identify dosing scenarios with no or mild cardiotoxicity observed. Key concentrations of DOX that induced cardiomyocyte toxicity in vitro were derived from studies of our own and others. A human population-based PBK model of DOX was developed and verified against pharmacokinetic data. The model was then used to predict plasma and extracellular and intracellular heart concentrations of DOX under selected clinical settings and compared with in vitro outcomes, based on several dose metrics: C_max (maximum concentration) or AUC (area under concentration-time curve) in free or total form of DOX. We found when using in vitro assays to predict cardiotoxicity for DOX, AUC is a better indicator. Our study illustrates that when appropriate dose metrics are used, it is possible to combine PBK modelling with in vitro-derived toxicity information to define margins of safety and predict low-risk human exposure levels.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

Semi-mechanistic PK/PD model to assess pulmonary targeting of beclomethasone dipropionate and its active metabolite

PURPOSE

The objective of this study was to describe the pulmonary targeting of beclomethasone dipropionate (BDP) and its active metabolite beclomethasone 17-monopropionate (BMP) in rats using a semi-mechanistic PK/PD model.

METHODS

Rat plasma and tissue concentrations of BDP and BMP, and tissue receptor occupancies of BMP after systemic and pulmonary delivery of BDP and BMP were integrated in a newly developed semi-mechanistic PK/PD model.

RESULTS

After IV administration of BDP, 95.4% of BDP was converted to BMP, while after pulmonary delivery of BDP, 46.6% of deposited BDP was absorbed as BMP. The developed semi-mechanistic PK model described plasma and tissue concentrations of BDP and BMP as well as receptor occupancies sufficiently well. The model incorporated dissolution, metabolic activation, and drug absorption processes to describe the local fate of BDP and BMP after systemic and pulmonary delivery. Dissolution rate constants of BDP and BMP were estimated to be 0.47/h and 2.01/h, respectively, and the permeabilities in central lung were estimated to be 15.0 and 2.9 × 10⁶ cm/s for BDP and BMP, respectively. The EC₅₀ of the binding of BMP to to the receptor was estimated to be 0.0017 ng/ml. Overall, receptor occupancies in the lung were more pronounced than those in the systemic circulation after pulmonary delivery of BDP or BMP. Simulations using the developed semi-mechanistic PK/PD model demonstrated that a slow dissolution rate and low permeability can improve pulmonary targeting.

CONCLUSIONS

A semi-mechanistic model was developed to describe the fate of an inhaled glucocorticoid pro-drug and its active metabolite in lung and the systemic circulation, both after pulmonary and systemic administration , thereby facilitating the understanding of the complex interplay between drug, prodrug and pharmacodynamic properties for quantifying the degree pulmonary targeting.

Food constituent- and herb-drug interactions in oncology: Influence of quantitative modelling on Drug labelling

AIMS

Herbal products, spices and/or fruits are perceived as inherently healthy; for instance, St. John's wort (SJW) is marketed as a natural antidepressant and patients often self-administer it concomitantly with oncology medications. However, food constituents/herbs can interfere with drug pharmacokinetics, with risk of altering pharmacodynamics and efficacy. The objective of this work was to develop a strategy to prioritize herb- or food constituent-drug interactions (FC-DIs) to better assess oncology drug clinical risk.

METHODS

Physiologically based pharmacokinetic (PBPK) models were developed by integrating in vitro parameters with the clinical pharmacokinetics of food constituents in grapefruit juice (bergamottin), turmeric (curcumin) or SJW (hyperforin). Perpetrator files were linked to verified victim PBPK models through appropriate interaction mechanisms (cytochrome P450 3A, breast cancer resistance protein, P-glycoprotein) and applied in prospective PBPK simulations to inform the likelihood and magnitude of changes in exposure to osimertinib, olaparib or acalabrutinib.

RESULTS

Reported FC-DIs with oncology drugs were well recovered, with absolute average fold error values of 1.10 (bergamottin), 1.05 (curcumin) and 1.01 (hyperforin). Prospective simulations with grapefruit juice and turmeric showed clinically minor to insignificant changes in exposure (<1.50-fold) to acalabrutinib, osimertinib and olaparib, but predicted 1.57-fold FC-DI risk between acalabrutinib and curcumin. Moderate DDI risk was expected when acalabrutinib, osimertinib or olaparib were dosed with SJW.

CONCLUSIONS

A model-informed decision tree based on mechanistic understanding of transporter and/or enzyme-mediated FC-DI is proposed based on bergamottin, curcumin and hyperforin FC-DI clinical data. Adopting this quantitative modelling approach should streamline herbal product safety assessments, assist in FC-DI management, and ultimately promote safe clinical use of oncology drugs.

A Novel Experimental and Theoretical Method for Estimating Albumin-Mediated Hepatic Uptake Based on the Albumin Binding Fraction in Plasma and Human PK Prediction Using a Physiologically-Based Pharmacokinetic Approach

Recently, protein-facilitated uptake has been suggested to be an important factor in the precise prediction of the pharmacokinetic (PK) profiles of drugs. In our previous study, a physiologically-based pharmacokinetic (PBPK) approach considering the mechanism of albumin-mediated hepatic uptake was developed for predicting human PK profiles. It was assumed that drugs affected by albumin-mediated hepatic uptake would bind only to albumin, which means that there would be over-estimation of the contribution of protein-facilitated uptake for a drug that could bind to multiple proteins. In this study, we developed a method that can evaluate the albumin binding fraction in plasma considering the affinity for other proteins. Based on the albumin binding fraction, the contribution of albumin-mediated hepatic uptake was theoretically estimated, and then the human PK profiles were predicted by our proposed PBPK approach incorporating this mechanism. As a result, the predicted human PK profiles agreed well with the observed ones, and the absolute average fold error of PK parameters was almost within a 1.5-fold error on average. These findings show the importance of considering protein-facilitated uptake and also suggest that our proposed PBPK approach can be useful in scientific discussions with regulatory authorities.

Understanding Mechanisms of Food Effect and Developing Reliable PBPK Models Using a Middle-out Approach

Over the last 10 years, 40% of approved oral drugs exhibited a significant effect of food on their pharmacokinetics (PK) and currently the only method to characterize the effect of food on drug absorption, which is recognized by the authorities, is to conduct a clinical evaluation. Within the pharmaceutical industry, there is a significant effort to predict the mechanism and clinical relevance of a food effect. Physiologically based pharmacokinetic (PBPK) models combining both drug-specific and physiology-specific data have been used to predict the effect of food on absorption and to reveal the underlying mechanisms. This manuscript provides detailed descriptions of how a middle-out modeling approach, combining bottom-up in vitro-based predictions with limited top-down fitting of key model parameters for clinical data, can be successfully used to predict the magnitude and direction of food effect when it is predicted poorly by a bottom-up approach. For nefazodone, a mechanistic clearance for the gut and liver was added, for furosemide, an absorption window was introduced, and for aprepitant, the biorelevant solubility was refined using multiple solubility measurements. In all cases, these adjustments were supported by literature data and showcased a rational approach to assess the factors limiting absorption and exposure.

Predicting human pharmacokinetics from preclinical data: volume of distribution

This tutorial introduces background and methods to predict the human volume of distribution (Vd) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑ T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd. Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.g., Vc, Vp, and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd is huge, physiological plausibility of all input and output data (e.g., r2-value of the allometric curve) may be reviewed for careful decision making.

Systematic Analysis of the Whole-Body Tissue Distribution and Fatty Acid Compositions of Membrane Lipids in CD1 and NMRI Mice and Wistar Rats

Understanding the tissue distribution of phospholipids and glycerolipids in animal models enables promoting the pharmacokinetic study of drugs and related PK predictions. The measurement of lipid compositions in animal models, usually mice and rats, without a standardized approach hindered the accuracy of PBPK investigation. In this work, high resolution mass spectrometry was applied to profile the tissue distribution of phospholipids and glycerolipids in 12 organs/tissues of mice and rats. Using this method, not only the amounts of phospholipids and glycerolipids in each organ/tissue but also the fatty acid compositions were acquired. In order to explore the interspecies specificity of lipid distribution in different organs/tissues, three animal species including CD1 mice, NMRI mice, and Wister rats were used in this systematic study. Globally, more organ specificity was observed. It was found that the brain is the organ containing the most abundant phosphatidylserine lipids (PSs) in all three animal models, leading to brain tissues having the most concentrated acidic phospholipids. Diverse fatty acid compositions in each lipid class were clearly revealed. Certain tissues/organs also had a specific selection of unique fatty acid compositions, for example, unreferenced FA(18 : 2) in the brain. It turned out that the access of free fatty acids affects the incorporation of acyl chain in phospholipids and glycerolipids. In the analysis, ether lipids were also profiled with the observation of dominant ePEs in brain tissues. However, little interspecies difference was found for fatty acid constituents and tissues distribution of phospholipids and glycerolipids.

Extension of the Mechanistic Tissue Distribution Model of Rodgers and Rowland by Systematic Incorporation of Lysosomal Trapping: Impact on Unbound Partition Coefficient and Volume of Distribution Predictions in the Rat

Physiologically based pharmacokinetic modeling has become a standard tool to predict drug distribution in early stages of drug discovery; however, this does not currently encompass lysosomal trapping. For basic lipophilic compounds, lysosomal sequestration is known to potentially influence intracellular as well as tissue distribution. The aim of our research was to reliably predict the lysosomal drug content and ultimately integrate this mechanism into pharmacokinetic prediction models. First, we further validated our previously presented method to predict the lysosomal drug content (Schmitt et al., 2019) for a larger set of compounds (n = 41) showing a very good predictivity. Using the lysosomal marker lipid bis(monoacylglycero)phosphate, we estimated the lysosomal volume fraction for all major tissues in the rat, ranging from 0.03% for adipose up to 5.3% for spleen. The pH-driven lysosomal trapping was then estimated and fully integrated into the mechanistic distribution model published by Rodgers et al. (2005) Predictions of Kpu improved for all lysosome-rich tissues. For instance, Kpu increased for nicotine 4-fold (spleen) and 2-fold (lung and kidney) and for quinidine 1.8-fold (brain), although for most other drugs the effects were much less (≤7%). Overall, the effect was strongest for basic compounds with a lower lipophilicity, such as nicotine, for which the unbound volume of distribution at steady-state prediction changed from 1.34 to 1.58 l/kg. For more lipophilic (basic) compounds or those that already show strong interactions with acidic phospholipids, the additional contribution of lysosomal trapping was less pronounced. Nevertheless, lysosomal trapping will also affect intracellular distribution of such compounds. SIGNIFICANCE STATEMENT: The estimation of the lysosomal content in all body tissues facilitated the incorporation of lysosomal sequestration into a general physiologically based pharmacokinetic model, leading to improved predictions as well as elucidating its influence on tissue and subcellular distribution in the rat.

Nonlinear Method to Predict the Distribution of Structurally Diverse Compounds between Blood and Tissue

In this work, methods for predicting the distribution of compounds between blood and tissue were investigated using nonlinear regression analysis. For the tissue/blood partition coefficient, 282 compounds and 810 activity data for seven tissues were selected. Twenty-four parameters were studied for each state of the compound. A study set with the most compounds and activity data in similar studies was established, and a model with good prediction ability was obtained. A total of 773 data points were randomly divided into two data sets: the training set contained 623 data points (n = 623, r = 0.822, s = 0.438, F = 142.2, and Q = 0.814) and the test set contained 150 data points (n = 150, r = 0.814, and s = 0.334). Furthermore, individual tissue/blood distribution coefficients were also studied in depth to obtain separate models for predicting the distribution coefficient of a drug between blood and a tissue. By applying these models, not only can the tissue/blood distribution coefficient or single tissue/blood distribution coefficient of the seven tissues and organs of the human body be predicted, but the distribution of the drug molecules in different states (neutral, cation, and anion) in the three tissue components can also be predicted.

Seeking Nonspecific Binding: Assessing the Reliability of Tissue Dilutions for Calculating Fraction Unbound

It has become commonplace (270+ article citations to date) to measure the fraction unbound (FrUn) of drugs in tissue homogenates and diluted plasma and then use a Correction Factor Equation (CFE) to extrapolate to the undiluted state. The CFE is based on assumptions of nonspecific binding with experimental use of very low drug concentrations. There are several possible determinants of apparent nonspecific binding as measured by methods such as equilibrium dialysis: true macromolecule binding and lipid partitioning along with receptor, enzyme, and transporter interactions. Theoretical calculations based on nonlinear protein binding indicate that the CFE will be most reliable to obtain FrUn when added drug concentration is small, binding constants are weak, protein concentrations are relatively high, and tissue dilution is minimal. When lipid partitioning is the sole factor determining apparent tissue binding, the CFE should be perfectly accurate. Use of very low drug concentrations, however, makes it more likely that specific binding to receptors and other targets may occur, and thus FrUn may reflect some binding to such components. Inclusion of trapped blood can clearly cause minor to marked discrepancies from purely tissue binding alone, which can be corrected. Furthermore, assessment of the occurrence of ionization/pH shifts, drug instability, and tissue metabolism may be necessary. Caution is needed in the use and interpretation of results from tissue dilution studies and other assessments of nonspecific binding, particularly for very strongly bound drugs with very small FrUn values and in tissues with metabolic enzymes, receptors, and trapped blood. SIGNIFICANCE STATEMENT: The use of tissue, plasma, and cell preparations to help obtain fraction unbound and tissue-to-plasma partition coefficients in pharmacokinetics has grown commonplace, especially for brain. This report examines theoretical, physiological, and experimental issues that need consideration before trusting such measurements and calculations.

Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem plus fosfomycin in paediatrics

AIMS

The objective of the current study was to evaluate paediatric dosing regimens for meropenem plus fosfomycin that generate sufficient coverage against multidrug-resistant bacteria.

METHODS

The physiologically based pharmacokinetic (PBPK) models of meropenem and fosfomycin were developed from previously published pharmacokinetic studies in five populations: healthy subjects of Japanese origin, and healthy adults, geriatric, paediatric and renally impaired of primarily Caucasian origins. Pharmacodynamic (PD) analyses were carried out by evaluating dosing regimens that achieved a ≥90% joint probability of target attainment (PTA), which was defined as the minimum of the marginal probabilities to achieve the target PD index of each antibiotic. For meropenem, the percentage of time over a 24-hour period wherein the free drug concentration was above the minimum inhibitory concentration (fT > MIC) of at least 40% was its PD target. The fosfomycin PD index was described by fAUC/MIC of at least 40.8.

RESULTS

For coadministration consisting of 20 mg/kg meropenem q8h as a 3-hour infusion and 35 mg/kg fosfomycin q8h also as a 3-hour infusion in a virtual paediatric population between 1 month and 12 years of age with normal renal function and a corresponding body weight between 3 and 50 kg, a joint PTA ≥ 90% is achieved at MICs of 16 and 64 mg/L for meropenem and fosfomycin coadministration, respectively, against Klebsiella pneumoniae and Pseudomonas aeruginosa.

CONCLUSION

The current study identified potentially effective paediatric dosing regimens for meropenem plus fosfomycin coadministration against multidrug-resistant bacteria.

Pharmacokinetics and Tolerability of the Novel Non-immunosuppressive Fingolimod Derivative, OSU-2S, in Dogs and Comparisons with Data in Mice and Rats

In this study, we characterized the pharmacokinetics of OSU-2S, a fingolimod-derived, non-immunosuppressive phosphatase activator, in mice, rats, and dogs, as well as tolerability and food effects in dogs. Across all species tested, plasma protein binding for OSU-2S was > 99.5%, and metabolic stability and hepatic intrinsic clearance were in the moderate range. OSU-2S did not significantly modulate CYP enzyme activity up until 50 μM, and Caco-2 data suggested low permeability with active efflux at 2 μM. Apparent oral bioavailability in mice was 16% and 69% at 10 and 50 mg/kg, respectively. In rats, bioavailability was 24%, 35%, and 28% at 10, 30, and 100 mg/kg, respectively, while brain/plasma ratio was 36 at 6-h post-dose at 30 mg/kg. In dogs, OSU-2S was well tolerated with oral capsule bioavailability of 27.5%. Plasma OSU-2S exposures increased proportionally over a 2.5-20 mg/kg dose range. After 4 weeks of 3 times weekly, oral administration (20 mg/kg), plasma AUC_last (26.1 μM*h), and C_max (0.899 μM) were nearly 2-fold greater than those after 1 week of dosing, and no food effects were observed. The elimination half-life (29.7 h), clearance (22.9 mL/min/kg), and plasma concentrations of repeated oral doses support a 3-times weekly dosing schedule in dogs. No significant CBC, serum biochemical, or histopathological changes were observed. OSU-2S has favorable oral PK properties similar to fingolimod in rodents and dogs and is well tolerated in healthy animals. This work supports establishing trials of OSU-2S efficacy in dogs with spontaneous tumors to guide its clinical development as a cancer therapeutic for human patients.

Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients

The development of predictive engines based on pharmacokinetic-physiological mathematical models for personalised dosage recommendations is an immature field. Nevertheless, these models are extensively applied during the design of new drugs. This study presents new advances in this subject, through a stable population of patients who underwent kidney transplantation and were prescribed tacrolimus. We developed 2 new population pharmacokinetic models based on a compartmental approach, with one following the physiologically based pharmacokinetic approach and both including circadian modulation of absorption and clearance variables. One of the major findings was an improved predictive capability for both models thanks to the consideration of circadian rhythms, both in estimating the population and in Bayesian individual customisation. This outcome confirms a plausible mechanism suggested by other authors to explain circadian patterns of tacrolimus concentrations. We also discovered significant intrapatient variability in tacrolimus levels a week after the conversion from a fast-release (Prograf) to a sustained-release formulation (Advagraf) using adaptive optimisation techniques, despite high adherence and controlled conditions. We calculated the intrapatient variability through parametric intrapatient variations, which provides a method for quantifying the mechanisms involved. We present a first application for the analysis of bioavailability changes in formulation conversion. The 2 pharmacokinetic models have demonstrated their capability as predictive engines for personalised dosage recommendations, although the physiologically based pharmacokinetic model showed better predictive behaviour.