Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology

Which Unbound Fraction Should We Use in the Well-Stirred Model for More Accurately Predicting Hepatic Clearance of Drugs for Humans?

As the hepatic clearance (CLH) of drugs becomes independent of hepatic blood flow rate, CLH depends primarily on intrinsic clearance (CLint). Incubations of microsomes or hepatocytes are commonly used to generate CLint. Therefore, CLint estimates corrected for binding to the in vitro systems and scaled to whole liver, are applied to a well-stirred liver model to obtain CLH predictions for drugs. In other words, CLint is extrapolated with the ratio of unbound fraction between the plasma (fup) and incubation medium (fuinc). However, this binding correction resulted to an important underprediction bias of CLH. Therefore, the approach considering fup and fuinc needs to be better understood for more precisely scaling CLint. The objective of this study was to explain the underprediction bias of CLH based on physicochemical properties of drugs. Analysis-ready datasets have been collected so that evaluation of the data generates a mechanistic understanding of the impact of unbound fraction on the prediction of CLH of human for 128 drugs. Experimental values of fuinc for liver are quantifying solely the binding to lipids in microsomes or hepatocytes in the absence of plasma proteins in the incubations. However, the experimental values of fup for plasma can estimate the binding to lipids and plasma proteins. Therefore, drugs binding primarily to lipids in the liver and plasma showed a less pronounced underprediction bias of CLH by using the ratios of fup/fuinc in the well-stirred model. In contrast, drugs binding primarily to the plasma proteins in the liver and plasma showed a larger underprediction bias of CLH. Furthermore, for the ionized drugs, values of fup and fuinc are not covering the pH gradient effect between plasma and hepatocytes, which also impacted the CLH predictions. For these reasons, a mechanistic approach was proposed to replace the conventional fup value with an adjusted fup (fu-adjusted) that accounts for differences in proteins/lipids binding and pH gradient effect between the liver and plasma. Hence, replacing fup with fu-adjusted did provide a universal and mechanisms-based approach removing the underprediction bias for all datasets of drugs. Overall, this study indicates which drug properties generated the largest underprediction bias of CLH and suggests that the Poulin et al. method referring to fu-adjusted could be the most proper unbound fraction to reduce that bias with the well-stirred model.

Application of Kirchhoff's Laws to pharmacologic and pharmacokinetic analyses

Recently, we introduced a straightforward approach to derive clearance and rate constant equations, without relying on differential equations, utilizing Kirchhoff's Laws, a well known physics methodology used to describe rate-defining processes either in series or parallel. Manuscripts from our laboratory have re-examined published experimental data, demonstrating that the Kirchhoff's Laws methodology can explain data previously considered anomalous, such as the following: (1) all experimental perfused liver clearance data conforming to the equation once thought to represent the unphysiological well stirred model, (2) instances where linear pharmacokinetic systemic bioavailability determinations exceed unity, (3) renal clearance being influenced by drug input processes, (4) statistically significant differences in bioavailability measures between urinary excretion and systemic concentration measurements, and (5) how the long-accepted steady-state clearance approach used in pharmacokinetics for the past half-century leads to unrealistic conclusions about the relationship between liver-to-blood Kpuu and hepatic availability FH. These findings demonstrate the potential for errors in pharmacokinetic evaluations that rely on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, both those that align with present pharmacokinetic theory, and those that do not. Although 3 publications have attempted to rebut our position, they fail to address unexplained experimental data, and we detail here why these analyses are invalid. Our discoveries are ongoing. Additionally, we briefly discuss the application of Kirchoff's Laws to saturable nonlinear kinetics, explaining increased pharmacodynamic response for extended vs immediate release dosage forms, as well as the advantages of successfully formulating high hepatic extraction drugs. SIGNIFICANCE STATEMENT: The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes anomalous published pharmacokinetic data that have previously been unexplained. We review 9 experimental outcomes in humans that are newly explained using the Kirchhoff's Laws approach, including the extension to deriving nonlinear saturable clearance relationships.

Characterizing the Hepatic Metabolic Pathway of Ketone Ester and Subsequent Metabolites Using Human and Rat Liver Fractions

Although exogenous ketogenic dietary supplements continue to grow in popularity, their pharmacokinetic properties have not been adequately studied, thus hindering their optimal use and benefits. Here, the metabolic characteristics of one such supplement (Veech ketone mono-ester ((R)-3-hydroxybutyl(R)-3-hydroxybutyrate) (KE)) were studied along with its metabolite- (R)-1,3-butanediol ((R)-1,3-BD), both of which are precursors and undergo metabolic conversion to (R)-beta-hydroxybutyrate (BHB). The metabolism of aldol (an aldehyde intermediate between the conversion of (R)-1,3-BD to (R)-BHB was also evaluated, as it is frequently not considered in any scientific discussion. The metabolic parameters were calculated using pooled human (mixed gender) and pooled rat (male and female) liver fractions. These were later used to estimate the hepatic extraction ratio and the hepatic clearance of these molecules. KE showed rapid and non-saturable clearance in human and rat liver fractions, even at concentrations as high as 15,000 μM. In the case of (R)-1,3-BD, there was saturable metabolism in rats and humans with Km and Vmax values of 8,000 μM and 27.1 nmol/min/mg of protein (humans), 19,300 μM and 113.5 nmol/min/mg of protein (male rats), and 11,910 μM and 75.8 nmol/min/mg of protein (female rats). The metabolism of aldol showed rapid and non-saturable hepatic clearance in human liver fractions.

Toward improved clearance predictions and distribution profiles employing the isolated perfused rat liver model: Experimental optimization

To reduce the drug attrition due to failure in clinical trials, an early accurate hepatic clearance (CLH) prediction for small molecule drugs is critical. However, the routinely used in vitro to in vivo extrapolation (IVIVE) methods to predict human CLH have led to significant underpredictions for many drug discovery programs. The ex vivo isolated perfused rat liver (IPRL) can be employed as a model to gain a mechanistic understanding of the root cause(s) of the frequent suboptimal performance of IVIVE methods. Although the IPRL perfusate composition is a critical factor to obtain reliable IPRL data to bridge the in vitro to in vivo clearance gap, no studies have experimentally explored the effect of different perfusate compositions on drug disposition profiles. Initially, in this study, acetaminophen and atazanavir, showing low or high protein binding, respectively, were tested in the IPRL system with 2 reperfusion media. One medium contained 3% bovine serum albumin, whereas the other was 10% rat blood to determine optimal experimental conditions, analogous to best practices in literature. This research provides new insights into the effect of IPRL perfusate composition on accurate CLH predictions and liver disposition profiles. Specifically, this study demonstrates for the first time that 3% bovine serum albumin and 0.01% bovine α-1-acid glycoprotein are essential in an IPRL experiment to mimic biorelevant conditions and to achieve in vivo-relevant unbound clearance values, alongside 10% blood to maintain liver viability and functionality. The unique mechanistic insights derived from these robust IPRL data will aid in the development of improved IVIVE methods for CLH. SIGNIFICANCE STATEMENT: This study explored the effect of perfusate composition on compound disposition profiles in an isolated perfused rat liver system. Reliable disposition profiles and improved clearance predictions were obtained, employing biorelevant plasma protein concentrations alongside 10% blood to obtain in vivo-relevant unbound clearance values and to preserve liver viability and functionality. Robust conditions are critical in isolated perfused rat liver experiments to gain mechanistic insights into the in vitro to in vivo clearance gap, enabling optimization of in vitro to in vivo extrapolation methods.

Hepatic and extra-hepatic metabolism of propylene glycol ethers in the context of central nervous system toxicity

Propylene glycol ethers (PGEs) are mixtures of an α-isomer and a β-isomer (β-PGE) that is oxidized via alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) to potentially neurotoxic alkoxy propionic acids (β-metabolites). While the liver is the primary organ for ADH- and ALDH-mediated metabolism, the contribution to the metabolism of β-PGEs by the blood-brain barrier (BBB) and the brain remains unknown. Here, we aimed to assess the neurotoxic potential of PGEs after systemic exposure by (1) comparing 3D HepaRG and human liver subcellular fraction (S9) for the in vitro determination of the kinetics of hepatic metabolism for β-PGEs, (2) evaluating the BBB-permeability of PGEs and β-metabolites, (3) determining the presence of ADH1 and ALDH2 and the extent of metabolization of β-PGEs in the BBB and brain. The results show that 3D HepaRG and S9 served as competent systems to estimate the enzymatic kinetic (clearance) for β-metabolite formation. We observed that PGEs and the β-metabolites could cross the BBB, based on their permeance across a cellular barrier consisting of the hCMEC/D3 cell line. Metabolic enzymes were not exclusive to the liver, as expression of ADH1 and ALDH2 was demonstrated using RT-qPCR, Western blot, and immunostainings in the BBB in vitro models and in BrainSpheres. Furthermore, LC-MS/MS quantification of the β-metabolites in all in vitro models revealed that 3D HepaRG had a similar metabolic capacity to primary human hepatocytes and that the amount of β-metabolite formed per protein in the BBB was approximately 10-30 % of that in the liver. We also demonstrated active metabolism in the BrainSpheres. In conclusion, the hepatic in vitro models provided data that will help to refine toxicokinetic models and predict internal exposures, thereby supporting the risk assessment of PGEs. In addition, the high permeance of the PGEs and the β-metabolites across the BBB increases the plausibility of neurotoxicity upon systemic exposure. This is further supported by the presence of active ADH1 and ALDH2 enzymes in the BBB in vitro systems and in BrainSpheres, suggesting metabolite formation in the central nervous system. Hence, we suggest that BBB-permeance and extra-hepatic metabolism of the β-PGEs may contribute to the neurotoxicity of PGEs.

Laboratory and physiological aspects of substitute metazoan models for in vivo pharmacotoxicological analysis

New methods are essential to characterize the performance of substitute procedures for detecting therapeutic action(s) of a chemical or key signal of toxicological events. Herein, it was discussed the applications and advantages of using arthropods, worms, and fishes in pharmacological and/or toxicology assessments. First of all, the illusion of similarity covers many differences between humans and mice, remarkably about liver injury and metabolism of xenobiotics. Using invertebrates, especially earthworms (Eisenia fetida), brine shrimps (Artemia salina, Daphnia magna), and insects (Drosophila melanogaster) and vertebrates as small fishes (Oryzias latipes, Pimephales promelas, Danio rerio) has countless advantages, including fewer ethical conflicts, short life cycle, high reproduction rate, simpler to handle, and less complex anatomy. They can be used to find contaminants in organic matters and water and are easier genetically engineered with orthologous-mutated genes to explore specific proteins involved in proliferative and hormonal disturbances, chemotherapy multidrug resistance, and carcinogenicity. As multicellular embryos, larvae, and mature organisms, they can be tested in bigger-sized replication platforms with 24-, 96-, or 384-multiwell plates as cheaper and faster ways to select hit compounds from drug-like libraries to predict acute, subacute or chronic toxicity, pharmacokinetics, and efficacy parameters of pharmaceutical, cosmetic, and personal care products. Meanwhile, sublethal exposures are designed to identify changes in reproduction, body weight, DNA damages, oxidation, and immune defense responses in earthworms and zebrafishes, and swimming behaviors in A. salina and D. rerio. Behavioral parameters also give specificities on sublethal effects that would not be detected in zebrafishes by OECD protocols.

Clearance prediction with three novel plated human hepatocyte models compared to conventional suspension assays: Assessment with 50 compounds and multiple donors

Incubation of drugs with suspension hepatocytes (SH) to determine intrinsic clearance is common in drug discovery. However, the limited duration of SH assays hampers clearance assessment of metabolically stable compounds. In turn, this has driven the development of alternative in vitro approaches to generate intrinsic clearance estimates. Culturing primary hepatocytes with supportive cells as co/tricultures has been shown to maintain morphology, viability, and drug-metabolizing enzyme function for weeks, permitting extended incubations. Another assay from our laboratory is the preloaded hepatocyte assay (preload assay), which involves preloading plated monoculture hepatocytes with compounds and measuring the loss from cells in drug-free media. This approach increases analytical sensitivity compared to assays that measure bulk compound loss in the cells plus medium. We conducted a systematic evaluation of the ability of coculture, triculture, and preload assay models to predict human in vivo clearance for 50 predominantly low-clearance compounds with a range of physicochemical properties, including equal numbers of compounds following or violating Lipinski's rule of 5, across 3 hepatocyte donors. The results were compared with SH. Co/tricultures exhibited lower inter-donor differences compared to the preload and SH assays, likely due to the blunting of environmental cues after 5 days in culture prior to compound introduction. All 3 plated models significantly reduced the number of compounds with insufficient turnover to calculate CLint,u compared to SH (SH: 40%; preload: 18%; cocultures: 8%; tricultures: 4%), exhibited strong interexperimental reproducibility and robust predictions of blood clearance (preload: 26/41; cocultures: 31/46; tricultures: 30/48 within 3-fold of observed). SIGNIFICANCE STATEMENT: Preloading plated hepatocytes with compounds and measuring the loss in drug-free media, or culturing hepatocytes with supportive cells as co/tricultures, facilitate quantitation of metabolically stable compounds in substrate depletion assays compared to suspension hepatocytes (SH). All 4 models exhibit robust estimates of CLint,u and CLb, but plated models allowed assessment of several compounds found to be too stable to evaluate in SH.

Rat PermQ: A permeability- and perfusion-based physiologically based pharmacokinetic model for improved prediction of drug concentration-time profiles in rat

A new rat permeability- and perfusion-limited physiologically based pharmacokinetic model, "rat PermQ," was developed with the goal of improving concentration-time (C-t) predictions of drugs. Similar to the previously published human PermQ, drugs can reversibly distribute between capillaries and interstitial fluid by fenestra or discontinuities in capillaries or by transcellular diffusion through endothelial cells. Drugs also can be partitioned into intracellular phospholipids and neutral lipids in the cytosol. For acidic drugs, active uptake transport and an empirical protein binding correction factor were considered. A shallow distribution compartment was added for basic drugs to account for early distribution. In vitro and in vivo experimental inputs were collected in-house or from the literature. C-t profiles were predicted for 7 drugs (2 acidic, 2 neutral, and 3 basic) with 3 models: Rodgers and Rowland (RR), a perfusion-limited membrane-based model, and rat PermQ. Results indicate the importance of consistent, species-specific in vitro inputs. In general, rat PermQ predicted C-t profiles at least as well as the other models. For acidic drugs, rat PermQ predictions improved with incorporation of uptake transport and the empirical protein binding factor. For neutral drugs, RR predicted digoxin C-t profiles better compared with rat PermQ, while midazolam predictions with rat PermQ were improved with the use of in-house in vitro experimental inputs. Rat PermQ predicted C-t profiles for all 3 bases better than RR and perfusion-limited membrane-based model, and addition of a shallow compartment greatly improved the predictions. Rat and human PermQ allowed several hypotheses to be simulated for putative uptake mechanisms for atenolol and glyburide. SIGNIFICANCE STATEMENT: A new physiologically based pharmacokinetic framework, rat PermQ, was developed. This model predicted plasma concentration-time profiles of the tested drugs as well as or better than published physiologically based pharmacokinetic models. PermQ allowed several hypotheses to be simulated for uptake mechanisms in rats and humans. The work highlights the importance of accurate in vitro parameters such as drug plasma protein binding and blood-to-plasma ratio. The model can aid in testing new hypotheses to explain poorly understood observations in distribution and elimination of drugs.

Structure-Based Discovery and Development of Highly Potent Dihydroorotate Dehydrogenase Inhibitors for Malaria Chemoprevention

Malaria remains a serious global health challenge, yet treatment and control programs are threatened by drug resistance. Dihydroorotate dehydrogenase (DHODH) was clinically validated as a target for treatment and prevention of malaria through human studies with DSM265, but currently no drugs against this target are in clinical use. We used structure-based computational tools including free energy perturbation (FEP+) to discover highly ligand efficient, potent, and selective pyrazole-based Plasmodium DHODH inhibitors through a scaffold hop from a pyrrole-based series. Optimized pyrazole-based compounds were identified with low nM-to-pM Plasmodium falciparum cell potency and oral activity in a humanized SCID mouse malaria infection model. The lead compound DSM1465 is more potent and has improved absorption, distribution, metabolism and excretion/pharmacokinetic (ADME/PK) properties compared to DSM265 that support the potential for once-monthly chemoprevention at a low dose. This compound meets the objective of identifying compounds with potential to be used for monthly chemoprevention in Africa to support malaria elimination efforts.

Establishing a physiologically based pharmacokinetic framework for aldehyde oxidase and dual aldehyde oxidase-CYP substrates

Aldehyde oxidase (AO) contributes to the clearance of many approved and investigational small molecule drugs, which are often dual substrates of AO and drug-metabolizing enzymes such as cytochrome P450s (CYPs). As such, the lack of established framework for quantitative translation of the clinical pharmacologic correlates of AO-mediated clearance represents an unmet need. This study aimed to evaluate the utility of physiologically based pharmacokinetic (PBPK) modeling in the development of AO and dual AO-CYP substrates. PBPK models were developed for capmatinib, idelalisib, lenvatinib, zaleplon, ziprasidone, and zoniporide, incorporating in vitro functional data from human liver subcellular fractions and human hepatocytes. Prediction of metabolic elimination with/without the additional empirical scaling factors (ESFs) was assessed. Clinical pharmacokinetics, human mass balance, and drug-drug interaction (DDI) studies with CYP3A4 modulators, where available, were used to refine/verify the models. Due to the lack of clinically significant AO-DDIs with known AO inhibitors, the fraction metabolized by AO (fmAO) was verified indirectly. Clearance predictions were improved by using ESFs (GMFE ≤1.4-fold versus up to fivefold with physiologically-based scaling only). Observed fmi from mass balance studies were crucial for model verification/refinement, as illustrated by capmatinib, where the fmAO (40%) was otherwise underpredicted up to fourfold. Subsequently, independent DDI studies with ketoconazole, itraconazole, rifampicin, and carbamazepine verified the fmCYP3A4, with predicted ratios of the area under the concentration-time curve (AUCR) within 1.5-fold of the observations. In conclusion, this study provides a novel PBPK-based framework for predicting AO-mediated pharmacokinetics and quantitative assessment of clinical DDI risks for dual AO-CYP substrates within a totality-of-evidence approach.

Real-world application of physiologically based pharmacokinetic models in drug discovery

The utility of physiologically based pharmacokinetic (PBPK) models in support of drug development has been well documented. During the discovery stage, PBPK modeling has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection, and early formulation assessment. Previous review articles have proposed model-building and application strategies for PBPK-based first-in-human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees based on literature reviews to guide the application of PBPK models in the discovery setting. The goal of this minireview is to provide additional guidance on the real-world application of PBPK models in support of the discovery stage of drug development, to assist in decision making. We have illustrated our recommended approach through description of case examples where PBPK models have been successfully applied to aid in human pharmacokinetic projection, candidate selection, and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including preverification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. SIGNIFICANCE STATEMENT: Through description of 3 case studies, this minireview highlights the fundamental principles of physiologically based pharmacokinetic application during drug discovery. These include preverification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.

Evaluating in vivo data for drug metabolism and transport: lessons from Kirchhoff's Laws

Mechanistic models of hepatic clearance have been evaluated for more than 50 years, with the first author of this mini-review serving as a co-author of the first paper proposing such a model. However, published quality experimental data are only consistent with the first of these models, designated as the well-stirred model, despite the universal recognition that this model is physiologically unrepresentative of what occurs with respect to liver metabolism and transport. Within the last 3 years, our laboratory has recognized that it is possible to derive clearance equations employing the concepts of Kirchhoff's Laws from physics, independent of the differential equation approach that has been utilized to derive reaction rates in chemistry. Here we review our published studies showing that the equation previously believed to be the well-stirred model, when hepatic basolateral transporters are not clinically relevant, is in fact the general equation for hepatic clearance when only systemic drug concentrations are measured, explaining why all experimental data fit this equation. To demonstrate that the equations deriving the mechanistic models of hepatic elimination for the past 50 years are not valid, we show that when calculating Kpuu, the ratio of unbound drug concentration in the liver to the unbound concentration of drug in the systemic circulation, for the well-stirred, parallel tube and dispersion models, Kpuu surprisingly can never exceed 1 and is a function of FH, the hepatic bioavailability following oral dosing. We believe that knowledgeable drug metabolism scientist and clinical pharmacologist will agree that this outcome is nonsensical.

Proof of Concept of an All-in-One System for Measuring Hepatic Influx, Egress, and Metabolic Clearance Based on the Extended Clearance Concept

Hepatic clearance (CLH ) prediction is a critical parameter to estimate human dose. However, CLH underpredictions are common, especially for slowly metabolized drugs, and may be attributable to drug properties that pose challenges for conventional in vitro absorption, distribution, metabolism, and elimination (ADME) assays, resulting in nonvalid data, which prevents in vitro to in vivo extrapolation and CLH predictions. Other processes, including hepatocyte and biliary distribution via transporters, can also play significant roles in CLH Recent advances in understanding the interplay of metabolism and drug transport for clearance processes have aided in developing the extended clearance model. In this study, we demonstrate proof of concept of a novel two-step assay enabling the measurement of multiple kinetic parameters from a single experiment in plated human primary hepatocytes with and without transporter and cytochrome P450 inhibitors-the hepatocyte uptake and loss assay (HUpLA). HUpLA accurately predicted the CLH of eight of the nine drugs (within twofold of the observed CLH ). Distribution clearances were within threefold of observed literature values in standard uptake and efflux assays. In comparison, the conventional suspension hepatocyte stability assay poorly predicted the CLH The CLH of only two drugs was predicted within twofold of the observed CLH Therefore, HUpLA is advantageous by enabling the measurement of enzymatic and transport processes concurrently within the same system, alleviating the need for applying scaling factors independently. The use of primary human hepatocytes enables physiologically relevant exploration of transporter-enzyme interplay. Most importantly, HUpLA shows promise as a sensitive measure for low-turnover drugs. Further evaluation across different drug characteristics is needed to demonstrate method robustness. SIGNIFICANCE STATEMENT: The hepatocyte uptake and loss assay involves measuring four commonly derived in vitro hepatic clearance endpoints. Since endpoints are generated within a single test system, it blunts experimental error originating from assays otherwise conducted independently. A key advantage is the concept of removing drug-containing media following intracellular drug loading, enabling the measurement of drug reappearance rate in media as well as the measurement of loss of total drug in the test system unencumbered by background quantities of drug in media otherwise present in a conventional assay.

Predicting Clearance with Simple and Permeability-Limited Physiologically Based Pharmacokinetic Frameworks: Comparison of Well-Stirred, Dispersion, and Parallel-Tube Liver Models

One-compartment (1C) and permeability-limited models were used to evaluate the ability of microsomal and hepatocyte intrinsic clearances to predict hepatic clearance. Well-stirred (WSM), parallel-tube (PTM), and dispersion (DM) models were evaluated within the liver as well as within whole-body physiologically based pharmacokinetic frameworks. It was shown that a linear combination of well-stirred and parallel-tube average liver blood concentrations accurately approximates dispersion model blood concentrations. Using a flow/permeability-limited model, a large systematic error was observed for acids and no systematic error for bases. A scaling factor that reduced interstitial fluid (ISF) plasma protein binding could greatly decrease the absolute average fold error (AAFE) for acids. Using a 1C model, a scalar to reduce plasma protein binding decreased the microsomal clearance AAFE for both acids and bases. With a permeability-limited model, only acids required this scalar. The mechanism of the apparent increased cytosolic concentrations for acids remains unknown. We also show that for hepatocyte intrinsic clearance in vitro-in vivo correlations (IVIVCs), a 1C model is mechanistically appropriate since hepatocyte clearance should represent the net clearance from ISF to elimination. A relationship was derived that uses microsomal and hepatocyte intrinsic clearance to solve for an active hepatic uptake clearance, but the results were inconclusive. Finally, the PTM model generally performed better than the WSM or DM models, with no clear advantage between microsomes and hepatocytes. SIGNIFICANCE STATEMENT: Prediction of drug clearance from microsomes or hepatocytes remains challenging. Various liver models (e.g., well-stirred, parallel-tube, and dispersion) have been mathematically incorporated into liver as well as whole-body physiologically based pharmacokinetic frameworks. Although the resulting models allow incorporation of pH partitioning, permeability, and active uptake for prediction of drug clearance, including these processes did not improve clearance predictions for both microsomes and hepatocytes.

How predictive are isolated perfused liver data of in vivo hepatic clearance? A meta-analysis of isolated perfused rat liver data

Isolated perfused rat liver (IPRL) experiments have been used to answer clearance-related questions, including evaluating the impact of pathological and physiological processes on hepatic clearance (CLH). However, to date, IPRL data has not been evaluated for in vivo CLH prediction accuracy.In addition to a detailed overview of available IPRL literature, we present an in-depth analysis of the performance of IPRL in CLH prediction.While the entire dataset poorly predicted CLH (GAFE = 3.2; 64% within 3-fold), IPRL conducted under optimal experimental conditions, such as in the presence of plasma proteins and with a perfusion rate within 2-fold of physiological liver blood flow and corrected for unbound fraction in the presence of red blood cells, can accurately predict rat CLH (GAFE = 2.0; 78% within 3-fold). Careful consideration of experimental conditions is needed to allow proper data analysis.Further, isolated perfused liver experiments in other species, including human livers, may allow us to address the current in vitro-in vivo disconnects of hepatic metabolic clearance and improve our methodology for CLH predictions.

Commentary: Pharmacokinetic Theory Must Consider Published Experimental Data

Recently, we have proposed simple methodology to derive clearance and rate constant equations, independent of differential equations, based on Kirchhoff's Laws, a common methodology from physics used to describe rate-defining processes either in series or parallel. Our approach has been challenged in three recent publications, two published in this journal, but notably what is lacking is that none evaluate experimental pharmacokinetic data. As reviewed here, manuscripts from our laboratory have evaluated published experimental data, demonstrating that the Kirchhoff's Laws approach explains (1) why all of the experimental perfused liver clearance data appear to fit the equation that was previously believed to be the well-stirred model, (2) why linear pharmacokinetic systemic bioavailability determinations can be greater than 1, (3) why renal clearance can be a function of drug input processes, and (4) why statistically different bioavailability measures may be found for urinary excretion versus systemic concentration measurements. Our most recent paper demonstrates (5) how the universally accepted steady-state clearance approach used by the field for the past 50 years leads to unrealistic outcomes concerning the relationship between liver-to-blood Kpuu and hepatic availability FH , highlighting the potential for errors in pharmacokinetic evaluations based on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, those that were previously adequately explained with present pharmacokinetic theory, and those that were not. The publications that have attempted to rebut our position do not address unexplained experimental data, and we show here why their analyses are not valid. SIGNIFICANCE STATEMENT: The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes published pharmacokinetic data that has previously been unexplained. Three recent publications claim to refute our proposed methodology; these publications only make theoretical arguments, do not evaluate experimental data, and never demonstrate that the Kirchhoff methodology provides incorrect interpretations of experimental pharmacokinetic data, including statistically significant data not explained by present pharmacokinetic theory. We demonstrate why these analyses are invalid.

First-in-Human Predictions of Hepatic Clearance for Drugs With the Well-Stirred Model: Comparative Assessment Between Models of Fraction Unbound Based Either on the Free Drug Hypothesis, Albumin-Facilitated Hepatic Uptake or Dynamic Binding Kinetics

The well-stirred model (WSM) is commonly used to predict the hepatic clearance in vivo (CLH) of drugs. The necessary intrinsic clearance of the unbound drug (CLint-in vitro-unbound) is generated in the in vitro assays in the presence of microsomes or hepatocytes but in the absence of plasma proteins. The value of CLint-in vitro-unbound can be extrapolated with the fraction unbound determined in vitro in plasma (fup) only if the fraction unbound in vivo in liver is the same. However, this approach resulted to a systematic underprediction bias of CLH. With the goal of reducing this bias, two new models of fraction unbound were published in this journal. These models estimate the binding kinetics of the rates of association and dissociation of the drug-protein complex and propose that more dissociation in the liver compared to plasma will increase the fraction unbound available for the metabolism. Consequently, these two models generated higher values of fraction unbound, implying a lower underprediction bias of CLH with the WSM. The first model was developed by Poulin et al. and is referring to the value of fup that is adjusted (fu-adjusted) to quantify the effect of a full dissociation of the drug-protein complex at the hepatocyte membrane in accordance with the theory of the albumin-facilitated hepatic uptake. A second model was developed by Yan et al. who presented a dynamic fraction unbound (fu-dynamic) measuring the real dissociation kinetics of the drug-protein complex with a new in vitro assay in the presence and absence of a recombinant liver enzyme in plasma. Therefore, the objective of this study was to make the first comparative assessment between these two models. The results indicate that, in general, the WSM combined with the values of fu-adjusted was the most accurate approach for predicting CLH. The WSM combined with the values of fu-dynamic has underperformed particularly with the acidic and neutral drugs binding to the albumin and presenting a low metabolic turnover in vitro. Therefore, the new in vitro assay for fu-dynamic gave an underprediction bias of CLH for these drug properties. However, the values of fu-adjusted are significantly higher than those values of fu-dynamic, and, this resulted to no underprediction bias, which is reinforcing the theory of the ALB-facilitated hepatic uptake. For the other neutral and acidic drugs, the models of fu-dynamic and fu-adjusted are in closer agreement. Finally, for the basic drugs, the models of fu-adjusted and fu-dynamic as well as a third model only considering a pH gradient effect on fup are almost accurately equivalent.

After 50 Years of Hepatic Clearance Models, Where Should We Go from Here? Improvements and Implications for Physiologically Based Pharmacokinetic Modeling

There is overwhelming preference for application of the unphysiologic, well-stirred model (WSM) over the parallel tube model (PTM) and dispersion model (DM) to predict hepatic drug clearance, CLH , despite that liver blood flow is dispersive and closer to the DM in nature. The reasoning is the ease in computation relating the hepatic intrinsic clearance ( CLint ), hepatic blood flow ( QH ), unbound fraction in blood ( fub ) and the transmembrane clearances ( CLin and CLef ) to CLH for the WSM. However, the WSM, being the least efficient liver model, predicts a lower EH that is associated with the in vitro CLint ( Vmax / Km ), therefore requiring scale-up to predict CLH in vivo. By contrast, the miniPTM, a three-subcompartment tank-in-series model of uniform enzymes, closely mimics the DM and yielded similar patterns for CLint versus EH , substrate concentration [S] , and KL / B , the tissue to outflow blood concentration ratio. We placed these liver models nested within physiologically based pharmacokinetic models to describe the kinetics of the flow-limited, phenolic substrate, harmol, using the WSM (single compartment) and the miniPTM and zonal liver models (ZLMs) of evenly and unevenly distributed glucuronidation and sulfation activities, respectively, to predict CLH For the same, given CLint ( Vmax and Km ), the WSM again furnished the lowest extraction ratio ( EH,WSM = 0.5) compared with the miniPTM and ZLM (>0.68). Values of EH,WSM were elevated to those for EH, PTM and EH, ZLM when the Vmax s for sulfation and glucuronidation were raised 5.7- to 1.15-fold. The miniPTM is easily manageable mathematically and should be the new normal for liver/physiologic modeling. SIGNIFICANCE STATEMENT: Selection of the proper liver clearance model impacts strongly on CLH predictions. The authors recommend use of the tank-in-series miniPTM (3 compartments mini-parallel tube model), which displays similar properties as the dispersion model (DM) in relating CLint and [ S ] to CLH as a stand-in for the DM, which best describes the liver microcirculation. The miniPTM is readily modified to accommodate enzyme and transporter zonation.

Discovery of the Clinical Candidate IDOR-1117-2520: A Potent and Selective Antagonist of CCR6 for Autoimmune Diseases

Migration of immune cells to sites of inflammation is a critical step in the body's response to infections but also during autoimmune flares. Chemokine receptors, members of the GPCR receptors, are instrumental in directing specific cell types to their target organs. Herein, we describe a highly potent small molecule antagonist of the chemokine receptor CCR6, which came out of fine-tuned structural elaborations from a proprietary HTS hit. Three main issues in the parent chemical series-cytotoxicity, phototoxicity, and hERG, were successfully solved. Biological characterization demonstrated that compound 45 (IDOR-1117-2520) is a selective and insurmountable antagonist of CCR6. In vivo proof-of-mechanism studies in a mouse lung inflammation model using a representative compound from the chemical class of 45 confirmed that the targeted CCR6+ cells were efficiently inhibited from migrating into the bronchoalveoli. Finally, ADMET and physicochemical properties were well balanced and the preclinical package warranted progress in the clinic.

Uridine 5'-Diphospho-glucuronosyltransferase 1A3 (UGT1A3) Prediction of Hepatic Clearance of Organic Anion Transporting Polypeptide 1B3 (OATP1B3) Substrate Telmisartan by Glucuronidation Using In Vitro-In Vivo Extrapolation (IVIVE)

BACKGROUND AND OBJECTIVE

The prediction of pharmacokinetic parameters for drugs metabolised by cytochrome P450 enzymes has been the subject of active research for many years, while the application of in vitro-in vivo extrapolation (IVIVE) techniques for non-cytochrome P450 enzymes has not been thoroughly evaluated. There is still no established quantitative method for predicting hepatic clearance of drugs metabolised by uridine 5'-diphospho-glucuronosyltransferases (UGTs), not to mention those which undergo hepatic uptake. The objective of the study was to predict the human hepatic clearance for telmisartan based on in vitro metabolic stability and hepatic uptake results.

METHODS

Telmisartan was examined in liver systems, allowing to estimate intrinsic clearance (CLint, in vitro) based on the substrate disappearance rate with the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Obtained CLint, in vitro values were corrected for corresponding unbound fractions. Prediction of human hepatic clearance was made from scaled unbound CLint, in vitro data with the use of the well-stirred model, and finally referenced to the literature value of observed clearance in humans, allowing determination of the essential scaling factors.

RESULTS

The in vitro scaled CLint, in vitro by UGT1A3 was assessed using three systems, human hepatocytes, liver microsomes, and recombinant enzymes. Obtained values were scaled and hepatic metabolism clearance was predicted, resulting in significant clearance underprediction. Utilization of the extended clearance concept (ECC) and hepatic uptake improved prediction of hepatic metabolism clearance. The scaling factors for hepatocytes, assessing the in vitro-in vivo difference, changed from sixfold difference to only twofold difference with the application of the ECC.

CONCLUSIONS

The study showed that taking into consideration hepatic uptake of a drug allows us to obtain satisfactory scaling factors, hence enabling the prediction of in vivo hepatic glucuronidation from in vitro data.

Improving In Vitro-In Vivo Extrapolation of Clearance Using Rat Liver Microsomes for Highly Plasma Protein-Bound Molecules

It is common practice in drug discovery and development to predict in vivo hepatic clearance from in vitro incubations with liver microsomes or hepatocytes using the well-stirred model (WSM). When applying the WSM to a set of approximately 3000 Novartis research compounds, 73% of neutral and basic compounds (extended clearance classification system [ECCS] class 2) were well-predicted within 3-fold. In contrast, only 44% (ECCS class 1A) or 34% (ECCS class 1B) of acids were predicted within 3-fold. To explore the hypothesis whether the higher degree of plasma protein binding for acids contributes to the in vitro-in vivo correlation (IVIVC) disconnect, 68 proprietary compounds were incubated with rat liver microsomes in the presence and absence of 5% plasma. A minor impact of plasma on clearance IVIVC was found for moderately bound compounds (fraction unbound in plasma [fup] ≥1%). However, addition of plasma significantly improved the IVIVC for highly bound compounds (fup <1%) as indicated by an increase of the average fold error from 0.10 to 0.36. Correlating fup with the scaled unbound intrinsic clearance ratio in the presence or absence of plasma allowed the establishment of an empirical, nonlinear correction equation that depends on fup Taken together, estimation of the metabolic clearance of highly bound compounds was enhanced by the addition of plasma to microsomal incubations. For standard incubations in buffer only, application of an empirical correction provided improved clearance predictions. SIGNIFICANCE STATEMENT: Application of the well-stirred liver model for clearance in vitro-in vivo extrapolation (IVIVE) in rat generally underpredicts the clearance of acids and the strong protein binding of acids is suspected to be one responsible factor. Unbound intrinsic in vitro clearance (CLint,u) determinations using rat liver microsomes supplemented with 5% plasma resulted in an improved IVIVE. An empirical equation was derived that can be applied to correct CLint,u-values in dependance of fraction unbound in plasma (fup) and measured CLint in buffer.

Introducing the Dynamic Well-Stirred Model for Predicting Hepatic Clearance and Extraction Ratio

The well-stirred model (WSM) incorporating the fraction of unbound drug (fu) to account for the effect of plasma binding on intrinsic clearance has been widely used for predicting hepatic clearance under the assumption that drug protein binding reaches equilibrium instantaneously. Our theoretical analysis reveals that the effect of protein binding on intrinsic clearance is better accounted for with the dynamic free fraction (fD), a measure of drug protein binding affinity, which leads to a putative dynamic well-stirred model (dWSM) without the instantaneous equilibrium assumption. Using recombinant CYP3A4 as the in vitro clearance system, we demonstrate that the binding effect of albumin on the intrinsic clearance of both highly bound midazolam and highly free verapamil is fully corrected by their corresponding fD values, respectively. On the other hand, fu only corrects the binding effect of albumin on the intrinsic clearance of verapamil, and yields severe over-correction of the intrinsic clearance of midazolam. The results suggest that the traditional WSM is suitable for highly free drugs like verapamil but not necessarily for highly bound drugs such as midazolam due to the violation of the instantaneous equilibrium assumption or under-estimating the true free drug concentration. In comparison, the dWSM incorporating fD holds true as long as drug elimination follows steady-state kinetics, and hence, it is more broadly applicable to drugs with different protein binding characteristics. Here we demonstrate with 36 diverse drugs, that the dWSM significantly improves the accuracy of predicting human hepatic clearance and liver extraction ratio from in vitro microsomal clearance data, highlighting the importance of drug plasma protein binding kinetics in addressing the under-prediction of hepatic clearance by the WSM.

Characterization and Applications of Permeabilized Hepatocytes in Drug Discovery

Hepatocytes are one of the most physiologically relevant in vitro liver systems for human translation of clearance and drug-drug interactions (DDI). However, the cell membranes of hepatocytes can limit the entry of certain compounds into the cells for metabolism and DDI. Passive permeability through hepatocytes can be different in vitro and in vivo, which complicates the human translation. Permeabilized hepatocytes offer a useful tool to probe mechanistic understanding of permeability-limited metabolism and DDI. Incubation with saponin of 0.01% at 0.5 million cells/mL and 0.05% at 5 million cells/mL for 5 min at 37°C completely permeabilized the plasma membrane of hepatocytes, while leaving the membranes of subcellular organelles intact. Permeabilized hepatocytes maintained similar enzymatic activity as intact unpermeabilized hepatocytes and can be stored at -80°C for at least 7 months. This approach reduces costs by preserving leftover hepatocytes. The relatively low levels of saponin in permeabilized hepatocytes had no significant impact on the enzymatic activity. As the cytosolic contents leak out from permeabilized hepatocytes, cofactors need to be added to enable metabolic reactions. Cytosolic enzymes will no longer be present if the media are removed after cells are permeabilized. Hence permeabilized hepatocytes with and without media removal may potentially enable reaction phenotyping of cytosolic enzymes. Although permeabilized hepatocytes work similarly as human liver microsomes and S9 fractions experimentally requiring addition of cofactors, they behave more like hepatocytes maintaining enzymatic activities for over 4 h. Permeabilized hepatocytes are a great addition to the drug metabolism toolbox to provide mechanistic insights.

Prediction of Human Clearance Using In Silico Models with Reduced Bias

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Interspecies comparison of metabolism of two novel prototype PFAS

As a result of proposed global restrictions and regulations on current-use per-and polyfluoroalkyl substances (PFAS), research on possible alternatives is highly required. In this study, phase I in vitro metabolism of two novel prototype PFAS in human and rat was investigated. These prototype chemicals are intended to be safer-by-design and expected to mineralize completely, and thus be less persistent in the environment compared to the PFAS available on the market. Following incubation with rat liver S9 (RL-S9) fractions, two main metabolites per initial substance were produced, namely an alcohol and a short-chain carboxylic acid. While with human liver S9 (HL-S9) fractions, only the short-chain carboxylic acid was detected. Beyond these major metabolites, two and five additional metabolites were identified at very low levels by non-targeted screening for the ether- and thioether-linked prototype chemicals, respectively. Overall, complete mineralization during the in vitro hepatic metabolism of these novel PFAS by HL-S9 and RL-S9 fractions was not observed. The reaction kinetics of the surfactants was determined by using the metabolite formation, rather than the substrate depletion approach. With rat liver enzymes, the formation rates of primary metabolite alcohols were at least two orders of magnitude higher than those of secondary metabolite carboxylic acids. When incubating with human liver enzymes, the formation rates of single metabolite carboxylic acids, were similar or smaller than those experienced in rat. It also indicates that the overall metabolic rate and clearance of surfactants are significantly higher in rat liver than in human liver. The maximum formation rate of the thioether congener exceeded 10-fold that of the ether in humans but were similar in rats. Overall, the results suggest that metabolism of the prototype chemicals followed a similar trend to those reported in studies of fluorotelomer alcohols.

Successful Prediction of Human Hepatic Concentrations of Transported Drugs Using the Proteomics-Informed Relative Expression Factor Approach

Tissue drug concentrations determine the efficacy and toxicity of drugs. When a drug is the substrate of transporters that are present at the blood:tissue barrier, the steady-state unbound tissue drug concentrations cannot be predicted from their corresponding plasma concentrations. To accurately predict transporter-modulated tissue drug concentrations, all clearances (CLs) mediating the drug's entry and exit (including metabolism) from the tissue must be accurately predicted. Because primary cells of most tissues are not available, we have proposed an alternative approach to predict such CLs, that is the use of transporter-expressing cells/vesicles (TECs/TEVs) and relative expression factor (REF). The REF represents the abundance of the relevant transporters in the tissue vs. in the TECs/TEVs. Here, we determined the transporter-based intrinsic CL of glyburide (GLB) and pitavastatin (PTV) in OATP1B1, OATP1B3, OATP2B1, and NTCP-expressing cells and MRP3-, BCRP-, P-gp-, and MRP2-expressing vesicles and scaled these CLs to in vivo using REF. These predictions fell within a priori set twofold range of the hepatobiliary CLs of GLB and PTV, estimated from their hepatic positron emission tomography imaging data: 272.3 and 607.8 mL/min for in vivo hepatic sinusoidal uptake CL, 47.8 and 17.4 mL/min for sinusoidal efflux CL, and 0 and 4.20 mL/min for biliary efflux CL, respectively. Moreover, their predicted hepatic concentrations (area under the hepatic concentration-time curve (AUC) and maximum plasma concentration (Cmax )), fell within twofold of their mean observed data. These data, together with our previous findings, confirm that the REF approach can successfully predict transporter-based drug CLs and tissue concentrations to enhance success in drug development.

A Complete Extension of Classical Hepatic Clearance Models Using Fractional Distribution Parameter fd in Physiologically Based Pharmacokinetics

The classical organ clearance models have been proposed to relate the plasma clearance CLp to probable mechanism(s) of hepatic clearance. However, the classical models assume the intrinsic capability of drug elimination (CLu,int) that is physically segregated from the vascular blood but directly acts upon the unbound drug concentration in the blood (fubCavg), and do not handle the transit-time delay between the inlet/outlet concentrations in their closed-form clearance equations. Therefore, we propose unified model structures that can address the internal blood concentration patterns of clearance organs in a more mechanistic/physiological manner, based on the fractional distribution parameter fd operative in PBPK. The basic partial/ordinary differential equations for four classical models are revisited/modified to yield a more complete set of extended clearance models, i.e., the Rattle, Sieve, Tube, and Jar models, which are the counterparts of the dispersion, series-compartment, parallel-tube, and well-stirred models. We demonstrate the feasibility of applying the resulting extended models to isolated perfused rat liver data for 11 compounds and an example dataset for in vitro-in vivo extrapolation of the intrinsic to the systemic clearances. Based on their feasibilities to handle such real data, these models may serve as an improved basis for applying clearance models in the future.

In vitro and in vivo pharmacokinetic characterization, chiral conversion and PBPK scaling towards human PK simulation of S-MRI-1867, a drug candidate for Hermansky-Pudlak syndrome pulmonary fibrosis

Hermansky-Pudlak syndrome (HPS) is a rare autosomal recessive disorder that affects lysosome-related organelles, often leading to fatal pulmonary fibrosis (PF). The search for a treatment for HPS pulmonary fibrosis (HPSPF) is ongoing. S-MRI-1867, a dual cannabinoid receptor 1 (CB1R)/inducible nitric oxide synthase (iNOS) inhibitor, has shown great promise for the treatment of several fibrotic diseases, including HPSPF. In this study, we investigated the in vitro ADME characteristics of S-MRI-1867, as well as its pharmacokinetic (PK) properties in mice, rats, dogs, and monkeys. S-MRI-1867 showed low aqueous solubility (< 1 µg/mL), high plasma protein binding (>99%), and moderate to high metabolic stability. In its preclinical PK studies, S-MRI-1867 exhibited moderate to low plasma clearance (CLp) and high steady-state volume of distribution (Vdss) across all species. Despite the low solubility and P-gp efflux, S-MRI-1867 showed great permeability and metabolic stability leading to a moderate bioavailability (21-60%) across mouse, rat, dog, and monkey. Since the R form of MRI-1867 is CB1R-inactive, we investigated the potential conversion of S-MRI-1867 to R-MRI-1867 in mice and found that the chiral conversion was negligible. Furthermore, we developed and validated a PBPK model that adequately fits the PK profiles of S-MRI-1867 in mice, rats, dogs, and monkeys using various dosing regimens. We employed this PBPK model to simulate the human PK profiles of S-MRI-1867, enabling us to inform human dose selection and support the advancement of this promising drug candidate in the treatment of HPSPF.

Challenges and Opportunities for In Vitro-In Vivo Extrapolation of Aldehyde Oxidase-Mediated Clearance: Toward a Roadmap for Quantitative Translation

Underestimation of aldehyde oxidase (AO)-mediated clearance by current in vitro assays leads to uncertainty in human dose projections, thereby reducing the likelihood of success in drug development. In the present study we first evaluated the current drug development practices for AO substrates. Next, the overall predictive performance of in vitro-in vivo extrapolation of unbound hepatic intrinsic clearance (CLint,u) and unbound hepatic intrinsic clearance by AO (CLint,u,AO) was assessed using a comprehensive literature database of in vitro (human cytosol/S9/hepatocytes) and in vivo (intravenous/oral) data collated for 22 AO substrates (total of 100 datapoints from multiple studies). Correction for unbound fraction in the incubation was done by experimental data or in silico predictions. The fraction metabolized by AO (fmAO) determined via in vitro/in vivo approaches was found to be highly variable. The geometric mean fold errors (gmfe) for scaled CLint,u (mL/min/kg) were 10.4 for human hepatocytes, 5.6 for human liver cytosols, and 5.0 for human liver S9, respectively. Application of these gmfe's as empirical scaling factors improved predictions (45%-57% within twofold of observed) compared with no correction (11%-27% within twofold), with the scaling factors qualified by leave-one-out cross-validation. A road map for quantitative translation was then proposed following a critical evaluation on the in vitro and clinical methodology to estimate in vivo fmAO In conclusion, the study provides the most robust system-specific empirical scaling factors to date as a pragmatic approach for the prediction of in vivo CLint,u,AO in the early stages of drug development. SIGNIFICANCE STATEMENT: Confidence remains low when predicting in vivo clearance of AO substrates using in vitro systems, leading to de-prioritization of AO substrates from the drug development pipeline to mitigate risk of unexpected and costly in vivo impact. The current study establishes a set of empirical scaling factors as a pragmatic tool to improve predictability of in vivo AO clearance. Developing clinical pharmacology strategies for AO substrates by utilizing mass balance/clinical drug-drug interaction data will help build confidence in fmAO.

Exploring the Boundaries for In Vitro-In Vivo Extrapolation: Use of Isolated Rat Hepatocytes in Co-culture and Impact of Albumin Binding Properties in the Prediction of Clearance of Various Drug Types

Prediction of hepatic clearance of drugs (via uptake or metabolism) from in vitro systems continues to be problematic, particularly when plasma protein binding is high. The following work explores simultaneous assessment of both clearance processes, focusing on a commercial hepatocyte-fibroblast co-culture system (HμREL) over a 24-hour period using six probe drugs (ranging in metabolic and transporter clearance and low-to-high plasma protein binding). A rat hepatocyte co-culture assay was established using drug depletion (measuring both medium and total concentrations) and cell uptake kinetic analysis, both in the presence and absence of plasma protein (1% bovine serum albumin). Secretion of endogenous albumin was monitored as a marker of viability, and this reached 0.004% in incubations (at a rate similar to in vivo synthesis). Binding to stromal cells was substantial and required appropriate correction factors. Drug concentration-time courses were analyzed both by conventional methods and a mechanistic cell model prior to in vivo extrapolation. Clearance assayed by drug depletion in conventional suspended rat hepatocytes provided a benchmark to evaluate co-culture value. Addition of albumin appeared to improve predictions for some compounds (where fraction unbound in the medium is less than 0.1); however, for high-binding drugs, albumin significantly limited quantification and thus predictions. Overall, these results highlight ongoing challenges concerning in vitro hepatocyte system complexity and limitations of practical expediency. Considering this, more reliable measurement of hepatically cleared compounds seems possible through judicious use of available hepatocyte systems, including co-culture systems, as described herein; this would include those compounds with low metabolic turnover but high active uptake clearance. SIGNIFICANCE STATEMENT: Co-culture systems offer a more advanced tool than standard hepatocytes, with the ability to be cultured for longer periods of time, yet their potential as an in vitro tool has not been extensively assessed. We evaluate the strengths and limitations of the HμREL system using six drugs representing various metabolic and transporter-mediated clearance pathways with various degrees of albumin binding. Studies in the presence/absence of albumin allow in vitro-in vivo extrapolation and a framework to maximize their utility.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

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.

The Disconnect in Intrinsic Clearance Determined in Human Hepatocytes and Liver Microsomes Results from Divergent Cytochrome P450 Activities

Candidate drugs may exhibit higher unbound intrinsic clearances (CLint,u) in human liver microsomes (HLMs) relative to human hepatocytes (HHs), posing a challenge as to which value is more predictive of in vivo clearance (CL). This work was aimed at better understanding the mechanism(s) underlying this 'HLM:HH disconnect' via examination of previous explanations, including passive permeability limited CL or cofactor exhaustion in hepatocytes. A series of structurally related, passively permeable (Papps > 5 × 10-6 cm/s), 5-azaquinazolines were studied in different liver fractions, and metabolic rates and routes were determined. A subset of these compounds demonstrated a significant HLM:HH (CLint,u ratio 2-26) disconnect. Compounds were metabolized via combinations of liver cytosol aldehyde oxidase (AO), microsomal cytochrome P450 (CYP) and flavin monooxygenase (FMO). For this series, the lack of concordance between CLint,u determined in HLM and HH contrasted with an excellent correlation of AO dependent CLint,u determined in human liver cytosol[Formula: see text], r2 = 0.95, P < 0.0001). The HLM:HH disconnect for both 5-azaquinazolines and midazolam was as a result of significantly higher CYP activity in HLM and lysed HH fortified with exogenous NADPH relative to intact HH. Moreover, for the 5-azaquinazolines, the maintenance of cytosolic AO and NADPH-dependent FMO activity in HH, relative to CYP, supports the conclusion that neither substrate permeability nor intracellular NADPH for hepatocytes were limiting CLint,u Further studies are required to identify the underlying cause of the lower CYP activities in HH relative to HLM and lysed hepatocytes in the presence of exogenous NADPH. SIGNIFICANCE STATEMENT: Candidate drugs may exhibit higher intrinsic clearance in human liver microsomes relative to human hepatocytes, posing a challenge as to which value is predictive of in vivo clearance. This work demonstrates that the difference in activity determined in liver fractions results from divergent cytochrome P450 but not aldehyde oxidase or flavin monooxygenase activity. This is inconsistent with explanations including substrate permeability limitations or cofactor exhaustion and should inform the focus of further studies to understand this cytochrome P450 specific disconnect phenomenon.

In Vitro-In Vivo Extrapolation of Hepatic Biotransformation Data for Fish. III. An In-depth Case Study with Pyrene

Computational models that predict chemical bioaccumulation in fish generally account for biotransformation using an apparent first-order whole-body rate constant (kB ; d-1 ). The use of such models requires, therefore, that methods exist for estimating kB , ideally without the need to expose live animals. One promising approach for estimating kB involves the extrapolation of measured in vitro intrinsic clearance (CLIN VITRO,INT ) to the whole animal (in vitro-in vivo extrapolation, [IVIVE]). To date, however, the accuracy of such predictions has been difficult to assess due to uncertainties associated with one or more extrapolation factors and/or a mismatch between fish used to generate in vitro data and those used to conduct in vivo exposures. In the present study we employed a combined in vitro and in vivo experimental approach to evaluate the IVIVE procedure using pyrene (PYR) as a model chemical. To the extent possible, measured rates of CLIN VITRO,INT were extrapolated to estimates of kB using extrapolation factors based on measured values. In vitro material (liver S9 fraction) was obtained from fish exposed to PYR in a controlled bioconcentration study protocol. Fish from the same study were then used to estimate in vivo kB values from an analysis of chemical depuration data. Averaged across four study groups, kB values estimated by IVIVE underestimated those determined from in vivo data by 2.6-fold. This difference corresponds to a 4.1-fold underestimation of true in vivo intrinsic clearance, assuming the liver is the only site of biotransformation. These findings are consistent with previous work performed using mammals and have important implications for use of measured CLIN VITRO,INT values in bioaccumulation assessments with fish. Environ Toxicol Chem 2023;42:1501-1515. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Hepatocyte intrinsic clearance (CL_int) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CL_p) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CL_p IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (f_u,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CL_p IVIVE approach. Results show that predictions of CL_p are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Ų, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CL_p were better for compounds with moderate-to-high hepatocyte CL_int (>10 μL/min/10⁶ cells), high passive cellular permeability (P_app > 100 nm/s), and moderate observed CL_p (5-50 mL/min/kg). Influences of plasma protein binding (f_u,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (P_app > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CL_p IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CL_p IVIVE. Our results indicate that the current practice of prospective CL_p IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CL_p IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CL_p IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

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.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Perspective on high-throughput bioanalysis to support in vitro assays in early drug discovery

As the desire for a shortened design/make/test/learn cycle increases in early drug discovery, the pressure to rapidly deliver drug metabolism pharmacokinetic data continues to rise. From a bioanalytical standpoint, in vitro assays are challenging because they are amenable to automation and thus capable of generating a high number of samples for analysis. To keep up with analysis demands, automated method development workflows, rapid sample analysis approaches and efficient data analysis software must be utilized. This work provides an outline of how we implemented those three aspects to provide bioanalytical support for in vitro drug metabolism pharmacokinetic assays, which include developing hundreds of mass spectrometry methods and analyzing thousands of samples per week, while delivering a median bioanalytical turnaround time of 1-2 business days.

New Methodology for Determining Plasma Protein Binding Kinetics Using an Enzyme Reporter Assay Coupling with High-Resolution Mass Spectrometry

Determination of drug binding kinetics in plasma is important yet extremely challenging. Accordingly, we introduce "dynamic free fraction" as a new binding parameter describing drug-protein binding kinetics. We demonstrate theoretically and experimentally that the dynamic free fraction can be determined by coupling the drug binding assay with a reporter enzyme in combination with high-resolution mass spectrometry measuring the relative initial steady-state rates of enzymatic reactions in the absence and presence of matrix proteins. This novel and simple methodology circumvents a long-standing challenge inherent in existing methods for determining binding kinetics constants, such as k_on and k_off, and enables assessment of the impact of protein binding kinetics on pharmaceutical properties of drugs. As demonstrated with nine model drugs, the predicted liver extraction ratio, a measure of efficiency of drug removal by the liver, correlates significantly better to the observed extraction ratio when using the dynamic free fraction (f_D) in place of the unbound fraction (f_u) of the drug in plasma. Similarly, the in vivo hepatic clearance of these drugs, a measure of liver drug elimination, is highly comparable to the clearance values calculated with the dynamic free fraction (f_D), which is markedly better than those calculated with the unbound fraction (f_u). In contrast to the prevailing view, these results indicate that protein binding kinetics is an important pharmacokinetic property of a drug. As plasma protein binding is one of the most important drug properties, this new methodology may represent a breakthrough and could have a real impact on the field.

Use of physiological based pharmacokinetic modeling for cross-species prediction of pharmacokinetic and tissue distribution profiles of a novel niclosamide prodrug

Introduction: Niclosamide (Nc) is an FDA-approved anthelmintic drug that was recently identified in a drug repurposing screening to possess antiviral activity against SARS-CoV-2. However, due to the low solubility and permeability of Nc, its in vivo efficacy was limited by its poor oral absorption. Method: The current study evaluated a novel prodrug of Nc (PDN; NCATS-SM4705) in improving in vivo exposure of Nc and predicted pharmacokinetic profiles of PDN and Nc across different species. ADME properties of the prodrug were determined in humans, hamsters, and mice, while the pharmacokinetics (PK) of PDN were obtained in mice and hamsters. Concentrations of PDN and Nc in plasma and tissue homogenates were measured by UPLC-MS/MS. A physiologically based pharmacokinetic (PBPK) model was developed based on physicochemical properties, pharmacokinetic and tissue distribution data in mice, validated by the PK profiles in hamsters and applied to predict pharmacokinetic profiles in humans. Results: Following intravenous and oral administration of PDN in mice, the total plasma clearance (CL_p) and volume of distribution at steady-state (Vd_ss) were 0.061-0.063 L/h and 0.28-0.31 L, respectively. PDN was converted to Nc in both liver and blood, improving the systemic exposure of Nc in mice and hamsters after oral administration. The PBPK model developed for PDN and in vivo formed Nc could adequately simulate plasma and tissue concentration-time profiles in mice and plasma profiles in hamsters. The predicted human CL_p/F and Vd_ss/F after an oral dose were 2.1 L/h/kg and 15 L/kg for the prodrug respectively. The predicted Nc concentrations in human plasma and lung suggest that a TID dose of 300 mg PDN would provide Nc lung concentrations at 8- to 60-fold higher than in vitro IC₅₀ against SARS-CoV-2 reported in cell assays. Conclusion: In conclusion, the novel prodrug PDN can be efficiently converted to Nc in vivo and improves the systemic exposure of Nc in mice after oral administration. The developed PBPK model adequately depicts the mouse and hamster pharmacokinetic and tissue distribution profiles and highlights its potential application in the prediction of human pharmacokinetic profiles.

Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.

Comparison of toxicokinetic properties of eleven analogues of Bisphenol A in pig after intravenous and oral administrations

Due to the restrictions of its use, Bisphenol A (BPA) has been replaced by many structurally related bisphenols (BPs) in consumer products. The endocrine disrupting potential similar to that of BPA has been described for several bisphenols, there is therefore an urgent need of toxicokinetic (TK) data for these emerging BPs in order to evaluate if their internal exposure could increase the risk of endocrine disruption. We investigated TK behaviors of eleven BPA substitutes (BPS, BPAF, BPB, BPF, BPM, BPZ, 3-3BPA, BP4-4, BPAP, BPP, and BPFL) by intravenous and oral administrations of mixtures of them to piglets and serial collection of blood over 72 h and urine over 24 h, to evaluate their disposition. Data were analyzed using nonlinear mixed-effects modeling and a comparison was made with TK predicted by the generic model HTTK package. The low urinary excretion of some BPs, in particular BPM, BPP and BPFL, is an important aspect to consider in predicting human exposure based on urine biomonitoring. Despite their structural similarities, for the same oral dose, all BPA analogues investigated showed a higher systemic exposure (area under the plasma concentration-time curve (AUC) of the unconjugated Bisphenol) than BPA (2 to 4 fold for 3-3BPA, BPAF, BPB and BPZ, 7-20 fold for BP4-4, BPAP, BPP, BPFL, BPF and BPM and 150 fold for BPS) due mainly to a considerable variation of oral bioavailability (proportion of BP administered by oral route that attains the systemic circulation unchanged). Given similarities in the digestive tract between pigs and humans, our TK data suggest that replacing BPA with some of its alternatives, particularly BPS, will likely lead to higher internal exposure to potential endocrine disruptive compounds. These findings are crucial for evaluating the risk of human exposure to these emerging BPs.

Is the Protein-Mediated Uptake of Drugs by Organic Anion Transporting Polypeptides a Real Phenomenon or an Artifact?

Plasma proteins or human serum albumin (HSA) have been reported to increase the in vitro intrinsic uptake clearance (CLint,uptake) of drugs by hepatocytes or organic anion transporting polypeptide (OATP)-transfected cell lines. This so-called protein-mediated uptake effect (PMUE) is thought to be due to an interaction between the drug-protein complex and the cell membrane causing an increase in the unbound drug concentration at the cell surface, resulting in an increase in the apparent CLint,uptake of the drug. To determine if the PMUE on OATP-mediated drug uptake is an artifact or a real phenomenon, we determined the effect of 1%, 2%, and 5% HSA on OATP1B1-mediated [human embryonic kidney (HEK)293 transfected cells] and passive CLint,uptake (mock HEK293 cells) on a cocktail of five statins. In addition, we determined the non-specific binding (NSB) of the statin-HSA complex to the cells/labware. The increase in uptake of atorvastatin, fluvastatin, and rosuvastatin in the presence of HSA was completely explained by the extent of NSB of the statin-HSA complex, indicating that the PMUE for these statins is an artifact. In contrast, this was not the case for OATP1B1-mediated uptake of pitavastatin and passive uptake of cerivastatin, suggesting that the PMUE is a real phenomenon for these drugs. Additionally, the PMUE on OATP1B1-mediated uptake of pitavastatin was confirmed by a decrease in its unbound IC50 in the presence of 5% HSA versus Hank's balanced salt solution buffer (HBSS). These data question the utility of routinely including plasma proteins or HSA in uptake experiments and the previous findings on PMUE on OATP-mediated drug uptake. SIGNIFICANCE STATEMENT: Here we report, for the first time, that the protein-mediated uptake effect (PMUE) on organic anion transporting polypeptide (OATP)-transported drugs could be an artifact of the non-specific binding (NSB) of the drug-albumin complex to cells/labware. Future experiments on PMUE must take into consideration such NSB. In addition, mechanisms other than PMUE need to be explored to explain the underprediction of in vivo OATP-mediated hepatic drug clearance from in vitro uptake studies.

Revisiting Cerebrospinal Fluid Flow Direction and Rate in Physiologically Based Pharmacokinetic Model

The bidirectional pulsatile movement of cerebrospinal fluid (CSF), instead of the traditionally believed unidirectional and constant CSF circulation, has been demonstrated. In the present study, the structure and parameters of the CSF compartments were revisited in our comprehensive and validated central nervous system (CNS)-specific, physiologically based pharmacokinetic (PBPK) model of healthy rats (LeiCNS-PK3.0). The bidirectional and site-dependent CSF movement was incorporated into LeiCNS-PK3.0 to create the new LeiCNS-PK“3.1” model. The physiological CSF movement rates in healthy rats that are unavailable from the literature were estimated by fitting the PK data of sucrose, a CSF flow marker, after intra-CSF administration. The capability of LeiCNS-PK3.1 to describe the PK profiles of other molecules was compared with that of the original LeiCNS-PK3.0 model. LeiCNS-PK3.1 demonstrated superior description of the CSF PK profiles of a range of small molecules after intra-CSF administration over LeiCNS-PK3.0. LeiCNS-PK3.1 also retained the same level of predictability of CSF PK profiles in cisterna magna after intravenous administration. These results support the theory of bidirectional and site-dependent CSF movement across the entire CSF space over unidirectional and constant CSF circulation in healthy rats, pointing out the need to revisit the structures and parameters of CSF compartments in CNS-PBPK models.

Evidence of the Need for Modified Well-stirred Model in In Vitro to In Vivo Extrapolation

In vitro to in vivo extrapolation (IVIVE), an approach for hepatic clearance (CL_H) prediction used worldwide, remains controversial due to systematic underprediction. Among the various probable factors, the original assumption of the hepatic mathematical model (i.e., the well-stirred model, WSM) may become problematic, leading to the underestimation of drug CL_H. Having a similar prerequisite that the well-stirred conditions are homogenous with perfectly mixed reactants, but using a different driving concentration, the modified well-stirred model (MWSM) stands apart from the WSM. However, we believe that both models should coexist so that the entire well-stirred scenario can be completely illustrated. Consequently, we collected published data from the literature and employed a logistic regression method to differentiate the optimal timing of use between WSM and MWSM in drug CL_H prediction. Generally, variances adopted in the regression, including partition coefficient (logP), fraction unbound (fu), volumes of distribution at steady-state (Vss), and mean residence time (MRT), corresponded to our assumption when protein-facilitated uptake was considered. Furthermore, a new empirical approach was introduced to allow practical use of the MWSM. The results showed that this model could provide a more precise prediction compared to previous empirical approaches. Therefore, these preliminary results not only delineated a more detailed structure and mechanism of MWSM but also highlighted its necessity and potential.

Utilizing virtual experiments to increase understanding of discrepancies involving in vitro-to-in vivo predictions of hepatic clearance

Predictions of xenobiotic hepatic clearance in humans using in vitro-to-in vivo extrapolation methods are frequently inaccurate and problematic. Multiple strategies are being pursued to disentangle responsible mechanisms. The objective of this work is to evaluate the feasibility of using insights gained from independent virtual experiments on two model systems to begin unraveling responsible mechanisms. The virtual culture is a software analog of hepatocytes in vitro, and the virtual human maps to hepatocytes within a liver within an idealized model human. Mobile objects (virtual compounds) map to amounts of xenobiotics. Earlier versions of the two systems achieved quantitative validation targets for intrinsic clearance (virtual culture) and hepatic clearance (virtual human). The major difference between the two systems is the spatial organization of the virtual hepatocytes. For each pair of experiments (virtual culture, virtual human), hepatocytes are configured the same. Probabilistic rules govern virtual compound movements and interactions with other objects. We focus on highly permeable virtual compounds and fix their extracellular unbound fraction at one of seven values (0.05–1.0). Hepatocytes contain objects that can bind and remove compounds, analogous to metabolism. We require that, for a subset of compound properties, per-hepatocyte compound exposure and removal rates during culture experiments directly predict corresponding measures made during virtual human experiments. That requirement serves as a cross-system validation target; we identify compound properties that enable achieving it. We then change compound properties, ceteris paribus, and provide model mechanism-based explanations for when and why measures made during culture experiments under- (or over-) predict corresponding measures made during virtual human experiments. The results show that, from the perspective of compound removal, the organization of hepatocytes within virtual livers is more efficient than within cultures, and the greater the efficiency difference, the larger the underprediction. That relationship is noteworthy because most in vitro-to-in vivo extrapolation methods abstract away the structural organization of hepatocytes within a liver. More work is needed on multiple fronts, including the study of an expanded variety of virtual compound properties. Nevertheless, the results support the feasibility of the approach and plan.