Prediction of Human Metabolic Clearance from In Vitro Systems: Retrospective Analysis and Prospective View

Determination of Intrinsic Clearance and Fraction Unbound in Human Liver Microsomes and In Vitro-In Vivo Extrapolation of Human Hepatic Clearance for Marketed Central Nervous System Drugs

OBJECTIVE

The objective of this study was to determine the apparent intrinsic clearance (Clint, app) and fraction unbound in human liver microsomes (fu, mic) of 86 marketed central nervous system (CNS) drugs and to predict the in vivo hepatic blood clearance (CLh, b).

METHODS

Clint, app in human liver microsomes (HLM) was determined by substrate depletion, and fu, mic was determined by equilibrium dialysis. The relationship between lipophilicity (logP) and unbound intrinsic clearance (Clint, u) was explored using the Biopharmaceutical Drug Disposition Classification System (BDDCS) and Extended Clearance Classification System (ECCS). The predicted hepatic blood clearance by direct scaling, conventional method and Poulin method using well-stirred (WS) and parallel-tube (PT) models were compared with observed values.

RESULTS

The Clint, app in HLM ranged from < 5.8 to 477 µl/min/mg. The fu, mic in HLM ranged from 0.02 to 1.0. The scaled Clint values ranged from < 5 to 4496 ml/min/kg. The metabolic rate increased with an increase in logP (logP ≥ 2.5) of the CNS compounds. The direct scaling and Poulin methods showed comparable results based on the percentage of clearance predictions within a two-fold error. The conventional method resulted in under-predictions of Clint, in vivo or CLh, b using the WS or PT models. The Poulin method is favored over the other methods based on the statistical parameters.

CONCLUSIONS

Experimental Clint, app and fu, mic for 86 CNS compounds were successfully determined, and the scaled clearance was used to predict the hepatic blood clearance of 34 drugs. The success of prospective clearance predictions using HLM is expected to be high for most of the lipophilic BDDCS class 1 and class 2 and ECCS class 2 CNS compounds. The Poulin method resulted in more accurate predictions falling within a two-fold error of the observed values using the WS or PT models.

DigiLoCS: A leap forward in predictive organ-on-chip simulations

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the conventional model and ii)explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Accurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

Biotransformation of carbamazepine and nicotine in juvenile American alligator (Alligator mississippiensis) in vitro hepatic S9 vs. in situ perfused liver

American alligators (Alligator mississippiensis) are apex predators and sentinel species in the coastal wetland ecosystem along the Gulf of Mexico. There is concern for alligator exposure and susceptibility to chemical contaminants due to their high trophic level and lower metabolic capability. At present, their hepatic biotransformation capacity to metabolize or detoxify contaminants has not been comprehensively determined. In this study, the hepatic biotransformation capability of juvenile American alligators to metabolize two commonly found environmental pharmaceuticals: carbamazepine (CBZ) or nicotine (NCT) was evaluated. The formation of their respective primary metabolites, i.e., carbamazepine-10,11-epoxide (CBZ-E) and cotinine (CTN), was evaluated at 10 μM (within the human therapeutic range). The in vitro S9 and a novel in situ liver perfusion assays were used to characterize and compare metabolic ability in isolated hepatic enzymes vs. whole organ (liver). For CBZ, the perfused livers exhibited only 30% of intrinsic formation clearance (CLf,int) relative to the S9 assay. The metabolism of NCT was not detectable in the S9 assay and was only observed in the perfused liver assay. Compared to the corresponding rat models (S9 or perfused livers),alligators' CLf,int was 2060% for CBZ and 50% for NCT of rats. Additionally, NCT exposure increased lactate levels in perfused livers indicating metabolic stress. This study provides insight into the hepatic capability of alligators to metabolize CBZ and NCT using an established in vitro (S9) system and a newly developed in situ liver perfusion system.

Use of physiologically based kinetic modeling to predict neurotoxicity and genotoxicity of methylglyoxal in humans

This study aimed to evaluate human neurotoxicity and genotoxicity risks from dietary and endogenous methylglyoxal (MGO), utilizing physiologically based kinetic (PBK) modeling-facilitated reverse dosimetry as a new approach methodology (NAM) to extrapolate in vitro toxicity data to in vivo dose-response predictions. A human PBK model was defined based on a newly developed and evaluated mouse model enabling the translation of in vitro toxicity data for MGO from human stem cell-derived neurons and WM-266-4 melanoma cells into quantitative human in vivo toxicity data and subsequent risk assessment by the margin of exposure (MOE) approach. The results show that the MOEs resulting from daily dietary intake did not raise a concern for endpoints for neurotoxicity including mitochondrial function, cytotoxicity, and apoptosis, while those for DNA adduct formation could not exclude a concern over genotoxicity. Endogenous MGO formation, especially under diabetic conditions, resulted in MOEs that raised concern not only for genotoxicity but also for some of the neurotoxicity endpoints evaluated. Thus, the results also point to the importance of taking the endogenous levels into account in the risk assessment of MGO.

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.

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

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.

Liver-on-chips for drug discovery and development

Recent FDA modernization act 2.0 has led to increasing industrial R&D investment in advanced in vitro 3D models such as organoids, spheroids, organ-on-chips, 3D bioprinting, and in silico approaches. Liver-related advanced in vitro models remain the prime area of interest, as liver plays a central role in drug clearance of compounds. Growing evidence indicates the importance of recapitulating the overall liver microenvironment to enhance hepatocyte maturity and culture longevity using liver-on-chips (LoC) in vitro. Hence, pharmaceutical industries have started exploring LoC assays in the two of the most challenging areas: accurate in vitro-in vivo extrapolation (IVIVE) of hepatic drug clearance and drug-induced liver injury. We examine the joint efforts of commercial chip manufacturers and pharmaceutical companies to present an up-to-date overview of the adoption of LoC technology in the drug discovery. Further, several roadblocks are identified to the rapid adoption of LoC assays in the current drug development framework. Finally, we discuss some of the underexplored application areas of LoC models, where conventional 2D hepatic models are deemed unsuitable. These include clearance prediction of metabolically stable compounds, immune-mediated drug-induced liver injury (DILI) predictions, bioavailability prediction with gut-liver systems, hepatic clearance prediction of drugs given during pregnancy, and dose adjustment studies in disease conditions. We conclude the review by discussing the importance of PBPK modeling with LoC, digital twins, and AI/ML integration with LoC.

Data set of fraction unbound values in the in vitro incubations for metabolic studies for better prediction of human clearance

In vitro-in vivo extrapolation is a commonly applied technique for liver clearance prediction. Various in vitro models are available such as hepatocytes, human liver microsomes, or recombinant cytochromes P450. According to the free drug theory, only the unbound fraction (fu) of a chemical can undergo metabolic changes. Therefore, to ensure the reliability of predictions, both specific and nonspecific binding in the model should be accounted. However, the fraction unbound in the experiment is often not reported. The study aimed to provide a detailed repository of the literature data on the compound's fu value in various in vitro systems used for drug metabolism evaluation and corresponding human plasma binding levels. Data on the free fraction in plasma and different in vitro models were supplemented with the following information: the experimental method used for the assessment of the degree of drug binding, protein or cell concentration in the incubation, and other experimental conditions, if different from the standard ones, species, reference to the source publication, and the author's name and date of publication. In total, we collected 129 literature studies on 1425 different compounds. The provided data set can be used as a reference for scientists involved in pharmacokinetic/physiologically based pharmacokinetic modelling as well as researchers interested in Quantitative Structure-Activity Relationship models for the prediction of fraction unbound based on compound structure. Database URL: https://data.mendeley.com/datasets/3bs5526htd/1.

A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans

PURPOSE

Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.

METHODS

This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy.

RESULTS

Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy.

CONCLUSION

The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.

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.

Mechanobiological stimulation in organ-on-a-chip systems reduces hepatic drug metabolic capacity in favor of regenerative specialization

Hepatic physiology depends on the liver's complex structural composition which among others, provides high oxygen supply rates, locally differential oxygen tension, endothelial paracrine signaling, as well as residual hemodynamic shear stress to resident hepatocytes. While functional improvements were shown by implementing these factors into hepatic culture systems, direct cause-effect relationships are often not well characterized-obfuscating their individual contribution in more complex microphysiological systems. By comparing increasingly complex hepatic in vitro culture systems that gradually implement these parameters, we investigate the influence of the cellular microenvironment to overall hepatic functionality in pharmacological applications. Here, hepatocytes were modulated in terms of oxygen tension and supplementation, endothelial coculture, and exposure to fluid shear stress delineated from oxygen influx. Results from transcriptomic and metabolomic evaluation indicate that particularly oxygen supply rates are critical to enhance cellular functionality-with cellular drug metabolism remaining comparable to physiological conditions after prolonged static culture. Endothelial signaling was found to be a major contributor to differential phenotype formation known as metabolic zonation, indicated by WNT pathway activity. Lastly, oxygen-delineated shear stress was identified to direct cellular fate towards increased hepatic plasticity and regenerative phenotypes at the cost of drug metabolic functionality - in line with regenerative effects observed in vivo. With these results, we provide a systematic evaluation of critical parameters and their impact in hepatic systems. Given their adherence to physiological effects in vivo, this highlights the importance of their implementation in biomimetic devices, such as organ-on-a-chip systems. Considering recent advances in basic liver biology, direct translation of physiological structures into in vitro models is a promising strategy to expand the capabilities of pharmacological models.

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.

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.

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.

Exploring the Pharmacokinetic Mysteries of the Liver: Application of Series Compartment Models of Hepatic Elimination

Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CLint ) and PBPK-operative tissue-to-plasma partition coefficient (Kp ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CLint and Kp decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CLint , the SCM yields a higher Kp when the liver perivenous region exhibits a lower CLint as compared with a high CLint at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CLint and the PBPK-operative Kp . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.

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.

Process and System Clearances in Pharmacokinetic Models: Our Basic Clearance Concepts Are Correct

Clearances are important parameters in pharmacokinetic (PK) models. All clearances in PK models are either process clearances that include diffusion, transport, and metabolism clearances or system clearances that include organ and systemic clearance. Clearance and volume of distribution are two independent parameters that characterize drug disposition in both individual compartments and systems of compartments. In this minireview, we show that systemic and organ clearances are net clearances that can be easily derived by partition analysis. When drugs are eliminated from the central compartment by first-order processes, systemic clearance is constant. When drugs are eliminated from a peripheral compartment, instantaneous systemic clearance will vary with time. However, average clearance and clearance at steady state will be constant and will equal dose divided by area under the curve. We show that peripheral elimination will not have a large impact on most pharmacokinetic analyses and that standard models of organ and systemic clearance are useful and appropriate. SIGNIFICANCE STATEMENT: There are two basic kinds of clearances used in pharmacokinetic models, process and system clearances. We show that organ and systemic clearances are net clearances with blood or plasma as the driving concentration. For linear pharmacokinetics, clearance is constant for elimination from the central compartment but varies with time for peripheral elimination. Despite the different kinds of clearance parameters and models, standard clearance models and concepts remain valid.

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.

Metabolic clearance of select opioids and opioid antagonists using hepatic spheroids and recombinant cytochrome P450 enzymes

The opioid crisis is a pressing public health issue, exacerbated by the emergence of more potent synthetic opioids, particularly fentanyl and its analogs. While competitive antagonists exist, their efficacy against synthetic opioids is largely unknown. Furthermore, due to the short durations of action of current antagonists, renarcotization remains a concern. In this study, metabolic activity was characterized for fentanyl-class opioids and common opioid antagonists using multiple in vitro systems, namely, cytochrome P450 (CYP) enzymes and hepatic spheroids, after which an in vitro-in vivo correlation was applied to convert in vitro metabolic activity to predictive in vivo intrinsic clearance. For all substrates, intrinsic hepatic metabolism was higher than the composite of CYP activities, due to fundamental differences between whole cells and single enzymatic reactions. Of the CYP isozymes investigated, 3A4 yielded the highest absolute and relative metabolism across all substrates, with largely negligible contributions from 2D6 and 2C19. Comparative analysis highlighted elevated lipophilicity and diminished CYP3A4 activity as potential considerations for the development of more efficacious opioid antagonists. Finally, antagonists with a high degree of molecular similarity exhibited comparable clearance, providing a basis for structure-metabolism relationships. Together, these results provide multiple screening criteria for early stage drug discovery involving opioid countermeasures.

Characterization of intravenous pharmacokinetics in Göttingen minipig and clearance prediction using established in vitro to in vivo extrapolation methodologies

1. The use of the Göttingen minipig as an animal model for drug safety testing and prediction of human pharmacokinetics (PK) continues to gain momentum in pharmaceutical research and development. The aim of this study was to evaluate in vitro to in vivo extrapolation (IVIVE) methodologies for prediction of hepatic, metabolic clearance (CL_hep,met) in Göttingen minipig, using a comprehensive set of compounds.2. In vivo clearance was determined in Göttingen minipig by intravenous cassette dosing and hepatocyte intrinsic clearance, plasma protein binding and non-specific incubation binding were determined in vitro. Prediction of CL_hep,met was performed by IVIVE using conventional and adapted formats of the well-stirred liver model.3. The best prediction of in vivo CL_hep,met from scaled in vitro kinetic data was achieved using an empirical correction factor based on a 'regression offset' of the IVIV relationship.4. In summary, these results expand the in vitro and in vivo PK knowledge in Göttingen minipig. We show regression corrected IVIVE provides superior prediction of in vivo CL_hep,met in minipig offering a practical, unified scaling approach to address systematic under-predictions. Finally, we propose a reference set for researchers to establish their own 'lab-specific' regression correction for IVIVE in minipig.

Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (β) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

Accurate prediction of in vivo hepatic clearance is an essential part of successful and efficient drug development; however, many investigators have recognized that there are significant limitations in the predictability of clearance with a tendency for underprediction for primarily metabolized drugs. Here, we examine the impact of adding serum or albumin into hepatocyte and microsomal incubations on the predictability of in vivo hepatic clearance. The addition of protein into hepatocyte incubations has been reported to improve the predictability for high clearance (extraction ratio) drugs and highly protein-bound drugs. Analyzing published data for 60 different drugs and 97 experimental comparisons (with 17 drugs being investigated from two to seven) we confirmed the marked underprediction of clearance. However, we could not validate any relevant improved predictability within twofold by the addition of serum to hepatocyte incubations or albumin to microsomal incubations. This was the case when investigating all measurements, or when subdividing analyses by extraction ratio, degree of protein binding, Biopharmaceutics Drug Disposition Classification System class, examining Extended Clearance Classification System class 1B drugs only, or drug charge. Manipulating characteristics of small data sets of like compounds and adding scaling factors can appear to yield good predictability, but the carryover of these methods to alternate drug classes and different laboratories is not evident. Improvement in predictability of poorly soluble compounds is greater than that for soluble compounds, but not to a meaningful extent. Overall, we cannot confirm that protein addition improves in vitro-in vivo extrapolation predictability to any clinically meaningful degree when considering all drugs and different subsets. SIGNIFICANCE STATEMENT: The addition of protein into microsomal or hepatocyte incubations has been widely proposed to improve hepatic clearance predictions. To date, studies examining this phenomenon have not included appropriate negative controls where predictability is achieved without protein addition and have been conducted with small data sets of similar compounds that don't apply to alternate drug classes. Here, an extensive analysis of published data for 60 drugs and 97 experimental comparisons couldn't validate any relevant clinically improved clearance predictability with protein addition.

Multi-Well Array Culture of Primary Human Hepatocyte Spheroids for Clearance Extrapolation of Slowly Metabolized Compounds

Accurate prediction of human pharmacokinetics using in vitro tools is an important task during drug development. Albeit, currently used in vitro systems for clearance extrapolation such as microsomes and primary human hepatocytes in suspension culture show reproducible turnover, the utility of these systems is limited by a rapid decline of activity of drug metabolizing enzymes. In this study, a multi-well array culture of primary human hepatocyte spheroids was compared to suspension and single spheroid cultures from the same donor. Multi-well spheroids remained viable and functional over the incubation time of 3 days, showing physiological excretion of albumin and α-AGP. Their metabolic activity was similar compared to suspension and single spheroid cultures. This physiological activity, the high cell concentration, and the prolonged incubation time resulted in significant turnover of all tested low clearance compounds (n = 8). In stark contrast, only one or none of the compounds showed significant turnover when single spheroid or suspension cultures were used. Using multi-well spheroids and a regression offset approach (log(CL_int) = 1.1 × + 0.85), clearance was predicted within 3-fold for 93% (13/14) of the tested compounds. Thus, multi-well spheroids represent a novel and valuable addition to the ADME in vitro tool kit for the determination of low clearance and overall clearance prediction. Graphical Abstract.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Pharmacokinetic Profile of Doxycycline in Koala Plasma after Weekly Subcutaneous Injections for the Treatment of Chlamydiosis

Simple Summary

Doxycycline is an antimicrobial used for treating chlamydial infections in various species, including the koala. The dose and route of administration used initially are based on first principles. Therefore, this study investigates the absorption, distribution, metabolism, and excretion of subcutaneous doxycycline injections, and evaluates the suitability of the current dosage regimen for inhibiting chlamydial pathogens. The results suggest that the current doxycycline dosage remained therapeutically effective for up to six days after each dose, with some accumulation over successive doses. All koalas in the study improved clinically and tested negative for chlamydial pathogens post-treatment before being released. This study contributes to determining the optimal dosage of doxycycline to treat chlamydiosis safely and effectively in infected koalas.

Abstract

Six mature, male koalas (Phascolarctos cinereus), with clinical signs of chlamydiosis, were administered doxycycline as a 5 mg/kg subcutaneous injection, once a week for four weeks. Blood was collected at standardised time points (T = 0 to 672 h) to quantify the plasma doxycycline concentrations through high-pressure liquid chromatography (HPLC). In five koalas, the doxycycline plasma concentration over the first 48 h appeared to have two distinct elimination gradients; therefore, a two-compartmental analysis was undertaken to describe the pharmacokinetic (PK) profile. The average ± SD maximum plasma concentration (C_max) was 312.30 ± 107.74 ng/mL, while the average time ± SD taken to reach the maximum plasma concentration (T_max) was 1.68 ± 1.49 h. The mean ± SD half-life of the distribution phase (T_1/2 α) and the elimination phase (T_1/2 β) were 10.51 ± 7.15 h and 82.93 ± 37.76 h, respectively. The average ± SD percentage of doxycycline binding to koala plasma protein was 83.65 ± 4.03% at three different concentrations, with a mean unbound fraction (fu) of 0.16. Using probability of target attainment modelling, doxycycline plasma concentrations were likely to inhibit 90% of pathogens with the doxycycline minimum inhibitory concentration (MIC) of 8.0–31.0 ng/mL, and the reported doxycycline MIC to inhibit Chlamydia pecorum isolates at the area under the curve/minimum inhibitory concentration (AUC/MIC) target of ≥24. All koalas were confirmed to be negative for Chlamydia pecorum using loop-mediated isothermal amplification (LAMP), from ocular and penile urethra swabs, three weeks after the last doxycycline injection.

Determination of In Vivo Biotransformation Kinetics Using Early-Time Biota Concentrations

Technical challenges have hampered the characterization of biotransformation kinetics-a critical link in understanding and predicting the toxicokinetics and ecotoxicology of organic compounds. A shortcut approach to characterize the in vivo biotransformation rate constant (k_M ) with incomplete pathway or metabolite details was proposed. The value of k_M can be derived as 2tln1fPC(t)) , with f_PC (t) being the molar equivalent fraction of the parent compound (PC) at an early time t in both constant exposure and decay source chemical uptake scenarios. The approximation-based k_M values agreed well with k_M values derived from rigorous fitting or toxicokinetic modeling (n = 42, root mean square error = 0.30) with accuracy exceeding those of typical toxicokinetic or partitioning models. The method is accurate when sampling time is adequately resolved (i.e., t < ln(2)/k_M ) but will likely produce biased k_M values with improper time-averaging. The approximate equation yields consistent theoretical expectations for fast and slow biotransformation reactions and is fully compatible with standard bioaccumulation and toxicity testing protocols. The simplification strategy circumvents statistical complications and numerical issues inherent in regressing or modeling the toxicokinetics of multimetabolite systems and may be adapted to similar problems at other physiological scales or ecotoxicological contexts. The method can help advance interspecies comparison of chemical metabolism and support the development of in vitro-in vivo extrapolations and in silico models needed for building next-generation ecological and health risk-assessment practices. Environ Toxicol Chem 2022;41:148-158. © 2021 SETAC.

Assessment of the Kochak-Benet Equation for Hepatic Clearance for the Parallel-Tube Model: Relevance of Classic Clearance Concepts in PK and PBPK

This report reviews concepts related to operation of the classic parallel-tube model (PTM) for hepatic disposition and examines two recent proposals of a newly derived equation to describe hepatic clearance (CL_H). It is demonstrated that the proposed equation is identical to a re-arrangement of an earlier relationship from Pang and Rowland and provides a means of calculation of intrinsic clearance (CL_int,PTM) rather than CL_H as posed. We further demonstrate how classic hepatic clearance models with an assumed CL_int, while subject to numerous limitations, remain highly useful and necessary in both traditional pharmacokinetics (PK) and physiologically based pharmacokinetic (PBPK) modeling.

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Variability of the unbound fraction in plasma (f_u) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f_u-estimates between labs for a range of different drugs, and to develop and validate an in silico f_u-prediction method and compare the results to the lab variability.A new in silico method with prediction accuracy (Q²) of 0.69 for log f_u was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f_u for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds.Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f_u-estimations and improving safety of drug candidates entering the clinical phase.

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

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

Latest impact of engineered human liver platforms on drug development

Drug-induced liver injury (DILI) is a leading cause of drug attrition, which is partly due to differences between preclinical animals and humans in metabolic pathways. Therefore, in vitro human liver models are utilized in biopharmaceutical practice to mitigate DILI risk and assess related mechanisms of drug transport and metabolism. However, liver cells lose phenotypic functions within 1–3 days in two-dimensional monocultures on collagen-coated polystyrene/glass, which precludes their use to model the chronic effects of drugs and disease stimuli. To mitigate such a limitation, bioengineers have adapted tools from the semiconductor industry and additive manufacturing to precisely control the microenvironment of liver cells. Such tools have led to the fabrication of advanced two-dimensional and three-dimensional human liver platforms for different throughput needs and assay endpoints (e.g., micropatterned cocultures, spheroids, organoids, bioprinted tissues, and microfluidic devices); such platforms have significantly enhanced liver functions closer to physiologic levels and improved functional lifetime to >4 weeks, which has translated to higher sensitivity for predicting drug outcomes and enabling modeling of diseased phenotypes for novel drug discovery. Here, we focus on commercialized engineered liver platforms and case studies from the biopharmaceutical industry showcasing their impact on drug development. We also discuss emerging multi-organ microfluidic devices containing a liver compartment that allow modeling of inter-tissue crosstalk following drug exposure. Finally, we end with key requirements for engineered liver platforms to become routine fixtures in the biopharmaceutical industry toward reducing animal usage and providing patients with safe and efficacious drugs with unprecedented speed and reduced cost.

Precisely adjusting the hepatic clearance of highly extracted drugs using the modified well-stirred model

Hepatic clearance has been widely studied for over 50 yr. Many models have been developed using either theoretical or empirical tests to predict drug metabolism. The well-stirred, parallel-tube, and dispersion metabolic models have been extensively discussed. However, to our knowledge, these models cannot fully describe all relevant scenarios in hepatic clearance. We addressed this issue using the isolated perfused rat liver technique with minor modifications. Diazepam was selected to illustrate different levels of drug plasma-protein binding by changing the added concentration of human serum albumin. The free fractions of diazepam at different albumin concentrations were assayed by rapid equilibrium dialysis. The experimental data provide new insights concerning an accepted formula used to describe hepatic clearance. Regarding drug concentrations passing through the liver, the driving force concentration (C_H,ss) in terms of C_in (influx in the liver) or C_out (efflux from the liver) needs to be carefully considered when determining drug hepatic and intrinsic clearances. The newly established model, termed the modified well-stirred model, which was derived from the original formula, successfully estimated hepatic drug metabolism. Using the modified well-stirred model, a theoretical driving force concentration of diazepam passing through the liver was evaluated. The model was further used to assess the predictability of in vitro to in vivo extrapolation. This study was not intended to refute the existing models, but rather to augment them using experimental data. The results stress the importance of proper calculation of dose when the drug clearance deviates from the prediction of the well-stirred model.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Prediction of a clinically effective dose of THY1773, a novel V1B receptor antagonist, based on preclinical data

THY1773 is a novel arginine vasopressin 1B (V1B ) receptor antagonist that is under development as an oral drug for the treatment of major depressive disorder (MDD). Here we report our strategy to predict a clinically effective dose of THY1773 for MDD in the preclinical stage, and discuss the important insights gained by retrospective analysis of prediction accuracy. To predict human pharmacokinetic (PK) parameters, several extrapolation methods from animal or in vitro data to humans were investigated. The fu correction intercept method and two-species-based allometry were used to extrapolate clearance from rats and dogs to humans. The physiologically based pharmacokinetics (PBPK)/receptor occupancy (RO) model was developed by linking free plasma concentration with pituitary V1B RO by the Emax model. As a result, the predicted clinically effective dose of THY1773 associated with 50% V1B RO was low enough (10 mg/day, or at maximum 110 mg/day) to warrant entering phase 1 clinical trials. In the phase 1 single ascending dose study, TS-121 capsule (active ingredient: THY1773) showed favorable PKs for THY1773 as expected, and in the separately conducted phase 1 RO study using positron emission tomography, the observed pituitary V1B RO was comparable to our prediction. Retrospective analysis of the prediction accuracy suggested that the prediction methods considering plasma protein binding, and avoiding having to apply unknown scaling factors obtained in animals to humans, would lead to better prediction. Selecting mechanism-based methods with reasonable assumptions would be critical for the successful prediction of a clinically effective dose in the preclinical stage of drug development.

Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL_int ) and fractional contribution of the metabolizing enzyme toward total metabolism (f_m ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL_int and f_m , 29 and 20 new drug applications were included for evaluation, respectively. For CL_int , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f_m , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL_int were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f_m from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f_m when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL_int and f_m still need to be optimized based on in vivo data.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Preclinical in vitro and in vivo pharmacokinetic properties of danicamtiv, a new targeted myosin activator for the treatment of dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a disease of the myocardium defined by left ventricular enlargement and systolic dysfunction leading to heart failure. Danicamtiv, a new targeted myosin activator designed for the treatment of DCM, was characterised in in vitro and in vivo preclinical studies. Danicamtiv human hepatic clearance was predicted to be 0.5 mL/min/kg from in vitro metabolic stability studies in human hepatocytes. For human, plasma protein binding was moderate with a fraction unbound of 0.16, whole blood-to-plasma partitioning ratio was 0.8, and danicamtiv showed high permeability and no efflux in a Caco-2 cell line. Danicamtiv metabolism pathways in vitro included CYP-mediated amide-cleavage, N-demethylation, as well as isoxazole- and piperidine-ring-opening. Danicamtiv clearance in vivo was low across species with 15.5, 15.3, 1.6, and 5.7 mL/min/kg in mouse, rat, dog, and monkey, respectively. Volume of distribution ranged from 0.24 L/kg in mouse to 1.7 L/kg in rat. Oral bioavailability ranged from 26% in mouse to 108% in dog. Simple allometric scaling prediction of human plasma clearance, volume of distribution, and half-life was 0.64 mL/min/kg, 0.98 L/kg, and 17.7 h, respectively. Danicamtiv preclinical attributes and predicted human pharmacokinetics supported advancement toward clinical development.

Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies

3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CL_int) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CL_int within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.

Comparison of In Vitro to In Vivo Extrapolation Approaches for Predicting Transporter-Mediated Hepatic Uptake Clearance Using Suspended Rat Hepatocytes

Clearance (CL) prediction remains a significant challenge in drug discovery, especially when complex processes such as drug transporters are involved. The present work explores various in vitro to in vivo extrapolation (IVIVE) approaches to predict hepatic CL driven by uptake transporters in rat. Broadly, two different IVIVE methods using suspended rat hepatocytes were compared: initial uptake CL (PSu,inf) and intrinsic metabolic CL (CLint,met) corrected by unbound hepatocytes to medium partition coefficient (Kpuu). Kpuu was determined by temperature method (Temp Kpuu,ss), homogenization method (Hom Kpuu,ss), and initial rate method (Kpuu,V0). In addition, the impact of bovine serum albumin (BSA) on each of these methods was investigated. Twelve compounds, which are known substrates of organic anion-transporting polypeptides representing diverse chemical matter, were selected for these studies. As expected, CLint,met alone significantly underestimated hepatic CL for all the test compounds. Overall, predicted hepatic CL using PSu,inf with BSA, Hom Kpuu,ss with BSA, and Temp Kpuu,ss showed the most robust correlation with in vivo rat hepatic CL. Adding BSA improved hepatic CL prediction for selected compounds when using the PSu,inf and Hom Kpuu,ss methods, with minimal impact on the Temp Kpuu,ss and Kpuu,V0 methods. None of the IVIVE approaches required an empirical scaling factor. These results suggest that supplementing rat hepatocyte suspension with BSA may be essential in drug discovery research for novel chemical matters to improve CL prediction. SIGNIFICANCE STATEMENT: The current investigation demonstrates that hepatocyte uptake assay supplemented with 4% bovine serum albumin is a valuable tool for estimating unbound hepatic uptake clearance (CL) and Kpuu. Based upon the extended clearance concept, direct extrapolation from these in vitro parameters significantly improved the overall hepatic CL prediction for organic anion-transporting polypeptide substrates in rat. This study provides a practical in vitro to in vivo extrapolation strategy for predicting transporter-mediated hepatic CL in early drug discovery.

Examining Uncertainty in In Vitro-In Vivo Extrapolation Applied in Fish Bioconcentration Models

In vitro biotransformation rates were determined for 30 chemicals, mostly fragrance ingredients, using trout liver S9 fractions (RT-S9) and incorporated into in vitro-in vivo extrapolation (IVIVE) models to predict bioconcentration factors (BCFs). Predicted BCFs were compared against empirical BCFs to explore potential major uncertainties involved in the in vitro methods and IVIVE models: (i) in vitro chemical test concentrations; (ii) different gill uptake rate constant calculations (k₁); (iii) protein binding (different calculations and measurement of the fraction of unbound chemical, f_U); (iv) species differences; and (v) extrahepatic biotransformation. Predicted BCFs were within 0.5 log units for 44% of the chemicals compared to empirical BCFs, whereas 56% were overpredicted by >0.5 log units. This trend of overprediction was reduced by alternative k₁ calculations to 32% of chemicals being overpredicted. Moreover, hepatic in vitro rates scaled to whole body biotransformation rates (k_B) were compared against in vivo k_B estimates. In vivo k_B was underestimated for 79% of the chemicals. Neither lowering the test concentration, nor incorporation of new measured f_U values, nor species matching avoided the tendency to overpredict BCFs indicating that further improvements to the IVIVE models are needed or extrahepatic biotransformation plays an underestimated role.

Are There Any Experimental Perfusion Data that Preferentially Support the Dispersion and Parallel-Tube Models over the Well-Stirred Model of Organ Elimination?

In reviewing previously published isolated perfused rat liver studies, we find no experimental data for high-clearance metabolized drugs that reasonably or unambiguously support preference for the dispersion and parallel-tube models versus the well-stirred model of organ elimination when only entering and exiting drug concentrations are available. It is likely that the investigators cited here may have been influenced by: 1) the unphysiologic aspects of the well-stirred model, which may have led them to undervalue the studies that directly test the various hepatic disposition models for high-clearance drugs (for which model differences are the greatest); 2) experimental assumptions made in the last century, which are no longer valid today, related to the predictability of in vivo outcomes from in vitro measures of drug elimination and the influence of albumin in hepatic drug uptake; and 3) a lack of critical review of previously reported experimental studies, resulting in inappropriate interpretation of the available experimental data. The number of papers investigating the theoretical aspects of the dispersion, parallel-tube, and well-stirred models of hepatic elimination greatly outnumber the papers that actually examine the experimental evidence available to substantiate these models. When all experimental studies that measure organ elimination using entering and exiting drug concentrations at steady state are critically reviewed, the simple but unphysiologic well-stirred model is the only model that can describe all trustworthy published available data. SIGNIFICANCE STATEMENT: Although the dispersion model of hepatic elimination more adequately reflects physiologic reality, there are no convincing experimental data that unambiguously favor this model. The well-stirred model can describe all well-designed perfusion studies with high-clearance drugs and nondrug substrates, but the field has not recognized this because of hesitation to accept a nonphysiologic model and flawed attempts to utilize in vitro-in vivo extrapolation approaches.