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.

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.

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.

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.

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.

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.

Tissue Drug Concentration

Blood flow enables the delivery of oxygen and nutrients to the different tissues of the human body. Drugs follow the same route as oxygen and nutrients; thus, drug concentrations in tissues are highly dependent on the blood flow fraction delivered to each of these tissues. Although the free drug concentration in blood is considered to correlate with pharmacodynamics, the pharmacodynamics of a drug is actually primarily commanded by the concentrations of drug in the aqueous spaces of bodily tissues. However, the concentrations of drug are not homogeneous throughout the tissues, and they rarely reflect the free drug concentration in the blood. This heterogeneity is due to differences in the blood flow fraction delivered to the tissues and also due to membrane transporters, efflux pumps, and metabolic enzymes. The rate of drug elimination from the body (systemic elimination) depends more on the driving force of drug elimination than on the free concentration of drug at the site from which the drug is being eliminated. In fact, the actual free drug concentration in the tissues results from the balance between the input and output rates. In the present paper, we develop a theoretical concept regarding solute partition between intravascular and extravascular spaces; discuss experimental research on aqueous/non-aqueous solute partitioning and clinical research on microdialysis; and present hypotheses to predict in-vivo elimination using parameters of in-vitro metabolism.

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.

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.

Evaluation and comparison of in vitro intrinsic clearance rates measured using cryopreserved hepatocytes from humans, rats, and rainbow trout

In vitro and in silico methods that can reduce the need for animal testing are being used with increasing frequency to assess chemical risks to human health and the environment. The rate of hepatic biotransformation is an important species-specific parameter for determining bioaccumulation potential and extrapolating in vitro bioactivity to in vivo effects. One approach to estimating hepatic biotransformation is to employ in vitro systems derived from liver tissue to measure chemical (substrate) depletion over time which can then be translated to a rate of intrinsic clearance (CL_int). In the present study, cryopreserved hepatocytes from humans, rats, and rainbow trout were used to measure CL_int values for 54 industrial and pesticidal chemicals at starting test concentrations of 0.1 and 1 μM. A data evaluation framework that emphasizes the behavior of Heat-Treated Controls (HTC) was developed to identify datasets suitable for rate reporting. Measured or estimated ("greater than" or "less than") CL_int values were determined for 124 of 226 (55 %) species-chemical-substrate concentration datasets with acceptable analytical chemistry. A large percentage of tested chemicals exhibited low HTC recovery values, indicating a substantial abiotic loss of test chemical over time. An evaluation of K_OW values for individual chemicals suggested that in vitro test performance declined with increasing chemical hydrophobicity, although differences in testing devices for mammals and fish also likely played a role. The current findings emphasize the value of negative controls as part of a rigorous approach to data quality assessment for in vitro substrate depletion studies. Changes in current testing protocols can be expected to result in the collection of higher quality data. However, poorly soluble chemicals are likely to remain a challenge for CL_int determination.

Impact of Plasma Protein Binding in Drug Clearance Prediction: A Data Base Analysis of Published Studies and Implications for In Vitro-In Vivo Extrapolation

Plasma protein-mediated uptake (PMU) and its effect on clearance (CL) prediction have been studied in various formats; however, a comprehensive analysis of the overall impact of PMU on CL parameters from hepatocyte assays (routinely used for IVIVE) has not previously been performed. The following work collated data reflecting the effect of PMU for 26 compounds with a wide variety of physicochemical, drug, and in vivo CL properties. PMU enhanced the unbound intrinsic clearance in vitro (CL_{int,u in vitro}) beyond that conventionally calculated using fraction unbound and was correlated with the unbound fraction of drug in vitro and in plasma (fu_p) and absolute unbound intrinsic clearance in vivo (CL_{int,u in vivo}) in both rat and human hepatocytes. PMU appeared to be more important for highly bound (fu_p < 0.1) and high CL_{int,u in vivo} drugs. These trends were independent of species, assay conditions, ionization, and extended clearance classification system group, although the type of plasma protein used in in vitro assays may require further investigation. Such generalized trends (spanning fu_p 0.0008-0.99) may suggest a generic mechanism behind PMU; however, multiple drug-dependent mechanisms are also possible. Using the identified relationship between the impact of PMU on CL_{int,u in vitro} and fu_p, PMU-enhanced predictions of CL_{int,u in vivo} were calculated for both transporter substrates and metabolically cleared drugs. PMU was accurately predicted, and incorporation of predicted PMU improved the IVIVE of hepatic CL, with an average fold error of 1.17 and >50% of compounds predicted within a 2-fold error for both rat and human data sets (n ≥ 100). SIGNIFICANCE STATEMENT: Current strategies for prediction of hepatic clearance from in vitro data are recognized to be inaccurate, but they do not account for PMU. The impact of PMU on CL_{int,u in vitro} is wide ranging and can be predicted based on fraction unbound in plasma and applied to CL_{int,u in vitro} values obtained by standard procedures in the absence of plasma protein. Such PMU-enhanced predictions improved IVIVE, and future studies may easily incorporate this PMU relationship to provide more accurate IVIVE.

Investigating the Theoretical Basis for In Vitro-In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways

Extensive studies have been conducted to predict in vivo metabolic clearance from in vitro human liver metabolism parameters (i.e., in vitro-in vivo extrapolation (IVIVE)) with little success. Here, deriving IVIVE from first principles, we show that the product of fraction unbound in the blood and the predicted in vivo intrinsic clearance determined from hepatocyte or microsomal incubations is the lower boundary condition for in vivo hepatic clearance and the prerequisite for IVIVE predictions to be valid, regardless of extraction ratio. For 60-80% of drugs evaluated here, this product is markedly less than the in vivo measured clearance, a result that violates the lower boundary of the predictive relationship. This can only be explained by (a) suboptimal in vitro metabolic stability assay conditions, (b) significant error in the assumption that in vitro intrinsic clearance determinations will predict in vivo intrinsic clearance simply by scaling-up the amount of enzyme (in vitro incubation to in vivo liver), and/or (c) the methods of determining fraction unbound are incorrect. We further suggest that widely employed organ blood flow values underpredict the effective blood flow within the organ by approximately 2.5-fold, thus impacting IVIVE of high clearance compounds. We propose future pathways that should be investigated in terms of the relationship to experimentally measured clearance values, rather than model-dependent intrinsic clearance. IVIVE outcome can be improved by estimating the ratio of unbound drug concentration in the liver tissue to the liver plasma, examining the assumption of the free drug theory (i.e., there are no transporter effects at the blood cell membrane) and the finding that the upper limit of organ clearance may be greater than blood flow entering the organ.