In Vitro To In Vivo Extrapolation Research Articles

An integrated in vitro approach to identifying chemically induced oxidative stress and toxicity in mitochondria

Growing concerns exist about increasing chemical usage and the potential health risks. Developing an efficient strategy to evaluate or predict the toxicity of chemicals is necessary. The mitochondria are essential organelles for cell maintenance and survival but also serve as one of the main targets of toxic chemicals. Mitochondria play an important role in the pathology of respiratory disease, and many environmental chemicals may induce impairment of the respiratory system through mitochondrial damage. This study aimed to develop integrated in vitro approaches to identify chemicals that could induce adverse health effects by increasing mitochondria-mediated oxidative stress using the H441 cells, which have a club-cell-like phenotype. Twenty-six environmental toxicants (biocides, phthalates, bisphenols, and particles) were tested, and each parameter was compared with eleven reference compounds. The inhibitory concentrations (IC20 and IC50) and benchmark doses (BMD) of the tested compounds were estimated from three in vitro assays, and the toxic concentration was determined. At the lowest IC20, the effects of compounds on mitochondrial reactive oxygen species (ROS) production and mitochondrial membrane potential (MMP) were compared. Principal component analysis and k-mean clustering were performed to cluster the chemicals that had comparable effects on the cells. Chemicals that induce mitochondrial damage at different concentrations were used for an in-depth high-tier assessment and classification as electron transport system (ETS) uncoupling or inhibiting agents. Additionally, using in vitro to in vivo extrapolation (IVIVE) tools, equivalent administration doses and maximum plasma concentrations of tested compounds in human were estimated. This study suggests an in vitro approach to identifying mitochondrial damage by integrating several in vitro toxicity tests and calculation modeling.

Animal-free assessment of developmental toxicity: Combining PBPK modeling with the ReproTracker assay

in vitro screening platforms to assess teratogenic potential of compounds are emerging rapidly. ReproTracker is a human induced pluripotent stem cells (hiPSCs)-based biomarker assay that is shown to identify the teratogenicity potential of new pharmaceuticals and chemicals reliably. In its current state, the assay is limited to identifying the potential teratogenic effects and does not immediately quantify a clinical dose relevant to the exposure of chemicals or drugs observable in mothers or fetuses. The goal of this study was to evaluate whether the ReproTracker assay can be extrapolated in vivo and quantitatively predict developmental toxicity exposure levels of two known human teratogens, thalidomide, and carbamazepine. Here, we utilized Physiologically Based Pharmacokinetic (PBPK) modeling to describe the pharmacokinetic behavior of these compounds and conducted an in vitro to in vivo extrapolation (IVIVE) approach to predict human equivalent effect doses (HEDs) that correspond with in vitro concentrations potentially associated with adverse outcomes in ReproTracker. The HEDs derived from the ReproTracker concentration predicted to cause developmental toxicity were close to the reported teratogenic human clinical doses and the HED derived from the rat or rabbit developmental toxicity study. The ReproTracker derived-HED revealed to be sensitive and protective of humans. Overall, this pilot study demonstrated the importance of integrating PBPK model in extrapolating and assessing developmental toxicity in vitro. The combination of these tools demonstrated that they could improve the safety assessment of drugs and chemicals without animal testing.

Dose Response, Dosimetric, and Metabolic Evaluations of Replacement PFAS Perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) Acid (HFPO-TeA).

Few studies are available on the environmental and toxicological effects of perfluoroalkyl ether carboxylic acids (PFECAs), such as GenX, which are replacing legacy PFAS in manufacturing processes. To collect initial data on the toxicity and toxicokinetics of a longer-chain PFECA, male and female Sprague Dawley rats were exposed to perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) acid (HFPO-TeA) by oral gavage for five days over multiple dose levels (0.3-335.2 mg/kg/day). Clinically, we observed mortality at doses >17 mg/kg/day and body weight changes at doses ≤17 mg/kg/day. For the 17 mg/kg/day dose level, T3 and T4 thyroid hormone concentrations were significantly decreased (p < 0.05) from controls and HFPO-TeA plasma concentrations were significantly different between sexes. Non-targeted analysis of plasma and in vitro hepatocyte assay extractions revealed the presence of another GenX oligomer, perfluoro-(2,5-dimethyl-3,6-dioxanonanoic) acid (HFPO-TA). In vitro to in vivo extrapolation (IVIVE) parameterized with in vitro toxicokinetic data predicted steady-state blood concentrations that were within seven-fold of those observed in the in vivo study, demonstrating reasonable predictivity. The evidence of thyroid hormone dysregulation, sex-based differences in clinical results and dosimetry, and IVIVE predictions presented here suggest that the replacement PFECA HFPO-TeA induces a complex and toxic exposure response in rodents.

Increasing confidence in new approach methodologies for inhalation risk assessment with multiple end point assays using 5-day repeated exposure to 1,3-dichloropropene

New Approach Methodologies (NAMs) are being widely used to reduce, refine, and replace, animal use in studying toxicology. For respiratory toxicology, this includes both in silico and in vitro alternatives to replace traditional in vivo inhalation studies. 1,3-Dichloropropene (1,3-DCP) is a volatile organic compound that is widely used in agriculture as a pre-planting fumigant. Short-term exposure of humans to 1,3-DCP can result in mucous membrane irritation, chest pain, headache, and dizziness. In our previous work, we exposed differentiated cells representing different parts of the respiratory epithelium to 1,3-DCP vapor, measured cytotoxicity, and did In Vitro to In Vivo Extrapolation (IVIVE). We have extended our previous study with 1,3-DCP vapors by conducting transcriptomics on acutely exposed nasal cultures and have implemented a separate 5-day repeated exposure with multiple endpoints to gain further molecular insight into our model. MucilAir™ Nasal cell culture models, representing the nasal epithelium, were exposed to six sub-cytotoxic concentrations of 1,3-DCP vapor at the air-liquid interface, and the nasal cultures were analyzed by different methodologies, including histology, transcriptomics, and glutathione (GSH) -depletion assays. We observed the dose-dependent effect of 1,3-DCP in terms of differential gene expression, change in cellular morphology from pseudostratified columnar epithelium to squamous epithelium, and depletion of GSH in MucilAir™ nasal cultures. The MucilAir™ nasal cultures were also exposed to 3 concentrations of 1,3-DCP using repeated exposure 4 h per day for 5 days and the histological analyses indicated changes in cellular morphology and a decrease in ciliated bodies and an increase in apoptotic bodies, with increasing concentrations of 1,3-DCP. Altogether, our results suggest that sub-cytotoxic exposures to 1,3-DCP lead to several molecular and cellular perturbations, providing significant insight into the mode-of-action (MoA) of 1,3-DCP using an innovative NAM model.

Innovations, Opportunities, and Challenges for Predicting Alteration in Drug-Metabolizing Enzyme and Transporter Activity in Specific Populations.

Drug-metabolizing enzymes and transporters (DMETs) are key regulators of the pharmacokinetics, efficacy, and toxicity of therapeutics. Over the past two decades, significant advancements in in vitro methodologies, targeted proteomics, in vitro to in vivo extrapolation methods, and integrated computational approaches such as physiologically based pharmacokinetic modeling have unequivocally contributed to improving our ability to quantitatively predict the role of DMETs in absorption, distribution, metabolism, and excretion and drug-drug interactions. However, the paucity of data regarding alterations in DMET activity in specific populations such as pregnant individuals, lactation, pediatrics, geriatrics, organ impairment, and disease states such as, cancer, kidney, and liver diseases and inflammation has restricted our ability to realize the full potential of these recent advancements. We envision that a series of carefully curated articles in a special supplementary issue of Drug Metabolism and Disposition will summarize the latest progress in in silico, in vitro, and in vivo approaches to characterize alteration in DMET activity and quantitatively predict drug disposition in specific populations. In addition, the supplementary issue will underscore the current scientific knowledge gaps that present formidable barriers to fully understand the clinical implications of altered DMET activity in specific populations and highlight opportunities for multistakeholder collaboration to advance our collective understanding of this rapidly emerging area. SIGNIFICANCE STATEMENT: This commentary highlights current knowledge and identifies gaps and key challenges in understanding the role of drug-metabolizing enzymes and transporters (DMETs) in drug disposition in specific populations. With this commentary for the special issue in Drug Metabolism and Disposition, the authors intend to increase interest and invite potential contributors whose research is focused or has aided in expanding the understanding around the role and impact of DMETs in drug disposition in specific populations.

Reproducibility of organ-level effects in repeat dose animal studies

This work estimates benchmarks for new approach method (NAM)performance in predictingorgan-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from theToxicityReference database (v2.1)for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, definedbyrepeated chemical only, chemical and species, or chemical and study type, were calculated. Concordancewas 39–88%, depending on organ, and was highest within species.Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) valueswhen available, was calculated by organ. Multilinear regression modeling,usingstudy descriptorsof organ-level effect values as covariates, was used to estimate total variance, mean square error(MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggeststudydescriptorsaccountedfor52–69% of totalvariance inorgan-levelLELs.RMSE ranged from0.41 to 0.68 log10-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from − 0.38 to − 0.19 log10mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, in vitro to in vivo extrapolation (IVIVE) was employed to compare bioactive concentrations from in vitro NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log10-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log10-mg/kg/day, with qualitative accuracy not exceeding 70%.

Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models.

Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass metabolism. The objective of the present study was to assess the predictability of human pharmacokinetics by using biphasic dissolution results obtained with the previously established BiPHa+ assay and PBPK tools. For six commercial drug products, formulated by different enabling technologies, the respective organic partitioning profiles were processed with two PBPK in silico modeling tools, namely PK-Sim and GastroPlus®, similar to extended-release dissolution profiles. Thus, a mechanistic dissolution/precipitation model of the assessed drug products was not required. The developed elimination/distribution models were used to simulate the pharmacokinetics of the evaluated drug products and compared with available human data. In essence, an in vitro to in vivo extrapolation (IVIVE) was successfully developed. Organic partitioning profiles obtained from the BiPHa+ dissolution analysis enabled highly accurate predictions of the pharmacokinetic behavior of the investigated drug products. In addition, PBPK models of (pro-)drugs with pronounced first-pass metabolism enabled adjustment of the solely passive diffusion predicting organic partitioning profiles, and increased prediction accuracy further.

Interpretation of Protein-Mediated Uptake of Statins by Hepatocytes Is Confounded by the Residual Statin-Protein Complex.

Inclusion of plasma (or plasma proteins) in human hepatocyte uptake studies narrows, but does not close, the gap in in vitro to in vivo extrapolation (IVIVE) of organic anion transporting polypeptide (OATP)-mediated hepatic clearance (CLh) of statins. We have previously shown that this "apparent" protein-mediated uptake effect (PMUE) of statins by OATP1B1-expressing cells, in the presence of 5% human serum albumin (HSA), is mostly an artifact caused by residual statin-HSA complex remaining in the uptake assay. We determined if the same was true with plated human hepatocytes (PHH) and if this artifact can be reduced using suspended human hepatocytes (SHH) and the oil-spin method. We quantified the uptake of a cocktail of five statins by PHH and SHH in the absence and presence of 5% HSA. After terminating the uptake assay, the amount of residual HSA was quantified by quantitative targeted proteomics. For both PHH and SHH, except for atorvastatin and cerivastatin, the increase in total, active, and passive uptake of the statins, in the presence of 5% HSA, was explained by the estimated residual stain-HSA complex. In addition, the increase in active statin uptake by SHH, where present, was marginal (<50%), much smaller than that observed with PHH. Such a marginal increase cannot bridge the gap in IVIVE of CLh of statins. These data disprove the prevailing hypotheses for the in vitro PMUE. A true PMUE should be evaluated using the uptake data corrected for the residual drug-protein complex. SIGNIFICANCE STATEMENT: We show that the apparent protein-mediated uptake (PMUE) of statins by human hepatocytes is largely confounded by residual statin when plated or suspended human hepatocytes are used. Therefore, mechanisms other than PMUE need to be explored to explain the underprediction of the in vivo human hepatic clearance of statins by human hepatocyte uptake assays.

Unravelling the Hepatic Elimination Mechanisms of Colistin.

Colistin is an antibiotic which is increasingly used as a last-resort therapy in critically-ill patients with multidrug resistant Gram-negative infections. The purpose of this study was to evaluate the mechanisms underlying colistin's pharmacokinetic (PK) behavior and to characterize its hepatic metabolism. In vitro incubations were performed using colistin sulfate with rat liver microsomes (RLM) and with rat and human hepatocytes (RH and HH) in suspension. The uptake of colistin in RH/HH and thefraction of unbound colistin in HH (fu,hep) was determined. In vitro to in vivo extrapolation (IVIVE) was employed to predict the hepatic clearance (CLh) of colistin. Slow metabolism was detected in RH/HH, with intrinsic clearance (CLint) values of 9.34± 0.50 and 3.25 ± 0.27mL/min/kg, respectively. Assuming the well-stirred model for hepatic drug elimination, the predicted rat CLh was 3.64± 0.22mL/min/kg which could explain almost 70% of the reported non-renal in vivo clearance. The predicted human CLh was 91.5 ± 8.83mL/min, which was within two-fold of the reported plasma clearance in healthy volunteers. When colistin was incubated together with the multidrug resistance-associated protein (MRP/Mrp) inhibitor benzbromarone, the intracellular accumulation of colistin in RH/HH increased significantly. These findings indicate the major role of hepatic metabolism in the non-renal clearance of colistin, while MRP/Mrp-mediated efflux is involved in the hepatic disposition of colistin. Our data provide detailed quantitative insights into the hereto unknown mechanisms responsible for non-renal elimination of colistin.

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.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

‘Cell Painting’ is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

Quantitative interpretation of ToxTracker dose-response data for potency comparisons and mode-of-action determination.

ToxTracker is an in vitro mammalian stem cell-based reporter assay that detects activation of specific cellular signaling pathways (DNA damage, oxidative stress, and/or protein damage) upon chemical exposure using flow cytometry. Here we used quantitative methods to empirically analyze historical control data, and dose-response data across a wide range of reference chemicals. First, we analyzed historical control data to define a fold-change threshold for identification of a significant positive response. Next, we used the benchmark dose (BMD) combined-covariate approach for potency ranking of a set of more than 120 compounds; the BMD values were used for comparative identification of the most potent inducers of each reporter. Lastly, we used principal component analysis (PCA) to investigate functional and statistical relationships between the ToxTracker reporters. The PCA results, based on the BMD results for all substances, indicated that the DNA damage (Rtkn, Bscl2) and p53 (Btg2) reporters are functionally complementary and indicative of genotoxic stress. The oxidative stress (Srxn1 and Blvrb) and protein stress (Ddit3) reporters are independent indicators of cellular stress, and essential for toxicological profiling using the ToxTracker assay. Overall, dose-response modeling of multivariate ToxTracker data can be used for potency ranking and mode-of-action determination. In the future, IVIVE (in vitro to in vivo extrapolation) methods can be employed to determine in vivo AED (administered equivalent dose) values that can in turn be used for human health risk assessment.

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability.

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

Cross species extrapolation of the disruption of thyroid hormone synthesis by oxyfluorfen using in vitro data, physiologically based pharmacokinetic (PBPK), and thyroid hormone kinetics models

The thyroid hormones play key roles in physiological processes such as regulation of the metabolic and cardiac systems as well as the development of the brain and surrounding sympathetic nervous system. Recent efforts to screen environmental chemicals for their ability to alter thyroid hormone synthesis, transport, metabolism and/or function have identified novel chemicals that target key processes in the thyroid pathway. One newly identified chemical, oxyfluorfen, is a diphenyl-ether herbicide used for control of annual broadleaf and grassy weeds in a variety of tree fruit, nut, vine, and field crops. Using in vitro high-throughput screening (HTS) assays, oxyfluorofen was identified to be a potent inhibitor of the thyroidal sodium-iodide symporter (NIS). To quantitatively assess this inhibition mechanism in vivo, we extrapolated in vitro NIS inhibition data to in vivo disruption of thyroid hormones synthesis in rats using physiologically based pharmacokinetic (PBPK) and thyroid hormone kinetics models. The overall computational model (chemical PBPK and THs kinetic sub-models) was calibrated against in vivo data for the levels of oxyfluorfen in thyroid tissue and serum and against serum levels of thyroid hormones triiodothyronine (T3) and thyroxine (T4) in rats. The rat thyroid model was then extrapolated to humans using human in vitro HTS data for NIS inhibition and the chemical specific hepatic clearance rate in humans. The overall species extrapolated PBPK-thyroid kinetics model can be used to predict dose–response (% drop in thyroid serum levels compared to homeostasis) relationships in humans. These relationships can be used to estimate points of departure for health risks related to a drop in serum levels of TH hormones based on HTS assays in vitro to in vivo extrapolation (IVIVE), toxicokinetics, and physiological principles.

Quantitative in vitro to in vivo extrapolation of genotoxicity data provides protective estimates of in vivo dose.

Genotoxicity assessment is a critical component in the development and evaluation of chemicals. Traditional genotoxicity assays (i.e., mutagenicity, clastogenicity, and aneugenicity) have been limited to dichotomous hazard classification, while other toxicity endpoints are assessed through quantitative determination of points-of-departures (PODs) for setting exposure limits. The more recent higher-throughput in vitro genotoxicity assays, many of which also provide mechanistic information, offer a powerful approach for determining defined PODs for potency ranking and risk assessment. In order to obtain relevant human dose context from the in vitro assays, in vitro to in vivo extrapolation (IVIVE) models are required to determine what dose would elicit a concentration in the body demonstrated to be genotoxic using in vitro assays. Previous work has demonstrated that application of IVIVE models to in vitro bioactivity data can provide PODs that are protective of human health, but there has been no evaluation of how these models perform with in vitro genotoxicity data. Thus, the Genetic Toxicology Technical Committee, under the Health and Environmental Sciences Institute, conducted a case study on 31 reference chemicals to evaluate the performance of IVIVE application to genotoxicity data. The results demonstrate that for most chemicals considered here (20/31), the PODs derived from in vitro data and IVIVE are health protective relative to in vivo PODs from animal studies. PODs were also protective by assay target: mutations (8/13 chemicals), micronuclei (9/12), and aneugenicity markers (4/4). It is envisioned that this novel testing strategy could enhance prioritization, rapid screening, and risk assessment of genotoxic chemicals.

Evidence of the need for modified well-stirred model in vitro to in vivo extrapolation

In vitro to in vivo extrapolation (IVIVE), an approach for hepatic clearance (CLH) 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 CLH. 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 CLH 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.

Developing an In Vitro to In Vivo Extrapolation (IVIVE) Model to Predict Human Milk-to-Plasma Drug Concentration Ratios.

Determining the amount of drug transferred into human milk is critical for benefit-risk analysis of taking medication while breastfeeding. In this study, we developed an in vitro and in vivo extrapolation (IVIVE) model to predict human milk/plasma (M/P) drug concentration ratios. Drug unionized fractions at pH 7.0 (Fni,7.0) and 7.4 (Fni,7.4), drug fractions unbound in human plasma (fup) and milk (fum), and in vitro cell permeability in both directions (efflux ratio, ER) were incorporated into the IVIVE model. A multiple regression Emax model was chosen to predict fum from fup and polar surface area (PSA). A total of 97 drugs with experimental ER from Caco-2 cells were used to test the IVIVE model. The M/P ratios predicted by the IVIVE model had a 1.93-fold geometric mean fold error (GMFE) and 72% of predictions were within two-fold error (Pw2FE), which were superior to the performance of previously reported five models. The IVIVE model showed a reasonable prediction accuracy for passive diffusion drugs (GMFE = 1.71-fold, Pw2FE = 82%, N = 50), BCRP substrates (BCRP: GMFE = 1.91-fold, Pw2FE = 60%, N = 5), and substrates of P-gp and BCRP (GMFE = 1.74-fold, Pw2FE = 75%, N = 8) and a lower prediction performance for P-gp substrates (GMFE = 2.51-fold, Pw2FE = 55%, N = 22). By fitting the observed M/P ratios of 39 P-gp substrates, an optimized ER (1.61) was generated to predict the M/P ratio of P-gp substrates using the developed IVIVE model. Compared with currently available in vitro models, the developed IVIVE model provides a more accurate prediction of the drug M/P ratio, especially for passive diffusion drugs. The model performance is expected to be further improved when more experimental fum and ER data are available.

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

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 (CLhep,met) in Göttingen minipig, using a comprehensive set of compounds. 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 CLhep,met was performed by IVIVE using conventional and adapted formats of the well-stirred liver model. The best prediction of in vivo CLhep,met from scaled in vitro kinetic data was achieved using an empirical correction factor based on a ‘regression offset’ of the IVIV relationship. 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 CLhep,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.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues.

The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOXqP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOXqP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues. VPA and a series of structural analogues were tested with the devTOXqP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses. The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data. This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOXqP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.