In Vitro-in Vivo Extrapolation Research Articles

Sex, Age, and Species Differences of Perfluorooctanoic Acid Modeled by Flow- versus Permeability-limited Physiologically-Based Pharmacokinetic Models.

This study aimed to investigate sex, age, and species differences of perfluorooctanoic acid (PFOA) using physiologically-based pharmacokinetic (PBPK) models in rats and humans. PBPK models were generally developed as either flow- or permeability-limited models. The flow-limited model is cost-effective and allows for human PK prediction through simple allometric scaling, while the permeability-limited model can incorporate detailed information on the disposition process through in vitro-in vivo extrapolation (IVIVE). PFOA was administered via oral or intravenous administration with 5mg/kg in male and female rats of different ages and the data was used to develop the PBPK models. Our results showed that both models successfully captured sex differences in rats, while only the flow-limited model with male rats and the permeability-limited model with both male and female rats provided comparable predictions in the human clinical study. More than the flow-limited model, the permeability-limited model effectively explained sex differences in rats and species differences through IVIVE. Additionally, the ontogeny-based mechanistic description of PFOA disposition enabled the interpretation of age- and sex-dependent pharmacokinetics. Although the flow-limited PBPK model lacked mechanistic interpretability compared to the permeability-limited model, it demonstrated reliable human prediction through simple allometric scaling. In conclusion, the permeability PBPK model could interpret age, sex, and species differences and it could improve the accuracy of human prediction.

Avapritinib Carries the Risk of Drug Interaction via Inhibition of UDP-Glucuronosyltransferase (UGT) 1A1.

Avapritinib is the only drug for adult patients with PDGFRA exon 18 mutated unresectable or metastatic Gastrointestinal Stromal Tumor (GIST). Although avapritinib has been approved by the FDA for three years, little is known about the risk of Drug-drug Interac-tions (DDIs) via UDP-glucuronyltransferases (UGTs) inhibition. The aim of the present study was to systematically evaluate the inhibitory effects of avapritinib against UGTs and to quantitatively estimate its potential DDIs risk in vivo. Recombinant human UGTs were employed to catalyze the glucuronidation of sub-strates in a range of concentrations of avapritinib. The kinetics analysis was performed to evaluate the inhibition types of avapritinib against UGTs. The quantitative prediction of DDIs was done using In vitro-in vivo Extrapolation (IVIVE). Avapritinib had a potent competitive inhibitory effect on UGT1A1. Quantitative predic-tion results showed that avapritinib administered at clinical doses might result in a 14.85% in-crease in the Area Under the Curve (AUC) of drugs primarily cleared by UGT1A1. Moreover, the Rgut value was calculated to be 18.44. Avapritinib has the potential to cause intestinal DDIs via the inhibition of UGT1A1. Additional attention should be paid when avapritinib is coadministered with UGT1A1 substrates.

In Vitro In Vivo Extrapolation and Bioequivalence Prediction for Immediate-Release Capsules of Cefadroxil Based on a Physiologically-Based Pharmacokinetic ACAT Model.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil.A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef®).The developed PBPK model satisfactorily predicted Cmax and AUC0-t after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef®) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for Cmax and AUC0-t in the FDA acceptable limits.The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.

Evaluation of the inhibitory effects of antigout drugs on human carboxylesterases in vitro

Gout is an immune-metabolic disease that frequently coexists with multiple comorbidities such as chronic kidney disease, cardiovascular disease and metabolic syndrome, therefore, it is often treated in combination with these complications. The present study aimed to evaluate the inhibitory effect of antigout drugs (allopurinol, febuxostat, topiroxostat, benzbromarone, lesinurad and probenecid) on the activity of the crucial phase I drug-metabolizing enzymes, carboxylesterases (CESs). 2-(2-benzoyl-3-methoxyphenyl) benzothiazole (BMBT) and fluorescein diacetate (FD) were utilized as the probe reactions to determine the activity of CES1 and CES2, respectively, through in vitro culturing with human liver microsomes. Benzbromarone and lesinurad exhibited strong inhibition towards CESs with Ki values of 2.16 and 5.15 μM for benzbromarone towards CES1 and CES2, respectively, and 2.94 μM for lesinurad towards CES2. In vitro-in vivo extrapolation (IVIVE) indicated that benzbromarone and lesinurad might disturb the metabolic hydrolysis of clinical drugs in vivo by inhibiting CESs. In silico docking showed that hydrogen bonds and hydrophobic interactions contributed to the intermolecular interactions of antigout drugs on CESs. Therefore, vigilant monitoring of potential drug-drug interactions (DDIs) is imperative when co-administering antigout drugs in clinical practice.

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).

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. 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. 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. 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.

In Vitro to In Vivo Scalars for Drug Clearance in Nonalcoholic Fatty Liver and Steatohepatitis.

In vitro-in vivo extrapolation (IVIVE) allows prediction of clinical outcomes across populations from in vitro data using specific scalars tailored to the biologic characteristics of each population. This study experimentally determined scalars for patients with varying degrees of nonalcoholic fatty liver disease (NAFLD), ranging from fatty liver to nonalcoholic steatohepatitis (NASH) and cirrhosis. Microsomal, S9, and cytosol fractions were extracted from 36 histologically normal and 66 NAFLD livers (27 nonalcoholic fatty liver [NAFL], 13 NASH, and 26 NASH with cirrhosis). Corrected microsomal protein per gram liver (MPPGL) progressively decreased with disease severity (26.8, 27.4, and 24.3 mg/g in NAFL, NASH, and NASH/cirrhosis, respectively, compared with 35.6 mg/g in normal livers; ANOVA, P < 0.001). Homogenate, S9, and cytosolic protein showed a consistent trend of decline in NASH/cirrhosis relative to normal control (post-hoc t test, P < 0.05). No differences across the groups were observed in homogenate, S9, cytosolic, and microsomal protein content in matched kidney samples. MPPGL-based scalars that combine protein content with liver size revealed that the reduction in MPPGL in NAFL and NASH was compensated by the reported increase in liver size (relative scalar ratios of 0.96 and 0.99, respectively), which was not the case with NASH/cirrhosis (ratio of 0.63), compared with healthy control. Physiologically based pharmacokinetics-informed global sensitivity analysis of the relative contribution of IVIVE scalars (hepatic CYP3A4 abundance, MPPGL, and liver size) to variability in exposure (area under the curve) to three CYP3A substrates (alprazolam, midazolam, and ibrutinib) revealed enzyme abundance as the most significant parameter, followed by MPPGL, whereas liver volume was the least impactful factor. SIGNIFICANCE STATEMENT: Nonalcoholic fatty liver disease-specific scalars necessary for extrapolation from in vitro systems to liver tissue are lacking. These are required in clearance prediction and dose selection in nonalcoholic fatty liver and steatohepatitis populations. Previously reported disease-driven changes have focused on cirrhosis, with no data on the initial stages of liver disease. The authors obtained experimental values for microsomal, cytosolic, and S9 fractions and assessed the relative impact of microsomal scalars on predicted exposure to substrate drugs using physiologically based pharmacokinetics.

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.

Fish Physiologically Based Toxicokinetic Modeling Approach for In Vitro-In Vivo and Cross-Species Extrapolation of Endocrine-Disrupting Chemicals in Risk Assessment.

High-throughput in vitro assays combined with in vitro-in vivo extrapolation (IVIVE) leverage in vitro responses to predict the corresponding in vivo exposures and thresholds of concern. The integrated approach is also expected to offer the potential for efficient tools to provide estimates of chemical toxicity to various wildlife species instead of animal testing. However, developing fish physiologically based toxicokinetic (PBTK) models for IVIVE in ecological applications is challenging, especially for plausible estimation of an internal effective dose, such as fish equivalent concentration (FEC). Here, a fish PBTK model linked with the IVIVE approach was established, with parameter optimization of chemical unbound fraction, pH-dependent ionization and hepatic clearance, and integration of temperature effect and growth dilution. The fish PBTK-IVIVE approach provides not only a more precise estimation of tissue-specific concentrations but also a reasonable approximation of FEC targeting the estrogenic potency of endocrine-disrupting chemicals. Both predictions were compared with in vivo data and were accurate for most indissociable/dissociable chemicals. Furthermore, the model can help determine cross-species variability and sensitivity among the five fish species. Using the available IVIVE-derived FEC with target pathways is helpful to develop predicted no-effect concentration for chemicals with similar mode of action and support screening-level ecological risk assessment.

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example.

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Prediction of CYP Down Regulation after Tusamitamab Ravtansine Administration (aDM4-Conjugate), Based on an In Vitro-In Vivo Extrapolation Approach.

Tusamitamab ravtansine is an antibody-drug conjugate (ADC) composed of a humanized monoclonal antibody (IgG1) and DM4 payload. Even if DM4 and its main metabolite methyl-DM4 (Me-DM4) circulate at low concentrations after ADC administration, their potential as perpetrators of cytochrome P450 mediated drug-drug interaction was assessed. In vitro studies in human hepatocytes indicated that Me-DM4 elicited a clear concentration-dependent down regulation of cytochrome P450 enzymes (CYP3A4, 1A2, and 2B6). Because DM4 was unstable under the incubation conditions studied, the in vitro constants could not be determined for this entity. Thus, to predict the clinical relevance of this observed downregulation, an in vitro-in vivo extrapolation (IVIVE) pharmacokinetic (PK) based approach was developed. To mitigate model prediction errors and because of their similar inhibitory effect on tubulin polymerization, the same downregulation constants were used for DM4 and Me-DM4. This approach describes the time course of decreasing CYP3A4, 1A2, and 2B6 enzyme amounts as a function of circulating concentrations of DM4 and Me-DM4 predicted from a population PK model. The developed IVIVE-PK model showed that the highest CYP abundance decrease was observed for CYP3A4, with a transient reduction of < 10% from baseline. The impact on midazolam exposure, as probe substrate of CYP3A, was then simulated based on a physiologically-based PK static method. The maximal CYP3A4 abundance reduction was associated with a predicted midazolam area under the curve (AUC) ratio of 1.14. To conclude, the observed in vitro downregulation of CYPs by Me-DM4 is not expected to have relevant clinical impact.

Inhibitory Effects of Tricyclic Antidepressants on Human Liver Microsomal Morphine Glucuronidation: Application of IVIVE to Predict Potential Drug-Drug Interactions in Humans.

Tricyclic antidepressants (TCAs) are commonly co-administered with morphine as an adjuvant analgesic. Nevertheless, there remains a lack of information concerning metabolic drug-drug interactions (DDIs) resulting from TCA inhibition on morphine glucuronidation. This study aimed to (i) examine the inhibitory effects of TCAs (viz., amitriptyline, clomipramine, imipramine, and nortriptyline) on human liver microsomal morphine 3- and 6-glucuronidation and (ii) evaluate the potential of DDI in humans by employing in vitro-in vivo extrapolation (IVIVE) approaches. The inhibition parameters for TCA inhibition on morphine glucuronidation were derived from the in vitro system containing 2% BSA. The Ki values were employed to predict the DDI magnitude in vivo by using static and dynamic mechanistic PBPK approaches Results: TCAs moderately inhibited human liver microsomal morphine glucuronidation, with clomipramine exhibiting the most potent inhibition potency. Amitriptyline, clomipramine, imipramine, and nortriptyline competitively inhibited morphine 3- and 6-glucuronide formation with the respective Ki values of 91 ± 7.5 and 82 ± 11 μM, 23 ± 1.3 and 14 ± 0.7 μM, 103 ± 5 and 90 ± 7 μM, and 115 ± 5 and 110 ± 3 μM. Employing the static mechanistic IVIVE, a prediction showed an estimated 20% elevation in the morphine AUC when co-administered with either clomipramine or imipramine, whereas the predicted increase was <5% for amitriptyline or nortriptyline. PBPK modelling predicted an increase of less than 10% in the morphine AUC due to the inhibition of clomipramine and imipramine in both virtual healthy and cirrhotic populations. The results suggest that the likelihood of potential clinical DDIs arising from tricyclic antidepressant inhibition on morphine glucuronidation is low.

Ciprofol is primarily glucuronidated by UGT1A9 and predicted not to cause drug-drug interactions with typical substrates of CYP1A2, CYP2B6, and CYP2C19

Ciprofol is a novel intravenous anesthetic agent. Its major glucuronide metabolite, M4, is found in plasma and urine. However, the specific isoforms of UDP-glucuronosyltransferases (UGTs) that metabolize ciprofol to M4 remain unknown. This study systematically characterized UGTs that contribute to the formation of M4 using human liver microsomes (HLM), human intestinal microsomes (HIM), and human recombinant UGTs. The inhibitory potential of ciprofol and M4 against major human UGTs and cytochrome P450 enzymes (P450s) was also explored. In vitro-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) simulations were performed to predict potential in vivo drug-drug interactions (DDIs) caused by ciprofol. Glucuronidation of ciprofol followed Michaelis-Menten kinetics in both HLM and HIM with apparent Km values of 345 and 412 μM, Vmax values of 2214 and 444 nmol min−1·mg protein−1, respectively. The in vitro intrinsic clearances (CLint = Vmax/Km) for ciprofol glucuronidation by HLM and HIM were 6.4 and 1.1 μL min−1·mg protein−1, respectively. Human recombinant UGT studies revealed that UGT1A9 is the predominant isoform mediating M4 formation, followed by UGT1A7, with UGT1A8 playing a minor role. Ciprofol competitively inhibited CYP1A2 (Ki = 12 μM) and CYP2B6 (Ki = 4.7 μM), and noncompetitively inhibited CYP2C19 (Ki = 29 μM). No time-dependent inhibition by ciprofol was noted for CYP1A2, CYP2B6, or CYP2C19. In contrast, M4 showed limited or no inhibitory effects against selected P450s. Neither ciprofol nor M4 inhibited UGTs significantly. Initial IVIVE suggested potential ciprofol-mediated inhibition of CYP1A2, CYP2B6, and CYP2C19 inhibition in vivo. However, PBPK simulations showed no significant effect on phenacetin, bupropion, and S-mephenytoin exposure or peak plasma concentration. Our findings are pertinent for future DDI studies of ciprofol as either a perpetrator or victim drug.

PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives.

Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1st of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. In vitro-in vivo extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Significance Statement Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.

IVIVE-PBPK based new approach methodology for addressing early life toxicity induced by Bisphenol A

Bisphenol A (BPA) is a known endocrine disruptor mimicking natural estrogens with the potential to affect human health, especially during prenatal and postnatal exposure at or below current acceptable daily intake levels. Different adverse effects of BPA are still under investigation, and multiple mechanisms of action remain unexplored. This may be one of the reasons for the continuously changing tolerable daily intake (TDI) of BPA with the emergence of new adverse health effects over time. In addition, translational modelling through in vitro-in vivo extrapolation (IVIVE) can act as prerequisite bridge for translating in-vitro finding into human risk assessment. The objective of this study was to conduct in-vitro experiments and utilize an IVIVE-pregnancy physiologically based pharmacokinetic (P-PBPK) modeling to investigate developmental neurotoxicity and embryotoxicity in humans. The data obtained from human embryonic stem cells-based assays (study conducted between October 2020–2021) were used for the IVIVE-P-PBPK models to obtain the human equivalent doses (HEDs) which were further extrapolated to reference doses (RfDs). The results showed that simulated mean RfDs (μg/kg/day) derived from the HSD3B1 and NFATC2 gene of embryotoxicity and neurodevelopmental toxicity tests, respectively, were 4.94 and 5.18. The simulated RfDs were close to the temporary-tolerable daily intake (t-TDI) recommended by European Food Safety Authority (EFSA) in 2015 (t-TDI: 4 μg/kg·bw) and higher than the TDI of 2023 (0.2 ng/kg·bw). In conclusion, in-vitro toxicogenomics dose-response data combined with PBPK modeling can become a promising alternative new approach methodology (NAM) to support decision-making in chemical risk assessment. Based on the simulated RfDs derived from this NAM, the t-TDI set by EFSA in 2015 may be considered a safe exposure limit for mothers and fetuses at the current BPA intake levels in Chinese mothers. This study provided an animal-free new strategy for NAMs based risk assessment by combining toxicogenomics and computational toxicology.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Quantitative Translation of Substrate Intrinsic Clearance from Recombinant CYP1A1 to Humans.

CYP1A1 is a cytochrome P450 family 1 enzyme that is mostly expressed in the extrahepatic tissues. To understand the CYP1A1 contribution to drug clearance in humans, we examined the in vitro-in vivo extrapolation (IVIVE) of intrinsic clearance (CLint) for a set of drugs that are in vitro CYP1A1 substrates. Despite being strong in vitro CYP1A1 substrates, 82% of drugs gave good IVIVE with predicted CLint within 2-3-fold of the observed values using human liver microsomes and hepatocytes, suggesting they were not in vivo CYP1A1 substrates due to the lack of extrahepatic contribution to CLint. Only three drugs (riluzole, melatonin and ramelteon) that are CYP1A2 substrates yielded significant underprediction of in vivo CLint up to 11-fold. The fold of CLint underprediction was linearly proportional to human recombinant CYP1A1 (rCYP1A1) CLint, indicating they were likely to be in vivo CYP1A1 substrates. Using these three substrates, a calibration curve can be developed to enable direct translation from in vitro rCYP1A1 CLint to in vivo extrahepatic contributions in humans. In vivo CYP1A1 substrates are planar and small, which is consistent with the structure of the active site. This is in contrast to the in vitro substrates, which include large and nonplanar molecules, suggesting rCYP1A1 is more accessible than what is in vivo. The impact of CYP1A1 on first-pass intestinal metabolism was also evaluated and shown to be minimal. This is the first study providing new insights on in vivo translation of CYP1A1 contributions to human clearance using in vitro rCYP1A1 data.

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

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

Bioconcentration Assessment in Fish Based on In Vitro Intrinsic Clearance: Predictivity of an Empirical Model Compared to In Vitro-In Vivo Extrapolation Models.

To estimate the bioconcentration factor (BCF), the in vitro intrinsic clearance (CLINVITRO,INT) from rainbow trout liver S9 fractions (RT-S9) can be applied to in vitro-in vivo extrapolation (IVIVE) models, yet uncertainties remain in model parameterization. An alternative model approach is evaluated: a regression model was built in the form log BCF = a × log Kow + b × log CLINVITRO,INT. The coefficients a and b were fitted based on a training set of 40 chemicals. A high robustness of the coefficients and good accuracy of BCF prediction were found on independent datasets of neutral organic chemicals (measured log Kow 3.3-6.2). BCF predictions were similar to or in better agreement with in vivo BCFs compared to IVIVE models (2.4- to 2.9- vs 2.8- to 3.6-fold misprediction) for training and test sets. Species-matched models (trout, carp) did not result in improvements. This study presents the largest dataset on CLINVITRO,INT and BCFs to assess predictivity of the RT-S9 assay. The robustness of the regression statistics on different datasets and the high statistical weight of the CLINVITRO,INT term illustrate the predictive power of the RT-S9 assay as an important step toward regulatory acceptance to replace animal experiments.

Potential risk of drug-drug interactions of ponatinib via inhibition against human UDP-glucuronosyltransferases

Ponatinib is an efficient oral tyrosine kinase inhibitor (TKI) for T315I-positive Ph + ALL and T315I-positive chronic myeloid leukemia (CML) or BCR-ABL when no other TKIs can be prescribed. In this research, we evaluated the inhibitory effects of ponatinib on human recombinant UDP-glucuronosyltransferases (UGTs) and predicted the magnitude of potential drug-drug interaction (DDI) risk of co-treatment with ponatinib and UGTs substrates by using in vitro-in vivo extrapolation (IVIVE) method. Our study presented that ponatinib showed a broad-spectrum inhibition against UGTs. Particularly, ponatinib exhibited potent inhibitory effects towards UGT1A7, UGT1A1, and UGT1A9 with IC50 values of 0.37, 0.41, and 0.89 μM, respectively, which might lead to clinically significant DDI.

Physiologically based pharmacokinetic model combined with reverse dose method to study the nephrotoxic tolerance dose of tacrolimus.

Nephrotoxicity is the most common side effect that severely limits the clinical application of tacrolimus (TAC), an immunosuppressive agent used in kidney transplant patients. This study aimed to explore the tolerated dose of nephrotoxicity of TAC in individuals with different CYP3A5 genotypes and liver conditions. We established a human whole-body physiological pharmacokinetic (WB-PBPK) model and validated it using data from previous clinical studies. Following the injection of 1mg/kg TAC into the tail veins of male rats, we developed a rat PBPK model utilizing the drug concentration-time curve obtained by LC-MS/MS. Next, we converted the established rat PBPK model into the human kidney PBPK model. To establish renal concentrations, the BMCL5 of the in vitro CCK-8 toxicity response curve (drug concentration range: 2-80mol/L) was extrapolated. To further investigate the acceptable levels of nephrotoxicity for several distinct CYP3A5 genotypes and varied hepatic function populations, oral dosing regimens were extrapolated utilizing in vitro-in vivo extrapolation (IVIVE). The PBPK model indicated the tolerated doses of nephrotoxicity were 0.14-0.185mg/kg (CYP3A5 expressors) and 0.13-0.155mg/kg (CYP3A5 non-expressors) in normal healthy subjects and 0.07-0.09mg/kg (CYP3A5 expressors) and 0.06-0.08mg/kg (CYP3A5 non-expressors) in patients with mild hepatic insufficiency. Further, patients with moderate hepatic insufficiency tolerated doses of 0.045-0.06mg/kg (CYP3A5 expressors) and 0.04-0.05mg/kg (CYP3A5 non-expressors), while in patients with moderate hepatic insufficiency, doses of 0.028-0.04mg/kg (CYP3A5 expressors) and 0.022-0.03mg/kg (CYP3A5 non-expressors) were tolerated. Overall, our study highlights the combined usage of the PBPK model and the IVIVE approach as a valuable tool for predicting toxicity tolerated doses of a drug in a specific group.