Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Predictions of tissue concentrations of myclobutanil, oxyfluorfen, and pronamide in rat and human after oral exposures via GastroPlusTM physiologically based pharmacokinetic modelling

Heritage agrochemicals like myclobutanil, oxyfluorfen, and pronamide, are extensively used in agriculture, with well-established studies on their animal toxicity. Yet, human toxicity assessment relies on conventional human risk assessment approaches including the utilization of animal-based ADME (Absorption, Distribution, Metabolism, and Excretion) data. In recent years, Physiologically Based Pharmacokinetic (PBPK) modelling approaches have played an increasing role in human risk assessment of many chemicals including agrochemicals. This study addresses the absence of PBPK-type data for myclobutanil, oxyfluorfen, and pronamide by generating in vitro data for key input PBPK parameters (Caco-2 permeability, rat plasma binding, rat blood to plasma ratio, and rat liver microsomal half-life), followed by generation of PBPK models for these three chemicals via the GastroPlusTM software. Incorporating these experimental input parameters into PBPK models, the prediction accuracy of plasma AUC (area under curve) was significantly improved. Validation against rat oral administration data demonstrated substantial enhancement. Steady-state plasma concentrations (Css) of pronamide aligned well with published data using measured PBPK parameters. Following validation, parent-based tissue concentrations for these agrochemicals were predicted in humans and rats after single or 30-day repeat exposure of 10 mg/kg/day. These predicted concentrations contribute valuable information for future human toxicity risk assessments of these agrochemicals.

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.

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.

Optimization of 1,2,4-Triazole-Based p97 Inhibitors for the Treatment of Cancer

The AAA+ ATPase p97 (valosin-containing protein, VCP) is a master regulator of protein homeostasis and therefore represents a novel target for cancer therapy. Starting from a known allosteric inhibitor, NMS-873, we systematically optimized this scaffold, in particular, by applying a benzene-to-acetylene isosteric replacement strategy, specific incorporation of F, and eutomer/distomer identification, which led to compounds that exhibited nanomolar biochemical and cell-based potency. In cellular pharmacodynamic assays, robust effects on biomarkers of p97 inhibition and apoptosis, including increased levels of ubiquitinated proteins, CHOP and cleaved caspase 3, were observed. Compound (R)-29 (UPCDC-30766) represents the most potent allosteric inhibitor of p97 reported to date.

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

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

Evaluation of a Five-Probe Metabolic Control Cocktail in Long-Term Cocultured Human Hepatocytes

Hepatocyte cocultures like HepatoPac have become more frequently used for the assessment of the intrinsic clearance of slowly metabolised drugs during drug discovery due to a superiority in enzymatic activity over time compared to liver microsomal fractions and suspended primary hepatocytes. However, the relatively high cost and practical limitations prevent several quality control compounds to be included in studies and the activities of many important metabolic enzymes are consequently often not monitored. In this study, we have evaluated the possibility for a cocktail approach of quality control compounds in the human HepatoPac system to ensure adequate activity of the major metabolising enzymes. Five reference compounds were selected based on their known metabolic substrate profile in order to capture major CYP and non-CYP metabolic pathways in the incubation cocktail. The intrinsic clearance of the reference compounds when incubated as singlets or in a cocktail was compared and no considerable difference was observed. We show here that a cocktail approach of quality control compounds allows for easy and efficient evaluation of the metabolic competency of the hepatic coculture system over an extended incubation period.

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier (ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Preclinical Characterization and Development on NAQ as a Mu Opioid Receptor Partial Agonist for Opioid Use Disorder Treatment

Mu opioid receptor (MOR) selective antagonists and partial agonists have clinical utility for the treatment of opioid use disorders (OUDs). However, the development of many has suffered due to their poor pharmacokinetic properties and/or rapid metabolism. Our recent efforts to identify MOR modulators have provided 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(isoquinoline-3-carboxamido)morphinan (NAQ), a low-efficacy partial agonist, that showed sub-nanomolar binding affinity to the MOR (K i 0.6 nM) with selectivity over the delta opioid receptor (δ/μ 241) and the kappa opioid receptor (κ/μ 48). Its potent inhibition of the analgesic effect of morphine (AD50 0.46 mg/kg) and precipitation of significantly less withdrawal symptoms even at 100-fold greater dose than naloxone represents a promising molecule for further development as a novel OUD therapeutic agent. Therefore, further in vitro and in vivo characterization of its pharmacokinetics and pharmacodynamics properties was conducted to fully understand its pharmaceutical profile. NAQ showed favorable in vitro ADMET properties and no off-target binding to several classes of GPCRs, enzymes, and ion channels. Following intravenous administration, 1 mg/kg dose of NAQ showed a similar in vivo pharmacokinetic profile to naloxone; however, orally administered 10 mg/kg NAQ demonstrated significantly improved oral bioavailability over both naloxone and naltrexone. Abuse liability assessment of NAQ in rats demonstrated that NAQ functioned as a less potent reinforcer than heroin. Chronic 5 day NAQ pretreatment decreased heroin self-administration in a heroin-vs-food choice procedure similar to the clinically used MOR partial agonist buprenorphine. Taken together, these studies provide evidence supporting NAQ as a promising lead to develop novel OUD therapeutics.

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict In Vivo Nrf2 Activation by EGCG and Its Colonic Metabolites in Humans

(-)-Epigallocatechin gallate (EGCG) is prone to microbial metabolism when reaching the colon. This study aimed to develop a human physiologically based kinetic (PBK) model for EGCG, with sub-models for its colonic metabolites gallic acid and pyrogallol. Results show that the developed PBK model could adequately predict in vivo time-dependent blood concentrations of EGCG after either the single or repeated oral administration of EGCG under both fasting and non-fasting conditions. The predicted in vivo blood C_max of EGCG indicates that the Nrf2 activation is limited, while concentrations of its metabolites in the intestinal tract may reach levels that are higher than that of EGCG and also high enough to activate Nrf2 gene transcription. Taken together, combining in vitro data with a human PBK model allowed the prediction of a dose-response curve for EGCG-induced Nrf2-mediated gene expression in humans and provided insights into the contribution of gut microbial metabolites to this effect.

Subcutaneous Catabolism of Peptide Therapeutics: Bioanalytical Approaches and ADME Considerations

Many peptide drugs such as insulin and glucagon-like peptide (GLP-1) analogues are successfully administered subcutaneously (SC). Following SC injection, peptides may undergo catabolism in the SC compartment before entering systemic circulation, which could compromise their bioavailability and in turn affect their efficacy.This review will discuss how both technology and strategy have evolved over the past years to further elucidate peptide SC catabolism.Modern bioanalytical technologies (particularly liquid chromatography-high-resolution mass spectrometry) and bioinformatics platforms for data mining has prompted the development of in silico, in vitro and in vivo tools for characterizing peptide SC catabolism to rapidly address proteolytic liabilities and, ultimately, guide the design of peptides with improved SC bioavailability.More predictive models able to recapitulate the interplay between SC catabolism and other factors driving SC absorption are highly desirable to improve in vitro/in vivo correlations.We envision the routine incorporation of in vitro and in vivo SC catabolism studies in ADME screening funnels to develop more effective peptide drugs for SC delivery.

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

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

Metabolism of Speciociliatine, an Overlooked Kratom Alkaloid for its Potential Pharmacological Effects

Speciociliatine, a diastereomer of mitragynine, is an indole-based alkaloid found in kratom (Mitragyna speciosa). Kratom has been widely used for the mitigation of pain and opioid dependence, as a mood enhancer, and/or as an energy booster. Speciociliatine is a partial µ-opioid agonist with a 3-fold higher binding affinity than mitragynine. Speciociliatine has been found to be a major circulating alkaloid in humans following oral administration of a kratom product. In this report, we have characterized the metabolism of speciociliatine in human and preclinical species (mouse, rat, dog, and cynomolgus monkey) liver microsomes and hepatocytes. Speciociliatine metabolized rapidly in monkey, rat, and mouse hepatocytes (in vitro half-life was 6.6 ± 0.2, 8.3 ± 1.1, 11.2 ± 0.7 min, respectively), while a slower metabolism was observed in human and dog hepatocytes (91.7 ± 12.8 and > 120 min, respectively). Speciociliatine underwent extensive metabolism, primarily through monooxidation and O-demethylation metabolic pathways in liver microsomes and hepatocytes across species. No human-specific or disproportionate metabolites of speciociliatine were found in human liver microsomes. The metabolism of speciociliatine was predominantly mediated by CYP3A4 with minor contributions by CYP2D6.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R² = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R² = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

Cytochrome P450 isoforms contribution, plasma protein binding, toxicokinetics of enniatin A in rats and in vivo clearance prediction in humans

Emerging mycotoxins, such as enniatin A (ENNA), are becoming a worldwide concern owing to their presence in different types of food and feed. However, comprehensive toxicokinetic data that links intake, exposure and toxicological effects of ENNA has not been elucidated yet. Therefore, the present study investigated the in vitro (rat and human) and in vivo (rat) toxicokinetic properties of ENNA. Towards this, an easily applicable and sensitive bioanalytical method was developed and validated for the estimation of ENNA in rat plasma. ENNA exhibited high plasma protein binding (99%), high hepatic clearance and mainly underwent metabolism via CYP3A4 (74%). The in-house predicted hepatic clearance (54 mL/min/kg) and observed in vivo rat clearance (55 mL/min/kg) were comparable. The predicted in vivo human hepatic clearance was 18 mL/min/kg. ENNA underwent slow absorption (T_max = 4 h) and rapid elimination following oral administration to rats. The absolute oral bioavailability was 47%. The toxicokinetic findings for ENNA from this study will help in designing and interpreting toxicological studies in rats. Besides, these findings could be used in physiologically based toxicokinetic (PBTK) model development for exposure predictions and risk assessment for ENNA in humans.

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 silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Volume of distribution at steady state (V_ss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human V_ss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed V_ss.The mean prediction error and % with <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed V_ss within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with <2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise V_ss in drug discovery applications.

Evaluation of ADMET PredictorTM in early discovery DMPK project work

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLM) and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff) and P-gp substrate identification (P-gp substrate) in the software ADMET Predictor^TM (AP) from Simulations Plus. The dataset consisted of a total of 4794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R²=0.79), guiding the synthesis of new compounds and for prioritzing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability and permeability by using artificial neural network (ANN) models in the optional Modeler^TM module of AP. Predictions of the test sets were performed with both the global and the local models and the R² value for linear regression for predicted versus measured HLM CL_int based on logarithmic data was 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and lab specific assay conditions for parameters which require biological assay systems. Significance Statement AP is useful early in projects for predicting and categorizing LogD, metabolic stability and permeability, to guide the synthesis of new compounds and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can give improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.

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

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

Investigating the glucuronidation and sulfation pathways contribution and disposition kinetics of Bisphenol S and its metabolites using LC-MS/MS-based nonenzymatic hydrolysis method

Despite showing serious health consequences and widespread exposure, the toxicokinetic information required to evaluate the health risks of BPS is insufficient. Thus, we aim to describe the comprehensive toxicokinetics of BPS and its glucuronide (BPS-G) and sulfate (BPS-S) metabolites in rats. Simultaneous quantification of BPS and its metabolites (authentic standards) was accomplished using UPLC-MS/MS method. BPS displayed rapid absorption, extensive metabolism and fast elimination after oral administration. Following intravenous administration, BPS exhibited CL (8.8 L/h/kg) higher than the rat hepatic blood flow rate suggesting the likelihood of extrahepatic clearance. The CL value differed from those reported previously (sheep and piglets) and the probable reason could be attributed to dose- and/or interspecies differences. BPS was extensively metabolized and excreted primarily through urine as BPS-G (∼56%). BPS and BPS-S exhibited a high protein binding capacity in comparison to BPS-G. In in vitro metabolic stability study, BPS was predominantly metabolized through glucuronidation. The predicted in vivo hepatic clearance of BPS suggested it to be a high and intermediate clearance chemical in rats and humans, respectively. The significant interspecies difference observed in the clearance of BPS between rats and humans indicated that toxicokinetics of BPS should be considered for health risk assessment in humans.

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q²) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R²) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.

Influence of Proteome Profiles and Intracellular Drug Exposure on Differences in CYP Activity in Donor-Matched Human Liver Microsomes and Hepatocytes

Human liver microsomes (HLM) and human hepatocytes (HH) are important in vitro systems for studies of intrinsic drug clearance (CLint) in the liver. However, the CLint values are often in disagreement for these two systems. Here, we investigated these differences in a side-by-side comparison of drug metabolism in HLM and HH prepared from 15 matched donors. Protein expression and intracellular unbound drug concentration (Kpuu) effects on the CLint were investigated for five prototypical probe substrates (bupropion-CYP2B6, diclofenac-CYP2C9, omeprazole-CYP2C19, bufuralol-CYP2D6, and midazolam-CYP3A4). The samples were donor-matched to compensate for inter-individual variability but still showed systematic differences in CLint. Global proteomics analysis outlined differences in HLM from HH and homogenates of human liver (HL), indicating variable enrichment of ER-localized cytochrome P450 (CYP) enzymes in the HLM preparation. This suggests that the HLM may not equally and accurately capture metabolic capacity for all CYPs. Scaling CLint with CYP amounts and Kpuu could only partly explain the discordance in absolute values of CLint for the five substrates. Nevertheless, scaling with CYP amounts improved the agreement in rank order for the majority of the substrates. Other factors, such as contribution of additional enzymes and variability in the proportions of active and inactive CYP enzymes in HLM and HH, may have to be considered to avoid the use of empirical scaling factors for prediction of drug metabolism.

Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans

Abstract

Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings.

Graphical abstract

Supplementary informations

The online version of this article (10.1007/s11030-021-10186-7) contains supplementary material, which is available to authorized users.

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

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

Evaluation of a Competitive Equilibrium Dialysis Approach for Assessing the Impact of Protein Binding on Clearance Predictions

Fraction unbound (f_u) is an important consideration when characterizing the ADME properties of drug candidates. For highly bound compounds, there can be low confidence in quantifying f_u introducing uncertainty in certain parameter estimations. Specifically, predictions of clearance (CL) rely on accurate f_u values measured in plasma (f_u,p) and microsomes (f_u,mic) to scale in vitro intrinsic CL to in vivo CL. However, determining the ratio of f_u,p/f_u,mic may circumvent the need to measure discrete binding values. The purpose of this study was to evaluate a plasma-to-microsome competitive equilibrium dialysis (cED) method to determine f_u,p/f_u,mic ratio (fuR) for nine physiochemically-distinct compounds, and to investigate the impact of altering microsomal concentrations on fuR. The values of fuR were comparable to ratios calculated from discretely measured f_u,p and f_u,mic values. Furthermore, increasing microsomal concentrations increased fuR for basic and neutral compounds. When using fuR values, there was a good in vitro-in vivo correlation (IVIVC) (≤3-fold observed in vivo CL). These results suggest that the cED method used to determine fuR may be an appropriate, alternative IVIVC approach. Application of cED may extend beyond IVIVC of CL to evaluate other parameters such as species differences in protein binding and free tissue to plasma ratios.

Evaluation of Erythromycin as a Tool to Assess CYP3A Contribution of Low Clearance Compounds in a Long-Term Hepatocyte Culture

Long-term hepatocyte culture systems such as HepatoPac are well suited to evaluate the metabolic turnover of low clearance (CL) drugs because of their sustained metabolic capacity and longer-term viability. Erythromycin (ERY), a moderate, mechanism-based inhibitor of CYP3A, was evaluated as a tool in the HepatoPac model to assess contribution of CYP3A to the clearance of drug candidates. ERY inhibited CYP3A activity by 58% and 80% at 3 and 10 μM, respectively, for up to 72 hours. At 30 µM, ERY inhibited midazolam hydroxylation by >85% for the entire 144-hour duration of the incubation. Alprazolam CL_int was inhibited 58% by 3 μM of ERY, 75% by 15 μM of ERY, 89% by 30 μM of ERY, and 94% by 60 μM of ERY. ERY (30 μM) did not markedly affect CL_int of substrates for several other major cytochrome P450 isoforms evaluated and did not markedly inhibit uridine diphosphoglucuronosyl transferase (UGT) isoforms 1A1, 1A3, 1A4, 1A6, 1A9, 2B7, or 2B15 as assessed using recombinant UGTs. ERY only mildly increased CYP3A4 gene expression by 2.1-fold (14% of rifampicin induction) at 120 µM, indicating that at effective concentrations for inhibition of CYP3A activity (30-60 µM), arylhydrocarbon receptor, constitutive androstane receptor, and pregnane-X-receptor activation are not likely to markedly increase levels of other drug-metabolizing enzymes or transporters. ERY at concentrations up to 60 µM was not toxic for up to 6 days of incubation. Use of ERY to selectively inhibit CYP3A in high-functioning, long-term hepatocyte models such as HepatoPac can be a valuable strategy to evaluate the contribution of CYP3A metabolism to the overall clearance of slowly metabolized drug candidates. SIGNIFICANCE STATEMENT: This work describes the use of erythromycin as a selective inhibitor of CYP3A to assess the contribution of CYP3A in the metabolism of compounds using long-term hepatocyte cultures.

In vitro-in vivo extrapolation of metabolic clearance using human liver microsomes: factors showing variability and their normalization

In vitro-in vivo extrapolation (IVIVE) using human liver microsomes has been widely used to predict metabolic clearance, but some of the factors used in the process of prediction show variability for the same compound: notably, microsomal intrinsic clearance values corrected by the unbound fraction (CL_{int, u}), physiological parameters used for scale-up, and the source of in vivo clearance data.The purpose of this study was to assess the correlation between in vitro and in vivo CL_int with a focus on factors showing variability using four cytochrome P450 (CYP)3A substrates.We surveyed in vivo clearance values in literature and also determined the microsomal CL_{int, u} values. A scaling factor (SF_direct) was defined as in vivo CL_int divided by the microsomal CL_{int, u}, which ranged from 1190 to 2310 (mg protein per kg body weight). The application of a mean SF_direct of 1600 (mg protein per kg body weight) and further normalization by the microsomal CL_{int, u} values of midazolam, the most commonly used substrate, resulted in improved prediction accuracy for CL_{int, u} values from various microsomal batches.The results suggest the normalization of variability might be useful for predicting the in vivo CL_int.

3D spheroids generated on carbon nanotube-functionalized fibrous scaffolds for drug metabolism and toxicity screening

The mechanical and electrical stimuli have a profound effect on the cellular behavior and function. In this study, a series of conductive nanofibrous scaffolds are developed by blend electrospinning of poly(styrene-co-maleic acid) (PSMA) and multiwalled-carbon nanotubes (CNTs), followed by grafting galactose as cell adhesion cues. When the mass ratios of CNTs to PSMA increase up to 5%, the alignment, Young's modulus and conductivity of fibrous scaffolds increase, whereas the average diameter, pore size and elongation at break decrease. Primary hepatocytes cultured on the scaffolds are self-assembled into 3D spheroids, which restores the hepatocyte polarity and sufficient expression of drug metabolism enzymes over an extended period of time. Among these conductive scaffolds, hepatocytes cultured on fibers containing 3% of CNTs (F3) show the highest clearance rates of model drugs, offering a better prediction of the in vivo data with a high correlation value. Moreover, the drug metabolism capability is maintained over 15 days and is more sensitive towards the inducers and inhibitors of metabolizing enzymes, demonstrating the applicability for drug-drug interaction studies. Thus, this culture system has been demonstrated as a reliable in vitro model for high-throughput screening of metabolism and toxicity in the early phases of drug development.

Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets

The fraction unbound in the incubation, f_u,inc, is an important parameter to consider in the evaluation of intrinsic clearance measurements performed in vitro in hepatocytes or microsomes. Reliable estimates of f_u,inc based on a compound's structure have the potential to positively impact the screening timelines in drug discovery. Previous works suggested that f_u,inc is primarily driven by passive processes and can be described using physicochemical properties such as lipophilicity and the protonation state of the molecule. While models based on these principles proved predictive in relatively small datasets that included marketed drugs, their applicability domain has not been extensively explored. The work presented here from the in silico ADME discussion group (part of the International Consortium for Innovation through Quality in Pharmaceutical Development, the IQ consortium) describes the accuracy of these models in large proprietary datasets that include several thousand of compounds across chemical space. Overall, the models do well for compounds with low lipophilicity. In other words, the equations correctly predict that f_u,inc is, in general, above 0.5 for compounds with a calculated logP of less than 3. When applied to lipophilic compounds, the models failed to produce quantitatively accurate predictions of f_u,inc, with a high risk of underestimating binding properties. These models can, therefore, be used quantitatively for less lipophilic compounds. On the other hand, internal machine-learning models using a company's own proprietary dataset also predict compounds with higher lipophilicity reasonably well. Additionally, the data shown indicate that microsomal binding is, in general, a good proxy for hepatocyte binding.

Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties

PURPOSE

To examine the interlaboratory variability in CL_int values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters.

METHODS

Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources.

RESULTS

Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CL_{int, in vivo} values, while those with non-uniform predictions typically had lower CL_{int, in vivo} values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions.

CONCLUSIONS

Caution should be taken when compiling in vitro CL_int values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.

Metabolic characteristics of Tanshinone I in human liver microsomes and S9 subcellular fractions

Tanshinone I (TSI) is a lipophilic diterpene in Salvia miltiorrhiza with versatile pharmacological activities. However, metabolic pathway of TSI in human is unknown. In this study, we determined major metabolites of TSI using a preparation of human liver microsomes (HLMs) by HPLC-UV and Q-Trap mass spectrometer. A total of 6 metabolites were detected, which indicated the presence of hydroxylation, reduction as well as glucuronidation. Selective chemical inhibition and purified cytochrome P450 (CYP450) isoform screening experiments revealed that CYP2A6 was primarily responsible for TSI Phase I metabolism. Part of generated hydroxylated TSI was glucuronidated via several glucuronosyltransferase (UGT) isoforms including UGT1A1, UGT1A3, UGT1A7, UGT1A9, as well as extrahepatic expressed isoforms UGT1A8 and UGT1A10. TSI could be reduced to a relatively unstable hydroquinone intermediate by NAD(P)H: quinone oxidoreductase 1 (NQO1), and then immediately conjugated with glucuronic acid by a panel of UGTs, especially UGT1A9, UGT1A1 and UGT1A8. Additionally, NQO1 could also reduce hydroxylated TSI to a hydroquinone intermediate, which was immediately glucuronidated by UGT1A1. The study demonstrated that hydroxylation, reduction as well as glucuronidation were the major pathways for TSI biotransformation, and six metabolites generated by CYPs, NQO1 and UGTs were found in HLMs and S9 subcellular fractions.

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability

The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with physiological parameters for a virtual population. For risk-based prioritization of chemicals, predicted bioactive equivalent doses were compared to demographic-specific inferences of exposure rates that were based on NHANES urinary analyte biomonitoring data. The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average for the total population when compared to previous methods. However, for some combinations of chemical and demographic groups the margin was reduced by as much as three quarters. This TK modeling framework allows targeted risk prioritization of chemicals for demographic groups of interest, including potentially sensitive life stages and subpopulations.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.

Hepatic Clearance Predictions from In Vitro-In Vivo Extrapolation and the Biopharmaceutics Drug Disposition Classification System

Predicting in vivo pharmacokinetic parameters such as clearance from in vitro data is a crucial part of the drug-development process. There is a commonly cited trend that drugs that are highly protein-bound and are substrates for hepatic uptake transporters often yield the worst predictions. Given this information, 11 different data sets using human microsomes and hepatocytes were evaluated to search for trends in accuracy, extent of protein binding, and drug classification based on the Biopharmaceutics Drug Disposition Classification System (BDDCS), which makes predictions about transporter effects. As previously reported, both in vitro systems (microsomes and hepatocytes) gave a large number of inaccurate results, defined as predictions falling more than 2-fold outside of in vivo values. The weighted average of the percentage of inaccuracy was 66.5%. BDDCS class 2 drugs, which are subject to transporter effects in vivo unlike class 1 compounds, had a higher percentage of inaccurate predictions and often had slightly larger bias. However, since the weighted average of the percentage of inaccuracy was still high in both classes (81.9% for class 2 and 62.3% for class 1), it may be currently hard to use BDDCS class to predict potential accuracy. The results of this study emphasize the need for improved in vitro to in vivo extrapolation experimental methods, as using physiologically based scaling is still not accurate, and BDDCS cannot currently help predict accurate results.

Comparison of methods for the prediction of human clearance from hepatocyte intrinsic clearance for a set of reference compounds and an external evaluation set

1. We compared direct scaling, regression model equation and the so-called "Poulin et al." methods to scale clearance (CL) from in vitro intrinsic clearance (CL_int) measured in human hepatocytes using two sets of compounds. One reference set comprised of 20 compounds with known elimination pathways and one external evaluation set based on 17 compounds development in Merck (MS). 2. A 90% prospective confidence interval was calculated using the reference set. This interval was found relevant for the regression equation method. The three outliers identified were justified on the basis of their elimination mechanism. 3. The direct scaling method showed a systematic underestimation of clearance in both the reference and evaluation sets. The "Poulin et al." and the regression equation methods showed no obvious bias in either the reference or evaluation sets. 4. The regression model equation was slightly superior to the "Poulin et al." method in the reference set and showed a better absolute average fold error (AAFE) of value 1.3 compared to 1.6. A larger difference was observed in the evaluation set were the regression method and "Poulin et al." resulted in an AAFE of 1.7 and 2.6, respectively (removing the three compounds with known issues mentioned above). A similar pattern was observed for the correlation coefficient. Based on these data we suggest the regression equation method combined with a prospective confidence interval as the first choice for the extrapolation of human in vivo hepatic metabolic clearance from in vitro systems.

Lipid-based oral delivery systems for skin deposition of a potential chemopreventive DIM derivative: characterization and evaluation

The objective of this study was to explore the oral route as a viable potential for the skin deposition of a novel diindolylmethane derivative (DIM-D) for chemoprevention activity. Various lipid-based oral delivery systems were optimized and compared for enhancing DIM-D's oral bioavailability and skin deposition. Preformulation studies were performed to evaluate the log P and solubility of DIM-D. Microsomal metabolism, P-glycoprotein efflux, and caco-2 monolayer permeability of DIM-D were determined. Comparative evaluation of the oral absorption and skin deposition of DIM-D-loaded various lipid-based formulations was performed in rats. DIM-D showed pH-dependent solubility and a high log P value. It was not a strong substrate of microsomal degradation and P-glycoprotein. SMEDDs comprised of medium chain triglycerides, monoglycerides, and kolliphor-HS15 (36.70 ± 0.42 nm). SNEDDs comprised of long chain triglycerides, cremophor RH40, labrasol, and TPGS (84.00 ± 14.14 nm). Nanostructured lipid carriers (NLC) consisted of compritol, miglyol, and surfactants (116.50 ± 2.12 nm). The blank formulations all showed >70 % cell viability in caco-2 cells. Differential Scanning Calorimetry confirmed the amorphization of DIM-D within the lipid matrices while Atomic Force Microscopy showed particle size distribution similar to the dynamic light scattering data. DIM-D also showed reduced permeation across caco-2 monolayer that was enhanced (p < 0.05) by SNEDDs in comparison to SMEDDs and NLC. Fabsolute for DIM-D SNEDDs, SMEDDs, and NLC was 0.14, 0.04, and 0.007, respectively. SNEDDs caused 53.90, 11.32, and 15.08-fold more skin deposition of DIM-D than the free drug, SMEDDs, and NLC, respectively, at 2 h following oral administration and shows a viable potential for use in skin cancer chemoprevention. Graphical Abstract ᅟ.

Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models

Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models-in vivo, in situ and in vitro-have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.