Extended Clearance Classification System Research Articles

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.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

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 (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) 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 CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, 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 CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 μL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,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 (Papp > 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 CLp 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 CLp IVIVE. Our results indicate that the current practice of prospective CLp 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 CLp 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 CLp 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.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CLint) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SFlin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CLint. These SFs contain both SFlin and an exponential SF (SFβ) of fraction unbound in plasma (fu,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CLint, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CLint. This observation is different than in rats, where rat liver microsomal CLint correlates well with in vivo CLint for compounds demonstrating permeability-limited metabolism. The good CLint IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Plasma Protein Binding Refinement of the Extended Clearance Classification System: Subclasses for Predicting Hepatic Uptake or Renal Clearance for Classes 1B and 3B.

The Extended Clearance Classification System (ECCS) was established to facilitate the timely anticipation of clearance rate determination according to the physicochemical characteristics of a given compound and invitro passive membrane permeability. Unfortunately, distinguishing between renal and hepatic uptake clearance mechanisms using ECCS class 3B is not possible. We determined the effects of plasma protein binding (PPB) on major hepatic organic anion transporting polypeptide (OATP) and renal organic aniontransporter (OAT) substrates. A modified ECCS could predict when renal or hepatic uptake mechanisms were the main clearance rate determinants (accounting for ≥ 70% of total clearance). A dataset of 66 human OATP and 41 OAT substrates was analyzed to determine the effect of PPB. A total of 63 acidic and zwitterionic, and high-molecular-weight (MW > 400Da) compounds, including 50 drugs in ECCS classes 1B and 3B, were reanalyzed considering their PPB. Statistical analyses revealed that hepatic uptake transporter (OATP1B1 and OATP1B3) substrates possess a high PPB rate of ≥ 90%, whereas OAT1 and/or OAT3 substrates possess low PPB rates of < 90%. By analyzing the 63 drugs on the basis of their PPB, the active hepatic uptakes of acids and zwitterions were determined to be the main clearance mechanisms, with PPB ≥ 90%, whereas renally eliminated drugs exhibited limited PPB (< 90%). Therefore, PPB is an effective parameter for defining clearance rate determination for acidic and zwitterionic drugs with high MWs. Using PPB as an additional parameter in ECCS, clearance mechanisms for class 1B and 3B compounds can be predicted, and OATP and OAT substrates may be readily distinguished.

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

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

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

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

Discovery and Optimization of Orally Bioavailable Phthalazone and Cinnolone Carboxylic Acid Derivatives as S1P2 Antagonists against Fibrotic Diseases.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease. Current treatments only slow down disease progression, making new therapeutic strategies compelling. Increasing evidence suggests that S1P2 antagonists could be effective agents against fibrotic diseases. Our compound collection was mined for molecules possessing substructure features associated with S1P2 activity. The weakly potent indole hit 6 evolved into a potent phthalazone series, bearing a carboxylic acid, with the aid of a homology model. Suboptimal pharmacokinetics of a benzimidazole subseries were improved by modifications targeting potential interactions with transporters, based on concepts deriving from the extended clearance classification system (ECCS). Scaffold hopping, as a part of a chemical enablement strategy, permitted the rapid exploration of the position adjacent to the carboxylic acid. Compound 38, with good pharmacokinetics and in vitro potency, was efficacious at 10 mg/kg BID in three different in vivo mouse models of fibrotic diseases in a therapeutic setting.

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

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

Clearance prediction for Amgen molecules against Extended Clearance Classification System (ECCS) and future directions

Early prediction of elimination pathways for new chemical entities can have a profound impact on drug discovery programs. The recently proposed Extended Clearance Classification System (ECCS) is a step in the right direction, providing a framework to help identify the major elimination pathway of a drug. A list of 42 Amgen small molecules was evaluated against the ECCS framework to assess its performance in retrospectively predicting their major elimination pathway. Here, we present a critical analysis of the chemical space defined by the ECCS framework with the aim of identifying its applicability and constraints. This evaluation highlights the critical need for periodic review and revision of ECCS, given that target constraints are moving molecules away from the traditional 'drug-like' physicochemical space.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction.

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

Cytochrome-P450-Mediated Drug-Drug Interactions of Substrate Drugs: Assessing Clinical Risk Based on Molecular Properties and an Extended Clearance Classification System.

Cytochrome-P450 (P450) isoforms are major drug-metabolizing enzymes implicated in the clearance and drug-drug interactions (DDIs) of diverse small-molecule drugs. Here, we evaluated the association between primary physicochemical descriptors of substrate drugs and their clinical DDI risk with P450 index (probe) inhibitors using an exhaustive clinical data set (n = 397, substrate-inhibitor pairs). Additionally, the ability of extended clearance classification system (ECCS), a categorical clearance mechanism model, to predict P450 DDI risk was assessed. The clinical data set indicated that basic and neutral compounds are probable candidates to show a higher magnitude of DDIs on P450 inhibition (i.e., plasma exposure change > twofold). Additionally, trends with lipophilicity were apparent for P450-based DDIs. ECCS class 2 drugs (high-permeability bases/neutrals) have higher probability to show moderate-to-strong DDIs with probe inhibitors of CYP1A2/2C19/2C9/2D6/3A, while ECCS class 1A/1B drugs are prone to interactions with inhibitors of CYP2C8 and CYP2C9. On the other hand, P450-based DDIs are notably small for classes 3A/3B/4. In conclusion, this study emphasizes the relevance of the ECCS framework in clearance characterization to evaluate victim DDI liabilities and aid chemists in mitigating risk during drug design.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Most Influential Physicochemical and In Vitro Assay Descriptors for Hepatotoxicity and Nephrotoxicity Prediction.

Drug-induced organ injury is a major reason for drug candidate attrition in preclinical and clinical drug development. The liver, kidneys, and heart have been recognized as the most common organ systems affected in safety-related attrition or the subject of black box warnings and postmarket drug withdrawals. In silico physicochemical property calculations and in vitro assays have been utilized separately in the early stages of the drug discovery and development process to predict drug safety. In this study, we combined physicochemical properties and in vitro cytotoxicity assays including mitochondrial dysfunction to build organ-specific univariate and multivariable logistic regression models to achieve odds ratios for the prediction of clinical hepatotoxicity, nephrotoxicity, and cardiotoxicity using 215 marketed drugs. The multivariable hepatotoxic predictive model showed an odds ratio of 6.2 (95% confidence interval (CI) 1.7-22.8) or 7.5 (95% CI 3.2-17.8) for mitochondrial inhibition or drug plasma Cmax >1 μM for drugs associated with liver injury, respectively. The multivariable nephrotoxicity predictive model showed an odds ratio of 5.8 (95% CI 2.0-16.9), 6.4 (95% CI 1.1-39.3), or 15.9 (95% CI 2.8-89.0) for drug plasma Cmax >1 μM, mitochondrial inhibition, or hydrogen-bond-acceptor atoms >7 for drugs associated with kidney injury, respectively. Conversely, drugs with a total polar surface area ≥75 Å were 79% (odds ratio 0.21, 95% CI 0.061-0.74) less likely to be associated with kidney injury. Drugs belonging to the extended clearance classification system (ECCS) class 4, where renal secretion is the primary clearance mechanism (low permeability drugs that are bases/neutrals), were 4 (95% CI 1.8-9.5) times more likely to to be associated withkidney injury with this data set. Alternatively, ECCS class 2 drugs, where hepatic metabolism is the primary clearance (high permeability drugs that are bases/neutrals) were 77% less likely (odds ratio 0.23 95% CI 0.095-0.54) to to be associated with kidney injury. A cardiotoxicity model was poorly defined using any of these drug physicochemical attributes. Combining in silico physicochemical properties descriptors along with in vitro toxicity assays can be used to build predictive toxicity models to select small molecule therapeutics with less potential to cause liver and kidney organ toxicity.

Reconciling Human-Canine Differences in Oral Bioavailability: Looking beyond the Biopharmaceutics Classification System.

The extrapolation of oral bioavailability (F) information between dogs and humans has had an important role in the drug development process, whether it be to support an assessment of potential human pharmaceutical formulations or to identify the bioavailability challenges that may be encountered in dogs. Accordingly, these interspecies extrapolations could benefit from a tool that helps identify those drug characteristics consistent with species similarities in F. Our initial effort to find such a tool led to an exploration of species differences as it pertained to the biopharmaceutics classification system (BCS). However, using a range of compounds, we concluded that solubility and permeability alone could not explain interspecies inconsistencies in estimates of F. Therefore, we have now extended our evaluation to include canine versus human comparisons of F based upon the biopharmaceutics drug disposition classification system (BDDCS) and the extended clearance classification system (ECCS). Using the same data as that in our initial BCS assessments, we conclude that although neither the BDDCS nor the ECCS can reliably improve our ability to determine when F will be similar in humans and dogs, the ECCS provides a mechanism to help define possible causes for observed human-canine inconsistencies.

Quantitative Contribution of Six Major Transporters to the Hepatic Uptake of Drugs: "SLC-Phenotyping" Using Primary Human Hepatocytes.

Hepatic uptake transporters [solute carriers (SLCs)], including organic anion transporting polypeptide (OATP) 1B1, OATP1B3, OATP2B1, sodium-dependent taurocholate cotransporting polypeptide (NTCP), and organic anion (OAT2) and organic cation (OCT1) transporters, play a key role in determining the systemic and liver exposure of chemically diverse drugs. Here, we established a phenotyping approach to quantify the contribution of the six SLCs, and passive diffusion, to the overall uptake using plated human hepatocytes (PHHs). First, selective inhibitor conditions were identified by screening about 20 inhibitors across the six SLCs using single-transfected human embryonic kidney 293 cells. Data implied rifamycin SV (20 µM) inhibits three OATPs, while rifampicin (5 µM) inhibits OATP1B1/1B3 only. Further, hepatitis B virus myristoylated-preS1 peptide (0.1 µM), quinidine (100 µM), and ketoprofen (100-300 µM) are relatively selective against NTCP, OCT1, and OAT2, respectively. Second, using these inhibitory conditions, the fraction transported (ft ) by the individual SLCs was characterized for 20 substrates with PHH. Generally, extended clearance classification system class 1A/3A (e.g., warfarin) and 1B/3B compounds (e.g., statins) showed predominant OAT2 and OATP1B1/1B3 contribution, respectively. OCT1-mediated uptake was prominent for class 2/4 compounds (e.g., metformin). Third, in vitro ft values were corrected using quantitative proteomics data to obtain "scaled ft " Fourth, in vitro-in vivo extrapolation of the scaled OATP1B1/1B3 ft was assessed, leveraging statin clinical drug-drug interaction data with rifampicin as the perpetrator. Finally, we outlined a novel stepwise strategy to implement phenotypic characterization of SLC-mediated hepatic uptake for new molecular entities and drugs in a drug discovery and development setting.

Organic Anion Transporter 2-Mediated Hepatic Uptake Contributes to the Clearance of High-Permeability-Low-Molecular-Weight Acid and Zwitterion Drugs: Evaluation Using 25 Drugs.

High-permeability-low-molecular-weight acids/zwitterions [i.e., extended clearance classification system class 1A (ECCS 1A) drugs] are considered to be cleared by metabolism with a minimal role of membrane transporters in their hepatic clearance. However, a marked disconnect in the in vitro-in vivo (IVIV) translation of hepatic clearance is often noted for these drugs. Metabolic rates measured using human liver microsomes and primary hepatocytes tend to underpredict. Here, we evaluated the role of organic anion transporter 2 (OAT2)-mediated hepatic uptake in the clearance of ECCS 1A drugs. For a set of 25 ECCS 1A drugs, in vitro transport activity was assessed using transporter-transfected cells and primary human hepatocytes. All but two drugs showed substrate affinity to OAT2, whereas four (bromfenac, entacapone, fluorescein, and nateglinide) also showed OATP1B1 activity in transfected cells. Most of these drugs (21 of 25) showed active uptake by plated human hepatocytes, with rifamycin SV (pan-transporter inhibitor) reducing the uptake by about 25%-95%. Metabolic turnover was estimated for 19 drugs after a few showed no measurable substrate depletion in liver microsomal incubations. IVIV extrapolation using in vitro data was evaluated to project human hepatic clearance of OAT2-alone substrates considering 1) uptake transport only, 2) metabolism only, and 3) transporter-enzyme interplay (extended clearance model). The transporter-enzyme interplay approach achieved improved prediction accuracy (average fold error = 1.9 and bias = 0.93) compared with the other two approaches. In conclusion, this study provides functional evidence for the role of OAT2-mediated hepatic uptake in determining the pharmacokinetics of several clinically important ECCS 1A drugs.

Navigating Transporter Sciences in Pharmacokinetics Characterization Using the Extended Clearance Classification System.

Membrane transporters play an important role in the absorption, distribution, clearance, and elimination of drugs. Supported by the pharmacokinetics data in human, several transporters including organic anion transporting polypeptide (OATP)1B1, OATP1B3, organic anion transporter (OAT)1, OAT3, organic cation transporter (OCT)2, multidrug and toxin extrusion (MATE) proteins, P-glycoprotein and breast cancer resistance protein are suggested to be of clinical relevance. An early understanding of the transporter role in drug disposition and clearance allows reliable prediction/evaluation of pharmacokinetics and changes due to drug-drug interactions (DDIs) or genetic polymorphisms. We recently proposed an extended clearance classification system (ECCS) based on simple drug properties (i.e., ionization, permeability, and molecular weight) to predict the predominant clearance mechanism. According to this framework, systemic clearance of class 1B and 3B drugs is likely determined by the OATP-mediated hepatic uptake. Class 3A and 4 drugs, and certain class 3B drugs, are predominantly cleared by renal, wherein, OAT1, OAT3, OCT2, and MATE proteins could contribute to their active renal secretion. Intestinal efflux and uptake transporters largely influence the oral pharmacokinetics of class 3A, 3B, and 4 drugs. We discuss the paradigm of applying the ECCS framework in mapping the role of clinically relevant drug transporters in early discovery and development; thereby implementing the right strategy to allow optimization of drug exposure and evaluation of clinical risk due to DDIs and pharmacogenomics.

Molecular properties associated with transporter-mediated drug disposition.

Membrane transporters play a key role in the absorption, distribution, clearance, elimination, and transport of drugs. Understanding the drug properties and structure activity relationships (SAR) for affinity to membrane transporters is critical to optimize clearance and pharmacokinetics during drug design. To facilitate the early identification of clearance mechanism, a framework named the extended clearance classification system (ECCS) was recently introduced. Using in vitro and physicochemical properties that are readily available in early drug discovery, ECCS has been successfully applied to identify major clearance mechanism and to implicate the role of membrane transporters in determining pharmacokinetics. While the crystal structures for most of the drug transporters are currently not available, ligand-based modeling approaches that use information obtained from the structure and molecular properties of the ligands have been applied to associate the drug-related properties and transporter-mediated disposition. The approach allows prospective prediction of transporter both substrate and/or inhibitor affinity and build quantitative structure-activity relationship (QSAR) to enable early optimization of pharmacokinetics, tissue distribution and drug-drug interaction risk. Drug design applications can be further improved through uncovering transporter protein crystal structure and generation of quality data to refine and develop viable predictive models.