Impact of nonspecific binding to microsomes and phospholipid on the inhibition of cytochrome P4502D6: implications for relating in vitro inhibition data to in vivo drug interactions

Unbound Fractions of PFAS in Human and Rodent Tissues: Rat Liver a Suitable Proxy for Evaluating Emerging PFAS?

Adverse health effects associated with exposures to perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a concern for public health and are driven by their elimination half-lives and accumulation in specific tissues. However, data on PFAS binding in human tissues are limited. Accumulation of PFAS in human tissues has been linked to interactions with specific proteins and lipids in target organs. Additional data on PFAS binding and unbound fractions (funbound) in whole human tissues are urgently needed. Here, we address this gap by using rapid equilibrium dialysis to measure the binding and funbound of 16 PFAS with 3 to 13 perfluorinated carbon atoms (ηpfc = 3-13) and several functional headgroups in human liver, lung, kidney, heart, and brain tissue. We compare results to mouse (C57BL/6 and CD-1) and rat tissues. Results show that funbound decreases with increasing fluorinated carbon chain length and hydrophobicity. Among human tissues, PFAS binding was generally greatest in brain > liver ≈ kidneys ≈ heart > lungs. A correlation analysis among human and rodent tissues identified rat liver as a suitable surrogate for predicting funbound for PFAS in human tissues (R2 ≥ 0.98). The funbound data resulting from this work and the rat liver prediction method offer input parameters and tools for toxicokinetic models for legacy and emerging PFAS.

Xenografted Tumors Share Comparable Fraction Unbound and Can Be Surrogated by Mouse Lung Tissue

Free (unbound) drug concentration at the site of action is the key determinant of biologic activity since only unbound drugs can exert pharmacological and toxicological effects. Unbound drug concentration in tumors for solid cancers is needed to understand/explain/predict pharmacokinetics, pharmacodynamics, and efficacy relations. Fraction unbound (fu ) in tumors is usually determined across several xenografted tumors derived from various cell lines in the drug discovery stage, which is time consuming and a resource burden. In this study, we determined the fu values for a set of diverse compounds (comprising acid, base, neutral, zwitterion, and covalent drugs) across five different xenografted tumors and five commercially available mouse tissues to explore the correlation of fu between tumors and the possibility of surrogate tissue(s) for tumor fu (fu,tumor) determination. The crosstumor comparison showed that fu,tumor values across tumors are largely comparable, and systematic tissue versus tumor comparison demonstrated that only lung tissue had comparable fu to all five tumors (fu values within twofold change for >80% compounds in both comparisons). These results indicated that mouse lung tissue can be used as a surrogate matrix for a fu,tumor assay. This study will increase efficiency in fu,tumor assessment and reduce animal use (adapting the replace, reduce, and refine principle) in drug discovery. SIGNIFICANCE STATEMENT: The free drug concept is a well accepted principle in drug discovery research. Currently, tumor fraction unbound (fu,tumor) is determined in several tumors derived from different cell lines to estimate free drug concentrations of a compound. The results from this study indicated that fu,tumor across xenografted tumors is comparable, and fu,tumor can be estimated using a surrogate tissue, mouse lung. The results will increase efficiency in fu,tumor assessment and reduce animal use in drug discovery.

Species-Specific Unbound Fraction Differences in Highly Bound PFAS: A Comparative Study across Human, Rat, and Mouse Plasma and Albumin

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of fluorinated compounds which have yet to undergo comprehensive investigation regarding potential adverse health effects and bioaccumulative properties. With long half-lives and accumulative properties, PFAS have been linked to several toxic effects in both non-clinical species such as rat and mouse as well as human. Although biological impacts and specific protein binding of PFAS have been examined, there is no study focusing on the species-specific fraction unbound (fu) in plasma and related toxicokinetics. Herein, a presaturation equilibrium dialysis method was used to measure and validate the binding of 14 individual PFAS with carbon chains containing 4 to 12 perfluorinated carbon atoms and several functional head-groups to albumin and plasma of mouse (C57BL/6 and CD-1), rat, and human. Equivalence testing between each species-matrix combination showed positive correlation between rat and human when comparing fu in plasma and binding to albumin. Similar trends in binding were also observed for mouse plasma and albumin. Relatively high Spearman correlations for all combinations indicate high concordance of PFAS binding regardless of matrix. Physiochemical properties of PFAS such as molecular weight, chain length, and lipophilicity were found to have important roles in plasma protein binding of PFAS.

Cytochrome P450 enzymes: a review on drug metabolizing enzyme inhibition studies in drug discovery and development

Assessment of drug candidate's potential to inhibit cytochrome P450 (CYP) enzymes remains crucial in pharmaceutical drug discovery and development. Both direct and time-dependent inhibition of drug metabolizing CYP enzymes by the concomitant administered drug is the leading cause of drug-drug interactions (DDIs), resulting in the increased toxicity of the victim drug. In this context, pharmaceutical companies have grown increasingly diligent in limiting CYP inhibition liabilities of drug candidates in the early stages and examining risk assessments throughout the drug development process. This review discusses different strategies and decision-making processes for assessing the drug-drug interaction risks by enzyme inhibition and lays particular emphasis on in vitro study designs and interpretation of CYP inhibition data in a stage-appropriate context.

How Science Is Driving Regulatory Guidances

This chapter provides regulatory perspectives on how to translate in vitro drug metabolism findings into in vivo drug-drug interaction (DDI) predictions and how this affects the decision of conducting in vivo DDI evaluation. The chapter delineates rationale and analyses that have supported the recommendations in the U.S. Food and Drug Administration (FDA) DDI guidances in terms of in vitro-in vivo extrapolation of cytochrome P450 (CYP) inhibition-mediated DDI potential for investigational new drugs and their metabolites as substrates or inhibitors. The chapter also describes the framework and considerations to assess UDP-glucuronosyltransferase (UGT) inhibition-mediated DDI potential for drugs as substrates or inhibitors. The limitations of decision criteria and further improvements needed are also discussed. Case examples are provided throughout the chapter to illustrate how decision criteria have been utilized to evaluate in vivo DDI potential from in vitro data.

Numerical Methods for Modeling Enzyme Kinetics

Differential equations are used to describe time-dependent changes in enzyme kinetics and pharmacokinetics. Analytical and numerical methods can be used to solve differential equations. This chapter describes the use of numerical methods in solving differential equations and its applications in characterizing the complexities observed in enzyme kinetics. A discussion is included on the use of numerical methods to overcome limitations of explicit equations in the analysis of metabolism kinetics, reversible inhibition kinetics, and inactivation kinetics. The chapter describes the advantages of using numerical methods when Michaelis-Menten assumptions do not hold.

Exploration of inhibition potential of isoniazid and its metabolites towards CYP2E1 in human liver microsomes through LC-MS/MS analysis

Isoniazid (INH) is the first-line anti-tubercular drug that is used both for the prophylaxis as well as the treatment of tuberculosis (TB). The patients with TB are more vulnerable to secondary infections and other health complications, hence, they are usually administered a cocktail of drugs. This increases the likelihood of drug-drug interactions (DDIs). INH is clinically proven to interact with drugs like phenytoin, carbamazepine, diazepam, triazolam, acetaminophen, etc. Most of such clinical observations have been supported by in vitro inhibition studies involving INH and cytochrome P450 (CYP) enzymes. A few published in vitro studies have explored the CYP2E1 inhibition potential of INH to explain its interactions with acetaminophen and other CY2E1 substrates, such as chlorzoxazone, but none of them were able to demonstrate any significant inhibition of the enzyme by the drug. It was reported that metabolites of INH, such as acetylhydrazine and hydrazine, were bioactivated by CYP2E1, highlighting that perhaps the drug metabolites were responsible for the mechanism based inhibition (MBI) of the enzyme. Therefore, the purpose of this investigation was to explore CYP2E1 enzyme inhibition potential of INH and its four major metabolites, viz., acetylisoniazid, isonicotinic acid, acetylhydrazine and hydrazine, using human liver microsomes (HLM). Additionally, we determined the fraction unbound in microsomal incubation (fu_mic) for all the five compounds using equilibrium dialysis assay. We observed that INH and its metabolites had lower propensity for microsomal binding, and the metabolites also lacked the potential to inhibit CYP2E1 enzyme, either by direct inhibition or through MBI. This suggests involvement of some other mechanism to explain interactions of INH with CY2E1 substrates, signifying need of further exploration.

Case Study 5: Predicting the Drug Interaction Potential for Inhibition of CYP2C8 by Montelukast

Predicting drug-drug interactions (DDIs) from in vitro data is made difficult by not knowing concentrations of substrate and inhibitor at the target site. For in vivo targets, this is understandable, since intracellular concentrations can differ from extracellular concentrations. More vexing is that the concentration of the drug at the target for some in vitro assays can also be unknown. This uncertainty has resulted in standard in vitro practices that cannot accurately predict human pharmacokinetics. This case study highlights the impact of drug distribution, both in vitro and in vivo, with the example of the drug interaction potential of montelukast.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Evidence-based strategies for the characterisation of human drug and chemical glucuronidation in vitro and UDP-glucuronosyltransferase reaction phenotyping

Enzymes of the UDP-glucuronosyltransferase (UGT) superfamily contribute to the elimination of drugs from almost all therapeutic classes. Awareness of the importance of glucuronidation as a drug clearance mechanism along with increased knowledge of the enzymology of drug and chemical metabolism has stimulated interest in the development and application of approaches for the characterisation of human drug glucuronidation in vitro, in particular reaction phenotyping (the fractional contribution of the individual UGT enzymes responsible for the glucuronidation of a given drug), assessment of metabolic stability, and UGT enzyme inhibition by drugs and other xenobiotics. In turn, this has permitted the implementation of in vitro - in vivo extrapolation approaches for the prediction of drug metabolic clearance, intestinal availability, and drug-drug interaction liability, all of which are of considerable importance in pre-clinical drug development. Indeed, regulatory agencies (FDA and EMA) require UGT reaction phenotyping for new chemical entities if glucuronidation accounts for ≥25% of total metabolism. In vitro studies are most commonly performed with recombinant UGT enzymes and human liver microsomes (HLM) as the enzyme sources. Despite the widespread use of in vitro approaches for the characterisation of drug and chemical glucuronidation by HLM and recombinant enzymes, evidence-based guidelines relating to experimental approaches are lacking. Here we present evidence-based strategies for the characterisation of drug and chemical glucuronidation in vitro, and for UGT reaction phenotyping. We anticipate that the strategies will inform practice, encourage development of standardised experimental procedures where feasible, and guide ongoing research in the field.

2020 FDA Drug-drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists

Background:

In January 2020, the US FDA published two final guidelines, one entitled “In vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry” and the other entitled “Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry”. These were updated from the 2017 draft in vitro and clinical DDI guidance.

Methods:

This study is aimed to provide an analysis of the updates along with a comparison of the DDI guidelines published by the European Medicines Agency (EMA) and Japanese Pharmaceuticals and Medical Devices Agency (PMDA) along with the current literature.

Results:

The updates were provided in the final FDA DDI guidelines and explained the rationale of those changes based on the understanding from research and literature. Furthermore, a comparison among the FDA, EMA, and PMDA DDI guidelines are presented in Tables 1, 2 and 3.

Conclusion:

The new 2020 clinical DDI guidance from the FDA now has even higher harmonization with the guidance (or guidelines) from the EMA and PMDA. A comparison of DDI guidance from the FDA 2017, 2020, EMA, and PMDA on CYP and transporter based DDI, mathematical models, PBPK, and clinical evaluation of DDI is presented in this review.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Hepatic clearance concepts and misconceptions: Why the well-stirred model is still used even though it is not physiologic reality?

The liver is the most important drug metabolizing organ, endowed with a plethora of metabolizing enzymes and transporters to facilitate drug entry and removal via metabolism and/or biliary excretion. For this reason, much focus surrounds the development of clearance concepts, which are based on normalizing the rate of removal to the input or arterial concentration. By so doing, some authors have recently claimed that it implies one specific model of hepatic elimination, namely, the widely used well-stirred or venous equilibration model (WSM). This commentary challenges this claim and aims to provide a comprehensive discussion of not only the WSM but other currently applied hepatic clearance models - the parallel tube model (PTM), the dispersion model (DM), the zonal liver model (ZLM), and the heterogeneous capillary transit time model of Goresky and co-workers (GM). The WSM, PTM, and DM differ in the patterns of internal blood flow, assuming bulk, plug, and dispersive flows, respectively, which render different degrees of mixing within the liver that are characterized by the magnitudes of the dispersion number (D_N), resulting in different implications concerning the (unbound) substrate concentration in liver (Cu_H). Early models assumed perfusion rate-limited distribution, which have since been modified to include membrane-limited transport. The recent developments associated with the misconceptions and the sensitivity of the models are hereby addressed. Since the WSM has been and will likely remain widely used, the pros and cons of this model relative to physiological reality are further discussed.

Impact of Lipid Partitioning on the Design, Analysis, and Interpretation of Microsomal Time-Dependent Inactivation

Nonspecific drug partitioning into microsomal membranes must be considered for in vitro-in vivo correlations. This work evaluated the effect of including lipid partitioning in the analysis of complex TDI kinetics with numerical methods. The covariance between lipid partitioning and multiple inhibitor binding was evaluated. Simulations were performed to test the impact of lipid partitioning on the interpretation of TDI kinetics, and experimental TDI datasets for paroxetine (PAR) and itraconazole (ITZ) were modeled. For most kinetic schemes, modeling lipid partitioning results in statistically better fits. For MM-IL simulations (KI,u = 0.1 µM, kinact = 0.1 minute-1), concurrent modeling of lipid partitioning for an fumic range (0.01, 0.1, and 0.5) resulted in better fits compared with post hoc correction (AICc: -526 vs. -496, -579 vs. -499, and -636 vs. -579, respectively). Similar results were obtained with EII-IL. Lipid partitioning may be misinterpreted as double binding, leading to incorrect parameter estimates. For the MM-IL datasets, when fumic = 0.02, MM-IL, and EII model fits were indistinguishable (δAICc = 3). For less partitioned datasets (fumic = 0.1 or 0.5), the inclusion of partitioning resulted in better models. The inclusion of lipid partitioning can lead to markedly different estimates of KI,u and kinact A reasonable alternate experimental design is nondilution TDI assays, with post hoc fumic incorporation. The best fit models for PAR (MIC-M-IL) and ITZ (MIC-EII-M-IL and MIC-EII-M-Seq-IL) were consistent with their reported mechanism and kinetics. Overall, experimental fumic values should be concurrently incorporated into TDI models with complex kinetics, when dilution protocols are used.

Quantitative Prediction of Human Hepatic Clearance for P450 and Non-P450 Substrates from In Vivo Monkey Pharmacokinetics Study and In Vitro Metabolic Stability Tests Using Hepatocytes

Accurate prediction of human pharmacokinetics for drugs remains challenging, especially for non-cytochrome P450 (P450) substrates. Hepatocytes might be suitable for predicting hepatic intrinsic clearance (CL_int) of new chemical entities, because they can be applied to various compounds regardless of the metabolic enzymes. However, it was reported that hepatic CL_int is underestimated in hepatocytes. The purpose of the present study was to confirm the predictability of human hepatic clearance for P450 and non-P450 substrates in hepatocytes and the utility of animal scaling factors for the prediction using hepatocytes. CL_int values for 30 substrates of P450, UDP-glucuronosyltransferase, flavin-containing monooxygenase, esterases, reductases, and aldehyde oxidase in human microsomes, human S9 and human, rat, and monkey hepatocytes were estimated. Hepatocytes were incubated in serum of each species. Furthermore, CL_int values in human hepatocytes were corrected with empirical, monkey, and rat scaling factors. CL_int values in hepatocytes for most compounds were underestimated compared to observed values regardless of the metabolic enzyme, and the predictability was improved by using the scaling factors. The prediction using human hepatocytes corrected with monkey scaling factor showed the highest predictability for both P450 and non-P450 substrates among the predictions using liver microsomes, liver S9, and hepatocytes with or without scaling factors. CL_int values by this method for 80% and 90% of all compounds were within 2- and 3-fold of observed values, respectively. This method is accurate and useful for estimating new chemical entities, with no need to care about cofactors, localization of metabolic enzymes, or protein binding in plasma and incubation mixture.

An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation

As explained by the free drug theory, the unbound fraction of drug has long been thought to drive the efficacy of a molecule. Thus, the fraction unbound term, or f_u, appears in equations for fundamental pharmacokinetic parameters such as clearance, and is used when attempting in vitro to in vivo extrapolation (IVIVE). In recent years though, it has been noted that IVIVE does not always yield accurate predictions, and that some highly protein bound ligands have more efficient uptake than can be explained by their unbound fractions. This review explores the evolution of f_u terms included when implementing IVIVE, the concept of protein-facilitated uptake, and the mechanisms that have been proposed to account for facilitated uptake.

Comparison of Species and Cell-Type Differences in Fraction Unbound of Liver Tissues, Hepatocytes, and Cell Lines

Fraction unbound (f_u) of liver tissue, hepatocytes, and other cell types is an essential parameter used to estimate unbound liver drug concentration and intracellular free drug concentration. f_u,liver and f_u,cell are frequently measured in multiple species and cell types in drug discovery and development for various applications. A comparison study of 12 matrices for f_u,liver and f_u,cell of hepatocytes in five different species (mouse, rat, dog, monkey, and human), as well as f_u,cell of Huh7 and human embryonic kidney 293 cell lines, was conducted for 22 structurally diverse compounds with the equilibrium dialysis method. Using an average bioequivalence approach, our results show that the average difference in binding to liver tissue, hepatocytes, or different cell types was within 2-fold of that of the rat f_u,liver Therefore, we recommend using rat f_u,liver as a surrogate for liver binding in other species and cell types in drug discovery. This strategy offers the potential to simplify binding studies and reduce cost, thereby enabling a more effective and practical determination of f_u for liver tissues, hepatocytes, and other cell types. In addition, f_u under hepatocyte stability incubation conditions should not be confused with f_u,cell, as one is a diluted f_u and the other is an undiluted f_u Cell density also plays a critical role in the accurate measurement of f_u,cell.

Inhibitory effects of sulfonylureas and non-steroidal anti-inflammatory drugs on in vitro metabolism of canagliflozin in human liver microsomes

Canagliflozin, used to treat type 2 diabetes mellitus (T2DM), is commonly co-administered with sulfonylureas. The objective of the present study was to evaluate the possible inhibitory effect of sulfonylureas and non-steroidal anti-inflammatory drugs (NSAIDs) on canagliflozin metabolism in vitro. Three sulfonylurea derivatives were evaluated as inhibitors: chlorpropamide, glimepiride and gliclazide. Two other NSAIDs were used as positive control inhibitors: niflumic acid and diclofenac. The rate of formation of canagliflozin metabolites was determined by HPLC analysis of in vitro incubations of canagliflozin as a substrate with and without inhibitors, using human liver microsomes (HLMs). Among sulfonylureas, glimepiride showed the most potent inhibitory effect against canagliflozin M7 metabolite formation, with an IC₅₀ value of 88 μm, compared to chlorpropamide and gliclazide with IC₅₀ values of more than 500 μm. Diclofenac inhibited M5 metabolite formation more than M7, with IC₅₀ values of 32 μm for M5 and 80 μm for M7. Niflumic acid showed no inhibition activity against M5 formation, but had relatively selective inhibitory potency against M7 formation, which is catalysed by UGT1A9, with an IC₅₀ value of 1.9 μm and an inhibition constant value of 0.8 μm. A clinical pharmacokinetic interaction between canagliflozin and sulfonylureas is unlikely. However, a possible clinically important drug interaction between niflumic acid and canagliflozin has been identified.

Drug Distribution. Part 1. Models to Predict Membrane Partitioning

PURPOSE

Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution.

METHODS

Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs.

RESULTS

The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs).

CONCLUSIONS

Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.

A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes

Correction for the nonspecific binding (NSB) of drugs to liver microsomes is essential for the accurate measurement of the kinetic parameters K_m and K_i, and hence in vitro-in vivo extrapolation to predict hepatic clearance and drug-drug interaction potential. Although a number of computational approaches for the estimation of drug microsomal NSB have been published, they generally rely on compound lipophilicity and charge state at the expense of other physicochemical and chemical properties. In this work, we report the development of a fragment-based hologram quantitative structure activity relationship (HQSAR) approach for the prediction of NSB using a database of 132 compounds. The model has excellent predictivity, with a noncross-validated r² of 0.966 and cross-validated r² of 0.680, with a predictive r² of 0.748 for an external test set comprising 34 drugs. The HQSAR method reliably predicted the fraction unbound in incubations of 95% of the training and test set drugs, excluding compounds with a steroid or morphinan 4,5-epoxide nucleus. Using the same data set of compounds, performance of the HQSAR method was superior to a model based on logP/D as the sole descriptor (predictive r² for the test set compounds, 0.534). Thus, the HQSAR method provides an alternative approach to laboratory-based procedures for the prediction of the NSB of drugs to liver microsomes, irrespective of the drug charge state (acid, base, or neutral).

The Reliability of Estimating Ki Values for Direct, Reversible Inhibition of Cytochrome P450 Enzymes from Corresponding IC50 Values: A Retrospective Analysis of 343 Experiments

In the present study, we conducted a retrospective analysis of 343 in vitro experiments to ascertain whether observed (experimentally determined) values of Ki for reversible cytochrome P450 (P450) inhibition could be reliably predicted by dividing the corresponding IC₅₀ values by two, based on the relationship (for competitive inhibition) in which Ki = IC₅₀/2 when [S] (substrate concentration) = Km (Michaelis-Menten constant). Values of Ki and IC₅₀ were determined under the following conditions: 1) the concentration of P450 marker substrate, [S], was equal to Km (for IC₅₀ determinations) and spanned Km (for Ki determinations); 2) the substrate incubation time was short (5 minutes) to minimize metabolism-dependent inhibition and inhibitor depletion; and 3) the concentration of human liver microsomes was low (0.1 mg/ml or less) to maximize the unbound fraction of inhibitor. Under these conditions, predicted Ki values, based on IC₅₀/2, correlated strongly with experimentally observed Ki determinations [r = 0.940; average fold error (AFE) = 1.10]. Of the 343 predicted Ki values, 316 (92%) were within a factor of 2 of the experimentally determined Ki values, and only one value fell outside a 3-fold range. In the case of noncompetitive inhibitors, Ki values predicted from IC₅₀/2 values were overestimated by a factor of nearly 2 (AFE = 1.85; n = 13), which is to be expected because, for noncompetitive inhibition, Ki = IC₅₀ (not IC₅₀/2). The results suggest that, under appropriate experimental conditions with the substrate concentration equal to Km, values of Ki for direct, reversible inhibition can be reliably estimated from values of IC₅₀/2.

Domperidone interacts with pioglitazone but not with ondansetron via common CYP metabolism in vitro

Domperidone (prokinetic agent) is frequently co-administered with pioglitazone (anitidiabetic) or ondansetron (antiemetic) in gastroparesis management. These drugs are metabolized via cytochome P-450 (CYP) 3A4, raising the possibility of interaction and adverse reactions. The concentration-dependent inhibitory effect of pioglitazone and ondansetron on domperidone hydroxylation was monitored in pooled human liver microsomes (HLM). Pioglitazone was further assessed as a mechanism-based inhibitor. Microsomal binding was evaluated in our assessment. In HLM, Vmax/Km estimates for monohydroxy domperidone formation decreased in presence of pioglitazone. Diagnostic plots indicated that pioglitazone inhibited domperidone in a partial mixed-type manner. The in vitro Ki was 1.52 µM. Predicted in vivo AUCi/AUC ratio was 1.98. Pioglitazone also exerted time-dependent inhibition on the metabolism of domperidone and the average remaining enzymatic activity decreased significantly upon preincubation with pioglitazone over 0-40 min. Diagnostic plots showed no inhibitory effect of ondansetron on domperidone hydroxylation. 6. In conclusion, pioglitazone inhibited domperidone metabolism in vitro through different complex mechanisms. Our in vitro data predict that the co-administration of these drugs can potentially trigger an in vivo drug-drug interaction.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Size- and time-dependent alteration in metabolic activities of human hepatic cytochrome P450 isozymes by gold nanoparticles via microsomal coincubations

Nano-sized particles are known to interfere with drug-metabolizing cytochrome P450 (CYP) enzymes, which can be anticipated to be a potential source of unintended adverse reactions, but the mechanisms underlying the inhibition are still not well understood. Herein we report a systematic investigation of the impacts of gold nanoparticles (AuNPs) on five major CYP isozymes under in vitro incubations of human liver microsomes (HLMs) with tannic acid (TA)-stabilized AuNPs in the size range of 5 to 100 nm. It is found that smaller AuNPs show more pronounced inhibitory effects on CYP2C9, CYP2C19, CYP2D6, and CYP3A4 in a dose-dependent manner, while 1A2 is the least susceptible to the AuNP inhibition. The size- and dose-dependent CYP-specific inhibition and the nonspecific drug-nanogold binding in the coincubation media can be significantly reduced by increasing the concentration ratio of microsomal proteins to AuNPs, probably via a noncompetitive mode. Remarkably, AuNPs are also found to exhibit a slow time-dependent inactivation of 2D6 and 3A4 in a β-nicotinamide adenine dinucleotide 2'-phosphate reduced tetrasodium salt hydrate (NADPH)-independent manner. During microsomal incubations, UV-vis spectroscopy, dynamic light scattering, and zeta-potential measurements were used to monitor the changes in particle properties under the miscellaneous AuNP/HLM/CYP dispersion system. An improved stability of AuNPs by mixing HLM with the gold nanocolloid reveals that the stabilization via AuNP-HLM interactions may occur on a faster time scale than the salt-induced nanoaggregation by incubation in phosphate buffer. The results suggest that the AuNP induced CYP inhibition can be partially attributed to its adhesion onto the enzymes to alter their structural conformations or onto the HLM membrane therefore impairing the integral membrane proteins. Additionally, AuNPs likely block the substrate pocket on the CYP surface, depending on both the particle characteristics and the structural diversity of the isozymes. These findings may represent additional mechanisms for the differential inhibitory effects arising from the coincubated AuNPs on the metabolic activities of the hepatic CYP isozymes.

Case study 4. Predicting the drug interaction potential for inhibition of CYP2C8 by montelukast

Predicting Drug-Drug Interactions (DDIs) from in vitro data is made difficult by not knowing concentrations of substrate and inhibitor at the target site. For in vivo targets, this is understandable, since intracellular concentrations can differ from extracellular concentrations. More vexing is that the concentration of the drug at the target for some in vitro assays can also be unknown. This uncertainty has resulted in standard in vitro practices that cannot accurately predict human pharmacokinetics. This case study highlights the impact of drug distribution, both in vitro and in vivo, with the example of the drug interaction potential of montelukast.

In vitro approaches to investigate cytochrome P450 activities: update on current status and their applicability

INTRODUCTION

Cytochromes P450 (CYPs) play a central role in the Phase I metabolism of drugs and other xenobiotics. It is estimated that CYPs can metabolize up to two-thirds of drugs present in humans. Over the past two decades, there have been numerous advances in in vitro methodologies to characterize drug metabolism and interaction involving CYPs.

AREAS COVERED

This review focuses on the use of in vitro methodologies to examine CYPs' role in drug metabolism and interaction. There is an emphasis on their current development, applicability, advantages and limitations as well as the use of in silico approaches in complementing and supporting in vitro data. The article also highlights the challenges in extrapolating in vitro data to in vivo situations.

EXPERT OPINION

Advances in in vitro methodologies have been made such that data can be used for in vivo prediction with comfortable degree of confidence. Improved assay designs and analytical techniques have permitted development of miniaturized assay format and automated system with improved sensitivity and throughput capacity. High-quality experimental designs and scientifically rigorous assessment/validation protocols remain crucial in developing reliable and robust in vitro models. With continued progress made in the field, in vitro methodologies will continually be employed in evaluating CYP activities in pharmaceutical industries and laboratories.

Simultaneous determination of multiple CYP inhibition constants using a cocktail-probe approach

To identify cytochrome P450 (CYP) drug-drug interaction (DDI) potential of a new chemical entity, the use of a specific clinically relevant probe substrate in the presence of a test compound is common place. In early discovery of new chemical entities, a balance of rigor, the ability to predict clinical DDI, and throughput is desired in an in vitro assay. This chapter describes a high-throughput CYP-mediated DDI assay method that balances these characteristics. The method utilizes a cassette approach using a cocktail of five selective probe substrates for the major clinically relevant CYPs involved in drug interactions. CYP1A2, 2C9, 2C19, 2D6, and 3A activities are assessed with liquid chromatography/tandem mass spectrometry (LC-MS/MS) quantification of metabolite formation. The method also outlines specific inhibitors to evaluate dynamic range and as a positive control. The benefits and needs for caution of this method are noted and discussed.

In vitro-to-in vivo predictions of drug-drug interactions involving multiple reversible inhibitors

INTRODUCTION

Predictions of drug-drug interactions (DDIs) are commonly performed for single inhibitors, but interactions involving multiple inhibitors also frequently occur. Predictions of such interactions involving stereoisomer pairs, parent/metabolite combinations and simultaneously administered multiple inhibitors are increasing in importance. This review provides the framework for predicting inhibitory DDIs of multiple inhibitors with any combination of reversible inhibition mechanism.

AREAS COVERED

The review provides an overview of the reliability of the in vitro determined reversible inhibition mechanism. Furthermore, the article provides a method to predict DDIs for multiple reversible inhibitors that allows substituting the inhibition constant (K(i)) with an inhibitor affinity (IC(50)) value determined at S << K(M).

EXPERT OPINION

A better understanding and the prediction methods of DDIs, resulting from multiple inhibitors, are important. The inhibition mechanism of a reversible inhibitor is often equivocal across studies and unreliable. Determination of the K(i) requires the assignment of reversible inhibition mechanism but in vitro-to-in vivo prediction of DDI risk can be achieved for multiple inhibitors from estimates of the inhibitor affinity (IC(50)) only, regardless of the inhibition mechanism.

Quantifying processes determining the free concentration of phenanthrene in Basal cytotoxicity assays

Difficulties may arise when extrapolating in vitro derived toxicity data to in vivo toxicity data because of the high variability and occasional low sensitivity of in vitro results. Differences in the free concentration of a test compound between in vitro and in vivo systems and between different in vitro systems may in part explain this variability and sensitivity difference. The aim of this study was to determine what assay components influence the free concentration of phenanthrene in a Balb/c 3T3 and RTgill-W1 MTT assay. Partition coefficients of phenanthrene to serum, well plate plastic, cells, and headspace were measured and subsequently used to model the free concentration of the compound in vitro. The estimated free concentration was compared to the free concentration measured in the assays using solid phase microextraction (SPME). Results indicate that the free concentration of phenanthrene, a relatively volatile and hydrophobic compound, is significantly reduced in a typical in vitro setup as it binds to matrices such as serum protein and well plate plastic. A reduction in free concentration due to increasing serum protein levels is accompanied by an increase in the median effect concentration (EC(50)) and can be modeled, with the exception of evaporation, using the partition coefficients of the compound to assay components.

Prediction of CYP2D6 drug interactions from in vitro data: evidence for substrate-dependent inhibition

Predicting the magnitude of potential drug-drug interactions is important for underwriting patient safety in the clinical setting. Substrate-dependent inhibition of cytochrome P450 enzymes may confound extrapolation of in vitro results to the in vivo situation. However, the potential for substrate-dependent inhibition with CYP2D6 has not been well characterized. The inhibition profiles of 20 known inhibitors of CYP2D6 were characterized in vitro against four clinically relevant CYP2D6 substrates (desipramine, dextromethorphan, metoprolol, and thioridazine) and bufuralol. Dextromethorphan exhibited the highest sensitivity to in vitro inhibition, whereas metoprolol was the least sensitive. In addition, when metoprolol was the substrate, inhibitors with structurally constrained amino moieties (clozapine, debrisoquine, harmine, quinidine, and yohimbine) exhibited at least a 5-fold decrease in inhibition potency when results were compared with those for dextromethorphan. Atypical inhibition kinetics were observed for these and other inhibitor-substrate pairings. In silico docking studies suggested that interactions with Glu216 and an adjacent hydrophobic binding pocket may influence substrate sensitivity and inhibition potency for CYP2D6. The in vivo sensitivities of the clinically relevant CYP2D6 substrates desipramine, dextromethorphan, and metoprolol were determined on the basis of literature drug-drug interaction (DDI) outcomes. Similar to the in vitro results, dextromethorphan exhibited the highest sensitivity to CYP2D6 inhibition in vivo. Finally, the magnitude of in vivo CYP2D6 DDIs caused by quinidine was predicted using desipramine, dextromethorphan, and metoprolol. Comparisons of the predictions with literature results indicated that the marked decrease in inhibition potency observed for the metoprolol-quinidine interaction in vitro translated to the in vivo situation.

Chemical inhibitors of cytochrome P450 isoforms in human liver microsomes: a re-evaluation of P450 isoform selectivity

The majority of marketed small-molecule drugs undergo metabolism by hepatic Cytochrome P450 (CYP) enzymes (Rendic 2002). Since these enzymes metabolize a structurally diverse number of drugs, metabolism-based drug-drug interactions (DDIs) can potentially occur when multiple drugs are coadministered to patients. Thus, a careful in vitro assessment of the contribution of various CYP isoforms to the total metabolism is important for predicting whether such DDIs might take place. One method of CYP phenotyping involves the use of potent and selective chemical inhibitors in human liver microsomal incubations in the presence of a test compound. The selectivity of such inhibitors plays a critical role in deciphering the involvement of specific CYP isoforms. Here, we review published data on the potency and selectivity of chemical inhibitors of the major human hepatic CYP isoforms. The most selective inhibitors available are furafylline (in co-incubation and pre-incubation conditions) for CYP1A2, 2-phenyl-2-(1-piperidinyl)propane (PPP) for CYP2B6, montelukast for CYP2C8, sulfaphenazole for CYP2C9, (-)-N-3-benzyl-phenobarbital for CYP2C19 and quinidine for CYP2D6. As for CYP2A6, tranylcypromine is the most widely used inhibitor, but on the basis of initial studies, either 3-(pyridin-3-yl)-1H-pyrazol-5-yl)methanamine (PPM) and 3-(2-methyl-1H-imidazol-1-yl)pyridine (MIP) can replace tranylcypromine as the most selective CYP2A6 inhibitor. For CYP3A4, ketoconazole is widely used in phenotyping studies, although azamulin is a far more selective CYP3A inhibitor. Most of the phenotyping studies do not include CYP2E1, mostly because of the limited number of new drug candidates that are metabolized by this enzyme. Among the inhibitors for this enzyme, 4-methylpyrazole appears to be selective.