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

Improvement in static and dynamic projections of drug-drug interactions caused by cytochrome P4503A time-dependent inhibitors through in vitro allosteric modulation by progesterone

Current drug discovery screens to assess the drug-drug interaction (DDI) risk caused by time-dependent inhibition (TDI) of cytochrome P450 (CYP) 3A4 are known to overpredict or produce false positives that do not translate in vivo. Recent work identified that inclusion of the allosteric modulator progesterone (PGS), at a concentration of 45 μM to human liver microsomal incubations, generated in vitro TDI values that replicated clinical DDI predictions for 2 well established mechanism-based inhibitors. Further application of this approach across a diverse set of compounds was undertaken in this study, with 56 molecules reported in literature as time-dependent inhibitors in vitro tested in the human liver microsomal TDI kinetic assay in the absence and presence of 45 μM PGS. No TDI signal was observed for 15 molecules under control conditions despite literature reports. For the remaining compounds observed to have a TDI signal under control conditions, presence of PGS modified the inactivation efficiency for 36 compounds and eliminated the TDI signal for 5 compounds that were false positives. In vitro kinetic values were incorporated into mechanistic static and dynamic physiologically based pharmacokinetic models to project DDIs. TDI parameters established in the presence of PGS decreased the magnitude of overprediction while maintaining a high sensitivity (96% and 100%) for the detection of TDI with improved specificity (69% and 89%) when using mechanistic static and dynamic models, respectively. Inclusion of PGS into in vitro TDI assays provides a simple, rapid, and cost-effective solution for identifying true CYP3A4 TDIs and improving TDI-related DDI predictions. SIGNIFICANCE STATEMENT: The impact of the previously determined optimal concentration of the allosteric modulator progesterone (45 μM) was evaluated across a set of 56 compounds reported to be time-dependent inhibitors in vitro. In vitro generated values were incorporated into mechanistic static and physiologically based pharmacokinetic models to predict extent of drug-drug interactions and compared to clinical reports. Inclusion of progesterone into the assay identified in vitro false positives and improved risk predictions.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Removal of Metal from Carboxypeptidase A Proceeds via a Split Pathway: Implications for the General Mechanisms of Metalloenzyme Inactivation by Chelating Agents

The chelation of protein-bound metal ions is typically thought to follow either a dissociative (D) or an associative (A) path. While the former mechanism involves the spontaneous dissociation of the metal from the protein prior to chelation, the latter route is characterized by the formation of an intermediate protein-metal-chelator ternary complex. Using the prototypical zinc protease carboxypeptidase A (CPA) and a variety of charged and neutral chelating agents, we demonstrate that inactivation of the enzyme (and likely other metalloproteins) proceeds through a split pathway with contributions from both D- and A-type mechanisms. In the case of charged chelators such as ethylenediaminetetraacetic acid (EDTA), the proportions of both paths could be tuned over a wide range through variation of the chelator concentration and the ionic strength, I (from ∼95% A type at low I values to ∼5% at high I values). By measuring the EDTA concentration and time dependence of CPA inactivation and fitting the obtained kinetic data to a modified time-dependent inhibition model, we obtained the rate constants for the A and D paths (kinact and koff, respectively) and the inhibition constant (KI) for the formation of the CPA-Zn2+-EDTA ternary complex, indicating that the decreased contribution of the A-type mechanism at high ionic strengths originates from a large (40-fold; at I = 0.5 M) increase in KI. This observation might be related to a triarginine motif in CPA that electrostatically steers negatively charged substrates into the active site and may therefore also guide carboxylate-bearing chelators toward the Zn2+ ion.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

CYP2J2-mediated metabolism of arachidonic acid in heart: A review of its kinetics, inhibition and role in heart rhythm control

Cytochrome P450 2 J2 (CYP2J2) is primarily expressed extrahepatically and is the predominant epoxygenase in human cardiac tissues. This highlights its key role in the metabolism of endogenous substrates. Significant scientific interest lies in cardiac CYP2J2 metabolism of arachidonic acid (AA), an omega-6 polyunsaturated fatty acid, to regioisomeric bioactive epoxyeicosatrienoic acid (EET) metabolites that show cardioprotective effects including regulation of cardiac electrophysiology. From an in vitro perspective, the accurate characterization of the kinetics of CYP2J2 metabolism of AA including its inhibition and inactivation by drugs could be useful in facilitating in vitro-in vivo extrapolations to predict drug-AA interactions in drug discovery and development. In this review, background information on the structure, regulation and expression of CYP2J2 in human heart is presented alongside AA and EETs as its endogenous substrate and metabolites. The in vitro and in vivo implications of the kinetics of this endogenous metabolic pathway as well as its perturbation via inhibition and inactivation by drugs are elaborated. Additionally, the role of CYP2J2-mediated metabolism of AA to EETs in cardiac electrophysiology will be expounded.

Investigation of N-Acetyltransferase 2-Mediated Drug Interactions of Amifampridine: In Vitro and In Vivo Evidence of Drug Interactions with Acetaminophen

Amifampridine is a drug used for the treatment of Lambert-Eaton myasthenic syndrome (LEMS) and was approved by the Food and Drug Administration (FDA) of the United States (US) in 2018. It is mainly metabolized by N-acetyltransferase 2 (NAT2); however, investigations of NAT2-mediated drug interactions with amifampridine have rarely been reported. In this study, we investigated the effects of acetaminophen, a NAT2 inhibitor, on the pharmacokinetics of amifampridine using in vitro and in vivo systems. Acetaminophen strongly inhibits the formation of 3-N-acetylamifmapridine from amifampridine in the rat liver S9 fraction in a mixed inhibitory manner. When rats were pretreated with acetaminophen (100 mg/kg), the systemic exposure to amifampridine significantly increased and the ratio of the area under the plasma concentration-time curve for 3-N-acetylamifampridine to amifampridine (AUC_m/AUC_p) decreased, likely due to the inhibition of NAT2 by acetaminophen. The urinary excretion and the amount of amifampridine distributed to the tissues also increased after acetaminophen administration, whereas the renal clearance and tissue partition coefficient (K_p) values in most tissues remained unchanged. Collectively, co-administration of acetaminophen with amifampridine may lead to relevant drug interactions; thus, care should be taken during co-administration.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

Fabrication and Characterization of Dextranase Nano-Entrapped Enzymes in Polymeric Particles Using a Novel Ultrasonication–Microwave Approach

In the current study, a novel method to improve the nano-entrapment of enzymes into Ca-alginate gel was investigated to determine the synergistic effects of ultrasound combined with microwave shock (UMS). The effects of UMS treatment on dextranase enzymes’ loading effectiveness (LE) and immobilization yield (IY) were investigated. By using FT-IR spectra and SEM, the microstructure of the immobilized enzyme (IE) was characterized. Additionally, the free enzyme was used as a control to compare the reusability and enzyme-kinetics characteristics of IEs produced with and without UMS treatments. The results demonstrated that the highest LE and IY were obtained when the IE was produced with a US of 40 W at 25 kHz for 15 min combined with an MS of 60 W at a shock rate of 20 s/min for 20 min, increasing the LE and the IY by 97.32 and 78.25%, respectively, when compared with an immobilized enzyme prepared without UMS treatment. In comparison with the control, UMS treatment dramatically raised the Vmax, KM, catalytic, and specificity constant values for the IE. The outcomes suggested that a microwave shock and ultrasound combination would be an efficient way to improve the immobilization of enzymes in biopolymer gel.

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as "Acute Effects", "NIOSH Toxicity Data", "Interactions", "Hepatotoxicity", "Carcinogenicity", "Symptoms", and "Human Toxicity Values" sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.

Atypical kinetics of cytochrome P450 enzymes in pharmacology and toxicology

Atypical kinetics are observed in metabolic reactions catalyzed by cytochrome P450 enzymes (P450). Yet, this phenomenon is regarded as experimental artifacts in some instances despite increasing evidence challenging the assumptions of typical Michaelis-Menten kinetics. As P450 play a major role in the metabolism of a wide range of substrates including drugs and endogenous compounds, it becomes critical to consider the impact of atypical kinetics on the accuracy of estimated kinetic and inhibitory parameters which could affect extrapolation of pharmacological and toxicological implications. The first half of this book chapter will focus on atypical non-Michaelis-Menten kinetics (e.g. substrate inhibition, biphasic and sigmoidal kinetics) as well as proposed underlying mechanisms supported by recent insights in mechanistic enzymology. In particular, substrate inhibition kinetics in P450 as well as concurrent drug inhibition of P450 in the presence of substrate inhibition will be further discussed. Moreover, mounting evidence has revealed that despite the high degree of sequence homology between CYP3A isoforms (i.e. CYP3A4 and CYP3A5), they have the propensities to exhibit vastly different susceptibilities and potencies of mechanism-based inactivation (MBI) with a common drug inhibitor. These experimental observations pertaining to the presence of these atypical isoform- and probe substrate-specific complexities in CYP3A isoforms by several clinically-relevant drugs will therefore be expounded and elaborated upon in the second half of this book chapter.

Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration

BACKGROUND

The concurrent administration of dronedarone and oral anti-coagulants is common because both are used in managing atrial fibrillation (AF). Dronedarone is a moderate inhibitor of the cytochrome P450 3A4 (CYP3A4) enzyme and P-glycoprotein (P-gp). Apixaban and rivaroxaban are P-gp and CYP3A4 substrates. This study aims to investigate the impact of exposure and bleeding risk of apixaban or rivaroxaban when co-administered with dronedarone using physiologically based pharmacokinetic/pharmacodynamic analysis.

METHODS

Modeling and simulation were conducted using Simcyp® Simulator. The parameters required for dronedarone modeling were collected from the literature. The developed dronedarone physiologically based pharmacokinetic (PBPK) model was verified using reported drug-drug interactions (DDIs) between dronedarone and CYP3A4 and P-gp substrates. The model was applied to evaluate the DDI potential of dronedarone on the exposure of apixaban 5 mg every 12 h or rivaroxaban 20 mg every 24 h in geriatric and renally impaired populations. DDIs precipitating major bleeding risks were assessed using exposure-response analyses derived from literature.

RESULTS

The model accurately described the pharmacokinetics of orally administered dronedarone in healthy subjects and accurately predicted DDIs between dronedarone and four CYP3A4 and P-gp substrates with fold errors <1.5. Dronedarone co-administration led to a 1.29 (90 % confidence interval (CI): 1.14-1.50) to 1.31 (90 % CI: 1.12-1.46)-fold increase in the area under concentration-time curve for rivaroxaban and 1.33 (90 % CI: 1.15-1.68) to 1.46 (90 % CI: 1.24-1.92)-fold increase for apixaban. The PD model indicated that dronedarone co-administration might potentiate the mean major bleeding risk of apixaban with a 1.45 to 1.95-fold increase. However, the mean major bleeding risk of rivaroxaban was increased by <1.5-fold in patients with normal or impaired renal function.

CONCLUSIONS

Dronedarone co-administration increased the exposure of rivaroxaban and apixaban and might potentiate major bleeding risks. Reduced apixaban and rivaroxaban dosing regimens are recommended when dronedarone is co-administered to patients with AF.

Epigenetics in drug disposition & drug therapy: symposium report of the 24th North American meeting of the International Society for the Study of Xenobiotics (ISSX)

The 24th North American International Society for the Study of Xenobiotics (ISSX) meeting, held virtually from September 13 to 17, 2021, embraced the theme of "Broadening Our Horizons." This reinforces a key mission of ISSX: striving to share innovative science related to drug discovery and development. Session speakers and the ISSX New Investigators Group, which supports the scientific and professional development of student and early career ISSX members, elected to highlight the scientific content presented during the captivating session titled, "Epigenetics in Drug Disposition & Drug Therapy." The impact genetic variation has on drug response is well established; however, this session underscored the importance of investigating the role of epigenetics in drug disposition and drug discovery. Session speakers, Drs. Ning, McClay, and Lazarus, detailed mechanisms by which epigenetic players including long non-coding RNA (lncRNAs), microRNA (miRNAs), DNA methylation, and histone acetylation can alter the expression of genes involved in pharmacokinetics, pharmacodynamics, and toxicity. Dr. Ning detailed current knowledge about miRNAs and lncRNAs and the mechanisms by which they can affect the expression of drug metabolizing enzymes (DMEs) and nuclear receptors. Dr. Lazarus discussed the potential role of miRNAs on UDP-glucuronosyltransferase (UGT) expression and activity. Dr. McClay provided evidence that aging alters methylation and acetylation of DMEs in the liver, affecting gene expression and activity. These topics, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are herein discussed, along with exciting future perspectives for epigenetics in drug disposition and drug discovery research.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Complex Cytochrome P450 Kinetics Due to Multisubstrate Binding and Sequential Metabolism. Part 2. Modeling of Experimental Data

Three CYP3A4 substrates, midazolam, ticlopidine, and diazepam, display non-Michaelis-Menten kinetics, form multiple primary metabolites, and are sequentially metabolized to secondary metabolites. We generated saturation curves for these compounds and analyzed the resulting datasets using a number of single-substrate and multisubstrate binding models. These models were parameterized using rate equations and numerical solutions of the ordinary differential equations. Multisubstrate binding models provided results superior to single-substrate models, and simultaneous modeling of multiple metabolites provided better results than fitting the individual datasets independently. Although midazolam datasets could be represented using standard two-substrate models, more complex models that include explicit enzyme-product complexes were needed to model the datasets for ticlopidine and diazepam. In vivo clearance predictions improved markedly with the use of in vitro parameters from the complex models versus the Michaelis-Menten equation. The results highlight the need to use sufficiently complex kinetic schemes instead of the Michaelis-Menten equation to generate accurate kinetic parameters. SIGNIFICANCE STATEMENT: The metabolism of midazolam, ticlopidine, and diazepam by CYP3A4 results in multiple metabolites and sequential metabolism. This study evaluates the use of rate equations and numerical methods to characterize the in vitro enzyme kinetics. Use of complex cytochrome P450 kinetic models is necessary to obtain accurate parameter estimates for predicting in vivo disposition.

Complex Cytochrome P450 Kinetics Due to Multisubstrate Binding and Sequential Metabolism. Part 1. Theoretical Considerations

Complexities in P450-mediated metabolism kinetics include multisubstrate binding, multiple-product formation, and sequential metabolism. Saturation curves and intrinsic clearances were simulated for single-substrate and multisubstrate models using derived velocity equations and numerical solutions of ordinary differential equations (ODEs). Multisubstrate models focused on sigmoidal kinetics because of their dramatic impact on clearance predictions. These models were combined with multiple-product formation and sequential metabolism, and simulations were performed with random error. Use of single-substrate models to characterize multisubstrate data can result in inaccurate kinetic parameters and poor clearance predictions. Comparing results for use of standard velocity equations with ODEs clearly shows that ODEs are more versatile and provide better parameter estimates. It would be difficult to derive concentration-velocity relationships for complex models, but these relationships can be easily modeled using numerical methods and ODEs. SIGNIFICANCE STATEMENT: The impact of multisubstrate binding, multiple-product formation, and sequential metabolism on the P450 kinetics was investigated. Numerical methods are capable of characterizing complicated P450 kinetics.

Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes

Cytochrome P450 3A (CYP3A) is a frequent target for time-dependent inhibition (TDI) that can give rise to drug-drug interactions (DDI). Yet many drugs that exhibit in vitro TDI for CYP3A do not result in DDI. There were 23 drugs with published clinical DDI evaluated for CYP3A TDI in human liver microsomes (HLM) and hepatocytes (HHEP), and these data were used in static and dynamic models for projecting DDI caused by inactivation of CYP3A in both liver and intestine. TDI parameters measured in HHEP, particularly the maximal rate of enzyme inactivation, were generally lower than those measured in HLM. In static models, the use of estimated average unbound organ exit concentrations offered the most accurate projections of DDI with geometric mean fold errors of 2.0 and 1.7 for HLM and HHEP, respectively. Use of maximum organ entry concentrations yielded marked overestimates of DDI. When evaluated in a binary fashion (i.e., projection of DDI of 1.25-fold or greater), data from HLM offered the greatest sensitivity (100%) and specificity (67%) and yielded no missed DDI when average unbound organ exit concentrations were used. In dynamic physiologically based pharmacokinetic modeling, accurate projections of DDI were obtained with geometric mean fold errors of 1.7 and 1.6 for HLM and HHEP, respectively. Sensitivity and specificity were 100% and 67% when using TDI data generated in HLM and Simcyp modeling. Overall, DDI caused by CYP3A-mediated TDI can be reliably projected using dynamic or static models. For static models, average organ unbound exit concentrations should be used as input values otherwise DDI will be markedly overestimated. SIGNIFICANCE STATEMENT: CYP3A time-dependent inhibitors (TDI) are important in the design and development of new drugs. The prevalence of CYP3A TDI is high among newly synthesized drug candidates, and understanding the potential need for running clinical drug-drug interaction (DDI) studies is essential during drug development. Ability to reliably predict DDI caused by CYP3A TDI has been difficult to achieve. We report a thorough evaluation of CYP3A TDI and demonstrate that DDI can be predicted when using appropriate models and input parameters generated in human liver microsomes or hepatocytes.

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.

Enantioselective inhibition of human CYP2C19 by the chiral pesticide ethofumesate: Prediction of pesticide-drug interactions in humans

Ethofumesate is a chiral herbicide that may display enantioselective behavior in humans. For this reason, the enantioselective potential of ethofumesate and its main metabolite ethofumesate-2-hydroxy to cause pesticide-drug interactions on cytochrome P450 forms (CYPs) has been evaluated by using human liver microsomes. Among the evaluated CYPs, CYP2C19 had its activity decreased by the ethofumesate racemic mixture (rac-ETO), (+)-ethofumesate ((+)-ETO), and (-)-ethofumesate ((-)-ETO). CYP2C19 inhibition was not time-dependent, but a strong inhibition potential was observed for rac-ETO (IC₅₀ = 5 ± 1 μmol L^-1), (+)-ETO (IC₅₀ = 1.6 ± 0.4 μmol L^-1), and (-)-ETO (IC₅₀ = 1.8 ± 0.4 μmol L^-1). The reversible inhibition mechanism was competitive, and the inhibition constant (K_i) values for rac-ETO (2.6 ± 0.4 μmol L^-1), (+)-ETO (1.5 ± 0.2 μmol L^-1), and (-)-ETO (0.7 ± 0.1 μmol L^-1) were comparable to the K_i values of strong CYP2C19 inhibitors. Inhibition of CYP2C19 by ethofumesate was enantioselective, being almost twice higher for (-)-ETO than for (+)-ETO, which indicates that this enantiomer may be a more potent inhibitor of this CYP form. For an in vitro-in vivo correlation, the Food and Drug Administration's (FDA) guideline on the assessment of drug-drug interactions used in the early stages of drug development was used. The FDA's R₁ values were estimated on the basis of the obtained ethofumesate K_i and distribution volume, metabolism, unbound plasma fraction, gastrointestinal and dermal absorption data available in the literature. The correlation revealed that ethofumesate probably inhibits CYP2C19 in vivo for both chronic (oral) and occupational (dermal) exposure scenarios.

Site-Directed Mutagenesis at the Molybdenum Pterin Cofactor Site of the Human Aldehyde Oxidase: Interrogating the Kinetic Differences Between Human and Cynomolgus Monkey

The estimation of the drug clearance by aldehyde oxidase (AO) has been complicated because of this enzyme's atypical kinetics and species and substrate specificity. Since human AO (hAO) and cynomolgus monkey AO (mAO) have a 95.1% sequence identity, cynomolgus monkeys may be the best species for estimating AO clearance in humans. Here, O6-benzylguanine (O6BG) and dantrolene were used under anaerobic conditions, as oxidative and reductive substrates of AO, respectively, to compare and contrast the kinetics of these two species through numerical modeling. Whereas dantrolene reduction followed the same linear kinetics in both species, the oxidation rate of O6BG was also linear in mAO and did not follow the already established biphasic kinetics of hAO. In an attempt to determine why hAO and mAO are kinetically distinct, we have altered the hAO V811 and F885 amino acids at the oxidation site adjacent to the molybdenum pterin cofactor to the corresponding alanine and leucine in mAO, respectively. Although some shift to a more monkey-like kinetics was observed for the V811A mutant, five more mutations around the AO cofactors still need to be investigated for this purpose. In comparing the oxidative and reductive rates of metabolism under anaerobic conditions, we have come to the conclusion that despite having similar rates of reduction (4-fold difference), the oxidation rate in mAO is more than 50-fold slower than hAO. This finding implies that the presence of nonlinearity in AO kinetics is dependent upon the degree of imbalance between the rates of oxidation and reduction in this enzyme. SIGNIFICANCE STATEMENT: Although they have as much as 95.1% sequence identity, human and cynomolgus monkey aldehyde oxidase are kinetically distinct. Therefore, monkeys may not be good estimators of drug clearance in humans.

Mechanisms of Herb-Drug Interactions Involving Cinnamon and CYP2A6: Focus on Time-Dependent Inhibition by Cinnamaldehyde and 2-Methoxycinnamaldehyde

Information is scarce regarding pharmacokinetic-based herb-drug interactions (HDI) with trans-cinnamaldehyde (CA) and 2-methoxycinnamaldehyde (MCA), components of cinnamon. Given the presence of cinnamon in food and herbal treatments for various diseases, HDIs involving the CYP2A6 substrates nicotine and letrozole with MCA (KS = 1.58 µM; Hill slope = 1.16) and CA were investigated. The time-dependent inhibition (TDI) by MCA and CA of CYP2A6-mediated nicotine metabolism is a complex process involving multiple mechanisms. Molecular dynamic simulations showed that CYP2A6's active site accommodates two dynamic ligands. The preferred binding orientations for MCA and CA were consistent with the observed metabolism: epoxidation, O-demethylation, and aromatic hydroxylation of MCA and cinnamic acid formation from CA. The percent remaining activity plots for TDI by MCA and CA were curved, and they were analyzed with a numerical method using models of varying complexity. The best-fit models support multiple inactivator binding, inhibitor depletion, and partial inactivation. Deconvoluted mass spectra indicated that MCA and CA modified CYP2A6 apoprotein with mass additions of 156.79 (142.54-171.04) and 132.67 (123.37-141.98), respectively, and it was unaffected by glutathione. Heme degradation was observed in the presence of MCA (48.5% ± 13.4% loss; detected by liquid chromatography-tandem mass spectrometry). In the absence of clinical data, HDI predictions were made for nicotine and letrozole using inhibition parameters from the best-fit TDI models and parameters scaled from rats. Predicted area under the concentration-time curve fold changes were 4.29 (CA-nicotine), 4.92 (CA-letrozole), 4.35 (MCA-nicotine), and 5.00 (MCA-letrozole). These findings suggest that extensive exposure to cinnamon (corresponding to ≈ 275 mg CA) would lead to noteworthy interactions. SIGNIFICANCE STATEMENT: Human exposure to cinnamon is common because of its presence in food and cinnamon-based herbal treatments. Little is known about the risk for cinnamaldehyde and methoxycinnamaldehyde, two components of cinnamon, to interact with drugs that are eliminated by CYP2A6-mediated metabolism. The interactions with CYP2A6 are complex, involving multiple-ligand binding, time-dependent inhibition of nicotine metabolism, heme degradation, and apoprotein modification. An herb-drug interaction prediction suggests that extensive exposure to cinnamon would lead to noteworthy interactions with nicotine.

How precise is quantitative prediction of pharmacokinetic effects due to drug-drug interactions and genotype from in vitro data? A comprehensive analysis on the example CYP2D6 and CYP2C19 substrates

Drug-drug interactions (DDI) and genomic variation (PG) can lead to dangerously high blood and tissue concentrations with some drugs but may be negligible with other drugs. Using a quantitative metaanalysis, we analyzed on the example of CYP2D6 and CYP2C19 substrates, how well the effects of DDI and PG can be predicted by in vitro methods. In addition, we analyzed the quantitative effect of prototypic inhibitors of the two enzymes in relation to their genetic deficiency. More than 600 published studies were screened which compared either human pharmacokinetics with and without comedication, or which compared human pharmacokinetics of deficient with extensive metabolizers, or which assessed metabolism by in vitro approaches. With human liver microsomes, the in vitro to in vivo agreement of fractional clearances was reasonably high if loss of substrate was quantified in the in vitro assays performed with and without enzyme specific inhibitors. Also a generally very high correlation between the clinical pharmacokinetic effects of inherited deficiency and inhibition by drug-drug interactions could be demonstrated. Most cases of poor correlation were explained by the lack of CYP2D6 versus CYP2C19 specificity of fluoxetine or by a poor knowledge of the quantitative contribution of the metabolic pathways if metabolite formation was quantified in the in vitro assays. The good correspondence of the in vitro data with clinical DDI and clinical PG studies may be a good basis for future application of these methods in drug development and drug therapy.