Human CYPs involved in drug metabolism: structures, substrates and binding affinities

Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes

The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%-84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites.

Structure-Function Analysis of Streptomyces griseolus CYP105A1 in the Metabolism of Nonsteroidal Anti-inflammatory Drugs

Streptomyces griseolus CYP105A1 exhibits monooxygenase activity to a wide variety of structurally different substrates with regio- and stereospecificity, making its application range broad. Our previous studies have shown that CYP105A1 wild type and its variants metabolize 12 types of nonsteroidal anti-inflammatory drugs (NSAIDs). In particular, the R84A variant exhibited a high activity against many NSAIDs. We successfully crystallized complexes of wild-type CYP105A1 (WT) and the R84A variant with diclofenac (DIF) or flufenamic acid (FLF). In the WT, the carboxyl group of DIF formed a charged hydrogen bond with Arg84. In contrast, in R84A, the carboxyl group formed two bidentate charged hydrogen bonds with Arg73. The C4' atom of the benzene ring of DIF, which undergoes hydroxylation by WT and R84A, was positioned approximately 4 Å from the heme iron. Binding of FLF was nearly the same in both WT and R84A. The carboxyl group of FLF formed charged hydrogen bonds with Arg73. In both WT and R84A, FLF appeared to be fixed by this charged hydrogen bonding with Arg73 during the reaction, and the C4' atom, which undergoes hydroxylation, must face the heme iron. Thus, the dihedral angles of the two N-C bonds connecting the two benzene rings of FLF needed to rotate by 78° and -71°, respectively. The temperature factors of the F-G loop, helix F, and helix G of R84A were remarkably higher than those of WT. This suggests that these regions in R84A are much more flexible compared to those of WT, which may consequently affect substrate binding and product release.

Characterization of the glucuronidating pathway of pectolinarigenin, the major active constituent of the Chinese medicine Daji, in humans and its influence on biological activities

ETHNOPHARMACOLOGICAL RELEVANCE

The Chinese medicine Daji (the aerial part of Cirsium japonicum DC.) and its charred product (Cirsii Japonici Herba Carbonisata) have been widely used as hemostatic agents or diuretic agents to prepare a variety of Chinese herbal formula. Pectolinarigenin (PEC), one of the most abundant constituents in both Daji and its charred product, has been considered as the key effective substance responsible for the major pharmacological activities of Daji, including hemostasis, hepatoprotective, anti-tumor and anti-osteoporosis effects. However, the major metabolic pathways of PEC in humans and the influence of PEC metabolism on its biological activities are poorly understood.

AIM OF THE STUDY

To characterize the main metabolic pathway(s) and key enzymes of PEC in human biological systems, as well as to reveal the influence of PEC metabolism on its biological activities.

MATERIALS AND METHODS

The metabolic stability assays of PEC were investigated in human liver microsomes (HLM). The O-glucuronide of PEC was biosynthesized and characterized by nuclear magnetic resonance (NMR) spectroscopy. The key enzymes responsible for O-glucuronidation of PEC in humans were assigned by performing UGT reaction phenotyping, chemical inhibition and enzymatic kinetic assays. The agonist effects of PEC and its O-glucuronide on nuclear factor erythroid2-related factor 2 (Nrf2), Peroxisome proliferator activated receptors (PPARα and PPARβ) were tested at the cellular level.

RESULTS

PEC could be readily metabolized to form a mono-O-glucuronide in both human liver microsome (HLM) and human intestinal microsome (HIM). The mono-O-glucuronide was bio-synthesized by mouse liver S9 and its structure was fully characterized as PEC-7-O-β-D-glucuronide (PEC-O-7-G). UGT1A1, UGT1A3 and UGT1A9 are key enzymes responsible for PEC-7-O-glucuronidation in HLM, while UGT1A1, UGT1A9 and 1A10 may play key roles in this reaction in HIM. Biological tests revealed that PEC displayed strong agonist effects on Nrf2, PPARα and PPARβ, whereas PEC-7-O-glucuronide showed relatively weak Nrf2 agonist effect and very weak PPAR agonist effects, indicating that PEC-7-O-glucuronidation strongly weaken its agonist effects on Nrf2 and PPAR.

CONCLUSIONS

Our results demonstrate that 7-O-glucuronidation is the major metabolic pathway of PEC in human tissues, while UGT1A1, 1A3 and 1A9 are key contributing enzymes responsible for PEC-7-O-glucuronidation in human liver. It is also found that PEC 7-O-glucuronidation significantly weakens the Nrf2 and PPAR agonist effects. All these findings are very helpful for the pharmacologists to deep understand the metabolic rates of PEC in humans.

Chemometrics as a valuable tool for evaluating interactions between antiretroviral drugs and food

AIMS

Clinically significant interactions with food occur for more than half of antiretroviral drugs. Different physiochemical properties deriving from the chemical structures of antiretroviral drugs may contribute to the variable food effect. Chemometric methods allow analysing a large number of interrelated variables concomitantly and visualizing correlations between them. We used a chemometric approach to determine the types of correlations among different features of antiretroviral drugs and food that may influence interactions.

METHODS

Thirty-three antiretroviral drugs were analysed: ten nucleoside reverse transcriptase inhibitors, six non-nucleoside reverse transcriptase inhibitors, five integrase strand transfer inhibitors, ten protease inhibitors, one fusion inhibitor and one HIV maturation inhibitor. Input data for the analysis were collected from already published clinical studies, chemical records and calculations. We constructed a hierarchical partial least squares (PLS) model with three response parameters: postprandial change of time to reach maximum drug concentration (ΔTmax ), albumin binding (%) and logarithm of partition coefficient (logP). Predictor parameters were the first two principal components of principal component analysis (PCA) models for six groups of molecular descriptors.

RESULTS

PCA models explained 64.4% to 83.4% of the variance of the original parameters (average: 76.9%), whereas the PLS model had four significant components and explained 86.2% and 71.4% of the variance in the sets of predictor and response parameters, respectively. We observed 58 significant correlations between ΔTmax , albumin binding (%), logP and constitutional, topological, hydrogen bonding and charge-based molecular descriptors.

CONCLUSIONS

Chemometrics is a useful and valuable tool for analysing interactions between antiretroviral drugs and food.

A deep dive into historical Ames study data for N-nitrosamine compounds

Mutagenicity data is a core component of the safety assessment data required by regulatory agencies for acceptance of new drug compounds, with the OECD-471 bacterial reverse mutation (Ames) assay most widely used as a primary screen to assess drug impurities for potential mutagenic risk. N-Nitrosamines are highly potent mutagenic carcinogens in rodent bioassays and their recent detection as impurities in pharmaceutical products has sparked increased interest in their safety assessment. Previous literature reports indicated that the Ames test might not be sensitive enough to detect the mutagenic potential of N-nitrosamines in order to accurately predict a risk of carcinogenicity. To explore this hypothesis, public Ames and rodent carcinogenicity data pertaining to the N-nitrosamine class of compounds was collated for analysis. Here we present how variations to the OECD 471-compliant Ames test, including strain, metabolic activation, solvent type and pre-incubation/plate incorporation methods, may impact the predictive performance for carcinogenicity. An understanding of optimal conditions for testing of N-nitrosamines may improve both the accuracy and confidence in the ability of the Ames test to identify potential carcinogens.

Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment

Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH₂ groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities.

Revisiting the mutagenicity and genotoxicity of N-nitroso propranolol in bacterial and human in vitro assays

Propranolol is a widely used β-blocker that can generate a nitrosated derivative, N-nitroso propranolol (NNP). NNP has been reported to be negative in the bacterial reverse mutation test (the Ames test) but genotoxic in other in vitro assays. In the current study, we systematically examined the in vitro mutagenicity and genotoxicity of NNP using several modifications of the Ames test known to affect the mutagenicity of nitrosamines, as well as a battery of genotoxicity tests using human cells. We found that NNP induced concentration-dependent mutations in the Ames test, both in two tester strains that detect base pair substitutions, TA1535 and TA100, as well as in the TA98 frameshift-detector strain. Although positive results were seen with rat liver S9, the hamster liver S9 fraction was more effective in bio-transforming NNP into a reactive mutagen. NNP also induced micronuclei and gene mutations in human lymphoblastoid TK6 cells in the presence of hamster liver S9. Using a panel of TK6 cell lines that each expresses a different human cytochrome P450 (CYP), CYP2C19 was identified as the most active enzyme in the bioactivation of NNP to a genotoxicant among those tested. NNP also induced concentration-dependent DNA strand breakage in metabolically competent 2-dimensional (2D) and 3D cultures of human HepaRG cells. This study indicates that NNP is genotoxic in a variety of bacterial and mammalian systems. Thus, NNP is a mutagenic and genotoxic nitrosamine and a potential human carcinogen.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

What Makes a Potent Nitrosamine? Statistical Validation of Expert-Derived Structure-Activity Relationships

The discovery of carcinogenic nitrosamine impurities above the safe limits in pharmaceuticals has led to an urgent need to develop methods for extending structure-activity relationship (SAR) analyses from relatively limited datasets, while the level of confidence required in that SAR indicates that there is significant value in investigating the effect of individual substructural features in a statistically robust manner. This is a challenging exercise to perform on a small dataset, since in practice, compounds contain a mixture of different features, which may confound both expert SAR and statistical quantitative structure-activity relationship (QSAR) methods. Isolating the effects of a single structural feature is made difficult due to the confounding effects of other functionality as well as issues relating to determining statistical significance in cases of concurrent statistical tests of a large number of potential variables with a small dataset; a naïve QSAR model does not predict any features to be significant after correction for multiple testing. We propose a variation on Bayesian multiple linear regression to estimate the effects of each feature simultaneously yet independently, taking into account the combinations of features present in the dataset and reducing the impact of multiple testing, showing that some features have a statistically significant impact. This method can be used to provide statistically robust validation of expert SAR approaches to the differences in potency between different structural groupings of nitrosamines. Structural features that lead to the highest and lowest carcinogenic potency can be isolated using this method, and novel nitrosamine compounds can be assigned into potency categories with high accuracy.

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

Spectroscopic observations of β-eudesmol binding to human cytochrome P450 isoforms 3A4 and 1A2, but not to isoforms 2C9, 2C19 and 2D6

β-Eudesmol is a sesquiterpenoid component o Atractylodes lancea with cytotoxic activity against cholangiocarcinoma. Its lipophilic nature makes β-eudesmol a likely substrate of human cytochrome P450 (P450) enzymes.Using ligand-binding difference spectroscopy, the affinities of this compound to recombinant CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4 were investigated in Escherichia coli membrane preparations.CYP3A4 showed a type I spectral change, with a binding constant Ks of 77 ± 23 (mean ± SD) μM at 0.5 μM P450 (Ks/[P450] ≈ 155). The reference substrate testosterone and the inhibitor fluconazole bound to the enzyme with apparent affinities of 86 ± 4 μM (type I) and 21 μM (type II), respectively. β-Eudesmol was bound to CYP3A4 in a non-cooperative manner (Hill coefficient n ≈ 0.8). CYP1A2 showed reverse type I difference spectra with either β-eudesmol or caffeine. The CYP1A2 affinity for β-eudesmol was higher (0.23 mM) than for caffeine (0.37 mM) but lower than for phenacetin (0.11 mM, type I). β-Eudesmol did not bind significantly to CYP2C9, CYP2C19, and CYP2D6.Confirmation of metabolic activity and studies on the involvement of other human P450 isoforms studies are required. Double-beam spectrometry is needed to validate Ks measurements made with a plate reader.

Salmonella typhimurium TA100 and TA1535 and E. coli WP2 uvrA are highly sensitive to detect the mutagenicity of short Alkyl-N-Nitrosamines in the Bacterial Reverse Mutation Test

Humans are exposed to low levels of N-nitrosamines via different sources. N-Nitrosamines have recently been detected as impurities in various marketed drugs and they are known mutagenic carcinogens belonging to the cohort of concern as referred to in the ICH M7 guideline. Despite their well-known mutagenic properties, there is ongoing discussion on the suitability of the bacterial reverse mutation assay and using induced rat liver S9 as the external source of metabolism to detect their mutagenic potential. Therefore, we have investigated the mutagenic potential of N-nitrosodimethylamine, N-nitrosodiethylamine, N-nitrosodipropylamine, and N-nitrosodibutylamine in vitro under various conditions. Our work showed that the bacterial reverse mutation assay applying plate incorporation or preincubation protocols and using Salmonella typhimurium strains TA100 and TA1535 and E. coli WP2 uvrA is suitable to predict the mutagenicity of n-nitrosamines in the presence of phenobarbital/β-naphthoflavone induced rat liver S9.

Developing Structure-Activity Relationships for N-Nitrosamine Activity

The detection of N-nitrosodimethylamine (NDMA) in several marketed drugs led regulatory agencies to require that N-nitrosamine risk assessments be performed on all marketed medical products [EMA/351053/2019 rev 1 (2019)]. Regulation of N-nitrosamine impurity levels in pharmaceutical drug substances and products is described in the ICH M7(R1) guideline where they are referred to as "cohort-of-concern" compounds as several are potent rodent carcinogens [Kroes et. al. 2004]. EMA, U.S. FDA and other regulatory agencies have set provisional acceptable daily intake limits for N-nitrosamines calculated from rodent carcinogenicity TD₅₀ values for experimentally measured N-nitrosamines or the measured TD₅₀ values of close analogs. The class-specific limit can be adjusted based upon a structure activity relationship analysis (SAR) and comparison with analogs having established carcinogenicity data [EMA/369136/2020, (2020)]. To investigate whether improvements in SARs can more accurately predict N-nitrosamine carcinogenic potency, an ad hoc workgroup of 23 companies and universities was established with the goals of addressing several scientific and regulatory issues including: reporting and review of N-nitrosamine mutagenicity and carcinogenicity reaction mechanisms, collection and review of available, public relevant experimental data, development of structure-activity relationships consistent with mechanisms for prediction of N-nitrosamine carcinogenic potency categories, and improved methods for calculating acceptable intake limits for N-nitrosamines based upon mechanistic analogs. Here we describe this collaboration and review our progress to date towards development of mechanistically based structure-activity relationships. We propose improving risk assessment of N-nitrosamines by first establishing the dominant reaction mechanism prior to retrieving an appropriate set of close analogs for use in read-across exercises.

Substrate Selectivity of Coumarin Derivatives by Human CYP1 Enzymes: In Vitro Enzyme Kinetics and In Silico Modeling

Of the three enzymes in the human cytochrome P450 family 1, CYP1A2 is an important enzyme mediating metabolism of xenobiotics including drugs in the liver, while CYP1A1 and CYP1B1 are expressed in extrahepatic tissues. Currently used CYP substrates, such as 7-ethoxycoumarin and 7-ethoxyresorufin, are oxidized by all individual CYP1 forms. The main aim of this study was to find profluorescent coumarin substrates that are more selective for the individual CYP1 forms. Eleven 3-phenylcoumarin derivatives were synthetized, their enzyme kinetic parameters were determined, and their interactions in the active sites of CYP1 enzymes were analyzed by docking and molecular dynamic simulations. All coumarin derivatives and 7-ethoxyresorufin and 7-pentoxyresorufin were oxidized by at least one CYP1 enzyme. 3-(3-Methoxyphenyl)-6-methoxycoumarin (19) was 7-O-demethylated by similar high efficiency [21-30 ML/(min·mol CYP)] by all CYP1 forms and displayed similar binding in the enzyme active sites. 3-(3-Fluoro-4-acetoxyphenyl)coumarin (14) was selectively 7-O-demethylated by CYP1A1, but with low efficiency [0.16 ML/(min mol)]. This was explained by better orientation and stronger H-bond interactions in the active site of CYP1A1 than that of CYP1A2 and CYP1B1. 3-(4-Acetoxyphenyl)-6-chlorocoumarin (20) was 7-O-demethylated most efficiently by CYP1B1 [53 ML/(min·mol CYP)], followed by CYP1A1 [16 ML/(min·mol CYP)] and CYP1A2 [0.6 ML/(min·mol CYP)]. Variations in stabilities of complexes between 20 and the individual CYP enzymes explained these differences. Compounds 14, 19, and 20 are candidates to replace traditional substrates in measuring activity of human CYP1 enzymes.

Dissecting the Structural Plasticity and Dynamics of Cytochrome P450 2B4 by Molecular Dynamics Simulations

The plasticity of cytochromes P450 (P450s) is known to contribute significantly to their catalytic capacity of metabolizing various substrates. Although numerous studies have been performed, factors governing the plasticity and dynamics of P450s are still not fully understood. In this study, taking CYP2B4 as an example, we dissect the protein plasticity and dynamics in different environments. CYP2B4 is featured by a high degree of plasticity, which exhibits open, closed, and intermediate states. By analyzing the CYP2B4 crystal structures, we identified the structural features for the closed, open, and intermediate states. Interestingly, formation of the dimer structure was found in the open and intermediate states. The subsequent molecular dynamics (MD) simulations of the open structure in water confirmed the importance of the dimer form in stabilizing the open conformations. MD simulations of the closed and open structures in the membrane environment and the free energies for opening the F-G cassette obtained from the umbrella sampling calculations indicate that the membrane environment is important for stabilizing the F-G cassette. The dynamical network analysis indicates that Asp105 on the B-C loop plays an important role in transiting the structure from the open to the intermediate state. Our results thus unveil the mechanisms of dimer formation and open-to-intermediate transition for CYP2B4 in the water and membrane environments.

Inhibition of human CYP1 enzymes by a classical inhibitor α-naphthoflavone and a novel inhibitor N-(3, 5-dichlorophenyl)cyclopropanecarboxamide: An in vitro and in silico study

Enzymes in the cytochrome P450 family 1 (CYP1) catalyze metabolic activation of procarcinogens and deactivation of certain anticancer drugs. Inhibition of these enzymes is a potential approach for cancer chemoprevention and treatment of CYP1-mediated drug resistance. We characterized inhibition of human CYP1A1, CYP1A2, and CYP1B1 enzymes by the novel inhibitor N-(3,5-dichlorophenyl)cyclopropanecarboxamide (DCPCC) and α-naphthoflavone (ANF). Depending on substrate, IC₅₀ values of DCPCC for CYP1A1 or CYP1B1 were 10-95 times higher than for CYP1A2. IC₅₀ of DCPCC for CYP1A2 was 100-fold lower than for enzymes in CYP2 and CYP3 families. DCPCC IC₅₀ values were 10-680 times higher than the ones of ANF. DCPCC was a mixed-type inhibitor of CYP1A2. ANF was a competitive tight-binding inhibitor of CYP1A1, CYP1A2, and CYP1B1. CYP1A1 oxidized DCPCC more rapidly than CYP1A2 or CYP1B1 to the same metabolite. Molecular dynamics simulations and binding free energy calculations explained the differences of binding of DCPCC and ANF to the active sites of all three CYP1 enzymes. We conclude that DCPCC is a more selective inhibitor for CYP1A2 than ANF. DCPCC is a candidate structure to modulate CYP1A2-mediated metabolism of procarcinogens and anticancer drugs.

Application of cytochrome P450 reactivity on the characterization of chemical compounds and its association with repeated-dose toxicity

Repeated-dose toxicity (RDT) studies are one of the critical studies to assess chemical safety. There have been some studies attempting to predict RDT endpoints based on chemical substructures, but it remains very difficult to establish such a method, and a more detailed characterization of chemical compounds seems necessary. Cytochrome P450s (P450s) comprise multiple forms with different substrate specificities and play important roles in both the detoxification and metabolic activation of xenobiotics. In this study, we investigated possible use of P450 reactivity of chemical compounds to classify the compounds. A total of 148 compounds with available rat RDT test data were used as test compounds and subjected to inhibition assays against 18 human and rat P450s. Among the tested compounds, 82 compounds inhibited at least one P450 form. Hierarchical clustering analyses using the P450 inhibitory profiles divided the 82 compounds into nine groups, some of which showed characteristic chemical and biological properties. Principal component analyses of the P450 inhibition data in combination with the calculated chemical descriptors demonstrated that P450 inhibition data were plotted differently than most chemical descriptors in the loading plots. Finally, association analyses between P450 inhibition and RDT endpoints showed that some endpoints related to the liver, kidney and hematology were significantly associated with the inhibition of some P450s. Our present results suggest that the P450 reactivity profiles can be used as novel descriptors for characterizing chemical compounds for the investigation of the toxicity mechanism and/or the establishment of a toxicity prediction model.

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Background:

Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.

Objective:

This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.

Results:

Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.

Conclusion:

This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.

Xenobiotic Metabolising Enzymes: Impact on Pathologic Conditions, Drug Interactions and Drug Design

Background:

The biotransformation of xenobiotics is a homeostatic defensive response of the body against bioactive invaders. Xenobiotic metabolizing enzymes, important for the metabolism, elimination and detoxification of exogenous agents, are found in most tissues and organs and are distinguished into phase I and phase II enzymes, as well as phase III transporters. The cytochrome P450 superfamily of enzymes plays a major role in the biotransformation of most xenobiotics as well as in the metabolism of important endogenous substrates such as steroids and fatty acids. The activity and the potential toxicity of numerous drugs are strongly influenced by their biotransformation, mainly accomplished by the cytochrome P450 enzymes, one of the most versatile enzyme systems.

Objective:

In this review, considering the importance of drug metabolising enzymes in health and disease, some of our previous research results are presented, which, combined with newer findings, may assist in the elucidation of xenobiotic metabolism and in the development of more efficient drugs.

Conclusion:

Study of drug metabolism is of major importance for the development of drugs and provides insight into the control of human health. This review is an effort towards this direction and may find useful applications in related medical interventions or help in the development of more efficient drugs.

Metabolic interactions of rosmarinic acid with human cytochrome P450 monooxygenases and uridine diphosphate glucuronosyltransferases

In light of the widespread use of herbal medicines containing rosmarinic acid (RA) and limited literature available thereon, we investigated the metabolic interactions of RA with human cytochrome P450 monooxygenases (CYPs) and uridine diphosphate glucuronosyltransferases (UGTs). The involvement of selected enzymes (CYP1A2, CYP2C19, CYP2E1, CYP3 A4, UGT1A1, UGT1A6, and UGT2B7) in the metabolism of RA and the inhibitory effect of RA on the enzyme activity were comprehensively evaluated using human recombinant isozyme system. Additionally, concentration-dependent RA metabolism mediated by phase I enzymes (including CYPs) or UGT was investigated in human liver microsome (HLM) system. A significant disappearance of RA was observed in the seven CYP and UGT isoforms studied, indicating their possible involvement in the metabolism of RA. Based on Michaelis-Menten kinetics, the metabolism study using HLM suggests that the UGT system may have a higher capacity and lower affinity for the metabolism of RA than phase I enzyme (including CYP) systems. Moreover, RA weakly inhibited CYP2C9 and 2E1 activities with IC50 values of 39.6 and 61.0 μM, respectively, while moderately inhibiting UGT1A1, 1A6, and 2B7 with IC50 values of 9.24, 19.1, and 23.4 μM, respectively. By constructing Line weaver-Burk plots, the type of inhibition exhibited by RA on CYP and UGT activities was determined as follows: CYP2C19, mixed inhibition; CYP2E1, UGT1A1, UGT1A6, and UGT2B7, competitive inhibition. Based on the comparison of the IC50 and Ki values obtained in the current study with the previously reported plasma concentrations of RA after oral dosing in humans, it is suggested that RA may significantly inhibit the activities of the tested UGTs, rather than CYPs, in clinical settings. Thus, the present study could provide a basis for further studies on clinically significant interactions between UGT substrate drugs and herbal medicines containing RA.

Estimation of fractions metabolized by hepatic CYP enzymes using a concept of inter-system extrapolation factors (ISEFs) - a comparison with the chemical inhibition method

BACKGROUND

For estimation of fractions metabolized (fm) by different hepatic recombinant human CYP enzymes (rhCYP), calculation of inter-system extrapolation factors (ISEFs) has been proposed.

METHODS

ISEF values for CYP1A2, CYP2C19 and CYP3A4/5 were measured. A CYP2C9 ISEF was taken from a previous report. Using a set of compounds, fractions metabolized by CYP enzymes (fm,CYP) values calculated with the ISEFs based on rhCYP data were compared with those from the chemical inhibition data. Oral pharmacokinetics (PK) profiles of midazolam were simulated using the physiologically based pharmacokinetics (PBPK) model with the CYP3A ISEF. For other CYPs, the in vitro fm,CYP values were compared with the reference fm,CYP data back-calculated with, e.g. modeling of test substrates by feeding clinical PK data.

RESULTS

In vitro-in vitro fm,CYP3A4 relationship between the results from rhCYP incubation and chemical inhibition was drawn as an exponential correlation with R2=0.974. A midazolam PBPK model with the CYP3A4/5 ISEFs simulated the PK profiles within twofold error compared to the clinical observations. In a limited number of cases, the in vitro methods could not show good performance in predicting fm,CYP1A2, fm,CYP2C9 and fm,CYP2C19 values as reference data.

CONCLUSIONS

The rhCYP data with the measured ISEFs provided reasonable calculation of fm,CYP3A4 values, showing slight over-estimation compared to chemical inhibition.

Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology

Although prediction of clearance using hepatocytes and liver microsomes has long played a decisive role in drug discovery, it is widely acknowledged that reliably accurate prediction is not yet achievable despite the predominance of hepatically cleared drugs. Physiologically mechanistic methodology tends to underpredict clearance by several fold, and empirical correction of this bias is confounded by imprecision across drugs. Understanding the causes of prediction uncertainty has been slow, possibly reflecting poor resolution of variables associated with donor source and experimental methods, particularly for the human situation. It has been reported that among published human hepatocyte predictions there was a tendency for underprediction to increase with increasing in vivo intrinsic clearance, suggesting an inherent limitation using this particular system. This implied an artifactual rate limitation in vitro, although preparative effects on cell stability and performance were not yet resolved from assay design limitations. Here, to resolve these issues further, we present an up-to-date and comprehensive examination of predictions from published rat as well as human studies (where n = 128 and 101 hepatocytes and n = 71 and 83 microsomes, respectively) to assess system performance more independently. We report a clear trend of increasing underprediction with increasing in vivo intrinsic clearance, which is similar both between species and between in vitro systems. Hence, prior concerns arising specifically from human in vitro systems may be unfounded and the focus of investigation in the future should be to minimize the potential in vitro assay limitations common to whole cells and subcellular fractions.

The Cytochrome P450 3A4 has three Major Conformations: New Clues to Drug Recognition by this Promiscuous Enzyme

We computed the channels of the 3A4 isoform of the cytochrome P450 3A4 (CYP) on the basis of 24 crystal structures extracted from the Protein Data Bank (PDB). We identified three major conformations (denoted C, O1 and O2) using an enhanced version of the CCCPP software that we developed for the present work, while only two conformations (C and O² ) are considered in the literature. We established the flowchart of definition of these three conformations in function of the structural and physicochemical parameters of the ligand. The channels are characterized with qualitative and quantitative parameters, and not only with their surrounding secondary structures as it is usually done in the literature.

Functioning of drug-metabolizing microsomal cytochrome P450s: In silico probing of proteins suggests that the distal heme 'active site' pocket plays a relatively 'passive role' in some enzyme-substrate interactions

PURPOSE

The currently held mechanistic understanding of microsomal cytochrome P450s (CYPs) seeks that diverse drug molecules bind within the deep-seated distal heme pocket and subsequently react at the heme centre. To explain a bevy of experimental observations and meta-analyses, we indulge a hypothesis that involves a "diffusible radical mediated" mechanism. This new hypothesis posits that many substrates could also bind at alternate loci on/within the enzyme and be reacted without the pertinent moiety accessing a bonding proximity to the purported catalytic Fe-O enzyme intermediate.

METHODS

Through blind and heme-distal pocket centered dockings of various substrates and non-substrates (drug molecules of diverse sizes, classes, topographies etc.) of microsomal CYPs, we explored the possibility of access of substrates via the distal channels, its binding energies, docking orientations, distance of reactive moieties (or molecule per se) to/from the heme centre, etc. We investigated specific cases like- (a) large drug molecules as substrates, (b) classical marker drug substrates, (c) class of drugs as substrates (Sartans, Statins etc.), (d) substrate preferences between related and unrelated CYPs, (e) man-made site-directed mutants' and naturally occurring mutants' reactivity and metabolic disposition, (f) drug-drug interactions, (g) overall affinities of drug substrate versus oxidized product, (h) meta-analysis of in silico versus experimental binding constants and reaction/residence times etc.

RESULTS

It was found that heme-centered dockings of the substrate/modulator drug molecules with the available CYP crystal structures gave poor docking geometries and distances from Fe-heme centre. In conjunction with several other arguments, the findings discount the relevance of erstwhile hypothesis in many CYP systems. Consequently, the newly proposed hypothesis is deemed a viable alternate, as it satisfies Occam's razor.

CONCLUSIONS

The new proposal affords expanded scope for explaining the mechanism, kinetics and overall phenomenology of CYP mediated drug metabolism. It is now understood that the heme-iron and the hydrophobic distal pocket of CYPs serve primarily to stabilize the reactive intermediate (diffusible radical) and the surface or crypts of the apoprotein bind to the xenobiotic substrate (and in some cases, the heme distal pocket could also serve the latter function). Thus, CYPs enhance reaction rates and selectivity/specificity via a hitherto unrecognized modality.

Computational Identification of the Paralogs and Orthologs of Human Cytochrome P450 Superfamily and the Implication in Drug Discovery

The human cytochrome P450 (CYP) superfamily consisting of 57 functional genes is the most important group of Phase I drug metabolizing enzymes that oxidize a large number of xenobiotics and endogenous compounds, including therapeutic drugs and environmental toxicants. The CYP superfamily has been shown to expand itself through gene duplication, and some of them become pseudogenes due to gene mutations. Orthologs and paralogs are homologous genes resulting from speciation or duplication, respectively. To explore the evolutionary and functional relationships of human CYPs, we conducted this bioinformatic study to identify their corresponding paralogs, homologs, and orthologs. The functional implications and implications in drug discovery and evolutionary biology were then discussed. GeneCards and Ensembl were used to identify the paralogs of human CYPs. We have used a panel of online databases to identify the orthologs of human CYP genes: NCBI, Ensembl Compara, GeneCards, OMA ("Orthologous MAtrix") Browser, PATHER, TreeFam, EggNOG, and Roundup. The results show that each human CYP has various numbers of paralogs and orthologs using GeneCards and Ensembl. For example, the paralogs of CYP2A6 include CYP2A7, 2A13, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 2F1, 2J2, 2R1, 2S1, 2U1, and 2W1; CYP11A1 has 6 paralogs including CYP11B1, 11B2, 24A1, 27A1, 27B1, and 27C1; CYP51A1 has only three paralogs: CYP26A1, 26B1, and 26C1; while CYP20A1 has no paralog. The majority of human CYPs are well conserved from plants, amphibians, fishes, or mammals to humans due to their important functions in physiology and xenobiotic disposition. The data from different approaches are also cross-validated and validated when experimental data are available. These findings facilitate our understanding of the evolutionary relationships and functional implications of the human CYP superfamily in drug discovery.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

Recent progress on structural bioinformatics research of cytochrome P450 and its impact on drug discovery

Cytochrome P450 is predominantly responsible for human drug metabolism, which is of critical importance for drug discovery and development. Structural bioinformatics focuses on analysis and prediction of three-dimentional structure of biological macromolecules and elucidation of structure-function relationship as well as identification of important binding interactions. Rapid advancement of structural bioinformatics has been made over the last decade. With more information available for CYP structures, the methods of structural bioinformatics may be used in the CYP field. In this review, we demonstrate three previous studies on CYP using the methods of structural bioinformatics, including the investigation of reasons for decrease of enzymatic activity of CYP1A2 caused by a peripheral mutation, the construction of a pharmacophore model specific to active site of CYP1A2 and the prediction of the functional consequences of single residue mutation in CYP. By illustrating these studies we attempt to show the potential role of structural bioinformatics in CYP research and help better understanding the importance of structural bioinformatics in drug designing.

In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors

We have previously established an in silico classification method ("CPathPred") to predict the major clearance pathways of drugs based on an empirical decision with only four physicochemical descriptors-charge, molecular weight, octanol-water distribution coefficient, and protein unbound fraction in plasma-using a rectangular method. In this study, we attempted to improve the prediction performance of the method by introducing a support vector machine (SVM) and increasing the number of descriptors. The data set consisted of 141 approved drugs whose major clearance pathways were classified into metabolism by CYP3A4, CYP2C9, or CYP2D6; organic anion transporting polypeptide-mediated hepatic uptake; or renal excretion. With the same four default descriptors as used in CPathPred, the SVM-based predictor (named "default descriptor SVM") resulted in higher prediction performance compared with a rectangular-based predictor judged by 10-fold cross-validation. Two SVM-based predictors were also established by adding some descriptors as follows: 1) 881 descriptors predicted in silico from the chemical structures of drugs in addition to 4 default descriptors ("885 descriptor SVM"); and 2) selected descriptors extracted by a feature selection based on a greedy algorithm with default descriptors ("feature selection SVM"). The prediction accuracies of the rectangular-based predictor, default descriptor SVM, 885 descriptor SVM, and feature selection SVM were 0.49, 0.60, 0.72, and 0.91, respectively, and the overall precision values for these four methods were 0.72, 0.77, 0.86, and 0.98, respectively. In conclusion, we successfully constructed SVM-based predictors with limited numbers of descriptors to classify the major clearance pathways of drugs in humans with high prediction performance.

Biotransformation in vitro: An essential consideration in the quantitative in vitro-to-in vivo extrapolation (QIVIVE) of toxicity data

Early consideration of the multiplicity of factors that govern the biological fate of foreign compounds in living systems is a necessary prerequisite for the quantitative in vitro-in vivo extrapolation (QIVIVE) of toxicity data. Substantial technological advances in in vitro methodologies have facilitated the study of in vitro metabolism and the further use of such data for in vivo prediction. However, extrapolation to in vivo with a comfortable degree of confidence, requires continuous progress in the field to address challenges such as e.g., in vitro evaluation of chemical-chemical interactions, accounting for individual variability but also analytical challenges for ensuring sensitive measurement technologies. This paper discusses the current status of in vitro metabolism studies for QIVIVE extrapolation, serving today's hazard and risk assessment needs. A short overview of the methodologies for in vitro metabolism studies is given. Furthermore, recommendations for priority research and other activities are provided to ensure further widespread uptake of in vitro metabolism methods in 21st century toxicology. The need for more streamlined and explicitly described integrated approaches to reflect the physiology and the related dynamic and kinetic processes of the human body is highlighted i.e., using in vitro data in combination with in silico approaches.

Estimation of biliary excretion of foreign compounds using properties of molecular structure

Biliary excretion is one of the main elimination pathways for drugs and/or their metabolites. Therefore, an insight into the structural profile of cholephilic compounds through accurate modelling of the biliary excretion is important for the estimation of clinical pharmacokinetics in early stages of drug discovery. The aim of this study was to develop quantitative structure-activity relationships as computational tools for the estimation of biliary excretion and identification of the molecular properties controlling this process. The study used percentage of dose excreted intact into bile measured in vivo in rat for a diverse dataset of 217 compounds. Statistical techniques were multiple linear regression analysis, regression trees, random forest and boosted trees. A simple regression tree model generated using the CART algorithm was the most accurate in the estimation of the percentage of bile excretion of compounds, and this outperformed the more sophisticated boosted trees and random forest techniques. Analysis of the outliers indicated that the models perform best when lipophilicity is not too extreme (log P < 5.35) and for compounds with molecular weight above 280 Da. Molecular descriptors selected by all these models including the top ten incorporated in boosted trees and random forest indicated a higher biliary excretion for relatively hydrophilic compounds especially if they are anionic or cationic, and have a large molecular size. A statistically validated molecular weight threshold for potentially significant biliary excretion was above 348 Da.

Prediction of amino acid residues participated in substrate recognition by cytochrome P450 subfamilies with broad substrate specificity

Cytochromes P450 comprise a large superfamily and several of their isoforms play a crucial role in metabolism of xenobiotics, including drugs. Although these enzymes demonstrate broad and cross-substrate specificity, different cytochrome P450 subfamilies exhibit certain selectivity for some types of substrates. Analysis of amino acid residues of the active sites of six cytochrome subfamilies (CYP1А, CYP2А, CYP2С, CYP2D, CYP2E and CYP3А) enables to define subfamily-specific patterns that consist of four residues. These residues are located on the periphery of the active sites of these cytochromes. We suggest that they can form a primary binding site at the entrance to the active site, defining cytochrome substrate recognition.

A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms

A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.

Plasticity of CYP2B enzymes: structural and solution biophysical methods

In the past three years, major advances in understanding cytochrome P450 2B (CYP2B) structure-function relationships have been made through determination of multiple ligand-bound and one ligand-free X-ray crystal structure of CYP2B4 and one ligand-bound X-ray crystal structure of CYP2B6. These structures have provided insight into the features that provide the high degree of plasticity of the enzymes. A combination of a phenylalanine cluster that allows for concerted movement of helices F through G and a conserved set of electrostatic interactions involving Arg(262) facilitates movement of this region to accommodate binding of ligands of various sizes without perturbing most of the P450 fold. Integrating solution based techniques such as NMR or deuterium exchange mass spectrometry (DXMS) with computational methods including molecular docking has provided further insight into enzyme behavior upon ligand binding. In addition, extended molecular dynamics simulations have provided a link between an open and a closed conformation of ligand-free CYP2B4 found in crystal structures. Other studies revealed the utility of rational engineering in improving stability of P450s to facilitate structural studies. The solution and computational results combined with the X-ray crystal structures yield a comprehensive picture of how these enzymes adopt different conformations to bind various ligands.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.

Toxicological actions of plant-derived and anthropogenic methylenedioxyphenyl-substituted chemicals in mammals and insects

The methylenedioxyphenyl (MDP) substituent is a structural feature present in many plant chemicals that deter foraging by predatory insects and herbivores. With increasing use of herbal extracts in alternative medicine, human exposure to MDP-derived plant chemicals may also be significant. Early studies found that most MDP agents themselves possess relatively low intrinsic toxicity, but strongly influence the actions of other xenobiotics in mammals and insects by modulating cytochrome P-450 (CYP)-dependent biotransformation. Thus, after exposure to MDP chemicals an initial phase of CYP inhibition is followed by a sustained phase of CYP induction. In insects CYP inhibition by MDP agents underlies their use as pesticide synergists, but analogous inhibition of mammalian CYP impairs the clearance of drugs and foreign compounds. Conversely, induction of mammalian CYP by MDP agents increases xenobiotic oxidation capacity. Exposure of insects to MDP-containing synergists in the environment, in the absence of coadministered pesticides, may also enhance xenobiotic detoxication. Finally, although most MDP agents are well tolerated, several, typified by safrole, aristolochic acid, and MDP-kavalactones, are associated with significant toxicities, including the risk of hepatotoxicity or tumorigenesis. Thus, the presence of MDP-substituted chemicals in the environment may produce a range of direct and indirect toxicities in target and nontarget species.