Comparative molecular field analysis and QSAR on substrates binding to cytochrome P450 2D6

Lipophilic Metabolic Efficiency (LipMetE) and Drug Efficiency Indices to Explore the Metabolic Properties of the Substrates of Selected Cytochrome P450 Isoforms

Cytochrome P450 (CYP450) enzymes belong to a superfamily of heme-containing proteins that are involved in the metabolism of structurally diverse endogenous and exogenous compounds. Various proof-of-concept studies indicate that metabolic stability and intrinsic clearance of CYP450 substrates are linked with the respective lipophilicity (log P or log D). This necessitates the normalization of lipophilicity (log P or log D) of a given CYP450 substrate with respect to its metabolic stability (LipMetE) and intrinsic clearance (log₁₀CL_int,u). Therefore, in this article, the LipMetE values of already known substrates of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, including some marketed drugs, have been calculated to elucidate the relationship between lipophilicity (log D _7.4) and in vitro clearance. Moreover, various drug efficiency metrics including lipophilic efficiency (LipE) and ligand efficiency (LE) have been evaluated, and the thresholds of these parameters have been defined for the CYP450 substrates exhibiting normalized LipMetE. Our results indicate that for a given range of LipMetE, greater the log D value of the substrate the more avidly it binds to a given CYP450 enzyme and shows more intrinsic clearance (log₁₀CL_int,u). Overall, the majority of the model substrates of CYP450 isoforms and already marketed drugs in our datasets exhibit log D _7.4 values of ∼2.5 with LipMetE values in the range of 0-2.5 and LipE values of ≤3. Overall, consideration of these parameters in ADME profiling could aid in reducing the drug failure rate in the later stages of clinical investigations.

Development of a Novel AOP for Cyp2F2-Mediated Lung Cancer in Mice

Traditional methods for carcinogenicity testing rely heavily on the rodent bioassay as the standard for identification of tumorigenic risk. As such, identification of species-specific outcomes and/or metabolism are a frequent argument for regulatory exemption. One example is the association of tumor formation in the mouse lung after exposure to Cyp2F2 ligands. The adverse outcome pathway (AOP) framework offers a theoretical platform to address issues of species specificity that is consistent, transparent, and capable of integrating data from new approach methodologies as well as traditional data streams. A central premise of the AOP concept is that pathway progression from the molecular initiating event (MIE) implies a definable “response-response” (R-R) relationship between each key event (KE) that drives the pathway towards a specific adverse outcome (AO). This article describes an AOP for lung cancer in the mouse from an MIE of Cyp2F2-specific reactive metabolite formation, advancing through KE that include protein and/or nucleic acid adducts, diminished Club Cell 10 kDa (CC10) protein expression, hyperplasia of CC10 deficient Club cells, and culminating in the AO of mixed-cell tumor formation in the distal airways. This tumor formation is independent of route of exposure and our AOP construct is based on overlapping mechanistic events for naphthalene, styrene, ethyl benzene, isoniazid, and fluensulfone in the mouse. This AOP is intended to accelerate the explication of an apparent mouse-specific outcome and serve as a starting point for a quantitative analysis of mouse-human differences in susceptibility to the tumorigenic effects of Cyp2F2 ligands.

The endogenous substrates of brain CYP2D

CYP2D6, one of the major cytochrome P450 isoforms present in the human brain, is associated with the incidence and prevalence of central nervous system (CNS) diseases. Human CYP2D6 and rat CYP2D are involved in the metabolism of various neurotransmitters and neurosteroids. Brain CYP2D can be regulated by endogenous steroids, including sex hormones. The alteration of CYP2D-mediated metabolism induced by endogenous steroids may cause changes in sensitivity to environmental and industrial toxins and carcinogens as well as physiological and pathophysiological processes controlled by biologically active compounds. This review summarizes the current knowledge regarding the distribution, endogenous substrates, and regulation of brain CYP2D.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

Three-dimensional quantitative structure-activity relationship analysis of inhibitors of human and rat cytochrome P4503A enzymes

Cytochrome P450 3A4 (CYP3A4) is a member of the CYP family and is an important enzyme in drug metabolism. A compound that inhibits CYP3A4 activity could also affect the pharmacokinetics of other substrates, resulting in drug-drug interactions (DDIs) that could cause side effects. Pharmacokinetic data from drug-development studies in rats often determine the dosage used in human clinical trials. It is therefore useful to understand differences in metabolism in different species at an early stage in drug development. Human and rat CYP3A enzymes show different inhibition profiles with different drugs, although the mechanisms involved are not yet clear. Here we built three-dimensional quantitative structure-activity relationship (3D-QSAR) models using structure-based comparative molecular field analysis (CoMFA), to predict the direct inhibitory activity of ligands for human CYP3A4 and rat CYP3A1, based on computer-ligand docking. The alignment of the ligand docking poses suggested that key amino acid-ligand interactions (e.g., Thr309 in CYP3A4 and Pro310 in CYP3A1) characterized the different potencies with which the ligands inhibited CYP3A4 and CYP3A1. The 3D-QSAR models for human and rat CYP3A family inhibitors predicted the potency of inhibitors and could be useful for assessing DDIs at an early stage in drug discovery.

The pharmacokinetics and pharmacogenetics of the antiemetic cyclizine in palliative care patients

CONTEXT

Cyclizine, an antihistaminic antiemetic, is commonly used in palliative care. Its pharmacokinetics have been poorly studied, and its metabolic pathway is unknown but may involve the genetically controlled cytochrome P450 2D6 (CYP2D6). If this is the case, the metabolic ratio of cyclizine to norcyclizine and efficacy/adverse effects may vary between patients according to their CYP2D6 genotype.

OBJECTIVES

To deduce the pharmacokinetics and antiemetic/sedative effects of cyclizine and relate these and its metabolic ratio to the CYP2D6 genotype in palliative care patients.

METHODS

Palliative care patients initiated on continuous cyclizine subcutaneous (SC) infusions had blood samples taken and efficacy/toxicity scores measured during the approach to steady state. Another group of patients at steady state receiving oral cyclizine had a single blood sample taken. Samples were analyzed to elucidate pharmacokinetic parameters and CYP2D6 genetics.

RESULTS

SC dosing group: The median (interquartile range) cyclizine half-life, volume of distribution, and clearance were 13 (7-48) hours, 23 (12-30)L/kg, and 15 (11-26)mL/min/kg, respectively. Nausea and sedation scores were 3.0 (1.2-5.7) and 5.0 (2.6-8.1), respectively, overall and did not vary with genotype (P=0.76 and 0.11, respectively). The median overall metabolic ratio at steady state was 4.9 (3.8-9.2) and did vary with CYP2D6 genotype (P=0.02). Oral dosing group: The median metabolic ratio was 2.1 (1.5-2.9) and did not vary with CYP2D6 genotype (P=0.37).

CONCLUSION

Palliative care patients have similar cyclizine pharmacokinetics to those reported in other patient groups. Cyclizine metabolism to norcyclizine may include CYP2D6 as the metabolic ratio varied with CYP2D6 genotype in the SC group.

Three-dimensional quantitative structure-activity relationship studies on UGT1A9-mediated 3-O-glucuronidation of natural flavonols using a pharmacophore-based comparative molecular field analysis model

Glucuronidation is often recognized as one of the rate-determining factors that limit the bioavailability of flavonols. Hence, design and synthesis of more bioavailable flavonols would benefit from the establishment of predictive models of glucuronidation using kinetic parameters [e.g., K(m), V(max), intrinsic clearance (CL(int)) = V(max)/K(m)] derived for flavonols. This article aims to construct position (3-OH)-specific comparative molecular field analysis (CoMFA) models to describe UDP-glucuronosyltransferase (UGT) 1A9-mediated glucuronidation of flavonols, which can be used to design poor UGT1A9 substrates. The kinetics of recombinant UGT1A9-mediated 3-O-glucuronidation of 30 flavonols was characterized, and kinetic parameters (K(m), V(max), CL(int)) were obtained. The observed K(m), V(max), and CL(int) values of 3-O-glucuronidation ranged from 0.04 to 0.68 μM, 0.04 to 12.95 nmol/mg/min, and 0.06 to 109.60 ml/mg/min, respectively. To model UGT1A9-mediated glucuronidation, 30 flavonols were split into the training (23 compounds) and test (7 compounds) sets. These flavonols were then aligned by mapping the flavonols to specific common feature pharmacophores, which were used to construct CoMFA models of V(max) and CL(int), respectively. The derived CoMFA models possessed good internal and external consistency and showed statistical significance and substantive predictive abilities (V(max) model: q(2) = 0.738, r(2) = 0.976, r(pred)(2) = 0.735; CL(int) model: q(2) = 0.561, r(2) = 0.938, r(pred)(2) = 0.630). The contour maps derived from CoMFA modeling clearly indicate structural characteristics associated with rapid or slow 3-O-glucuronidation. In conclusion, the approach of coupling CoMFA analysis with a pharmacophore-based structural alignment is viable for constructing a predictive model for regiospecific glucuronidation rates of flavonols by UGT1A9.

Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule

BACKGROUND

Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models for predicting substrate specificity of major isoforms of P450, in order to understand whether a given drug will be metabolized or not. This paper describes an in-silico method for predicting the metabolizing capability of major isoforms (e.g. CYP 3A4, 2D6, 1A2, 2C9 and 2C19).

RESULTS

All models were trained and tested on 226 approved drug molecules. Firstly, 2392 molecular descriptors for each drug molecule were calculated using various softwares. Secondly, best 41 descriptors were selected using general and genetic algorithm. Thirdly, Support Vector Machine (SVM) based QSAR models were developed using 41 best descriptors and achieved an average accuracy of 86.02%, evaluated using fivefold cross-validation. We have also evaluated the performance of our model on an independent dataset of 146 drug molecules and achieved average accuracy 70.55%. In addition, SVM based models were developed using 26 Chemistry Development Kit (CDK) molecular descriptors and achieved an average accuracy of 86.60%.

CONCLUSIONS

This study demonstrates that SVM based QSAR model can predict substrate specificity of major CYP isoforms with high accuracy. These models can be used to predict isoform responsible for metabolizing a drug molecule. Thus these models can used to understand whether a molecule will be metabolized or not. This is possible to develop highly accurate models for predicting substrate specificity of major isoforms using CDK descriptors. A web server MetaPred has been developed for predicting metabolizing isoform of a drug molecule http://crdd.osdd.net/raghava/metapred/.

New insights into the structural characteristics and functional relevance of the human cytochrome P450 2D6 enzyme

To date, the crystal structures of at least 12 human CYPs (1A2, 2A6, 2A13, 2C8, 2C9, 2D6, 2E1, 2R1, 3A4, 7A1, 8A1, and 46A1) have been determined. CYP2D6 accounts for only a small percentage of all hepatic CYPs (< 2%), but it metabolizes approximately 25% of clinically used drugs with significant polymorphisms. CYP2D6 also metabolizes procarcinogens and neurotoxins, such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, 1,2,3,4-tetrahydroquinoline, and indolealkylamines. Moreover, the enzyme utilizes hydroxytryptamines and neurosteroids as endogenous substrates. Typical CYP2D6 substrates are usually lipophilic bases with an aromatic ring and a nitrogen atom, which can be protonated at physiological pH. Substrate binding is generally followed by oxidation (5-7 A) from the proposed nitrogen-Asp301 interaction. A number of homology models have been constructed to explore the structural features of CYP2D6, while antibody studies also provide useful structural information. Site-directed mutagenesis studies have demonstrated that Glu216, Asp301, Phe120, Phe481, and Phe483 play important roles in determining the binding of ligands to CYP2D6. The structure of human CYP2D6 has been recently determined and shows the characteristic CYP fold observed for other members of the CYP superfamily. The lengths and orientations of the individual secondary structural elements in the CYP2D6 structure are similar to those seen in other human CYP2 members, such as CYP2C9 and 2C8. The 2D6 structure has a well-defined active-site cavity located above the heme group with a volume of approximately 540 A(3), which is larger than equivalent cavities in CYP2A6 (260 A(3)), 1A2 (375 A(3)), and 2E1 (190 A(3)), but smaller than those in CYP3A4 (1385 A(3)) and 2C8 (1438 A(3)). Further studies are required to delineate the molecular mechanisms involved in CYP2D6 ligand interactions and their implications for drug development and clinical practice.

Computer-based substrate specifity prediction for cytochrome P450

Cytochrome P450 is important class of enzymes metabolizing numerous drugs. The composition and activity of these enzymes are determined the drug distribution in organism, its pharmacological and toxic effect. Thus the prediction of the behaviour of compounds in organism is essential for discovery and development of new drugs in the early stages of this process. The different isoforms of cytochrome P450 can oxidized wide range of chemical compounds and their substrate specifity do not correlate with their taxonomical classification. The main methods of cytochrome P450 substrate specifity prediction is reviewed. These methods divided based on primary informations that used: prediction based on amino acid sequences, ligand-based (pharmacophore and QSAR models) and structure-based (molecular docking, affinity prediction) methods. The common problem of cytochrome P450 substrate prediction and advantage and disadvantages of these methods are discussed.

Molecular properties and CYP2D6 substrates: central nervous system therapeutics case study and pattern analysis of a substrate database

CYP2D6 substrate status is a critical Go/No Go decision criteria in central nervous system (CNS) drug discovery efforts because the polymorphic nature of CYP2D6 can lead to variable patient safety and drug efficacy. In addition, CYP2D6 is disproportionately involved in the metabolism of CNS drugs compared with other drug classes. Therefore, identifying trends in small molecule properties of CNS-penetrant compounds that can help discriminate potential CYP2D6 substrates from nonsubstrates would allow additional prioritization in the synthesis and biological evaluation of new therapeutic candidates. We report here the conversion of the CNS drug minaprine from substrate to nonsubstrate, as well as the conversion of the related CNS drug minozac from nonsubstrate to substrate, through the use of analog synthesis and CYP2D6 enzyme kinetic analyses. No single molecular property strongly correlated with substrate status for this 3-amino-4-methyl-6-phenylpyridazine scaffold, although molecular volume and charge appeared to be indirectly related. A parsed database of CYP2D6 substrates across diverse chemical structures was assembled and analyzed for physical property trends correlating with substrate status. We found that a complex interplay of properties influenced CYP2D6 substrate status and that the particular chemical scaffold affects which properties are most prominent. The results also identified an unexpected issue in CNS drug discovery, in that some property trends correlative with CYP2D6 substrates overlap previously reported properties that correlate with CNS penetrance. These results suggest the need for a careful balance in the design and synthesis of new CNS therapeutic candidates to avoid CYP2D6 substrate status while maintaining CNS penetrance.

Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450

BACKGROUND

Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s).

OBJECTIVE

This review discusses the recent advances of these methods and the implications that are specific for P450s.

METHODS

Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions.

CONCLUSION

P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.

Comparative molecular field analysis of flavonoid inhibitors of the PIM-1 kinase

The PIM-1 protein, the product of the pim-1 oncogene, is a serine/threonine kinase. Dysregulation of the PIM-1 kinase has been implicated in the development of human malignancies including lymphomas, leukemias, and prostate cancer. Comparative molecular field analysis (CoMFA) is a 3-D QSAR technique that has been widely used, with notable success, to correlate biological activity with the steric and electrostatic properties of ligands. We have used a set of 15 flavonoid inhibitors of the PIM-1 kinase, aligned de novo by common substructure, to generate a CoMFA model for the purpose of elucidating the steric and electrostatic properties involved in flavonoid binding to the PIM-1 kinase. Partial least squares correlation between observed and predicted inhibitor potency (expressed as -logIC50), using a non-cross-validated partial least squares analysis, generated a non-cross-validated q2=0.805 for the training set (n=15) of flavonoids. The CoMFA generated steric map indicated that the PIM-1-binding site was sterically hindered, leading to more efficient binding of planar molecules over (R) or (S) compounds. The electrostatic map identified that positive charges near the flavonoid atom C8 and negative charges near C4' increased flavonoid binding. The CoMFA model accurately predicted the potency of a test set of flavonoids (n=6), generating a correlation between observed and predicted potency of q2=0.825. CoMFA models generated from additional alignment rules, which were guided by co-crystal structure ligand orientations, did not improve the correlative value of the model. Superimposing the PIM-1 kinase crystal structure onto the CoMFA contours validated the steric and electrostatic maps, elucidating the amino acid residues that potentially contribute to the CoMFA fields. Thus we have generated the first predictive model that may be used for the rational design of small-molecule inhibitors of the PIM-1 kinase.

Pharmacophoric Fingerprint Method (TOPP) for 3D-QSAR Modeling: Application to CYP2D6 Metabolic Stability

The application of a new 3-point pharmacophore-fingerprinting package (TOPP, Triplets Of Pharmacophoric Points) to develop QSAR models is discussed. In the CYP2D6 metabolic stability case, these 3D pharmacophoric fingerprints have shown to be as valid as other 3D descriptors and 2D features. Interestingly, it was found in the 3D models that the use of more realistic substrate conformations, by an additional docking step, did not improve the statistical results significantly. A detailed analysis of the generated pharmacophoric hypotheses is consistent with the previously proposed dual interaction mode of substrates within the active site of CYP2D6.

Recursive Partitioning for the Prediction of Cytochromes P450 2D6 and 1A2 Inhibition: Importance of the Quality of the Dataset

The purpose of this study was to explore the use of detailed biological data in combination with a statistical learning method for predicting the CYP1A2 and CYP2D6 inhibition. Data were extracted from the Aureus-Pharma highly structured databases which contain precise measures and detailed experimental protocol concerning the inhibition of the two cytochromes. The methodology used was Recursive Partitioning, an easy and quick method to implement. The building of models was preceded by the evaluation of the chemical space covered by the datasets. The descriptors used are available in the MOE software suite. The models reached at least 80% of Accuracy and often exceeded this percentage for the Sensitivity (Recall), Specificity, and Precision parameters. CYP2D6 datasets provided 11 models with Accuracy over 80%, while CYP1A2 datasets counted 5 high-accuracy models. Our models can be useful to predict the ADME properties during the drug discovery process and are indicated for high-throughput screening.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.

Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs

The site of biotransformation, the extent and rate of metabolism and the number of active metabolic pathways are among the most important characteristics of the pharmacokinetics of a drug. The catalytic activity of drug metabolizing enzymes is likely the most influential determinant of the pharmacokinetic variability. Metabolic stability is the prerequisite for sustaining the therapeutically relevant concentrations. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. A variety of computational approaches are currently available for predicting different cytochrome P450 (CYP)-related metabolism endpoints. The present review will describe these approaches and their impact on drug development process. Indications on the available software for the implementation will also be given.

In silico approaches for predicting ADME properties of drugs

Combinatorial chemistry and high-throughput screening have increased the possibility of finding new lead compounds at much shorter time periods than conventional medicinal chemistry. However, too much promising drug candidates often fail because of unsatisfactory ADME properties. In silico ADME studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, many in silico approaches for predicting ADME properties of compounds from their chemical structure have been developed, ranging from data-based approaches such as quantitative structure-activity relationship (QSAR), similarity searches, and 3-dimensional QSAR, to structure-based methods such as ligand-protein docking and pharmacophore modelling. In addition, several methods of integrating ADME properties to predict pharmacokinetics at the organ or body level have been studied. In this article, we briefly summarize in silico ADME approaches.