Predicting drug metabolism: experiment and/or computation?

Kirchmair, Johannes; Göller, Andreas H.; Lang, Dieter; Kunze, Jens; Testa, Bernard; Wilson, Ian D.; Glen, Robert C.; Schneider, Gisbert

doi:10.1038/nrd4581

Cited by 388 publications

(356 citation statements)

References 145 publications

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“…Acting in phase I metabolism, the enzyme family of cytochrome P450 is estimated to transform about 75% of marketed drugs (1) and numerous in silico approaches for the prediction of cytochrome P450-mediated metabolism have emerged to date (2,3). Although the majority of metabolism prediction studies focuses on phase I, the significance of phase II metabolism is generally underestimated (4) and to this day, computer-based models for the prediction of phase II metabolism remain scarce (5).…”

mentioning

confidence: 99%

In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

Rakers

Schumacher

Meinl

et al. 2016

Journal of Biological Chemistry

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Acting during phase II metabolism, sulfotransferases (SULTs) serve detoxification by transforming a broad spectrum of compounds from pharmaceutical, nutritional, or environmental sources into more easily excretable metabolites. However, SULT activity has also been shown to promote formation of reactive metabolites that may have genotoxic effects. SULT subtype 1E1 (SULT1E1) was identified as a key player in estrogen homeostasis, which is involved in many physiological processes and the pathogenesis of breast and endometrial cancer. The development of an in silico prediction model for SULT1E1 ligands would therefore support the development of metabolically inert drugs and help to assess health risks related to hormonal imbalances. Here, we report on a novel approach to develop a model that enables prediction of substrates and inhibitors of SULT1E1. Molecular dynamics simulations were performed to investigate enzyme flexibility and sample protein conformations. Pharmacophores were developed that served as a cornerstone of the model, and machine learning techniques were applied for prediction refinement. The prediction model was used to screen the DrugBank (a database of experimental and approved drugs): 28% of the predicted hits were reported in literature as ligands of SULT1E1. From the remaining hits, a selection of nine molecules was subjected to biochemical assay validation and experimental results were in accordance with the in silico prediction of SULT1E1 inhibitors and substrates, thus affirming our prediction hypotheses.Metabolism plays an important role in drug discovery and appropriate metabolic profiles of new drug candidates should be taken into consideration during the process of drug development. Acting in phase I metabolism, the enzyme family of cytochrome P450 is estimated to transform about 75% of marketed drugs (1) and numerous in silico approaches for the prediction of cytochrome P450-mediated metabolism have emerged to date (2, 3). Although the majority of metabolism prediction studies focuses on phase I, the significance of phase II metabolism is generally underestimated (4) and to this day, computer-based models for the prediction of phase II metabolism remain scarce (5).Among the predominant phase II enzyme families are the soluble sulfotransferases that form a gene superfamily termed SULT. These enzymes regulate the sulfonation of smaller molecules such as endogenous hormones, neurotransmitters, and xenobiotic substances from pharmaceutical, nutritional, or environmental sources. Based on sequence similarity, functional human SULTs are divided into two main families (SULT1 and SULT2) and further into subfamilies that exhibit individual, but somewhat overlapping substrate specificities (6). Influencing the level of female sex hormones (estrogens), SULT subtype 1E1 (SULT1E1) shows a specific substrate preference for physiological estrogenic compounds (K m ϭ 5 nM for estradiol (7)) and has been extensively investigated in experimental studies.In general, sulfonation reactions in which a sulfona...

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confidence: 99%

In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

Rakers

Schumacher

Meinl

et al. 2016

Journal of Biological Chemistry

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“…Likewise, with SAR or statistical approaches, the metabolic step may be implicit. Whilst there are computational methods to predict metabolites (Kirchmair et al 2015), they can predict a large number of metabolites, many of which are irrelevant (i.e. while they are theoretically possible they are not formed in biological systems), from which it can thus be difficult to identify those important for toxicity.…”

Section: Advances In Software For Identifying Structural Alertsmentioning

confidence: 99%

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Boobis

Brown

Cronin

et al. 2017

Critical Reviews in Toxicology

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(2017) Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation, Critical Reviews in Toxicology, 47:8, 710-732, DOI: 10.1080/10408444.2017 The threshold of toxicological concern (TTC) approach is a resource-effective de minimis method for the safety assessment of chemicals, based on distributional analysis of the results of a large number of toxicological studies. It is being increasingly used to screen and prioritize substances with low exposure for which there is little or no toxicological information. The first step in the approach is the identification of substances that may be DNA-reactive mutagens, to which the lowest TTC value is applied. This TTC value was based on the analysis of the cancer potency database and involved a number of assumptions that no longer reflect the state-of-the-science and some of which were not as transparent as they could have been. Hence, review and updating of the database is proposed, using inclusion and exclusion criteria reflecting current knowledge. A strategy for the selection of appropriate substances for TTC determination, based on consideration of weight of evidence for genotoxicity and carcinogenicity is outlined. Identification of substances that are carcinogenic by a DNA-reactive mutagenic mode of action and those that clearly act by a non-genotoxic mode of action will enable the protectiveness to be determined of both the TTC for DNA-reactive mutagenicity and that applied by default to substances that may be carcinogenic but are unlikely to be DNA-reactive mutagens (i.e. for Cramer class I-III compounds). Critical to the application of the TTC approach to substances that are likely to be DNA-reactive mutagens is the reliability of the software tools used to identify such compounds. Current methods for this task are reviewed and recommendations made for their application.

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“…[49] [50,51] Due to the inhibitory activity of drug molecules on these enzymes, the pharmacokinetics-related drug-drug interaction can lead to toxic or undesired side-effects which are consequences of lower clearance and accumulation of the drugs and their metabolites. [52][53][54] In In silico pharmacokinetics evaluation supports more evidence to conclude the potential drugs for experimental testing. According to analyzed results, CYP2C9 is the least targeted isoform while CYP2D6 is the most targeted one, followed by CYPA4.…”

Section: Pharmacokinetics Evaluationmentioning

confidence: 91%

Exploring Anti-Hyperglycemic Potential of Alkaloid Compounds From Catharanthus Roseus G. Don (L) Against Various Type Ii Diabetes Targets by in Silico Virtual Screening

Le¹

2017

WJPPS

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Background: Catharanthus roseus (L.) G. Don (C. roseus) is a prominent anticancer herb having high alkaloid content. Besides known anticancer effect, this plant has been also traditionally used as an effective anti-diabetic treatment in many Asian countries, such as India, Malaysia, and Vietnam. Some in vivo and in vitro studies on its compounds and fractional extracts have confirmed its strong anti-hyperglycemia. This fact raises a question whether alkaloid compounds probably play any function in inhibitory activity on proteins related to Type 2 Diabetes Mellitus (T2DM). This study, therefore, was conducted not only to assess the role of alkaloid compounds in glucoregulatory pathway but also investigate other chemical groups treating T2DM besides known ones such as polyphenols and terpenoids. Ly et al. World Journal of Pharmacy and Pharmaceutical Sciencesprotein's active sites. Besides, pharmacokinetics of these compounds were also evaluate to support the conclusion of potent drug candidates for T2DM treatment. Method: Molecular Dynamics (MD) simulation was processed on pre-docking protein to assess the their stability.Then, the structure-based virtual screening was processed to evaluate the binding free energy of the alkaloid compounds when they were in contact with the protein active sites. Potent compounds whose binding free energy was greater than or equal to those of the control ligands were selected for pharmacophore analysis to investigate which functional residue got involve in the interaction. Besides, pharmacokinetics evaluation was done to assess the adverse drug interaction. All the in silico results were combined to explain and suggest potent compounds. Results: All structure of protein was simulated to assess the stability before processing structure-based virtual screening. After structure-based screening, 12 potential compounds were sellected. Then, pharmacokinetics of all compounds was evaluated. Enzyme CYP2C9 is considered to be the least targeted isoform while CYP2D6 is the most targeted one, followed by CYPA4. Structure-based pharmacophore features of these compounds were also analyzed to understand their molecular interaction modes and explain the differences in binding free energy. Conclusion: After combining all the in silico result, there were 8 compounds (com_05, com_07, com_16, com_26, com_27, com_28, com_46, and com_56) were suggested to be potent drug candidates for experimental confirmation. Besides, com_56(Strictosidine lactam) and com_46 (Mitraphyllline) are considered to be two best top-hit ligands with extremely high docked score. These ligands, however, well targeted on 11-β-HSD1. Moreover, since 4 in 8 compounds well targeted on 11-β-HSD1, this protein is undoubtly confirmed as the most suitable receptor for in vitro testing. On the other hand, although com_21 (Alstonine) was temporarily rejected due to targeting on up to 3 three CYP enzyme, the potential role of this compound should be carefully re-assess because it is the only multi-target compounds toward to 4 pr...

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Predicting drug metabolism: experiment and/or computation?

Cited by 388 publications

References 145 publications

In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Exploring Anti-Hyperglycemic Potential of Alkaloid Compounds From Catharanthus Roseus G. Don (L) Against Various Type Ii Diabetes Targets by in Silico Virtual Screening

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