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Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
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Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
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Wishart DS, Tian S, Allen D, Oler E, Peters H, Lui V, Gautam V, Djoumbou-Feunang Y, Greiner R, Metz T. BioTransformer 3.0-a web server for accurately predicting metabolic transformation products. Nucleic Acids Res 2022; 50:W115-W123. [PMID: 35536252 PMCID: PMC9252798 DOI: 10.1093/nar/gkac313] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 05/04/2022] [Indexed: 11/15/2022] Open
Abstract
BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.
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Affiliation(s)
- David S Wishart
- To whom correspondence should be addressed. Tel: +1 780 492 8574;
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dana Allen
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vicki W Lui
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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The FDA-Approved Drug Cobicistat Synergizes with Remdesivir To Inhibit SARS-CoV-2 Replication In Vitro and Decreases Viral Titers and Disease Progression in Syrian Hamsters. mBio 2022; 13:e0370521. [PMID: 35229634 PMCID: PMC8941859 DOI: 10.1128/mbio.03705-21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Combinations of direct-acting antivirals are needed to minimize drug resistance mutations and stably suppress replication of RNA viruses. Currently, there are limited therapeutic options against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and testing of a number of drug regimens has led to conflicting results. Here, we show that cobicistat, which is an FDA-approved drug booster that blocks the activity of the drug-metabolizing proteins cytochrome P450-3As (CYP3As) and P-glycoprotein (P-gp), inhibits SARS-CoV-2 replication. Two independent cell-to-cell membrane fusion assays showed that the antiviral effect of cobicistat is exerted through inhibition of spike protein-mediated membrane fusion. In line with this, incubation with low-micromolar concentrations of cobicistat decreased viral replication in three different cell lines including cells of lung and gut origin. When cobicistat was used in combination with remdesivir, a synergistic effect on the inhibition of viral replication was observed in cell lines and in a primary human colon organoid. This was consistent with the effects of cobicistat on two of its known targets, CYP3A4 and P-gp, the silencing of which boosted the in vitro antiviral activity of remdesivir in a cobicistat-like manner. When administered in vivo to Syrian hamsters at a high dose, cobicistat decreased viral load and mitigated clinical progression. These data highlight cobicistat as a therapeutic candidate for treating SARS-CoV-2 infection and as a potential building block of combination therapies for COVID-19.
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
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Roy H, Nandi S. In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery. Curr Pharm Des 2020; 25:3292-3305. [PMID: 31481001 DOI: 10.2174/1381612825666190903155935] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 09/01/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly. METHODS To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status. RESULTS The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound. CONCLUSION It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.
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Affiliation(s)
- Harekrishna Roy
- Nirmala College of Pharmacy, Mangalagiri, Guntur, Affiliated to Acharya Nagarjuna University, Andhra Pradesh-522503, India
| | - Sisir Nandi
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur-244713, India
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Dmitriev AV, Lagunin AA, Karasev DА, Rudik AV, Pogodin PV, Filimonov DA, Poroikov VV. Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Curr Top Med Chem 2019; 19:319-336. [PMID: 30674264 DOI: 10.2174/1568026619666190123160406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
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Affiliation(s)
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russian Federation.,Pirogov Russian National Research Medical University, Moscow, RussiaN Federation
| | | | | | - Pavel V Pogodin
- Institute of Biomedical Chemistry, Moscow, Russian Federation
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Xiong Y, Qiao Y, Kihara D, Zhang HY, Zhu X, Wei DQ. Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates. Curr Drug Metab 2019; 20:229-235. [PMID: 30338736 DOI: 10.2174/1389200219666181019094526] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 08/05/2018] [Accepted: 08/06/2018] [Indexed: 12/23/2022]
Abstract
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.
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Affiliation(s)
- Yi Xiong
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanhua Qiao
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN 47907, United States
| | - Hui-Yuan Zhang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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9
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Four Major Channels Detected in the Cytochrome P450 3A4: A Step toward Understanding Its Multispecificity. Int J Mol Sci 2019; 20:ijms20040987. [PMID: 30823507 PMCID: PMC6412807 DOI: 10.3390/ijms20040987] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/12/2019] [Accepted: 02/20/2019] [Indexed: 12/27/2022] Open
Abstract
We computed the network of channels of the 3A4 isoform of the cytochrome P450 (CYP) on the basis of 16 crystal structures extracted from the Protein Data Bank (PDB). The calculations were performed with version 2 of the CCCPP software that we developed for this research project. We identified the minimal cost paths (MCPs) output by CCCPP as probable ways to access to the buried active site. The algorithm of calculation of the MCPs is presented in this paper, with its original method of visualization of the channels. We found that these MCPs constitute four major channels in CYP3A4. Among the many channels proposed by Cojocaru et al. in 2007, we found that only four of them open in 3A4. We provide a refined description of these channels together with associated quantitative data.
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Djoumbou-Feunang Y, Fiamoncini J, Gil-de-la-Fuente A, Greiner R, Manach C, Wishart DS. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform 2019; 11:2. [PMID: 30612223 PMCID: PMC6689873 DOI: 10.1186/s13321-018-0324-5] [Citation(s) in RCA: 224] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 12/22/2018] [Indexed: 12/17/2022] Open
Abstract
Background A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance. Results To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found. Conclusion BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data. Electronic supplementary material The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Jarlei Fiamoncini
- INRA, Human Nutrition Unit, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.,Department of Food and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | | | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.,Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, T6G 2E8, Canada
| | - Claudine Manach
- INRA, Human Nutrition Unit, Université Clermont Auvergne, 63000, Clermont-Ferrand, France
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada. .,Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.
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Don CG, Smieško M. Out‐compute drug side effects: Focus on cytochrome P450 2D6 modeling. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1366] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Charleen G. Don
- Department of Pharmaceutical SciencesUniversity of BaselBaselSwitzerland
| | - Martin Smieško
- Department of Pharmaceutical SciencesUniversity of BaselBaselSwitzerland
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12
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Pang X, Zhang B, Mu G, Xia J, Xiang Q, Zhao X, Liu A, Du G, Cui Y. Screening of cytochrome P450 3A4 inhibitors via in silico and in vitro approaches. RSC Adv 2018; 8:34783-34792. [PMID: 35547066 PMCID: PMC9086869 DOI: 10.1039/c8ra06311g] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 09/26/2018] [Indexed: 12/29/2022] Open
Abstract
Cytochrome P450 3A4 (CYP3A4) is an important member of the CYP family and responsible for metabolizing a broad range of drugs. It is necessary to establish virtual screening models for predicting CYP3A4 inhibitors.
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Affiliation(s)
- Xiaocong Pang
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
| | - Baoyue Zhang
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
| | - Guangyan Mu
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
| | - Jie Xia
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Qian Xiang
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
| | - Xia Zhao
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
| | - Ailin Liu
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Guanhua Du
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Yimin Cui
- Department of Pharmacy
- Peking University First Hospital
- Beijing
- China
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Pertusi DA, Moura ME, Jeffryes JG, Prabhu S, Walters Biggs B, Tyo KEJ. Predicting novel substrates for enzymes with minimal experimental effort with active learning. Metab Eng 2017; 44:171-181. [PMID: 29030274 DOI: 10.1016/j.ymben.2017.09.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 08/20/2017] [Accepted: 09/18/2017] [Indexed: 01/26/2023]
Abstract
Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.
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Affiliation(s)
- Dante A Pertusi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Matthew E Moura
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - James G Jeffryes
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, United States
| | - Siddhant Prabhu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Bradley Walters Biggs
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States.
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Dixit VA, Deshpande S. Advances in Computational Prediction of Regioselective and Isoform-Specific Drug Metabolism Catalyzed by CYP450s. ChemistrySelect 2016. [DOI: 10.1002/slct.201601051] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Vaibhav A. Dixit
- Department of Pharmaceutical Chemistry; School of Pharmacy and Technology Management (SPTM), Shri Vile Parle Kelavani Mandal's (SVKM's) Narsee Monjee Institute of Management Studies (NMIMS), Mukesh Patel Technology Park, Babulde, Bank of Tapi River; Mumbai-Agra Road Shirpur, Dist. Dhule−425405 India
| | - Shirish Deshpande
- Department of Pharmaceutical Chemistry; School of Pharmacy and Technology Management (SPTM), Shri Vile Parle Kelavani Mandal's (SVKM's) Narsee Monjee Institute of Management Studies (NMIMS), Mukesh Patel Technology Park, Babulde, Bank of Tapi River; Mumbai-Agra Road Shirpur, Dist. Dhule−425405 India
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15
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Kesharwani SS, Nandekar PP, Pragyan P, Rathod V, Sangamwar AT. Characterization of differences in substrate specificity among CYP1A1, CYP1A2 and CYP1B1: an integrated approach employing molecular docking and molecular dynamics simulations. J Mol Recognit 2016; 29:370-90. [PMID: 26916064 DOI: 10.1002/jmr.2537] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Revised: 12/16/2015] [Accepted: 01/08/2016] [Indexed: 01/05/2023]
Abstract
Recent trends in new drug discovery of anticancer drugs have made oncologists more aware of the fact that the new drug discovery must target the developing mechanism of tumorigenesis to improve the therapeutic efficacy of antineoplastic drugs. The drugs designed are expected to have high affinity towards the novel targets selectively. Current research highlights overexpression of CYP450s, particularly cytochrome P450 1A1 (CYP1A1), in tumour cells, representing a novel target for anticancer therapy. However, the CYP1 family is identified as posing significant problems in selectivity of anticancer molecules towards CYP1A1. Three members have been identified in the human CYP1 family: CYP1A1, CYP1A2 and CYP1B1. Although sequences of the three isoform have high sequence identity, they have distinct substrate specificities. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics, design novel antitumour compounds that could be specifically metabolized by only CYP1A1 to mediate their antitumour activity and elucidate the reasons for differences in substrate specificity profile among the three proteins. In the present study, we employed a combination of computational methodologies: molecular docking and molecular dynamics simulations. We utilized eight substrates for elucidating the difference in substrate specificity of the three isoforms. Lastly, we conclude that the substrate specificity of a particular substrate depends upon the type of the active site residues, the dynamic motions in the protein structure upon ligand binding and the physico-chemical characteristics of a particular ligand. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Siddharth S Kesharwani
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Prajwal P Nandekar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Preeti Pragyan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Vijay Rathod
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Abhay T Sangamwar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
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Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction. PLoS Comput Biol 2016; 12:e1004495. [PMID: 26871706 PMCID: PMC4752336 DOI: 10.1371/journal.pcbi.1004495] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 08/03/2015] [Indexed: 11/19/2022] Open
Abstract
Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.
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Olsen L, Oostenbrink C, Jørgensen FS. Prediction of cytochrome P450 mediated metabolism. Adv Drug Deliv Rev 2015; 86:61-71. [PMID: 25958010 DOI: 10.1016/j.addr.2015.04.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 03/30/2015] [Accepted: 04/27/2015] [Indexed: 10/23/2022]
Abstract
Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.
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Abstract
Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.
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Pertusi DA, Stine AE, Broadbelt LJ, Tyo KEJ. Efficient searching and annotation of metabolic networks using chemical similarity. ACTA ACUST UNITED AC 2014; 31:1016-24. [PMID: 25417203 DOI: 10.1093/bioinformatics/btu760] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 11/11/2014] [Indexed: 11/14/2022]
Abstract
MOTIVATION The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. RESULTS Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. AVAILABILITY AND IMPLEMENTATION Python files will be available for download at http://tyolab.northwestern.edu/tools/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dante A Pertusi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Andrew E Stine
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
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von Grafenstein S, Fuchs JE, Huber MM, Bassi A, Lacetera A, Ruzsanyi V, Troppmair J, Amann A, Liedl KR. Precursors for cytochrome P450 profiling breath tests from an in silico screening approach. J Breath Res 2014; 8:046001. [PMID: 25233885 DOI: 10.1088/1752-7155/8/4/046001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The family of cytochrome P450 enzymes (CYPs) is a major player in the metabolism of drugs and xenobiotics. Genetic polymorphisms and transcriptional regulation give a complex patient-individual CYP activity profile for each human being. Therefore, personalized medicine demands easy and non-invasive measurement of the CYP phenotype. Breath tests detect volatile organic compounds (VOCs) in the patients' exhaled air after administration of a precursor molecule. CYP breath tests established for individual CYP isoforms are based on the detection of (13)CO2 or (14)CO2 originating from CYP-catalyzed oxidative degradation reactions of isotopically labeled precursors.We present an in silico work-flow aiming at the identification of novel precursor molecules, likely to result in VOCs other than CO2 upon oxidative degradation as we aim at label-free precursor molecules. The ligand-based work-flow comprises five parts: (1) CYP profiling was encoded as a decision tree based on 2D molecular descriptors derived from established models in the literature and validated against publicly available data extracted from the DrugBank. (2) Likely sites of metabolism were identified by reactivity and accessibility estimation for abstractable hydrogen radical. (3) Oxidative degradation reactions (O- and N-dealkylations) were found to be most promising in the release of VOCs. Thus, the CYP-catalyzed oxidative degradation reaction was encoded as SMIRKS (a programming language style to implement reactions based on the SMARTS description) to enumerate possible reaction products. (4) A quantitative structure property relation (QSPR) model aiming to predict the Henry constant H was derived from data for 488 organic compounds and identifies potentially VOCs amongst CYP reaction products. (5) A blacklist of naturally occurring breath components was implemented to identify marker molecules allowing straightforward detection within the exhaled air.Evident oxidative degradation reactions served as test case for the screening approach. Comparisons to metabolism data from literature support the results' plausibility. Thus, a large scale screening for potential novel breath test precursor using the presented five stage work-flow is promising.
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Affiliation(s)
- Susanne von Grafenstein
- Department of Theoretical Chemistry and Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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Lin TH, Tsai TL. Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM. J Theor Biol 2014; 356:85-97. [DOI: 10.1016/j.jtbi.2014.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 04/01/2014] [Accepted: 04/16/2014] [Indexed: 10/25/2022]
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22
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Liew CY, Yap CW. QSAR and Predictors of Eye and Skin Effects. Mol Inform 2013; 32:281-90. [PMID: 27481523 DOI: 10.1002/minf.201200119] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 01/16/2013] [Indexed: 01/19/2023]
Abstract
In this study, the ensemble of features and training samples was examined with a collection of support vector machines. The effects of data sampling methods, ratio of positive to negative compounds, and types of base models combiner to produce ensemble models were explored. The ensemble method was applied to produce four separate in silico models to classify the labels for eye/skin corrosion (H314), skin irritation (H315), serious eye damage (H318), and eye irritation (H319), which are defined in the "Globally Harmonized System of Classification and Labelling of Chemicals". To the best of our knowledge, the training set used in this work is one of the largest (made of publicly available data) with acceptable prediction performances. These models were distributed via PaDEL-DDPredictor (http://padel.nus.edu.sg/software/padelddpredictor) that can be downloaded freely for public use.
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Affiliation(s)
- Chin Yee Liew
- Pharmaceutical Data Exploration Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543 fax: +65-67791554
| | - Chun Wei Yap
- Pharmaceutical Data Exploration Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543 fax: +65-67791554.
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Peach ML, Zakharov AV, Liu R, Pugliese A, Tawa G, Wallqvist A, Nicklaus MC. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med Chem 2012; 4:1907-32. [PMID: 23088273 PMCID: PMC3992830 DOI: 10.4155/fmc.12.150] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.
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Affiliation(s)
- Megan L Peach
- Basic Science Program, SAIC-Frederick, Inc.: CADD Group, Chemical Biology Laboratory, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Alexey V Zakharov
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Ruifeng Liu
- DoD Biotechnology HPC Software Applications Institute, US Army Medical Research & Materiel Command, 2405 Whittier Drive, Frederick, MD 21702, USA
| | - Angelo Pugliese
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
- Computer-Aided Drug Design at Cancer Research UK, Beatson Laboratories, Drug Discovery Programme, Switchback Road, Bearsden, Glasgow, G61 1BD, UK
| | - Gregory Tawa
- DoD Biotechnology HPC Software Applications Institute, US Army Medical Research & Materiel Command, 2405 Whittier Drive, Frederick, MD 21702, USA
| | - Anders Wallqvist
- DoD Biotechnology HPC Software Applications Institute, US Army Medical Research & Materiel Command, 2405 Whittier Drive, Frederick, MD 21702, USA
| | - Marc C Nicklaus
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
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Zaretzki J, Rydberg P, Bergeron C, Bennett KP, Olsen L, Breneman CM. RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes. J Chem Inf Model 2012; 52:1637-59. [PMID: 22524152 DOI: 10.1021/ci300009z] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date. A comprehensive investigation into the importance of different descriptor classes for identifying the regioselectivity mediated by each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a density functional theory (DFT) reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by Optibrium and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2 (83.0%), 2A6 (85.7%), 2B6 (82.1%), 2C19 (86.2%), 2C8 (83.8%), 2C9 (84.5%), 2D6 (85.9%), 2E1 (82.8%), 3A4 (82.3%), and merged (86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.
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Affiliation(s)
- Jed Zaretzki
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52:617-48. [PMID: 22339582 PMCID: PMC3317594 DOI: 10.1021/ci200542m] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
![]()
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.
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Affiliation(s)
- Johannes Kirchmair
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
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Carbon-Mangels M, Hutter MC. Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets. Mol Inform 2011; 30:885-95. [PMID: 27468108 DOI: 10.1002/minf.201100069] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Accepted: 08/19/2011] [Indexed: 11/12/2022]
Abstract
Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over-determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to model the probability distribution of descriptors values used for binary classification using n-fold cross-validation. As a measure for the discriminative power of the classifiers, the symmetric form of the Kullback-Leibler divergence of their probability distributions was computed. We found that the most relevant descriptors possess a Gaussian-like distribution of their values, show the largest divergences, and therefore appear most often in the cross-validation scenario. The results were compared to those of the LASSO feature selection method applied to multiple decision trees and support vector machine approaches for data sets of substrates and nonsubstrates of three Cytochrome P450 isoenzymes, which comprise strongly unbalanced compound distributions. In contrast to decision trees and support vector machines, the performance of Bayesian estimates is less affected by unbalanced data sets. This strategy reveals those descriptors that allow a simple linear separation of the classes, whereas the superior accuracy of decision trees and support vector machines can be attributed to nonlinear separation, which are in turn more prone to overfitting.
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Affiliation(s)
- Miriam Carbon-Mangels
- Section of Biostatistics, Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines, Paul-Ehrlich-Straße 51-59, 63225 Langen, Germany
| | - Michael C Hutter
- Center for Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany phone/fax: +49 681 302 70703/70702.
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Mishra NK. Computational modeling of P450s for toxicity prediction. Expert Opin Drug Metab Toxicol 2011; 7:1211-31. [DOI: 10.1517/17425255.2011.611501] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Zaretzki J, Bergeron C, Rydberg P, Huang TW, Bennett KP, Breneman CM. RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4. J Chem Inf Model 2011; 51:1667-89. [PMID: 21528931 DOI: 10.1021/ci2000488] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.
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Affiliation(s)
- Jed Zaretzki
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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Saraceno M, Massarelli I, Imbriani M, James TL, Bianucci AM. Optimizing QSAR Models for Predicting Ligand Binding to the Drug-Metabolizing Cytochrome P450 Isoenzyme CYP2D6. Chem Biol Drug Des 2011; 78:236-51. [DOI: 10.1111/j.1747-0285.2011.01137.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Ramesh M, Bharatam PV. CYP isoform specificity toward drug metabolism: analysis using common feature hypothesis. J Mol Model 2011; 18:709-20. [DOI: 10.1007/s00894-011-1105-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2011] [Accepted: 04/20/2011] [Indexed: 02/02/2023]
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Bucher J, Riedmaier S, Schnabel A, Marcus K, Vacun G, Weiss TS, Thasler WE, Nüssler AK, Zanger UM, Reuss M. A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes. BMC SYSTEMS BIOLOGY 2011; 5:66. [PMID: 21548957 PMCID: PMC3117731 DOI: 10.1186/1752-0509-5-66] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2010] [Accepted: 05/06/2011] [Indexed: 12/13/2022]
Abstract
Background The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models. Results In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics. Conclusions A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.
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Affiliation(s)
- Joachim Bucher
- Institute of Biochemical Engineering, Allmandring, and Center Systems Biology, Nobelstraße, University of Stuttgart, Germany
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Rossato G, Ernst B, Smiesko M, Spreafico M, Vedani A. Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts. ChemMedChem 2011; 5:2088-101. [PMID: 21038340 DOI: 10.1002/cmdc.201000358] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Drug metabolism, toxicity, and their interaction profiles are major issues in the drug-discovery and lead-optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome-mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug-drug, and food-drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross-validated r²=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y-scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false-positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org).
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Affiliation(s)
- Gianluca Rossato
- Institute of Molecular Pharmacy, Pharmacenter, University of Basel, Switzerland
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Klon AE. Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development. Expert Opin Drug Metab Toxicol 2011; 6:821-33. [PMID: 20465523 DOI: 10.1517/17425255.2010.489550] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
IMPORTANCE OF THE FIELD The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. AREAS COVERED IN THIS REVIEW The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. WHAT THE READER WILL GAIN This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. TAKE HOME MESSAGE A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.
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Affiliation(s)
- Anthony E Klon
- Ansaris, Computational Chemistry, Four Valley Square, 512 East Township Line Road, Blue Bell, PA 19422, USA.
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Nasonov AF. Computational methods and software in computer-aided combinatorial library design. RUSS J GEN CHEM+ 2011. [DOI: 10.1134/s1070363210120248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Vasanthanathan P, Lastdrager J, Oostenbrink C, Commandeur JNM, Vermeulen NPE, Jørgensen FS, Olsen L. Identification of CYP1A2 ligands by structure-based and ligand-based virtual screening. MEDCHEMCOMM 2011. [DOI: 10.1039/c1md00087j] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
This introductory chapter gives a brief overview of the history of cheminformatics, and then summarizes some recent trends in computing, cultures, open systems, chemical structure representation, docking, de novo design, fragment-based drug design, molecular similarity, quantitative structure-activity relationships (QSAR), metabolite prediction, the use of phamacophores in drug discovery, data reduction and visualization, and text mining. The aim is to set the scene for the more detailed exposition of these topics in the later chapters.
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Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, Holmes Chapel, Cheshire, UK
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Hecht D. Applications of machine learning and computational intelligence to drug discovery and development. Drug Dev Res 2010. [DOI: 10.1002/ddr.20402] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- David Hecht
- Southwestern College, Chula Vista, California
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Xu C, Mager DE. Quantitative structure–pharmacokinetic relationships. Expert Opin Drug Metab Toxicol 2010; 7:63-77. [DOI: 10.1517/17425255.2011.537257] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Xie Z, Zhang T, Wang JF, Chou KC, Wei DQ. The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4. Comput Biol Med 2010; 40:845-52. [DOI: 10.1016/j.compbiomed.2010.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2010] [Revised: 08/09/2010] [Accepted: 09/21/2010] [Indexed: 10/18/2022]
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40
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Lewis DFV, Ito Y. Human CYPs involved in drug metabolism: structures, substrates and binding affinities. Expert Opin Drug Metab Toxicol 2010; 6:661-74. [PMID: 20402561 DOI: 10.1517/17425251003674380] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD There is current interest in the CYPs primarily due to their important role in the Phase I metabolism of foreign compounds, including pharmaceuticals, agrochemicals and environmental pollutants, to which mankind is exposed. AREAS COVERED IN THIS REVIEW The roles of the human CYPs are introduced in the context of using structural modelling and quantitative structure-activity relationships for rationalizing substrate binding, selectivity and rates of metabolism, particularly for drugs in current clinical use. The importance of compound lipophilicity in both substrate binding and metabolic rate is emphasised, together with the employment of an automated docking method (AutoDock) for estimating binding energy and likely route of metabolism for drug substrates. WHAT THE READER WILL GAIN The location of key interacting groups on both substrate and enzyme tends to define the preferred outcome of CYP-mediated drug metabolism in the majority of cases investigated thus far. This enables one to draw up a simple model of the important features present in the binding sites of CYPs which relate to substrate selectivity and likely positions of metabolism. TAKE HOME MESSAGE For the major CYPs involved in human drug metabolism, it would appear that there is a relatively well-defined key distance, in terms of number of intervening atoms, between the main sites of binding and CYP-mediated metabolism.
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Affiliation(s)
- David F V Lewis
- University of Surrey, Faculty of Health and Medical Sciences, Surrey, UK.
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Mishra NK, Agarwal S, Raghava GPS. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol 2010; 10:8. [PMID: 20637097 PMCID: PMC2912882 DOI: 10.1186/1471-2210-10-8] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Accepted: 07/16/2010] [Indexed: 11/24/2022] Open
Abstract
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/.
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Affiliation(s)
- Nitish K Mishra
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India
| | - Sandhya Agarwal
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India
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Michielan L, Moro S. Pharmaceutical Perspectives of Nonlinear QSAR Strategies. J Chem Inf Model 2010; 50:961-78. [DOI: 10.1021/ci100072z] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Lisa Michielan
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
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Vasanthanathan P, Olsen L, Jørgensen FS, Vermeulen NPE, Oostenbrink C. Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations. Drug Metab Dispos 2010; 38:1347-54. [PMID: 20413725 DOI: 10.1124/dmd.110.032946] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structure-based ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method, and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent application of the model to an external test set gives an error of 2.1 kJ/mol, which is remarkably good, considering the simplicity of the model. The structures of the protein-ligand interactions are further analyzed, again demonstrating the large versatility and plasticity of the cytochrome P450 active site.
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Affiliation(s)
- Poongavanam Vasanthanathan
- Leiden/Amsterdam Center for Drug Research, Division of Molecular Toxicology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Hasegawa K, Koyama M, Funatsu K. Quantitative Prediction of Regioselectivity Toward Cytochrome P450/3A4 Using Machine Learning Approaches. Mol Inform 2010; 29:243-9. [PMID: 27462767 DOI: 10.1002/minf.200900086] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2009] [Accepted: 02/11/2010] [Indexed: 11/11/2022]
Abstract
In the drug discovery process, it is important to know the properties of both drug candidates and their metabolites. Fast and precise prediction of metabolites is essential. However, it has been difficult to predict metabolites because of the complexity of the mechanism of cytochrome P450/3A4 (CYP 3A4), which is the main metabolite enzyme of drugs. In this study, we focus on the regioselectivity of CYP 3A4, i.e., the selectivity of metabolic sites. We have developed a model to predict the regioselectivity of drug candidates by using machine learning (ML) approaches.
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Affiliation(s)
- Kiyoshi Hasegawa
- Chugai Pharmaceutical Company, Kamakura Research, Laboratories, Kajiwara 200, Kamakura, Kanagawa 247-8530, Japan
| | - Michio Koyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 03-5841-7751 fax: (+81) 03-5841-7771
| | - Kimito Funatsu
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 03-5841-7751 fax: (+81) 03-5841-7771.
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Ewing T, Feher M. Forecasting CYP2D6 and CYP3A4 Risk with a Global/Local Fusion Model of CYP450 Inhibition. Mol Inform 2010; 29:127-41. [DOI: 10.1002/minf.200900040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2009] [Accepted: 09/23/2009] [Indexed: 11/12/2022]
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Wang B, Yang LP, Zhang XZ, Huang SQ, Bartlam M, Zhou SF. New insights into the structural characteristics and functional relevance of the human cytochrome P450 2D6 enzyme. Drug Metab Rev 2010; 41:573-643. [PMID: 19645588 DOI: 10.1080/03602530903118729] [Citation(s) in RCA: 122] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
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.
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Affiliation(s)
- Bo Wang
- Department of Pediatrics, Guangdong Women and Children's Hospital, Guangzhou, China
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Freitas RF, Bauab RL, Montanari CA. Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4. J Chem Inf Model 2010; 50:97-109. [DOI: 10.1021/ci900074t] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- R. F. Freitas
- Grupo de Estudos em Química Medicinal de Produtos Naturais - NEQUIMED-PN, Instituto de Química de São Carlos - Universidade de São Paulo, 13560-970 - São Carlos - SP, Brazil
| | - R. L. Bauab
- Grupo de Estudos em Química Medicinal de Produtos Naturais - NEQUIMED-PN, Instituto de Química de São Carlos - Universidade de São Paulo, 13560-970 - São Carlos - SP, Brazil
| | - C. A. Montanari
- Grupo de Estudos em Química Medicinal de Produtos Naturais - NEQUIMED-PN, Instituto de Química de São Carlos - Universidade de São Paulo, 13560-970 - São Carlos - SP, Brazil
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Veselovsky A, Sobolev B, Zharkova M, Archakov A. Computer-based substrate specifity prediction for cytochrome P450. ACTA ACUST UNITED AC 2010; 56:90-100. [DOI: 10.18097/pbmc20105601090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
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.
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Schuster D, Kern L, Hristozov DP, Terfloth L, Bienfait B, Laggner C, Kirchmair J, Grienke U, Wolber G, Langer T, Stuppner H, Gasteiger J, Rollinger JM. Applications of integrated data mining methods to exploring natural product space for acetylcholinesterase inhibitors. Comb Chem High Throughput Screen 2010; 13:54-66. [PMID: 20214575 PMCID: PMC3547168 DOI: 10.2174/138620710790218212] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Nature, especially the plant kingdom, is a rich source for novel bioactive compounds that can be used as lead compounds for drug development. In order to exploit this resource, the two neural network-based virtual screening techniques novelty detection with self-organizing maps (SOMs) and counterpropagation neural network were evaluated as tools for efficient lead structure discovery. As application scenario, significant descriptors for acetylcholinesterase (AChE) inhibitors were determined and used for model building, theoretical model validation, and virtual screening. Top-ranked virtual hits from both approaches were docked into the AChE binding site to approve the initial hits. Finally, in vitro testing of selected compounds led to the identification of forsythoside A and (+)-sesamolin as novel AChE inhibitors.
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Affiliation(s)
- Daniela Schuster
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
- Inte:Ligand Softwareentwicklung und Consulting GmbH, Clemens-Maria-Hofbauer-Gasse 6, A-2344 Maria Enzersdorf, Austria
| | - Lisa Kern
- Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
| | | | - Lothar Terfloth
- Molecular Networks GmbH, Henkestr. 91, D-91052 Erlangen, Germany
| | - Bruno Bienfait
- Molecular Networks GmbH, Henkestr. 91, D-91052 Erlangen, Germany
| | - Christian Laggner
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
| | - Johannes Kirchmair
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
- Inte:Ligand Softwareentwicklung und Consulting GmbH, Clemens-Maria-Hofbauer-Gasse 6, A-2344 Maria Enzersdorf, Austria
| | - Ulrike Grienke
- Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
| | - Gerhard Wolber
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
- Inte:Ligand Softwareentwicklung und Consulting GmbH, Clemens-Maria-Hofbauer-Gasse 6, A-2344 Maria Enzersdorf, Austria
| | - Thierry Langer
- Prestwick Chemical Inc., Boulevard Gonthier d’Andernach, 67100 Illkirch, France
| | - Hermann Stuppner
- Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
| | - Johann Gasteiger
- Molecular Networks GmbH, Henkestr. 91, D-91052 Erlangen, Germany
| | - Judith M. Rollinger
- Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria and Center of Molecular Biosciences Innsbruck (CMBI)
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