1
|
Shamovsky I, Ripa L, Narjes F, Bonn B, Schiesser S, Terstiege I, Tyrchan C. Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations. J Med Chem 2021; 64:8545-8563. [PMID: 34110134 DOI: 10.1021/acs.jmedchem.1c00514] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Aromatic and heteroaromatic amines (ArNH2) are activated by cytochrome P450 monooxygenases, primarily CYP1A2, into reactive N-arylhydroxylamines that can lead to covalent adducts with DNA nucleobases. Hereby, we give hands-on mechanism-based guidelines to design mutagenicity-free ArNH2. The mechanism of N-hydroxylation of ArNH2 by CYP1A2 is investigated by density functional theory (DFT) calculations. Two putative pathways are considered, the radicaloid route that goes via the classical ferryl-oxo oxidant and an alternative anionic pathway through Fenton-like oxidation by ferriheme-bound H2O2. Results suggest that bioactivation of ArNH2 follows the anionic pathway. We demonstrate that H-bonding and/or geometric fit of ArNH2 to CYP1A2 as well as feasibility of both proton abstraction by the ferriheme-peroxo base and heterolytic cleavage of arylhydroxylamines render molecules mutagenic. Mutagenicity of ArNH2 can be removed by structural alterations that disrupt geometric and/or electrostatic fit to CYP1A2, decrease the acidity of the NH2 group, destabilize arylnitrenium ions, or disrupt their pre-covalent transition states with guanine.
Collapse
|
2
|
Abstract
![]()
As
a field, computational toxicology is concerned with using in silico models to predict and understand the origins of
toxicity. It is fast, relatively inexpensive, and avoids the ethical
conundrum of using animals in scientific experimentation. In this
perspective, we discuss the importance of computational models in
toxicology, with a specific focus on the different model types that
can be used in predictive toxicological approaches toward mutagenicity
(SARs and QSARs). We then focus on how quantum chemical methods, such
as density functional theory (DFT), have previously been used in the
prediction of mutagenicity. It is then discussed how DFT allows for
the development of new chemical descriptors that focus on capturing
the steric and energetic effects that influence toxicological reactions.
We hope to demonstrate the role that DFT plays in understanding the
fundamental, intrinsic chemistry of toxicological reactions in predictive
toxicology.
Collapse
Affiliation(s)
- Piers A Townsend
- Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Matthew N Grayson
- Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| |
Collapse
|
3
|
Van Bossuyt M, Raitano G, Honma M, Van Hoeck E, Vanhaecke T, Rogiers V, Mertens B, Benfenati E. New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test. Toxicol Lett 2020; 329:80-84. [PMID: 32360788 DOI: 10.1016/j.toxlet.2020.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/18/2020] [Accepted: 04/22/2020] [Indexed: 10/24/2022]
Abstract
A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.
Collapse
Affiliation(s)
- Melissa Van Bossuyt
- Scientific Direction Chemical and Physical Health Risks, Sciensano, Brussels, Belgium; Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Giuseppa Raitano
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Masamitsu Honma
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki, Japan
| | - Els Van Hoeck
- Scientific Direction Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Tamara Vanhaecke
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Vera Rogiers
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Birgit Mertens
- Scientific Direction Chemical and Physical Health Risks, Sciensano, Brussels, Belgium.
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| |
Collapse
|
4
|
Townsend PA, Grayson MN. Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors. J Chem Inf Model 2019; 59:5099-5103. [DOI: 10.1021/acs.jcim.9b00966] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
5
|
Morita T, Shigeta Y, Kawamura T, Fujita Y, Honda H, Honma M. In silico prediction of chromosome damage: comparison of three (Q)SAR models. Mutagenesis 2019; 34:91-100. [PMID: 30085209 DOI: 10.1093/mutage/gey017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 07/23/2018] [Indexed: 11/12/2022] Open
Abstract
Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.
Collapse
Affiliation(s)
- Takeshi Morita
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Yoshiyuki Shigeta
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Tomoko Kawamura
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Yurika Fujita
- R&D Safety Science Research, Kao Corporation, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, Japan
| | - Hiroshi Honda
- R&D Safety Science Research, Kao Corporation, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, Japan
| | - Masamitsu Honma
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| |
Collapse
|
6
|
Screening of pharmacokinetic properties of fifty dihydropyrimidin(thi)one derivatives using a combo of in vitro and in silico assays. Eur J Pharm Sci 2017; 109:334-346. [DOI: 10.1016/j.ejps.2017.08.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 08/16/2017] [Accepted: 08/18/2017] [Indexed: 11/19/2022]
|
7
|
Bailey WJ, Glaab W. Derisking drug-induced liver injury from bench to bedside. CURRENT OPINION IN TOXICOLOGY 2017. [DOI: 10.1016/j.cotox.2017.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
8
|
Use of in silico models for prioritization of heat-induced food contaminants in mutagenicity and carcinogenicity testing. Arch Toxicol 2017; 91:3157-3174. [PMID: 28091709 DOI: 10.1007/s00204-016-1924-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 12/20/2016] [Indexed: 01/01/2023]
Abstract
Numerous Maillard reaction and lipid oxidation products are present in processed foods such as heated cereals, roasted meat, refined oils, coffee, and juices. Due to the lack of experimental toxicological data, risk assessment is hardly possible for most of these compounds. In the present study, an in silico approach was employed for the prediction of the toxicological endpoints mutagenicity and carcinogenicity on the basis of the structure of the respective compound, to examine (quantitative) structure-activity relationships for more than 800 compounds. Five software tools for mutagenicity prediction (T.E.S.T., SARpy, CAESAR, Benigni-Bossa, and LAZAR) and three carcinogenicity prediction tools (CAESAR, Benigni-Bossa, and LAZAR) were combined to yield so-called mutagenic or carcinogenic scores for every single substance. Alcohols, ketones, acids, lactones, and esters were predicted to be mutagenic and carcinogenic with low probability, whereas the software tools tended to predict a considerable mutagenic and carcinogenic potential for thiazoles. To verify the in silico predictions for the endpoint mutagenicity experimentally, twelve selected compounds were examined for their mutagenic potential using two different validated in vitro test systems, the bacterial reverse mutation assay (Ames test) and the in vitro micronucleus assay. There was a good correlation between the results of the Ames test and the in silico predictions. However, in the case of the micronucleus assay, at least three substances, 2-amino-6-methylpyridine, 6-heptenoic acid, and 2-methylphenol, were clearly positive although they were predicted to be non-mutagenic. Thus, software tools for mutagenicity prediction are suitable for prioritization among large numbers of substances, but these predictions still need experimental verification.
Collapse
|
9
|
Barber C, Cayley A, Hanser T, Harding A, Heghes C, Vessey JD, Werner S, Weiner SK, Wichard J, Giddings A, Glowienke S, Parenty A, Brigo A, Spirkl HP, Amberg A, Kemper R, Greene N. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained. Regul Toxicol Pharmacol 2015; 76:7-20. [PMID: 26708083 DOI: 10.1016/j.yrtph.2015.12.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 12/13/2015] [Accepted: 12/15/2015] [Indexed: 11/27/2022]
Abstract
The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound.
Collapse
Affiliation(s)
- Chris Barber
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Alex Cayley
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
| | - Thierry Hanser
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Alex Harding
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Crina Heghes
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Jonathan D Vessey
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Stephane Werner
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Sandy K Weiner
- Janssen Research & Development, 1400 McKean Road, Spring House, PA, 19477, United States
| | - Joerg Wichard
- Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, S 116, D-13353, Berlin, Germany
| | - Amanda Giddings
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Susanne Glowienke
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Alexis Parenty
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Hans-Peter Spirkl
- Sanofi-Aventis Deutschland GmbH, R&D DSAR/Preclinical Safety FF, Industriepark Hoechst, Bldg. H823, Room 116, D-65926, Frankfurt, Germany
| | - Alexander Amberg
- Sanofi-Aventis Deutschland GmbH, R&D DSAR/Preclinical Safety FF, Industriepark Hoechst, Bldg. H823, Room 116, D-65926, Frankfurt, Germany
| | - Ray Kemper
- Vertex Pharmaceuticals, Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, United States
| | - Nigel Greene
- Compound Safety Prediction, Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT, 06340, United States
| |
Collapse
|
10
|
Aiba née Kaneko M, Hirota M, Kouzuki H, Mori M. Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system. J Toxicol Sci 2015; 40:77-98. [DOI: 10.2131/jts.40.77] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
11
|
Abstract
Computational approaches offer the attraction of being both fast and cheap to run being able to process thousands of chemical structures in a few minutes. As with all new technology, there is a tendency for these approaches to be hyped up and claims of reliability and performance may be exaggerated. So just how good are these computational methods?
Collapse
Affiliation(s)
- Nigel Greene
- Worldwide Medicinal Chemistry
- Pfizer Inc. Groton
- CT 06340, USA
| | - William Pennie
- Drug Safety Research and Evaluation
- Takeda Pharmaceuticals International Inc
- Cambridge, USA
| |
Collapse
|
12
|
Valencia A, Prous J, Mora O, Sadrieh N, Valerio LG. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol Appl Pharmacol 2013; 273:427-34. [PMID: 24090816 DOI: 10.1016/j.taap.2013.09.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2013] [Revised: 09/04/2013] [Accepted: 09/19/2013] [Indexed: 11/18/2022]
Abstract
As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry(SM), a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84±1% sensitivity, 81±1% specificity, 83±1% concordance and 79±1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity.
Collapse
Affiliation(s)
- Antoni Valencia
- Prous Institute for Biomedical Research, Rambla de Catalunya, 135, 3-2, Barcelona 08008, Spain
| | | | | | | | | |
Collapse
|
13
|
Abstract
Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.
Collapse
|
14
|
Bakhtyari NG, Raitano G, Benfenati E, Martin T, Young D. Comparison of in silico models for prediction of mutagenicity. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2013; 31:45-66. [PMID: 23534394 DOI: 10.1080/10590501.2013.763576] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.
Collapse
|
15
|
Naven RT, Greene N, Williams RV. Latest advances in computational genotoxicity prediction. Expert Opin Drug Metab Toxicol 2012; 8:1579-87. [DOI: 10.1517/17425255.2012.724059] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
16
|
Modi S, Li J, Malcomber S, Moore C, Scott A, White A, Carmichael P. Integrated in silico approaches for the prediction of Ames test mutagenicity. J Comput Aided Mol Des 2012; 26:1017-33. [DOI: 10.1007/s10822-012-9595-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2011] [Accepted: 08/09/2012] [Indexed: 02/04/2023]
|
17
|
Valerio LG, Choudhuri S. Chemoinformatics and chemical genomics: potential utility of in silico methods. J Appl Toxicol 2012; 32:880-9. [PMID: 22886396 DOI: 10.1002/jat.2804] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 06/26/2012] [Accepted: 06/27/2012] [Indexed: 12/24/2022]
Abstract
Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.
Collapse
Affiliation(s)
- Luis G Valerio
- Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
| | | |
Collapse
|
18
|
Valerio, LG, Cross KP. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*. Toxicol Appl Pharmacol 2012; 260:209-21. [DOI: 10.1016/j.taap.2012.03.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Revised: 02/24/2012] [Accepted: 03/02/2012] [Indexed: 10/28/2022]
|
19
|
Dobo KL, Greene N, Fred C, Glowienke S, Harvey JS, Hasselgren C, Jolly R, Kenyon MO, Munzner JB, Muster W, Neft R, Vijayaraj Reddy M, White AT, Weiner S. In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: An industry survey. Regul Toxicol Pharmacol 2012; 62:449-55. [DOI: 10.1016/j.yrtph.2012.01.007] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Accepted: 01/23/2012] [Indexed: 11/16/2022]
|
20
|
Inroads to predict in vivo toxicology-an introduction to the eTOX Project. Int J Mol Sci 2012; 13:3820-3846. [PMID: 22489185 PMCID: PMC3317745 DOI: 10.3390/ijms13033820] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2011] [Revised: 01/30/2012] [Accepted: 03/14/2012] [Indexed: 12/20/2022] Open
Abstract
There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.
Collapse
|
21
|
Understanding of the mass spectrometric fragmentation pathways of a few potentially genotoxic haloaniline isomers in their protonated form by collision-induced dissociation. J Pharm Biomed Anal 2011; 56:721-7. [DOI: 10.1016/j.jpba.2011.07.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2011] [Revised: 07/15/2011] [Accepted: 07/19/2011] [Indexed: 01/21/2023]
|
22
|
McCarren P, Springer C, Whitehead L. An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential. J Cheminform 2011; 3:51. [PMID: 22107807 PMCID: PMC3277490 DOI: 10.1186/1758-2946-3-51] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2011] [Accepted: 11/22/2011] [Indexed: 11/29/2022] Open
Abstract
Background In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training. Results In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight. Conclusions Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.
Collapse
Affiliation(s)
- Patrick McCarren
- Novartis Institutes for Biomedical Research, 100 Technology Square, Cambridge, MA 02139, USA.
| | | | | |
Collapse
|
23
|
Nigsch F, Lounkine E, McCarren P, Cornett B, Glick M, Azzaoui K, Urban L, Marc P, Müller A, Hahne F, Heard DJ, Jenkins JL. Computational methods for early predictive safety assessment from biological and chemical data. Expert Opin Drug Metab Toxicol 2011; 7:1497-511. [DOI: 10.1517/17425255.2011.632632] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
24
|
Abstract
In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.
Collapse
Affiliation(s)
- Luis G Valerio
- Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
| |
Collapse
|
25
|
|
26
|
Hillebrecht A, Muster W, Brigo A, Kansy M, Weiser T, Singer T. Comparative Evaluation ofin SilicoSystems for Ames Test Mutagenicity Prediction: Scope and Limitations. Chem Res Toxicol 2011; 24:843-54. [DOI: 10.1021/tx2000398] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
27
|
McCarren P, Bebernitz GR, Gedeck P, Glowienke S, Grondine MS, Kirman LC, Klickstein J, Schuster HF, Whitehead L. Avoidance of the Ames test liability for aryl-amines via computation. Bioorg Med Chem 2011; 19:3173-82. [PMID: 21524589 DOI: 10.1016/j.bmc.2011.03.066] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2011] [Revised: 03/25/2011] [Accepted: 03/30/2011] [Indexed: 11/19/2022]
Abstract
Aryl-amines are commonly used synthons in modern drug discovery, however a minority of these chemical templates have the potential to cause toxicity through mutagenicity. The toxicity mostly arises through a series of metabolic steps leading to a reactive electrophilic nitrenium cation intermediate that reacts with DNA nucleotides causing mutation. Highly detailed in silico calculations of the energetics of chemical reactions involved in the metabolic formation of nitrenium cations have been performed. This allowed a critical assessment of the accuracy and reliability of using a theoretical formation energy of the DNA-reactive nitrenium intermediate to correlate with the Ames test response. This study contains the largest data set reported to date, and presents the in silico calculations versus the in vitro Ames response data in the form of beanplots commonly used in statistical analysis. A comparison of this quantum mechanical approach to QSAR and knowledge-based methods is also reported, as well as the calculated formation energies of nitrenium ions for thousands of commercially available aryl-amines generated as a watch-list for medicinal chemists in their synthetic optimization strategies.
Collapse
Affiliation(s)
- Patrick McCarren
- Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA
| | | | | | | | | | | | | | | | | |
Collapse
|