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Rane R, Satpute B, Kumar D, Suryawanshi M, Prabhune AG, Gawade B, Mahajan A, Pawar A, Sakat S. Mutagenic and genotoxic in silico QSAR prediction of dimer impurity of gliflozins; canagliflozin, dapaglifozin, and emphagliflozin and in vitro evaluation by Ames and micronucleus test. Drug Chem Toxicol 2024:1-10. [PMID: 39072496 DOI: 10.1080/01480545.2024.2378768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 06/14/2024] [Accepted: 07/06/2024] [Indexed: 07/30/2024]
Abstract
Canagliflozin, Dapagliflozin, and Empagliflozin, glucagon-like peptide-1 receptor agonists, are indicated for managing type II diabetes. Although the genotoxicity profiles of these drugs are well-explored, limited information exists regarding the genotoxic potential of their impurities. In this investigation, the dimer impurities of Canagliflozin, Dapagliflozin, and Empagliflozin underwent both in silico and in vitro assessments for mutagenic potential. Tester strains of Salmonella typhimurium and Escherichia coli were subjected to the Ames test, utilizing concentrations of up to 1 µg per plate, with and without the presence of metabolic activation. Evaluation of micronucleus induction in TK6 cells was conducted through a micronucleus test, exploring concentrations up to 500 µg/mL, with or without the presence of exogenous metabolic activation. Under the specific test conditions, the dimer impurities of Canagliflozin, Dapagliflozin, and Empagliflozin showed no evidence of mutagenicity or clastrogenicity, establishing their in vitro classification as nonmutagenic. These findings align with negative in silico predictions from quantitative structure-activity relationship (QSAR) analyses for mutagenicity and genotoxicity of the dimer impurities. Collectively, these studies contribute clinically relevant safety information by confirming that the dimer impurities of Canagliflozin, Dapagliflozin, and Empagliflozin are nonmutagenic and nongenotoxic, emphasizing the consistency between in silico and in vitro data.
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Affiliation(s)
- Rajesh Rane
- Department of Pharmaceutical Chemistry, BVDU Poona College of Pharmacy, Pune, India
| | - Bharat Satpute
- Department of Pharmaceutical Chemistry, BVDU Poona College of Pharmacy, Pune, India
| | - Dileep Kumar
- Department of Pharmaceutical Chemistry, BVDU Poona College of Pharmacy, Pune, India
| | - Mugdha Suryawanshi
- Department of Pharmaceutical Chemistry, BVDU Poona College of Pharmacy, Pune, India
| | | | - Bapu Gawade
- Director, Cleanchem Life Sciences Pvt. Ltd., Navi Mumbai, Maharashtra, India
| | - Anand Mahajan
- Department of Pharmaceutical Chemistry, Goa College of Pharmacy, Panaji, Goa, India
| | - Atmaram Pawar
- Department of Pharmaceutics, BVDU Poona College of Pharmacy, Pune, India
| | - Sachin Sakat
- Director, Shribios Innovations Pvt. Ltd, Pune, Maharashtra, India
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Amado PM, Woodley C, Cristiano MLS, O’Neill PM. Recent Advances of DprE1 Inhibitors against Mycobacterium tuberculosis: Computational Analysis of Physicochemical and ADMET Properties. ACS OMEGA 2022; 7:40659-40681. [PMID: 36406587 PMCID: PMC9670723 DOI: 10.1021/acsomega.2c05307] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 10/21/2022] [Indexed: 05/14/2023]
Abstract
Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) is a critical flavoenzyme in Mycobacterium tuberculosis, catalyzing a vital step in the production of lipoarabinomannan and arabinogalactan, both of which are essential for cell wall biosynthesis. Due to its periplasmic localization, DprE1 is a susceptible target, and several compounds with diverse scaffolds have been discovered that inhibit this enzyme, covalently or noncovalently. We evaluated a total of ∼1519 DprE1 inhibitors disclosed in the literature from 2009 to April 2022 by performing an in-depth analysis of physicochemical descriptors and absorption, distribution, metabolism, excretion, and toxicity (ADMET), to gain new insights into these properties in DprE1 inhibitors. Several molecular properties that should facilitate the design and optimization of future DprE1 inhibitors are described, allowing for the development of improved analogues targeting M. tuberculosis.
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Affiliation(s)
- Patrícia
S. M. Amado
- Center
of Marine Sciences - CCMAR, University of
Algarve, P-8005-039 Faro, Portugal
- Department
of Chemistry and Pharmacy, FCT, University
of Algarve, P-8005-039 Faro, Portugal
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, United Kingdom
| | - Christopher Woodley
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, United Kingdom
| | - Maria L. S. Cristiano
- Center
of Marine Sciences - CCMAR, University of
Algarve, P-8005-039 Faro, Portugal
- Department
of Chemistry and Pharmacy, FCT, University
of Algarve, P-8005-039 Faro, Portugal
- Email
for M.L.S.C.:
| | - Paul M. O’Neill
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, United Kingdom
- Email for P.M.O.:
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Golbamaki A, Golbamaki N, Sizochenko N, Rasulev B, Leszczynski J, Benfenati E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties. Nanotoxicology 2018; 12:1113-1129. [PMID: 29888633 DOI: 10.1080/17435390.2018.1478999] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials' data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure-activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.
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Affiliation(s)
- Azadi Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Nazanin Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Natalia Sizochenko
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,c Department of Computer Science , Dartmouth College, Sudikoff Lab , Hanover , NH , USA
| | - Bakhtiyor Rasulev
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,d Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA
| | - Jerzy Leszczynski
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA
| | - Emilio Benfenati
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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Legehar A, Xhaard H, Ghemtio L. IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data. J Cheminform 2016; 8:33. [PMID: 27303447 PMCID: PMC4906584 DOI: 10.1186/s13321-016-0141-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 05/20/2016] [Indexed: 02/05/2023] Open
Abstract
Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.FDA approved drug data integration for predictive modeling ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ashenafi Legehar
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland
| | - Henri Xhaard
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland ; Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland
| | - Leo Ghemtio
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland
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Su BH, Tu YS, Lin C, Shao CY, Lin OA, Tseng YJ. Rule-Based Prediction Models of Cytochrome P450 Inhibition. J Chem Inf Model 2015; 55:1426-34. [DOI: 10.1021/acs.jcim.5b00130] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bo-Han Su
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Yi-shu Tu
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Chieh Lin
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Chi-Yu Shao
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Olivia A. Lin
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Yufeng J. Tseng
- Graduate Institute of Biomedical Electronics
and Bioinformatics and §Department of Computer
Science and Information Engineering, National Taiwan University, No.
1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
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An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment. Regul Toxicol Pharmacol 2015; 71:388-97. [PMID: 25656493 DOI: 10.1016/j.yrtph.2015.01.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 01/20/2015] [Accepted: 01/21/2015] [Indexed: 11/22/2022]
Abstract
The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data. However, the proprietary nature of internal structures prevents sharing of data and the full OECD compliance of such models. This analysis investigated whether using in-house internal compounds for training models is needed and substantially impacts the results of in silico genotoxicity assessments, or whether using commercial-off-the-shelf (COTS) packages such as Derek Nexus or Leadscope provide adequate performance. We demonstrated that supplementation of COTS packages with a Support Vector Machine (SVM) QSAR model trained on combined in-house and public data does, in fact, improve coverage and accuracy, and reduces the number of compounds needing experimental assessment, i.e., the liability load. This result indicates that there is added value in models trained on both internal and public structures and incorporating such models as part of a consensus approach improves the overall evaluation. Lastly, we optimized an in silico consensus decision-making approach utilizing two COTS models and an internal (SVM) model to minimize false negatives.
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