1
|
Toropova AP, Toropov AA. The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172119. [PMID: 38569951 DOI: 10.1016/j.scitotenv.2024.172119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/05/2024]
Abstract
Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
2
|
Ouabane M, Zaki K, Tabti K, Alaqarbeh M, Sbai A, Sekkate C, Bouachrine M, Lakhlifi T. Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations. Comput Biol Med 2024; 169:107880. [PMID: 38211383 DOI: 10.1016/j.compbiomed.2023.107880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 01/13/2024]
Abstract
It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R2 = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q2 = 0.954, QF12 = 0.946, QF22 = 0.938, QF32 = 0.947, Rm2 = 0.878, RMSE = 0.187, MAE = 0.151, FTraining = 390, FInvisible = 218, FCalibration = 240, RTest2 = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.
Collapse
Affiliation(s)
- Mohamed Ouabane
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco; Chemistry-Biology Applied to the Environment URL CNRT 13, Chemistry Department, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Khadija Zaki
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Kamal Tabti
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Marwa Alaqarbeh
- Basic Science Department, Prince Al Hussein Bin Abdullah II Academy for Civil Protection, Al-Balqa Applied University, Al-Salt, 19117, Jordan
| | - Abdelouahid Sbai
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Chakib Sekkate
- Chemistry-Biology Applied to the Environment URL CNRT 13, Chemistry Department, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Mohammed Bouachrine
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco; Higher School of Technology-Khenifra (EST-Khenifra), University of Sultan Moulay Slimane, PB 170, Khenifra, 54000, Morocco
| | - Tahar Lakhlifi
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Science, Moulay Ismail University, Meknes, Morocco.
| |
Collapse
|
3
|
Toropova AP, Toropov AA. Quasi-SMILES as a basis to build up models of endpoints for nanomaterials. ENVIRONMENTAL TECHNOLOGY 2023; 44:4460-4467. [PMID: 35748421 DOI: 10.1080/09593330.2022.2093655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model's predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| |
Collapse
|
4
|
Toropova AP, Toropov AA. Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity. Amino Acids 2023; 55:1437-1445. [PMID: 37707646 DOI: 10.1007/s00726-023-03322-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/24/2023] [Indexed: 09/15/2023]
Abstract
The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| |
Collapse
|
5
|
Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry. MUTATION RESEARCH. GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2023; 891:503684. [PMID: 37770141 DOI: 10.1016/j.mrgentox.2023.503684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/24/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023]
Abstract
Most quantitative structure-property/activity relationships (QSPRs/QSARs) techniques involve using different programs separately for generating molecular descriptors and separately for building models based on available descriptors. Here, the capabilities of the CORAL program are evaluated. A user of the program should apply as the basis for models the representation of the molecular structure by means of the simplified molecular input-line entry system (SMILES) as well as experimental data on the endpoint of interest. The local symmetry of SMILES is a novel composition of symmetrically represented symbols, which are three 'xyx', four 'xyyx', or five symbols 'xyzyx'. We updated our CORAL software using this optimal, new flexible descriptor, sensitive to the symmetric composition of a specific part of the molecule. Computational experiments have shown that taking account of these attributes of SMILES can improve the predictive potential of models for the mutagenicity of nitroaromatic compounds. In addition, the above computational experiments have confirmed the advantage of using the index of ideality of correlation (IIC) and the correlation intensity index (CII) for Monte Carlo optimization of the correlation weights for various attributes of SMILES, including the local symmetry. The average value of the coefficient of determination for the validation set (five different models) without fragments of local symmetry is 0.8589 ± 0.025, whereas using fragments of local symmetry improves this criterion of the predictive potential up to 0.9055 ± 0.010.
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
6
|
Huang R, Liu H, Wei Z, Jiang Y, Pan K, Wang X, Kong J. Insights into the quantitative structure-activity relationship for ionic liquids: a bibliometric mapping analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:95054-95076. [PMID: 37581727 DOI: 10.1007/s11356-023-29285-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 08/07/2023] [Indexed: 08/16/2023]
Abstract
Environmental protection and sustainability is the development goal that countries all over the world are pursuing. Ionic liquids (ILs), as a new type of green material, have a great application prospect. And the quantitative structure-activity relationship (QSAR) is significant for the research of ILs. To better understand the role played by QSAR in the research of ILs, 4139 literatures published in the WOS database from 2002 to 2022 were used for bibliometric analysis, and different types of knowledge maps were mapped to obtain the current status and trends of IL research applied QSAR. The distribution pattern of the literature output chronology, country, institution, author cooperation, and major source journals can be obtained through the research of the distribution of literature. Through core literature, dual-map overlays, and evolutionary path analysis, the research knowledge base was obtained mainly including ionic liquid toxicological properties research, environmental protection and sustainability, ionic liquid design, and mild steel corrosion inhibition; through the co-occurrence and evolution of keywords, the current research hotspots are basic properties of ILs, corrosion inhibition of mild steel, the effect of toxicity on the environment, QSAR modeling methods, solvent application of ILs, and drug design.
Collapse
Affiliation(s)
- Rui Huang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Hui Liu
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China.
- State Key Laboratory Cultivation Base for Gas Geology and Gas Control, Henan Polytechnic University, Jiaozuo, 454000, China.
| | - Ze Wei
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Yi Jiang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Kai Pan
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Xin Wang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jie Kong
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| |
Collapse
|
7
|
Tajiani F, Ahmadi S, Lotfi S, Kumar P, Almasirad A. In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization. BMC Chem 2023; 17:87. [PMID: 37496005 PMCID: PMC10373329 DOI: 10.1186/s13065-023-00999-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model ([Formula: see text]= 0.727, [Formula: see text]= 0.628, [Formula: see text]= 0.642, and [Formula: see text]=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC50 were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC50 of eight natural flavonols with pIC50 more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).
Collapse
Affiliation(s)
- Faezeh Tajiani
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, 19395-4697, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
8
|
Toropov AA, Toropova AP, Leszczynska D, Leszczynski J. Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:1852. [PMID: 37368282 DOI: 10.3390/nano13121852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/30/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure-property-activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson, MS 39217-0510, USA
| |
Collapse
|
9
|
Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity. Toxicol In Vitro 2023:105629. [PMID: 37307858 DOI: 10.1016/j.tiv.2023.105629] [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: 11/22/2022] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average value of the determination coefficient for the validation set is 0.8737 ± 0.0312.
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
10
|
Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity. TOXICS 2023; 11:toxics11050419. [PMID: 37235234 DOI: 10.3390/toxics11050419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
11
|
Goyal S, Rani P, Chahar M, Hussain K, Kumar P, Sindhu J. Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies. J Biomol Struct Dyn 2023; 41:13616-13631. [PMID: 37010991 DOI: 10.1080/07391102.2023.2193991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/03/2023] [Indexed: 04/04/2023]
Abstract
Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R2validation = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. In silico molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of -10.46 to -14.80. Molecular dynamics simulation of 100 ns was performed for ED01 and NED05. The results revealed that the protein-ligand complex bearing NED05 was more stable than lead ED01 exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Surbhi Goyal
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Payal Rani
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Monika Chahar
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Khalid Hussain
- Department of AS&H, Mewat Engineering College, Palla, Nuh, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| |
Collapse
|
12
|
Toropov AA, Di Nicola MR, Toropova AP, Roncaglioni A, Dorne JLCM, Benfenati E. Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles. CHEMOSPHERE 2023; 312:137224. [PMID: 36375610 DOI: 10.1016/j.chemosphere.2022.137224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
Simplified molecular input-line entry systems (SMILES) are the representation of the molecular structure that can be used to establish quantitative structure-property/activity relationships (QSPRs/QSARs) for various endpoints expressed as mathematical functions of the molecular architecture. Quasi-SMILES is extending the traditional SMILES by means of additional symbols that reflect experimental conditions. Using the quasi-SMILES models of toxicity to tadpoles gives the possibility to build up models by taking into account the time of exposure. Toxic effects of experimental situations expressed via 188 quasi-SMILES (the negative logarithm of molar concentrations which lead to lethal 50% tadpoles effected during 12 h, 24 h, 48 h, 72 h, and 96 h) were modelled with good results (the average determination coefficient for the validation sets is about 0.97). In this way, we developed new models for this amphibian endpoint, which is poorly studied.
Collapse
Affiliation(s)
- A A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - M R Di Nicola
- IRCCS San Raffaele Hospital, Unit of Dermatology, Milan, Italy
| | - A P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - A Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - J L C M Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
| | - E Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| |
Collapse
|
13
|
Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes. Sci Rep 2022; 12:21708. [PMID: 36522400 PMCID: PMC9755126 DOI: 10.1038/s41598-022-26279-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC50 are recognized and used for the mechanistic interpretation of structural attributes.
Collapse
|
14
|
Toropova AP, Toropov AA, Fjodorova N. Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna. NANOIMPACT 2022; 28:100427. [PMID: 36113716 DOI: 10.1016/j.impact.2022.100427] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 06/15/2023]
Abstract
Quasi-SMILES is an extension of the traditional SMILES. The classic SMILES is a way to represent the molecular structure. The quasi-SMILES is a way to describe all eclectic conditions that are able to affect the activity of a substance or a mixture. Nano-QSAR for prediction of toxicity of Nano-mixtures built up using the database on the corresponding experimental data. Models built up for five random splits of available data in training and validation sets are suggested. The Monte Carlo method of optimization is applied to calculate so-called optimal descriptors. The optimization was carried out with two criteria of predictive potential. These are the so-called index of ideality of correlation (IIC) and correlation intensity index (CII). Applying CII gives the better statistical quality of models for toxicity Nano-mixtures towards Daphnia Magna. The statistical quality of the best model follows the determination coefficients 0.987 (training set) and 0.980 (validation set).
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy
| | | |
Collapse
|
15
|
Toropov AA, Kjeldsen F, Toropova AP. Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials. CHEMOSPHERE 2022; 303:135086. [PMID: 35618064 DOI: 10.1016/j.chemosphere.2022.135086] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 06/15/2023]
Abstract
Quasi-SMILES deviate from traditional SMILES (simplified molecular input-line entry system) by the extension of additional symbols that encode for conditions of an experiment. Descriptors calculated with SMILES are useful for the development of quantitative structure-property/activity relationships (QSPRs/QSARs), while descriptors calculated with quasi-SMILES can be useful for the development of quantitative models of experimental results obtained under different conditions. Here, this approach has been applied for the development of generalized models using aquatic nanotoxicity data (i.e., related to fish and daphnia). The statistical quality of the above models (pLC50) is quite good with a determination coefficient for the external validation set ranging from 0.62 to 0.71 and RMSE ranging from 0.58 to 0.60. The principle of the approach includes splitting the experimental data into three random distributions defining training, calibration, and validation sets.
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230, Odense, Denmark.
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
| |
Collapse
|
16
|
Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
Collapse
Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
| |
Collapse
|
17
|
Tabti K, Elmchichi L, Sbai A, Maghat H, Bouachrine M, Lakhlifi T. Molecular modelling of antiproliferative inhibitors based on SMILES descriptors using Monte-Carlo method, docking, MD simulations and ADME/Tox studies. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2110246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Kamal Tabti
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Larbi Elmchichi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Abdelouahid Sbai
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Hamid Maghat
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| | - Mohammed Bouachrine
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
- High School of Technology Khenifra, Sultan Moulay Sliman University, Benimellal, Morocco
| | - Tahar Lakhlifi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
| |
Collapse
|
18
|
Toropov AA, Di Nicola MR, Toropova AP, Roncaglioni A, Carnesecchi E, Kramer NI, Williams AJ, Ortiz-Santaliestra ME, Benfenati E, Dorne JLCM. A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 830:154795. [PMID: 35341855 PMCID: PMC9535814 DOI: 10.1016/j.scitotenv.2022.154795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/16/2022] [Accepted: 03/20/2022] [Indexed: 04/15/2023]
Abstract
Amphibian populations are undergoing a global decline worldwide. Such decline has been attributed to their unique physiology, ecology, and exposure to multiple stressors including chemicals, temperature, and biological hazards such as fungi of the Batrachochytrium genus, viruses such as Ranavirus, and habitat reduction. There are limited toxicity data for chemicals available for amphibians and few quantitative structure-activity relationship (QSAR) models have been developed and are publicly available. Such QSARs provide important tools to assess the toxicity of chemicals particularly in a data poor context. QSARs provide important tools to assess the toxicity of chemicals particularly when no toxicological data are available. This manuscript provides a description and validation of a regression-based QSAR model to predict, in a quantitative manner, acute lethal toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica). QSAR models for acute median lethal molar concentrations (LC50-12 h) of waterborne chemicals using the Monte Carlo method were developed. The statistical characteristics of the QSARs were described as average values obtained from five random distributions into training and validation sets. Predictions from the model gave satisfactory results for the overall training set (R2 = 0.72 and RMSE = 0.33) and were even more robust for the validation set (R2 = 0.96 and RMSE = 0.11). Further development of QSAR models in amphibians, particularly for other life stages and species, are discussed.
Collapse
Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Matteo R Di Nicola
- Unit of Dermatology and Cosmetology, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy; Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands.
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Edoardo Carnesecchi
- Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Evidence Management Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
| | - Nynke I Kramer
- Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands; Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands.
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, Durham, USA.
| | - Manuel E Ortiz-Santaliestra
- Instituto de Investigación en Recursos Cinegéticos (IREC) UCLM-CSIC-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain.
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Jean-Lou C M Dorne
- Methodology and Scientific Support Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
| |
Collapse
|
19
|
Pandey V, Sharma K, Raghav N. Ligand-based modeling of semicarbazones and thiosemicarbazones derivatives as Cathepsin B, H, and L inhibitors: A multi-target approach. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
20
|
Ničkčović VP, Nikolić GR, Nedeljković BM, Mitić N, Danić SF, Mitić J, Marčetić Z, Sokolović D, Veselinović AM. In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition. CHEMICAL PAPERS 2022; 76:4393-4404. [PMID: 35400796 PMCID: PMC8977062 DOI: 10.1007/s11696-022-02170-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 03/05/2022] [Indexed: 11/03/2022]
|
21
|
Ahmadi S, Ketabi S, Qomi M. CO 2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization. NEW J CHEM 2022. [DOI: 10.1039/d2nj00596d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The first report of quasi-SMILES-based QSPR models for CO2 capture of MOFs based on experimental data.
Collapse
Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Sepideh Ketabi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mahnaz Qomi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
- Active Pharmaceutical Ingredients Research (APIRC), Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
22
|
Prediction of pEC50(M) and molecular docking study for the selective inhibition of arachidonate 5-lipoxygenase. UKRAINIAN BIOCHEMICAL JOURNAL 2021. [DOI: 10.15407/ubj93.06.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
23
|
Kumar P, Kumar A. Correlation intensity index (CII) as a benchmark of predictive potential: Construction of quantitative structure activity relationship models for anti-influenza single-stranded DNA aptamers using Monte Carlo optimization. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.131205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
|
24
|
Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies. Comput Biol Med 2021; 138:104876. [PMID: 34598068 DOI: 10.1016/j.compbiomed.2021.104876] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 12/29/2022]
Abstract
In an effort to explore a class of novel antidiabetic agents, we have made an effort to synergize the α-amylase inhibitory potential of 1,3-benzothiazole and 1,3,4-oxadiazole scaffolds by combining the two into a single structure via an ether linkage. The structure of synthesized benzothiazole clubbed oxadiazole derivatives are established by different spectral techniques. The synthesized hybrids are evaluated for their in vitro inhibitory potential against α-amylase. Compound 8f is found to be the most potent with a significant inhibition (87.5 ± 0.74% at 50 μg/mL, 82.27 ± 1.85% at 25 μg/mL and 79.94 ± 1.88% at 12.5 μg/mL) when compared to positive control acarbose (77.96 ± 2.06%, 71.17 ± 0.60%, 67.24 ± 1.16% at 50 μg/mL, 25 μg/mL and 12.5 μg/mL concentration). Molecular docking of the most potent enzyme inhibitor, 8f, shows promising interaction with the binding site of biological macromolecule Aspergillus oryzae α-amylase (PDB ID: 7TAA) and human pancreatic α-amylase (PDB ID: 3BAJ). To a step further, in-depth QSAR studies show a significant correlation between the experimental and the predicted inhibitory activities with the best Rvalidation2= 0.8701. The developed QSAR model can provide ample information about the structural features responsible for the increase and decrease of inhibitory activity. The mechanistic interpretation of the structure-activity relationship (SAR) is done with the help of combined computational calculations i.e. molecular docking and QSAR. Finally, molecular dynamic simulations are performed to get an insight into the binding mode of the most potent derivative with α-amylase from A. oryzae (PDB ID: 7TAA) and human pancreas (PDB ID: 3BAJ).
Collapse
|
25
|
Toropova AP, Toropov AA. Can the Monte Carlo method predict the toxicity of binary mixtures? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39493-39500. [PMID: 33755888 DOI: 10.1007/s11356-021-13460-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R2=0.95, and RMSE=0.375.
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
| |
Collapse
|
26
|
Toropov AA, Toropova AP. Quasi-SMILES as a basis for the development of models for the toxicity of ZnO nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145532. [PMID: 33578164 DOI: 10.1016/j.scitotenv.2021.145532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/07/2021] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The application of nanomaterials is expanding. Therefore, it is necessary to investigate the relationship between the structure and toxicity of different nanomaterials. Quasi-SMILES is a line of symbols which are codes of corresponding conditions of experiments aimed to estimate the toxicity of ZnO nanoparticles towards the rat via intraperitoneal injections. By means of the Monte Carlo method, the so-called correlation weights for fragments of quasi-SMILES can be calculated. Having the numerical data on the correlation weights one can build up a one-variable model for the toxicity. The checking up of the approach with five random splits of all available data on results of thirty-six experiments into a sub-system of training and sub-system of validation has confirmed the significance of the statistical quality of models obtained with the above approach. The average determination coefficient equal to 0.957 (dispersion 0.010) and average root mean square error equal to 7.25 [mg/kg] (dispersion 0.59 [mg/kg]).
Collapse
Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| |
Collapse
|
27
|
Evaluation of molecular structure based descriptors for the prediction of pEC50(M) for the selective adenosine A2A Receptor. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
|
28
|
Toropova AP, Toropov AA, Benfenati E. The self-organizing vector of atom-pairs proportions: use to develop models for melting points. Struct Chem 2021. [DOI: 10.1007/s11224-021-01778-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
29
|
In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7. Comput Biol Med 2021; 132:104346. [PMID: 33774271 DOI: 10.1016/j.compbiomed.2021.104346] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 03/13/2021] [Accepted: 03/13/2021] [Indexed: 01/27/2023]
Abstract
The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results.
Collapse
|
30
|
Toropov AA, Toropova AP. The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law? Toxicol Lett 2021; 340:133-140. [PMID: 33484841 DOI: 10.1016/j.toxlet.2021.01.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/23/2020] [Accepted: 01/16/2021] [Indexed: 12/01/2022]
Abstract
Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R2 = 0.60; RMSE = 0.63).
Collapse
Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| |
Collapse
|
31
|
Kumar A, Kumar P. Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 402:123777. [PMID: 33254788 DOI: 10.1016/j.jhazmat.2020.123777] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/30/2020] [Accepted: 08/15/2020] [Indexed: 05/23/2023]
Abstract
The assessment of cytotoxicity of quantum dots is very essential for environmental and health risk analysis. In the present work we have modelled HeLa cell cytotoxicity of sixty one CdSe quantum dots with ZnS shell as a function of its experimental conditions and molecular construction using quasiSMILES representations. The index of ideality of correlation helps in the building of ten statistically significant models having good fitting ability with value of R2 ranging from 0.8414 to 0.9609 for the training set. The split 5 model is rated as the best model with values of R2, Q2F1, Q2F2 and Q2F3 as 0.8964, 0.8267, 0.8264 and 0.8777 respectively for the calibration set. The extraction of features causing increase and decrease of cytotoxicity of quantum dots indicates importance of neutral surface charge, surface modified with protein, 72 h exposure time, combination of MTT assay with surface protein in decreasing the cytotoxicity. Amphiphilic polymer, polyol ligand with neutral charge, 0.5 - 0.6 nm quantum dot diameter with lipid ligand and unmodified positively charged surface are grouped in toxicity enhancer features. Further, consensus modelling using split 5 and 8 patterns enhances the prediction quality by increasing the R2val to 0.9361 and 0.9656 respectively.
Collapse
Affiliation(s)
- Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, 125001, India.
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| |
Collapse
|
32
|
Perić V, Golubović M, Lazarević M, Marjanović V, Kostić T, Đorđević M, Milić D, Veselinović AM. Development of potential therapeutics for pain treatment by inducing Sigma 1 receptor antagonism – in silico approach. NEW J CHEM 2021. [DOI: 10.1039/d1nj00883h] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for pain treatment.
Collapse
Affiliation(s)
- Velimir Perić
- Department for Cardiac Surgery
- Clinic for Anaesthesiology and Intensive Therapy
- Clinical Center Niš
- Niš
- Serbia
| | - Mladjan Golubović
- Department for Cardiac Surgery
- Clinic for Anaesthesiology and Intensive Therapy
- Clinical Center Niš
- Niš
- Serbia
| | - Milan Lazarević
- Faculty of Medicine
- Department of Chemistry
- Medical School of Niš
- University of Niš
- 18000 Niš
| | - Vesna Marjanović
- Faculty of Medicine
- Department of Chemistry
- Medical School of Niš
- University of Niš
- 18000 Niš
| | - Tomislav Kostić
- Faculty of Medicine
- Department of Chemistry
- Medical School of Niš
- University of Niš
- 18000 Niš
| | - Miodrag Đorđević
- Faculty of Medicine
- Department of Chemistry
- Medical School of Niš
- University of Niš
- 18000 Niš
| | - Dragan Milić
- Faculty of Medicine
- Department of Chemistry
- Medical School of Niš
- University of Niš
- 18000 Niš
| | | |
Collapse
|
33
|
How the CORAL software can be used to select compounds for efficient treatment of neurodegenerative diseases? Toxicol Appl Pharmacol 2020; 408:115276. [DOI: 10.1016/j.taap.2020.115276] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 08/21/2020] [Accepted: 10/07/2020] [Indexed: 12/26/2022]
|
34
|
Ahmadi S, Lotfi S, Kumar P. A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:935-950. [PMID: 33179988 DOI: 10.1080/1062936x.2020.1842495] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.
Collapse
Affiliation(s)
- S Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University , Tehran, Iran
| | - S Lotfi
- Department of Chemistry, Payame Noor University (PNU) , Tehran, Iran
| | - P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra, Haryana, India
| |
Collapse
|
35
|
Toropov AA, Sizochenko N, Toropova AP, Leszczynska D, Leszczynski J. Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII). J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.113929] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
36
|
Toropov AA, Toropova AP. Correlation intensity index: Building up models for mutagenicity of silver nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 737:139720. [PMID: 32554036 DOI: 10.1016/j.scitotenv.2020.139720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/21/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| |
Collapse
|
37
|
Stošić B, Janković R, Stošić M, Marković D, Stanković D, Sokolović D, Veselinović AM. In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA. Comput Biol Chem 2020; 88:107318. [DOI: 10.1016/j.compbiolchem.2020.107318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 06/07/2020] [Accepted: 06/22/2020] [Indexed: 11/25/2022]
|
38
|
Moné MJ, Pallocca G, Escher SE, Exner T, Herzler M, Bennekou SH, Kamp H, Kroese ED, Leist M, Steger-Hartmann T, van de Water B. Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience. Arch Toxicol 2020; 94:3581-3592. [PMID: 32886186 PMCID: PMC7502065 DOI: 10.1007/s00204-020-02866-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 08/12/2020] [Indexed: 01/22/2023]
Abstract
In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.
Collapse
Affiliation(s)
- M J Moné
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - G Pallocca
- CAAT-Europe at the University of Konstanz, Constance, Germany
| | - S E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - T Exner
- Edelweiss Connect GmbH, Basel, Switzerland
| | - M Herzler
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | | | - H Kamp
- BASF SE, Ludwigshafen, Germany
| | - E D Kroese
- TNO Innovation for Life, Utrecht, The Netherlands
| | - Marcel Leist
- CAAT-Europe at the University of Konstanz, Constance, Germany.
- In Vitro Toxicology and Biomedicine, Department Inaugurated By the Doerenkamp-Zbinden Foundation at the University of Konstanz, University of Konstanz, 78457, Constance, Germany.
| | - T Steger-Hartmann
- Investigational Toxicology, Bayer AG, Pharmaceuticals, Berlin, Germany
| | - B van de Water
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| |
Collapse
|
39
|
Kumar A, Kumar P. Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking. Struct Chem 2020. [DOI: 10.1007/s11224-020-01629-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
40
|
Jafari K, Fatemi MH, Toropova AP, Toropov AA. Correlation Intensity Index (CII) as a criterion of predictive potential: Applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids. Chem Phys Lett 2020. [DOI: 10.1016/j.cplett.2020.137614] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
|
41
|
Toropov AA, Toropova AP, Benfenati E. 'Ideal correlations' for the predictive toxicity to Tetrahymena pyriformis. Toxicol Mech Methods 2020; 30:605-610. [PMID: 32718259 DOI: 10.1080/15376516.2020.1801928] [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] [Indexed: 01/17/2023]
Abstract
OBJECTIVES Predictive models for toxicity to Tetrahymena pyriformis are an important component of natural sciences. The present study aims to build up a predictive model for the endpoint using the so-called index of ideality of correlation (IIC). Besides, the comparison of the predictive potential of these models with the predictive potential of models suggested in the literature is the task of the present study. METHODS The Monte Carlo technique is a tool to build up the predictive model applied in this study. The molecular structure is represented via a simplified molecular input-line entry system (SMILES). The IIC is a statistical characteristic sensitive to both the correlation coefficient and mean absolute error. Applying of the IIC to build up quantitative structure-activity relationships (QSARs) for the toxicity to Tetrahymena pyriformis improves the predictive potential of those models for random splits into the training set and the validation set. The calculation was carried out with CORAL software (http://www.insilico.eu/coral). RESULTS The statistical quality of the suggested models is incredibly good for the external validation set, but the statistical quality of the models for the training set is modest. This is the paradox of ideal correlation, which is obtained with applying the IIC. CONCLUSIONS The Monte Carlo technique is a convenient and reliable way to build up a predictive model for toxicity to Tetrahymena pyriformis. The IIC is a useful statistical criterion for building up predictive models as well as for the assessment of their statistical quality.
Collapse
Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| |
Collapse
|
42
|
Kumar A, Sindhu J, Kumar P. In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation. J Biomol Struct Dyn 2020; 39:5014-5025. [DOI: 10.1080/07391102.2020.1784286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology, Hisar, Haryana, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| |
Collapse
|
43
|
Bagri K, Kumar A, Nimbhal M, Kumar P. Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1770753] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Kiran Bagri
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Manisha Nimbhal
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| |
Collapse
|
44
|
Toropov AA, Toropova AP, Marzo M, Benfenati E. Use of the index of ideality of correlation to improve aquatic solubility model. J Mol Graph Model 2020; 96:107525. [DOI: 10.1016/j.jmgm.2019.107525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 11/27/2019] [Accepted: 12/23/2019] [Indexed: 12/18/2022]
|
45
|
The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity. Struct Chem 2020. [DOI: 10.1007/s11224-020-01525-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
46
|
Toropova AP, Toropov AA, Carnesecchi E, Benfenati E, Dorne JL. The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:13339-13347. [PMID: 32020455 DOI: 10.1007/s11356-020-07820-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
Models for water solubility of pesticides suggested in this manuscript are important data from point of view of ecologic engineering. The Index of Ideality of Correlation (IIC) of groups of quantitative structure-property relationships (QSPRs) for water solubility of pesticides related to the calibration sets was used to identify good in silico models. This comparison confirmed the high IIC set provides better statistical quality of the model for the validation set. Though there are large databases on solubility, the reliable prediction of the endpoint for new substances which are potential pesticides is an important ecologic task. Unfortunately, predictive models for various endpoints suffer overtraining, and the IIC serves to avoid or at least reduce this. Thus, the approach suggested has both theoretical and economic effects for ecology.
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Edoardo Carnesecchi
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
- Institute for Risk Assessment Sciences, Utrecht University, PO Box 80177, 3508 TD, Utrecht, The Netherlands
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, 43126, Parma, Italy
| |
Collapse
|
47
|
Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. CHEMOSPHERE 2020; 242:125192. [PMID: 31677509 DOI: 10.1016/j.chemosphere.2019.125192] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF1) and index of ideality correlation target function (TF2) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF2 improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF2 reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.
Collapse
Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
| |
Collapse
|
48
|
Zivkovic M, Zlatanovic M, Zlatanovic N, Djordjevic Jocic J, Golubović M, Veselinović AM. Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach. NEW J CHEM 2020. [DOI: 10.1039/c9nj05967a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for glaucoma treatment.
Collapse
Affiliation(s)
- Maja Zivkovic
- Faculty of Medicine
- Department of Ophthalmology
- University of Nis
- Nis
- Serbia
| | | | | | | | - Mladjan Golubović
- Clinic for Anesthesiology and Intensive Care
- Clinical Center Nis
- Nis
- Serbia
| | | |
Collapse
|
49
|
Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. The index of ideality of correlation: models of the flash points of ternary mixtures. NEW J CHEM 2020. [DOI: 10.1039/d0nj00121j] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reliable information related to the flash point of ternary mixtures assists in the rational classification of different ternary mixtures of liquids.
Collapse
Affiliation(s)
- Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology
- Department of Environmental Health Science
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS
- 20156 Milano
- Italy
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology
- Department of Environmental Health Science
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS
- 20156 Milano
- Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center
- Department of Civil and Environmental Engineering
- Jackson State University
- Jackson
- USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center
- Department of Chemistry
- Physics and Atmospheric Sciences
- Jackson State University
- Jackson
| |
Collapse
|
50
|
Duhan M, Singh R, Devi M, Sindhu J, Bhatia R, Kumar A, Kumar P. Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. J Biomol Struct Dyn 2019; 39:91-107. [DOI: 10.1080/07391102.2019.1704885] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Meenakshi Duhan
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Rimpy Bhatia
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology, Hisar, Haryana, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| |
Collapse
|