1
|
Varsou DD, Kolokathis PD, Antoniou M, Sidiropoulos NK, Tsoumanis A, Papadiamantis AG, Melagraki G, Lynch I, Afantitis A. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation. Comput Struct Biotechnol J 2024; 25:47-60. [PMID: 38646468 PMCID: PMC11026727 DOI: 10.1016/j.csbj.2024.03.020] [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: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
Collapse
Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
| | | | | | | | - Andreas Tsoumanis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| | - Anastasios G. Papadiamantis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Antreas Afantitis
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| |
Collapse
|
2
|
Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Masand V, Humane V, Mali S, Alzahrani AYA, Rashid S, Elossaily GM. Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in- vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor. J Biomol Struct Dyn 2024:1-31. [PMID: 38385447 DOI: 10.1080/07391102.2024.2319104] [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: 08/24/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024]
Abstract
A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay Masand
- Department of Chemistry, Amravati, Maharashtra, India
| | - Vivek Humane
- Department of Chemistry, Shri R. R. Lahoti Science college, Morshi District: Amravati, Maharashtra, India
| | - Suraj Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Gehan M Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
| |
Collapse
|
3
|
Cao H, Peng J, Zhou Z, Yang Z, Wang L, Sun Y, Wang Y, Liang Y. Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17762-17773. [PMID: 36282672 DOI: 10.1021/acs.est.2c04400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pose potential environmental issues and health risks. However, little is known about emerging PFAS bioaccumulation to assess their chemical safety. This study focuses specifically on the large and high-quality data set of fluorochemicals from the related environmental and pharmaceutical chemicals databases, and machine learning (ML) models were developed for the classification prediction of the unbound fraction of compounds in plasma. A comprehensive evaluation of the ML models shows that the best blending model yields an accuracy of 0.901 for the test set. The predictions suggest that most PFAS (∼92%) have a high binding fraction in plasma. Introduction of alkaline amino groups is likely to reduce the binding affinities of PFAS with plasma proteins. Molecular dynamics simulations indicate a clear distinction between the high and low binding fractions of PFAS. These computational workflows can be used to predict the bioaccumulation of emerging PFAS and are also helpful for the molecular design of PFAS to prevent the release of high-bioaccumulation compounds into the environment.
Collapse
Affiliation(s)
- Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Jianhua Peng
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zhen Zhou
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yawei Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| |
Collapse
|
4
|
Guan R, Liu W, Li N, Cui Z, Cai R, Wang Y, Zhao C. Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 337:122620. [PMID: 37769706 DOI: 10.1016/j.envpol.2023.122620] [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: 05/24/2023] [Revised: 09/03/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.
Collapse
Affiliation(s)
- Ruining Guan
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Wencheng Liu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Ningqi Li
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Zeyang Cui
- School of Information Science & Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Ruitong Cai
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Yawei Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Chunyan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China.
| |
Collapse
|
5
|
Seal S, Yang H, Trapotsi MA, Singh S, Carreras-Puigvert J, Spjuth O, Bender A. Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data. J Cheminform 2023; 15:56. [PMID: 37268960 DOI: 10.1186/s13321-023-00723-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023] Open
Abstract
The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC > 0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces.
Collapse
Affiliation(s)
- Srijit Seal
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Maria-Anna Trapotsi
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Satvik Singh
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, UK
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
| |
Collapse
|
6
|
Oubahmane M, Hdoufane I, Delaite C, Sayede A, Cherqaoui D, El Allali A. Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations. Pharmaceuticals (Basel) 2023; 16:ph16040608. [PMID: 37111365 PMCID: PMC10146715 DOI: 10.3390/ph16040608] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/08/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.
Collapse
Affiliation(s)
- Mehdi Oubahmane
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Ismail Hdoufane
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Christelle Delaite
- Laboratoire de Photochimie et d'Ingénierie Macromoléculaires (LPIM), Ecole Nationale Supérieure de Chimie de Mulhouse, Université de Haute-Alsace, 68100 Mulhouse, France
| | - Adlane Sayede
- University Artois, CNRS, Centrale Lille, University Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), F-62300 Lens, France
| | - Driss Cherqaoui
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
| |
Collapse
|
7
|
Feng JJ, Sun XF, Zeng EY. Predicted health and environmental hazards of liquid crystal materials via quantitative structure-property relationship modeling. JOURNAL OF HAZARDOUS MATERIALS 2023; 446:130592. [PMID: 36580781 DOI: 10.1016/j.jhazmat.2022.130592] [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: 10/04/2022] [Revised: 11/26/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Liquid crystal materials (LCMs) are considered as emerging contaminants with high persistent and bioaccumulative potentials, but their toxicological effects are not well understood. To address this issue, a list of 1431 LCMs commercially available in the market was established through literature reviews and surveys of LCM suppliers. Toxicological properties of 221 target LCMs were derived from the Classification and Labeling Inventory by the European Chemicals Agency. More than 80 % of target LCMs likely pose adverse effects on human health or aquatic ecosystems. Two quantitative structure-property relationship (QSPR) models developed from the toxicological properties of LCMs achieved approximately 90 % accuracy in external data sets. The probability-based approach was more efficient in defining the applicability domain for the QSPR models than a range- or distance-based approach. The highest accuracy was achieved for chemicals within the probability-based applicability domain. The QSPR models were applied to predict health and environmental hazards of 1210 LCMs that had not been notified to the Classification and Labeling Inventory, and 301 and 94 LCMs were recognized as posing potential hazards to human health and the environment, respectively. The present study highlights the potential detrimental effects of LCMs and offers a specific in silico technique for screening hazardous LCMs.
Collapse
Affiliation(s)
- Jing-Jing Feng
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China
| | - Xiang-Fei Sun
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China
| | - Eddy Y Zeng
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China; Research Center of Low Carbon Economy for Guangzhou Region, Key Laboratory of Philosophy and Social Science in Guangdong Province of Community of Life for Man and Nature, Jinan University, Guangzhou 510632, China.
| |
Collapse
|
8
|
Liman W, Oubahmane M, Hdoufane I, Bjij I, Villemin D, Daoud R, Cherqaoui D, El Allali A. Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus. Molecules 2022; 27:molecules27092729. [PMID: 35566079 PMCID: PMC9099611 DOI: 10.3390/molecules27092729] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 11/16/2022] Open
Abstract
Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.
Collapse
Affiliation(s)
- Wissal Liman
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Mehdi Oubahmane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Ismail Hdoufane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Imane Bjij
- Institut Supérieur des Professions Infirmières et Techniques de Santé (ISPITS), Dakhla 73000, Morocco;
| | - Didier Villemin
- Ecole Nationale Supérieure d’Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique, UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, 14118 Caen, France;
| | - Rachid Daoud
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Driss Cherqaoui
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
- Correspondence:
| |
Collapse
|
9
|
Huchthausen J, Henneberger L, Mälzer S, Nicol B, Sparham C, Escher BI. High-Throughput Assessment of the Abiotic Stability of Test Chemicals in In Vitro Bioassays. Chem Res Toxicol 2022; 35:867-879. [PMID: 35394761 DOI: 10.1021/acs.chemrestox.2c00030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Abiotic stability of chemicals is not routinely tested prior to performing in vitro bioassays, although abiotic degradation can reduce the concentration of test chemicals leading to the formation of active or inactive transformation products, which may lead to misinterpretation of bioassay results. A high-throughput workflow was developed to measure the abiotic stability of 22 test chemicals in protein-rich aqueous media under typical bioassay conditions at 37 °C for 48 h. These test chemicals were degradable in the environment according to a literature review. The chemicals were extracted from the exposure media at different time points using a novel 96-pin solid-phase microextraction. The conditions were varied to differentiate between various reaction mechanisms. For most hydrolyzable chemicals, pH-dependent degradation in phosphate-buffered saline indicated that acid-catalyzed hydrolysis was less important than reactions with hydroxide ions. Reactions with proteins were mainly responsible for the depletion of the test chemicals in the media, which was simulated by bovine serum albumin (BSA) and glutathione (GSH). 1,2-Benzisothiazol-3(2H)-one, 2-methyl-4-isothiazolinone, and l-sulforaphane reacted almost instantaneously with GSH but not with BSA, indicating that GSH is a good proxy for reactivity with electrophilic amino acids but may overestimate the actual reaction with three-dimensional proteins. Chemicals such as hydroquinones or polyunsaturated chemicals are prone to autoxidation, but this reaction is difficult to differentiate from hydrolysis and could not be simulated by the oxidant N-bromosuccinimide. Photodegradation played a minor role because cells are exposed in incubators in the dark and simulations with high light intensities did not yield realistic degradation. Stability predictions from various in silico prediction models for environmental conditions can give initial indications of the stability but were not always consistent with the experimental stability in bioassays. As the presented workflow can be performed in high throughput under realistic bioassay conditions, it can be used to provide an experimental database for developing bioassay-specific stability prediction models.
Collapse
Affiliation(s)
- Julia Huchthausen
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research─UFZ, Permoserstr. 15, DE-04318 Leipzig, Germany
| | - Luise Henneberger
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research─UFZ, Permoserstr. 15, DE-04318 Leipzig, Germany
| | - Sophia Mälzer
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research─UFZ, Permoserstr. 15, DE-04318 Leipzig, Germany
| | - Beate Nicol
- Safety and Environmental Assurance Centre, Unilever, Colworth House, Sharnbrook, Bedford MK44 1LQ, U.K
| | - Chris Sparham
- Safety and Environmental Assurance Centre, Unilever, Colworth House, Sharnbrook, Bedford MK44 1LQ, U.K
| | - Beate I Escher
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research─UFZ, Permoserstr. 15, DE-04318 Leipzig, Germany.,Environmental Toxicology, Center for Applied Geoscience, Eberhard Karls University Tübingen, DE-72076 Tübingen, Germany
| |
Collapse
|
10
|
In-silico approaches towards the profiling of some anti-dengue virus as potent inhibitors against dengue NS-5 receptor. SCIENTIFIC AFRICAN 2021. [DOI: 10.1016/j.sciaf.2021.e00907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
11
|
Kutsarova S, Mehmed A, Cherkezova D, Stoeva S, Georgiev M, Petkov T, Chapkanov A, Schultz TW, Mekenyan OG. Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox. Regul Toxicol Pharmacol 2021; 125:105015. [PMID: 34293429 DOI: 10.1016/j.yrtph.2021.105015] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 11/17/2022]
Abstract
A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R2. The mechanistic and predictive coverages were >0.85.
Collapse
Affiliation(s)
- Stela Kutsarova
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Aycel Mehmed
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Daniela Cherkezova
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Stoyanka Stoeva
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Marin Georgiev
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Todor Petkov
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Atanas Chapkanov
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Terry W Schultz
- The University of Tennessee, College of Veterinary Medicine, Knoxville, TN, 37996-4500, USA
| | - Ovanes G Mekenyan
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria.
| |
Collapse
|
12
|
Sakamuru S, Zhao J, Xia M, Hong H, Simeonov A, Vaisman I, Huang R. Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors. J Chem Inf Model 2021; 61:2675-2685. [PMID: 34047186 DOI: 10.1021/acs.jcim.1c00439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.
Collapse
Affiliation(s)
- Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States.,Bioinformatics and Computational Biology, School of Systems Biology, College of Science, George Mason University, Manassas, Virginia 20110, United States
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), Jefferson, Arkansas 72079, United States
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Iosif Vaisman
- Bioinformatics and Computational Biology, School of Systems Biology, College of Science, George Mason University, Manassas, Virginia 20110, United States
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| |
Collapse
|
13
|
Singh AK, Bilal M, Iqbal HMN, Raj A. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 770:144561. [PMID: 33736422 DOI: 10.1016/j.scitotenv.2020.144561] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 02/05/2023]
Abstract
The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.
Collapse
Affiliation(s)
- Anil Kumar Singh
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hafiz M N Iqbal
- Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico.
| | - Abhay Raj
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
| |
Collapse
|
14
|
Petkov P, Ivanova H, Schultz T, Mekenyan O. Criteria for assessing the reliability of toxicity predictions: I. TIMES Ames mutagenicity model. ACTA ACUST UNITED AC 2021. [DOI: 10.1016/j.comtox.2020.100143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
15
|
Abstract
Skin permeation is an essential biological property of small organic compounds our body is exposed to, such as drugs in topic formulations, cosmetics, and environmental toxins. Despite the limited availability of experimental data, there is a lack of systematic analysis and structure. We present a novel resource on skin permeation data that collects all measurements available in the literature and systematically structures experimental conditions. Besides the skin permeation value kp, it includes experimental protocols such as skin source site, skin layer used, preparation technique, storage conditions, as well as test conditions such as temperature, pH as well as the type of donor and acceptor solution. It is important to include these parameters in the assessment of the skin permeation data. In addition, we provide an analysis of physicochemical properties and chemical space coverage, laying the basis for applicability domain determination of insights drawn from the collected data points. The database is freely accessible under https://huskindb.drug-design.de or https://doi.org/10.7303/syn21998881 .
Collapse
|
16
|
Masinja W, Elliott C, Modi S, Enoch SJ, Cronin MTD, McInnes EF, Currie RA. Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients. Toxicol In Vitro 2020; 70:105017. [PMID: 33038465 DOI: 10.1016/j.tiv.2020.105017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/25/2020] [Accepted: 10/05/2020] [Indexed: 01/22/2023]
Abstract
Alternatives to mammalian testing are highly desirable to predict the skin sensitisation potential of agrochemical active ingredients (AI). The GARD assay, a stimulated, dendritic cell-like, cell line measuring genomic signatures, was evaluated using twelve AIs (seven sensitisers and five non-sensitisers) and the results compared with historical results from guinea pig or local lymph node assay (LLNA) studies. Initial GARD results suggested 11/12 AIs were sensitisers and six concurred with mammalian data. Conformal predictions changed one AI to a non-sensitiser. An AI identified as non-sensitising in the GARD assay was considered a potent sensitiser in the LLNA. In total 7/12 GARD results corresponded with mammalian data. AI chemistries might not be comparable to the GARD training set in terms of applicability domains. Whilst the GARD assay can replace mammalian tests for skin sensitisation evaluation for compounds including cosmetic ingredients, further work in agrochemical chemistries is needed for this assay to be a viable replacement to animal testing. The work conducted here is, however, considered exploratory research and the methodology needs further development to be validated for agrochemicals. Mammalian and other alternative assays for regulatory safety assessments of AIs must provide confidence to assign the appropriate classification for human health protection.
Collapse
Affiliation(s)
- William Masinja
- Syngenta, International Research Centre, Jealott's Hill, Bracknell, Berks RG42 6EY, United Kingdom; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom.
| | - Claire Elliott
- Syngenta, International Research Centre, Jealott's Hill, Bracknell, Berks RG42 6EY, United Kingdom; Penman Consulting Limited, Aspect House, Waylands Avenue, Wantage, Oxon OX12 9FF, United Kingdom
| | - Sandeep Modi
- Syngenta, International Research Centre, Jealott's Hill, Bracknell, Berks RG42 6EY, United Kingdom
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Elizabeth F McInnes
- Syngenta, International Research Centre, Jealott's Hill, Bracknell, Berks RG42 6EY, United Kingdom
| | - Richard A Currie
- Syngenta, International Research Centre, Jealott's Hill, Bracknell, Berks RG42 6EY, United Kingdom
| |
Collapse
|
17
|
Burggraaff L, Lenselink EB, Jespers W, van Engelen J, Bongers BJ, González MG, Liu R, Hoos HH, van Vlijmen HWT, IJzerman AP, van Westen GJP. Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors. J Chem Inf Model 2020; 60:4283-4295. [PMID: 32343143 PMCID: PMC7525794 DOI: 10.1021/acs.jcim.9b01204] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
![]()
Kinases are frequently
studied in the context of anticancer drugs.
Their involvement in cell responses, such as proliferation, differentiation,
and apoptosis, makes them interesting subjects in multitarget drug
design. In this study, a workflow is presented that models the bioactivity
spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC,
and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and
PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding
inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based
models were included, which were thoroughly benchmarked and optimized.
A virtual screening was performed to test the workflow for one of
the main targets, RET kinase. This resulted in 5 novel and chemically
dissimilar RET inhibitors with remaining RET activity of <60% (at
a concentration of 10 μM) and similarities with known RET inhibitors
from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors
were assessed in a concentration range and proved to be modestly active
with a pIC50 value of 5.1 for the most active compound.
The experimental validation of inhibitors for RET strongly indicates
that the multitarget workflow is able to detect novel inhibitors for
kinases, and hence, this workflow can potentially be applied in polypharmacology
modeling. We conclude that this approach can identify new chemical
matter for existing targets. Moreover, this workflow can easily be
applied to other targets as well.
Collapse
Affiliation(s)
- Lindsey Burggraaff
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Eelke B Lenselink
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Willem Jespers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Department of Cell and Molecular Biology, Uppsala University, Uppsala 75124, Sweden
| | - Jesper van Engelen
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Brandon J Bongers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Marina Gorostiola González
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Rongfang Liu
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Holger H Hoos
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Herman W T van Vlijmen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Janssen Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Adriaan P IJzerman
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| |
Collapse
|
18
|
Mervin LH, Afzal AM, Engkvist O, Bender A. Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions. J Chem Inf Model 2020; 60:4546-4559. [DOI: 10.1021/acs.jcim.0c00476] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lewis H. Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Avid M. Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Mölndal SE-431 83, Sweden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1TN, U.K
| |
Collapse
|
19
|
Mando H, Hassan A, Gharaghani S. Novel and Predictive QSAR Model for Steroidal and Nonsteroidal 5α- Reductase Type II Inhibitors. Curr Drug Discov Technol 2020; 18:317-332. [PMID: 32208118 DOI: 10.2174/1570163817666200324170457] [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: 09/04/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 11/22/2022]
Abstract
AIMS AND OBJECTIVE In this study, a novel quantitative structure activity relationship (QSAR) model has been developed for inhibitors of human 5-alpha reductase type II, which are used to treat benign prostate hypertrophy (BPH). METHODS The dataset consisted of 113 compounds-mainly nonsteroidal-with known inhibitory concentration. Then 3D structures of compounds were optimized and molecular structure descriptors were calculated. The stepwise multiple linear regression was used to select descriptors encoding the inhibitory activity of the compounds. Multiple linear regression (MLR) was used to build up the linear QSAR model. RESULTS The results obtained revealed that the descriptors which best describe the activity were atom type electropological state, carbon type, radial distribution function (RDF), barysz matrix and molecular linear free energy relation. The suggested model could achieve satisfied square correlation coefficient of R2 = 0.72, higher than of many previous studies, indicating its superiority. Rigid validation criteria were met using external data with Q2 ˃ 0.5 and R2 = 0.75, reflecting the predictive power of the model. CONCLUSION The QSAR model was applied for screening botanical components of herbal preparations used to treat BPH, and could predict the activity of some, among others, making reasonable attribution to the proposed effect of these preparations. Gamma tocopherol was found to be an active inhibitor, in consistence with many previous studies, anticipating the power of this model in the prediction of new candidate molecules and suggesting further investigations.
Collapse
Affiliation(s)
- Huda Mando
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Ahmad Hassan
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| |
Collapse
|
20
|
An approach to identify new antihypertensive agents using Thermolysin as model: In silico study based on QSARINS and docking. ARAB J CHEM 2019. [DOI: 10.1016/j.arabjc.2016.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
21
|
Toropova AP, Toropov AA. Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task? Curr Top Med Chem 2019; 19:2643-2657. [PMID: 31702504 DOI: 10.2174/1568026619666191105111817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/02/2019] [Accepted: 09/04/2019] [Indexed: 12/23/2022]
Abstract
Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| |
Collapse
|
22
|
Vo AH, Van Vleet TR, Gupta RR, Liguori MJ, Rao MS. An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation. Chem Res Toxicol 2019; 33:20-37. [DOI: 10.1021/acs.chemrestox.9b00227] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Andy H. Vo
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Terry R. Van Vleet
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Rishi R. Gupta
- Information Research, Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Michael J. Liguori
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Mohan S. Rao
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| |
Collapse
|
23
|
El-Atawneh S, Hirsch S, Hadar R, Tam J, Goldblum A. Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists. J Chem Inf Model 2019; 59:3996-4006. [PMID: 31433190 DOI: 10.1021/acs.jcim.9b00577] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metabolic abnormalities, while avoiding the psychoactive effects in the central nervous system. We applied our in-house algorithm, iterative stochastic elimination, to produce a ligand-based model that distinguishes between CB1R antagonists and random molecules by physicochemical properties only. We screened ∼2 million commercially available molecules and found that about 500 of them are potential candidates to antagonize the CB1R. We applied a few criteria for peripheral activity and narrowed that set down to 30 molecules, out of which 15 could be purchased. Ten out of those 15 showed good affinity to the CB1R and two of them with nanomolar affinities (Ki of ∼400 nM). The eight molecules with top affinities were tested for activity: two compounds were pure antagonists, and five others were inverse agonists. These molecules are now being examined in vivo for their peripheral versus central distribution and subsequently will be tested for their effects on obesity in small animals.
Collapse
|
24
|
Yousefinejad S, Mahboubifar M, Eskandari R. Quantitative structure-activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks. Malar J 2019; 18:310. [PMID: 31521174 PMCID: PMC6744662 DOI: 10.1186/s12936-019-2941-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 08/27/2019] [Indexed: 01/09/2023] Open
Abstract
Background After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds. Methods In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg–Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity. Results The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R2), mean square error (MSE). For instance R2train = 0.947, R2val = 0.959, R2test = 0.920 shows the potential of the suggested model for the prediction of 3D7 activity. Different statistical approaches such as and applicability domain (AD) and y-scrambling was also showed the validity of models. Conclusion QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial–error runs during synthesis.
Collapse
Affiliation(s)
- Saeed Yousefinejad
- Research Center for Health Sciences, Institute of Health, Department of Occupational Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Marjan Mahboubifar
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Rayhaneh Eskandari
- Department of Chemistry, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| |
Collapse
|
25
|
Dimitrov SD, Dermen IA, Dimitrova NH, Vasilev KG, Schultz TW, Mekenyan OG. Mechanistic relationship between biodegradation and bioaccumulation. Practical outcomes. Regul Toxicol Pharmacol 2019; 107:104411. [PMID: 31226393 DOI: 10.1016/j.yrtph.2019.104411] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/04/2019] [Accepted: 06/17/2019] [Indexed: 10/26/2022]
Abstract
According to the REACH Regulation, for all substances manufactured or imported in amounts of 10 or more tons per year, that are not exempted from the registration requirement, a Chemical Safety Assessment (CSA) must be conducted. According to CSA criteria, for these substances persistent, bioaccumulative and toxic (PBT), and very persistent and very bioaccumulative (vPvB) assessment is requested. In order to reduce the number of applications of the expensive bioaccumulation test it seems useful to search thresholds for other related parameters above which no bioaccumulation is observed. Given the known relationship between ready biodegradability and bioaccumulation, one such parameter is biodegradation. This article addresses this relationship in searching for BOD threshold above which no vB and B chemicals could be observed. It was found that the regulatory criteria for persistency could be used for identification of not vB and B chemicals. In addition, fish liver metabolism is determined as the most significant factor in reducing of maximum bioaccumulation potential of the chemicals. It was found that parameters associated with the models simulating fish metabolism could be also used for identification of not vB and B chemicals.
Collapse
Affiliation(s)
- Sabcho D Dimitrov
- Laboratory of Mathematical Chemistry, University "Prof. As. Zlatarov", 8010, Bourgas, Bulgaria
| | - Irina A Dermen
- Laboratory of Mathematical Chemistry, University "Prof. As. Zlatarov", 8010, Bourgas, Bulgaria.
| | - Nadezhda H Dimitrova
- Laboratory of Mathematical Chemistry, University "Prof. As. Zlatarov", 8010, Bourgas, Bulgaria.
| | - Krasimir G Vasilev
- Laboratory of Mathematical Chemistry, University "Prof. As. Zlatarov", 8010, Bourgas, Bulgaria.
| | - Terry W Schultz
- The University of Tennessee, College of Veterinary Medicine, 2407 River Drive, Knoxville, TN, 37996-4500, USA.
| | - Ovanes G Mekenyan
- Laboratory of Mathematical Chemistry, University "Prof. As. Zlatarov", 8010, Bourgas, Bulgaria.
| |
Collapse
|
26
|
Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
27
|
Luque Ruiz I, Gómez-Nieto MÁ. Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index. J Chem Inf Model 2019; 59:2785-2804. [DOI: 10.1021/acs.jcim.9b00264] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Irene Luque Ruiz
- Department of Computing and Numerical Analysis, University of Córdoba, Albert Einstein Building, Campus de Rabanales, E-14071, Córdoba, Spain
| | - Miguel Ángel Gómez-Nieto
- Department of Computing and Numerical Analysis, University of Córdoba, Albert Einstein Building, Campus de Rabanales, E-14071, Córdoba, Spain
| |
Collapse
|
28
|
Junker T, Coors A, Schüürmann G. Compartment-Specific Screening Tools for Persistence: Potential Role and Application in the Regulatory Context. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2019; 15:470-481. [PMID: 30638305 DOI: 10.1002/ieam.4125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/01/2018] [Accepted: 01/09/2019] [Indexed: 06/09/2023]
Abstract
The persistence assessment under the European Union regulation Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) relies on compartment-specific degradation half-lives derived from laboratory simulation studies with surface water, aquatic sediment, or soil. Although these data are given priority, they are not available for most of the compounds. Therefore, according to the Integrated Assessment and Testing Strategy (ITS) for persistence assessment, results from ready biodegradability tests (RBTs) are used within a persistence screening to decide whether a substance is considered as "not persistent" or "potentially persistent." However, ready biodegradability is currently tested only in water. Consequently, there is a lack of approaches that include the soil and sediment compartments for persistence assessment at the screening level. In previous studies, compartment-specific screening tools for water-sediment (Water-Sediment Screening Tool [WSST]) and soil (Soil Screening Tool [SST]) were developed based on the existing test guideline Organisation for Economic Development and Co-operation (OECD TG 301C [MITI (Ministry of International Trade and Industry, Japan) test]). The test systems MITI, WSST, and SST were successfully applied to determine sound and reliable biodegradation data for 15 test compounds. In the present study, these results are used within the scope of a new alternative persistence screening approach, the Compartment-Specific Persistence Screening (CSPS). Compared to the persistence screening under REACH, the CSPS is a more conservative approach that provides additional reasonable results, particularly for compounds that sorb to sediment and soil, and for which the current standard persistence screening might be insufficient. Thus, the CSPS can be used to identify potentially persistent and nonpersistent compounds in the regulatory context by a comprehensive assessment that includes water, soil, and sediment. Moreover, experimentally determined half-lives from the compartment-specific screening tools can be used as input for multimedia models that estimate, for example, overall persistence (Pov ). The application of fixed half-life factors to extrapolate from water to soil and sediment, which is here demonstrated to be inappropriate, can thereby be avoided. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.
Collapse
Affiliation(s)
| | - Anja Coors
- ECT Oekotoxikologie GmbH, Flörsheim, Germany
| | - Gerrit Schüürmann
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Leipzig, Germany
- Institute of Organic Chemistry, Technical University Bergakademie Freiberg, Freiberg, Germany
| |
Collapse
|
29
|
Petkov PI, Schultz TW, Honma M, Yamada T, Kaloyanova E, Mekenyan OG. Validation of the performance of TIMES genotoxicity models with EFSA pesticide data. Mutagenesis 2019; 34:83-90. [PMID: 30445516 DOI: 10.1093/mutage/gey035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This study validates the performance of the TIssue MEtabolism Simulator (TIMES) genotoxicity models with data on pesticide chemicals included in a recently released European Food Safety Authority (EFSA) genotoxicity database. The EFSA database is biased towards negative chemicals. A comparison of substances included in the EFSA database and TIMES genotoxicity databases showed that the majority of the EFSA pesticides is not included in the TIMES genotoxicity databases and, thus, out of the applicability domains of the current TIMES models. However, the EFSA genotoxicity database provides an opportunity to expand the TIMES models. Where there is overlap of substances, consistency between EFSA and TIMES databases for the chemicals with documented data is found to be high (>80%) with respect to the Ames data and lower than the Ames data with respect to chromosomal aberration (CA) and mouse lymphoma assay (MLA) data. No conclusion for consistency with respect to micronucleus test and comet genotoxicity data can be provided due to the limited number of overlapping substances. Specificity of the models is important, given the prevalence of negative genotoxicity data in the EFSA database. High specificity (>80%) is obtained for prediction of the EFSA pesticides with Ames data. Moreover, this high specificity of the TIMES Ames models is not dependant on pesticides being within the domains. Specificity of the TIMES CA and MLA models is lower (>40%) to pesticides for out of domain. Sensitivity of TIMES in vitro and in vivo models cannot be properly estimated due to the small number of positive chemicals in the EFSA database.
Collapse
Affiliation(s)
- Petko I Petkov
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria
| | - Terry W Schultz
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria.,College of Veterinary Medicine, The University of Tennessee, 2407 River Dr, Knoxville, USA
| | - Masamitsu Honma
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria.,Division of Genetics and Mutagenesis, National Institute of Health Sciences, Tonomachi, kawasaki-ku, Kanagawa, Japan
| | - Takashi Yamada
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria.,Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, kawasaki-ku, Kanagawa, Japan
| | - Elena Kaloyanova
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria
| | - Ovanes G Mekenyan
- Laboratory of Mathematical Chemistry (LMC), University "Prof. D-R Assen Zlatarov"-Burgas, Burgas, Bulgaria
| |
Collapse
|
30
|
Idakwo G, Luttrell J, Chen M, Hong H, Zhou Z, Gong P, Zhang C. A review on machine learning methods for in silico toxicity prediction. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2019; 36:169-191. [PMID: 30628866 DOI: 10.1080/10590501.2018.1537118] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.
Collapse
Affiliation(s)
- Gabriel Idakwo
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Joseph Luttrell
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Minjun Chen
- b Division of Bioinformatics and Biostatistics, National Center for Toxicological Science , US Food and Drug Administration , Jefferson , Arkansas , USA
| | - Huixiao Hong
- b Division of Bioinformatics and Biostatistics, National Center for Toxicological Science , US Food and Drug Administration , Jefferson , Arkansas , USA
| | - Zhaoxian Zhou
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Ping Gong
- c Environmental Laboratory , US Army Engineer Research and Development Center , Vicksburg , Mississippi , USA
| | - Chaoyang Zhang
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| |
Collapse
|
31
|
Hanser T, Barber C, Guesné S, Marchaland JF, Werner S. Applicability Domain: Towards a More Formal Framework to Express the Applicability of a Model and the Confidence in Individual Predictions. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2019. [DOI: 10.1007/978-3-030-16443-0_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
32
|
Toma C, Gadaleta D, Roncaglioni A, Toropov A, Toropova A, Marzo M, Benfenati E. QSAR Development for Plasma Protein Binding: Influence of the Ionization State. Pharm Res 2018; 36:28. [PMID: 30591975 PMCID: PMC6308215 DOI: 10.1007/s11095-018-2561-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/17/2018] [Indexed: 01/05/2023]
Abstract
Purpose This study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain. Methods We retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families. Results Internal validation of the selected models returned Q2 values close to 0.60. External validation also gave r2 values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r2 0.74 in external validation. Conclusions Performance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule. Electronic supplementary material The online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy.
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Andrey Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Alla Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| |
Collapse
|
33
|
Yousefinejad S, Mahboubifar M, Rasekh S. Prediction of different antibacterial activity in a new set of formyl hydroxyamino derivatives with potent action on peptide deformylase using structural information. Struct Chem 2018. [DOI: 10.1007/s11224-018-1242-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
34
|
Ruiz IL, Gómez-Nieto MÁ. Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes. Molecules 2018; 23:molecules23112756. [PMID: 30356020 PMCID: PMC6278359 DOI: 10.3390/molecules23112756] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 10/14/2018] [Accepted: 10/22/2018] [Indexed: 11/30/2022] Open
Abstract
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.
Collapse
Affiliation(s)
- Irene Luque Ruiz
- Department of Computing and Numerical Analysis, Campus Universitario de Rabanales, Albert Einstein Building, University of Córdoba, E-14071 Córdoba, Spain.
| | - Miguel Ángel Gómez-Nieto
- Department of Computing and Numerical Analysis, Campus Universitario de Rabanales, Albert Einstein Building, University of Córdoba, E-14071 Córdoba, Spain.
| |
Collapse
|
35
|
Kaneko H. Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping. Mol Inform 2018; 38:e1800088. [PMID: 30259699 DOI: 10.1002/minf.201800088] [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] [Received: 07/27/2018] [Accepted: 08/30/2018] [Indexed: 01/11/2023]
Abstract
This paper introduces two generative topographic mapping (GTM) methods that can be used for data visualization, regression analysis, inverse analysis, and the determination of applicability domains (ADs). In GTM-multiple linear regression (GTM-MLR), the prior probability distribution of the descriptors or explanatory variables (X) is calculated with GTM, and the posterior probability distribution of the property/activity or objective variable (y) given X is calculated with MLR; inverse analysis is then performed using the product rule and Bayes' theorem. In GTM-regression (GTMR), X and y are combined and GTM is performed to obtain the joint probability distribution of X and y; this leads to the posterior probability distributions of y given X and of X given y, which are used for regression and inverse analysis, respectively. Simulations using linear and nonlinear datasets and quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) datasets confirm that GTM-MLR and GTMR enable data visualization, regression analysis, and inverse analysis considering appropriate ADs. Python and MATLAB codes for the proposed algorithms are available at https://github.com/hkaneko1985/gtm-generativetopographicmapping.
Collapse
Affiliation(s)
- Hiromasa Kaneko
- Department of Applied Chemistry, Meiji University 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan
| |
Collapse
|
36
|
Luechtefeld T, Hartung T. Computational approaches to chemical hazard assessment. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2018; 34:459-478. [PMID: 29101769 PMCID: PMC5848496 DOI: 10.14573/altex.1710141] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Indexed: 01/10/2023]
Abstract
Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.
Collapse
Affiliation(s)
- Thomas Luechtefeld
- Johns Hopkins Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Thomas Hartung
- Johns Hopkins Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
| |
Collapse
|
37
|
Liu R, Glover KP, Feasel MG, Wallqvist A. General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity. J Chem Inf Model 2018; 58:1561-1575. [DOI: 10.1021/acs.jcim.8b00114] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ruifeng Liu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States
| | - Kyle P. Glover
- Defense Threat Reduction Agency, Aberdeen Proving Ground, Maryland 21010, United States
| | - Michael G. Feasel
- U.S. Army—Edgewood Chemical Biological Center, Operational Toxicology, Aberdeen Proving Ground, Maryland 21010, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States
| |
Collapse
|
38
|
Fitzpatrick JM, Roberts DW, Patlewicz G. An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:439-468. [PMID: 29676182 PMCID: PMC6077848 DOI: 10.1080/1062936x.2018.1455223] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 03/17/2018] [Indexed: 06/08/2023]
Abstract
Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.
Collapse
Affiliation(s)
- Jeremy M Fitzpatrick
- National Center for Computational Toxicology (NCCT), US Environmental Protection Agency (US EPA), 109 T W Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - David W Roberts
- School of Pharmacy, Liverpool John Moores University, Byrom Street, Liverpool, UK
| | - Grace Patlewicz
- National Center for Computational Toxicology (NCCT), US Environmental Protection Agency (US EPA), 109 T W Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| |
Collapse
|
39
|
Nendza M, Kühne R, Lombardo A, Strempel S, Schüürmann G. PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 616-617:97-106. [PMID: 29107783 DOI: 10.1016/j.scitotenv.2017.10.317] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 10/30/2017] [Accepted: 10/30/2017] [Indexed: 06/07/2023]
Abstract
Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour.
Collapse
Affiliation(s)
- Monika Nendza
- Analytical Laboratory AL-Luhnstedt, Bahnhofstraße 1, 24816 Luhnstedt, Germany.
| | - Ralph Kühne
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany.
| | - Anna Lombardo
- IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Environmental Chemistry and Toxicology Laboratory, via La Masa 19, 20156 Milan, Italy.
| | | | - Gerrit Schüürmann
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany; Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Strasse 29, 09596 Freiberg, Germany.
| |
Collapse
|
40
|
Kaneko H. Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels. J Chem Inf Model 2018; 58:480-489. [PMID: 29425038 DOI: 10.1021/acs.jcim.7b00649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.
Collapse
Affiliation(s)
- Hiromasa Kaneko
- Department of Applied Chemistry, School of Science and Technology, Meiji University , 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
| |
Collapse
|
41
|
Patel M, Chilton ML, Sartini A, Gibson L, Barber C, Covey-Crump L, Przybylak KR, Cronin MTD, Madden JC. Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert. J Chem Inf Model 2018; 58:673-682. [DOI: 10.1021/acs.jcim.7b00523] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Mukesh Patel
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Martyn L. Chilton
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Andrea Sartini
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Laura Gibson
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Chris Barber
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Liz Covey-Crump
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, England
| | - Katarzyna R. Przybylak
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England
| | - Mark T. D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England
| | - Judith C. Madden
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England
| |
Collapse
|
42
|
Grisoni F, Ballabio D, Todeschini R, Consonni V. Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach. Methods Mol Biol 2018; 1800:3-53. [PMID: 29934886 DOI: 10.1007/978-1-4939-7899-1_1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.
Collapse
Affiliation(s)
- Francesca Grisoni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy.
| | - Davide Ballabio
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Roberto Todeschini
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Viviana Consonni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| |
Collapse
|
43
|
Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling. Methods Mol Biol 2018; 1800:141-169. [PMID: 29934891 DOI: 10.1007/978-1-4939-7899-1_6] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
In the context of human safety assessment through quantitative structure-activity relationship (QSAR) modeling, the concept of applicability domain (AD) has an enormous role to play. The Organization of Economic Co-operation and Development (OECD) for QSAR model validation recommended as principle 3 "A defined domain of applicability" to be present for a predictive QSAR model. The study of AD allows estimating the uncertainty in the prediction for a particular molecule based on how similar it is to the training compounds which are used in the model development. In the current scenario, AD represents an active research topic, and many methods have been designed to estimate the competence of a model and the confidence in its outcome for a given prediction task. Thus, characterization of interpolation space is significant in defining the AD. The diverse set of reported AD methods was constructed through different hypotheses and algorithms. These multiplicities of methodologies mystify the end users and make the comparison of the AD for different models a complex issue to address. We have attempted to summarize in this chapter the important concepts of AD including particulars of the available methods to compute the AD along with their thresholds and criteria for estimating AD through training set interpolation in the descriptor space. The idea about transparent domain and decision domain are also discussed. To help readers determine the AD in their projects, practical examples together with available open source software tools are provided.
Collapse
|
44
|
Dimitrova NH, Dermen IA, Todorova ND, Vasilev KG, Dimitrov SD, Mekenyan OG, Ikenaga Y, Aoyagi T, Zaitsu Y, Hamaguchi C. CATALOGIC 301C model - validation and improvement. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:511-524. [PMID: 28728491 DOI: 10.1080/1062936x.2017.1343255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 06/13/2017] [Indexed: 06/07/2023]
Abstract
In Europe, REACH legislation encourages the use of alternative in silico methods such as (Q)SAR models. According to the recent progress of Chemical Substances Control Law (CSCL) in Japan, (Q)SAR predictions are also utilized as supporting evidence for the assessment of bioaccumulation potential of chemicals along with read across. Currently, the effective use of read across and QSARs is examined for other hazards, including biodegradability. This paper describes the results of external validation and improvement of CATALOGIC 301C model based on more than 1000 tested new chemical substances of the publication schedule under CSCL. CATALOGIC 301C model meets all REACH requirements to be used for biodegradability assessment. The model formalism built on scientific understanding for the microbial degradation of chemicals has a well-defined and transparent applicability domain. The model predictions are adequate for the evaluation of the ready degradability of chemicals.
Collapse
Affiliation(s)
- N H Dimitrova
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - I A Dermen
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - N D Todorova
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - K G Vasilev
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - S D Dimitrov
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - O G Mekenyan
- a Laboratory of Mathematical Chemistry , University "Prof. As. Zlatarov" , Bourgas , Bulgaria
| | - Y Ikenaga
- b Chemical Management Center, National Institute of Technology and Evaluation (NITE) , Japan
| | - T Aoyagi
- b Chemical Management Center, National Institute of Technology and Evaluation (NITE) , Japan
| | - Y Zaitsu
- b Chemical Management Center, National Institute of Technology and Evaluation (NITE) , Japan
| | - C Hamaguchi
- b Chemical Management Center, National Institute of Technology and Evaluation (NITE) , Japan
| |
Collapse
|
45
|
Quantitative prediction of ionization effect on human skin permeability. Int J Pharm 2017; 522:222-233. [DOI: 10.1016/j.ijpharm.2017.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 03/05/2017] [Accepted: 03/06/2017] [Indexed: 12/12/2022]
|
46
|
Nendza M, Müller M, Wenzel A. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2017; 19:429-437. [PMID: 28165522 DOI: 10.1039/c6em00600k] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log Kow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.
Collapse
Affiliation(s)
- Monika Nendza
- Analytical Laboratory AL-Luhnstedt, Bahnhofstraße 1, 24816 Luhnstedt, Germany.
| | - Martin Müller
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Auf dem Aberg 1, 57392 Schmallenberg, Germany
| | - Andrea Wenzel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Auf dem Aberg 1, 57392 Schmallenberg, Germany
| |
Collapse
|
47
|
Abstract
Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.
Collapse
|
48
|
Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
Collapse
Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
| | | | | |
Collapse
|
49
|
Consensus of classification trees for skin sensitisation hazard prediction. Toxicol In Vitro 2016; 36:197-209. [DOI: 10.1016/j.tiv.2016.07.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 07/08/2016] [Accepted: 07/21/2016] [Indexed: 11/20/2022]
|
50
|
Shah I, Liu J, Judson RS, Thomas RS, Patlewicz G. Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. Regul Toxicol Pharmacol 2016; 79:12-24. [DOI: 10.1016/j.yrtph.2016.05.008] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 04/20/2016] [Accepted: 05/03/2016] [Indexed: 02/03/2023]
|