1
|
He S, Segura Abarrategi J, Bediaga H, Arrasate S, González-Díaz H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:535-555. [PMID: 38774585 PMCID: PMC11106676 DOI: 10.3762/bjnano.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/23/2024] [Indexed: 05/24/2024]
Abstract
Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.
Collapse
Affiliation(s)
- Shan He
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
- Painting Department, Fine Arts Faculty, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Instituto Biofisika (UPV/EHU-CSIC), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
2
|
Furxhi I, Kalapus M, Costa A, Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections. Nanotoxicology 2023; 17:529-544. [PMID: 37885250 DOI: 10.1080/17435390.2023.2268163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023]
Abstract
Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.
Collapse
Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Limerick, Ireland
| | - Michal Kalapus
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Anna Costa
- CNR-ISSMC Istituto di Scienza, Tecnologia e Sostenibilità per lo Sviluppo dei Materiali Ceramici, Faenza, Italy
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- QSAR Lab Ltd, Gdansk, Poland
| |
Collapse
|
3
|
Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
Collapse
Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| |
Collapse
|
4
|
Azizi M, Jahanban-Esfahlan R, Samadian H, Hamidi M, Seidi K, Dolatshahi-Pirouz A, Yazdi AA, Shavandi A, Laurent S, Be Omide Hagh M, Kasaiyan N, Santos HA, Shahbazi MA. Multifunctional nanostructures: Intelligent design to overcome biological barriers. Mater Today Bio 2023; 20:100672. [PMID: 37273793 PMCID: PMC10232915 DOI: 10.1016/j.mtbio.2023.100672] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/24/2023] [Accepted: 05/18/2023] [Indexed: 06/06/2023] Open
Abstract
Over the past three decades, nanoscience has offered a unique solution for reducing the systemic toxicity of chemotherapy drugs and for increasing drug therapeutic efficiency. However, the poor accumulation and pharmacokinetics of nanoparticles are some of the key reasons for their slow translation into the clinic. The is intimately linked to the non-biological nature of nanoparticles and the aberrant features of solid cancer, which together significantly compromise nanoparticle delivery. New findings on the unique properties of tumors and their interactions with nanoparticles and the human body suggest that, contrary to what was long-believed, tumor features may be more mirage than miracle, as the enhanced permeability and retention based efficacy is estimated to be as low as 1%. In this review, we highlight the current barriers and available solutions to pave the way for approved nanoformulations. Furthermore, we aim to discuss the main solutions to solve inefficient drug delivery with the use of nanobioengineering of nanocarriers and the tumor environment. Finally, we will discuss the suggested strategies to overcome two or more biological barriers with one nanocarrier. The variety of design formats, applications and implications of each of these methods will also be evaluated.
Collapse
Affiliation(s)
- Mehdi Azizi
- Department of Tissue Engineering and Biomaterials, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran
- Dental Implants Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Rana Jahanban-Esfahlan
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hadi Samadian
- Dental Implants Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
- Department of Molecular Medicine, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Masoud Hamidi
- Université Libre de Bruxelles (ULB), École Polytechnique de Bruxelles-BioMatter Unit, Avenue F.D. Roosevelt, 50 - CP 165/61, 1050, Brussels, Belgium
| | - Khaled Seidi
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Amirhossein Ahmadieh Yazdi
- Department of Molecular Medicine, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Amin Shavandi
- Université Libre de Bruxelles (ULB), École Polytechnique de Bruxelles-BioMatter Unit, Avenue F.D. Roosevelt, 50 - CP 165/61, 1050, Brussels, Belgium
| | - Sophie Laurent
- General, Organic and Biomedical Chemistry Unit, Faculty of Medicine and Pharmacy, Research Institute for Health Sciences and Technology, University of Mons – UMONS, Mons, Belgium
| | - Mahsa Be Omide Hagh
- Immunology Research Center, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nahid Kasaiyan
- Department of Nephrology and Hypertension, University Medical Center Utrecht, 3508 GA, Utrecht, Netherlands
| | - Hélder A. Santos
- Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands
- W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, FI-00014, Helsinki, Finland
| | - Mohammad-Ali Shahbazi
- Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands
- W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands
| |
Collapse
|
5
|
Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
Collapse
Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| |
Collapse
|
6
|
Banaye Yazdipour A, Masoorian H, Ahmadi M, Mohammadzadeh N, Ayyoubzadeh SM. Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review. Nanotoxicology 2023; 17:62-77. [PMID: 36883698 DOI: 10.1080/17435390.2023.2186279] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.
Collapse
Affiliation(s)
- Alireza Banaye Yazdipour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.,Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hoorie Masoorian
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahnaz Ahmadi
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
7
|
Toropova AP, Toropov AA. Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 823:153747. [PMID: 35149067 DOI: 10.1016/j.scitotenv.2022.153747] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
8
|
Roy J, Roy K. Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to Escherichia coli: categorization and data gap filling for untested metal oxides. Nanotoxicology 2022; 16:152-164. [PMID: 35166631 DOI: 10.1080/17435390.2022.2038299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Metal oxide nanoparticles (MeOxNPs) production is expected to increase every year exponentially, and their potential to cause adverse effect to the environment and human health will also expand rapidly. Hence, risk assessment of nanoparticles (NPs) is necessary to design ecosafe products. However, experimental ecotoxicological assessments are time-consuming requiring a lot of resources. Therefore, researchers rely on alternative in silico approaches to predict the behavior of NPs in the biological system. Quantitative structure - toxicity relationship (QSTR) has been adopted as a potential method to predict the cytotoxicity of untested NPs. Hence, in the present study, multiple linear regression (MLR) models were developed using 17 MeOxNPs on Escherichia coli (E. coli) bacteria cells under both light and dark conditions. The models were developed applying Small Dataset Modeler software, version 1.0.0 (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) which generates models with a limited number of data points. Periodic table-based descriptors (both 1st and 2nd generation) were used for the modeling purpose. Two statistically significant MLR models based on photo-induced toxicity (Q(LOO)2= 0.612, R2 = 0.726) and dark-based toxicity (Q(LOO)2= 0.627, R2 = 0.770) were developed. From the developed models, we interpreted that increase in valency and oxidation state of the metal will decrease the cytotoxicity whereas the atomic radius of the metal and electronegativity of MeOxNPs influence the toxicity toward E. coli cells. The MLR models were validated using different internal validation metrics. Additionally, we have collected 42 MeOxNPs as an external set to observe the predictive power of the two developed MLR models and categorize them into toxic and non-toxic classes. The chemical features selected in the developed models are important for understanding the mechanisms of nanotoxicity. Thus, the developed models can be a scientific basis for designing safer NPs.
Collapse
Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| |
Collapse
|
9
|
Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. NANOSCALE 2021; 13:17854-17870. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.
Collapse
Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
10
|
Kar S, Pathakoti K, Tchounwou PB, Leszczynska D, Leszczynski J. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies. CHEMOSPHERE 2021; 264:128428. [PMID: 33022504 PMCID: PMC7919734 DOI: 10.1016/j.chemosphere.2020.128428] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/23/2020] [Accepted: 09/21/2020] [Indexed: 05/25/2023]
Abstract
The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.
Collapse
Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | - Kavitha Pathakoti
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Paul B Tchounwou
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Danuta Leszczynska
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
| |
Collapse
|
11
|
Ortega-Tenezaca B, González-Díaz H. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. NANOSCALE 2021; 13:1318-1330. [PMID: 33410431 DOI: 10.1039/d0nr07588d] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.
Collapse
Affiliation(s)
- Bernabé Ortega-Tenezaca
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain and Amazon State University UEA, Puyo, Pastaza, Ecuador and Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain and Center for Investigation on Technologies of Information and Communication (CITIC), University of Coruña (UDC), Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain and IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
12
|
Chan C, Du S, Dong Y, Cheng X. Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems. Curr Top Med Chem 2021; 21:92-114. [PMID: 33243123 PMCID: PMC8191596 DOI: 10.2174/1568026620666201126162945] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/16/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023]
Abstract
Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.
Collapse
Affiliation(s)
- Chun Chan
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Shi Du
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Yizhou Dong
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Engineering; The Center for Clinical and Translational Science; The Comprehensive Cancer Center; Dorothy M. Davis Heart & Lung Research Institute; Department of Radiation Oncology, The Ohio State University, Columbus, OH 43210, USA
| | - Xiaolin Cheng
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Biophysics Graduate Program, Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
13
|
Abdelsattar AS, Dawoud A, Helal MA. Interaction of nanoparticles with biological macromolecules: a review of molecular docking studies. Nanotoxicology 2020; 15:66-95. [PMID: 33283572 DOI: 10.1080/17435390.2020.1842537] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The high frequency of using engineered nanoparticles in various medical applications entails a deep understanding of their interaction with biological macromolecules. Molecular docking simulation is now widely used to study the binding of different types of nanoparticles with proteins and nucleic acids. This helps not only in understanding the mechanism of their biological action but also in predicting any potential toxicity. In this review, the computational techniques used in studying the nanoparticles interaction with biological macromolecules are covered. Then, a comprehensive overview of the docking studies performed on various types of nanoparticles will be offered. The implication of these predicted interactions in the biological activity and/or toxicity is also discussed for each type of nanoparticles.
Collapse
Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray and Determination of Structure of Matter, Zewail City of Science and Technology, Giza, Egypt
| | - Alyaa Dawoud
- Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, Egypt
| | - Mohamed A Helal
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt.,Medicinal Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
| |
Collapse
|
14
|
Halder AK, Melo A, Cordeiro MNDS. A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles. CHEMOSPHERE 2020; 244:125489. [PMID: 31812055 DOI: 10.1016/j.chemosphere.2019.125489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/19/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.
Collapse
Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
| | - André Melo
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
| |
Collapse
|
15
|
Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules 2020; 25:E1375. [PMID: 32197324 PMCID: PMC7144386 DOI: 10.3390/molecules25061375] [Citation(s) in RCA: 233] [Impact Index Per Article: 58.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/16/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
Collapse
Affiliation(s)
- Xiaoqian Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiu Li
- School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, China;
| | - Xubo Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| |
Collapse
|
16
|
Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
Collapse
Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
| |
Collapse
|
17
|
Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
Collapse
Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
| |
Collapse
|
18
|
Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J. Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique. Anticancer Agents Med Chem 2019; 19:148-153. [PMID: 30360729 DOI: 10.2174/1871520618666181025122318] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 09/08/2017] [Accepted: 03/21/2018] [Indexed: 01/27/2023]
Abstract
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.
Collapse
Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Emilio Benfenati
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental; Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, United States
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, United States
| |
Collapse
|
19
|
Forest V, Hochepied JF, Pourchez J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chem Res Toxicol 2019; 32:1320-1326. [PMID: 31243983 DOI: 10.1021/acs.chemrestox.9b00022] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.
Collapse
Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| | - Jean-François Hochepied
- MINES ParisTech , PSL Research University , MAT - Centre des matériaux, CNRS UMR 7633 , BP 87 91003 Evry , France.,UCP, ENSTA ParisTech , Université Paris-Saclay , 828 bd des Maréchaux , 91762 Palaiseau cedex , France
| | - Jérémie Pourchez
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| |
Collapse
|
20
|
Álvarez-Machancoses Ó, Fernández-Martínez JL. Using artificial intelligence methods to speed up drug discovery. Expert Opin Drug Discov 2019; 14:769-777. [DOI: 10.1080/17460441.2019.1621284] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Óscar Álvarez-Machancoses
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Spain
| | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Spain
| |
Collapse
|
21
|
Furxhi I, Murphy F, Poland CA, Sheehan B, Mullins M, Mantecca P. Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics. Nanotoxicology 2019; 13:827-848. [PMID: 31140895 DOI: 10.1080/17435390.2019.1595206] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.
Collapse
Affiliation(s)
- Irini Furxhi
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Finbarr Murphy
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Craig A Poland
- b ELEGI/Colt Laboratory , Queen's Medical Research Institute, University of Edinburgh , Edinburgh , Scotland
| | - Barry Sheehan
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Martin Mullins
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Paride Mantecca
- c Department of Earth and Environmental Sciences , Particulate Matter and Health Risk (POLARIS) Research Centre University of Milano Bicocca , Milano , Italy
| |
Collapse
|
22
|
Concu R, D. S. Cordeiro MN, Munteanu CR, González-Díaz H. PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms. J Proteome Res 2019; 18:2735-2746. [DOI: 10.1021/acs.jproteome.8b00949] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Riccardo Concu
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - M. Natália. D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Cristian R. Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
- INIBIC Biomedical Research Institute of Coruña, CHUAC University Hospital, 15006 A Coruña, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
23
|
Bayal M, Janardhanan P, Tom E, Chandran N, Devadathan S, Ranjeet D, Unniyampurath U, Pilankatta R, Nair SS. Cytotoxicity of nanoparticles - Are the size and shape only matters? or the media parameters too?: a study on band engineered ZnS nanoparticles and calculations based on equivolume stress model. Nanotoxicology 2019; 13:1005-1020. [DOI: 10.1080/17435390.2019.1602678] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Manikanta Bayal
- Department of Physics, Central University of Kerala, Periye, Kasaragod, India
| | - Prajit Janardhanan
- Department of Biochemistry and Molecular Biology, Central University of Kerala, Periye, Kasaragod, India
| | - Emmanuel Tom
- Department of Physics, Central University of Kerala, Periye, Kasaragod, India
| | - Neeli Chandran
- Department of Physics, Central University of Kerala, Periye, Kasaragod, India
| | - S. Devadathan
- Department of Biochemistry and Molecular Biology, Central University of Kerala, Periye, Kasaragod, India
| | - D. Ranjeet
- Department of Biochemistry and Molecular Biology, Central University of Kerala, Periye, Kasaragod, India
| | | | - Rajendra Pilankatta
- Department of Biochemistry and Molecular Biology, Central University of Kerala, Periye, Kasaragod, India
| | - Swapna S. Nair
- Department of Physics, Central University of Kerala, Periye, Kasaragod, India
| |
Collapse
|
24
|
Roy J, Ojha PK, Roy K. Risk assessment of heterogeneous TiO2-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors. Nanotoxicology 2019; 13:701-716. [DOI: 10.1080/17435390.2019.1593543] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| |
Collapse
|
25
|
Nocedo-Mena D, Cornelio C, Camacho-Corona MDR, Garza-González E, Waksman de Torres N, Arrasate S, Sotomayor N, Lete E, González-Díaz H. Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. J Chem Inf Model 2019; 59:1109-1120. [PMID: 30802402 DOI: 10.1021/acs.jcim.9b00034] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN s) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.
Collapse
Affiliation(s)
- Deyani Nocedo-Mena
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Carlos Cornelio
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - María Del Rayo Camacho-Corona
- Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Elvira Garza-González
- Servicio de Gastroenterología, Hospital Universitario, Dr. Eleuterio González , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Noemi Waksman de Torres
- Facultad de Medicina , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Sonia Arrasate
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Nuria Sotomayor
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Esther Lete
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Biscay , Spain
| |
Collapse
|
26
|
González-Durruthy M, Manske Nunes S, Ventura-Lima J, Gelesky MA, González-Díaz H, Monserrat JM, Concu R, Cordeiro MND. MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors. J Chem Inf Model 2018; 59:86-97. [DOI: 10.1021/acs.jcim.8b00631] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Michael González-Durruthy
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - Silvana Manske Nunes
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Juliane Ventura-Lima
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Marcos A. Gelesky
- Post-Graduate Program in Technological and Environmental Chemistry, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II, College of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Bizkaia, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia, Spain
| | - José M. Monserrat
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Riccardo Concu
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - M. Natália D.S. Cordeiro
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| |
Collapse
|
27
|
Afantitis A, Melagraki G, Tsoumanis A, Valsami-Jones E, Lynch I. A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints. Nanotoxicology 2018; 12:1148-1165. [PMID: 30182778 DOI: 10.1080/17435390.2018.1504998] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.
Collapse
Affiliation(s)
| | | | | | - Eugenia Valsami-Jones
- b School of Geography Earth and Environmental Sciences , University of Birmingham , Birmingham , United Kingdom
| | - Iseult Lynch
- b School of Geography Earth and Environmental Sciences , University of Birmingham , Birmingham , United Kingdom
| |
Collapse
|
28
|
Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials. Comput Biol Med 2018; 99:161-172. [PMID: 29933127 DOI: 10.1016/j.compbiomed.2018.06.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 06/13/2018] [Accepted: 06/13/2018] [Indexed: 01/05/2023]
Abstract
The present study is based on the application of a multivariate statistical analysis approach for the selection of optimal descriptors of nanomaterials with the objective of robust qualitative modeling of their toxicity. A novel data mining protocol has been developed for the selection of an optimal subset of descriptors of nanomaterials by using the well-known multivariate method principal component analysis (PCA). The selected subsets of descriptors were validated for qualitative modeling of the toxicity of nanomaterials in the PC space. The analysis and validation of the proposed schemes were based on five decisive nanomaterial toxicity data sets available in the published literature. Optimal descriptors were selected on the basis of the maximum loading criteria and using a threshold value of cumulative variance ≤90% on PC directions. A maximum inter-class separation(B) and the minimum intra-classes separation(A) were obtained for toxic vs. nontoxic nanomaterials in the PC space with the selected subsets of optimal descriptors compared to their other combinations for each of the datasets.
Collapse
|
29
|
Aligning nanotoxicology with the 3Rs: What is needed to realise the short, medium and long-term opportunities? Regul Toxicol Pharmacol 2017; 91:257-266. [DOI: 10.1016/j.yrtph.2017.10.021] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 09/24/2017] [Accepted: 10/19/2017] [Indexed: 11/20/2022]
|
30
|
González-Durruthy M, Werhli AV, Seus V, Machado KS, Pazos A, Munteanu CR, González-Díaz H, Monserrat JM. Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Sci Rep 2017; 7:13271. [PMID: 29038520 PMCID: PMC5643473 DOI: 10.1038/s41598-017-13691-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/25/2017] [Indexed: 01/30/2023] Open
Abstract
The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.
Collapse
Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil.
| | - Adriano V Werhli
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Vinicius Seus
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Karina S Machado
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain.,RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - José M Monserrat
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| |
Collapse
|
31
|
Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol Lett 2017; 275:57-66. [DOI: 10.1016/j.toxlet.2017.03.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 03/24/2017] [Accepted: 03/24/2017] [Indexed: 01/13/2023]
|
32
|
Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co 3O 4, and TiO 2). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017; 139:404-407. [PMID: 28192776 DOI: 10.1016/j.ecoenv.2017.01.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/25/2017] [Accepted: 01/31/2017] [Indexed: 06/06/2023]
Abstract
Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
| |
Collapse
|
33
|
González-Durruthy M, Alberici LC, Curti C, Naal Z, Atique-Sawazaki DT, Vázquez-Naya JM, González-Díaz H, Munteanu CR. Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants. J Chem Inf Model 2017; 57:1029-1044. [PMID: 28414908 DOI: 10.1021/acs.jcim.6b00458] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .
Collapse
Affiliation(s)
| | | | | | | | | | - José M Vázquez-Naya
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU , 48940, Leioa, Bizkaia, Spain.,IKERBASQUE, Basque Foundation for Science , 48011, Bilbao, Bizkaia, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain.,Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC) , A Coruña, 15006, Spain
| |
Collapse
|
34
|
Toropova AP, Toropov AA. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. J Theor Biol 2017; 416:113-118. [DOI: 10.1016/j.jtbi.2017.01.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 12/25/2016] [Accepted: 01/06/2017] [Indexed: 10/20/2022]
|
35
|
Toropova AP, Achary PGR, Toropov AA. Quasi-SMILES for Nano-QSAR Prediction of Toxic Effect of Al2O3 Nanoparticles. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The level of malondialdehyde (MDA) in wet tissue of different organs is utilized as a measure of toxic effect. The numerical data on the concentration of MDA in wet tissue of liver, kidneys, brain, and heart of rat is examined as the endpoint which are impacted by different dose (mg/kg), exposure time (3 and 14 days) and single oral treatment of aluminium nano-oxide (Al2O3) with 30 nm or 40 nm. An attempt to develop predictive model for this endpoint has been carried out in this work. SMILES is a traditional tool to represent molecular structure for QSPRs/QSARs. In contrast to traditional SMILES, so-called quasi-SMILES can be a tool to build up quantitative features – property / activity relationships (QFPRs/QFARs) for endpoints which are not defined by solely molecular structure, but by a group of physicochemical and/or biochemical conditions. The quasi-SMILES is the representation of the above eclectic conditions whereas the QFPR/QFAR are models of endpoints which are modified under impacts of these eclectic conditions.
Collapse
Affiliation(s)
| | - P. Ganga Raju Achary
- Institute of Technical Education and Research (ITER), Siksha ‘O'Anusandhan University, India
| | | |
Collapse
|
36
|
Lynch I, Afantitis A, Leonis G, Melagraki G, Valsami-Jones E. Strategy for Identification of Nanomaterials’ Critical Properties Linked to Biological Impacts: Interlinking of Experimental and Computational Approaches. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2017. [DOI: 10.1007/978-3-319-56850-8_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
37
|
Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Leszczynska D, Leszczynski J. Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2016; 35:2691-2697. [PMID: 27110865 DOI: 10.1002/etc.3466] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 04/18/2016] [Accepted: 04/21/2016] [Indexed: 06/05/2023]
Abstract
Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r2 = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | | | | | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, Jackson, Mississippi, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| |
Collapse
|
38
|
Lin Z, Monteiro-Riviere NA, Kannan R, Riviere JE. A computational framework for interspecies pharmacokinetics, exposure and toxicity assessment of gold nanoparticles. Nanomedicine (Lond) 2016; 11:107-19. [DOI: 10.2217/nnm.15.177] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Aim: To develop a comprehensive computational framework to simulate tissue distribution of gold nanoparticles (AuNP) across several species. Materials & methods: This framework was built on physiologically based pharmacokinetic modeling, calibrated and evaluated with multiple independent datasets. Results: Rats and pigs seem to be more appropriate models than mice in animal-to-human extrapolation of AuNP pharmacokinetics and that the dose and age should be considered. Incorporation of in vitro and/or in vivo cellular uptake and toxicity data into the model improved toxicity assessment of AuNP. Conclusion: These results partially explain the current low translation rate of nanotechnology-based drug delivery systems from mice to humans. This simulation approach may be applied to other nanomaterials and provides guidance to design future translational studies.
Collapse
Affiliation(s)
- Zhoumeng Lin
- Institute of Computational Comparative Medicine (ICCM), Kansas State University, Manhattan, KS 66506, USA
| | - Nancy A Monteiro-Riviere
- Nanotechnology Innovation Center of Kansas State (NICKS), Kansas State University, Manhattan, KS 66506, USA
| | - Raghuraman Kannan
- Department of Radiology, University of Missouri, Columbia, MO 65211, USA
| | - Jim E Riviere
- Institute of Computational Comparative Medicine (ICCM), Kansas State University, Manhattan, KS 66506, USA
| |
Collapse
|
39
|
Halder A, Goodarzi M. Recent Advances in Multi-Task QSAR Modeling for Drug Design. PHARMACEUTICAL SCIENCES 2015. [DOI: 10.15171/ps.2015.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
|
40
|
Zanni R, Galvez-Llompart M, García-Domenech R, Galvez J. Latest advances in molecular topology applications for drug discovery. Expert Opin Drug Discov 2015; 10:945-57. [DOI: 10.1517/17460441.2015.1062751] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
41
|
González-Durruthy M, Monserrat JM, Alberici LC, Naal Z, Curti C, González-Díaz H. Mitoprotective activity of oxidized carbon nanotubes against mitochondrial swelling induced in multiple experimental conditions and predictions with new expected-value perturbation theory. RSC Adv 2015. [DOI: 10.1039/c5ra14435c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies.
Collapse
Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Jose Maria Monserrat
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Luciane C. Alberici
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Zeki Naal
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Carlos Curti
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
| |
Collapse
|