1
|
Dasgupta I, Das T, Das B, Gayen S. Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:909-924. [PMID: 39076688 PMCID: PMC11285082 DOI: 10.3762/bjnano.15.75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/01/2024] [Indexed: 07/31/2024]
Abstract
Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.
Collapse
Affiliation(s)
- Indrasis Dasgupta
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Totan Das
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Biplab Das
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| |
Collapse
|
2
|
Liao R, Zhuang Y, Li X, Chen K, Wang X, Feng C, Yin G, Zhu X, Lin J, Zhang X. Unveiling protein corona composition: predicting with resampling embedding and machine learning. Regen Biomater 2023; 11:rbad082. [PMID: 38213739 PMCID: PMC10781662 DOI: 10.1093/rb/rbad082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/25/2023] [Accepted: 09/02/2023] [Indexed: 01/13/2024] Open
Abstract
Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.
Collapse
Affiliation(s)
- Rong Liao
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Yan Zhuang
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Xiangfeng Li
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Ke Chen
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Xingming Wang
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Cong Feng
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Guangfu Yin
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Xiangdong Zhu
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Jiangli Lin
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| | - Xingdong Zhang
- College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China
| |
Collapse
|
3
|
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
|
4
|
Xu K, Li S, Zhou Y, Gao X, Mei J, Liu Y. Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery. Pharmaceutics 2023; 15:pharmaceutics15041064. [PMID: 37111551 PMCID: PMC10144056 DOI: 10.3390/pharmaceutics15041064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 03/28/2023] Open
Abstract
Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.
Collapse
Affiliation(s)
- Ke Xu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shilin Li
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangkai Zhou
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinglong Gao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Mei
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying Liu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- GBA National Institute for Nanotechnology Innovation, Guangzhou 510700, China
- Correspondence: ; Tel.: +86-1082-545-526
| |
Collapse
|
5
|
Sang L, Wang Y, Zong C, Wang P, Zhang H, Guo D, Yuan B, Pan Y. Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiO 2 and Heavy Metals: QSAR Model Apply Random Forest Algorithm after Clustering Analysis. Molecules 2022; 27:molecules27186125. [PMID: 36144857 PMCID: PMC9500633 DOI: 10.3390/molecules27186125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022] Open
Abstract
With the development and application of nanomaterials, their impact on the environment and organisms has attracted attention. As a common nanomaterial, nano-titanium dioxide (nano-TiO2) has adsorption properties to heavy metals in the environment. Quantitative structure-activity relationship (QSAR) is often used to predict the cytotoxicity of a single substance. However, there is little research on the toxicity of interaction between nanomaterials and other substances. In this study, we exposed human renal cortex proximal tubule epithelial (HK-2) cells to mixtures of eight heavy metals with nano-TiO2, measured absorbance values by CCK-8, and calculated cell viability. PLS and two ensemble learning algorithms are used to build multiple QSAR models for data sets, and the test set R2 is increased from 0.38 to 0.78 and 0.85, and RMSE is decreased from 0.18 to 0.12 and 0.10. After selecting the better random forest algorithm, the K-means clustering algorithm is used to continue to optimize the model, increasing the test set R2 to 0.95 and decreasing the RMSE to 0.08 and 0.06. As a reliable machine algorithm, random forest can be used to predict the toxicity of the mixture of nano-metal oxides and heavy metals. The cluster analysis can effectively improve the stability and predictability of the model, and provide a new idea for the prediction of cytotoxicity model in the future.
Collapse
Affiliation(s)
- Leqi Sang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Yunlin Wang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Cheng Zong
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Pengfei Wang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Huazhong Zhang
- Department of Emergency Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210006, China
| | - Dan Guo
- Department of Preventive Health Branch, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, China
| | - Beilei Yuan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
- Correspondence: (B.Y.); (Y.P.); Tel.: +86-25-5813-9553 (B.Y.)
| | - Yong Pan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
- Correspondence: (B.Y.); (Y.P.); Tel.: +86-25-5813-9553 (B.Y.)
| |
Collapse
|
6
|
Yuan B, Wang P, Sang L, Gong J, Pan Y, Hu Y. QNAR modeling of cytotoxicity of mixing nano-TiO 2 and heavy metals. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 208:111634. [PMID: 33396154 DOI: 10.1016/j.ecoenv.2020.111634] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/04/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO2 and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula Dmix= [Formula: see text] respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology.
Collapse
Affiliation(s)
- Beilei Yuan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.
| | - Pengfei Wang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Leqi Sang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Junhui Gong
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Yong Pan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Yanhui Hu
- Safety Assessment and Research Center for Drug, Pesticide and Veterinary Drug of Jiangsu Province, Nanjing Medical University, Nanjing, Jiangsu, China.
| |
Collapse
|
7
|
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
|
8
|
Russo DP, Yan X, Shende S, Huang H, Yan B, Zhu H. Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties. Anal Chem 2020; 92:13971-13979. [PMID: 32970421 DOI: 10.1021/acs.analchem.0c02878] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.
Collapse
Affiliation(s)
- Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States
| | - Xiliang Yan
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Institute of Environmental Research at Greater Bay, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Sunil Shende
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Department of Computer Science, Rutgers University, 227 Penn Street, Camden, New Jersey 08102, United States
| | | | - Bing Yan
- Institute of Environmental Research at Greater Bay, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.,School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Department of Chemistry, Rutgers University, 315 Penn Street, Camden, New Jersey 08102, United States
| |
Collapse
|
9
|
Kudrinskiy A, Zherebin P, Gusev A, Shapoval O, Pyee J, Lisichkin G, Krutyakov Y. New Relevant Descriptor of Linear QNAR Models for Toxicity Assessment of Silver Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E1459. [PMID: 32722446 PMCID: PMC7466614 DOI: 10.3390/nano10081459] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/17/2020] [Accepted: 07/23/2020] [Indexed: 01/22/2023]
Abstract
The use of silver nanoparticles (NPs) in medical, industrial and agricultural fields is becoming more widespread every year. This leads to an increasing number of experimental toxicological and microbiological studies of silver NPs aimed at establishing the risk-benefit ratio for their application. The following key parameters affecting the biological activity of silver dispersions are traditionally taken into consideration: mean diameter of NPs, surface potential of NPs and equilibrium concentration of Ag+. These characteristics are mainly predetermined by the chemical nature of the capping agent used for stabilization. However, the extent to which they influence the biological activity and the toxicity of silver NPs varies greatly. In this work, dispersions of silver NPs stabilized with a wide array of substances of different chemical nature were used for quantitative evaluation of whether the various measurable properties of silver NPs fit as descriptors of linear QNAR (quantitative nanostructure-activity relationship) models for silver NP toxicity evaluation with respect to a model eukaryotic microorganism-Saccharomyces cerevisiae yeast cells. It was shown that among the factors that determine silver NP toxicity, the charge of particles, their colloidal stability and the ability to generate Ag+ ions carry more importance than the descriptors related to the particle size. A significant synergistic effect between the ζ-potential and the colloidal stability of silver NPs on their toxicity was also discovered. Following this, a new descriptor has been proposed for the integral characterization of the silver dispersion colloidal stability. According to the obtained data, it can be considered applicable for building QNAR models of higher efficacy. The validity testing of the proposed model for theoretical prediction of silver NP toxicity using a wide range of living organisms has shown that this new descriptor correlates with toxicity much better compared to most traditionally used descriptors. Consequently, it seems promising in terms of being used not only in situations involving the rather narrow array of the objects tested, but also for the construction of silver NP toxicity models with respect to other living organisms.
Collapse
Affiliation(s)
- Alexey Kudrinskiy
- Department of Chemistry, Lomonosov Moscow State University, Lenin Hills 1-3, 119991 Moscow, Russia; (A.K.); (P.Z.); (G.L.)
- National Research Center “Kurchatov Institute”, pl. Akademika Kurchatova 1, 123182 Moscow, Russia
| | - Pavel Zherebin
- Department of Chemistry, Lomonosov Moscow State University, Lenin Hills 1-3, 119991 Moscow, Russia; (A.K.); (P.Z.); (G.L.)
| | - Alexander Gusev
- Research Institute for Environmental Science and Biotechnology, Derzhavin Tambov State University, str. Moskovskaya 10, 392000 Tambov, Russia;
- Department of Functional Nanosystems and High-Temperature Materials, National University of Science and Technology “MISIS”, 119991 Moscow, Russia
- Engineering Center, Plekhanov Russian University of Economics, Stremyanny Lane 36, 117997 Moscow, Russia
| | - Olga Shapoval
- Pryanishnikov Russian Scientific Research Institute of Agrochemistry, str. Pryanishnikova 31a, 127550 Moscow, Russia;
| | - Jaeho Pyee
- Department of Molecular Biology, Dankook University, 119 Dandae str., Cheonan 31116, Korea;
| | - Georgy Lisichkin
- Department of Chemistry, Lomonosov Moscow State University, Lenin Hills 1-3, 119991 Moscow, Russia; (A.K.); (P.Z.); (G.L.)
| | - Yurii Krutyakov
- Department of Chemistry, Lomonosov Moscow State University, Lenin Hills 1-3, 119991 Moscow, Russia; (A.K.); (P.Z.); (G.L.)
- National Research Center “Kurchatov Institute”, pl. Akademika Kurchatova 1, 123182 Moscow, Russia
| |
Collapse
|
10
|
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
|
11
|
Wang W, Yan X, Zhao L, Russo DP, Wang S, Liu Y, Sedykh A, Zhao X, Yan B, Zhu H. Universal nanohydrophobicity predictions using virtual nanoparticle library. J Cheminform 2019; 11:6. [PMID: 30659400 PMCID: PMC6689884 DOI: 10.1186/s13321-019-0329-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 01/10/2019] [Indexed: 12/12/2022] Open
Abstract
To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design.
Collapse
Affiliation(s)
- Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Xiliang Yan
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.,School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Shenqing Wang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China
| | - Yin Liu
- Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing, 100085, China
| | - Alexander Sedykh
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.,Sciome, Research Triangle Park, NC, 27709, USA
| | - Xiaoli Zhao
- Department of Physiological Sciences, Eastern Virginia Medical School, Norfolk, VA, 23507, USA
| | - Bing Yan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China.,School of Environment, Jinan University, Guangzhou, 510632, China
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA. .,Department of Chemistry, Rutgers University, 315 Penn St., Camden, NJ, 08102, USA. .,College of Life Science and Bio-Engineering, Beijing University of Technology, Beijing, 100124, China.
| |
Collapse
|
12
|
Luan F, Wang T, Tang L, Zhang S, Cordeiro MNDS. Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach. Molecules 2018; 23:molecules23051002. [PMID: 29695132 PMCID: PMC6099972 DOI: 10.3390/molecules23051002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/20/2018] [Accepted: 04/21/2018] [Indexed: 11/16/2022] Open
Abstract
Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.
Collapse
Affiliation(s)
- Feng Luan
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Ting Wang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Lili Tang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Shuang Zhang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - M Natália Dias Soeiro Cordeiro
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| |
Collapse
|
13
|
Wang W, Sedykh A, Sun H, Zhao L, Russo DP, Zhou H, Yan B, Zhu H. Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS NANO 2017; 11:12641-12649. [PMID: 29149552 PMCID: PMC5772766 DOI: 10.1021/acsnano.7b07093] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.
Collapse
Affiliation(s)
- Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Alexander Sedykh
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Sciome, Research Triangle Park, North Carolina 27709, United States
| | - Hainan Sun
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Hongyu Zhou
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Bing Yan
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
- Corresponding Authors. (B. Yan): . (H. Zhu):
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- Corresponding Authors. (B. Yan): . (H. Zhu):
| |
Collapse
|