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Bilgi E, Winkler DA, Oksel Karakus C. Identifying factors controlling cellular uptake of gold nanoparticles by machine learning. J Drug Target 2024; 32:66-73. [PMID: 38009690 DOI: 10.1080/1061186x.2023.2288995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 11/18/2023] [Indexed: 11/29/2023]
Abstract
There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.
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Affiliation(s)
- Eyup Bilgi
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
- Department, of Material Science and Engineering, Izmir Institute of Technology, Izmir, Turkey
| | - David A Winkler
- School of Biochemistry & Chemistry, La Trobe University, Bundoora, VIC, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
- School of Pharmacy, University of Nottingham, Nottingham, UK
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Fang F, Chen X. Carrier-Free Nanodrugs: From Bench to Bedside. ACS NANO 2024; 18:23827-23841. [PMID: 39163559 DOI: 10.1021/acsnano.4c09027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Carrier-free nanodrugs with extraordinary active pharmaceutical ingredient (API) loading (even 100%), avoidable carrier-induced toxicity, and simple synthetic procedures are considered as one of the most promising candidates for disease theranostics. Substantial studies and the commercial success of "carrier-free" nanocrystals have demonstrated their strong clinical potential. However, their practical translations remain challenging and are impeded by unpredictable assembly processes, insufficient delivery efficiency, and an unclear in vivo fate. In this Perspective, we systematically outline the contemporary and emerging carrier-free nanodrugs based on diverse APIs, as well as highlight their opportunities and challenges in clinical translation. Looking ahead, further improvements in design and preparation, drug delivery, in vivo efficacy, and safety of carrier-free nanomedicines are essential to facilitate their translation from the bench to bedside.
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Affiliation(s)
- Fang Fang
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Theranostics Center of Excellence (TCE), Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
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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.
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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
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Moncho S, Serrano-Candelas E, de Julián-Ortiz JV, Gozalbes R. A review on the structural characterization of nanomaterials for nano-QSAR models. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:854-866. [PMID: 39015425 PMCID: PMC11250003 DOI: 10.3762/bjnano.15.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/28/2024] [Indexed: 07/18/2024]
Abstract
Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.
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Affiliation(s)
- Salvador Moncho
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
| | | | - Jesús Vicente de Julián-Ortiz
- Universitat de València, Facultad de Farmacia, Departamento de Química Física, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Avda. Vicent Andrés Estellés 0, 46100 Burjassot, Spain
| | - Rafael Gozalbes
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
- MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain
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Bessa CDPB, Feu AE, de Menezes RPB, Scotti MT, Lima JMG, Lima ML, Tempone AG, de Andrade JP, Bastida J, Borges WDS. Multitarget anti-parasitic activities of isoquinoline alkaloids isolated from Hippeastrum aulicum (Amaryllidaceae). PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155414. [PMID: 38503155 DOI: 10.1016/j.phymed.2024.155414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/02/2024] [Accepted: 02/03/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Chagas disease and leishmaniasis affect a significant portion of the Latin American population and still lack efficient treatments. In this context, natural products emerge as promising compounds for developing more effective therapies, aiming to mitigate side effects and drug resistance. Notably, species from the Amaryllidaceae family emerge as potential reservoirs of antiparasitic agents due to the presence of diverse biologically active alkaloids. PURPOSE To assess the anti-Trypanosoma cruzi and anti-Leishmania infantum activity of five isolated alkaloids from Hippeastrum aulicum Herb. (Amaryllidaceae) against different life stages of the parasites using in silico and in vitro assays. Furthermore, molecular docking was employed to evaluate the interaction of the most active alkaloids. METHODS Five natural isoquinoline alkaloids isolated in suitable quantities for in vitro testing underwent preliminary in silico analysis to predict their potential efficacy against Trypanosoma cruzi (amastigote and trypomastigote forms) and Leishmania infantum (amastigote and promastigote forms). The in vitro antiparasitic activity and mammalian cytotoxicity were investigated with a subsequent comparison of both analysis (in silico and in vitro) findings. Additionally, this study employed the molecular docking technique, utilizing cruzain (T. cruzi) and sterol 14α-demethylase (CYP51, L. infantum) as crucial biological targets for parasite survival, specifically focusing on compounds that exhibited promising activities against both parasites. RESULTS Through computational techniques, it was identified that the alkaloids haemanthamine (1) and lycorine (8) were the most active against T. cruzi (amastigote and trypomastigote) and L. infantum (amastigote and promastigote), while also revealing unprecedented activity of alkaloid 7‑methoxy-O-methyllycorenine (6). The in vitro analysis confirmed the in silico tests, in which compound 1 presented the best activities against the promastigote and amastigote forms of L. infantum with half-maximal inhibitory concentration (IC50) 0.6 µM and 1.78 µM, respectively. Compound 8 exhibited significant activity against the amastigote form of T. cruzi (IC50 7.70 µM), and compound 6 demonstrated activity against the trypomastigote forms of T. cruzi and amastigote of L. infantum, with IC50 values of 89.55 and 86.12 µM, respectively. Molecular docking analyses indicated that alkaloids 1 and 8 exhibited superior interaction energies compared to the inhibitors. CONCLUSION The hitherto unreported potential of compound 6 against T. cruzi trypomastigotes and L. infantum amastigotes is now brought to the forefront. Furthermore, the acquired dataset signifies that the isolated alkaloids 1 and 8 from H. aulicum might serve as prototypes for subsequent structural refinements aimed at the exploration of novel leads against both T. cruzi and L. infantum parasites.
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Affiliation(s)
- Carliani Dal Piero Betzel Bessa
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil
| | - Amanda Eiriz Feu
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil
| | - Renata Priscila Barros de Menezes
- Programa de Pós-graduação em Produtos Naturais e Sintéticos Bioativos (PgPNSB), Universidade Federal da Paraíba, Campus I, Cidade Universitária, João Pessoa 58051-900, Brazil
| | - Marcus Tullius Scotti
- Programa de Pós-graduação em Produtos Naturais e Sintéticos Bioativos (PgPNSB), Universidade Federal da Paraíba, Campus I, Cidade Universitária, João Pessoa 58051-900, Brazil
| | | | - Marta Lopes Lima
- School of Life Sciences, University of Dundee, Scotland DD1 4HN, United Kingdom
| | | | - Jean Paulo de Andrade
- Departamento de Medicina Traslacional, Facultad de Medicina, Escuela de Química y Farmacia, Universidad Católica del Maule, Talca 3480112, Chile
| | - Jaume Bastida
- Departament de Biologia, Sanitat i Medi Ambient, Facultat de Farmàcia i Ciències de l´Alimentació, Universitat de Barcelona, Barcelona 08028, Spain
| | - Warley de Souza Borges
- Programa de Pós-Graduação em Química, Departamento de Química, Universidade Federal do Espírito Santo, Vitória-ES 29075-910, Brazil.
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Lee J. Development of quantitative structure activity relationships (QSARs) for predicting the aggregation of TiO 2 nanoparticles under favorable conditions. Heliyon 2024; 10:e27966. [PMID: 38571612 PMCID: PMC10987904 DOI: 10.1016/j.heliyon.2024.e27966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/08/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
This study developed multi-linear regression (MLR) quantitative structure-activity relationships (QSARs) to predict n-TiO2 aggregation in the presence of high concentrations of representative emerging organic contaminants (EOCs), which presented favorable conditions to interaction with n-TiO2. The largest diameter change (Δ 517 nm at 0 h and Δ 1164 nm at 12 h) of n-TiO2 was observed by estrone, while the smallest diameter change (Δ -114 nm at 0 h and - 4 nm at 12 h) was observed by lincomycin during experimental periods. In addition, the zeta potential changes of n-TiO2 were observed that the biggest changes were observed by 17β-estradiol (-1.3 mV) and alachlor (-10.02 mV) at 0 h, while 17β-estradiol (-1.31 mV) and pendimethalin (-11.4 mV) showed the biggest changes at 12 h comparing to control. These changes of n-TiO2 diameter and zeta potential may implicate the effects of unique physico-chemical properties of each EOC on the surface modification of n-TiO2. Based on the interaction results, this study investigated the QSARs between n-TiO2 aggregation and physico-chemical descriptors of EOCs with 7 representative descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., # of HBD) for predicting n-TiO2 aggregation rate kinetics at 0 h and 12 h by applying MATLAB statistical methods (model 1 - fitlm and model 2 - stepwiselm). In a model 1, QSARs showed the good coefficients of determination (R2 = 0.92) at 0 h and (R2 = 0.87) at 12 h with 7 descriptors. In a model 2, QSARs showed the goodness of fit of a model (R2 = 0.9998) with 8 descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., #HBD, pKa⋅#H bond donors) at 0 h, while QSARs showed the coefficients of determination (R2 = 0.68) with 2 descriptors (pKa, M.V.) at 12 h. Particularly, we observed that some descriptors of EOCs such as pKa and # of HBD having polarity have more influenced on the n-TiO2 aggregation rate kinetics. Our developed QSARs demonstrated that the 7 descriptors of EOCs were significantly effective descriptors for predicting n-TiO2 aggregation rate kinetics in favorable conditions, which may implicate the complexity interactions between heterogeneous surfaces of n-TiO2 and physico-chemical properties of EOCs.
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Affiliation(s)
- Jaewoong Lee
- Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [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: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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Lei L, Zhang L, Han Z, Chen Q, Liao P, Wu D, Tai J, Xie B, Su Y. Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:123093. [PMID: 38072027 DOI: 10.1016/j.envpol.2023.123093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 01/26/2024]
Abstract
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10-3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.
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Affiliation(s)
- Lang Lei
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Liangmao Zhang
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Zhibang Han
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Qirui Chen
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Pengcheng Liao
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Dong Wu
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Jun Tai
- Shanghai Environmental Sanitation Engineering Design Institute Co., Ltd., Shanghai, 200232, China
| | - Bing Xie
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Yinglong Su
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
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Muñoz-Vega MC, López-Hernández S, Sierra-Chavarro A, Scotti MT, Scotti L, Coy-Barrera E, Herrera-Acevedo C. Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors. Molecules 2023; 29:179. [PMID: 38202763 PMCID: PMC10779987 DOI: 10.3390/molecules29010179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/02/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
The critical enzyme dihydrofolate reductase-thymidylate synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. Through consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in silico predictions were then validated through in vitro assays to examine the inhibitory capacity of the top-ranked naturally occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4'-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.
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Affiliation(s)
- Maria Camila Muñoz-Vega
- Department of Chemical Engineering, Universidad ECCI, Bogotá, Distrito Capital 111311, Colombia; (M.C.M.-V.); (S.L.-H.); (A.S.-C.)
- Laboratorio de Investigación en Biocatálisis y Biotransformaciones (LIBB), Grupo de Investigación en Ingeniería de los Procesos Agroalimentarios y Biotecnológicos (GIPAB), Departamento de Química Universidad del Valle, Cali 760042, Colombia
| | - Sofía López-Hernández
- Department of Chemical Engineering, Universidad ECCI, Bogotá, Distrito Capital 111311, Colombia; (M.C.M.-V.); (S.L.-H.); (A.S.-C.)
| | - Adrián Sierra-Chavarro
- Department of Chemical Engineering, Universidad ECCI, Bogotá, Distrito Capital 111311, Colombia; (M.C.M.-V.); (S.L.-H.); (A.S.-C.)
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (M.T.S.); (L.S.)
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (M.T.S.); (L.S.)
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá 250247, Colombia;
| | - Chonny Herrera-Acevedo
- Department of Chemical Engineering, Universidad ECCI, Bogotá, Distrito Capital 111311, Colombia; (M.C.M.-V.); (S.L.-H.); (A.S.-C.)
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (M.T.S.); (L.S.)
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Gawne PJ, Ferreira M, Papaluca M, Grimm J, Decuzzi P. New Opportunities and Old Challenges in the Clinical translation of Nanotheranostics. NATURE REVIEWS. MATERIALS 2023; 8:783-798. [PMID: 39022623 PMCID: PMC11251001 DOI: 10.1038/s41578-023-00581-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/27/2023] [Indexed: 07/20/2024]
Abstract
Nanoparticle-based systems imbued with both diagnostic and therapeutic functions, known as nanotheranostics, have enabled remarkable progress in guiding focal therapy, inducing active responses to endogenous and exogenous biophysical stimuli, and stratifying patients for optimal treatment. However, although in recent years more nanotechnological platforms and techniques have been implemented in the clinic, several important challenges remain that are specific to nanotheranostics. In this Review, we first discuss some of the many ways of 'constructing' nanotheranostics, focusing on the different imaging modalities and therapeutic strategies. We then outline nanotheranostics that are currently used in humans at different stages of clinical development, identifying specific advantages and opportunities. Finally, we define critical steps along the winding road of preclinical and clinical development and suggest actions to overcome technical, manufacturing, regulatory and economical challenges for the safe and effective clinical translation of nanotheranostics.
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Affiliation(s)
- Peter J. Gawne
- UCL Cancer Institute, University College London, London, UK
- Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary, University of London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Miguel Ferreira
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Marisa Papaluca
- School of Public Health, Imperial College of London, South Kensington CampusLondon, UK
| | - Jan Grimm
- Molecular Pharmacology Program and Department of Radiology, Memorial Sloan-Kettering Cancer, Center, New York, NY, USA
| | - Paolo Decuzzi
- Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia, Via, Morego 30, 16163, Genoa, IT
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Roy J, Pore S, Roy K. Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO 2 with periodic table descriptors using machine learning approaches. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2023; 14:939-950. [PMID: 37736658 PMCID: PMC10509545 DOI: 10.3762/bjnano.14.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/30/2023] [Indexed: 09/23/2023]
Abstract
Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99-0.89), Q2 values (0.64-0.77), and Q2F1 values (0.99-0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Souvik Pore
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
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12
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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.
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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
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13
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Wang X, Li F, Teng Y, Ji C, Wu H. Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:162103. [PMID: 36764549 DOI: 10.1016/j.scitotenv.2023.162103] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/19/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The wide application of TiO2-based engineered nanoparticles (nTiO2) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO2 to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO2 on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) - catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) - glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with high accuracy and balanced accuracy for both training sets and external validation sets. Furthermore, the best model revealed the predominant factors (exposure concentration, exposure periods, and exposure matrix) influencing the oxidative stress induced by nTiO2. These results showed that high exposure concentrations and short exposure-intervals tended to cause oxidative damage to bivalves. In addition, gills and digestive glands could be vulnerable to nTiO2-induced oxidative damage as tissues/organs differences were the important factors controlling MDA activity. This study provided insights into important nano-features responsible for the different indicators of oxidative stress and thereby extended the application of machine learning approaches in toxicological assessment for nanoparticles.
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Affiliation(s)
- Xiaoqing Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China.
| | - Yuefa Teng
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
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14
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Gomes SIL, Roca CP, Pokhrel S, Mädler L, Scott-Fordsmand JJ, Amorim MJB. TiO 2 nanoparticles' library toxicity (UV and non-UV exposure) - High-throughput in vivo transcriptomics reveals mechanisms. NANOIMPACT 2023; 30:100458. [PMID: 36858316 DOI: 10.1016/j.impact.2023.100458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 06/03/2023]
Abstract
The hazards of nanomaterials/nanoparticles (NMs/NPs) are mostly assessed using individual NMs, and a more systematic approach, using many NMs, is needed to evaluate its risks in the environment. Libraries of NMs, with a range of identified different but related characters/descriptors allow the comparison of effects across many NMs. The effects of a custom designed Fe-doped TiO2 NMs library containing 11 NMs was assessed on the soil model Enchytraeus crypticus (Oligochaeta), both with and without UV (standard fluorescent) radiation. Effects were analyzed at organism (phenotypic, survival and reproduction) and gene expression level (transcriptomics, high-throughput 4x44K microarray) to understand the underlying mechanisms. A total of 48 microarrays (20 test conditions) were done plus controls (UV and non-UV). Unique mechanisms induced by TiO2 NPs exposure included the impairment in RNA processing for TiO2_10nm, or deregulated apoptosis for 2%FeTiO2_10nm. Strikingly apparent was the size dependent effects such as induction of reproductive effects via smaller TiO2 NPs (≤12 nm) - embryo interaction, while larger particles (27 nm) caused reproductive effects through different mechanisms. Also, phagocytosis was affected by 12 and 27 nm NPs, but not by ≤11 nm. The organism level study shows the integrated response, i.e. the result after a cascade of events. While uni-cell models offer key mechanistic information, we here deliver a combined biological system level (phenotype and genotype), seldom available, especially for environmental models.
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Affiliation(s)
- Susana I L Gomes
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Carlos P Roca
- Department of Ecoscience, Aarhus University, C.F. Møllers Alle 4, DK-8000, Aarhus, Denmark
| | - Suman Pokhrel
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany; Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Lutz Mädler
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany; Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | | | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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15
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Mahmoudi M, Landry MP, Moore A, Coreas R. The protein corona from nanomedicine to environmental science. NATURE REVIEWS. MATERIALS 2023; 8:1-17. [PMID: 37361608 PMCID: PMC10037407 DOI: 10.1038/s41578-023-00552-2] [Citation(s) in RCA: 104] [Impact Index Per Article: 104.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 05/15/2023]
Abstract
The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.
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Affiliation(s)
- Morteza Mahmoudi
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI USA
| | - Markita P. Landry
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA USA
- Innovative Genomics Institute, Berkeley, CA USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA USA
- Chan Zuckerberg Biohub, San Francisco, CA USA
| | - Anna Moore
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI USA
| | - Roxana Coreas
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA USA
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16
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Luna IS, Souza TAD, da Silva MS, Franca Rodrigues KAD, Scotti L, Scotti MT, Mendonça-Junior FJB. Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents. Eur J Med Chem 2023; 250:115223. [PMID: 36848847 DOI: 10.1016/j.ejmech.2023.115223] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/23/2023]
Abstract
The leishmaniasis is a neglected disease caused by a group of protozoan parasites from the genus Leishmania whose treatment is limited, obsolete, toxic, and ineffective in certain cases. These characteristics motivate researchers worldwide to plan new therapeutic alternatives for the treatment of leishmaniasis, where the use of cheminformatics tools applied to computer-assisted drug design has allowed research to make great advances in the search for new drugs candidates. In this study, a series of 2-amino-thiophene (2-AT) derivatives was screened virtually using QSAR tools, ADMET filters and prediction models, allowing direct the synthesis of compounds, which were evaluated in vitro against promastigotes and axenic amastigotes of Leishmania amazonensis. The combination of different descriptors and machine learning methods led to obtaining robust and predictive QSAR models, which was obtained from a dataset composed of 1862 compounds extracted from the ChEMBL database, with correct classification rates ranging from 0.53 (for amastigotes) to 0.91 (for promastigotes), allowing to select eleven 2-AT derivatives, which do not violate Lipinski's rules, exhibit good druglikeness, and with probability ≤70% of potential activity against the two evolutionary forms of the parasite. All compounds were properly synthesized and 8 of them were shown to be active at least against one of the evolutionary forms of the parasite with IC50 values lower than 10 μM, being more active than the reference drug meglumine antimoniate, and showing low or no citotoxicity against macrophage J774.A1 for the most part. Compounds 8CN and DCN-83, respectively, are the most active against promastigote and amastigote forms, with IC50 values of 1.20 and 0.71 μM, and selectivity indexes (SI) of 36.58 and 119.33. Structure Activity Relationship (SAR) study was carried out and allowed to identify some favorable and/or essential substitution patterns for the leishmanial activity of 2-AT derivatives. Taken together, these findings demonstrate that the use of ligand-based virtual screening proved to be quite effective and saved time, effort, and money in the selection of potential anti-leishmanial agents, and confirm, once again that 2-AT derivatives are promising hit compounds for the development of new anti-leishmanial agents.
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Affiliation(s)
- Isadora Silva Luna
- Laboratory of Synthesis and Drug Delivery, State University of Paraiba, João Pessoa, PB, Brazil; Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Thalisson Amorim de Souza
- Multiuser Laboratory Center of Characterization and Analysis, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Marcelo Sobral da Silva
- Multiuser Laboratory Center of Characterization and Analysis, Federal University of Paraiba, João Pessoa, PB, Brazil
| | | | - Luciana Scotti
- Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Francisco Jaime Bezerra Mendonça-Junior
- Laboratory of Synthesis and Drug Delivery, State University of Paraiba, João Pessoa, PB, Brazil; Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil.
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17
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Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. CARBON 2023; 204:484-494. [PMID: 36845527 PMCID: PMC9957041 DOI: 10.1016/j.carbon.2022.12.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.
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Affiliation(s)
- Tong Wang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Daniel P. Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
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18
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Toropova AP, Toropov AA, Fjodorova N. In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES. Int J Mol Sci 2023; 24:ijms24032058. [PMID: 36768396 PMCID: PMC9917241 DOI: 10.3390/ijms24032058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R2 = 0.806 ± 0.061.
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Affiliation(s)
- Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy
- Correspondence: ; Tel.: +39-02-3901-4595
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy
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19
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Hu W, Wang C, Gao D, Liang Q. Toxicity of transition metal nanoparticles: A review of different experimental models in the gastrointestinal tract. J Appl Toxicol 2023; 43:32-46. [PMID: 35289422 DOI: 10.1002/jat.4320] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022]
Abstract
The development of nanotechnology is becoming a major trend nowadays. Nanoparticles (NPs) have been widely used in fields including food, biomedicine, and cosmetics, endowing NPs more opportunities to enter the human body. It is well-known that the gut microbiome plays a key role in human health, and the exposure of intestines to NPs is unavoidable. Accordingly, the toxicity of NPs has attracted more attention than before. This review mainly highlights recent advances in the evaluation of NPs' toxicity in the gastrointestinal system from the existing cell-based experimental models, such as the original mono-culture models, co-culture models, three-dimensional (3D) culture models, and the models established on microfluidic chips, to those in vivo experiments, such as mice models, Caenorhabditis elegans models, zebrafish models, human volunteers, as well as computer-simulated toxicity models. Owing to these models, especially those more biomimetic models, the outcome of the toxicity of NPs acting in the gastrointestinal tract can get results closer to what happened inside the real human microenvironment.
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Affiliation(s)
- Wanting Hu
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.,Center for Synthetic and Systems Biology, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China
| | - Chenlong Wang
- Center for Synthetic and Systems Biology, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China
| | - Dan Gao
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
| | - Qionglin Liang
- Center for Synthetic and Systems Biology, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China
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20
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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21
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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22
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Wyrzykowska E, Mikolajczyk A, Lynch I, Jeliazkova N, Kochev N, Sarimveis H, Doganis P, Karatzas P, Afantitis A, Melagraki G, Serra A, Greco D, Subbotina J, Lobaskin V, Bañares MA, Valsami-Jones E, Jagiello K, Puzyn T. Representing and describing nanomaterials in predictive nanoinformatics. NATURE NANOTECHNOLOGY 2022; 17:924-932. [PMID: 35982314 DOI: 10.1038/s41565-022-01173-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.
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Affiliation(s)
| | - Alicja Mikolajczyk
- QSAR Lab Ltd, Gdańsk, Poland
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | | | - Nikolay Kochev
- Ideaconsult Ltd, Sofia, Bulgaria
- Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | | | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | - Angela Serra
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Dario Greco
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Julia Subbotina
- School of Physics, University College Dublin, Belfield, Dublin, Ireland
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin, Ireland
| | - Miguel A Bañares
- Instituto de Catálisis y Petroleoquimica, ICP CSIC, Madrid, Spain
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Karolina Jagiello
- QSAR Lab Ltd, Gdańsk, Poland
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd, Gdańsk, Poland.
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.
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23
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Xu K, Bechu A, Basu N, Ghoshal S, Moores A, George S. Hazard Profiling of Commercially Relevant Quantum Dot Components Revealed Synergistic Interactions between Heavy Metals and Polymers. Chem Res Toxicol 2022; 35:1457-1466. [PMID: 35943131 DOI: 10.1021/acs.chemrestox.1c00382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Commercially used quantum dots (QDs) exemplify complex nanomaterials with multiple components, though little is known about the type of interactions between these components in determining the overall toxicity of this material. We synthesized and characterized a functional QD (CdSe/ZnS_P&E) that was identical in structure and composition to a patented and commercially applied QD and the combinations of its components (CdSe, CdSe/ZnS, ZnS, CdSe_P&E, ZnS_P&E, and P&E). Cells exposed to incremental concentrations of these materials were investigated for cell viability and cellular perturbations, contributing to a final common pathway of cell death using high-content screening assays in model human intestinal epithelial cells (HIEC-6). The concentrations that resulted in a loss of 20% cell viability (EC20 values) for each tested component were used for estimating the combination index (CI) to evaluate synergistic or antagonistic effects between the components. Complete QD (core/shell-polymer) showed the highest toxic potential due to synergistic interactions between core and surface functional groups. The cationic polymer coating enhanced cellular uptake of the QD, ensuing lysosome acidification and release of heavy metal ions to the intracellular milieu, and caused oxidative stress and cytotoxicity. Overall, this study advances our understanding of the collective contribution of individual components of a functional QD toward its toxic potential and emphasizes the need to study multilayered nanomaterials in their entirety for hazard characterization.
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Affiliation(s)
- Ke Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal H3A 0G4, Canada
| | - Aude Bechu
- Department of Chemistry, McGill University, Montreal H3A 0G4, Canada
| | - Niladri Basu
- Department of Natural Resources Sciences, McGill University, Montreal H3A 0G4, Canada
| | - Subhasis Ghoshal
- Department of Civil Engineering, McGill University, Montreal H3A 0G4, Canada
| | - Audrey Moores
- Department of Natural Resources Sciences, McGill University, Montreal H3A 0G4, Canada.,Centre for Green Chemistry and Catalysis, Department of Chemistry, McGill University, Montreal H3A 0G4, Canada.,Department of Mining and Materials Engineering, McGill University, Montreal H3A 0G4, Canada
| | - Saji George
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal H3A 0G4, Canada
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24
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Domingues C, Santos A, Alvarez-Lorenzo C, Concheiro A, Jarak I, Veiga F, Barbosa I, Dourado M, Figueiras A. Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology. ACS NANO 2022; 16:9994-10041. [PMID: 35729778 DOI: 10.1021/acsnano.2c00128] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Worldwide nanotechnology development and application have fueled many scientific advances, but technophilic expectations and technophobic demands must be counterbalanced in parallel. Some of the burning issues today are the following: (1) Where is nano today? (2) How good are the communication and investment networks between academia/research and governments? (3) Is there any spotlight application for nanotechnology? Nanomedicine is a particular arm of nanotechnology within the healthcare landscape, focused on diagnosis, treatment, and monitoring of emerging (such as coronavirus disease 2019, COVID-19) and contemporary (including diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer) diseases. However, it may only represent the bright side of the coin. In fact, in the recent past, the concept of nanotoxicology has emerged to address the dark shadows of nanomedicine. The nanomedicine field requires more nanotoxicological studies to identify undesirable effects and guarantee safety. Here, we provide an overall perspective on nanomedicine and nanotoxicology as central pieces of the giant puzzle of nanotechnology. First, the impact of nanotechnology on education and research is highlighted, followed by market trends and scientific output tendencies. In the next section, the nanomedicine and nanotoxicology dilemma is addressed through the interplay of in silico, in vitro, and in vivo models with the support of omics and microfluidic approaches. Lastly, a reflection on the regulatory issues and clinical trials is provided. Finally, some conclusions and future perspectives are proposed for a clearer and safer translation of nanomedicines from the bench to the bedside.
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Affiliation(s)
- Cátia Domingues
- Univ. Coimbra, Faculty of Pharmacy, Galenic and Pharmaceutical Technology Laboratory, 3000-548 Coimbra, Portugal
- LAQV-REQUIMTE, Galenic and Pharmaceutical Technology Laboratory, Faculty of Pharmacy, Univ. Coimbra, 3000-548 Coimbra, Portugal
- Univ. Coimbra, Institute for Clinical and Biomedical Research (iCBR) Area of Environment Genetics and Oncobiology (CIMAGO), Faculty of Medicine, 3000-548 Coimbra, Portugal
| | - Ana Santos
- Univ. Coimbra, Faculty of Pharmacy, Galenic and Pharmaceutical Technology Laboratory, 3000-548 Coimbra, Portugal
| | - Carmen Alvarez-Lorenzo
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, iMATUS, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Angel Concheiro
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, iMATUS, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Ivana Jarak
- Univ. Coimbra, Faculty of Pharmacy, Galenic and Pharmaceutical Technology Laboratory, 3000-548 Coimbra, Portugal
| | - Francisco Veiga
- Univ. Coimbra, Faculty of Pharmacy, Galenic and Pharmaceutical Technology Laboratory, 3000-548 Coimbra, Portugal
- LAQV-REQUIMTE, Galenic and Pharmaceutical Technology Laboratory, Faculty of Pharmacy, Univ. Coimbra, 3000-548 Coimbra, Portugal
| | - Isabel Barbosa
- Univ. Coimbra, Faculty of Pharmacy, Phamaceutical Chemistry Laboratory, 3000-548 Coimbra, Portugal
| | - Marília Dourado
- Univ. Coimbra, Institute for Clinical and Biomedical Research (iCBR) Area of Environment Genetics and Oncobiology (CIMAGO), Faculty of Medicine, 3000-548 Coimbra, Portugal
- Univ. Coimbra, Center for Health Studies and Research of the University of Coimbra (CEISUC), Faculty of Medicine, 3000-548 Coimbra, Portugal
- Univ. Coimbra, Center for Studies and Development of Continuous and Palliative Care (CEDCCP), Faculty of Medicine, 3000-548 Coimbra, Portugal
| | - Ana Figueiras
- Univ. Coimbra, Faculty of Pharmacy, Galenic and Pharmaceutical Technology Laboratory, 3000-548 Coimbra, Portugal
- LAQV-REQUIMTE, Galenic and Pharmaceutical Technology Laboratory, Faculty of Pharmacy, Univ. Coimbra, 3000-548 Coimbra, Portugal
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25
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Yang H, Li X, Zheng X, Zhi H, Tang G, Ke Y, Liu B, Ma H. Comparing the toxicity of iodinated X-ray contrast media on eukaryote- and prokaryote-based quantified microarray assays. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 240:113678. [PMID: 35653977 DOI: 10.1016/j.ecoenv.2022.113678] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
This study compared the toxicity mechanisms of four X-ray-based iodinated contrast media (ICM) on Escherichia coli (E. coli) and yeast microarray assays, aiming to determine the diverse toxicity mechanisms among different exposed organisms and the relationship between toxicity and their physical and chemical characteristics. The conventional phenotypic endpoint cytotoxicity and the change in reactive oxygen species (ROS) level were employed in conjunction with toxicogenomics to quantify changes in the gene/protein biomarker level in the regulation of different damage/repair pathways. The results showed that molecular toxicity endpoints, named transcriptional effect level index (TELI) and protein effect level index (PELI) for E. coli and yeast, respectively, correlated well with the phenotypic endpoints. Temporal altered gene/protein expression profiles revealed dynamic and complex damage/toxic mechanisms. In particular, compared with E. coli cells, yeast cells exposed to ICM exhibited significantly higher stress intensity and diverse stress types, resulting in stress or damage to the organism. The toxic mechanisms of ICM are concentration/property-dependent and relevant to the cellular structure and defense systems in prokaryotes and eukaryotes. In particular, the toxicity of ionic ICM is higher than that of non-ionic ICM, and eukaryotes are more susceptible than prokaryotes to ICM exposure.
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Affiliation(s)
- Heyun Yang
- State Key Laboratory of Eco-hydraulics in North west Arid Region, Xi'an University of Technology, Xi'an 710048, China
| | - Xiaoliang Li
- State Key Laboratory of Eco-hydraulics in North west Arid Region, Xi'an University of Technology, Xi'an 710048, China
| | - Xing Zheng
- State Key Laboratory of Eco-hydraulics in North west Arid Region, Xi'an University of Technology, Xi'an 710048, China.
| | - Hegang Zhi
- College of Agricultural and Environmental Sciences, University of California, Davis 95616, United States
| | - Gang Tang
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Yanchun Ke
- Aerospace Kaitian environmental technology co. ltd., Changsha 410100, China
| | - Bao Liu
- State Key Laboratory of Eco-hydraulics in North west Arid Region, Xi'an University of Technology, Xi'an 710048, China
| | - Hao Ma
- State Key Laboratory of Eco-hydraulics in North west Arid Region, Xi'an University of Technology, Xi'an 710048, China
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26
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Zushi Y. Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS. Anal Chem 2022; 94:9149-9157. [PMID: 35700270 PMCID: PMC9246259 DOI: 10.1021/acs.analchem.2c01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
With advances in
machine learning (ML) techniques, the quantitative
structure–activity relationship (QSAR) approach is becoming
popular for evaluating chemicals. However, the QSAR approach requires
that the chemical structure of the target compound is known and that
it should be convertible to molecular descriptors. These requirements
lead to limitations in predicting the properties and toxicities of
chemicals distributed in the environment as in the PubChem database;
the structural information on only 14% of compounds is available.
This study proposes a new ML-based QSAR approach that can predict
the properties and toxicities of compounds using analytical descriptors
of mass spectrum and retention index obtained via gas chromatography–mass
spectrometry without requiring exact structural information. The model
was developed based on the XGBoost ML method. The root-mean-square
errors (RMSEs) for log Ko-w, log (molecular weight), melting point,
boiling point, log (vapor pressure), log (water solubility), log (LD50) (rat, oral), and log (LD50) (mouse, oral) are
0.97, 0.052, 51, 23, 0.74, 1.1, 0.74, and 0.6, respectively. The model
performed well on a chemical standard mixture measurement, with similar
results to those of model validation. It also performed well on a
measurement of contaminated oil with spectral deconvolution. These
results indicate that the model is suitable for investigating unknown-structured
chemicals detected in measurements. Any online user can execute the
model through a web application named Detective-QSAR (http://www.mixture-platform.net/Detective_QSAR_Med_Open/). The analytical descriptor-based approach is expected to create
new opportunities for the evaluation of unknown chemicals around us.
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Affiliation(s)
- Yasuyuki Zushi
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.,Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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27
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Stoliński F, Rybińska-Fryca A, Gromelski M, Mikolajczyk A, Puzyn T. NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO 2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells. Nanotoxicology 2022; 16:276-289. [PMID: 35713578 DOI: 10.1080/17435390.2022.2080609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO2-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO2-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.
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Affiliation(s)
- Filip Stoliński
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | | | - Alicja Mikolajczyk
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
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28
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Barros de Menezes RP, Fechine Tavares J, Kato MJ, da Rocha Coelho FA, Sousa Dos Santos AL, da Franca Rodrigues KA, Sessions ZL, Muratov EN, Scotti L, Tullius Scotti M. Natural Products from Annonaceae as Potential Antichagasic Agents. ChemMedChem 2022; 17:e202200196. [PMID: 35678042 DOI: 10.1002/cmdc.202200196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/06/2022] [Indexed: 11/12/2022]
Abstract
Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.
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Affiliation(s)
- Renata Priscila Barros de Menezes
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Josean Fechine Tavares
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Massuo Jorge Kato
- Instituto de Química, Universidade de São Paulo, 05508-000, São Paulo, SP, Brazil
| | | | | | | | - Zoe L Sessions
- Molecular Modeling Lab, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, 27599, NC, USA
| | - Eugene N Muratov
- Molecular Modeling Lab, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, 27599, NC, USA
| | - Luciana Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
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29
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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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30
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Chew AK, Pedersen JA, Van Lehn RC. Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors. ACS NANO 2022; 16:6282-6292. [PMID: 35289596 DOI: 10.1021/acsnano.2c00301] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gold nanoparticles are versatile materials for biological applications because their properties can be modulated by assembling ligands on their surface to form monolayers. However, the physicochemical properties and behaviors of monolayer-protected nanoparticles in biological environments are difficult to anticipate because they emerge from the interplay of ligand-ligand and ligand-solvent interactions that cannot be readily inferred from ligand chemical structure alone. In this work, we demonstrate that quantitative nanostructure-activity relationship (QNAR) models can employ descriptors calculated from molecular dynamics simulations to predict nanoparticle properties and cellular uptake. We performed atomistic molecular dynamics simulations of 154 monolayer-protected gold nanoparticles and calculated a small library of simulation-derived descriptors that capture nanoparticle structural and chemical properties in aqueous solution. We then parametrized QNAR models using interpretable regression algorithms to predict experimental measurements of nanoparticle octanol-water partition coefficients, zeta potentials, and cellular uptake obtained from a curated database. These models reveal that simulation-derived descriptors can accurately predict experimental trends and provide physical insight into what descriptors are most important for obtaining desired nanoparticle properties or behaviors in biological environments. Finally, we demonstrate model generalizability by predicting cell uptake trends for 12 nanoparticles not included in the original data set. These results demonstrate that QNAR models parametrized with simulation-derived descriptors are accurate, generalizable computational tools that could be used to guide the design of monolayer-protected gold nanoparticles for biological applications without laborious trial-and-error experimentation.
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Affiliation(s)
- Alex K Chew
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Joel A Pedersen
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Reid C Van Lehn
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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31
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Forest V. Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment. NANOMATERIALS 2022; 12:nano12081346. [PMID: 35458054 PMCID: PMC9031966 DOI: 10.3390/nano12081346] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022]
Abstract
The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, Etablissement Français du Sang, INSERM, U1059 Sainbiose, Centre CIS, F-42023 Saint-Etienne, France
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Computational Modeling of Mixture Toxicity. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2425:561-587. [PMID: 35188647 DOI: 10.1007/978-1-0716-1960-5_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Environmental pollution has become an inevitable problem and a relevant global issue of the twenty-first century. The fast industrial growth has caused the production and release of various chemical species and multicomponent mixtures to the environment which affect the entire living world adversely. Various industrial regulatory agencies are working in this domain to regulate the production of chemical entities, proper release of chemical wastes, and the risk assessment of the industrial and hazardous chemicals; however, they mostly rely upon the single chemical risk assessment instead of considering the toxicity of multicomponent mixtures. In this era of chemical advances, single chemical exposure is a myth. The entire living world is always being exposed to the environmental chemical mixtures but the scarcity of toxicity data of chemical mixtures is a serious concern. The nature of toxicity of mixtures is entirely different and complex from the individual chemicals because of the interactions (synergism/antagonism) among the mixture components. Various regulatory authorities and the scientific world have come up with a handful of methodologies and guidelines for evaluating the harmful effects of the multicomponent mixtures, though there is no such significant, standard, and reliable approach for the toxicity evaluation of chemical mixtures and their management across diverse fields. Toxicity experimentations on laboratory animals are troublesome, time-consuming, costly, and unethical. Thus, to reduce the animal experimentations, the scientific communities, regulatory agencies, and the industries are now depending upon the already proven computational alternatives. The computational approaches are capable of predicting toxicities, prioritizing chemicals, and their risk assessment. Besides these, the in silico methods are cost-effective, less time-consuming, and easy to understand. It has been found out that most of the in silico toxicity predictions are on single chemicals and till date there are very few computational studies available for chemical mixtures in the scientific literature. Therefore, the current chapter illustrates the importance of determination of toxicity of mixtures, the conventional methods for toxicity evaluation of chemical mixtures, and the role of in silico methods to assess the toxicity, followed by the types of various computational methods used for such purpose. Additionally, few successful applications of computational tools in toxicity prediction of mixtures have been discussed in detail. At the end of this chapter, we have discussed some future perspectives toward the role and applications of in silico techniques for toxicity prediction of mixtures.
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Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27041366. [PMID: 35209156 PMCID: PMC8878085 DOI: 10.3390/molecules27041366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 12/04/2022]
Abstract
Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.
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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.
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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
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de Menezes RPB, Viana JDO, Muratov E, Scotti L, Scotti MT. Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity. Curr Issues Mol Biol 2022; 44:383-408. [PMID: 35723407 PMCID: PMC8929062 DOI: 10.3390/cimb44010028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/08/2022] [Accepted: 01/09/2022] [Indexed: 11/28/2022] Open
Abstract
Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.
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Affiliation(s)
- Renata Priscila Barros de Menezes
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Jéssika de Oliveira Viana
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA;
| | - Luciana Scotti
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
- Correspondence: ; Tel.: +55-83-998690415
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Ouassil N, Pinals RL, Del Bonis-O’Donnell JT, Wang JW, Landry MP. Supervised learning model predicts protein adsorption to carbon nanotubes. SCIENCE ADVANCES 2022; 8:eabm0898. [PMID: 34995109 PMCID: PMC8741178 DOI: 10.1126/sciadv.abm0898] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.
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Affiliation(s)
- Nicholas Ouassil
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Rebecca L. Pinals
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Jeffrey W. Wang
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Markita P. Landry
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Innovative Genomics Institute (IGI), Berkeley, CA 94720, USA
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, CA 94720, USA
- Corresponding author.
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37
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Ouassil N, Pinals RL, Del Bonis-O'Donnell JT, Wang JW, Landry MP. Supervised learning model predicts protein adsorption to carbon nanotubes. SCIENCE ADVANCES 2022. [PMID: 34995109 DOI: 10.5281/zenodo.5640140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.
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Affiliation(s)
- Nicholas Ouassil
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Rebecca L Pinals
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Jeffrey W Wang
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Markita P Landry
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Innovative Genomics Institute (IGI), Berkeley, CA 94720, USA
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, CA 94720, USA
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38
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Davids J, Ashrafian H. AIM in Nanomedicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Lu B, Jan Hendriks A, Nolte TM. A generic model based on the properties of nanoparticles and cells for predicting cellular uptake. Colloids Surf B Biointerfaces 2022; 209:112155. [PMID: 34678608 DOI: 10.1016/j.colsurfb.2021.112155] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 10/20/2022]
Abstract
Nanoparticles (NPs) are widely used in industry and technology due to their small size and versatility, which makes them easy to enter organisms and pose threats to human and ecological health. Given the particularity and complex structure of NPs, statistical models alone cannot reliably predict uptake. Hence, we developed a generic model for predicting the cellular uptake of NPs with organic coatings, based on physicochemical interactions underlying uptake. The model utilized the concentration, experimental conditions and properties of NPs viz. size, surface coating and coverage. These parameters were converted to surface energy components and surface potentials, and combined with the components and potential for a cell membrane. For NPs uptake, we constructed energetic profiles and barriers for adsorption and permeation onto/through cell membranes. The relationships derived were compared to experimental uptake data. The model provided accurate and robust uptake estimates for neutrally charged unhalogenated NPs and six different cell types. We envision that the model provides a reference for cellular accumulation of neutral NPs and (ecological/human) risk assessment of NPs or microparticles.
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Affiliation(s)
- Bingqing Lu
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, 6500 GL Nijmegen, The Netherlands.
| | - A Jan Hendriks
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, 6500 GL Nijmegen, The Netherlands
| | - Tom M Nolte
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, 6500 GL Nijmegen, The Netherlands
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Varsou DD, Ellis LJA, Afantitis A, Melagraki G, Lynch I. Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna. CHEMOSPHERE 2021; 285:131452. [PMID: 34265725 DOI: 10.1016/j.chemosphere.2021.131452] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO2 NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO2 NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.
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Affiliation(s)
| | - Laura-Jayne A Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK
| | | | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece.
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
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41
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Gupta R, Chen Y, Xie H. In vitro dissolution considerations associated with nano drug delivery systems. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2021; 13:e1732. [PMID: 34132050 PMCID: PMC8526385 DOI: 10.1002/wnan.1732] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 12/17/2022]
Abstract
Nano drug delivery systems (NDDS) offer promising solution for the translation of future nanomedicines. As bioavailability and therapeutic outcomes can be improved by altering the drug release from these NDDS, it becomes essential to thoroughly understand their drug release kinetics. Moreover, U.S. Food and Drug Administration requires critical evaluation of potential safety, efficacy, and public health impacts of nanomaterials. Spiraling up market share of NDDS has also stimulated the pharmaceutical industry to develop their cost-effective generic versions after the expiry of patent and associated exclusivity. However, unlike the conventional dosage forms, the in vivo disposition of NDDS is highly intricate and different from their in vitro behavior. Significant challenges exist in the establishment of in vitro-in vivo correlation (IVIVC) due to incomplete understanding of nanoparticles' in vivo biofate and its impact on in vitro experimental protocols. A rational design of dissolution may serve as quality and quantity control tool and help develop a meaningful IVIVC for favorable economic implications. Clinically relevant drug product specifications (critical quality attributes) can be identified by establishing a link between in vitro performance and in vivo exposure. In vitro dissolution may also play a pivotal role to understand the dissolution-mediated clearance and safety of NDDS. Prevalent in vitro dissolution methods for NDDS and their limitations are discussed in this review, among which USP 4 is gaining more interest recently. Researchers are working diligently to develop biorelevant in vitro release assays to ensure optimal therapeutic performance of generic versions of these NDDS. This article focuses on these studies and presents important considerations for the future development of clinically relevant in vitro release methods. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.
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Affiliation(s)
- Ritu Gupta
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
| | - Yuan Chen
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
| | - Huan Xie
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
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42
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Herrera-Acevedo C, Dos Santos Maia M, Cavalcanti ÉBVS, Coy-Barrera E, Scotti L, Scotti MT. Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach. Mol Divers 2021; 25:2411-2427. [PMID: 32909084 DOI: 10.1007/s11030-020-10139-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 08/30/2020] [Indexed: 12/20/2022]
Abstract
Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.
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Affiliation(s)
- Chonny Herrera-Acevedo
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, PB, 58051-900, Brazil
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá, 250247, Colombia
| | - Mayara Dos Santos Maia
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, PB, 58051-900, Brazil
| | | | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá, 250247, Colombia
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, PB, 58051-900, Brazil
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, PB, 58051-900, Brazil.
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Arya SS, Lenka SK, Cahill DM, Rookes JE. Designer nanoparticles for plant cell culture systems: Mechanisms of elicitation and harnessing of specialized metabolites. Bioessays 2021; 43:e2100081. [PMID: 34608646 DOI: 10.1002/bies.202100081] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 09/08/2021] [Accepted: 09/08/2021] [Indexed: 11/07/2022]
Abstract
Plant cell culture systems have become an attractive and sustainable approach to produce high-value and commercially significant metabolites under controlled conditions. Strategies involving elicitor supplementation into plant cell culture media are employed to mimic natural conditions for increasing the metabolite yield. Studies on nanoparticles (NPs) that have investigated elicitation of specialized metabolism have shown the potential of NPs to be a substitute for biotic elicitors such as phytohormones and microbial extracts. Customizable physicochemical characteristics allow the design of monodispersed-, stimulus-responsive-, and hormone-carrying-NPs of precise geometries to enhance their elicitation capabilities based on target metabolite/plant cell culture type. We contextualize advances in NP-mediated elicitation, especially stimulation of specialized metabolic pathways, the underlying mechanisms, impacts on gene regulation, and NP-associated cytotoxicity. The novelty of the concept lies in unleashing the potential of designer NPs to enhance yield, harness metabolites, and transform nanoelicitation from exploratory investigations to a commercially viable strategy.
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Affiliation(s)
- Sagar S Arya
- School of Life and Environmental Sciences, Deakin University, Geelong Campus at Waurn Ponds, Geelong, Victoria, Australia.,TERI-Deakin Nanobiotechnology Centre, The Energy and Resources Institute, Gurugram, Haryana, India
| | - Sangram K Lenka
- TERI-Deakin Nanobiotechnology Centre, The Energy and Resources Institute, Gurugram, Haryana, India
| | - David M Cahill
- School of Life and Environmental Sciences, Deakin University, Geelong Campus at Waurn Ponds, Geelong, Victoria, Australia
| | - James E Rookes
- School of Life and Environmental Sciences, Deakin University, Geelong Campus at Waurn Ponds, Geelong, Victoria, Australia
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Nademi Y, Tang T, Uludağ H. Modeling Uptake of Polyethylenimine/Short Interfering RNA Nanoparticles in Breast Cancer Cells Using Machine Learning. ADVANCED NANOBIOMED RESEARCH 2021. [DOI: 10.1002/anbr.202000106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Affiliation(s)
- Yousef Nademi
- Department of Chemical and Materials Engineering Donadeo Innovation Centre for Engineering University of Alberta Edmonton AB T6G 1H9 Canada
| | - Tian Tang
- Department of Mechanical Engineering Donadeo Innovation Centre for Engineering University of Alberta Edmonton AB T6G 1H9 Canada
| | - Hasan Uludağ
- Department of Chemical and Materials Engineering Donadeo Innovation Centre for Engineering University of Alberta Edmonton AB T6G 1H9 Canada
- Department of Biomedical Engineering Donadeo Innovation Centre for Engineering University of Alberta Edmonton AB T6G 1H9 Canada
- Faculty of Pharmacy and Pharmaceutical Sciences Donadeo Innovation Centre for Engineering University of Alberta Edmonton AB T6G 1H9 Canada
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45
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Gomes SIL, Amorim MJB, Pokhrel S, Mädler L, Fasano M, Chiavazzo E, Asinari P, Jänes J, Tämm K, Burk J, Scott-Fordsmand JJ. Machine learning and materials modelling interpretation of in vivo toxicological response to TiO 2 nanoparticles library (UV and non-UV exposure). NANOSCALE 2021; 13:14666-14678. [PMID: 34533558 DOI: 10.1039/d1nr03231c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.
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Affiliation(s)
- Susana I L Gomes
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Suman Pokhrel
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Lutz Mädler
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Matteo Fasano
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Eliodoro Chiavazzo
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Pietro Asinari
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- INRIM, Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino 10135, Italy
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Kaido Tämm
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Jaanus Burk
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Janeck J Scott-Fordsmand
- Department of Bioscience, Aarhus University, Vejlsovej 25, PO BOX 314, DK-8600 Silkeborg, Denmark
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46
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Ji Z, Guo W, Sakkiah S, Liu J, Patterson TA, Hong H. Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials. NANOMATERIALS 2021; 11:nano11061599. [PMID: 34207026 PMCID: PMC8234318 DOI: 10.3390/nano11061599] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 12/19/2022]
Abstract
Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.
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Varsou DD, Koutroumpa NM, Sarimveis H. Automated Grouping of Nanomaterials and Read-Across Prediction of Their Adverse Effects Based on Mathematical Optimization. J Chem Inf Model 2021; 61:2766-2779. [PMID: 34029462 DOI: 10.1021/acs.jcim.1c00199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In this study, a computational workflow is presented for grouping engineered nanomaterials (ENMs) and for predicting their toxicity-related end points. A mixed integer-linear optimization program (MILP) problem is formulated, which automatically filters out the noisy variables, defines the grouping boundaries, and develops specific to each group predictive models. The method is extended to the multidimensional space, by considering the ENM characterization categories (e.g., biological, physicochemical, biokinetics, image etc.) as different dimensions. The performance of the proposed method is illustrated through the application to benchmark data sets and comparison with alternative predictive modeling approaches. The trained models using the above data sets were made publicly available through a user-friendly web service.
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Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, Athens, 157 80, Greece
| | | | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Athens, 157 80, Greece
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48
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Herrera-Acevedo C, Flores-Gaspar A, Scotti L, Mendonça-Junior FJB, Scotti MT, Coy-Barrera E. Identification of Kaurane-Type Diterpenes as Inhibitors of Leishmania Pteridine Reductase I. Molecules 2021; 26:molecules26113076. [PMID: 34063939 PMCID: PMC8196580 DOI: 10.3390/molecules26113076] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/18/2021] [Accepted: 05/18/2021] [Indexed: 12/16/2022] Open
Abstract
The current treatments against Leishmania parasites present high toxicity and multiple side effects, which makes the control and elimination of leishmaniasis challenging. Natural products constitute an interesting and diverse chemical space for the identification of new antileishmanial drugs. To identify new drug options, an in-house database of 360 kauranes (tetracyclic diterpenes) was generated, and a combined ligand- and structure-based virtual screening (VS) approach was performed to select potential inhibitors of Leishmania major (Lm) pteridine reductase I (PTR1). The best-ranked kauranes were employed to verify the validity of the VS approach through LmPTR1 enzyme inhibition assay. The half-maximal inhibitory concentration (IC50) values of selected bioactive compounds were examined using the random forest (RF) model (i.e., 2β-hydroxy-menth-6-en-5β-yl ent-kaurenoate (135) and 3α-cinnamoyloxy-ent-kaur-16-en-19-oic acid (302)) were below 10 μM. A compound similar to 302, 3α-p-coumaroyloxy-ent-kaur-16-en-19-oic acid (302a), was also synthesized and showed the highest activity against LmPTR1. Finally, molecular docking calculations and molecular dynamics simulations were performed for the VS-selected, most-active kauranes within the active sites of PTR1 hybrid models, generated from three Leishmania species that are known to cause cutaneous leishmaniasis in the new world (i.e., L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes to other species-dependent variants of this enzyme.
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Affiliation(s)
- Chonny Herrera-Acevedo
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (C.H.-A.); (L.S.)
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá 250247, Colombia;
| | - Areli Flores-Gaspar
- Departamento de Química, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá 250247, Colombia
- Correspondence: (A.F.-G.); (M.T.S.); Tel.: +57-1-650-00-00 (ext. 1526) (A.F.-G.); +55-83-99869-0415 (M.T.S.)
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (C.H.-A.); (L.S.)
| | | | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil; (C.H.-A.); (L.S.)
- Correspondence: (A.F.-G.); (M.T.S.); Tel.: +57-1-650-00-00 (ext. 1526) (A.F.-G.); +55-83-99869-0415 (M.T.S.)
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá 250247, Colombia;
- Departamento de Química, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá 250247, Colombia
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Soltani M, Moradi Kashkooli F, Souri M, Zare Harofte S, Harati T, Khadem A, Haeri Pour M, Raahemifar K. Enhancing Clinical Translation of Cancer Using Nanoinformatics. Cancers (Basel) 2021; 13:2481. [PMID: 34069606 PMCID: PMC8161319 DOI: 10.3390/cancers13102481] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/08/2021] [Accepted: 05/16/2021] [Indexed: 12/14/2022] Open
Abstract
Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.
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Affiliation(s)
- Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi Univesity of Technology, Tehran 14176-14411, Iran
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Farshad Moradi Kashkooli
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Souri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Tina Harati
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Atefeh Khadem
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Haeri Pour
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Kaamran Raahemifar
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), State College, Penn State University, Pennsylvania, PA 16801, USA
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
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50
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Liu G, Yan X, Wang S, Yu Q, Jia J, Yan B. Elucidation of the Critical Role of Core Materials in PM 2.5-Induced Cytotoxicity by Interrogating Silica- and Carbon-Based Model PM 2.5 Particle Libraries. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:6128-6139. [PMID: 33825456 DOI: 10.1021/acs.est.1c00001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
An insoluble core with adsorbed pollutants constitutes the most toxic part of PM2.5 particles. However, the toxicological difference between carbon and silica cores remains unknown. Here, we employed 32-membered carbon- and silica-based model PM2.5 libraries that each was loaded with four toxic airborne pollutants including Cr(VI), As(III), Pb2+, and BaP in all possible combinations to explore their contributions to cytotoxicity in normal human bronchial cells. The following three crucial findings were revealed: (1) more adsorption of polar pollutants in a silica core (such as Cr(VI), As(III), and Pb2+) and nonpolar ones in a carbon core (such as BaP); (2) about 41% more cell uptake of carbon- than silica-based particles; and (3) about 59% less toxicity in silica- than carbon-based particles when pollutants other than Cr(VI) were loaded. This was reversed after Cr(VI) loading (silica particles were 56% more toxic). The difference maker is that compared to stable silica, carbon particles reduce Cr(VI) to less toxic Cr(III). Our findings highlight the different roles of carbon and silica cores in inducing health risks of PM2.5 particles.
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Affiliation(s)
- Guohong Liu
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
| | - Xiliang Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Shenqing Wang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
| | - Qianhui Yu
- School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China
| | - Jianbo Jia
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Bing Yan
- Institute of Environmental Research at Greater Bay Area, 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, Qingdao 266237, China
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