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He S, Nader K, Abarrategi JS, Bediaga H, Nocedo-Mena D, Ascencio E, Casanola-Martin GM, Castellanos-Rubio I, Insausti M, Rasulev B, Arrasate S, González-Díaz H. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J Nanobiotechnology 2024; 22:435. [PMID: 39044265 PMCID: PMC11267683 DOI: 10.1186/s12951-024-02660-9] [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: 04/18/2024] [Accepted: 06/24/2024] [Indexed: 07/25/2024] Open
Abstract
Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.
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Affiliation(s)
- Shan He
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Karam Nader
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Deyani Nocedo-Mena
- Faculty of Physical Mathematical Sciences, Autonomous University of Nuevo León, San Nicolás de los Garza, 66455, Nuevo León, México
| | - Estefania Ascencio
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Idoia Castellanos-Rubio
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Maite Insausti
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Leioa, 48940, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, 48940, Bizkaia, Basque Country, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011, Biscay, Spain
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Maity T, Balachandran AK, Krishnamurthy LP, Nagar KL, Upadhyayula RS, Sengupta S, Maiti PK. Data-Driven Approaches to Predict Dendrimer Cytotoxicity. ACS OMEGA 2024; 9:24899-24906. [PMID: 38882163 PMCID: PMC11173563 DOI: 10.1021/acsomega.4c01775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 06/18/2024]
Abstract
Dendrimers are employed as functional elements in contrast agents and are proposed as nontoxic vehicles for drug delivery. Toxicity is a property that is to be evaluated for this novel class of bionanomaterials for in vivo applications. The current research is hampered due to the lack of structured data sets for toxicity studies for dendrimers. In this work, we have built a data set by curating literature for toxicity data and augmented it with structural and physicochemical features. We present a comprehensive, feature-rich database of dendrimer toxicity measured across various cell lines for prediction, design, and optimization studies. We have also explored novel computational approaches for predicting dendrimer cytotoxicity. We demonstrate superior outcomes for toxicity prediction using essential regression in the space of small data sets.
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Affiliation(s)
- Tarun Maity
- Centre for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Anandu K Balachandran
- Accenture Labs, Technology & Innovation, Ecospace, Bellandur, Bengaluru 560087, India
| | | | - Karthik L Nagar
- Accenture Labs, Technology & Innovation, Ecospace, Bellandur, Bengaluru 560087, India
| | | | - Shubhashis Sengupta
- Accenture Labs, Technology & Innovation, Ecospace, Bellandur, Bengaluru 560087, India
| | - Prabal K Maiti
- Centre for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bengaluru 560012, India
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Toropova AP, Toropov AA. The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172119. [PMID: 38569951 DOI: 10.1016/j.scitotenv.2024.172119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/05/2024]
Abstract
Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.
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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.
| | - 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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He S, Segura Abarrategi J, Bediaga H, Arrasate S, González-Díaz H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:535-555. [PMID: 38774585 PMCID: PMC11106676 DOI: 10.3762/bjnano.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/23/2024] [Indexed: 05/24/2024]
Abstract
Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.
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Affiliation(s)
- Shan He
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
- Painting Department, Fine Arts Faculty, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Instituto Biofisika (UPV/EHU-CSIC), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Gakis GP, Aviziotis IG, Charitidis CA. A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials. NANOSCALE 2023; 15:16432-16446. [PMID: 37791566 DOI: 10.1039/d3nr03174h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.
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Affiliation(s)
- G P Gakis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
| | - I G Aviziotis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
| | - C A Charitidis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
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Ningthoujam SS, Nath R, Kityania S, Mazumder PB, Dutta Choudhury M, Talukdar AD, Nahar L, Sarker SD. R software for QSAR analysis in phytopharmacological studies. PHYTOCHEMICAL ANALYSIS : PCA 2023; 34:709-728. [PMID: 37392081 DOI: 10.1002/pca.3239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable. OBJECTIVE The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity. RESULTS The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model. CONCLUSION QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.
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Affiliation(s)
| | - Rajat Nath
- Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India
| | - Sibashish Kityania
- Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India
| | | | | | - Anupam Das Talukdar
- Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India
| | - Lutfun Nahar
- Laboratory of Growth Regulators, Institute of Experimental Botany, The Czech Academy of Sciences and Palacký University, Olomouc, Czech Republic
| | - Satyajit D Sarker
- Centre for Natural Products Discovery (CNPD), School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
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Furxhi I, Kalapus M, Costa A, Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections. Nanotoxicology 2023; 17:529-544. [PMID: 37885250 DOI: 10.1080/17435390.2023.2268163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023]
Abstract
Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.
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Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Limerick, Ireland
| | - Michal Kalapus
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Anna Costa
- CNR-ISSMC Istituto di Scienza, Tecnologia e Sostenibilità per lo Sviluppo dei Materiali Ceramici, Faenza, Italy
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- QSAR Lab Ltd, Gdansk, Poland
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Furxhi I, Willighagen E, Evelo C, Costa A, Gardini D, Ammar A. A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability. NANOIMPACT 2023; 31:100475. [PMID: 37423508 DOI: 10.1016/j.impact.2023.100475] [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: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. METHODOLOGY To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. RESULTS Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. CONCLUSIONS This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Ireland; Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Egon Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Chris Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Anna Costa
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Davide Gardini
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
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Shirokii N, Din Y, Petrov I, Seregin Y, Sirotenko S, Razlivina J, Serov N, Vinogradov V. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207106. [PMID: 36772908 DOI: 10.1002/smll.202207106] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/09/2023] [Indexed: 05/11/2023]
Abstract
Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.
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Affiliation(s)
- Nikolai Shirokii
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yevgeniya Din
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Ilya Petrov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yurii Seregin
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Sofia Sirotenko
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Julia Razlivina
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Nikita Serov
- Advanced Engineering School, Almetyevsk State Oil Institute, Almetyevsk, Russia
| | - Vladimir Vinogradov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
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Banaye Yazdipour A, Masoorian H, Ahmadi M, Mohammadzadeh N, Ayyoubzadeh SM. Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review. Nanotoxicology 2023; 17:62-77. [PMID: 36883698 DOI: 10.1080/17435390.2023.2186279] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.
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Affiliation(s)
- Alireza Banaye Yazdipour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.,Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hoorie Masoorian
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahnaz Ahmadi
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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12
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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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13
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Computational Indicator Approach for Assessment of Nanotoxicity of Two-Dimensional Nanomaterials. NANOMATERIALS 2022; 12:nano12040650. [PMID: 35214977 PMCID: PMC8879952 DOI: 10.3390/nano12040650] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/30/2022] [Accepted: 02/11/2022] [Indexed: 12/17/2022]
Abstract
The increasing growth in the development of various novel nanomaterials and their biomedical applications has drawn increasing attention to their biological safety and potential health impact. The most commonly used methods for nanomaterial toxicity assessment are based on laboratory experiments. In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. An affordable, cost-effective numerical modeling approach thus can reduce the need for in vitro and in vivo testing and predict the properties of designed or developed nanomaterials. We propose here a new in silico method for rapid cytotoxicity assessment of two-dimensional nanomaterials of arbitrary chemical composition by using free energy analysis and molecular dynamics simulations, which can be expressed by a computational indicator of nanotoxicity (CIN2D). We applied this approach to five well-known two-dimensional nanomaterials promising for biomedical applications: graphene, graphene oxide, layered double hydroxide, aloohene, and hexagonal boron nitride nanosheets. The results corroborate the available laboratory biosafety data for these nanomaterials, supporting the applicability of the developed method for predictive nanotoxicity assessment of two-dimensional nanomaterials.
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14
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Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
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Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
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15
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Furxhi I. Health and environmental safety of nanomaterials: O Data, Where Art Thou? NANOIMPACT 2022; 25:100378. [PMID: 35559884 DOI: 10.1016/j.impact.2021.100378] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 06/15/2023]
Abstract
Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Ireland; Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
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16
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Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. NANOSCALE 2021; 13:17854-17870. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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17
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Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int J Mol Sci 2021; 22:ijms222111519. [PMID: 34768951 PMCID: PMC8584266 DOI: 10.3390/ijms222111519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 10/22/2021] [Indexed: 12/22/2022] Open
Abstract
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.
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18
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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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19
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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20
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Toropova AP, Toropov AA, Leszczynski J, Sizochenko N. Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2021; 86:103665. [PMID: 33895354 DOI: 10.1016/j.etap.2021.103665] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al2O3, CuO, Fe2O3, Fe3O4, SiO2, TiO2, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics, and Atmospheric Sciences, Jackson State University, Jackson, MS, USA
| | - Natalia Sizochenko
- Department of Informatics, Postdoctoral Institute for Computational Studies, Enfield, NH, USA; The Ronin Institute of Independent Scholarship, Montclair, NJ, USA.
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21
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Zhang H, Barnard AS. Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles. NANOSCALE 2021; 13:11887-11898. [PMID: 34190263 DOI: 10.1039/d1nr02258j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning models are known to be sensitive to the features used to train them, but there is currently no way to predict the impact of using different features prior to feature extraction. This is particularly important to fields such as nanotechnology that are highly multi-disciplinary, and samples can be characterised many different ways depending on the preferences of individual researchers. Does it matter if nanomaterials are described using the interatomic coordinations or more complex order parameters? In this study we compare results of supervised and unsupervised learning on a single set of gold nanoparticles that has been characterised by two different descriptors, each with a unique feature space. We find that there are some consistencies, and model selection is descriptor-agnostic, but the level of detail and the type of information that can be extracted from the results is sensitive to the way the particles are described. Unsupervised clustering revealed that an atomistic descriptor provides a finer-grained interpretation and clusters that are sub-clusters of a more sophisticated crystallographic descriptor, which is consistent with both how the features were calculated, and how they are interpreted in the domain. A supervised classifier revealed that the types of features responsible for the separation are related to the bulk structure, regardless of the descriptor, but capture different types of information. For both the atomistic and crystallographic descriptor the gradient boosting decision tree classifier gave superior results of F1-scores of 0.96 and 0.98, respectively, with excellent precision and recall, even though the clustering presented a challenging multi-classification problem.
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Affiliation(s)
- Haonan Zhang
- School of Computing, Australian National University, Acton 2601, Australia.
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22
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Mirzaei M, Furxhi I, Murphy F, Mullins M. A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:1774. [PMID: 34361160 PMCID: PMC8308172 DOI: 10.3390/nano11071774] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/13/2021] [Accepted: 07/06/2021] [Indexed: 12/22/2022]
Abstract
The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model's validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.
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Affiliation(s)
- Mahsa Mirzaei
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
| | - Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
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23
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Subramanian N, Palaniappan A. NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features. ACS OMEGA 2021; 6:11729-11739. [PMID: 34056326 PMCID: PMC8154018 DOI: 10.1021/acsomega.1c01076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 04/14/2021] [Indexed: 05/30/2023]
Abstract
Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).
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Affiliation(s)
- Nilesh
Anantha Subramanian
- Department
of Medical Nanotechnology and Department of Bioinformatics, School of Chemical and BioTechnology, SASTRA Deemed
University, Thanjavur 613401, India
| | - Ashok Palaniappan
- Department
of Medical Nanotechnology and Department of Bioinformatics, School of Chemical and BioTechnology, SASTRA Deemed
University, Thanjavur 613401, India
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24
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Ortega-Tenezaca B, González-Díaz H. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. NANOSCALE 2021; 13:1318-1330. [PMID: 33410431 DOI: 10.1039/d0nr07588d] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.
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Affiliation(s)
- Bernabé Ortega-Tenezaca
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain and Amazon State University UEA, Puyo, Pastaza, Ecuador and Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain and Center for Investigation on Technologies of Information and Communication (CITIC), University of Coruña (UDC), Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain and IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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25
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Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H. Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). ACS Chem Neurosci 2021; 12:203-215. [PMID: 33347281 DOI: 10.1021/acschemneuro.0c00687] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.
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Affiliation(s)
- Ivo E. Sampaio-Dias
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José E. Rodríguez-Borges
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Víctor Yáñez-Pérez
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Sonia Arrasate
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Javier Llorente
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Pharmacology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Harbil Bediaga
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Physical Chemistry, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Dolores Viña
- Dept. of Pharmacology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - María Isabel Loza
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Xerardo García-Mera
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humberto González-Díaz
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Basque Center for Biophysics (CSIC UPV/EHU), University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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26
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Chan C, Du S, Dong Y, Cheng X. Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems. Curr Top Med Chem 2021; 21:92-114. [PMID: 33243123 PMCID: PMC8191596 DOI: 10.2174/1568026620666201126162945] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/16/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023]
Abstract
Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.
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Affiliation(s)
- Chun Chan
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Shi Du
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Yizhou Dong
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Engineering; The Center for Clinical and Translational Science; The Comprehensive Cancer Center; Dorothy M. Davis Heart & Lung Research Institute; Department of Radiation Oncology, The Ohio State University, Columbus, OH 43210, USA
| | - Xiaolin Cheng
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Biophysics Graduate Program, Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
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27
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Ortega-Tenezaca B, Quevedo-Tumailli V, Bediaga H, Collados J, Arrasate S, Madariaga G, Munteanu CR, Cordeiro MND, González-Díaz H. PTML Multi-Label Algorithms: Models, Software, and Applications. Curr Top Med Chem 2020; 20:2326-2337. [DOI: 10.2174/1568026620666200916122616] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/19/2020] [Accepted: 07/20/2020] [Indexed: 12/17/2022]
Abstract
By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible
to develop predictive models for a variety of response targets. Such combination often known as
Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle
various physical, and chemical properties of different organisms, complex biological or material
systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold
of diverse chemical and biological data into a single computational framework that can then be applied
for screening lead chemicals as well as to find clues for improving the targeted response(s).
PTML models have thus been extremely helpful in drug or material design efforts and found to be
predictive and applicable across a broad space of systems. After a brief outline of the applied methodology,
this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other
applications. Finally, we cover the development of software available nowadays for setting up PTML
models from large datasets.
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Affiliation(s)
| | | | - Harbil Bediaga
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Jon Collados
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Gotzon Madariaga
- Department of Condensed Matter Physics, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruna, Spain
| | - M. Natália D.S. Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
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28
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Cabrera-Andrade A, López-Cortés A, Munteanu CR, Pazos A, Pérez-Castillo Y, Tejera E, Arrasate S, González-Díaz H. Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds. ACS OMEGA 2020; 5:27211-27220. [PMID: 33134682 PMCID: PMC7594149 DOI: 10.1021/acsomega.0c03356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/06/2020] [Indexed: 06/11/2023]
Abstract
Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.
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Affiliation(s)
- Alejandro Cabrera-Andrade
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Carrera
de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
| | - Andrés López-Cortés
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Centro
de Investigación Genética y Genómica, Facultad
de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito 170129, Ecuador
| | - Cristian R. Munteanu
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
- Centro de
Investigación en Tecnologías de la Información
y las Comunicaciones (CITIC), Campus de
Elviña s/n, A Coruña 15071, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
| | - Yunierkis Pérez-Castillo
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Escuela
de Ciencias Físicas y Matemáticas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Eduardo Tejera
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Facultad
de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Sonia Arrasate
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
| | - Humbert González-Díaz
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
- Ikerbasque,
Basque Foundation for Science, Bilbao 48011, Biscay, Spain
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29
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Rybińska-Fryca A, Mikolajczyk A, Puzyn T. Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept. NANOSCALE 2020; 12:20669-20676. [PMID: 33048104 DOI: 10.1039/d0nr05220e] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.
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Affiliation(s)
| | - Alicja Mikolajczyk
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
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30
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Kleandrova VV, Speck-Planche A. PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6. Curr Top Med Chem 2020; 20:1661-1676. [DOI: 10.2174/1568026620666200607190951] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/12/2019] [Accepted: 01/05/2020] [Indexed: 01/23/2023]
Abstract
Background:
Alzheimer’s disease is characterized by a progressive pattern of cognitive and
functional impairment, which ultimately leads to death. Computational approaches have played an important
role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational
models reported to date have been focused on only one protein associated with Alzheimer's,
while relying on small datasets of structurally related molecules.
Objective:
We introduce the first model combining perturbation theory and machine learning based on
artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three
Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase
1 (HDAC1), and histone deacetylase 6 (HDAC6).
Methods:
The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on
a classification approach to predict chemicals as active or inactive.
Results:
The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training
and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model
permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget
inhibitory activity. Based on this information, we assembled ten molecules from several fragments
with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the
remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of
the designed molecules complied with Lipinski’s rule of five and its variants.
Conclusion:
This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's
therapies.
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Affiliation(s)
- Valeria V. Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080, Moscow, Russian Federation
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. Box 8940577, San Joaquin, Santiago, Chile
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31
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Singh AV, Ansari MHD, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K, Kanase A, Singh R, Laux P, Luch A. Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine. Adv Healthc Mater 2020; 9:e1901862. [PMID: 32627972 DOI: 10.1002/adhm.201901862] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/17/2020] [Indexed: 12/22/2022]
Abstract
Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Mohammad Hasan Dad Ansari
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Daniel Rosenkranz
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Romi Singh Maharjan
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Fabian L Kriegel
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Kaustubh Gandhi
- Bosch Sensortec GmbH, Gerhard-Kindler-Straße 9, Reutlingen, 72770, Germany
| | - Anurag Kanase
- Department of Bioengineering, Northeastern University, Boston, MA, 02215, USA
| | - Rishabh Singh
- Rajarshi Shahu College of Engineering, Pune, Maharashtra, 411033, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
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32
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Urista DV, Carrué DB, Otero I, Arrasate S, Quevedo-Tumailli VF, Gestal M, González-Díaz H, Munteanu CR. Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. BIOLOGY 2020; 9:biology9080198. [PMID: 32751710 PMCID: PMC7465777 DOI: 10.3390/biology9080198] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/22/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022]
Abstract
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
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Affiliation(s)
- Diana V. Urista
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
| | - Diego B. Carrué
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
| | - Iago Otero
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
| | - Viviana F. Quevedo-Tumailli
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Universidad Estatal Amazónica UEA, Km. 2 1/2 vía Puyo a Tena (paso lateral), Puyo 160150, Pastaza, Ecuador
| | - Marcos Gestal
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Biomedical Research Institute of A Coruña (INIBIC), Hospital Teresa Herrera, Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
- IKERBASQUE, Basque Foundation for Science, Alameda Urquijo 36, 48011 Bilbao, Spain
- Basque Centre for Biophysics CSIC-UPVEHU, University of Basque Country UPV/EHU, Barrio Sarriena, 48940 Leioa, Spain
| | - Cristian R. Munteanu
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Biomedical Research Institute of A Coruña (INIBIC), Hospital Teresa Herrera, Xubias de Arriba 84, 15006 A Coruña, Spain
- Correspondence:
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Furxhi I, Murphy F. Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning. Int J Mol Sci 2020; 21:E5280. [PMID: 32722414 PMCID: PMC7432486 DOI: 10.3390/ijms21155280] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/31/2022] Open
Abstract
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, V42V384 Newcastle, Limerick, Ireland;
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Malaviya P, Shukal D, Vasavada AR. Nanotechnology-based Drug Delivery, Metabolism and Toxicity. Curr Drug Metab 2020; 20:1167-1190. [PMID: 31902350 DOI: 10.2174/1389200221666200103091753] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 10/02/2019] [Accepted: 11/23/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Nanoparticles (NPs) are being used extensively owing to their increased surface area, targeted delivery and enhanced retention. NPs have the potential to be used in many disease conditions. Despite widespread use, their toxicity and clinical safety still remain a major concern. OBJECTIVE The purpose of this study was to explore the metabolism and toxicological effects of nanotherapeutics. METHODS Comprehensive, time-bound literature search was done covering the period from 2010 till date. The primary focus was on the metabolism of NP including their adsorption, degradation, clearance, and bio-persistence. This review also focuses on updated investigations on NPs with respect to their toxic effects on various in vitro and in vivo experimental models. RESULTS Nanotechnology is a thriving field of biomedical research and an efficient drug delivery system. Further their applications are under investigation for diagnosis of disease and as medical devices. CONCLUSION The toxicity of NPs is a major concern in the application of NPs as therapeutics. Studies addressing metabolism, side-effects and safety of NPs are desirable to gain maximum benefits of nanotherapeutics.
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Affiliation(s)
- Pooja Malaviya
- Department of Cell and Molecular Biology, Iladevi Cataract and IOL Research Centre, Memnagar, Ahmedabad 380052, India.,Ph.D. Scholars, Manipal Academy of Higher Education, Manipal, India
| | - Dhaval Shukal
- Department of Cell and Molecular Biology, Iladevi Cataract and IOL Research Centre, Memnagar, Ahmedabad 380052, India.,Ph.D. Scholars, Manipal Academy of Higher Education, Manipal, India
| | - Abhay R Vasavada
- Department of Cell and Molecular Biology, Iladevi Cataract and IOL Research Centre, Memnagar, Ahmedabad 380052, India
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, Montemore MM, González-Díaz H. PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy. Mol Pharm 2020; 17:2612-2627. [PMID: 32459098 DOI: 10.1021/acs.molpharmaceut.0c00308] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.
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Affiliation(s)
- Ricardo Santana
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States.,University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain.,Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Piedad Gañán
- Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain
| | - Enrique Onieva
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain
| | - Matthew M Montemore
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Basque Center for Biophysics, Spanish National Research Council (CSIC)-University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Basque Country, Spain
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H. Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. NANOSCALE 2020; 12:13471-13483. [PMID: 32613998 DOI: 10.1039/d0nr01849j] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.
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Affiliation(s)
- Ricardo Santana
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain. and Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Piedad Gañán
- Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
| | - Enrique Onieva
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain.
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain. and IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain and Biofisika Institue CSIC-UPVEHU, University of Basque Country UPV/EHU, 48940, Leioa, Spain
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Ali I, Mukhtar SD, Ali HS, Scotti MT, Scotti L. Advances in Nanoparticles as Anticancer Drug Delivery Vector: Need of this Century. Curr Pharm Des 2020; 26:1637-1649. [DOI: 10.2174/1381612826666200203124330] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/02/2019] [Indexed: 12/17/2022]
Abstract
Background:
Nanotechnology has contributed a great deal to the field of medical science. Smart drugdelivery
vectors, combined with stimuli-based characteristics, are becoming increasingly important. The use of
external and internal stimulating factors can have enormous benefits and increase the targeting efficiency of
nanotechnology platforms. The pH values of tumor vascular tissues are acidic in nature, allowing the improved
targeting of anticancer drug payloads using drug-delivery vectors. Nanopolymers are smart drug-delivery vectors
that have recently been developed and recommended for use by scientists because of their potential targeting
capabilities, non-toxicity and biocompatibility, and make them ideal nanocarriers for personalized drug delivery.
Method:
The present review article provides an overview of current advances in the use of nanoparticles (NPs) as
anticancer drug-delivery vectors.
Results:
This article reviews the molecular basis for the use of NPs in medicine, including personalized medicine,
personalized therapy, emerging vistas in anticancer therapy, nanopolymer targeting, passive and active targeting
transports, pH-responsive drug carriers, biological barriers, computer-aided drug design, future challenges and
perspectives, biodegradability and safety.
Conclusions:
This article will benefit academia, researchers, clinicians, and government authorities by providing a
basis for further research advancements.
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Affiliation(s)
- Imran Ali
- Department of Chemistry, College of Sciences, Taibah University, Al-Medina Al-Munawara – 41477, Saudi Arabia
| | - Sofi D. Mukhtar
- Department of Chemistry, Jamia Millia Islamia (Central University) New Delhi-110025, India
| | - Heyam S. Ali
- Department of Pharmaceutics, University of Khartoum, Khartoum, Sudan
| | - Marcus T. Scotti
- Cheminformatics Laboratory- Postgraduate Program in Natural Products and Synthetic Bioactive, Federal University of Paraíba-Campus I 58051-970, João Pessoa, PB, Brazil
| | - Luciana Scotti
- Teaching and Research Management - University Hospital, Cheminformatics Laboratory- Postgraduate Program in Natural Products and Synthetic Bioactive, Federal University of Paraíba-Campus I, 58051-970, João Pessoa, PB, Brazil
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Halder AK, Melo A, Cordeiro MNDS. A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles. CHEMOSPHERE 2020; 244:125489. [PMID: 31812055 DOI: 10.1016/j.chemosphere.2019.125489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/19/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
| | - André Melo
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva Caracuel E, González-Díaz H. PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives. ACS COMBINATORIAL SCIENCE 2020; 22:129-141. [PMID: 32011854 DOI: 10.1021/acscombsci.9b00166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (nij) with a considerable combination of assay conditions (cj). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of cj. They are focused on >500 different biological activity parameters (c0), >340 different targets (c1), >6200 types of cell (c2), >120 organisms of assay (c3), and >60 assay strains (c4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundación Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain
- Grupo de Investigación sobre Nuevos Materiales, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Piedad Gañán
- Facultad de Ingeniería Química, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | | | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
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Carracedo-Reboredo P, Corona R, Martinez-Nunes M, Fernandez-Lozano C, Tsiliki G, Sarimveis H, Aranzamendi E, Arrasate S, Sotomayor N, Lete E, Munteanu CR, González-Díaz H. MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry. Curr Top Med Chem 2019; 20:305-317. [PMID: 31878856 DOI: 10.2174/1568026620666191226092431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 11/22/2022]
Abstract
AIMS Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain.,Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Ramiro Corona
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Mikel Martinez-Nunes
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Georgia Tsiliki
- Institute for the Management of Information Systems, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Campus, 15780, Athens, Greece.,Pharma-Informatics Unit, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Eider Aranzamendi
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Nuria Sotomayor
- Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Esther Lete
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Cristian Robert Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Humbert González-Díaz
- Basque Center for Biophysics, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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Buglak AA, Zherdev AV, Dzantiev BB. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019; 24:molecules24244537. [PMID: 31835808 PMCID: PMC6943593 DOI: 10.3390/molecules24244537] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/12/2022] Open
Abstract
Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.
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Affiliation(s)
- Andrey A. Buglak
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Physical Faculty, St. Petersburg State University, 7/9 Universitetskaya Naberezhnaya, 199034 St. Petersburg, Russia
- Correspondence: ; Tel.: +7-(495)-954-27-32
| | - Anatoly V. Zherdev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severny Proezd 1, 142432 Chernogolovka, Moscow Region, Russia
| | - Boris B. Dzantiev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
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44
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Santana R, Onieva E, Zuluaga R, Duardo-Sánchez A, Gañán P. Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union. Curr Top Med Chem 2019; 20:324-332. [PMID: 31804168 DOI: 10.2174/1568026619666191205152538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/07/2019] [Accepted: 09/10/2019] [Indexed: 11/22/2022]
Abstract
AIMS Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. BACKGROUND Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. OBJECTIVE Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. METHODS A systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. RESULTS It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. CONCLUSION To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain.,Faculty of Engineering, University of Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain.,Grupo de Investigación Sobre Nuevos Materiales, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Enrique Onieva
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain
| | - Robin Zuluaga
- Facultad de Ingenieria Agroindustrial, Universidad Pontificia Bolivariana UPB, 050031, Medellin, Colombia
| | - Aliuska Duardo-Sánchez
- Department of Public Law, Law and the Human Genome Research Group, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain
| | - Piedad Gañán
- Facultad de Ingeniería Quimica, Universidad Pontificia Bolivariana UPB, 050031, Medellin, Colombia
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45
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models. NANOSCALE 2019; 11:21811-21823. [PMID: 31691701 DOI: 10.1039/c9nr05070a] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Nano-systems for cancer co-therapy including vitamins or vitamin derivatives have showed adequate results to continue with further research studies to better understand them. However, the number of different combinations of drugs, vitamins, nanoparticle types, coating agents, synthesis conditions, and system types (nanocapsules, micelles, etc.) to be tested is very large generating a high cost in experimentations. In this context, there are reports of large datasets of preclinical assays of compounds (like in the ChEMBL database) and increasing but yet limited reports of experimental measurements of nano-systems per se. On the other hand, Machine Learning is gaining momentum in Nanotechnology and Pharmaceutical Sciences as a tool for rational design of new drugs and drug-release nano-systems. In this work, we propose to combine Perturbation Theory principles and Machine Learning to develop a PTML model for rational selection of the components of cancer co-therapy drug-vitamin release nano-systems (DVRNs). In doing so, we apply information fusion techniques with 2 data sets: (1) a large ChEMBL dataset of >36 000 preclinical assays of vitamin derivatives and a new dataset of >1000 outcomes of DVRNs, collected herein from the literature for the first time. The ChEMBL dataset used covers a considerable number of assay conditions (cjvit) each one with multiple levels. These conditions included >504 biological activity parameters (c0vit), >340 types of proteins (c1vit), >650 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit). Regarding the DVRNs, there are 25 different types of nano-systems (njn), with up to 16 conditions (cjn) including also different levels such as 8 biological activity parameters (c0n), 9 raw nanomaterials (c4n), 15 assay cells (c11n), etc. In the first stage, we used Moving Average operators to quantify the perturbations (deviations) in all input variables with respect to the conditions. After that, we used multiplicative PT operators to carry out data fusion, and dimension reduction, and Linear Discriminant Analysis (LDA) to seek the PTML model. The best PTML model found showed values of specificity, sensitivity, and accuracy in the range of 83-88% in training and external validation series for >130 000 cases (DVRNs vs. ChEMBL data pairs) formed after data fusion. To the best of our knowledge, this is the first general purpose model for the rational design of DVRNs for cancer co-therapy.
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Diez-Alarcia R, Yáñez-Pérez V, Muneta-Arrate I, Arrasate S, Lete E, Meana JJ, González-Díaz H. Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [ 35S]GTPγS Binding Assays. ACS Chem Neurosci 2019; 10:4476-4491. [PMID: 31618004 DOI: 10.1021/acschemneuro.9b00302] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.
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Affiliation(s)
- Rebeca Diez-Alarcia
- Centro de Investigación Biomédica en Red en Salud Mental, 48940 Leioa, Spain
| | | | | | | | | | - J. Javier Meana
- Centro de Investigación Biomédica en Red en Salud Mental, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Biophysics Institute, CSIC-UPV/EHU, University of the Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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47
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Pérez-Parras Toledano J, García-Pedrajas N, Cerruela-García G. Multilabel and Missing Label Methods for Binary Quantitative Structure-Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions. J Chem Inf Model 2019; 59:4120-4130. [PMID: 31514503 DOI: 10.1021/acs.jcim.9b00611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The prediction of adverse drug reactions in the discovery of new medicines is highly challenging. In the task of predicting the adverse reactions of chemical compounds, information about different targets is often available. Although we can focus on every adverse drug reaction prediction separately, multilabel approaches have been proven useful in many research areas for taking advantage of the relationship among the targets. However, when approaching the prediction problem from a multilabel point of view, we have to deal with the lack of information for some labels. This missing labels problem is a relevant issue in the field of cheminformatics approaches. This paper aims to predict the adverse drug reaction of commercial drugs using a multilabel approach where the possible presence of missing labels is also taken into consideration. We propose the use of multilabel methods to deal with the prediction of a large set of 27 different adverse reaction targets. We also propose the use of multilabel methods specifically designed to deal with the missing labels problem to test their ability to solve this difficult problem. The results show the validity of the proposed approach, demonstrating a superior performance of the multilabel method compared with the single-label approach in addressing the problem of adverse drug reaction prediction.
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Affiliation(s)
- José Pérez-Parras Toledano
- University of Córdoba , Department of Computing and Numerical Analysis, Campus de Rabanales , Albert Einstein Building , E-14071 Córdoba , Spain
| | - Nicolás García-Pedrajas
- University of Córdoba , Department of Computing and Numerical Analysis, Campus de Rabanales , Albert Einstein Building , E-14071 Córdoba , Spain
| | - Gonzalo Cerruela-García
- University of Córdoba , Department of Computing and Numerical Analysis, Campus de Rabanales , Albert Einstein Building , E-14071 Córdoba , Spain
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48
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Zhang H, Mao J, Qi HZ, Ding L. In silico prediction of drug-induced developmental toxicity by using machine learning approaches. Mol Divers 2019; 24:1281-1290. [PMID: 31486961 DOI: 10.1007/s11030-019-09991-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 08/28/2019] [Indexed: 02/05/2023]
Abstract
Some drugs and xenobiotics have the potential to disturb homeostasis, normal growth, differentiation, development or behavior during prenatal development or postnatally until puberty. Assessment of the developmental toxicity is one of the important safety considerations incorporated by international regulatory agencies. In this investigation, seven machine learning methods, including naïve Bayes, support vector machine, recursive partitioning, k-nearest neighbor, C4.5 decision tree, random forest and Adaboost, were used to build binary classification models for developmental toxicity. Among these models, the naïve Bayes classifier represented the best predictive performance and stability, which gave 91.11% overall prediction accuracy, 91.50% balanced accuracy and 0.818 MCC for the training set, and generated 83.93% concordance, 81.85% balanced accuracy and 0.627 MCC for the test set. The application domains were analyzed, and only one chemical in the test set was identified as outside the application domain. In addition, 10 important molecular descriptors related to developmental toxicity were selected by the genetic algorithm, which may contribute to explanation of the mechanisms of developmental toxicants. The best naïve Bayes classification model should be employed as alternative method for qualitative prediction of chemical-induced developmental toxicity in early stages of drug development.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China. .,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
| | - Jun Mao
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Hua-Zhao Qi
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Lan Ding
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
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49
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Vásquez-Domínguez E, Armijos-Jaramillo VD, Tejera E, González-Díaz H. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm 2019; 16:4200-4212. [PMID: 31426639 DOI: 10.1021/acs.molpharmaceut.9b00538] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c0), 55 protein accessions (c1), 83 cell lines (c2), 64 organisms of assay (c3), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.
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Affiliation(s)
- Emilia Vásquez-Domínguez
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Vinicio Danilo Armijos-Jaramillo
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Eduardo Tejera
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Spain
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50
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Tang J, Wang Y, Li Y, Zhang Y, Zhang R, Xiao Z, Luo Y, Guo X, Tao L, Lou Y, Xue W, Zhu F. Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics. Curr Pharm Des 2019; 25:1536-1553. [PMID: 31258068 DOI: 10.2174/1381612825666190618123306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 06/11/2019] [Indexed: 11/22/2022]
Abstract
Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Xueying Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
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