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Gackowski M, Pluskota R, Koba M. Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques. Curr Comput Aided Drug Des 2024; 20:798-810. [PMID: 37309763 DOI: 10.2174/1573409919666230612144407] [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: 02/15/2023] [Revised: 04/27/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. OBJECTIVES The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. METHODS The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. RESULTS ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. CONCLUSION The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.
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Affiliation(s)
- Marcin Gackowski
- Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, PL-85089, Bydgoszcz, Poland
| | - Robert Pluskota
- Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, PL-85089, Bydgoszcz, Poland
| | - Marcin Koba
- Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, PL-85089, Bydgoszcz, Poland
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Bhuvaneswari S, Aakash VB, Ramalakshmi N, Arunkumar S. Quantitative Structure–Activity Relationship Analysis and Validation of New DNA Gyrase Inhibitors. Pharm Chem J 2022. [DOI: 10.1007/s11094-021-02513-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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3
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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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Developing quantitative structure–retention relationship model to prediction of retention factors of some alkyl-benzenes in nano-LC. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2019. [DOI: 10.1007/s13738-019-01624-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Lamon L, Asturiol D, Vilchez A, Ruperez-Illescas R, Cabellos J, Richarz A, Worth A. Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 9:143-151. [PMID: 31008416 PMCID: PMC6472618 DOI: 10.1016/j.comtox.2018.12.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/05/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023]
Abstract
Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs.
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Affiliation(s)
- L. Lamon
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - D. Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - A. Vilchez
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - R. Ruperez-Illescas
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - J. Cabellos
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - A. Richarz
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - A. Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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Lata S, Vikas. Concentration dependent adsorption of aromatic organic compounds by SWCNTs: Quantum-mechanical descriptors for nano-toxicological studies of biomolecules and agrochemicals. J Mol Graph Model 2018; 85:232-241. [PMID: 30227368 DOI: 10.1016/j.jmgm.2018.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/12/2018] [Accepted: 08/20/2018] [Indexed: 12/30/2022]
Abstract
For evaluating the environmental risk associated using carbon nanotubes (CNTs), a successful prediction is desired for the adsorption of organic compounds (OCs) by CNTs at different adsorbate concentrations. This is most often achieved through poly-parameter linear solvation energy relationships (LSERs) based on solvatochromic descriptors. This study examines the real predictivity of the existing LSERs for predicting the adsorption of OCs by single-walled CNTs (SWCNTs) while comparing it with that of the models developed in the present work using quantum-mechanical descriptors. The real predictivity of the quantum-mechanical models and existing LSERs is compared using state-of-the-art statistical procedures employing an external prediction set of compounds not used in the model development. The quantum-mechanically computed mean polarizability, but originating from the interactions between electrons of parallel spin, is found to play an essential role in the adsorption of OCs by SWCNTs. Besides the solvatochromic descriptors (McGowan volume and molar excess refractivity), the instantaneous inter-electronic interactions, captured through electron-correlation based quantum-mechanical descriptors, are found to significantly affect the adsorption at varying adsorbate concentration. The models developed using a combination of quantum-mechanical and solvatochromic descriptors are found to be quite reliable. The models proposed were further employed to predict the adsorption of agrochemicals such as insecticides, pesticides, herbicides, as well as adsorption of endocrine disruptors and biomolecules such as nucleobases and steroid hormones. These are predicted to be strongly adsorbed by SWCNTs with Progesterone and Guanine exhibiting maximal interaction with the SWCNTs among biomolecules. The quantum-mechanical descriptors proposed in this work can be used for the risk assessment of SWCNTs in systems where adsorption is the primary process.
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Affiliation(s)
- Suman Lata
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India
| | - Vikas
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India.
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Park CH, Tocci E, Fontananova E, Bahattab MA, Aljlil SA, Drioli E. Mixed matrix membranes containing functionalized multiwalled carbon nanotubes: Mesoscale simulation and experimental approach for optimizing dispersion. J Memb Sci 2016. [DOI: 10.1016/j.memsci.2016.04.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Conversano F, Pisani P, Casciaro E, Di Paola M, Leporatti S, Franchini R, Quarta A, Gigli G, Casciaro S. Automatic Echographic Detection of Halloysite Clay Nanotubes in a Low Concentration Range. NANOMATERIALS (BASEL, SWITZERLAND) 2016; 6:E66. [PMID: 28335194 PMCID: PMC5302578 DOI: 10.3390/nano6040066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 04/01/2016] [Accepted: 04/05/2016] [Indexed: 12/29/2022]
Abstract
Aim of this work was to investigate the automatic echographic detection of an experimental drug delivery agent, halloysite clay nanotubes (HNTs), by employing an innovative method based on advanced spectral analysis of the corresponding "raw" radiofrequency backscatter signals. Different HNT concentrations in a low range (5.5-66 × 1010 part/mL, equivalent to 0.25-3.00 mg/mL) were dispersed in custom-designed tissue-mimicking phantoms and imaged through a clinically-available echographic device at a conventional ultrasound diagnostic frequency (10 MHz). The most effective response (sensitivity = 60%, specificity = 95%), was found at a concentration of 33 × 1010 part/mL (1.5 mg/mL), representing a kind of best compromise between the need of enough particles to introduce detectable spectral modifications in the backscattered signal and the necessity to avoid the losses of spectral peculiarity associated to higher HNT concentrations. Based on theoretical considerations and quantitative comparisons with literature-available results, this concentration could also represent an optimal concentration level for the automatic echographic detection of different solid nanoparticles when employing a similar ultrasound frequency. Future dedicated studies will assess the actual clinical usefulness of the proposed approach and the potential of HNTs for effective theranostic applications.
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Affiliation(s)
- Francesco Conversano
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
| | - Paola Pisani
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
| | - Ernesto Casciaro
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
| | - Marco Di Paola
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
| | - Stefano Leporatti
- National Research Council, Institute of Nanotechnology, Lecce 73100, Italy.
| | - Roberto Franchini
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
| | - Alessandra Quarta
- National Research Council, Institute of Nanotechnology, Lecce 73100, Italy.
| | - Giuseppe Gigli
- National Research Council, Institute of Nanotechnology, Lecce 73100, Italy.
| | - Sergio Casciaro
- National Research Council, Institute of Clinical Physiology, Lecce 73100, Italy.
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Kharisov BI, Kharissova OV, Dimas AV. The dispersion, solubilization and stabilization in “solution” of single-walled carbon nanotubes. RSC Adv 2016. [DOI: 10.1039/c6ra13187e] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Methods for the solubilization and dispersion of single-walled carbon nanotubes in water and organic solvents by physical and chemical methods have been reviewed.
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