Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach

Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure–property–activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.

[1]  Y. Li,et al.  Source toxicity characteristics of short- and medium-chain chlorinated paraffin in multi-environmental media: Product source toxicity, molecular source toxicity and food chain migration control through silica methods. , 2023, The Science of the total environment.

[2]  Ernesto Alfaro-Moreno,et al.  The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions. , 2023, Drug and chemical toxicology.

[3]  E. Benfenati,et al.  Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential , 2022, SAR and QSAR in environmental research.

[4]  F. Kjeldsen,et al.  Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials. , 2022, Chemosphere.

[5]  A. Toropova,et al.  The system of self-consistent models for the uptake of nanoparticles in PaCa2 cancer cells , 2021, Nanotoxicology.

[6]  M. Farahmandjou,et al.  The predictive model for band gap prediction of metal oxide nanoparticles based on quasi-SMILES , 2021, Structural Chemistry.

[7]  A. Toropova,et al.  The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: a pun or a reliable law? , 2021, Toxicology letters.

[8]  S. Ahmadi.  Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. , 2020, Chemosphere.

[9]  M. Fatemi,et al.  Application of nano-quantitative structure–property relationship paradigm to develop predictive models for thermal conductivity of metal oxide-based ethylene glycol nanofluids , 2020, Journal of Thermal Analysis and Calorimetry.

[10]  A. Toropova,et al.  Does the Index of Ideality of Correlation Detect the Better Model Correctly? , 2019, Molecular informatics.

[11]  Hyung-Gi Byun,et al.  Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. , 2019, Chemosphere.

[12]  I. Raška,et al.  The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models. , 2019, The Science of the total environment.

[13]  Subhabrata Majumdar,et al.  Editorial: Beware of Naïve q2, use True q2: Some Comments on QSAR Model Building and Cross Validation. , 2018, Current computer-aided drug design.

[14]  Hyung-Gi Byun,et al.  Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. , 2018, Chemical research in toxicology.

[15]  S. Yalkowsky,et al.  Estimation of Melting Points of Organics. , 2017, Journal of pharmaceutical sciences.

[16]  Andrey A Toropov,et al.  The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? , 2017, Mutation research.

[17]  Jerzy Leszczynski,et al.  QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. , 2013, Chemosphere.

[18]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[19]  G. Box.  Science and Statistics , 1976 .

[20]  G. Gakis,et al.  Metal and metal oxide nanoparticle toxicity: Moving towards a more holistic structure-activity approach , 2023, Environmental Science: Nano.

[21]  M. Qomi,et al.  CO2 Uptake Prediction of Metal-Organic Frameworks Using Quasi-SMILES and Monte Carlo Optimization , 2022, New Journal of Chemistry.

[22]  OECD GUIDELINES FOR THE TESTING OF CHEMICALS , 2014 .

[23]  A. Hasan,et al.  Organisation for Economic Co-operation and Development , 2007 .

[24]  Colin Stacey,et al.  FOR ECONOMIC CO-OPERATION AND DEVELOPMENT Policy Brief OCTOBER 2006 Young Drivers : The Road to Safety , 2022 .