The system of self-consistent models for the uptake of nanoparticles in PaCa2 cancer cells

References

• Ahmadi, S., A. P. Toropova, and A. A. Toropov. 2020. “Correlation Intensity Index: mathematical Modeling of Cytotoxicity of Metal Oxide Nanoparticles.” Nanotoxicology 14 (8): 1118–1126. doi:10.1080/17435390.2020.1808252.
   PubMed Web of Science ®Google Scholar
• Box, G. E. P. 1976. “Science and Statistics.” Journal of the American Statistical Association 71 (356): 791–799. doi:10.1080/01621459.1976.10480949.
   Web of Science ®Google Scholar
• Concu, R., V. V. Kleandrova, A. Speck-Planche, and M. N. D. S. Cordeiro. 2017. “Probing the Toxicity of Nanoparticles: A Unified in Silico Machine Learning Model Based on Perturbation Theory.” Nanotoxicology 11 (7): 891–906. doi:10.1080/17435390.2017.1379567.
   PubMed Web of Science ®Google Scholar
• Doweyko, A. M. 2008. “QSAR: Dead or Alive?” Journal of Computer-Aided Molecular Design 22 (2): 81–89. doi:10.1007/s10822-007-9162-7.
   PubMed Web of Science ®Google Scholar
• Fourches, D., D. Pu, C. Tassa, R. Weissleder, S. Y. Shaw, R. J. Mumper, and A. Tropsha. 2010. “Quantitative Nanostructure-Activity Relationship Modeling.” ACS Nano 4 (10): 5703–5712. doi:10.1021/nn1013484.
   PubMed Web of Science ®Google Scholar
• Jafari, K., M. H. Fatemi, A. P. Toropova, and A. A. Toropov. 2020. “Correlation Intensity Index (CII) as a Criterion of Predictive Potential: Applying to Model Thermal Conductivity of Metal Oxide-Based Ethylene Glycol Nanofluids.” Chemical Physics Letters 754: 137614. doi:10.1016/j.cplett.2020.137614.
   Google Scholar
• Kuehr, S., V. Kosfeld, and C. Schlechtriem. 2021. “Bioaccumulation Assessment of Nanomaterials Using Freshwater Invertebrate Species.” Environmental Sciences Europe 33 (1): 9. doi:10.1186/s12302-020-00442-2.
   Web of Science ®Google Scholar
• Lei, C., Y. Sun, D. C. W. Tsang, and D. Lin. 2018. “Environmental Transformations and Ecological Effects of Iron-Based Nanoparticles.” Environmental Pollution 232: 10–30. doi:10.1016/j.envpol.2017.09.052.
   PubMed Web of Science ®Google Scholar
• Leszczynski, J. 2010. “Bionanoscience: Nano Meets Bio at the Interface.” Nature Nanotechnology 5 (9): 633–634. doi:10.1038/nnano.2010.182.
   PubMed Web of Science ®Google Scholar
• Luan, F., V. V. Kleandrova, H. Gonzalez-Diaz, J. M. Ruso, A. Melo, A. Speck-Planche, and M. N. D. S. Cordeiro. 2014. “Computer-Aided Nanotoxicology: assessing Cytotoxicity of Nanoparticles under Diverse Experimental Conditions by Using a Novel QSTR-Perturbation Approach.” Nanoscale 6 (18): 10623–10630. doi:10.1039/c4nr01285b.
   PubMed Web of Science ®Google Scholar
• Majumdar, S., and S. C. Basak. 2018. “ Editorial: Beware of Naïve q2, use True q2: Some Comments on QSAR Model Building and Cross Validation.” Current Computer-Aided Drug Design 14 (1): 5–6. doi:10.2174/157340991401180321112006.
   PubMed Web of Science ®Google Scholar
• Melagraki, G., and A. Afantitis. 2014. “Enalos InSilicoNano Platform: An Online Decision Support Tool for the Design and Virtual Screening of Nanoparticles.” RSC Advances. 4 (92): 50713–50725. doi:10.1039/c4ra07756c.
   Web of Science ®Google Scholar
• Ojha, P. K., I. Mitra, R. N. Das, and K. Roy. 2011. “Further Exploring rm2 Metrics for Validation of QSPR Models.” Chemometrics and Intelligent Laboratory Systems 107 (1): 194–205. doi:10.1016/j.chemolab.2011.03.011.
   Web of Science ®Google Scholar
• Ortega-Tenezaca, B., and H. González-Díaz. 2021. “IFPTML Mapping of Nanoparticle Antibacterial Activity: Vs. pathogen Metabolic Networks.” Nanoscale 13 (2): 1318–1330. doi:10.1039/d0nr07588d.
   PubMed Web of Science ®Google Scholar
• Peters, G.-J. Y., M. de Bruin, and R. Crutzen. 2015. “Everything Should Be as Simple as Possible, but No Simpler: Towards a Protocol for Accumulating Evidence regarding the Active Content of Health Behaviour Change Interventions.” Health Psychology Review 9 (1): 1–14. doi:10.1080/17437199.2013.848409.
   PubMed Web of Science ®Google Scholar
• Qi, R., Y. Pan, J. Cao, Z. Jia, and J. Jiang. 2020. “The Cytotoxicity of Nanomaterials: Modeling Multiple Human Cells Uptake of Functionalized Magneto-Fluorescent Nanoparticles via nano-QSAR.” Chemosphere 249: 126175. doi:10.1016/j.chemosphere.2020.126175.
   PubMed Web of Science ®Google Scholar
• Shao, H., H. Lu, G. Xu, and B. Marian. 2017. “Integration into Plant Biology and Soil Science Has Provided Insights into the Total environment.” Science Total Environment 579: 928–929. doi:10.1016/j.scitotenv.2016.10.167.
   PubMed Web of Science ®Google Scholar
• Speck-Planche, A., and M. N. D. S. Cordeiro. 2017. “De Novo Computational Design of Compounds Virtually Displaying Potent Antibacterial Activity and Desirable in Vitro ADMET Profiles.” Medicinal Chemistry Research 26 (10): 2345–2356. doi:10.1007/s00044-017-1936-4.
   Web of Science ®Google Scholar
• Speck-Planche, A., V. V. Kleandrova, F. Luan, and M. N. D. S. Cordeiro. 2015. “Computational Modeling in Nanomedicine: prediction of Multiple Antibacterial Profiles of Nanoparticles Using a Quantitative Structure-Activity Relationship Perturbation Model.” Nanomedicine 10 (2): 193–204. doi:10.2217/nnm.14.96.
   PubMed Web of Science ®Google Scholar
• Speck-Planche, A., V. V. Kleandrova, J. M. Ruso, and M. N. D. S. Cordeiro. 2016. “First Multitarget Chemo-Bioinformatic Model to Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens.” Journal of Chemical Information and Modeling 56 (3): 588–598. doi:10.1021/acs.jcim.5b00630.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., and A. A. Toropov. 2017a. “CORAL: Binary Classifications (Active/Inactive) for Drug-Induced Liver Injury.” Toxicology Letters 268: 51–57. doi:10.1016/j.toxlet.2017.01.011.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., and A. A. Toropov. 2017b. “The Index of Ideality of Correlation: A Criterion of Predictability of QSAR Models for Skin Permeability?” The Science of the Total Environment 586: 466–472. doi:10.1016/j.scitotenv.2017.01.198.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., and A. A. Toropov. 2019. “Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?” Current Topics in Medicinal Chemistry 19 (29): 2643–2657. doi:10.2174/1568026619666191105111817.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., A. A. Toropov, S. Manganelli, C. Leone, D. Baderna, E. Benfenati, and R. Fanelli. 2016. “Quasi-SMILES as a Tool to Utilize Eclectic Data for Predicting the Behavior of Nanomaterials.” NanoImpact 1: 60–64. doi:10.1016/j.impact.2016.04.003.
   Web of Science ®Google Scholar
• Toropov, A. A., N. Sizochenko, A. P. Toropova, D. Leszczynska, and J. Leszczynski. 2020. “Advancement of Predictive Modeling of Zeta Potentials (ζ) in Metal Oxide Nanoparticles with Correlation Intensity Index (CII).” Journal of Molecular Liquids 317: 113929. doi:10.1016/j.molliq.2020.113929.
   Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2015. “Quasi-QSAR for Mutagenic Potential of Multi-Walled Carbon-Nanotubes.” Chemosphere 124 (1): 40–46. doi:10.1016/j.chemosphere.2014.10.067.
   PubMedGoogle Scholar
• Toropov, A. A., and A. P. Toropova. 2019. “QSAR as a Random Event: Criteria of Predictive Potential for a Chance Model.” Structural Chemistry 30 (5): 1677–1683. doi:10.1007/s11224-019-01361-6.
   Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2020. “Correlation Intensity Index: Building up Models for Mutagenicity of Silver Nanoparticles.” The Science of the Total Environment 737: 139720. doi:10.1016/j.scitotenv.2020.139720.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2021a. “The Unreliability of the Reliability Criteria in the Estimation of QSAR for Skin Sensitivity: A Pun or a Reliable law??” Toxicology Letters 340: 133–140. doi:10.1016/j.toxlet.2021.01.015.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2021b. “Quasi-SMILES as a Basis for the Development of Models for the Toxicity of ZnO Nanoparticles.” The Science of the Total Environment 772: 145532. doi:10.1016/j.scitotenv.2021.145532.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., A. P. Toropova, T. Puzyn, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski. 2013. “QSAR as a Random Event: Modeling of Nanoparticles Uptake in PaCa2 Cancer cells.” Chemosphere 92 (1): 31–37. doi:10.1016/j.chemosphere.2013.03.012.
   PubMed Web of Science ®Google Scholar
• Weininger, D. 1988. “SMILES, a Chemical Language and Information System: 1: Introduction to Methodology and Encoding Rules.” Journal of Chemical Information and Modeling 28 (1): 31–36. doi:10.1021/ci00057a005.
   Web of Science ®Google Scholar
• Weissleder, R., K. Kelly, E. Y. Sun, T. Shtatland, and L. Josephson. 2005. “Cell-Specific Targeting of Nanoparticles by Multivalent Attachment of Small Molecules.” Nature Biotechnology 23 (11): 1418–1423. doi:10.1038/nbt1159.
   PubMed Web of Science ®Google Scholar
• Winkler, D. A., F. R. Burden, B. Yan, R. Weissleder, C. Tassa, C. Shaw, and V. C. Epa. 2014. “Modelling and Predicting the Biological Effects of Nanomaterials.” SAR and QSAR in Environmental Research 25 (2): 161–172. doi:10.1080/1062936X.2013.874367.
   PubMed Web of Science ®Google Scholar