The study was carried out to develop an efficient approach for prediction the genotoxicity of carbon nanotubes. The experimental data on the bacterial reverse mutation test (TA100) on multi-walled carbon nanotubes (MWCNTs) was collected from the literature and examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint was built up. The model is represented by a function of: (i) dose (µg/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) two types of MWCNTs. The above listed conditions were represented by so-called quasi-SMILES. Simplified molecular input-line entry system (SMILES) is a tool for representation of molecular structure. The quasi-SMILES is a tool to represent physicochemical and / or biochemical conditions for building up a predictive model. Thus, instead of well-known paradigm of predictive modeling “endpoint is a mathematical function of molecular structure” a fresh paradigm “endpoint is a mathematical function of available eclectic data (conditions) is suggested.
Toporova AP, Toporov AA, Rallo R, Leszczynska D, Leszczynski J. Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes. Int. J. Environ. Res., 2016, 10(1): 59-64
Historically, market pressure has often resulted in scientific innovation being made available to consumers even before we were fully aware of its ins and outs. This was notably the case with asbestos, and the same scenario could very well be repeating with nanotechnology if proper safety assessment studies are not conducted and political measures taken. According to some of the latest forecasts, the nano- technology market will grow to reach US$ 75.8 (EUR 65.8) billion by 2020. And while engineered nanoparticles (eNPs) are already widespread in the likes of cosmetics, paint and electronics, we still don’t know much about their possible long- term effects on biological systems.
To gain a better understanding, scientists still rely heavily on animal testing — in spite of efforts from animal protection activists, scientists and policy makers to put the focus on alternative testing methods. In line with the EU’s efforts to implement appropriate testing strategies and with a view to overcoming the current obstacles to a wider adoption of in silico methods, Prof. Robert Rallo, coordinator of MODERN, initiated the MODERN project in January 2013.
A couple of months before the end of the project, he tells us about its achievements and expected impact on eNP toxicity assessment methods.
ISSN 1831-9947 (printed version)
ISSN 1977-4028 (PDF, EPUB)
In silico exploratory study using structure-activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay
The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure-activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.
Kamath P, Raitano G, Fernández A, Rallo R, Benfenati E.In silico exploratory study using structure-activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay. SAR and QSAR in Environmental Research, 2015, 11, xxxx-xxx
- DOI: 10.1080/1062936X.2015.1108932
Analysis of trends in nanotoxicology data and the development of data-driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a “Toy Dataset” presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
Marchese-Robinson RL, Cronin RTD, Richarz AN, Rallo R. An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology. Beilstein Journal of Nanotechnology, 2015, 6, 1978-1999
- DOI: 10.3762/bjnano.6.202
Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability
Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely availablein silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models.
Fernández A, Rallo R, Giralt F. Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability. Environmental Research, 2015, 142, 161-168
- DOI: 10.1016/j.envres.2015.06.031
The empirical parameters of copolymerization Q-e have been examined as an endpoint for establishing the quantitative structure – property relationships (QSPRs). The possibility to build up QSPR for these parameters is demonstrated for 22 transfer chain agents. Data for 20 taken in the literature and two were investigated in direct experiment. The statistical qualities of the models for parameter e together with the negative decimal logarithm of Q × 10−4 (pQ) are quite good. The mechanistic interpretation for these models are suggested and discussed.
Toropova AP, Toropov AA, Kudyshkin VO, Rallo R. Prediction of the Q-e parameters from structures of transfer chain agents. J. Polym. Res. 2015, 22:128
- DOI: 10.1007/s10965-015-0778-3
Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure – Activity Relationships (PCSAR)
Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical de- scriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selec- tion were in line with already published literature, and the computational model constructed on these features had a good ac- curacy (R2LOO=0.76 and R2LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical de- scriptors were independent of the specific proteins that form the corona.
Kamath P, Fernández A, Giralt F, Rallo R. Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure – Activity Relationships (PCSAR). Current Topics in Medicinal Chemistry, 2015, (15)18
- DOI: 10.2174/1568026615666150506152808
- PUBMED: 25961528