“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling

doi:10.1016/j.chemolab.2015.07.007

Chemometrics and Intelligent Laboratory Systems

Volume 147, 15 October 2015, Pages 1-13

https://doi.org/10.1016/j.chemolab.2015.07.007 Get rights and content

Abstract

Nanotechnology is a branch of science and technology that comes with lots of industrial applications and potential benefits to the society. But the risk associated with the nanomaterials towards human health and environment is of major concern. Quantitative structure-activity relationship (QSAR) studies for modeling activities or properties of nanoparticles (nano-QSAR modeling) can be employed to study the factors governing the toxicity of nanomaterials. We have developed a variety of software tools under the NanoBRIDGES project (http://nanobridges.eu/) which will assist in performing QSAR and nano-QSAR modeling. These user friendly tools are standalone, and openly accessible from NanoBRIDGES official website (http://nanobridges.eu/software/), DTC laboratory website (http://dtclab.webs.com/software-tools) and Jadavpur University official website (http://teqip.jdvu.ac.in/QSAR_Tools/). In this paper, we have described the theoretical background of each software tool including its algorithm and its applicability in the nano-QSAR modeling.

Introduction

Nanotechnology holds incredible promise benefits in a wide range of industrial applications, from health care, cosmetics, food safety, environmental science to material science, information and communication technology, transportation, and many others. The potential benefits to society include stronger, lighter, more durable materials, remote sensing and tracking devices related to food quality and spoilage, improved systems to control, prevent, and remediate pollution problems or cost-effective development and use of renewable energy sources. The use of nanomaterials for commercial products has increased exponentially, while the safety of such products has been left aside with little or no assessments. Therefore, there is a major concern about the new risk that nanotechnology could bring for humans and the environment [1].

According to recent studies, nanomaterials may endanger human health through the potential induction of cytogenetic, genotoxic, mutagenic and ecotoxic effects [1], [2], [3]. Many physico-chemical properties of pristine nanomaterials are interdependent and therefore cannot be varied systematically independently from other characteristics. Since nanoparticles under biological or environmental conditions may introduce the effect of agglomeration, aggregation, heteroaggregation, and biomolecule coating, it is still largely unknown which properties govern their toxicity [4]. Without doubts, a comprehensive characterization and risk assessment of nanoparticles are essential to understand their behavior as well as potential toxic effects. However, due to ethical and financial constraints it is practically infeasible to test all possible nanoparticle types, sizes and concentrations. It is therefore important to develop reliable alternatives to animal testing that enable modeling the relationships between the structure, properties, molecular interactions and toxicity of engineered nanoparticles [1].

The most promising approach that could be applied for this purpose is quantitative structure-activity relationships (QSAR) [5], [6].The QSAR approach is based on defining mathematical dependencies between the variance in molecular structures (encoded by so-called molecular descriptors), and the variance in a given physico-chemical or biological property (so-called endpoint) in a set of compounds. In practice, this means that if one has experimentally measured substituent constants, other physico-chemical properties or calculated some molecular parameters for a group of similar chemicals and toxicological data are available only for a part of this group, one is able to interpolate the lacking data from the molecular descriptors and a suitable mathematical model. Such predictive computational models could help to reduce the number and cost of synthesis and further requirements of characterization and testing as well as to design nanoparticles having the properties required for their future applications that are simultaneously safe for human health and the environment [7].

The concept of QSAR for nanoparticles (nano-QSAR) was already proved [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. However, developing in silico models for nanoparticles is a new and still evolving area of research. There are serious limitations related to developing nano-QSARs [7].The first one is a lack of sufficiently numerous and systematic experimental data as well as appropriate descriptors able to express specificity of nanostructure, while the second one is a lack of rational structure-activity modeling procedures and software tools to screen large numbers of nanoparticles and facilitate nano-QSAR modeling. The existing methods and modeling protocols should be specifically profiled for nanoparticles [7].This paper presents and discusses open access, standalone software tools developed under the NanoBRIDGES project [18](http://nanobridges.eu/), for performing QSAR and nano-QSAR modeling. The software tools reported here can be used at various stages of QSAR and nano-QSAR modeling as shown in the Fig. 1.

Section snippets

Software tools

The software tools discussed here are developed under NanoBRIDGES project to assist nano-QSAR modeling. However, note that almost all the software tools are not limited to just nano-QSAR modeling but one can also effectively use them for performing QSAR modeling in general. All the tools are standalone, open access, and freely available at the NanoBRIDGES official website (http://nanobridges.eu/software/), DTC lab website (http://dtclab.webs.com/software-tools), and Jadavpur University official

Validation of the tools and additional information

The software tools discussed here are validated by performing manual calculations on known data sets and/or confirmed from the output with well-known commercial software like MINITAB. [22] Also, we would like to mention some additional tools, although not developed under NanoBRIDGES project, but are available at the DTC lab website (http://dtclab.webs.com/software-tools) for validation of the QSAR models such as MLRplus validation tool (to develop MLR model and perform complete validation, i.e.

Availability and requirements

Download links:

NanoBRIDGES official website (http://nanobridges.eu/software/);
DTC lab website (http://dtclab.webs.com/software-tools); and
Jadavpur University official website (http://teqip.jdvu.ac.in/QSAR_Tools/).

Operating Systems:

C++ Programs: Windows (32 bit, 64 bit)
Java Programs: Platform independent (Windows, Linux, Macs)

Programming Language: C++, Java

External Libraries used:

Java Programs:

Java Statistical Classes (JSC) (http://www.jsc.nildram.co.uk/)
Apache Commons Mathematics Library (//commons.apache.org/proper/commons-math/

Independent testing

The NanoBRIDGES software tools were separately tested by two independent experts in the QSAR field. The signed review reports in full are uploaded as the Supplementary Information. Below are given unedited excerpts from the original reports:

Review 1 (Written by Dr. Andrey A Toropov, Istituto di Ricerche Farmacologiche Mario Negri, Italy, andrey.toropov@marionegri.it)

“The checking up has shown:

(i)
Each tool is downloadable
(ii)
Each tool is described in detail in manual
(iii)
Each tool is properly working

“This

Conclusion

In this paper, we have discussed various software tools that are developed under the NanoBRIDGES project. The tools are standalone, user friendly and openly accessible from NanoBRIDGES official website (http://nanobridges.eu/software/), DTC lab website (http://dtclab.webs.com/software-tools) and Jadavpur University official website (http://teqip.jdvu.ac.in/QSAR_Tools/). As discussed, the tools are not only limited to carry out nano-QSAR modeling but they can also be used to perform general QSAR

Acknowledgement

The authors are grateful for the financial support from the European Commission through the Marie Curie IRSES program, NanoBRIDGES project (FP7-PEOPLE-2011-IRSES, Grant Agreement number 295128) and through NanoPUZZLES project ( grant agreement #309837). PA thanks the Department of Biotechnology (DBT), New Delhi for financial assistance. RBA thanks the Indian Council of Medical Research (ICMR),New Delhi for financial assistance in the form of senior research fellowship (SRF).

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¹: These authors have contributed equally to this work.

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Software Description“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling

Abstract

Introduction

Section snippets

Software tools

Validation of the tools and additional information

Availability and requirements

Independent testing

Conclusion

Acknowledgement

Adv. Drug Deliv. Rev.

Nano Today

Toxicol. In Vitro

Ecotoxicol. Environ. Saf.

Toxicology

J. Mol. Graph. Model.

Eur. J. Pharm. Sci.

Chemom. Intell. Lab. Syst.

Chemom. Intell. Lab. Syst.

Anal. Chim. Acta

Chemom. Intell. Lab. Syst.

Anal. Chim. Acta

Anal. Chim. Acta

Comput. Biomed. Res.

Expert Syst. Appl.

Nanomaterials and nanoparticles: sources and toxicity

Biointerphases

Mechanisms of nanoparticle-induced oxidative stress and toxicity

BioMed. Res. Int.

Nano (Q) SAR: Challenges, pitfalls and perspectives

Nanotoxicology

Modelling and predicting the biological effects of nanomaterials

SAR QSAR Environ. Res.

Toward the development of "nano-QSAR": Advances and challenges

Small

Quantitative nanostructure–activity relationship modeling

ACS Nano

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

Nat. Nanotechnol.

Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles

Comb. Chem. High Throughput Screen.

Modeling biological activities of nanoparticles

Nano Lett.

Software Description
“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling