Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model

Accepted Manuscript Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model Ouahid Ben Ghanem, Syed Nasir Shaha, Jean-Marc Lévêque, M.I. Abdul Mutalib, Mohanad El-Harbawi, Amir Sada Khan, Mohamad Sahban Alnarabiji, Hamada AlAbsi, Zahoor Ullah PII:

S0045-6535(17)31979-3

DOI:

10.1016/j.chemosphere.2017.12.018

Reference:

CHEM 20398

To appear in:

ECSN

Received Date: 14 September 2017 Revised Date:

29 November 2017

Accepted Date: 4 December 2017

Please cite this article as: Ghanem, O.B., Shaha, S.N., Lévêque, J.-M., Mutalib, M.I.A., El-Harbawi, M., Khan, A.S., Alnarabiji, M.S., Al-Absi, H., Ullah, Z., Study of the antimicrobial activity of cyclic cationbased ionic liquids via experimental and group contribution QSAR model, Chemosphere (2018), doi: 10.1016/j.chemosphere.2017.12.018. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Study of the Antimicrobial Activity of Cyclic Cation-Based Ionic Liquids via Experimental

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and Group Contribution QSAR Model

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Ouahid Ben Ghanema*, Syed Nasir Shahab, Jean-Marc Lévêquec, M.I. Abdul Mutaliba, Mohanad

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El-Harbawid, Amir Sada Khane,f, Mohamad Sahban Alnarabijia, Hamada Al-Absig, Zahoor

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Ullahh a

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Faculty of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Malaysia

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Department of Chemical Engineering, COMSATS institute of information Technology, Lahore c

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LCME/SCeM, Univ. Savoie Mont Blanc, LCME, F-73000 Chambéry, France

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Chemical Engineering Department, King Saud University, Riyadh 11421, Kingdom of Saudi Arabia

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Centre of Research in Ionic Liquids (CORIL), Department of Chemical Engineering, Universiti Teknologi PETRONAS, 31750 Tronoh, Perak, Malaysia

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Department of Chemistry, University of Science and Technology, Bannu 28100, Khyber Pakhtunkhwa, Pakistan

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Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, 93350 Kuching Sarawak, Malaysia

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Department of Chemistry, The Balochistan University of IT, Engineering and Management

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Sciences (BUITEMS), Takatu Campus, Quetta 87100, Pakistan

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* Corresponding author:

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E-mail: [email protected]

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Abstract

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Over the past decades, Ionic liquids (ILs) have gained considerable attention from the scientific

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community in reason of their versatility and performance in many fields. However, they

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nowadays remain mainly for laboratory scale use. The main barrier hampering their use in a

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larger scale is their questionable ecological toxicity. This study investigated the effect of

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hydrophobic and hydrophilic cyclic cation-based ILs against four pathogenic bacteria that infect

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humans. For that, cations, either of aromatic character (imidazolium or pyridinium) or of non-

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aromatic nature, (pyrrolidinium or piperidinium), were selected with different alkyl chain lengths

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and combined with both hydrophilic and hydrophobic anionic moieties. The results clearly

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demonstrated that introducing of hydrophobic anion namely bis((trifluoromethyl)sulfonyl)amide,

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[NTF2] and the elongation of the cations substitutions dramatically affect ILs toxicity behaviour.

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The established toxicity data [50% effective concentration (EC50)] along with similar endpoint

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collected from previous work against Aeromonas hydrophila were combined to developed

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quantitative structure-activity relationship (QSAR) model for toxicity prediction. The model was

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developed and validated in the light of Organization for Economic Co-operation and

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Development (OECD) guidelines strategy, producing good correlation coefficient R2 of 0.904

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and small mean square error (MSE) of 0.095. The reliability of the QSAR model was further

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determined using k-fold cross validation.

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Keywords: ionic liquids, antimicrobial activity, EC50, functional group contribution, QSAR

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1. Introduction The use of greener solvents in industrial processes has been gaining attention over the last two

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decades because of the alarming and daily increased rates of pollutants discharged into the

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ecosystem worldwide. Ionic liquids (ILs) comprise a large chemical class of millions of

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compounds that theoretically exist. Their negligible vapour pressure, non-flammability, high

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thermal and chemical stability, alteration capabilities, and other properties have given them a

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green image within research and development community [1-3]. ILs are defined as organic salts

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with melting points typically below 100 °C. These neoteric solvents are thought to be a greener

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and more useful chemical class that can be used to replace toxic and volatile solvents. Above all,

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their non-volatile character may greatly contribute to reduce drastically amounts of atmospheric

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pollutants daily released from chemical industry plants by substituting traditional organic

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molecular solvents, which are one of the main sources of VOCs. However, their non-volatility is

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not the only factor driving their environmental fate. In fact, the green image typically associated

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with ILs because of their negligible vapour pressure has been questioned [4-6]. Several studies

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have shown indeed that some ILs could be even more toxic to aquatic organisms than their

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starting materials and volatile organic solvents that they were supposed to replace [7, 8].

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Furthermore, their considerable water solubility for most of them, even for those claimed as

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hydrophobic allow their facilitated dispersion into aquatic systems, increasing concerns

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regarding their pollution power [9-12]. Therefore, evaluating their overall toxicity has become a

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burgeoning field of investigation.

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Microorganisms have short generation times, ecological and industrial relevance, and quick

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growth [13-16]. Therefore, they can serve as ideal starting points for IL structure–activity

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relationship investigations. However, the antimicrobial activity of various kind of ILs have been

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investigated using quantitative and qualitative methods. Several studies have clearly

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demonstrated that numerous kinds of ILs have the capability to inhibit the bacterial growth of the

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microorganisms [17-19]. On the other hand, some research groups focused on the development

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of novel-nontoxic ILs. For example, Prydderch et al., [20] developed 10 mandelic acid based ILs

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with low antimicrobial activity against 13 bacterial species they were screened against.

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Quantitative and qualitative methods can be used to assess the antimicrobial potency of ILs

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against microbial species, including disc diffusion and well diffusion tests, minimum inhibitory

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concentration, and the minimum bactericidal/fungicidal concentration. The evaluation of

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antimicrobial activity using quantitative methods such as micro-dilution testing are limited

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compared to qualitative assays [21]. The evaluation of the antimicrobial activity of ILs, i.e.

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inhibition of bacterial growth, could demonstrate their potential in different applications [22].

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For example, high antimicrobial activity character can be a valuable property in medicine

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application; in contrast, it may seriously hinder their biotechnology applications.

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In this study, 24 ILs comprised of cyclic cations, particularly imidazolium, pyridinium,

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piperidinium, and pyrrolidinium, all paired with different anions and alkyl chain substitutes

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varying from C2 to C10, were synthesized and characterized. Their antimicrobial potencies were

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assessed against four human pathogenic bacteria as 50% effective concentration (EC50) to

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identify the toxic/nontoxic structural elements in order to draw the map for designing low toxic

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ILs.

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The established EC50 against Aeromonas hydrophila and other data against same organism were

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collected from previous studies to develop a linear quantitative structure-activity relationship

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(QSAR) model linking the microbial activity of the studied ILs to their molecular structures. The

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QSAR models have been developed within the guidelines of the Organization of Economic Co-

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operation and Development (OECD) for regulatory QSAR models [23, 24], and validated using

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k-fold cross validation technique to confirm their stability. The second goal focuses on the

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validation of the experimental results using the QSAR model based on the group contribution

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method. The prediction of the antimicrobial activity against human pathogens bacteria through

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QSAR techniques has been rarely reported in literatures [25]. Therefore, finding an obvious

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relation between the structural elements of ILs and their response on the selected organism

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(EC50) using prediction model will promote more confidence to the use of QSAR technique as an

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alternative but unavoidable toxicity assays.

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2. Materials and Experimental 2.1 Ionic Liquids

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The ILs used for antimicrobial activity measurement in this study were as follows: 1-butyl-3-

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methylpyridinium

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[C4mpy][SCN],

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methylpyridinium

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[C8mpy][Br],

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methylpyridinium

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[C10mpy][SCN],

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methylpyrrolidinium

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[C8mpyrr][Br],

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methylpiperidinium

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[C6mpip][Br],

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methylpiperidinium

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methylpiperidinium bis((trifluoromethyl)sulfonyl)amide, [C8mpip][NTf2], 1-butylpyridinium

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bis((trifluoromethyl)sulfonyl)amide,

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bis((trifluoromethyl)sulfonyl)amide,

[C8py][NTf2],

1-butyl-3-methylpyrrolidinium

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bis((trifluoromethyl)sulfonyl)amide,

[C4mpyrr][NTf2],

1-octyl-3-methylpyrrolidinium

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bis((trifluoromethyl)sulfonyl)amide,

[C8mpyrr][NTf2],

1-butyl-3-methylimidazolium

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bis((trifluoromethyl)sulfonyl)amide,

[C4mim][NTf2],

1-octyl-3-methylimidazolium

bromide,

[C4mpy][Br],

1-butyl-3-methylpyridinium

1-hexyl-3-methylpyridinium

[C6mpy][SCN],

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thiocyanate,

1-octyl-3-methylpyridinium bromide,

bromide,

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[C4mpyrr][Br],

1-decyl-1-methylpyrrolidinium [C4mpip][Br],

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bromide,

1-octyl-1-methylpiperidinium

bromide,

bromide, 1-decyl-3thiocyanate,

[C2mpyrr][Br],

1-octyl-1-methylpyrrolidinium bromide,

[C10mpyrr][Br],

1-hexyl-1-methylpiperidinium bromide,

bis((trifluoromethyl)sulfonyl)amide,

[C4py][NTf2],

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1-hexyl-3-

[C8mpy][SCN],

1-decyl-3-methylpyridinium

1-ethyl-1-methylpyrrolidinium bromide,

[C6mpy][Br],

1-octyl-3-methylpyridinium

thiocyanate,

[C10mpy][Br],

thiocyanate,

1-butyl-1bromide, 1-butyl-1bromide,

[C8mpip][Br],

1-butyl-3-

[C4mpip][NTf2],

1-octyl-3-

1-octylpyridinium

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bis((trifluoromethyl)sulfonyl)amide, [C8mim][NTf2], 1-octyl-3-methylimidazolium thiocyanate,

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[C8mim][SCN]. The studied ILs were synthesized and characterized according to established

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procedures [26-28], syntheses routes and purification methods are provided in the supplementary

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information. 2.2 Microorganisms

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Four strains were used for antimicrobial activity assessment including the gram-positive strains Listeria

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monocytogenes L4 and Staphylococcus aureus S244 and gram-negative strain Escherichia coli E149 and

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Aeromonas hydrophila A97. These strains were obtained from the Institute of Medical Research (IMR)

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Kuala Lumpur, Malaysia.

2.3 Antimicrobial activity assay

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The micro-broth dilution test was conducted according to the standard protocol (CLSI-M07-A9,

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2008) developed by the Clinical and Laboratory Standard Institute Pennsylvania, USA, to

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determine the EC50 values of the studied ILs against human pathogenic bacteria [29, 30]. A

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single colony of each strain was first collected from a prepared petri dish plate in agar media

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(MHA), transferred to a 5 mL sterile broth media (MHB), and incubated at 37 °C overnight.

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Prior to prepare the 96-well plate, a standard stock solution equivalent to 0.5 McFarland

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standards [31] was prepared and confirmed by UV-analysis at 620 nm. The optical density was

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approximately 0.1 at 620 nm. Next, a 96-well plate for each IL was prepared as follows: 100 µL

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of pure MHB media was first injected in the second to last rows. Next, 100 µL of IL solution was

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added to the first two horizontal rows, followed by two-fold dilutions from the second to the

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seventh rows. The last horizontal row was left untreated as a control. To the wells, 100 µL of

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standard bacterial solution was added. The plate was incubated at 37 °C for 24 h. A microplate

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reader [Multiskan™ FC Microplate Photometer (Type.357), Thermo Scientific, Waltham, MA,

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USA] was used to determine EC50 values. Seven concentrations were tested to plot bacterial

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growth verses IL concentrations.

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2.4 Preparation of QSAR dataset, and molecular descriptors A QSAR model was developed to predict the antimicrobial activity of the ILs against A.

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hydrophila. The EC50 values established in this study were combined with other data obtained

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from the literature against the same bacterial species [32, 33]. A total diverse dataset comprised

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of 52 ILs featuring 4 different cations, and 11 organic and inorganic anions and alkyl chain

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lengths varying from C2 to C12.

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Molecular descriptors were developed based on the functional group contribution method [34,

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35]. IL structure features were divided into three main groups including cations, anions, and

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alkyl substituents. The cation subgroup consisted of four studied cations. The anions were

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classified based on their expected toxicity contribution. Three anion subgroups were set:

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hydrophilic, hydrophobic, and amino acid anions. This division enabled detection of the anion

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toxicity contribution based on their water nature and presence of amino and carboxylic acids in

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the case of amino acid anions. Previous studies have reported that aquatic organisms are very

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sensitive to increased alkyl chain lengths. Given this effect on IL-related toxicity, substituent

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subgroups were presented in the dataset using five descriptors. The descriptors are described in

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detail in Table 1, the full names and ILs structures used for the QSAR development were

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provided in the Supplementary Information, Table S.2, Fig.S.3 and Fig.S.4.

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2.5 Development and validation of the QSAR model

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Multiple linear regression (MLR) method was used to build the QSAR model, this model used to

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represent the mathematical relationship between the dependent and independent variables. The

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dependent variable is the [log (EC50)] in mM of the studied ILs against A. hydrophila, whereas

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the independent variables are the 12 descriptors used to represent the structural elements of the

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ILs set, Table 1. According to the selected descriptors in this work, the multiple linear regression

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model can be written as follow:

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( ) .   =  + a ∗  + a ∗  + a ∗ C  + a ∗  + a ∗ ! " +

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a# ∗ ! $" + % ∗ !

+ a& ∗ R  + a( ∗ ) + a ∗ )# + a ∗ R & + a ∗ )*+

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(1)

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Where a0, a1, …, a12 are defined as regression coefficients, and the Cim, Cpy, …, Rlong are the

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descriptors.

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The developed model was statistically evaluated to ensure its usefulness and predictive ability as

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screening tool for future use. However, the importance of each descriptor was first measured

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using pValue in order to exclude the insignificant ones. Therefore, descriptors with pValue

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greater than 0.05 were excluded. This is crucial step to ensure that the final QSAR model will be

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constructed using the influential descriptors only. After that, the final developed QSAR model

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was evaluated using correlation coefficient (R2), adjusted correlation coefficient (R2adj), and

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mean square error (MSE). In addition, F-test, and p-value were also used to evaluate the fitness

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of the QSAR model [36-38].

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Furthermore, validation of the model was conducted using k-fold cross validation technique. The

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matrix of the dataset was randomly divided into five sub-samples. Four sub-samples were used

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for the learning stage, whereas the remaining sub-sample was used to evaluate the model

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stability. The process was repeated five times, and the average correlation coefficient of the k-

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fold cross validation technique was then reported as (Q2)[39].

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3. Result and discussion 3.1 Antimicrobial activity

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Twenty-four ILs were synthesized and characterized. The synthesis procedures along with 1H

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and

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was conducted to assess the EC50 values of the studied ILs against the four selected

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microorganisms and were evaluated using the ELISA plate reader and are given Table 2. The

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influence of structural elements on antimicrobial activity was evaluated. Briefly, the influence of

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the pyridinium, pyrrolidinium, and piperidinium cations on the toxicity of ILs was studied using

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bromide as a counter anion. The results include also EC50 values of imidazolium-based ILs

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determined in our previous study [32].

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Table 1. Molecular descriptors based on group contribution methods

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Molecular descriptor

Cation (C)

Cim

Cpyrr Cpip R2

Influence of imidazolium cation. Value = 1 if it exists and 0 if not. Influence of pyridinium cation. Value = 1 if it exists and 0 if not. Influence of pyrrolidinium cation. Value = 1 if it exists and 0 if not. Influence of piperidinium cation. Value = 1 if it exists and 0 if not.

Influence of ethyl or hydroxyethyl substituents. Value = 1 if it exists and 0 if not. Influence of butyl substituent. Value = 1 if it exists and 0 if not. Influence of hexyl substituent. Value = 1 if it exists and 0 if not. Influence of octyl substituent. Value = 1 if it exists and 0 if not. Influence of decyl and dodecyl substituents. Value = 1 if it exists and 0 if not. Influence of hydrophilic anions: Cl, Br, SCN, DCA, and BF4. Value = 1 if it exists and 0 if not. Influence of hydrophobic anion: (NTf2). Value = 1 if it exists and 0 if not.

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Cpy

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C NMR Spectra are given in Supplementary Information. An antimicrobial activity test

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R6 R8

Rlong

Anion (A)

Aphilic Aphobic

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Influence of amino acid anions: Gly, Ala, Ser, Pro, and Asn. Value = 1 if it exists and 0 if not.

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The reported EC50 values indicate that the toxicity effect of the cations lies in their water nature

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and structural features rather than in the number of carbon atoms in the cycle. For example,

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aromatic cations paired with three carbon atoms within the cation ring such as imidazolium

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exhibit higher toxicity than piperidinium cation, which corresponds to the imidazolium ring with

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the addition of two carbon atoms. Aromatic cations, particularly imidazolium and pyridinium,

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clearly showed higher toxicity than non-aromatic pyrrolidinium and piperidinium cations as

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shown in Fig. 1. The higher toxicity of the aromatic based ILs can be explained by the higher

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solubility in water of these moieties as previously reported [10, 40, 41]. Moreover, the least toxic

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IL moiety was found to belong to pyrrolidinium-based ILs. For instance, the recorded EC50 value

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of [C4mpyrr][Br] against A. hydrophila was 111.7 mM, whereas those for [C4mpip][Br] and

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[C4mpy][Br] were 75.45 and 37.78 mM, respectively. The EC50 value for the imidazolium

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counterpart [C4mim][Br] was 47.17 mM [32]. It is clear that pyrrolidinium-based ILs are much

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less toxic than other cyclic cation-based ILs, which is in good agreement with previous studies

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[42, 43]. The reported EC50 indicates that the increase in toxicity follows the trend: pyrrolidinium

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< piperidinium < pyridinium ≤ imidazolium.

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Table 2. Antimicrobial activity of studied ILs presented in EC50 in mM No

ILs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

[C4mpy][Br] [C4mpy][SCN] [C6mpy][Br] [C6mpy][SCN] [C8mpy][Br] [C8mpy][SCN] [C10mpy][Br] [C10mpy][SCN] [C2mpyrr][Br] [C4mpyrr][Br] [C8mpyrr][Br] [C10mpyrr][Br] [C4mpip][Br] [C6mpip][Br] [C8mpip][Br] [C8mim][SCN] [C4mim][NTf2]

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* Imidazolium-based IL results were taken from ref [32] for comparison.

EC50, mM

A. hydrophila

E. coli

37.78 ± 0.066 61.27 ± 0.061 8.44 ± 0.048 10.76 ± 0.000 0.93 ± 0.078 0.54 ± 0.030 0.28 ± 0.055 0.02 ± 0.054 220.43 ± 0.046 111.70 ± 0.047 2.98 ± 0.048 0.47 ± 0.078 75.45 ± 0.064 11.46 ± 0.067 2.01 ± 0.072 2.44 ± 0.021 1.68 ± 0.048

45.12 ± 0.057 55.47 ± 0.069 9.75 ± 0.043 14.29 ± 0.0 1.09 ± 0.076 0.57 ± 0.028 0.27 ± 0.052 0.02 ± 0.060 222.19 ± 0.034 108.88 ± 0.047 3.22 ± 0.046 0.52 ± 0.070 80.4 ± 0.062 10.83 ± 0.067 1.91 ± 0.073 2.53 ± 0.023 1.64 ± 0.048

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Fig. 1. Influence of alkyl chain length on toxicity of different cations towards E. coli and L. monocytogenes*.

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L. monocytogenes 52.87 ± 0.049 59.31 ± 0.064 10.64 ± 0.042 12.23 ± 0.0 1.32 ± 0.074 0.68 ± 0.038 0.36 ± 0.038 0.030 ± 0.083 223.87 ± 0.029 107.64 ± 0.045 5.25 ± 0.042 0.81 ± 0.076 82.03 ± 0.062 13.76 ± 0.062 2.01 ± 0.070 3.17 ± 0.020 1.7 ± 0.047

S. aureus 38.41 ± 0.063 50.08 ± 0.076 7.10 ± 0.046 11.11 ± 0.0 1.01 ± 0.074 0.49 ± 0.032 0.23 ± 0.052 0.030 ± 0.050 210.52 ± 0.0338 107.41 ± 0.048 3.67 ± 0.044 0.53 ± 0.077 67.83 ± 0.070 11.02 ± 0.065 2.05 ± 0.067 2.33 ± 0.024 1.68 ± 0.043

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[C4py][NTf2] [C4mpyrr][NTf2] [C4mpip][NTf2] [C8mim][NTf2] [C8py] [NTf2] [C8mpyrr][NTf2] [C8mpip] [NTf2]

2.33 ± 0.034 2.73 ± 0.038 2.14 ± 0.048 0.40 ± 0.050 0.49 ± 0.034 0.53 ± 0.068 0.86 ± 0.047

2.3 ± 0.038 3.29 ± 0.042 2.05 ± 0.049 0.35 ± 0.060 0.47 ± 0.039 0.64 ± 0.078 0.90 ± 0.044

2.43 ± 0.040 3.86 ± 0.052 1.86 ± 0.059 0.37 ± 0.060 0.50 ± 0.040 0.75 ± 0.100 0.95 ± 0.043

2.39 ± 0.037 2.53 ± 0.026 2.04 ± 0.046 0.31 ± 0.060 0.43 ± 0.038 0.53 ± 0.058 0.86 ± 0.039

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The toxicity of hydrophobic ILs was studied using eight compounds bearing two different alkyl

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chains (butyl and octyl) with the same anion, [NTf2] which is the unique hydrophobic anion

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included in this study. The reported EC50 values for the hydrophobic moiety clearly shows that

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[NTf2] anion inhibits the growth of the studied organisms even when it was paired with short

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alkyl substituents. Fig 2. shows a comparison between [Br] and [NTF2] based ILs. For instance,

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it can be seen that the EC50 of [C4mpyrr][Br] against E.coli decreased from 108.88 mM to 3.29

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mM as a result of switching the anion to [NTF2]. The same conclusion can be drawn for the other

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ILs with different cations, as it can be seen in Table 2 and Fig 2. These findings are in good

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agreement with previous studies which reported the strong effect of the hydrophobic anion on the

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toxicity behavior of the ILs [19, 21]. In contrast, the studied hydrophilic anions [Br] and [SCN]

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showed marginal influence on the toxicity behavior of the ILs.

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The EC50 values of gentamicin were previously evaluated against three of the microorganisms

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found in this work, including E. coli, A. hydrophila, and L. monocytogenes, which were 1.86–

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2.00 mM [33]. Although gentamicin is one of the most effective antibiotics, many ILs evaluated

16

in this study were found to be more active against bacteria than gentamicin. More specifically,

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ILs with long alkyl chains (C8 and C10) with pyridinium cation and/or [NTF2] anion showed high

18

toxicity. The extension of the alkyl side chain leads to an increase in the ILs hydrophobicity,

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hence increasing the toxicity drastically. For example, all [NTF2] derivatives bearing an octyl

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side alkyl chain revealed EC50 values of 0.40–0.86 mM, reflecting their high antimicrobial

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activity, this was also observed for pyridinium derivatives with C8 or C10 side alkyl chains.

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It was early reported that the increase of the alkyl chain length and the presence of hydrophobic

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anions were found to be the dominant elements responsible for enhancing the toxicity of ILs on

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aquatic organisms such as daphnia magna, fish, and green algae [5, 22, 44-46]. The same

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conclusion can be drawn from this present work, and therefore pathogenic bacterial species can

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be strongly recommended for the studying of the ecotoxicity impact of the new designing

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chemicals more effectively.

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Thus, the contribution of IL structural elements on toxicity can be classified into two categories:

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Structural elements that possess high levels of toxicity when present within IL structures such as alkyl side chain starting from C8 or [NTf2] hydrophobic anion.

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Structural elements showing no obvious effect on increasing toxicity, such as cyclic cations, short to medium alkyl chain lengths, and hydrophilic anions.

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It is also worth to highlight that the studied microorganisms showed similar sensitivities when

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exposed to the same ILs in most cases. Therefore, the choice was made to develop the prediction

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QSAR model against one organism (A. hydrophila).

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E . coli L. monocytoge nes

10 7. 64

10 8. 88

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1. 7 2

TF

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1. 64

1. 86

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TF

[N

rr

p]

4

[C

m

py

pi

m

4

[C

]

2. 05

2. 43

]

]

TF

2

4

rr

py

4

[C

m

4

[C

M AN U

py ] [N

] [N

im m

2

TF

r] ] [B

r] m

4

[C

m 4

[C

1

py

p] pi

rr py m 4

[C

][B

[B

][ B

r]

r]

0

2. 3

3. 29

3. 86

SC

20

2

40

[C

45 .1 2

42 .5

52 .8 7

RI PT

82 .0 3

60

43 .2 1

-1

80

50

EC in mmol.L

80 .4

100

2

Fig. 2. Comparison between the toxicity of hydrophobic and hydrophilic anion based ILs*.

3

* Imidazolium-based IL results were taken from ref [32] for comparison.

4

3.2 Model development

The matrix representative of the dataset of this work was comprised of Log EC50 values (mM) of

6

52 ILs and 12 descriptors used to represent their structural elements, as can be seen in Table 3.

7

The model was constructed using two optimization steps. At first, a MLR QSAR model was

8

constructed using the whole dataset to identify the importance of each descriptor. As a result, the

9

first constructed QSAR model was written as follows:

AC C

EP

TE D

5



10


( ) .   = 2.012 − 0.051 ∗  − 0.129 ∗  + 0.353 ∗ C  + 0.121 ∗

11

 − 0.146 ∗ ! " − 1.132 ∗ ! $" − 0.242 ∗ R  − 0.295 ∗ ) − 0.799 ∗ )# − 1.536 ∗

12

R & − 2.740 ∗ )*+

13

However, and as shown in eqn 2 and Table 4, the model failed to identify any potential effect of

14

amino acid descriptor (AAA) on the toxicity, as the numerical value of its coefficient was zero,

(2)

14

ACCEPTED MANUSCRIPT

reflecting an undefined pValue. Therefore, the first QSAR model was constructed with 11

2

descriptors, Eqn 2. Based on the reported pValue for the first model, eight descriptors were

3

excluded as they have pValues greater 0.05, Table 4. The final model was then constructed using

4

the remaining four descriptors namely Aphobic, R6, R8, and R≥10, eqn 3.

5

Statistical analysis comparison between the first and final QSAR models was conducted as

6

shown in Table 5; the comparison demonstrated that the proposed final model was more reliable

7

than the first one. Moreover, the F-test increased from 46.5 to 111 when moving from the first to

8

the final model. In contrast, the p-value of the two models decreased from 2.88E-19 to 2.54E-24.

9

These two parameters clearly indicate a significant linear relationship between the selected

10

descriptors and toxicity. Fig. 3. shows the relationship between the experimental and predicted

11

log EC50. The final model showed a good correlation coefficient (R2) of 0.904 and low MSE of

12

0.095. However, a small reduction in the correlation coefficient of the final model was observed

13

compared to the one reported for the first model as a result of excluding 8 descriptors. The final

14

QSAR prediction model can be written as follows:

15


( ) .   = 1.653 − 0.972 ∗ ! $" − 0.634 ∗ )# − 1.284 ∗ R & − 2.546 ∗ )*+

16

(3)

EP

TE D

M AN U

SC

RI PT

1

17

Table 3. Molecular descriptors presenting ionic liquids structures with log EC50 against A.

19

hydrophila No 1 2 3 4 5 6 7 8 9

AC C

18

ILs

Cim

Cpy

Cpyrr

Cpip

Aphilic

Aphobic

AAA

R2

R4

R6

R8

R≥10

Log EC50

[C4mim][SCN] [C4mim][Br] [C4mim][Cl] [C6mim][BF4] [C6mim][Br] [C6mim][Cl] [C6mim][SCN] [C8mim][SCN] [C8mim][Cl]

0 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0

0 0 0 1 1 1 1 0 0

0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0

1.571 1.674 1.880 1.137 1.105 0.931 0.939 0.387 0.170

15

ACCEPTED MANUSCRIPT

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0

1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0

RI PT

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0

SC

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0

M AN U

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1

TE D

[C8mim][Br] [C8mim][DCA] [C8mim][BF4] [C10mim][Br] [C10mim][BF4] [C10mim][DCA] [C10mim][SCN] [C12mim][Br] [C2OHmim][Pro] [C2OHmim][Ser] [C2OHmim][Ala] [C2OHmim][Gly] [C8mim][Ala] [C8mim][Ser] [C8mim][Asn] [C8mim][Pro] [C8mim][Gly] [C4mpy][Br] [C4mpy][SCN] [C6mpy][Br] [C6mpy][SCN] [C8mpy][Br] [C8mpy][SCN] [C10mpy][Br] [C10mpy][SCN] [C2mpyrr][Br] [C4mpyrr][Br] [C8mpyrr][Br] [C10mpyrr][Br] [C4mpip][Br] [C6mpip][Br] [C8mpip][Br] [C4mim][NTf2] [C4py][NTf2] [C4mpyrr][NTf2] [C4mpip][NTf2] [C8mim][NTf2] [C8py] [NTf2] [C8mpyrr][NTf2] [C8mpip] [NTf2] [2OHim][Cl] [2OHimC][Cl] [2OHimC4][Cl]

EP

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

The accuracy and reliability of the model was further evaluated using a cross-validation

2

technique. The validation test showed high stability of the MLR model as the correlation

3

coefficient (Q2), and MSE values of the k-fold cross validation test were 0.907 and 0.124,

4

respectively. The reported parameters for the validation test were nearly equal to those recorded

5

for the main model, as shown in Table 5.

6 7

Table 4. Descriptor coefficients for QSAR models and their statistical importance parameter pValue

AC C

1

16

0.097 0.025 0.155 -1.000 -0.745 -0.523 -1.000 -1.301 1.695 1.886 1.409 1.543 0.439 0.674 0.301 0.610 0.450 1.577 1.787 0.926 1.032 -0.032 -0.268 -0.553 -1.699 2.343 2.048 0.474 -0.328 1.878 1.059 0.303 0.225 0.367 0.436 0.330 -0.398 -0.310 -0.276 -0.066 1.815 1.551 1.494

pValue

(Intercept)

2.012

8.15E-05

1.653

2.53E-26

Cim

-0.051

0.872

Removed

-

Cpy

-0.129

0.687

Removed

-

Cpyrr

0.353

0.289

Removed

-

Cpip

0.121

0.716

Removed

-

Aphilic

-0.146

0.308

Removed

-

Aphobic

-1.132

1.68E-07

-0.972

3.79E-10

AAA

0.000

NAN

Removed

-

R2

-0.242

R4

-0.295

R6

-0.799

R8

-1.536

Rlong

-2.740

SC

Descriptor

Coefficient of the first MLR model

RI PT

pValue

Coefficient of the final MLR model

M AN U

ACCEPTED MANUSCRIPT

0.469

Removed

-

0.350

Removed

-

0.015

-0.634

3.57E-05

1.22E-05

-1.284

9.45E-17

8.18E-11

-2.546

6.91E-24

*

2

The positive or negative coefficient for each descriptor was used to interpret the prediction

3

models. For instance, although cation descriptors were excluded, their coefficients were clearly

4

related to the experimental findings. As shown in Table 4 for the first model, the descriptors

5

presenting the aromatic cations with negative coefficients indicating their high toxicity

6

behaviour. Non-aromatic cation descriptors instead showed positive coefficients, indicating their

7

low toxicity. Additionally, pyrrolidinium cation showed the highest positive coefficient among

8

the studied cations, demonstrating its low toxic character. Based on the first QSAR model

9

observation, it can be seen that the cation core influenced the toxicity rate in the following

10

sequence: Cpy > Cim > Cpip > Cpyrr. This trend is similar to that highlighted in the experimental

11

section in which the order of the Cpy and Cim descriptors was changed.

AC C

EP

NAN: Not a number

TE D

1

17

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

1

Fig .3. Predicted versus measured log EC50 of studied ionic liquids for A. hydrophila using

3

equation 2.

4

Table 5. Statistical comparison of first and final QSAR models

5

MLR coefficient MLR coefficient of first model final model MSE 0.085 0.095 2 R 0.927 0.904 2 R adj 0.907 0.896 Q2 0.933 0.907 MSE cross validation 0.152 0.124 F-test 46.5 111 p-value 2.88E-19 2.54E-24 Moreover, the descriptors of alkyl chain substitutes had negative coefficients, increasing with

6

side chain elongation. This indicates their major contribution in the reduction of log EC50 values,

7

reflecting increasing toxicity. The numerical values of the alkyl chain substitute coefficients in

8

the first and final models were increased as alkyl chain length increased, Fig. 4. For instance, in

9

the first model, the numerical value of the longest alkyl chain used in the study (R≥10) was 11-

10

fold greater than that of the shortest alkyl chain (R2). This clearly shows that increasing the alkyl

TE D

2

AC C

EP

Estimated coefficient

18

ACCEPTED MANUSCRIPT

chain length reduces log EC50 values, indicating the high impact of alkyl chain elongation on

2

toxicity; these results agree with the experimental analysis. Negative coefficients and numerical

3

values greater than the one reported for R6 descriptor substitutes were observed for the [NTF2]

4

anion. The negative and high numerical value of the [NTF2] descriptor supports the experimental

5

finding that the [NTF2] anion drastically affects the overall ILs toxicity as compared to the

6

negligible effect reported for hydrophilic and amino acids anions.

RI PT

1

0.0

Numerical values of substituents descriptors (Rn)

SC

cofficients of final QSAR cofficients of first QSAR

M AN U

-0.5

-1.0

-1.5

-2.0

1

9 10 11

3

4

5

6

7

8

9

10

11

Alkyl length of the substituents

Fig. 4. Alkyl lengths of substituents vs numerical values for their descriptors

EP

8

2

AC C

7

TE D

-2.5

3.3 Compliance with the OECD guidelines.

12

In the previous section, the attempt was made to construct QSAR model in the light of the

13

proposed OECD guidelines. However, the present study involves a dataset of 52 ILs with

14

specific toxicity endpoint conducted under the same protocol against Aeromonas hydrophila,

15

which obeys to the OECD principle 1. The multiple linear regression technique employed for

16

model development is direct, transparent and reproducible and thereby obeys the unambiguity

19

ACCEPTED MANUSCRIPT

criterion of OECD principle 2. The domain of applicability defining the chemical space was a

2

clear as the model is limited for ILs with cyclic, hydrophobic and hydrophilic nature, (OECD

3

principle 3). Various statistical metrics describing the goodness-of-fit, which included R2, MSE,

4

validation test (Q2, MSE), etc., (OECD principle 4). Finally, the final model was able to

5

recognize the most toxic elements ([NTF2] and alkyl substitutions), OECD principle 5 (a

6

mechanistic interpretation). 4. Conclusion

SC

7

RI PT

1

The antimicrobial activities of 24 hydrophilic and hydrophobic ILs against four bacteria

9

pathogenic to humans were evaluated in this study. The results clearly showed a strong

10

relationship between the structural elements of ILs and their toxicity behaviour. Designing low

11

toxic ILs is possible by using a combination of structural elements that possess low toxicity such

12

as short alkyl substitutes, hydrophilic anions, and nonaromatic cations. The long alkyl substitutes

13

and hydrophobic anion [NTF2] being the most toxic structural elements. Group contribution

14

method was used to develop a reliable QSAR model for predicting IL toxicity in the early design

15

stage. The first and final models agreed well with all experimental findings. The experimental

16

results were validated with the first and final QSAR models and showed good correlation

17

coefficients greater than 0.9 in all cases. This agreement between the QSAR model and

18

experimental results indicate that the descriptors used to represent IL structural elements and the

19

development technique were successful. The final model was constructed with fewer but

20

influential descriptors.

21

Acknowledgements

22

This work was funded by the YAYASAN UTP Project No: 0153AAA20 and Centre of research

23

for Ionic Liquid (CORIL). The author would like also to thank Universiti Teknologi

AC C

EP

TE D

M AN U

8

20

ACCEPTED MANUSCRIPT

PETRONAS. Mohanad El-Harbawi also extend his appreciation to the Deanship of Scientific

2

Research at King Saud University for supporting this work through research group no. RGP-303.

3

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Highlights Several hydrophilic and hydrophobic ILs were synthesized and characterized. Antimicrobial activity was assessed against four human pathogenic bacteria.

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Hydrophobic ionic liquids were more toxic than their hydrophilic counterparts. The group contribution method was used for QSAR model development.

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A linear QSAR model was used to fit antimicrobial activity to IL structure elements.