Antibacterial activity of griseofulvin analogues as an example of drug repurposing
Graphical abstract
Forty two griseofulvin derivates were evaluated for their antibacterial activity based on the predictions using Way2Drug computational drug repurposing platform, according to which it may have antibacterial activity. Docking studies were perform to find possible mechanism of antibacterial activity.
Introduction
Micro-organisms resistant to antimicrobials threaten the health of many people worldwide, particularly in developing countries, as both old and new infectious diseases remain a dangerous threat to public health. Morbidity and mortality rates have increased due to the low effectiveness of available medications and the development of resistant strains. The number of new classes of antibiotics in the drug discovery process has been declining, so there is a need for new approaches for the treatment of bacterial infections [1], [2], [3]. One attractive alternative is repurposing of drugs that have been approved previously for other diseases. Indeed, they have the advantage that their pharmacological and toxicological profiles have been well characterized [4], [5], [6], [7], [8], [9], [10], and the cost of repurposing is lower than that for de-novo drug development. Furthermore, many potential drugs never reach clinical testing, and fewer than 15% of compounds that enter clinical development ultimately receive approval despite the majority of them being deemed safe [11]. For these compounds, finding new indications can rapidly bring benefits to patients [6]. One recent example is the discovery of antifungal activity of the known antibacterial drugs polymyxin B and colistin, including action on multi-drug-resistant fungal strains [12].
The authors created a computational platform, Way2Drug, which offers tools for in-silico drug repurposing [13], including a database of drugs that have been launched worldwide and PASS (Prediction of Activity Spectra for Substances) software for prediction of biological activity spectra based on the structural formula of active pharmaceutical ingredients [14]. A comparative study demonstrated that this approach outperformed several other accessible web services in the prediction of new indications for well-known and recently patented repurposed drugs [15]. This approach was used for the prediction of antifungal activity of 42 synthesized griseofulvin derivatives [16], and the predicted biological activity profiles also included antibacterial action.
During the systematic analysis of antibacterial activity predictions for the launched drugs using the Way2Drugs platform, this activity was predicted with probability Pa=0.445 for griseofulvin. The relatively low Pa value estimated for the antibacterial activity of griseofulvin means that the similarity of this compound to the structures of antibacterial agents included in the PASS training set is rather low; thus, if the prediction is confirmed by experiment, this may result in the discovery of a new chemical entity [17].
It has been reported previously that griseofulvin has weak antibacterial activity against Staphylococcus aureus [minimal inhibitory concentration (MIC) >128 µg/mL] [18] and Bacillus subtilis (MIC>100 µg/mL) [19]. However, some antibacterial agents demonstrate potent activity against some bacteria, yet are almost completely inactive against other bacteria [20]. Thus, the authors decided to evaluate the probability of antibacterial activity for 42 recently synthesized and published griseofulvin derivatives [16]. If antibacterial activity is predicted for the majority of the synthesized compounds, it may be considered as typical for a particular chemical series [21] and should be tested by experiment. Moreover, evaluating the particular activity of several dozen griseofulvin analogues may be the first step for application of the so-called ‘SOSA’ (selective optimization of side activities) approach [22]. Antibacterial activity was predicted for 33 of the 42 compounds, so the authors decided to test the antibacterial activity of griseofulvin and its derivatives on a panel of Gram-positive and Gram-negative bacteria.
It should be mentioned that these compounds were tested for antifungal activity, which was much higher than that of reference drugs [16].
This paper describes the results of in-silico and in-vitro evaluation of the antibacterial activity of griseofulvin derivatives.
Section snippets
PASS predictions
PASS implemented in the Way2Drug platform is based on a robust analysis of structure–activity relationships (SAR) in a heterogeneous training set that currently includes several hundred thousand active compounds from different chemical classes [23]. SAR analysis is performed using multi-level neighbourhood of atoms descriptors and naïve Bayes method [24]. The average accuracy of prediction is approximately 0.95. Approximately 7000 antibacterial agents are included in the PASS training set; for
Chemistry
Synthesis and characterization of these compounds have been reported previously [19].
Computational prediction of antibacterial activity
As antibacterial activity was predicted for griseofulvin (see Fig. 1), the authors decided to use PASS Online to predict antibacterial activity and molecular mechanisms for 42 recently published griseofulvin derivatives [16]. The results are given in Table 1.
As shown in Table 1, in addition to the general term ‘antibacterial action’, two molecular mechanisms were also predicted in PASS Online: ‘protein
Conclusion
Forty-two compounds were evaluated for antibacterial activity using an in-silico computer-aided approach implemented in PASS software. Analysis of data on analogues of the studied compounds identified using a similarity search in the Clarivate Analytics Integrity portal confirmed their novelty in the field of antibacterials, which corresponded to the relatively low probability of those activities estimated by PASS.
Experimental evaluation of antibacterial activity revealed that all of the
Acknowledgements
Computational predictions of biological activity using the Way2Drug computational platform and PASS software, and analysis of molecular similarity using Clarivate Analytics Integrity (Dmitry Druzhilovskiy and Vladimir Poroikov) were performed in the framework of the Russian State Academies of Sciences Fundamental Research Program for 2013–2020.
Declarations
Funding: None.
Competing interests: None declared.
Ethical approval: Not required.
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