No abstract

With the pressing issue of antibiotic resistance, there is a constant need for new antibiotics. However, the fact that traditional methods of drug discovery are expensive and time-consuming has discouraged the pharmaceutical industry, leaving the burden of discovery to research institutions. This is where quantitative structure-activity relationship (QSAR) methods become a key tool in fighting multidrug-resistant bacteria, seeing as they provide useful information for the rational design of new active molecules at a minimal cost. A variety of linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of the molecules. QSAR models have proven to be effective in rapidly providing lead compound candidates against resistant bacteria such as methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas spp., Bacillus subtilis, or Mycobacterium tuberculosis. Moreover, QSAR methods allow for a deeper analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles. The information obtained from QSAR studies makes optimizing an existing drug simpler, which is a cost-effective approach to obtain new treatments against increasingly resistant bacteria.

Aim: Due to antibiotic resistance and the lack of investment in antimicrobial R&D, quantitative structure–activity relationship (SAR) methods appear as an ideal approach for the discovery of new antibiotics. Result & methodology: Molecular topology and linear discriminant analysis were used to construct a model to predict activity against Escherichia coli. This model establishes new SARs, of which, molecular size and complexity ( Nclass), stand out for their discriminant power. This model was used for the virtual screening of the Index Merck database, with results showing a high success rate as well as a moderate restriction. Conclusion: The model efficiently finds new active compounds. The topological index Nclass can act as a filter in other quantitative structure–activity relationship models predicting activity against E. coli.

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

Experimentation in mammals is a long and expensive process in which ethical aspects must be considered, which has led the scientific community to develop alternative models such as that of Galleria mellonella. This model is a cost and time effective option to act as a filter in the drug discovery process. The main limitation of this model is the lack of variety in the solvents used to administer compounds, which limits the compounds that can be studied using this model. Five aqueous (DMSO, MeOH, acetic acid, HCl and NaOH) and four non-aqueous (olive oil, isopropyl myristate, benzyl benzoate and ethyl oleate) solvents was assessed to be used as vehicles for toxicity and antimicrobial activity in vivo assays. All the tested solvents were innocuous at the tested concentrations except for NaOH, which can be used at a maximum concentration of 0.5 M. The toxicity of two additional compounds, 5-aminosalicylic acid and DDT, was also assessed. The results obtained allow for the testing of a broader range of compounds using wax moth larvae. This model appears as an alternative to mammal models, by acting as a filter in the drug development process and reducing costs and time invested in new drugs.

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

Fluorimetric analysis is still a growing line of research in the determination of a wide range of organic compounds, including pharmaceuticals and pesticides, which makes necessary the development of new strategies aimed at improving the performance of fluorescence determinations as well as the sensitivity and, especially, the selectivity of the newly developed analytical methods. In this paper are presented applications of a useful and growing tool suitable for fostering and improving research in the analytical field. Experimental screening, molecular connectivity and discriminant analysis are applied to organic compounds to predict their fluorescent behaviour after their photodegradation by UV irradiation in a continuous flow manifold (multicommutation flow assembly). The screening was based on online fluorimetric measurement and comprised pre-selected compounds with different molecular structures (pharmaceuticals and some pesticides with known 'native' fluorescent behaviour) to study their changes in fluorescent behaviour after UV irradiation. Theoretical predictions agree with the results from the experimental screening and could be used to develop selective analytical methods, as well as helping to reduce the need for expensive, time-consuming and trial-and-error screening procedures.

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four equations were constructed, named DF1, DF2, DF3, and DF4, all with good statistical parameters such as Fisher–Snedecor’s F (over 25 in all cases), Wilk’s lambda (below 0.36 in all cases) and percentage of correct classification (over 80% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. From the four discriminant functions, it can be extracted that the presence of sp3 carbons, ramifications, and secondary amine groups in a molecule enhance antibacterial activity, whereas the presence of 5-member rings, sp2 carbons, and sp2 oxygens hinder it. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of antibacterial activity.