Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Exploring the Anti-Adherence Potential of Skt35 to Combat Catheter-Associated Staphylococcus aureus Infections: Efficacy, Toxicity and Mechanism of Action

Catheter-associated urinary tract infections (CAUTIs), often caused by biofilm-forming Staphylococcus aureus, present significant clinical challenges. Skt35, a dioxopiperidinamide derivative of cinnamic acid, was investigated for its potential antibacterial and antibiofilm activities against S. aureus biofilms. The antibacterial effect of Skt35 was assessed using the zone of inhibition and microdilution methods, revealing a minimum inhibitory concentration (MIC) of 250 µM. Antibiofilm properties were confirmed through crystal violet assays, scanning electron microscopy and confocal laser scanning microscopy, showing significant biofilm inhibition at the Sub-MIC. In an in vitro bladder model, Skt35-coated silicone catheter tubes exhibited significant antiadhesive effects. Zebrafish embryo tests indicated no toxicity at concentrations up to 125 µM. Molecular docking and simulation analysis revealed strong binding affinities of Skt35 to Accessory Gene Regulator A (-7.9 kcal/mol) and Lux Small protein (-4.96 kcal/mol), suggesting potential disruption of quorum sensing and gene expression in S. aureus, making it a promising candidate for catheter coatings to prevent CAUTIs.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Synthesis of Quinolones and Zwitterionic Quinolonate Derivatives with Broad-Spectrum Antibiotic Activity

Quinolones are one of the most extensively used therapeutic families of antibiotics. However, the increase in antibiotic-resistant bacteria has rendered many of the available compounds useless. After applying our prediction model of activity against E. coli to a library of 1000 quinolones, two quinolones were selected to be synthesized. Additionally, a series of zwitterionic quinolonates were also synthesized. Quinolones and zwitterionic quinolonates were obtained by coupling the corresponding amine with reagent 1 in acetonitrile. Antibacterial activity was assessed using a microdilution method. All the compounds presented antibacterial activity, especially quinolones 2 and 3, selected by the prediction model, which had broad-spectrum activity. Furthermore, a new type of zwitterionic quinolonate with antibacterial activity was found. These compounds can lead to a new line of antimicrobials, as the structures, and, therefore, their properties, are easily adjustable in the amine in position 4 of the pyridine ring.

Antibiotic resistant bacteria: current situation and treatment options to accelerate the development of a new antimicrobial arsenal

INTRODUCTION

Antibiotic resistance is one of the biggest public health threats worldwide. Currently, antibiotic-resistant bacteria kill 700,000 people every year. These data represent the near future in which we find ourselves, a "post-antibiotic era" where the identification and development of new treatments are key. This review is focused on the current and emerging antimicrobial therapies which can solve this global threat.

AREAS COVERED

Through a literature search using databases such as Medline and Web of Science, and search engines such as Google Scholar, different antimicrobial therapies were analyzed, including pathogen-oriented therapy, phagotherapy, microbiota and antivirulent therapy. Additionally, the development pathways of new antibiotics were described, emphasizing on the potential advantages that the combination of a drug repurposing strategy with the application of mathematical prediction models could bring to solve the problem of AMRs.

EXPERT OPINION

This review offers several starting points to solve a single problem: reducing the number of AMR. The data suggest that the strategies described could provide many benefits to improve antimicrobial treatments. However, the development of new antimicrobials remains necessary. Drug repurposing, with the application of mathematical prediction models, is considered to be of interest due to its rapid and effective potential to increase the current therapeutic arsenal.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

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.

New Pharmacokinetic and Microbiological Prediction Equations to Be Used as Models for the Search of Antibacterial Drugs

Currently, the development of resistance of Enterobacteriaceae bacteria is one of the most important health problems worldwide. Consequently, there is a growing urge for finding new compounds with antibacterial activity. Furthermore, it is very important to find antibacterial compounds with a good pharmacokinetic profile too, which will lead to more efficient and safer drugs. In this work, we have mathematically described a series of antibacterial quinolones by means of molecular topology. We have used molecular descriptors and related them to various pharmacological properties by using multilinear regression (MLR) analysis. The regression functions selected by presenting the best combination of a number of quality and validation metrics allowed for the reliable prediction of clearance (CL), and minimum inhibitory concentration 50 against Enterobacter aerogenes (MIC50Ea) and Proteus mirabilis (MIC50Pm). The obtained results clearly reveal that the combination of molecular topology methods and MLR provides an excellent tool for the prediction of pharmacokinetic properties and microbiological activities in both new and existing compounds with different pharmacological activities.

Molecular Topology for the Search of New Anti-MRSA Compounds

The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DF_MRSA1, DF_MRSA2, DF_MRSA3 and DF_MRSA4 (DF_MRSA was built in a previous study), all with good statistical parameters, such as Fisher-Snedecor F (>68 in all cases), Wilk's lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.