Structural biology approaches to antibacterial drug discovery

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Drug target central

BACKGROUND

One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

OBJECTIVE

Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development.

CONCLUSION

Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.

A class of selective antibacterials derived from a protein kinase inhibitor pharmacophore

As the need for novel antibiotic classes to combat bacterial drug resistance increases, the paucity of leads resulting from target-based antibacterial screening of pharmaceutical compound libraries is of major concern. One explanation for this lack of success is that antibacterial screening efforts have not leveraged the eukaryotic bias resulting from more extensive chemistry efforts targeting eukaryotic gene families such as G protein-coupled receptors and protein kinases. Consistent with a focus on antibacterial target space resembling these eukaryotic targets, we used whole-cell screening to identify a series of antibacterial pyridopyrimidines derived from a protein kinase inhibitor pharmacophore. In bacteria, the pyridopyrimidines target the ATP-binding site of biotin carboxylase (BC), which catalyzes the first enzymatic step of fatty acid biosynthesis. These inhibitors are effective in vitro and in vivo against fastidious gram-negative pathogens including Haemophilus influenzae. Although the BC active site has architectural similarity to those of eukaryotic protein kinases, inhibitor binding to the BC ATP-binding site is distinct from the protein kinase-binding mode, such that the inhibitors are selective for bacterial BC. In summary, we have discovered a promising class of potent antibacterials with a previously undescribed mechanism of action. In consideration of the eukaryotic bias of pharmaceutical libraries, our findings also suggest that pursuit of a novel inhibitor leads for antibacterial targets with active-site structural similarity to known human targets will likely be more fruitful than the traditional focus on unique bacterial target space, particularly when structure-based and computational methodologies are applied to ensure bacterial selectivity.

Identifying targets for drug discovery using bioinformatics

BACKGROUND

Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them.

OBJECTIVE

The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time.

METHODS

Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with.

RESULTS

We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.