Antibacterial

Antibacterial destroys bacteria or suppresses their growth or their ability to reproduce. Chemicals such as chlorine, and antibiotic drugs all have antibacterial properties. Such products do not reduce the risk for symptoms of viral infectious diseases in otherwise healthy persons.

LAH4
PAβN dihydrochloride
100929-99-5
100986-85-4
Levofloxacin
100986-85-4
101363-10-4
Rufloxacin
101363-10-4
Tigemonam
102507-71-1
Cadazolid
1025097-10-2
102-65-8
Sulfaclozine
102-65-8
103060-53-3
Daptomycin
103060-53-3
104010-37-9
Ceftiofur Sodium
104010-37-9
104376-79-6
10592-13-9
Doxycycline HCl
10592-13-9
106017-08-7
106017-08-7
1070-11-7
Ethambutol HCl
1070-11-7
1071638-38-4
MAC13243
1071638-38-4
1075240-43-5
Omadacycline tosylate
1075240-43-5

Background


Impact of Genome Research on Development of New Antibacterials

Comparative genome analysis provided novel approaches for antibacterial drug discovery in the pharmaceutical industry and academia. Importantly, the antibiotic classes currently used in clinical practice target only a limited number of cellular functions: cell wall biosynthesis, protein biosynthesis, nucleic acid metabolism, and DNA replication. Consequently, the pharmaceutical industry in the late 1990s started to transfer genome information into target identification programs to identify novel target structures in alternative cellular pathways. This strategy should lead to the identification of novel compounds that inhibit biosynthetic pathways presently not addressed by current antibacterials. The advantage of such a strategy is claimed to lie in the avoidance of resistance development and crossresistance to convential antibiotics. This is not necessarily the fact, however, the probability that inhibition of a novel target can overcome current resistance problems is very high.

Microbial Genomics and Antimicrobial Drug Discovery

The investigation of novel targets in new pathways has been suggested to be especially promising as it is assumed that antimicrobials targeting new pathways will exhibit most likely no cross-resistance to commonly used antimicrobials and resistance development will be delayed. Genome-wide gene inactivation studies in several pathogens provided valuable information on how many potentially essential genes are encoded within the genome of pathogenic bacteria. Depending on the organisms studied and the used methods, the number of essential genes broadly ranges between 220 and 250, for example, for Haemophilus influenzae, Neisseria gonorrhoeae, Salmonella enterica sv. Typhimurium, Streptrococcus pneumoniae, and more than 500 for S. aureus. However, it can be calculated that the number of putative broad-spectrum targets is probably below 100. Recently, a study dealing with Salmonella metabolic pathways suggested a shortage of new metabolic targets for broad-spectrum antibiotics. In a comprehensive in vitro and in vivo approach 155 promising targets to treat Salmonella infections have been identified, 64 of which were also conserved in other important pathogens, such as S. aureus, Enterococcus faecalis, S. pneumoniae, and H. influenzae. Almost all of these targets belong to pathways already inhibited by current antibiotics (peptidoglycan biosynthesis, folate biosynthesis, isoprenoid biosynthesis, fatty acid biosynthesis, tRNA synthases) or pathways previously considered for antimicrobial development. These studies imply that, although a substantial number of proteins are conserved in relevant pathogens and are essential for bacterial growth, only a very limited number of proteins are suitable as antibacterial target. Potential targets can further be prioritized on the basis of defined criteria including essentiality, conservation in a range of pathogens, and nonexistence or low similarity in humans.

Another basic requirement of a selected target is preferentially an understanding of the function of the gene product at the level of its biochemical activity. Generally, genome sequence comparison using bioinformatic platforms conventionally allows the assignment of a function to a gene identified through DNA sequencing via similarity to a characterized protein by linear comparisons of DNA and protein sequences. This approach has several limitations as it is not possible to assign the function of proteins that lack an obvious homolog. Consequently, a significant proportion of each complete genome is functionally unannotated. Recent advances in bioinformatics, however, have been applied that deduce protein function on the basis of properties other than amino acid sequence similarity.

These methods use both theoretical prediction based on phylogeny and experimental data such as expression profiles and proteome data to identify functional linkages between proteins. The usefulness of these approaches has been demonstrated by the assignment of about half of the 2500 uncharacterized Saccharomyces cerevisiae proteins. In addition, novel software tools can be used when sequence comparisons fail to determine the function of a protein with known structure but unknown function. Direct comparison of three-dimensional (3D) protein structures is superior to simple sequence alignment, because the function of a protein is more directly a consequence of its form than its sequence. The rate of structure determination has increased dramatically, and current structural genomics projects will impact significantly assignment of function to unknown proteins by providing sufficient information to allow other protein sequences to be modeled accurately.

Reference:

Selzer, P. M. (Ed.). (2009). Antiparasitic and antibacterial drug discovery: from molecular targets to drug candidates (Vol. 1). John Wiley & Sons.