84
States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests and use of more expensive drugs.
The comparative genomics associated with Pan-genomics, subtractive genomics,
structural bioinformatics, and metabolic pathways analysis approaches are currently
applied to reach the development of new antibiotics and fight antimicrobial resistance. Targeted drug development retains major challenges from candidate selection
to in vitro and in vivo experiments and clinical trials. Yet, the advances in scientific
knowledge and research and development, the advent of omics approaches for
example, genomics, transcriptomics, proteomics, and bioinformatics breakthroughs
conduct to a ‘big-data era’ that improved identification of putative targets via the
application of in silico tools that shortened the timeline in a cost-efficient manner.
In this chapter, we are focusing on different bioinformatics strategies for prioritizing
drug targets in pathogens.
Keywords Antimicrobial resistance · Comparative genomics · Next generation
sequencing · Pan-genomics · Subtractive genomics · Prioritizing drug targets ·
Metabolic pathway reconstruction
4.1 Introduction
Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure to the environment; however, the indiscriminate use of antimicrobials
is accelerating its progression (Holmes et al. 2016). The development of resistance
happens when microorganisms evolve the mechanism to evade damage e.g. drug
inactivation/alteration, efflux pumps, porin loss, biofilm formation, reduced intracellular drug accumulation, modification of drug binding sites, caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals,
antimalarials, and anthelmintics, which involves genetic changes (Santajit and
Indrawattana, 2016). As a result, the medicine/treatment become ineffective and the
infection persists in the body, increasing the risk of spread to others, prolonged illness, disability, and death. Likewise, major medical procedures such as organ transplantation, cancer chemotherapy, diabetes management, and surgery, would be
compromised. Infections with resistant pathogens also prompt a higher health care
cost (estimated bugged of $20 billion annually in the United States) compared to
non-resistant infections due to longer duration of illness/hospitalization, additional
tests and use of more expensive drugs (Marston et al. 2016).
Awareness of antimicrobial traits is essential to comprehend the gain of resistance and how to overcome it. Other relevant factors that influence the prevalence of
resistance are the pathogen drug and pathogen host interactions, the rate of the
microorganism mutation, cross-resistance information, selection of co-resistance to
unrelated drugs, and the transmission rates between human, animals, and the environment. Hence, the education of health care professionals and the general
M. Santana et al.
States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests and use of more expensive drugs.
The comparative genomics associated with Pan-genomics, subtractive genomics,
structural bioinformatics, and metabolic pathways analysis approaches are currently
applied to reach the development of new antibiotics and fight antimicrobial resistance. Targeted drug development retains major challenges from candidate selection
to in vitro and in vivo experiments and clinical trials. Yet, the advances in scientific
knowledge and research and development, the advent of omics approaches for
example, genomics, transcriptomics, proteomics, and bioinformatics breakthroughs
conduct to a ‘big-data era’ that improved identification of putative targets via the
application of in silico tools that shortened the timeline in a cost-efficient manner.
In this chapter, we are focusing on different bioinformatics strategies for prioritizing
drug targets in pathogens.
Keywords Antimicrobial resistance · Comparative genomics · Next generation
sequencing · Pan-genomics · Subtractive genomics · Prioritizing drug targets ·
Metabolic pathway reconstruction
4.1 Introduction
Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure to the environment; however, the indiscriminate use of antimicrobials
is accelerating its progression (Holmes et al. 2016). The development of resistance
happens when microorganisms evolve the mechanism to evade damage e.g. drug
inactivation/alteration, efflux pumps, porin loss, biofilm formation, reduced intracellular drug accumulation, modification of drug binding sites, caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals,
antimalarials, and anthelmintics, which involves genetic changes (Santajit and
Indrawattana, 2016). As a result, the medicine/treatment become ineffective and the
infection persists in the body, increasing the risk of spread to others, prolonged illness, disability, and death. Likewise, major medical procedures such as organ transplantation, cancer chemotherapy, diabetes management, and surgery, would be
compromised. Infections with resistant pathogens also prompt a higher health care
cost (estimated bugged of $20 billion annually in the United States) compared to
non-resistant infections due to longer duration of illness/hospitalization, additional
tests and use of more expensive drugs (Marston et al. 2016).
Awareness of antimicrobial traits is essential to comprehend the gain of resistance and how to overcome it. Other relevant factors that influence the prevalence of
resistance are the pathogen drug and pathogen host interactions, the rate of the
microorganism mutation, cross-resistance information, selection of co-resistance to
unrelated drugs, and the transmission rates between human, animals, and the environment. Hence, the education of health care professionals and the general
M. Santana et al.