Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5352_Библиотеки_им_академика_М_И_Перельмана

The antibiotic is a type of antimicrobial substance that acts against bacteria by destroying them. As one of the most important types of antibacterial agents for ghting bacterial infections, antibiotic medica­tions are used widely in the treatment and prevention of such infections due to their effectiveness. A number of antibiotics are capable of killing or inhibiting the growth of bacteria. Antibiotics also have the ability to kill protozoa in a limited number of cases. It is important to note that there are several types of antibiotics: Aminoglycosides, carbapenems, cephalosporins, uoroquinolones, glycopeptides and lipoglycopeptides (such as vancomycin), macrolides (such as erythromycin and azithromycin), mono­bactams (aztreonam), oxazolidinones (such as linezolid and tedizolid), penicillins, polypeptides, rifamy­cins, sulfonamides, and streptogramins (including quinupristin and dalfopristin) along with tetracyclines are also used. It should be noted that carbapenems, cephalosporins, monobactams, and penicillins fall under the beta-lactam antibiotic class, a type of antibiotic which is characterized by a chemical structure called a beta-lactam ring. There are other antibiotics that are not included in the classes above, including chloramphenicol, clindamycin, daptomycin, fosfomycin, lefamulin, metronidazole, mupirocin, nitrofu­rantoin, and tigecycline.

There are increasing levels of antimicrobial resistance (AMR) in clinically signicant bacteria, which is undermining the efcacy of existing antibiotics and causing alarming levels of worldwide morbidity and mortality.1 There is an estimation that 2.8 million infections are caused by antibiotic-resistant bacte­ria in the United States each year, with 35,000 deaths resulting from such untreatable infections, accord­ing to the Centers for Disease Control and Prevention. It has also been suggested by current research that in some cases the solution may be part of the problem itself. It has been shown that antibiotics can damage the gut microbiome signicantly, resulting in a reduction of species diversity and encouraging the evolution and dissemination of AMR genes.2 The antibiotics that are under clinical trial are generally analogs to existing drugs which have already developed mechanisms of AMR, which underscores the need for novel approaches in the discovery of antibiotics.

It has been shown that articial intelligence is an efcient tool that can be used to combat AMR, expediting and improving conventional protocols and strategies, ensuring a smart, digitized patient care system and healthcare system, as well as reducing mistaken human actions.66 It has been found that articial intelligence can help in patient investigations, disease diagnosis, antibiotic prescribing, identi­cation of AMR markers, discovery and design of novel drugs with minimal toxicity and relatively low chances of developing AMR, and stewardship of antimicrobials. The importance of AI-driven software and mobile apps for analyzing and interpreting antimicrobial susceptibility tests, especially in low- and middle-income countries (LMIC) where healthcare professionals and technicians are hard to access and unavailable, cannot be overstated.

Beta-lactamases are diverse family of microbial enzymes that hydrolyze the cyclic amide bond of sus­ceptible to beta-lactam antibiotics. By studying the effects and functioning of beta-lactamase enzymes, it is possible to gain a better understanding of the mechanisms by which microorganisms develop resistance to antibiotics. Finding compounds that have the potential to combat these microorganisms is very impor­tant since AMR is one of the top ten global health threats facing mankind at this time. Using machine learning algorithms, the bioactivity of a few avonoids and terpenoids from plants was assessed against microorganisms that carry beta-lactamases. To understand the origin of the bioactivity of compounds, a large dataset of more than 62,000 compounds, as well as their potency values against beta-lactamase AmpC, has been obtained through ChEMBL and used in a quantitative structure–activity relationship (QSAR) model to identify the originator of their bioactivity. A number of ngerprint descriptors and predictive models have been constructed, and the results have been presented. Figu re 12.1 shows the summary of workow, data extraction, modeling, and model performance.

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M concentration of similar but not targeted ODNs.

A majority of human respiratory infections are caused by S. pneumoniae, and beta-lactam antibiotics have been used for decades to treat infections related to S. pneumoniae as well as other respiratory patho­gens. Among pneumococci, beta-lactam resistance has steadily increased, and it is mainly attributed to the modication of penicillin-binding proteins (PBPs) that reduces the binding afnity of antibiotics to these proteins. It should be noted, however, that the high variability of PBPs in clinical isolates as well as their mosaic gene structure hamper the ability to predict the level of resistance according to the sequence of the PBP gene.

By using a label-free SERS technique, a stacked autoencoder (SAE)-based DNN was used to iden­tify methicillin-resistant S. aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria with relatively high accuracy. In this study, the performance of the DNN was compared with that of the tra­ditional classiers. Since the SERS technique provides high levels of signal-to-noise ratio (SNR) data, it was possible to detect some subtle differences between MRSA and MSSA in terms of their relative band intensities. SAE-based DNNs are capable of learning features from raw data and classifying them with an accuracy of 97.66%. As a result, the model was able to discriminate bacteria with an area under the curve (AUC) of 0.99. The SAE-based DNN was found to be more accurate and to have a higher AUC than traditional classiers. Statistics also supports the results. As can be seen from these results, deep learning has great potential to be used to characterize and detect antibiotic-resistant bacteria in the spec­tral data generated by SERS in a very effective manner.

A more effective way of prescribing antibiotics is needed in order to delay the spread of antibiotic resistance in the future. Through the use of sequencing technologies coupled with the use of trained neu­ral network algorithms for the interpretation of genotype-to-phenotype information, it will be possible to reduce the time needed for the identication of antibiotic susceptibility proles from days to hours by utilizing sequencing technologies.

Various factors contribute to antibiotic resistance in modern times, including overprescription of anti­biotics, faulty infection–prevention strategies, pollution in overcrowded places, or overuse of antibiotics in agriculture. Furthermore, the pharmaceutical industry is showing a decreasing interest in researching and testing new antibiotics. Lastly, the high costs associated with the development of antibiotics are one of the reasons for this problem. There has been a growing body of research on deep learning and antibi­otic resistance.75 It was the aim of the study to highlight the techniques that are being developed in order to assist in the identication of new antibiotics to aid in the lengthy process of identifying them, with technology such as articial intelligence. It is expected that AI will shorten the preclinical phase of drug development since it will be able to generate many substances in a short period of time using algorithms that are created using machine learning (ML) techniques such as neural networks (NNs) or deep learning (DL). There has recently been a study that used a text mining system that incorporated DL algorithms in order to aid and speed up the process of curation of data.

There are a number of new and old methods being used in the identication of new antibiotics, includ­ing QSAR methods combined with ML or Raman spectroscopy and MALDI-TOF MS combined with neural networks, offering faster and easier interpretations of results. Thus, AI techniques serve as an important additional tool for researchers and clinicians in the race to nd new methods of overcoming the resistance of bacteria in order to improve treatment outcomes. Figure 12.5 depicts the future perspec­tives of antibiotic discovery through the use of articial intelligence.