1. Investigación

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.

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.

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.

Since 2017, the SWICEU team has developed various informative actions and innovative gamification supports to educate and raise awareness about antimicrobial resistance (AMR) and the correct use of antibiotics among the general population especially among young people. This case study presents the results obtained in the last 5 years with the strategies carried out by this team, composed of students and professors of Health Sciences, Industrial Design Engineering, and Communication Sciences at CEU Cardenal Herrera University (CEU UCH) in Valencia (Spain). Over the past 5 years, playful educational supports have been developed to make the health problem of bacterial resistance and the action of antibiotics more understandable among young people. The dissemination media used, with the same objective of teaching and raising awareness about AMR in a creative and innovative way, have been selected according to the trends in digital communication and use of scientific and health content provided by the most recent studies carried out among the Spanish population. These strategies have included decalogues or “tips” with useful advice, infographics, YouTube videos, Twitter threads, online challenges on Kahoot, stories on Instagram, use of QR codes, etc. These actions have also obtained diffusion in the media and have been awarded by different national and international entities. The good results obtained in the case under study allow us to establish recommendations for the design of innovative educational gamification and dissemination supports on AMR, especially aimed at younger audiences.

In May 2022, the UK International Health Regulations National Focal Point notified World Health Organization of 176 cases of severe acute hepatitis of unknown etiology in children under 10 years of age. From that moment on, cases of severe acute hepatitis of unknown origin in children began to be reported in several countries. As of June 17, 2022, a total of 991 cases had been reported in 35 countries worldwide, 50 children needed a liver transplant and 28 patients died. According to information published by ECDC, 449 cases have been detected in 21 EU countries. The children were between 1 month and 16 years of age. Adenovirus was detected in 62.2% of the analyzed samples. So far, the cause of these cases is unknown and many hypotheses remain open, but hepatitis A–E viruses and COVID-19 vaccines have been ruled out. A possible hypothesis has been published to explain the cause of these cases of severe hepatitis, according to which it could be a consequence of adenovirus infection in the intestine in healthy children previously infected with SARS-CoV-2. No other clear epidemiological risk factors have been identified to date. Thus, at this time, the etiology of the current cases of hepatitis remains under active investigation.

Antimicrobial resistance (AMR) remains a serious global health problem. Spain is the fifth country in Europe with the highest consumption of antibiotics, due in part to ignorance of the good use of these drugs and the problem of AMR. To avoid a post-antibiotic era, adequate training on this problem is key to create social awareness. This study aimed to evaluate the impact that the SWICEU project, an academic program about antibiotic discovery, has had on the knowledge of AMR and rational use of antimicrobials in preuniversity students from seven schools in the province of Valencia during five academic years (2017–2021), as well as to evaluate the level of satisfaction of university and pre-university students who have participated in the project. For this study, a survey was carried out with multiple-choice questions with a single correct answer to evaluate the knowledge acquired by pre-university students before and after the project. A satisfaction survey was also designed with a Likert scale from the lowest to the highest level of satisfaction for the two groups of students after the project. Data on knowledge surveys indicated an increase in the mean number of correct answers after the sessions. In satisfaction surveys, we highlighted the issue that referred to the project’s recommendation. The data obtained confirm this project as a valuable activity, as it allows learning about AMR and the rational use of antibiotics in a pleasing and attractive way for young pre-university and university students.

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

Alzheimer’s (AD) and Parkinson’s diseases (PD) are the two most prevalent neurodegenerative disorders in human populations. Epidemiological studies have shown that patients suffering from either condition present a reduced overall risk of cancer than controls (i.e., inverse comorbidity), suggesting that neurodegeneration provides a protective effect against cancer. Reduced risks of several site-specific tumors, including colorectal, lung, and prostate cancers, have also been observed in AD and PD. By contrast, an increased risk of melanoma has been described in PD patients (i.e., direct comorbidity). Therefore, a fundamental question to address is whether these associations are due to shared genetic and molecular factors or are explained by other phenomena, such as flaws in epidemiological studies, exposure to shared risk factors, or the effect of medications. To this end, we first evaluated the transcriptomes of AD and PD post-mortem brain tissues derived from the hippocampus and the substantia nigra and analyzed their similarities to those of a large panel of 22 site-specific cancers, which were obtained through differential gene expression meta-analyses of array-based studies available in public repositories. Genes and pathways that were deregulated in both disorders in each analyzed pair were examined. Second, we assessed potential genetic links between AD, PD, and the selected cancers by establishing interactome-based overlaps of genes previously linked to each disorder. Then, their genetic correlations were computed using cross-trait LD score regression and GWAS summary statistics data. Finally, the potential role of medications in the reported comorbidities was assessed by comparing disease-specific differential gene expression profiles to an extensive collection of differential gene expression signatures generated by exposing cell lines to drugs indicated for AD, PD, and cancer treatment (LINCS L1000). We identified significant inverse associations of transcriptomic deregulation between AD hippocampal tissues and breast, lung, liver, and prostate cancers, and between PD substantia nigra tissues and breast, lung, and prostate cancers. Moreover, significant direct (same direction) associations of deregulation were observed between AD and PD and brain and thyroid cancers, as well as between PD and kidney cancer. Several biological processes, including the immune system, oxidative phosphorylation, PI3K/AKT/mTOR signaling, and the cell cycle, were found to be deregulated in both cancer and neurodegenerative disorders. Significant genetic correlations were found between PD and melanoma and prostate cancers. Several drugs indicated for the treatment of neurodegenerative disorders and cancer, such as galantamine, selegiline, exemestane, and estradiol, were identified as potential modulators of the comorbidities observed between neurodegeneration and cancer.

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for di erent pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds e ectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.