Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.

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.

Hepatitis E and pregnancy: current state.

Hepatitis E virus (HEV) is responsible for more than 50% of acute viral hepatitis cases in endemic countries. Approximately 2 billion individuals live in hepatitis E-endemic areas and, therefore, are at risk of infection. According to World Health Organization, HEV causes about 20.1 million infections and 70 000 deaths every year. In developing countries with poor sanitation, this disease is transmitted through contaminated water and is associated with large outbreaks, affecting hundreds or thousands of people. In developed countries, autochthonous cases of HEV have been increasingly recognized in the past several years. Hepatitis E virus typically causes an acute, self-limiting illness similar to other acute viral hepatitis, such as hepatitis A or B, with about 0.2% to 1% mortality rate in the general population. However, the course of hepatitis E in pregnancy is different than the mild self-constraining infection described in other populations. During pregnancy, HEV infection can take a fulminant course, resulting in fulminant hepatic failure, membrane rupture, spontaneous abortions, and stillbirths. Studies from various developing countries have shown a high incidence of HEV infection in pregnancy with a significant proportion of pregnant women progressing to fulminant hepatitis with a fatality rate of up to 30%. The present review will highlight new aspects of the HEV infection and pregnancy.

Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis.

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

Antibiotic resistant bacteria: current situation and treatment options to accelerate the development of a new antimicrobial arsenal.

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.

Antibiotic resistance is currently one of the biggest public health threats worldwide. Right now, antibiotic-resistant bacteria kill 700,000 people every year. Many of the available antibiotics are useless against drug resistant bacteria. The present and near future in which we find ourselves is a post-antibiotic era, where the antibiotics we have are unable to combat the bacterial infections that are emerging. In this review, published studies were accessed to explore different techniques that are available to improve existing treatment options. Currently, these strategies cannot replace antibiotic therapy. The reviewed knowledge presents these alternatives as adjuvants to antibiotic treatments. Therefore, research into new antibiotics remains important. This review exposes that the repurposing of known drugs as antibiotics could contribute to the cost-effective search of new antibiotics in a faster and cost-effective way compared to traditional development methods of new antibiotics. The review emphasizes the urgency of identifying new pharmacological targets that can aid in the development of new therapies, and to improve known alternative therapies. Drug repurposing can greatly shorten the time and cost of development of new antibiotics. This strategy adds to the value of certain commercialized molecules, recovering part of the investment made by the pharmaceutic industry. In addition, it provides greater knowledge about other alternative antibiotic therapies and about the mechanisms by which bacteria develop antibiotic resistance.

Severe Acute Hepatitis of Unknown Origin in Children: What Do We Know Today?

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.

Evaluation of knowledge about antibiotics and engagement with a research experience on antimicrobial resistance between pre-university and university students for five school years (2017-2021).

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 pre-university 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.

Synthesis of Quinolones and Zwitterionic Quinolonate Derivatives with Broad-Spectrum Antibiotic Activity.

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.

Innovative gamification and outreach tools to raise awareness about antimicrobial resistance.

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.

A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework.

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.

Transcriptomic and Genetic Associations between Alzheimer's Disease, Parkinson's Disease, and Cancer.

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.

Neisseria gonorrhoeae Infections.

Gonorrhea is a sexually transmitted disease with a high morbidity burden. Despite having guidelines for its treatment, the incidence of the disease follows an increasing trend worldwide. This is mainly due to the appearance of antibiotic-resistant strains, inefficient diagnostic methods and poor sexual education. Without an effective vaccine available, the key priorities for the control of the disease include sexual education, contact notification, epidemiological surveillance, diagnosis and effective antibiotic treatment. This Special Issue focuses on some of these important issues such as the molecular mechanisms of the disease, diagnostic tests and different treatment strategies to combat gonorrhea.

Evaluation of the Impact of the Tiny Earth Project on the Knowledge About Antibiotics of Pre-university Students in the Province of Valencia on Three Different School Years (2017-2020).

According to the World Health Organization (WHO), antibacterial resistance is a serious problem worldwide. In Spain, knowledge about the use of antibiotics is scarce, being the third country with the highest consumption of antibiotics in the world and the first in Europe. This problem is due, partly, to the abusive use of these drugs in human medicine, livestock, and agriculture. The objective of this study was to evaluate the impact that the Tiny Earth project has had on the antibiotic knowledge in pre-university students. To do this, a survey was conducted before and after the Tiny Earth project in three different school years (2017-2020) to 322 pre-university students belonging to seven schools in the province of Valencia. The survey consisted of 12 multiple-choice questions with a single valid answer. We observed 67.6% success at the beginning and 81.2% at the end. These data indicate that they correctly answered an average of 1.64 more questions after completing the project. In view of the results, we can affirm that the Tiny Earth project has contributed to an improvement in scientific knowledge and awareness of the correct use of antibiotics and the emergence of resistances by pre-university students, which could also be transmitted to their social environment, thus improving awareness global on these issues.

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against Escherichia coli.

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 different 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 effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

Molecular Topology for the Discovery of New Broad-Spectrum Antibacterial Drugs.

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four equations were constructed, named DF1, DF2, DF3, and DF4, all with good statistical parameters such as Fisher-Snedecor's F (over 25 in all cases), Wilk's lambda (below 0.36 in all cases) and percentage of correct classification (over 80% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. From the four discriminant functions, it can be extracted that the presence of sp3 carbons, ramifications, and secondary amine groups in a molecule enhance antibacterial activity, whereas the presence of 5-member rings, sp2 carbons, and sp2 oxygens hinder it. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of antibacterial activity.

Present and Future of Carbapenem-resistant Enterobacteriaceae (CRE) Infections.

Carbapenem-resistant Enterobacteriaceae (CRE) have become a public health threat worldwide. There are three major mechanisms by which Enterobacteriaceae become resistant to carbapenems: enzyme production, efflux pumps and porin mutations. Of these, enzyme production is the main resistance mechanism. There are three main groups of enzymes responsible for most of the carbapenem resistance: KPC (Klebsiella pneumoniae carbapenemase) (Ambler class A), MBLs (Metallo-ß-Lactamases) (Ambler class B) and OXA-48-like (Ambler class D). KPC-producing Enterobacteriaceae are endemic in the United States, Colombia, Argentina, Greece and Italy. On the other hand, the MBL NDM-1 is the main carbapenemase-producing resistance in India, Pakistan and Sri Lanka, while OXA-48-like enzyme-producers are endemic in Turkey, Malta, the Middle-East and North Africa. All three groups of enzymes are plasmid-mediated, which implies an easier horizontal transfer and, thus, faster spread of carbapenem resistance worldwide. As a result, there is an urgent need to develop new therapeutic guidelines to treat CRE infections. Bearing in mind the different mechanisms by which Enterobacteriaceae can become resistant to carbapenems, there are different approaches to treat infections caused by these bacteria, which include the repurposing of already existing antibiotics, dual therapies with these antibiotics, and the development of new ß-lactamase inhibitors and antibiotics.

Detection and Characterization of Extended-Spectrum Beta-Lactamases-Producing Escherichia coli in Animals.

The detection of multidrug-resistant bacteria is a growing problem; however, the role of domesticated animals in the propagation of antimicrobial resistance has barely been studied. The aim of this study was to identify extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli strains in domestic animal feces to assess their antimicrobial resistance profile and carry out molecular characterization of the ß-lactamases. A total of 325 samples were collected from eight animal species. Of these, 34 bacterial isolates were identified as E. coli. The antibiotic resistance profile of the E. coli strains was as follows: 100% resistant to amoxicillin, aztreonam, and cephalosporins; 58.8% resistant to nalidixic acid, ciprofloxacin, and trimethoprim/sulfamethoxazole; 41.2% resistant to gentamicin and tobramycin; 11.8% resistant and 32.4% intermediate to cefoxitin; 97.1% sensible and 2.9% intermediate to amoxicillin/clavulanate; and 100% sensible to ertapenem, minocycline, imipenem, meropenem, amikacin, nitrofurantoin, fosfomycin, and colistin. All 34 E. coli strains met criteria for ESBL production. In total, 46 ß-lactamase genes were detected: 43.5% blaTEM, 30.4% blaCTX-M (23.9% blaCTX-M-1 and 6.5% blaCTX-M-9), and 26.1% blaSHV (17.4% blaSHV-5 and 8.7% blaSHV-12). All the ß-lactamases were found in dogs except for four blaSHV found in falcons. No plasmidic AmpC genes were found. The high prevalence of ESBL-producing E. coli strains in animals could become a zoonotic transmission vector.

Topological index Nclass as a factor determining the antibacterial activity of quinolones against Escherichia coli.

Aim: Due to antibiotic resistance and the lack of investment in antimicrobial R&D, quantitative structure-activity relationship (SAR) methods appear as an ideal approach for the discovery of new antibiotics. Result & methodology: Molecular topology and linear discriminant analysis were used to construct a model to predict activity against Escherichia coli. This model establishes new SARs, of which, molecular size and complexity (Nclass), stand out for their discriminant power. This model was used for the virtual screening of the Index Merck database, with results showing a high success rate as well as a moderate restriction. Conclusion: The model efficiently finds new active compounds. The topological index Nclass can act as a filter in other quantitative structure-activity relationship models predicting activity against E. coli.

New solvent options for in vivo assays in the Galleria mellonella larvae model.

Experimentation in mammals is a long and expensive process in which ethical aspects must be considered, which has led the scientific community to develop alternative models such as that of Galleria mellonella. This model is a cost and time effective option to act as a filter in the drug discovery process. The main limitation of this model is the lack of variety in the solvents used to administer compounds, which limits the compounds that can be studied using this model. Five aqueous (DMSO, MeOH, acetic acid, HCl and NaOH) and four non-aqueous (olive oil, isopropyl myristate, benzyl benzoate and ethyl oleate) solvents was assessed to be used as vehicles for toxicity and antimicrobial activity in vivo assays. All the tested solvents were innocuous at the tested concentrations except for NaOH, which can be used at a maximum concentration of 0.5 M. The toxicity of two additional compounds, 5-aminosalicylic acid and DDT, was also assessed. The results obtained allow for the testing of a broader range of compounds using wax moth larvae. This model appears as an alternative to mammal models, by acting as a filter in the drug development process and reducing costs and time invested in new drugs.

Future Prospects for Neisseria gonorrhoeae Treatment.

Gonorrhea is a sexually transmitted disease with a high morbidity burden. Incidence of this disease is rising due to the increasing number of antibiotic-resistant strains. Neisseria gonorrhoeae has shown an extraordinary ability to develop resistance to all antimicrobials introduced for its treatment. In fact, it was recently classified as a “Priority 2” microorganism in the World Health Organization (WHO) Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery and Development of New Antibiotics. Seeing as there is no gonococcal vaccine, control of the disease relies entirely on prevention, diagnosis, and, especially, antibiotic treatment. Different health organizations worldwide have established treatment guidelines against gonorrhea, mostly consisting of dual therapy with a single oral or intramuscular dose. However, gonococci continue to develop resistances to all antibiotics introduced for treatment. In fact, the first strain of super-resistant N.gonorrhoeae was recently detected in the United Kingdom, which was resistant to ceftriaxone and azithromycin. The increase in the detection of resistant gonococci may lead to a situation where gonorrhea becomes untreatable. Seeing as drug resistance appears to be unstoppable, new treatment options are necessary in order to control the disease. Three approaches are currently being followed for the development of new therapies against drug-resistant gonococci: (1) novel combinations of already existing antibiotics; (2) development of new antibiotics; and (3) development of alternative therapies which might slow down the appearance of resistances. N. gonorrhoeae is a public health threat due to the increasing number of antibiotic-resistant strains. Current treatment guidelines are already being challenged by this superbug. This has led the scientific community to develop new antibiotics and alternative therapies in order to control this disease.

Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus.

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis. This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this activity, while the presence of tertiary amines decreases it. This model was applied to a combinatorial library of a thousand and one 6-fluoroquinolones, from which 117 theoretical active molecules were obtained. The compound 10 and five new quinolones were tested against MRSA. They all showed some activity against MRSA, although compounds 6, 8 and 9 showed anti-MRSA activity similar to ciprofloxacin. This model was also applied to 263 theoretical antibacterial agents described by us in a previous work, from which 34 were predicted as theoretically active. Anti-MRSA activity was found bibliographically in 9 of them (ensuring at least 26% of success), and from the rest, 3 compounds were randomly chosen and tested, finding mitomycin C to be more active than ciprofloxacin. The results demonstrate the utility of the molecular topology approaches for identifying new drugs active against MRSA.