A folate inhibitor exploits metabolic differences in Pseudomonas aeruginosa for narrow-spectrum targeting.

Pseudomonas aeruginosa is a leading cause of hospital-acquired infections for which the development of antibiotics is urgently needed. Unlike most enteric bacteria, P. aeruginosa lacks enzymes required to scavenge exogenous thymine. An appealing strategy to selectively target P. aeruginosa is to disrupt thymidine synthesis while providing exogenous thymine. However, known antibiotics that perturb thymidine synthesis are largely inactive against P. aeruginosa.Here we characterize fluorofolin, a dihydrofolate reductase (DHFR) inhibitor derived from Irresistin-16, that exhibits significant activity against P. aeruginosa in culture and in a mouse thigh infection model. Fluorofolin is active against a wide range of clinical P. aeruginosa isolates resistant to known antibiotics. Metabolomics and in vitro assays using purified folA confirm that fluorofolin inhibits P. aeruginosa DHFR. Importantly, in the presence of thymine supplementation, fluorofolin activity is selective for P. aeruginosa. Resistance to fluorofolin can emerge through overexpression of the efflux pumps MexCD-OprJ and MexEF-OprN, but these mutants also decrease pathogenesis. Our findings demonstrate how understanding species-specific genetic differences can enable selective targeting of important pathogens while revealing trade-offs between resistance and pathogenesis.

Small Molecule Benzothiophene with In Vivo Efficacy in a Mouse Model of Drug-Resistant Enterococcus faecium Infection.

Hospital-acquired infections, caused by ESKAPE bacteria, are a challenging global public health concern, in part due to the emergence of drug-resistant strains. While profiling a diverse set of compounds for in vitro activity versus this class of bacteria, we noted that the benzothiophene JSF-2827 exhibited promising antibacterial activity against Enterococcus faecium. A hit evolution campaign ensued, involving the design, synthesis, and biological assay of analogues designed to address early issues such as a short mouse liver microsome half-life and a modest mouse pharmacokinetic profile. Among these derivatives, JSF-3269 was found to exhibit an enhanced profile and in vivo efficacy in an immunocompetent mouse model of acute, drug-resistant E. faecium infection. The findings suggest a rationale for the further evolution of this promising series to afford a novel therapeutic strategy to treat drug-resistant E. faecium infection.

Linezolid does not improve bactericidal activity of rifampin-containing first-line regimens in animal models of TB meningitis.

Tuberculous meningitis (TB meningitis) is the most devastating form of tuberculosis (TB) and there is a critical need to optimize treatment. Linezolid is approved for multidrug resistant TB and has shown encouraging results in retrospective TB meningitis studies, with several clinical trials underway assessing its additive effects on high-dose (35 mg/kg/day) or standard-dose (10 mg/kg/day) rifampin-containing regimens. However, the efficacy of adjunctive linezolid to rifampin-containing first-line TB meningitis regimens and the tissue pharmacokinetics (PK) in the central nervous system (CNS) are not known. We therefore conducted cross-species studies in two mammalian (rabbits and mice) models of TB meningitis to test the efficacy of linezolid when added to the first-line TB regimen and measure detailed tissue PK (multicompartmental positron emission tomography [PET] imaging and mass spectrometry). Addition of linezolid did not improve the bactericidal activity of the high-dose rifampin-containing regimen in either animal model. Moreover, the addition of linezolid to standard-dose rifampin in mice also did not improve its efficacy. Linezolid penetration (tissue/plasma) into the CNS was compartmentalized with lower than previously reported brain and cerebrospinal fluid (CSF) penetration, which decreased further two weeks after initiation of treatment. These results provide important data regarding the addition of linezolid for the treatment of TB meningitis.

Liquid chromatography-mass spectrometry-based protocol to measure drug accumulation in Mycobacterium tuberculosis and its host cell.

The extent to which a drug accumulates in Mycobacterium tuberculosis (Mtb) and its host cell can affect treatment efficacy. We describe protocols measuring drug accumulation in Mtb, macrophages, and Mtb-infected macrophages. The method leverages drug extraction from the cellular lysate and drug-level quantification by liquid chromatography-mass spectrometry. The general methodology has broad applicability and can quantify drug accumulation in other cell types, while being extended to quantification of drug metabolites formed within the cell under study. For complete details on the use and execution of this protocol, please refer to Lavin et al. (2021).1.

Redesign of Rifamycin Antibiotics to Overcome ADP-Ribosylation-Mediated Resistance.

Rifamycin antibiotics are a valuable class of antimicrobials for treating infections by mycobacteria and other persistent bacteria owing to their potent bactericidal activity against replicating and non-replicating pathogens. However, the clinical utility of rifamycins against Mycobacterium abscessus is seriously compromised by a novel resistance mechanism, namely, rifamycin inactivation by ADP-ribosylation. Using a structure-based approach, we rationally redesign rifamycins through strategic modification of the ansa-chain to block ADP-ribosylation while preserving on-target activity. Validated by a combination of biochemical, structural, and microbiological studies, the most potent analogs overcome ADP-ribosylation, restored their intrinsic low nanomolar activity and demonstrated significant in vivo antibacterial efficacy. Further optimization by tuning drug disposition properties afforded a preclinical candidate with remarkable potency and an outstanding pharmacokinetic profile.

Mycobacterium tuberculosis KasA as a drug target: Structure-based inhibitor design.

Recent studies have reported the ß-ketoacyl-acyl carrier protein KasA as a druggable target for Mycobacterium tuberculosis. This review summarizes the current status of major classes of KasA inhibitors with an emphasis on significant contributions from structure-based design methods leveraging X-ray crystal structures of KasA alone and in complex with inhibitors. The issues addressed within each inhibitor class are discussed while detailing the characterized interactions with KasA and structure-activity relationships. A critical analysis of these findings should lay the foundation for new KasA inhibitors to study the basic biology of M. tuberculosis and to form the basis of new antitubercular molecules of clinical significance with activity against drug-sensitive and drug-resistant infections.

A Novel Oral GyrB/ParE Dual Binding Inhibitor Effective against Multidrug-Resistant Neisseria gonorrhoeae and Other High-Threat Pathogens.

Drug-resistant Neisseria gonorrhoeae is a serious global health concern. New drugs are needed that can overcome existing drug resistance and limit the development of new resistances. Here, we describe the small molecule tricyclic pyrimidoindole JSF-2414 [8-(6-fluoro-8-(methylamino)-2-((2-methylpyrimidin-5-yl)oxy)-9H-pyrimido[4,5-b]indol-4-yl)-2-oxa-8-azaspiro[4.5]decan-3-yl)methanol], which was developed to target both ATP-binding regions of DNA gyrase (GyrB) and topoisomerase (ParE). JSF-2414 displays potent activity against N. gonorrhoeae, including drug-resistant strains. A phosphate pro-drug, JSF-2659, was developed to facilitate oral dosing. In two different animal models of Neisseria gonorrhoeae vaginal infection, JSF-2659 was highly efficacious in reducing microbial burdens to the limit of detection. The parent molecule also showed potent in vitro activity against high-threat Gram-positive organisms, and JSF-2659 was shown in a deep tissue model of vancomycin-resistant Staphylococcus aureus (VRSA) and a model of Clostridioides difficile-induced colitis to be highly efficacious and protective. JSF-2659 is a novel preclinical drug candidate against high-threat multidrug resistant organisms with low potential to develop new resistance.

Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents.

The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.

Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection.

Rickettsia is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, and concerns over drug resistance is driving a need to identify novel treatments for these obligate intracellular bacteria. Utilizing an uvGFP plasmid reporter, we developed a screen for identifying anti-rickettsial small molecule inhibitors using Rickettsia canadensis as a model organism. The screening data were utilized to train a Bayesian model to predict growth inhibition in this assay. This two-pronged methodology identified anti-rickettsial compounds, including duartin and JSF-3204 as highly specific, efficacious, and noncytotoxic compounds. Both molecules exhibited in vitro growth inhibition of R. prowazekii, the causative agent of epidemic typhus. These small molecules and the workflow, featuring a high-throughput phenotypic screen for growth inhibitors of intracellular Rickettsia spp. and machine learning models for the prediction of growth inhibition of an obligate intracellular Gram-negative bacterium, should prove useful in the search for new therapeutic strategies to treat infections from Rickettsia spp. and other obligate intracellular bacteria.

Alternative approaches utilizing click chemistry to develop next-generation analogs of solithromycin.

The marked rise in bacterial drug resistance has created an urgent need for novel antibacterials belonging to new drug classes and ideally possessing new mechanisms of action. The superior biological activity of solithromycin against streptococci and other bacteria causative of community-acquired pneumonia pathogens, compared to telithromycin and other macrolides encouraged us to extensively explore this class of antibiotics. We, thus, present the design and synthesis of a novel series of solithromycin analogs. Three main strategies were pursued in structure-activity relationship studies covering the N-11 side chain and the desosamine motif, which are both chief elements for establishing strong interactions with the bacterial ribosome as the molecular target. Minimal inhibitory concentration assays were determined to assess the in vitro potency of the various analogs in relation to solithromycin. Two analogs exhibited improved activity compared to solithromycin against resistant strains, which can be assessed in further pre-clinical studies.

Metagenome-Guided Analogue Synthesis Yields Improved Gram-Negative-Active Albicidin- and Cystobactamid-Type Antibiotics.

Natural products are a major source of new antibiotics. Here we utilize biosynthetic instructions contained within metagenome-derived congener biosynthetic gene clusters (BGCs) to guide the synthesis of improved antibiotic analogues. Albicidin and cystobactamid are the first members of a new class of broad-spectrum ρ-aminobenzoic acid (PABA)-based antibiotics. Our search for PABA-specific adenylation domain sequences in soil metagenomes revealed that BGCs in this family are common in nature. Twelve BGCs that were bio-informatically predicted to encode six new congeners were recovered from soil metagenomic libraries. Synthesis of these six predicted structures led to the identification of potent antibiotics with changes in their spectrum of activity and the ability to circumvent resistance conferred by endopeptidase cleavage enzymes.

Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus.

We present the application of Bayesian modeling to identify chemical tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151 was predicted by modeling and then empirically demonstrated to be active against in vitro cultured clinical methicillin- and vancomycin-resistant strains while also exhibiting efficacy in a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism (IBDM) approach to probe the mechanism by which JSF-3151 is transformed within the bacteria. We also identify and then validate two mechanisms of resistance in S. aureus: one mechanism involves increased expression of a lipocalin protein, and the other arises from the loss of function of an azoreductase. The computational and experimental approaches, discovery of an antibacterial agent, and elucidated resistance mechanisms collectively hold promise to advance our understanding of therapeutic regimens for drug-resistant S. aureus.

Targeting Mycobacterium tuberculosis response to environmental cues for the development of effective antitubercular drugs.

Sensing and response to environmental cues, such as pH and chloride (Cl-), is critical in enabling Mycobacterium tuberculosis (Mtb) colonization of its host. Utilizing a fluorescent reporter Mtb strain in a chemical screen, we have identified compounds that dysregulate Mtb response to high Cl- levels, with a subset of the hits also inhibiting Mtb growth in host macrophages. Structure-activity relationship studies on the hit compound "C6," or 2-(4-((2-(ethylthio)pyrimidin-5-yl)methyl)piperazin-1-yl)benzo[d]oxazole, demonstrated a correlation between compound perturbation of Mtb Cl- response and inhibition of bacterial growth in macrophages. C6 accumulated in both bacterial and host cells, and inhibited Mtb growth in cholesterol media, but not in rich media. Subsequent examination of the Cl- response of Mtb revealed an intriguing link with bacterial growth in cholesterol, with increased transcription of several Cl--responsive genes in the simultaneous presence of cholesterol and high external Cl- concentration, versus transcript levels observed during exposure to high external Cl- concentration alone. Strikingly, oral administration of C6 was able to inhibit Mtb growth in vivo in a C3HeB/FeJ murine infection model. Our work illustrates how Mtb response to environmental cues can intersect with its metabolism and be exploited in antitubercular drug discovery.

Assessment of carbapenems in a mouse model of Mycobacterium tuberculosis infection.

We present further study of a subset of carbapenems, arising from a previously reported machine learning approach, with regard to their mouse pharmacokinetic profiling and subsequent study in a mouse model of sub-acute Mycobacterium tuberculosis infection. Pharmacokinetic metrics for such small molecules were compared to those for meropenem and biapenem, resulting in the selection of two carbapenems to be assessed for their ability to reduce M. tuberculosis bacterial loads in the lungs of infected mice. The original syntheses of these two carbapenems were optimized to provide multigram quantities of each compound. One of the two experimental carbapenems, JSF-2204, exhibited efficacy equivalent to that of meropenem, while both were inferior to rifampin. The lessons learned in this study point toward the need to further enhance the pharmacokinetic profiles of experimental carbapenems to positively impact in vivo efficacy performance.

Random Forest Model Prediction of Compound Oral Exposure in the Mouse.

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.

Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA.

Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require ß-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additional molecules binding to this protein. We now describe structure-based pharmacophores, docking and machine learning studies with Assay Central as a computational tool for the identification of small molecules targeting KasA. We then tested these compounds using nanoscale differential scanning fluorimetry and microscale thermophoresis. Of note, we identified several molecules including the Food and Drug Administration (FDA)-approved drugs sildenafil and flubendazole with K d values between 30-40 µM. This may provide additional starting points for further optimization.

Pruned Machine Learning Models to Predict Aqueous Solubility.

Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology. METHODS: Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >105 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits. RESULTS: Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae. CONCLUSIONS: This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.

Morphological profiling of tubercle bacilli identifies drug pathways of action.

Morphological profiling is a method to classify target pathways of antibacterials based on how bacteria respond to treatment through changes to cellular shape and spatial organization. Here we utilized the cell-to-cell variation in morphological features of Mycobacterium tuberculosis bacilli to develop a rapid profiling platform called Morphological Evaluation and Understanding of Stress (MorphEUS). MorphEUS classified 94% of tested drugs correctly into broad categories according to modes of action previously identified in the literature. In the other 6%, MorphEUS pointed to key off-target activities. We observed cell wall damage induced by bedaquiline and moxifloxacin through secondary effects downstream from their main target pathways. We implemented MorphEUS to correctly classify three compounds in a blinded study and identified an off-target effect for one compound that was not readily apparent in previous studies. We anticipate that the ability of MorphEUS to rapidly identify pathways of drug action and the proximal cause of cellular damage in tubercle bacilli will make it applicable to other pathogens and cell types where morphological responses are subtle and heterogeneous.