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.

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.

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.

Structural basis of DSF recognition by its receptor RpfR and its regulatory interaction with the DSF synthase RpfF.

The diffusible signal factors (DSFs) are a family of quorum-sensing autoinducers (AIs) produced and detected by numerous gram-negative bacteria. The DSF family AIs are fatty acids, differing in their acyl chain length, branching, and substitution but having in common a cis-2 double bond that is required for their activity. In both human and plant pathogens, DSFs regulate diverse phenotypes, including virulence factor expression, antibiotic resistance, and biofilm dispersal. Despite their widespread relevance to both human health and agriculture, the molecular basis of DSF recognition by their cellular receptors remained a mystery. Here, we report the first structure-function studies of the DSF receptor regulation of pathogenicity factor R (RpfR). We present the X-ray crystal structure of the RpfR DSF-binding domain in complex with the Burkholderia DSF (BDSF), which to our knowledge is the first structure of a DSF receptor in complex with its AI. To begin to understand the mechanistic role of the BDSF-RpfR contacts observed in the biologically important complex, we have also determined the X-ray crystal structure of the RpfR DSF-binding domain in complex with the inactive, saturated isomer of BDSF, dodecanoic acid (C12:0). In addition to these ligand-receptor complex structures, we report the discovery of a previously overlooked RpfR domain and show that it binds to and negatively regulates the DSF synthase regulation of pathogenicity factor F (RpfF). We have named this RpfR region the RpfF interaction (FI) domain, and we have determined its X-ray crystal structure alone and in complex with RpfF. These X-ray crystal structures, together with extensive complementary in vivo and in vitro functional studies, reveal the molecular basis of DSF recognition and the importance of the cis-2 double bond to DSF function. Finally, we show that throughout cellular growth, the production of BDSF by RpfF is post-translationally controlled by the RpfR N-terminal FI domain, affecting the cellular concentration of the bacterial second messenger bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP). Thus, in addition to describing the molecular basis for the binding and specificity of a DSF for its receptor, we describe a receptor-synthase interaction regulating bacterial quorum-sensing signaling and second messenger signal transduction.

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.

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.

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.

Enhanced respiration prevents drug tolerance and drug resistance in Mycobacterium tuberculosis.

Persistence, manifested as drug tolerance, represents a significant obstacle to global tuberculosis control. The bactericidal drugs isoniazid and rifampicin kill greater than 99% of exponentially growing Mycobacterium tuberculosis (Mtb) cells, but the remaining cells are persisters, cells with decreased metabolic rate, refractory to killing by these drugs, and able to generate drug-resistant mutants. We discovered that the combination of cysteine or other small thiols with either isoniazid or rifampicin prevents the formation of drug-tolerant and drug-resistant cells in Mtb cultures. This effect was concentration- and time-dependent, relying on increased oxygen consumption that triggered enhanced production of reactive oxygen species. In infected murine macrophages, the addition of N-acetylcysteine to isoniazid treatment potentiated the killing of Mtb Furthermore, we demonstrate that the addition of small thiols to Mtb drug treatment shifted the menaquinol/menaquinone balance toward a reduced state that stimulates Mtb respiration and converts persister cells to metabolically active cells. This prevention of both persister cell formation and drug resistance leads ultimately to mycobacterial cell death. Strategies to enhance respiration and initiate oxidative damage should improve tuberculosis chemotherapies.

Non-classical transpeptidases yield insight into new antibacterials.

Bacterial survival requires an intact peptidoglycan layer, a three-dimensional exoskeleton that encapsulates the cytoplasmic membrane. Historically, the final steps of peptidoglycan synthesis are known to be carried out by D,D-transpeptidases, enzymes that are inhibited by the ß-lactams, which constitute >50% of all antibacterials in clinical use. Here, we show that the carbapenem subclass of ß-lactams are distinctly effective not only because they inhibit D,D-transpeptidases and are poor substrates for ß-lactamases, but primarily because they also inhibit non-classical transpeptidases, namely the L,D-transpeptidases, which generate the majority of linkages in the peptidoglycan of mycobacteria. We have characterized the molecular mechanisms responsible for inhibition of L,D-transpeptidases of Mycobacterium tuberculosis and a range of bacteria including ESKAPE pathogens, and used this information to design, synthesize and test simplified carbapenems with potent antibacterial activity.

The Natural Product Eugenol Is an Inhibitor of the Ebola Virus In Vitro.

PURPOSE: Since the 2014 Ebola virus (EBOV) outbreak in West Africa there has been considerable effort towards developing drugs to treat Ebola virus disease and yet to date there is no FDA approved treatment. This is important as at the time of writing this manuscript there is an ongoing outbreak in the Democratic Republic of the Congo which has killed over 1000. METHODS: We have evaluated a small number of natural products, some of which had shown antiviral activity against other pathogens. This is exemplified with eugenol, which is found in high concentrations in multiple essential oils, and has shown antiviral activity against feline calicivirus, tomato yellow leaf curl virus, Influenza A virus, Herpes Simplex virus type 1 and 2, and four airborne phages. RESULTS: Four compounds possessed EC50 values less than or equal to 11 µM. Of these, eugenol, had an EC50 of 1.3 µM against EBOV and is present in several plants including clove, cinnamon, basil and bay. Eugenol is much smaller and structurally unlike any compound that has been previously identified as an inhibitor of EBOV, therefore it may provide new mechanistic insights. CONCLUSION: This compound is readily accessible in bulk quantities, is inexpensive, and has a long history of human consumption, which endorses the idea for further assessment as an antiviral therapeutic. This work also suggests that a more exhaustive assessment of natural product libraries against EBOV and other viruses is warranted to improve our ability to identify compounds that are so distinct from FDA approved drugs.

Synthesis and biological evaluation of solithromycin analogs against multidrug resistant pathogens.

Novel antibacterial drugs that treat multidrug resistant pathogens are in high demand. We have synthesized analogs of solithromycin using Cu(I)-mediated click chemistry. Evaluation of the analogs using Minimum Inhibitory Concentration (MIC) assays against resistant Staphylococcus aureus, Escherichia coli, and multidrug resistant pathogens Enterococcus faecium and Acinetobacter baumannii showed they possess potencies similar to those of solithromycin, thus demonstrating their potential as future therapeutics to combat the existential threat of multidrug resistant pathogens.

Optimization of N-benzyl-5-nitrofuran-2-carboxamide as an antitubercular agent.

The optimization campaign for a nitrofuran antitubercular hit (N-benzyl-5-nitrofuran-2-carboxamide; JSF-3449) led to the design, synthesis, and biological profiling of a family of analogs. These compounds exhibited potent in vitro antitubercular activity (MIC = 0.019-0.20 µM) against the Mycobacterium tuberculosis H37Rv strain and low in vitro cytotoxicity (CC50 = 40->120 µM) towards Vero cells. Significant improvements in mouse liver microsomal stability and mouse pharmacokinetic profile were realized by introduction of an α, α-dimethylbenzyl moiety. Among these compounds, JSF-4088 is highlighted due to its in vitro antitubercular potency (MIC = 0.019 µM) and Vero cell cytotoxicity (CC50 > 120 µM). The findings suggest a rationale for the continued evolution of this promising series of antitubercular small molecules.

Novel Pyrimidines as Antitubercular Agents.

Mycobacterium tuberculosis infection is responsible for a global pandemic. New drugs are needed that do not show cross-resistance with the existing front-line therapeutics. A triazine antitubercular hit led to the design of a related pyrimidine family. The synthesis of a focused series of these analogs facilitated exploration of their in vitro activity, in vitro cytotoxicity, and physiochemical and absorption-distribution-metabolism-excretion properties. Select pyrimidines were then evaluated for their pharmacokinetic profiles in mice. The findings suggest a rationale for the further evolution of this promising series of antitubercular small molecules, which appear to share some similarities with the clinical compound PA-824 in terms of activation, while highlighting more general guidelines for the optimization of small-molecule antitubercular agents.

Discovery of MRSA active antibiotics using primary sequence from the human microbiome.

Here we present a natural product discovery approach, whereby structures are bioinformatically predicted from primary sequence and produced by chemical synthesis (synthetic-bioinformatic natural products, syn-BNPs), circumventing the need for bacterial culture and gene expression. When we applied the approach to nonribosomal peptide synthetase gene clusters from human-associated bacteria, we identified the humimycins. These antibiotics inhibit lipid II flippase and potentiate ß-lactam activity against methicillin-resistant Staphylococcus aureus in mice, potentially providing a new treatment regimen.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 µM, 1 µM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Naïve Bayesian Models for Vero Cell Cytotoxicity.

PURPOSE: To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds. METHODS: Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem. RESULTS: Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors. CONCLUSIONS: The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.

Antimicrobials Inspired by Nonribosomal Peptide Synthetase Gene Clusters.

Bacterial culture broth extracts have been the starting point for the development of numerous therapeutics. However, only a small fraction of bacterial biosynthetic diversity is accessible using this strategy. Here, we apply a discovery approach that bypasses the culturing step entirely by bioinformatically predicting small molecule structures from the primary sequences of the biosynthetic gene clusters. These structures are then chemically synthesized to give synthetic-bioinformatic natural products (syn-BNPs). Using this approach, we screened syn-BNPs inspired by nonribosomal peptide synthetases against microbial pathogens, and discovered an antibiotic for which no resistance could be identified and an antifungal agent with activity against diverse fungal pathogens.

Mycobacterium abscessus l,d-Transpeptidases Are Susceptible to Inactivation by Carbapenems and Cephalosporins but Not Penicillins.

As a growing number of clinical isolates of Mycobacterium abscessus are resistant to most antibiotics, new treatment options that are effective against these drug-resistant strains are desperately needed. The majority of the linkages in the cell wall peptidoglycan of M. abscessus are synthesized by nonclassical transpeptidases, namely, the l,d-transpeptidases. Emerging evidence suggests that these enzymes represent a new molecular vulnerability in this pathogen. Recent studies have demonstrated that inhibition of these enzymes by the carbapenem class of ß-lactams determines their activity against Mycobacterium tuberculosis Here, we studied the interactions of ß-lactams with two l,d-transpeptidases in M. abscessus, namely, LdtMab1 and LdtMab2, and found that both the carbapenem and cephalosporin, but not penicillin, subclasses of ß-lactams inhibit these enzymes. Contrary to the commonly held belief that combination therapy with ß-lactams is redundant, doripenem and cefdinir exhibit synergy against both pansusceptible M. abscessus and clinical isolates that are resistant to most antibiotics, which suggests that dual-ß-lactam therapy has potential for the treatment of M. abscessus Finally, we solved the first crystal structure of an M. abscessus l,d-transpeptidase, LdtMab2, and using substitutions of critical amino acids in the catalytic site and computational simulations, we describe the key molecular interactions between this enzyme and ß-lactams, which provide an insight into the molecular basis for the relative efficacy of different ß-lactams against M. abscessus.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. METHODS: Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). RESULTS: "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. CONCLUSIONS: Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.