Novel Pyrimidines as Antitubercular Agents

Design and one-pot synthesis of new substituted pyrrolo[1,2-a]thieno[3,2-e]pyrimidine as potential antitumor agents: in vitro and in vivo studies

A new efficient and versatile one-pot three-component synthesis of substituted pyrrolo[1,2-a]thieno[3,2-e]pyrimidine derivatives has been developed. It is based on a multistep cascade reaction from 2-aminothiophenes and 2-hydroxy-4-oxobut-2-enoic acids, and derivatives of cyanoacetic acid catalyzed by diisopropylethylamine. As a result, novel pyrrolo[1,2-a]thieno[3,2-e]pyrimidine derivatives (21 compounds) were synthesized in a mild reaction conditions with a high yield. The structures of the developed compounds were confirmed by NMR and elemental analysis. The influence of electron-withdrawing or electron-donor substituents on the antitumor activity of the developed compounds has been identified. In vitro screening analysis of 21 compounds revealed six lead candidates (12aa, 12dc, 12hc, 12ic, 12lb, and 12mb) that demonstrated the most significant antitumor activity against B16-F10, 4T1 and CT26 cells. Necrosis/apoptosis assay showed that apoptosis was the predominant mechanism of cell death. Molecular docking analysis revealed several potential targets for tested compounds, i.e. phosphatidylinositol 5-phosphate 4-kinase (PI5P4K2C), proto-oncogene serine/threonine-protein kinase (Pim-1), nicotinamide phosphoribosyltransferase (NAMPT) and dihydrofolate reductase (DHFR). The lead compound (12aa) can effectively induce cell apoptosis, possesses a high yield (98 %) and requires low-cost starting chemicals for its synthesis. In vivo experiments with melanoma-bearing mice confirmed that 12aa compound resulted in the significant tumor inhibition on 15 d after the therapy. In particular, tumor volume was ∼0.19 cm3 for 50 mg/kg versus ∼2.39 cm3 in case of untreated mice and tumor weight was ∼71.6 mg for 50 mg/kg versus ∼452.4 mg when considered untreated mice. Thus, our results demonstrated the high potential of the 12aa compound in the treatment of melanoma and can be recommended for further preclinical studies.

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.

In vitro bioevaluation and docking study of dihydrosphingosine and ethambutol analogues against sensitive and multi-drug resistant Mycobacterium tuberculosis

Tuberculosis remains a major public health problem and one of the top ten causes of death worldwide. The alarming increase in multidrug-resistant and extensively resistant variants (MDR, pre-XDR, and XDR) makes the disease more difficult to treat and control. New drugs that act against MDR/XDR strains are needed for programs to contain this major epidemic. The present study aimed to evaluate new compounds related to dihydro-sphingosine and ethambutol against sensitive and pre-XDR Mycobacterium strains, as well as to characterize the pharmacological activity through in vitro and in silico approaches in mmpL3 protein. Of the 48 compounds analyzed, 11 demonstrated good to moderate activity on sensitive and MDR Mycobacterium tuberculosis (Mtb), with a Minimum Inhibitory Concentration (MIC) ranging from 1.5 to 8 μM. They presented 2 to 14 times greater potency of activity when compared to ethambutol in pre-XDR strain, and demonstrated a selectivity index varying between 2.21 and 82.17. The substance 12b when combined with rifampicin, showed a synergistic effect (FICI = 0.5) on sensitive and MDR Mtb. It has also been shown to have a concentration-dependent intracellular bactericidal effect, and a time-dependent bactericidal effect in M. smegmatis and pre-XDR M. tuberculosis. The binding mode of the compounds in its cavity was identified through molecular docking and using a predicted structural model of mmpL3. Finally, we observed by transmission electron microscopy the induction of damage to the cell wall integrity of M. tuberculosis treated with the substance 12b. With these findings, we demonstrate the potential of a 2-aminoalkanol derivative to be a prototype substance and candidate for further optimization of molecular structure and anti-tubercular activity in preclinical studies.

Microwave Induced Green Synthesis: Sustainable Technology for Efficient Development of Bioactive Pyrimidine Scaffolds

Microwave radiation is used as a heating source during the synthesis of heterocyclic compounds. The heating mechanisms involved in microwave-induced synthesis include dipolar polarization and ionic conduction. This heating technology follows the green protocol as it involves the use of recyclable organic solvents during synthesis. The microwave heating approach offers a faster rate of reaction, easier work-up procedure, and higher product yield with purity and also reduces environmental pollution. So, microwave heating is applied as a sustainable technology for the efficient production of pyrimidine compounds as one of the heterocyclic moieties. Pyrimidine is a six-membered nitrogenous heterocyclic compound that plays a significant role due to several therapeutic applications. This moiety acts as an essential building block for generating drug candidates with diverse biological activities, including anti-cancer (capecitabine), anti-thyroid (propylthiouracil), antihistaminic (pemirolast), antimalarial (pyrimethamine), antidiabetic (alloxan), antihypertensive (minoxidil), anti-inflammatory (octotiamine), antifungal (cyprodinil), antibacterial (sulfamethazine), etc. This review is focused on the synthesis of pyrimidine analogs under microwave irradiation technique and the study of their therapeutic potentials.

Anti-Mycobacterial Activity of Flavonoid and Pyrimidine Compounds

We evaluated the anti-mycobacterial effect of a flavonoid 5,7-dihydroxy-2-(4-hydroxyphenyl) 4H-chromen-4-one (1) and two pyrimidines, 4-hydroxy-2-dimethylamino-5-nitroso-6-aminopyrimidine (2) and 2-chloro-5-n-nonylpyrimidine (3) in vitro against Mycobacterium tuberculosis (M. tuberculosis, H37Ra) and Mycobacterium avium (M. avium), using a Microplate Alamar Blue Assay (MABA). The effects of the compounds 1-3 in combination with first- and second-line anti-TB drugs isoniazid, rifampicin, cycloserine, and clarithromycin on the growth of M. tuberculosis and M. avium were also evaluated in in vitro assays. As a single agent, compounds 1 and 2 exhibited modest activity while compound 3 was the most effective against M. tuberculosis and M. avium. When compounds 1-3 were evaluated at lower than 50% of their inhibitory concentrations in a two-drug combination with isoniazid or rifampicin, they showed additive to synergistic interactions. This inhibitory effect was improved when each of the three compounds was tested together in a three-drug combination with two of the first-line anti-TB drugs. Compounds 1-3 also demonstrated strong synergistic interaction in combination with cycloserine and clarithromycin in inhibiting the growth of M. tuberculosis and M. avium, respectively. This study demonstrated that compounds 1-3 have potential to be developed as effective anti-TB agents with combined use.

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.

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.

A Convenient Cascade Strategy Towards the Synthesis of Novel Substituted Morpholinopyrimidines and Pyrimidooxazapines

Abstract:

A simple and novel protocol for the facile synthesis of bicyclic hybrid molecular framework of morpholinopyrimidines having six-membered pyrimidine ring fused with the morpholine unit is established. The method has been successfully employed to synthesize both regioisomers of the morpholinopyrimidine derivatives in good to high yields. The strategy is further extended successfully to synthesize bicyclic pyrimidooxazapine derivatives bearing the six-membered aromatic pyrimidine unit fused with seven-membered aliphatic oxazapine fragment.

K2S2O8-mediated coupling of 6-amino-7-aminomethyl-thiazolino-pyridones with aldehydes to construct amyloid affecting pyrimidine-fused thiazolino-2-pyridones

We herein present the synthesis of diversely functionalized pyrimidine fused thiazolino-2-pyridones via K2S2O8-mediated oxidative coupling of 6-amino-7-(aminomethyl)-thiazolino-2-pyridones with aldehydes. The developed protocol is mild, has wide substrate scope, and does not require transition metal catalyst or base. Some of the synthesized compounds have an ability to inhibit the formation of Amyloid-β fibrils associated with Alzheimer's disease, while others bind to mature amyloid-β and α-synuclein fibrils.

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.

Responding to environmental cues such as pH and chloride is critical in enabling Mycobacterium tuberculosis to colonize its host. A chemical screen using an M. tuberculosis strain bearing a fluorescent reporter identifies a compound that perturbs the bacterial response to chloride and inhibits its growth in a murine infection model.

Design, Synthesis, Molecular Modeling and Antitumor Evaluation of Novel Indolyl-Pyrimidine Derivatives with EGFR Inhibitory Activity

Scaffolds hybridization is a well-known drug design strategy for antitumor agents. Herein, series of novel indolyl-pyrimidine hybrids were synthesized and evaluated in vitro and in vivo for their antitumor activity. The in vitro antiproliferative activity of all compounds was obtained against MCF-7, HepG2, and HCT-116 cancer cell lines, as well as against WI38 normal cells using the resazurin assay. Compounds 1-4 showed broad spectrum cytotoxic activity against all these cancer cell lines compared to normal cells. Compound 4g showed potent antiproliferative activity against these cell lines (IC₅₀ = 5.1, 5.02, and 6.6 μM, respectively) comparable to the standard treatment (5-FU and erlotinib). In addition, the most promising group of compounds was further evaluated for their in vivo antitumor efficacy against EAC tumor bearing mice. Notably, compound 4g showed the most potent in vivo antitumor activity. The most active compounds were evaluated for their EGFR inhibitory (range 53-79%) activity. Compound 4g was found to be the most active compound against EGFR (IC₅₀ = 0.25 µM) showing equipotency as the reference treatment (erlotinib). Molecular modeling study was performed on compound 4g revealed a proper binding of this compound inside the EGFR active site comparable to erlotinib. The data suggest that compound 4g could be used as a potential anticancer agent.

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.

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.

A Preclinical Candidate Targeting Mycobacterium tuberculosis KasA

Published Mycobacterium tuberculosis β-ketoacyl-ACP synthase KasA inhibitors lack sufficient potency and/or pharmacokinetic properties. A structure-based approach was used to optimize existing KasA inhibitor DG167. This afforded indazole JSF-3285 with a 30-fold increase in mouse plasma exposure. Biochemical, genetic, and X-ray studies confirmed JSF-3285 targets KasA. JSF-3285 offers substantial activity in an acute mouse model of infection and in the corresponding chronic infection model, with efficacious reductions in colony-forming units at doses as low as 5 mg/kg once daily orally and improvement of the efficacy of front-line drugs isoniazid or rifampicin. JSF-3285 is a promising preclinical candidate for tuberculosis.

Antitubercular Triazines: Optimization and Intrabacterial Metabolism

The triazine antitubercular JSF-2019 was of interest due to its in vitro efficacy and the nitro group shared with the clinically relevant delamanid and pretomanid. JSF-2019 undergoes activation requiring F420H2 and one or more nitroreductases in addition to Ddn. An intrabacterial drug metabolism (IBDM) platform was leveraged to demonstrate the system kinetics, evidencing formation of NO⋅ and a des-nitro metabolite. Structure-activity relationship studies focused on improving the solubility and mouse pharmacokinetic profile of JSF-2019 and culminated in JSF-2513, relying on the key introduction of a morpholine. Mechanistic studies with JSF-2019, JSF-2513, and other triazines stressed the significance of achieving potent in vitro efficacy via release of intrabacterial NO⋅ along with inhibition of InhA and, more generally, the FAS-II pathway. This study highlights the importance of probing IBDM and its potential to clarify mechanism of action, which in this case is a combination of NO⋅ release and InhA inhibition.

Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F₄₂₀H₂-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.

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.

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.