Brain endothelial STING1 activation by Plasmodium-sequestered heme promotes cerebral malaria via type I IFN response.

Cerebral malaria (CM) is a life-threatening form of Plasmodium falciparum infection caused by brain inflammation. Brain endothelium dysfunction is a hallmark of CM pathology, which is also associated with the activation of the type I interferon (IFN) inflammatory pathway. The molecular triggers and sensors eliciting brain type I IFN cellular responses during CM remain largely unknown. We herein identified the stimulator of interferon response cGAMP interactor 1 (STING1) as the key innate immune sensor that induces Ifnß1 transcription in the brain of mice infected with Plasmodium berghei ANKA (Pba). This STING1/IFNß-mediated response increases brain CXCL10 governing the extent of brain leukocyte infiltration and blood-brain barrier (BBB) breakdown, and determining CM lethality. The critical role of brain endothelial cells (BECs) in fueling type I IFN-driven brain inflammation was demonstrated in brain endothelial-specific IFNß-reporter and STING1-deficient Pba-infected mice, which were significantly protected from CM lethality. Moreover, extracellular particles (EPs) released from Pba-infected erythrocytes activated the STING1-dependent type I IFN response in BECs, a response requiring intracellular acidification. Fractionation of the EPs enabled us to identify a defined fraction carrying hemoglobin degradation remnants that activates STING1/IFNß in the brain endothelium, a process correlated with heme content. Notably, stimulation of STING1-deficient BECs with heme, docking experiments, and in vitro binding assays unveiled that heme is a putative STING1 ligand. This work shows that heme resultant from the parasite heterotrophic activity operates as an alarmin, triggering brain endothelial inflammatory responses via the STING1/IFNß/CXCL10 axis crucial to CM pathogenesis and lethality.

Unveiling Novel ERCC1-XPF Complex Inhibitors: Bridging the Gap from In Silico Exploration to Experimental Design.

Modifications in DNA repair pathways are recognized as prognostic markers and potential therapeutic targets in various cancers, including non-small cell lung cancer (NSCLC). Overexpression of ERCC1 correlates with poorer prognosis and response to platinum-based chemotherapy. As a result, there is a pressing need to discover new inhibitors of the ERCC1-XPF complex that can potentiate the efficacy of cisplatin in NSCLC. In this study, we developed a structure-based virtual screening strategy targeting the inhibition of ERCC1 and XPF interaction. Analysis of crystal structures and a library of small molecules known to act against the complex highlighted the pivotal role of Phe293 (ERCC1) in maintaining complex stability. This residue was chosen as the primary binding site for virtual screening. Using an optimized docking protocol, we screened compounds from various databases, ultimately identifying more than one hundred potential inhibitors. Their capability to amplify cisplatin-induced cytotoxicity was assessed in NSCLC H1299 cells, which exhibited the highest ERCC1 expression of all the cell lines tested. Of these, 22 compounds emerged as promising enhancers of cisplatin efficacy. Our results underscore the value of pinpointing crucial molecular characteristics in the pursuit of novel modulators of the ERCC1-XPF interaction, which could be combined with cisplatin to treat NSCLC more effectively.

Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening.

Urease is a metalloenzyme that catalyzes the hydrolysis of urea, and its modulation has an important role in both the agricultural and medical industry. Even though numerous molecules have been tested against ureases of different species, their clinical translation has been limited due to chemical and metabolic stability as well as side effects. Therefore, screening new compounds against urease would be of interest in part due to rising concerns regarding antibiotic resistance. In this work, we collected and curated a diverse set of 2640 publicly available small-molecule inhibitors of jack bean urease and developed a classifier using a random forest machine learning method with high predictive performance. In addition, the physicochemical features of compounds were paired with molecular docking and protein-ligand fingerprint analysis to gather insight into the current activity landscape. We observed that the docking score could not differentiate active from inactive compounds within each chemical family, but scores were correlated with compound activity when all compounds were considered. Additionally, a decision tree model was built based on 2D and 3D Morgan fingerprints to mine patterns of the known active-class compounds. The final machine learning model showed good prediction performance against the test set (81% and 77% precision for active and inactive compounds, respectively). Finally, this model was employed, as a proof-of-concept, on an in-house library to predict new hits that were then tested against urease and found to be active. This is, to date, the largest, most diverse dataset of compounds used to develop predictive in silico models. Overall, the results highlight the usefulness of using machine learning classifiers and molecular docking to predict novel urease inhibitors.

Exploring the Chemical Space of Urease Inhibitors to Extract Meaningful Trends and Drivers of Activity.

Blocking the catalytic activity of urease has been shown to have a key role in different diseases as well as in different agricultural applications. A vast array of molecules have been tested against ureases of different species, but the clinical translation of these compounds has been limited due to challenges of potency, chemical and metabolic stability as well as promiscuity against other proteins. The design and development of new compounds greatly benefit from insights from previously tested compounds; however, no large-scale studies surveying the urease inhibitors' chemical space exist that can provide an overview of developed compounds to data. Therefore, given the increasing interest in developing new compounds for this target, we carried out a comprehensive analysis of the activity landscape published so far. To do so, we assembled and curated a data set of compounds tested against urease. To the best of our knowledge, this is the largest data set of urease inhibitors to date, composed of 3200 compounds of diverse structures. We characterized the data set in terms of chemical space coverage, molecular scaffolds, distribution with respect to physicochemical properties, as well as temporal trends of drug development. Through these analyses, we highlighted different substructures and functional groups responsible for distinct activity and inactivity against ureases. Furthermore, activity cliffs were assessed, and the chemical space of urease inhibitors was compared to DrugBank. Finally, we extracted meaningful patterns associated with activity using a decision tree algorithm. Overall, this study provides a critical overview of urease inhibitor research carried out in the last few decades and enabled finding underlying SAR patterns such as under-reported chemical functional groups that contribute to the overall activity. With this work, we propose different rules and practical implications that can guide the design or selection of novel compounds to be screened as well as lead optimization.

Bromoditerpenes from the Red Seaweed Sphaerococcus coronopifolius as Potential Cytotoxic Agents and Proteasome Inhibitors and Related Mechanisms of Action.

Seaweeds are a great source of compounds with cytotoxic properties with the potential to be used as anticancer agents. This study evaluated the cytotoxic and proteasome inhibitory activities of 12R-hydroxy-bromosphaerol, 12S-hydroxy-bromosphaerol, and bromosphaerol isolated from Sphaerococcus coronopifolius. The cytotoxicity was evaluated on malignant cell lines (A549, CACO-2, HCT-15, MCF-7, NCI-H226, PC-3, SH-SY5Y, and SK-MEL-28) using the MTT and LDH assays. The ability of compounds to stimulate the production of hydrogen peroxide (H2O2) and to induce mitochondrial dysfunction, the externalization of phosphatidylserine, Caspase-9 activity, and changes in nuclear morphology was also studied on MCF-7 cells. The ability to induce DNA damage was also studied on L929 fibroblasts. The proteasome inhibitory activity was estimated through molecular docking studies. The compounds exhibited IC50 values between 15.35 and 53.34 µM. 12R-hydroxy-bromosphaerol and 12S-hydroxy-bromosphaerol increased the H2O2 levels on MCF-7 cells, and bromosphaerol induced DNA damage on fibroblasts. All compounds promoted a depolarization of mitochondrial membrane potential, Caspase-9 activity, and nuclear condensation and fragmentation. The compounds have been shown to interact with the chymotrypsin-like catalytic site through molecular docking studies; however, only 12S-hydroxy-bromosphaerol evidenced interaction with ALA20 and SER169, key residues of the proteasome catalytic mechanism. Further studies should be outlined to deeply characterize and understand the potential of those bromoditerpenes for anticancer therapeutics.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Revisiting Proteasome Inhibitors: Molecular Underpinnings of Their Development, Mechanisms of Resistance and Strategies to Overcome Anti-Cancer Drug Resistance.

Proteasome inhibitors have shown relevant clinical activity in several hematological malignancies, namely in multiple myeloma and mantle cell lymphoma, improving patient outcomes such as survival and quality of life, when compared with other therapies. However, initial response to the therapy is a challenge as most patients show an innate resistance to proteasome inhibitors, and those that respond to the therapy usually develop late relapses suggesting the development of acquired resistance. The mechanisms of resistance to proteasome inhibition are still controversial and scarce in the literature. In this review, we discuss the development of proteasome inhibitors and the mechanisms of innate and acquired resistance to their activity-a major challenge in preclinical and clinical therapeutics. An improved understanding of these mechanisms is crucial to guiding the design of new and more effective drugs to tackle these devastating diseases. In addition, we provide a comprehensive overview of proteasome inhibitors used in combination with other chemotherapeutic agents, as this is a key strategy to combat resistance.

Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2.

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

Synthesis of Computationally Designed 2,5(6)-Benzimidazole Derivatives via Pd-Catalyzed Reactions for Potential E. coli DNA Gyrase B Inhibition.

A pharmacophore model for inhibitors of Escherichia coli's DNA Gyrase B was developed, using computer-aided drug design. Subsequently, docking studies showed that 2,5(6)-substituted benzimidazole derivatives are promising molecules, as they possess key hydrogen bond donor/acceptor groups for an efficient interaction with this bacterial target. Furthermore, 5(6)-bromo-2-(2-nitrophenyl)-1H-benzimidazole, selected as a core molecule, was prepared on a multi-gram scale through condensation of 4-bromo-1,2-diaminobenzene with 2-nitrobenzaldehyde using a sustainable approach. The challenging functionalization of the 5(6)-position was carried out via palladium-catalyzed Suzuki-Miyaura and Buchwald-Hartwig amination cross-coupling reactions between N-protected-5-bromo-2-nitrophenyl-benzimidazole and aryl boronic acids or sulfonylanilines, with yields up to 81%. The final designed molecules (2-(aminophen-2-yl)-5(6)-substituted-1H-benzimidazoles), which encompass the appropriate functional groups in the 5(6)-position according to the pharmacophore model, were obtained in yields up to 91% after acid-mediated N-boc deprotection followed by Pd-catalyzed hydrogenation. These groups are predicted to favor interactions with DNA gyrase B residues Asn46, Asp73, and Asp173, aiming to promote an inhibitory effect.

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma.

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-7.

Breakthroughs in Medicinal Chemistry [...].

In silico, NMR and pharmacological evaluation of an hydroxyoxindole cholinesterase inhibitor.

From a screening study of various potential inhibitors for cholinesterases (ChEs), compound (rac)-1 (4-((3-hydroxy-2-oxo-3-phenylindolin-1-yl) methyl) piperidin-1-ium chloride) showed an IC50 of 18 µM for butyrylcholinesterase (BuChE). Herein we present a toxicological and pharmacological evaluation of (rac)-1 to determine its potential for use as an alternative ChE inhibitor for the treatment of Alzheimer's disease. The strategy adopted included in vivo and ex vivo studies with mouse models, Molecular Modelling and Saturation Transfer Difference (STD) NMR studies. Preliminary molecular docking studies were conducted with both (R) and (S)-1 with acetylcholinesterase (AChE) and BuChE, prior to advancing to the mouse model, and indeed favorable interactions were observed, with (R)-1 showing the best binding with AChE and (S)-1 with BuChE. STD-NMR studies were used to successfully validate these results. Toxicological studies were also conducted using the Artemia salina model, with donepezil as reference. It was found that in the in vivo mouse studies that (rac)-1 presented a slightly better inhibition of AChE (0.096 µmol.min-1.mg-1) than donepezil (0.112 µmol.min-1.mg-1) and the same level of inhibition for BuChE as donepezil (0.014 µmol.min-1.mg-1).

Chemical Patterns of Proteasome Inhibitors: Lessons Learned from Two Decades of Drug Design.

Drug discovery now faces a new challenge, where the availability of experimental data is no longer the limiting step, and instead, making sense of the data has gained a new level of importance, propelled by the extensive incorporation of cheminformatics and bioinformatics methodologies into the drug discovery and development pipeline. These enable, for example, the inference of structure-activity relationships that can be useful in the discovery of new drug candidates. One of the therapeutic applications that could benefit from this type of data mining is proteasome inhibition, given that multiple compounds have been designed and tested for the last 20 years, and this collection of data is yet to be subjected to such type of assessment. This study presents a retrospective overview of two decades of proteasome inhibitors development (680 compounds), in order to gather what could be learned from them and apply this knowledge to any future drug discovery on this subject. Our analysis focused on how different chemical descriptors coupled with statistical tools can be used to extract interesting patterns of activity. Multiple instances of the structure-activity relationship were observed in this dataset, either for isolated molecular descriptors (e.g., molecular refractivity and topological polar surface area) as well as scaffold similarity or chemical space overlap. Building a decision tree allowed the identification of two meaningful decision rules that describe the chemical parameters associated with high activity. Additionally, a characterization of the prevalence of key functional groups gives insight into global patterns followed in drug discovery projects, and highlights some systematically underexplored parts of the chemical space. The various chemical patterns identified provided useful insight that can be applied in future drug discovery projects, and give an overview of what has been done so far.

Pyridine-Containing Macrocycles Display MMP-2/9 Inhibitory Activity and Distinct Effects on Migration and Invasion of 2D and 3D Breast Cancer Models.

The role of metalloproteinases (MMPs) on the migration and invasion of cancer cells has been correlated with tumor aggressiveness, namely with the up-regulation of MMP-2 and 9. Herein, two pyridine-containing macrocyclic compounds, [15]pyN5 and [16]pyN5, were synthesized, chemically characterized and evaluated as potential MMP inhibitors for breast cancer therapy using 3D and 2D cellular models. [15]pyN5 and [16]pyN5 (5-20 µM) showed a marked inhibition of MMPs activity (100% at concentrations ≥ 7.5 µM) when compared to ARP-100, a known MMP inhibitor. The inhibitory activity of [15]pyN5 and [16]pyN5 was further supported through in silico docking studies using Goldscore and ChemPLP scoring functions. Moreover, although no significant differences were observed in the invasion studies in the presence of all MMPs inhibitors, cell migration was significantly inhibited by both pyridine-containing macrocycles at concentrations above 5 µM in 2D cells (p < 0.05). In spheroids, the same effect was observed, but only with [16]pyN5 at 20 µM and ARP-100 at 40 µM. Overall, [15]pyN5 and [16]pyN5 led to impaired breast cancer cell migration and revealed to be potential inhibitors of MMPs 2 and 9.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-6.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of Editorials that is published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...].

Unveiling the Pathogenic Molecular Mechanisms of the Most Common Variant (p.K329E) in Medium-Chain Acyl-CoA Dehydrogenase Deficiency by in Vitro and in Silico Approaches.

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common genetic disorder affecting the mitochondrial fatty acid ß-oxidation pathway. The mature and functional form of human MCAD (hMCAD) is a homotetramer assembled as a dimer of dimers (monomers A/B and C/D). Each monomer binds a FAD cofactor, necessary for the enzyme's activity. The most frequent mutation in MCADD results from the substitution of a lysine with a glutamate in position 304 of mature hMCAD (p.K329E in the precursor protein). Here, we combined in vitro and in silico approaches to assess the impact of the p.K329E mutation on the protein's structure and function. Our in silico results demonstrated for the first time that the p.K329E mutation, despite lying at the dimer-dimer interface and being deeply buried inside the tetrameric core, seems to affect the tetramer surface, especially the ß-domain that forms part of the catalytic pocket wall. Additionally, the molecular dynamics data indicate a stronger impact of the mutation on the protein's motions in dimer A/B, while dimer C/D remains similar to the wild type. For dimer A/B, severe disruptions in the architecture of the pockets and in the FAD and octanoyl-CoA binding affinities were also observed. The presence of unaffected pockets (C/D) in the in silico studies may explain the decreased enzymatic activity determined for the variant protein (46% residual activity). Moreover, the in silico structural changes observed for the p.K329E variant protein provide an explanation for the structural instability observed experimentally, namely, the disturbed oligomeric profile, thermal stability, and conformational flexibility, with respect to the wild-type.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview.

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

3-Hydroxypyrrolidine and (3,4)-dihydroxypyrrolidine derivatives: inhibition of rat intestinal α-glucosidase.

Thirteen pyrrolidine-based iminosugar derivatives have been synthesized and evaluated for inhibition of α-glucosidase from rat intestine. The compounds studied were the non-hydroxy, mono-hydroxy and dihydroxypyrrolidines. All the compounds were N-benzylated apart from one. Four of the compounds had a carbonyl group in the 2,5-position of the pyrrolidine ring. The most promising iminosugar was the trans-3,4-dihydroxypyrrolidine 5 giving an IC50 of 2.97±0.046 and a KI of 1.18 mM. Kinetic studies showed that the inhibition was of the mixed type, but predominantly competitive for all the compounds tested. Toxicological assay results showed that the compounds have low toxicity. Docking studies showed that all the compounds occupy the same region as the DNJ inhibitor on the enzyme binding site with the most active compounds establishing similar interactions with key residues. Our studies suggest that a rotation of ∼90° of some compounds inside the binding pocket is responsible for the complete loss of inhibitory activity. Despite the fact that activity was found only in the mM range, these compounds have served as simple molecular tools for probing the structural features of the enzyme, so that inhibition can be improved in further studies.

Inferring molecular inhibition potency with AlphaFold predicted structures.

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Development of New Drugs to Treat Tuberculosis Based on the Dinitrobenzamide Scaffold.

Tuberculosis (TB) continues to be a major global health challenge and a leading cause of death from infectious diseases. Inspired by the results from a previous work by our group on antimycobacterial N-alkylnitrobenzamides, which are structurally related to the nitrobenzamide family of decaprenylphosphoryl-ß-d-ribose oxidase (DprE1) inhibitors, the present study explored a broad array of substituted benzamides. We particularly focused on previously unexplored 3,5-dinitrobenzamide derivatives. Starting with 3,5-dinitrobenzoic acid, we synthesized a diverse library of amides, incorporating both linear and cyclic amine moieties and also assessed the impact of terminal aromatic groups connected through ether, ester, or amide bonds on the bioactivity of the compounds. The synthesis primarily utilized nucleophilic addition/elimination, SN2, and Mitsunobu reactions. The activity was impacted mainly by two structural features, the addition of an aromatic moiety as a terminal group and the type of linker. The most interesting compounds (c2, d1, and d2, MIC = 0.031 µg/mL) exhibited activities against Mycobacterium Tuberculosis (Mtb) H37Rv comparable to isoniazid. Complementary computational studies helped elucidate potential interactions with DprE1, enhancing our understanding of the molecular basis of their action. Our findings suggest that the most active compounds provide a promising foundation for the continued development of new antimycobacterial agents.