Drug Discovery Approaches Research Articles

Anthricin-induced hyperactive proteasome and its molecular mechanism

Recently, targeted protein degradation has attracted increasing interest as a new drug discovery approach. This method aims to control the function of drug targets by inducing their degradation through protein degradation systems such as the proteasome. Concurrently, compounds that enhance proteasome activity have also garnered attention. In 2023, we reported that anthricin (also known as 4-deoxypodophyllotoxin), a natural product that belongs to the lignan family, enhances proteasome activity. However, whether this enhancement was because of increased proteasome expression or improved proteasome function remains unclear. In this study, we investigated the structure–activity relationship of anthricin and its analogs in enhancing proteasome activity, the effects of anthricin on proteasome-related gene expression, and the direct binding between anthricin and the proteasome using pull-down assay. Moreover, we assessed the interaction between anthricin and the proteasome using molecular dynamics (MD) simulations. The results showed that anthricin does not induce proteasome-related gene expression, but instead binds to the β-subunit of the proteasome, bringing the side chains of three amino acid residues (Thr1, Asp17, and Lys33) at the catalytic site closer together, thereby inducing a hyperactive state. To the best of our knowledge, this study is the first to suggest the mechanism of proteasome activity enhancement by anthricin at the molecular level. The findings could contribute to the development of new chemotypes to enhance the effects of targeted protein degraders by regulating proteasome activity.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation.

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

Studying Target-Engagement of Anti-Infectives by Solvent-Induced Protein Precipitation and Quantitative Mass Spectrometry.

Antimicrobial resistance (AMR) poses a serious threat to global health. The rapid emergence of resistance contrasts with the slow pace of antimicrobial development, emphasizing the urgent need for innovative drug discovery approaches. This study addresses a critical bottleneck in early drug development by introducing integral solvent-induced protein precipitation (iSPP) to rapidly assess the target-engagement of lead compounds in extracts of pathogenic microorganisms under close-to-physiological conditions. iSPP measures the change in protein stability against solvent-induced precipitation in the presence of ligands. The iSPP method for bacteria builds upon established SPP procedures and features optimized denaturation gradients and minimized sample input amounts. The effectiveness of the iSPP workflow was initially demonstrated through a multidrug target-engagement study. Using quantitative mass spectrometry (LC-MS/MS), we successfully identified known drug targets of seven different antibiotics in cell extracts of four AMR-related pathogens: the three Gram-negative bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and the Gram-positive bacterium Staphylococcus aureus. The iSPP method was ultimately applied to demonstrate target-engagement of compounds derived from target-based drug discovery. We employed five small molecules targeting three enzymes in the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway─a promising focus for anti-infective drug development. The study showcases iSPP adaptability and efficiency in identifying anti-infective drug targets, advancing early-stage drug discovery against AMR.

Integrated virtual screening and MD simulation study to discover potential inhibitors of mycobacterial electron transfer flavoprotein oxidoreductase.

Tuberculosis (TB) continues to be a major global health burden, with high incidence and mortality rates, compounded by the emergence and spread of drug-resistant strains. The limitations of current TB medications and the urgent need for new drugs targeting drug-resistant strains, particularly multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB, underscore the pressing demand for innovative anti-TB drugs that can shorten treatment duration. This has led to a focus on targeting energy metabolism of Mycobacterium tuberculosis (Mtb) as a promising approach for drug discovery. This study focused on repurposing drugs against the crucial mycobacterial protein, electron transfer flavoprotein oxidoreductase (EtfD), integral to utilizing fatty acids and cholesterol as a carbon source during infection. The research adopted an integrative approach, starting with virtual screening of approved drugs from the ZINC20 database against EtfD, followed by molecular docking, and concluding with molecular dynamics (MD) simulations. Diacerein, levonadifloxacin, and gatifloxacin were identified as promising candidates for repurposing against TB based on their strong binding affinity, stability, and interactions with EtfD. ADMET analysis and anti-TB sensitivity predictions assessed their pharmacokinetic and therapeutic potential. Diacerein and levonadifloxacin, previously unexplored in anti-tuberculous therapy, along with gatifloxacin, known for its efficacy in drug-resistant TB, have broad-spectrum antimicrobial properties and favorable pharmacokinetic profiles, suggesting potential as alternatives to current TB treatments, especially against resistant strains. This study underscores the efficacy of computational drug repurposing, highlighting bacterial energy metabolism and lipid catabolism as fruitful targets. Further research is necessary to validate the clinical suitability and efficacy of diacerein, levonadifloxacin, and gatifloxacin, potentially enhancing the arsenal against global TB.

Current Status of Computational Approaches for Small Molecule Drug Discovery.

2024 has been an exciting year for computational sciences, with the Nobel Prize in Physics awarded for "artificial neural network" and the Nobel Prize in Chemistry presented for "protein structure prediction and design". Given the rapid advancements in Computer-Aided Drug Design (CADD) and Artificial Intelligence in Drug Discovery (AIDD), a document summarizing their current standing and future directions would be timely and relevant to the readership of Journal of Medicinal Chemistry. This piece of commentary aims to highlight recent developments, key challenges, and potential synergies between these fields, contributing to ongoing discussions in the literature and scientific blogs.

Revolutionizing new drug discovery: Harnessing AI and machine learning to overcome traditional challenges and accelerate targeted therapies

Designing highly targeted, selective drugs with desirable absorption, distribution, metabolism, excretion, and pharmacokinetic (PK) profiles; single-digit nanomolar efficacy; and a wider therapeutic index are challenging. In the traditional drug discovery process, researchers screen thousands of chemical compounds during pre-clinical development, progressing through hit identification, lead optimization, and candidate selection to shortlist – potential clinical candidates with favorable PK profiles, high tolerability, and manageable toxicity. The selected candidate must demonstrate sufficient efficacy in treating the target disease in humans. Despite these efforts, the success rate of the pre-clinical candidate to sail through Phase I, Phase II, and Phase III in clinical trials remains exceedingly low. Supported by powerful data-driven tools, artificial intelligence (AI) has transformed this traditional drug discovery process by enabling the analysis of large quantities of omics, phenotypic, and expression data to identify the biological mechanisms of pathological conditions and in turn identify druggable proteins or genes. The generative AI-powered toolbox creates novel compounds from scratch, assists scientists in optimizing druggability attributes, and bridges the differences between animal and human physiology and anatomy to predict potential toxicity in humans with high accuracy. This review discusses the bottlenecks in the traditional drug discovery approach, the impact of AI and machine learning (ML) in drug discovery, and potential challenges associated with AI/ML adoption.

The ALS drug riluzole binds to the C-terminal domain of SARS-CoV-2 nucleocapsid protein and has antiviral activity.

Nucleoproteins (N)play an essential role in virus assembly and are less prone to mutation than other viral structural proteins, making them attractive targets for drug discovery. Using an NMR fragment-based drug discovery approach, we identified the 1,3-benzothiazol-2-amine (BZT) group as a scaffold to develop potential antivirals for SARS-CoV-2 nucleocapsid (N)protein. A thorough characterization of BZT derivatives using NMR, X-ray crystallography, antiviral activity assays, and intrinsic fluorescence measurements revealed their binding in the C-terminal domain (CTD) domain of the N protein, to residues Arg 259, Trp 330, and Lys 338, coinciding with the nucleotide binding site. Our most effective compound exhibits a slightly better affinity than GTP and the ALS drug riluzole, also identified during the screening, and displays notable viral inhibition activity. A virtual screening of 218 BZT-based compounds revealed a potential extended binding site that could be exploited for the future development of new SARS-CoV-2 antivirals.

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

Morphological and Molecular Profiling of Amyloid-β Species in Alzheimer's Pathogenesis.

Alzheimer's disease (AD) is the most common form of dementia around the world (~ 65%). Here, we portray the neuropathology of AD, biomarkers, and classification of amyloid plaques (diffuse, non-cored, dense core, compact). Tau pathology and its involvement with Aβ plaques and cell death are discussed. Amyloid cascade hypotheses, aggregation mechanisms, and molecular species formed in vitro and in vivo (on- and off-pathways) are described. Aβ42/Aβ40 monomers, dimers, trimers, Aβ-derived diffusible ligands, globulomers, dodecamers, amylospheroids, amorphous aggregates, protofibrils, fibrils, and plaques are characterized (structure, size, morphology, solubility, toxicity, mechanistic steps). An update on AD-approved drugs by regulatory agencies, along with new Aβ-based therapies, is presented. Beyond prescribing Aβ plaque disruptors, cholinergic agonists, or NMDA receptor antagonists, other therapeutic strategies (RNAi, glutaminyl cyclase inhibitors, monoclonal antibodies, secretase modulators, Aβ aggregation inhibitors, and anti-amyloid vaccines) are already under clinical trials. New drug discovery approaches based on "designed multiple ligands", "hybrid molecules", or "multitarget-directed ligands" are also being put forward and may contribute to tackling this highly debilitating and fatal form of human dementia.

Orphan GPCRs in Neurodegenerative Disorders: Integrating Structural Biology and Drug Discovery Approaches.

Neurodegenerative disorders, particularly Alzheimer's and Parkinson's diseases, continue to challenge modern medicine despite therapeutic advances. Orphan G-protein-coupled receptors (GPCRs) have emerged as promising targets in the central nervous system, offering new avenues for drug development. This review focuses on the structural biology of orphan GPCRs implicated in these disorders, providing a comprehensive analysis of their molecular architecture and functional mechanisms. We examine recent breakthroughs in structural determination techniques, such as cryo-electron microscopy and X-ray crystallography, which have elucidated the intricate conformations of these receptors. The review highlights how structural insights inform our understanding of orphan GPCR activation, ligand binding and signaling pathways. By integrating structural data with molecular pharmacology, we explore the potential of structure-guided approaches in developing targeted therapeutics toward orphan GPCRs. This structural-biology-centered perspective aims to deepen our comprehension of orphan GPCRs and guide future drug discovery efforts in neurodegenerative disorders.

Identification of high-affinity Monoamine oxidase B inhibitors for depression and Parkinson's disease treatment: bioinformatic approach of drug repurposing.

Depression and Parkinson's disease (PD) are devastating psychiatric and neurological disorders that require the development of novel therapeutic interventions. Drug repurposing targeting predefined pharmacological targets is a widely use approach in modern drug discovery. Monoamine oxidase B (MAO-B) is a critical protein implicated in Depression and PD. In this study, we undertook a systematic exploration of repurposed drugs as potential inhibitors of MAO-B. Exploring a library of 3,648 commercially available drug molecules, we conducted virtual screening using a molecular docking approach to target the MAO-B binding pocket. Two promising drug molecules, Brexpiprazole and Trifluperidol, were identified based on their exceptional binding potential and drug profiling. Subsequently, all-atom molecular dynamics (MD) simulations were performed on the MAO-B-ligand complexes for a trajectory of 300 nanoseconds (ns). Simulation results demonstrated that the binding of Brexpiprazole and Trifluperidol induced only minor structural alterations in MAO-B and showed significant stabilization throughout the simulation trajectory. Overall, the finding suggests that Brexpiprazole and Trifluperidol exhibit strong potential as repurposed inhibitors of MAO-B that might be explored further in experimental investigations for the development of targeted therapies for depression and PD.

Cheminformatic evaluation of the multi-protein binding potential of some diselenide derivatives: A plausible drug discovery approach for leishmaniasis

Over twelve million individuals worldwide suffer from leishmaniasis, and an additional billion people are at risk in places where the disease is prevalent. This work was motivated by the lack of vaccination against leishmaniasis and the shortcomings of current anti-leishmanial treatments. Large compound libraries can be screened using computational methods, which can also be used to analyze pharmacokinetic features, explore protein–ligand interactions, and quickly, accurately, and precisely create novel pharmacological entities. Virtual docking screening was performed to assess the multi-protein targeting capability of certain diselenides using four Leishmania protein targets—2XOX, 3SPX, 5ZWY, and 6K91. Following the Lipinski filter's screening of a subset of analogues, density functional theory (DFT), molecular dynamics (MD) simulation analysis, and ADMET profiles were used to profile the lead compounds. The overall average binding affinity of ligands to target proteins is as follows: 3SPX (− 184.998 kcal/mol) > 6K91 (− 180.114 kcal/mol) > 2XOX (− 176.581 kcal/mol) > 5ZWY (− 157.198 kcal/mol). The focus is on ligands that target all investigated proteins with binding scores greater than the average binding scores for the proteins as a whole for which seven compounds were identified that also showed higher MolDock scores than both reference drugs (Miltefosine and Pentamidine). Further screening showed four compounds (4, 7, 25, and 26) passed Lipinski’s test, making them orally bioavailable with excellent ADMET properties that compared well to both reference medications. The DFT analysis and MD simulation results indicate these compounds' favorability, stability, and reactivity in binding to the studied targets, 3SPX and 6K91 with 25_3SPX and 4_6K91 showing the highest binding free energy (MM-GBSA) of − 88.35 kcal/mol and − 90.29 kcal/mol respectively. Conclusively, the chosen diselenides could be developed as prospective anti-leishmanial pharmacological compounds or as useful scaffolds for creating more potent anti-leishmanial drugs.

Perry Disease: Current Outlook and Advances in Drug Discovery Approach to Symptomatic Treatment.

Perry disease (PeD) is a rare, neurodegenerative, genetic disorder inherited in an autosomal dominant manner. The disease manifests as parkinsonism, with psychiatric symptoms on top, such as depression or sleep disorders, accompanied by unexpected weight loss, central hypoventilation, and aggregation of DNA-binding protein (TDP-43) in the brain. Due to the genetic cause, no causal treatment for PeD is currently available. The only way to improve the quality of life of patients is through symptomatic therapy. This work aims to review the latest data on potential PeD treatment, specifically from the medicinal chemistry and computer-aided drug design (CADD) points of view. We select proteins that might represent therapeutic targets for symptomatic treatment of the disease: monoamine oxidase B (MAO-B), serotonin transporter (SERT), dopamine D2 (D2R), and serotonin 5-HT1A (5-HT1AR) receptors. We report on compounds that may be potential hits to develop symptomatic therapies for PeD and related neurodegenerative diseases and relieve its symptoms. We use Phase pharmacophore modeling software (version 2023.08) implemented in Schrödinger Maestro as a ligand selection tool. For each of the chosen targets, based on the resolved protein-ligand structures deposited in the Protein Data Bank (PDB) database, pharmacophore models are proposed. We review novel, active compounds that might serve as either hits for further optimization or candidates for further phases of studies, leading to potential use in the treatment of PeD.

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

AbstractThe CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors.

Screening of Some Ayurvedic Phytochemicals to Identify Potential Inhibitors against SARS-CoV-2 Mpro by In Silico Computational Approach

Background: The classical drug discovery approach demands more than a decade of strenuous exploration and substantial monetary or economic support, which is difficult in pandemic conditions, such as COVID-19. Methods: The main purpose of this work was to ascertain the best inhibitors to combat the SARS-CoV-2 Mpro (PDB ID: 6LU7) target. To achieve this, we conducted a molecular docking screening of 35 phytochemicals from eight different medicinal plants. Using a structure-based drug design of molecular docking, we studied the binding affinities and found 35 molecules that showed greater or identical affinity towards the target than the N3 inhibitor. Additionally, we conducted MD simula-tions for the 6LU7-schaftoside complex. Results: The docking analysis has identified several promising phytochemicals with great binding attraction towards the key target. The phytoconstituent, schaftoside (-8.7 kcal/mol), demonstrated the most binding attraction with the target via 6 conventional hydrogen bonds. Additionally, 2'-O-methyl cajanone (-8.3 kcal/mol), isoschaftoside (-8.0 kcal/mol), cajaflavonone (-8.0 kcal/mol), and co-crystal N3 inhibitor (-7.8 kcal/mol) also displayed significant binding affinity. Interestingly, schaftoside and 2’-O-methyl cajanone showed the most promising activities with their low binding energies. Conclusion: After thorough analysis, some compounds were found on elite docking sites that re-sembled drugs and had a harmless ADMET profile. Based on the study, it can be concluded that the compounds mentioned earlier possess the ability to be reused as potent inhibitors against the COVID-19 pandemic.

Small chemical molecules regulating the phytohormone signalling alter the plant's physiological processes to improve stress adaptation, growth and productivity.

Small chemical molecules are attractive agents for improving the plant processes associated with plant growth and stress tolerance. Recent advances in chemical biology and structure-assisted drug discovery approaches have opened up new avenues in plant biology to discover new drug-like molecules to improve plant processes for sustained food production. Several compounds targeting phytohormone biosynthesis or signalling cascades were designed to alter plant physiological mechanisms. Altering Abscisic acid synthesis and its signalling process can improve drought tolerance, and the processes targeted are reversible. Molecules targeting cytokinin, Auxin, and gibberellic acid regulate plant physiological processes and can potentially improve plant growth, biomass and productivity. The potential of molecules may be exploited as agrochemicals to enhance agricultural productivity. The discovery of small molecules provides new avenues to improve crop production in changing climatic conditions and the nutritional quality of foods. We present the rational combinations of small molecules with inhibitory and co-stimulatory effects and discuss future opportunities in this field.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis -regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical–genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

Lasamide, a Potent Human Carbonic Anhydrase Inhibitor from the Market: Inhibition Profiling and Crystallographic Studies.

Lasamide is a synthetic precursor and a contaminant of the diuretic Furosemide manufacturing process and represents a highly valuable building block for fragment-based drug discovery approaches. We assessed the ability of Lasamide to inhibit in vitro the human-expressed Carbonic Anhydrases by means of the stopped-flow technique, and we assessed its binding modes within hCAs II and XII-mimic catalytic clefts by X-ray crystallography. Interestingly, an unprecedented crystal form for the hCA IX mimic H-tag is reported and discussed herein.

Design, synthesis, and evaluation of chalcone derivatives as xanthine oxidase inhibitors

Xanthine oxidase (XO) is an important enzyme that catalyzes the oxidation of hypoxanthine to xanthine and xanthine to uric acid in the catabolism of purines in humans. This makes XO a well-recognized target in alleviating hyperuricemia. The present study adapted a structure-based drug discovery approach to develop potent and low-toxicity XO inhibitors with the chalcone skeleton. We introduced a carboxyl group and a hydroxyl group to the B ring and modified the A ring. 35 chalcone derivatives were designed and synthesized. All the 35 derivatives exhibited higher XO inhibition activities (IC50 = 0.064–0.559 μM) compared with allopurinol (IC50 = 2.588 μM). Their high affinity was attributed to strong hydrogen bond interactions formed between the introduced carboxyl and hydroxyl groups with key amino acid residues in XO. SAR analysis disclosed that carboxyl, hydroxyl, ethyl (12c), methylamino (12h), dimethylamino (12i), indolin (13k), and indol (13l) groups played important roles in improving the whole molecules’ inhibition potency against XO. ADME predictions and cytotoxicity assays suggested their pharmacokinetic characteristics and biocompatibility were desirable. Additionally, 12c exhibited a significant hypouricemic effect on potassium oxonate-induced hyperuricemia rats after orally administrated at a dose range of 10–40 mg/kg, representing a promising anti-hyperuricemia potential for further optimization and development.

Discovery of anti-infective compounds against Mycobacterium marinum after biotransformation of simple natural stilbenes by a fungal secretome.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, remains a serious threat to human health worldwide and the quest for new anti-tubercular drugs is an enduring and demanding journey. Natural products (NPs) have played a significant role in advancing drug therapy of infectious diseases. This study evaluated the suitability of a high-throughput infection system composed of the host amoeba Dictyostelium discoideum (Dd) and Mycobacterium marinum (Mm), a close relative of Mtb, to identify anti-infective compounds. Growth of Dd and intracellular Mm were quantified by using luminescence and fluorescence readouts in phenotypic assays. The system was first benchmarked with a set of therapeutic anti-Mtb antibiotics and then used to screen a library of biotransformed stilbenes. The study confirmed both efficacy of established antibiotics such as rifampicin and bedaquiline, with activities below defined anti-mycobacterium susceptibility breakpoints, and the lack of activity of pyrazinamide against Mm. The screening revealed the promising anti-infective activities of trans-δ-viniferins and in particular of two compounds 17 and 19 with an IC50 of 18.1 μM, 9 μM, respectively. Both compounds had no activity on Mm in broth. Subsequent exploration via halogenation and structure-activity relationship studies led to the identification of derivatives with improved selectivity and potency. The modes of action of the anti-infective compounds may involve inhibition of mycobacterial virulence factors or boosting of host defense. The study highlights the potential of biotransformation and NP-inspired derivatization approaches for drug discovery and underscores the utility of the Dd-Mm infection system in identifying novel anti-infective compounds.