Avenues For Drug Discovery Research Articles

SnapShot: Targeted protein degradation.

Targeted protein degradation strategies leverage endogenous cellular degradation machinery to selectively eliminate a protein of interest. Emerging technologies are opening avenues in drug discovery and functional characterization of intracellular, membrane, and extracellular proteins. To view this SnapShot, open or download the PDF.

Predicting biological activity and design of 5-HT6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

Anti-Schistosomal activity and ADMET properties of 1,2,5-oxadiazinane-containing compound synthesized by visible-light photoredox catalysis.

The incorporation of saturated nitrogen-containing heterocycle 1,2,5-oxadiazinane into small molecules represents a compelling avenue in drug discovery due to its unexplored behavior within biological systems and incomplete protocols for synthesis. In this study, we present 1,2,5-oxadiazinane, an innovative heterocyclic bioisostere of piperizin-2-one and novel chemotype of the anti-schistosomal drug praziquantel (PZQ), which has been the only clinical drug available for three decades. PZQ is associated with significant drawbacks, including poor solubility, a bitter taste, and low metabolic stability. Therefore, the discovery of a new class of anti-schistosomal agents is imperative. To address this challenge, we introduce a pioneering method for the synthesis of 1,2,5-oxadiazinane derivatives through the cycloaddition of nitrones with N,N,N',N'-tetraalkyldiaminomethane in the presence of an IrIII complex photosensitizer. This transformative reaction offers a streamlined route to various kinds of 1,2,5-oxadiazinanes that is characterized by mild reaction conditions and broad substrate scope. Mechanistic investigations suggest that the photoredox pathway underlies the [3 + 3] photocycloaddition process. Thus, based on bioisosteric replacement, we identified a remarkable molecule as a new chemotype of a potent anti-schistosomal compound that not only exhibits superior solubility, but also retains the potent biological activity inherent to PZQ.

Ameliorative Effects of Essential Oils on Diabetes Mellitus: A Review.

Diabetes mellitus (DM) is a metabolic disorder and is responsible for the death of more than 4.2 million people in 2019. Synthetic drugs for DM like metformin have been reported to induce numerous complications and side effects. Reports suggested that essential plant oil has been used as an herbal remedy to lower blood glucose levels. Essential oils (EOs) are complex combinations of small molecules obtained from plants via the process of steam distillation and several solvents. EOs have already shown great efficacy as antimicrobials, anti-inflammatory, hepatoprotective, and anti-hypertensive. This review aims to summarize some potential EOs that have been reported to have anti-diabetic activity both in preclinical and clinical aspects while summarizing the probable mechanism of action. The authors went through a vast number of articles from various scientific databases like Google Scholar, PubMed, and Web of Science. It was found that EO from a total of 20 plants has been pre-clinically investigated to have anti-diabetic potential. Besides this, clinical studies have reported the antidiabetic efficacy of EOs from Nigella sativa and Cuminum cyminum at different concentrations. Bioactive phytoconstituents like carvacrol, thymol, α- pinene, via . obtained from EOs ameliorate DM by inhibiting α-GLUC, α-amylase, lipase enzymes and increasing GLUT-4 expression, AKT phosphorylation, via . Although fewer in number, EOs from plant sources have demonstrated significant efficacy in DM. Proper elucidation of the anti-diabetic efficacy of the EOs may open up new avenues for drug discovery and development subjected to clinical studies.

Three-Dimensional Cell Culture Scaffold Supports Capillary-Like Network Formation by Endothelial Cells Derived from Porcine-Induced Pluripotent Stem Cells.

Endothelial cells (EC) can be generated from porcine-induced pluripotent stem cells (piPSC), but poor efficiency in driving EC differentiation hampers their application and efficacy. Additionally, the culture of piPSC-derived EC (piPSC-EC) on three-dimensional (3D) scaffolds has not been fully reported yet. Here, we report a method to improve the generation of EC differentiation from piPSC and to facilitate their culture on 3D scaffolds, providing a potential resource for in vitro drug testing and the generation of tissue-engineered vascular grafts. We initiated the differentiation of piPSC into EC by seeding them on laminin 411 and employing a three-stage protocol, which involved the use of distinct EC differentiation media supplemented with CHIR99021, BMP4, VEGF, and bFGF. piPSC-EC not only expressed EC markers such as CD31, VE-cadherin, and von Willebrand factor (vWF) but also exhibited an upregulation of EC marker genes, including CD31, CD34, VEGFR2, VE-cadherin, and vWF. They exhibited functional characteristics similar to those of porcine coronary artery endothelial cells (PCAEC), such as tube formation and Dil-Ac-LDL uptake. Furthermore, when cultured on 3D scaffolds, piPSC-EC developed a 3D morphology and were capable of forming an endothelial layer and engineering capillary-like networks, though these lacked lumen structures. Our study not only advances the generation of EC from piPSC through an inhibitor and growth factor cocktail but also provides a promising approach for constructing vascular network-like structures. Importantly, these findings open new avenues for drug discovery in vitro and tissue engineering in vivo.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition.

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.

A review on molecular scissoring with CRISPR/Cas9 genome editing technology.

Genome editing is a technology to make specific changes in the DNA of a cell or an organism. It has significantly altered the landscape of life sciences, facilitating the establishment of exceedingly customized genetic modifications. Among various genome editing technologies, the CRISPR/Cas9 system, a specific endonuclease induces a double stranded DNA break and enabling modifications to the genome, has surfaced as a formidable and adaptable instrument. Its significance cannot be overstated, as it not only allows for the manipulation of genomes in model organisms but also holds great potential for revolutionary advances in medicine, particularly in treating genetic diseases. This review paper explores the remarkable journey of CRISPR/Cas9, its natural function, mechanisms, and transformative impact on genome editing and finally the use of artificial intelligence and other intelligent manufacturing tools used. The introduction provides the background on genome editing, emphasizing the emergence and significance of CRISPR/Cas9. Subsequent sections comprehensively elucidate its natural function, disease modeling, agriculture, and biotechnology, address therapeutic applications, and ongoing clinical trials while also discussing prospects and ethical implications. We summarized the key findings, indicating that CRISPR/Cas9 has empowered the creation of disease-specific animal models. This provides invaluable insights into pathogenic mechanisms and opens new avenues for drug discovery, reaffirming the transformative impact of CRISPR/Cas9 on genome editing. Finally we discussed the importance of continued research and collaboration for comprehensive utilization of the inherent capabilities of this molecular precision tool in shaping forthcoming advancements.

Hypersensitivity to BKCa channel opening in persistent post-traumatic headache

BackgroundLarge conductance calcium-activated potassium (BKCa) channels have been implicated in the neurobiological underpinnings of migraine. Considering the clinical similarities between migraine and persistent post-traumatic headache (PPTH), we aimed to examine whether MaxiPost (a BKCa channel opener) could induce migraine-like headache in persons with PPTH.MethodsThis is a randomized double-blind, placebo-controlled, two-way crossover study from September 2023 to December 2023. Eligible participants were adults with PPTH after mild traumatic brain injury who reported having no personal history of migraine. The randomized participants received a single dose of either MaxiPost (0.05 mg/min) or placebo (isotonic saline) that was infused intravenously over 20 minutes. The two experiment sessions were scheduled at least one week apart to avoid potential carryover effects. The primary endpoint was the induction of migraine-like headache after MaxiPost as compared to placebo within 12 hours of drug administration. The secondary endpoint was the area under the curve (AUC) values for headache intensity scores between MaxiPost and placebo over the same 12-hour observation period.ResultsTwenty-one adult participants (comprising 14 females and 7 males) with PPTH were enrolled and completed both experiment sessions. The proportion of participants who developed migraine-like headache was 11 (52%) of 21 participants after MaxiPost infusion, in contrast to four (19%) participants following placebo (P = .02). Furthermore, the median headache intensity scores, represented by AUC values, were higher following MaxiPost than after placebo (P < .001).ConclusionsOur results indicate that BKCa channel opening can elicit migraine-like headache in persons with PPTH. Thus, pharmacologic blockade of BKCa channels might present a novel avenue for drug discovery. Additional investigations are nonetheless needed to confirm these insights and explore the therapeutic prospects of BKCa channel blockers in managing PPTH.ClinicalTrials.gov IdentifierNCT05378074.

Bis‐Ureido‐Substituted Benzenesulfonamides: Evaluation of Their Antibacterial, Anticholinesterase, and Cytotoxicity Properties

AbstractIn this study, we present the re‐synthesis of a series of twelve bis‐ureido‐substituted benzenesulfonamides, focusing on their potential as antibacterial, anticholinesterase, and cytotoxic agents. First, the antibacterial assessment of these compounds indicated varying activity levels across different bacterial strains, with S. aureus displaying resistance to all compounds, while compounds 9 and 11 exhibited promise against E. faecalis with a MIC value of 31.25 μg/mL. Additionally, P. aeruginosa showed resistance to compounds containing 4‐aminobenzene sulfonamides, whereas derivatives such as (8–11) and compound 19 displayed notable activity against E. coli, comparable to Ampicillin. Second, their anticholinesterase activities were examined, with a focus on their potential role in addressing neurological disorders, particularly Alzheimer′s disease. The findings indicated that all synthesized compounds showed a significant inhibitory effect on both AChE and BChE enzymes. Compound 11 demonstrated the most effective inhibition on the AChE enzyme with an IC50 value of 0.2160±0.09 nM, while compound 18 exhibited the most potent inhibition on the BChE enzyme with an IC50 value of 0.2257±0.06 nM. Finally, cytotoxicity studies were conducted across various cell lines, including breast, lung, and prostate cancer cells and normal cells. The results revealed that compound 12 emerged as the most potent tested compound against breast cancer cells with no cytotoxic effect on CRL‐4010 normal breast epithelial cells. The research presented here not only highlights the multifaceted pharmacological potential of bis‐ureido‐substituted benzenesulfonamides, but also indicates their potential as versatile compounds for antibacterial, anticholinesterase, and cytotoxicity applications, opening up new avenues for drug discovery and development.

MinActionPath2: path generation between different conformations of large macromolecular assemblies by action minimization.

Recent progress in solving macromolecular structures and assemblies by cryogenic electron microscopy techniques enables sampling of their conformations in different states that are relevant to their biological function. Knowing the transition path between these conformations would provide new avenues for drug discovery. While the experimental study of transition paths is intrinsically difficult, in-silico methods can be used to generate an initial guess for those paths. The Elastic Network Model (ENM), along with a coarse-grained representation (CG) of the structures are among the most popular models to explore such possible paths. Here we propose an update to our software platform MinActionPath that generates non-linear transition paths based on ENM and CG models, using action minimization to solve the equations of motion. The new website enables the study of large structures such as ribosomes or entire virus envelopes. It provides direct visualization of the trajectories along with quantitative analyses of their behaviors at http://dynstr.pasteur.fr/servers/minactionpath/minactionpath2_submission.

Houttuynia cordata Thunb: Ethnobotanical and availability notes in the Cu Chi district, Vietnam

Houttuynia cordata, a plant with a long history of traditional medicinal and culinary uses across different cultures, has shown effectiveness in treating various ailments in Southeast Asia. The Cu Chi district of southern Vietnam, is home to 35 out of the country's 54 ethnic minority groups, making it an ideal location to investigate the traditional uses and availability of H. cordata within the local community. The study findings reveal notable differences in leaf anatomy, thickness, and vascular bundles, which can be attributed to variations in soil conditions. The Phu My Hung area, characterized by lower soil moisture and nutrient content, indicates more pronounced drought conditions. H. cordata from this region exhibits higher antioxidant capacity and develops lignified cells as an adaptation to dry conditions, thereby enhancing water use efficiency. The findings of this study highlight the significant implications of the distinct populations of H. cordata belonging to the same species. These populations have undergone morphological and physiological adaptations in response to their specific ecological conditions. Notably, the H. cordata population from drought-prone areas such as Phu My Hung exhibits remarkable potential for medicinal applications. This suggests that the unique environmental pressures in these regions have influenced the plant's composition and properties, making it a valuable resource for medicinal research and potential therapeutic developments. The identification and utilization of such adaptations in H. cordata populations could have far-reaching implications in the field of medicine, offering new avenues for drug discovery and the treatment of various ailments.

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

Text-Guided Molecule Generation with Diffusion Language Model

Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rely on an autoregressive architecture. In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion models to address the limitations of autoregressive methods. TGM-DLM updates token embeddings within the SMILES string collectively and iteratively, using a two-phase diffusion generation process. The first phase optimizes embeddings from random noise, guided by the text description, while the second phase corrects invalid SMILES strings to form valid molecular representations. We demonstrate that TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for additional data resources. Our findings underscore the remarkable effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Antisense and Functional Nucleic Acids in Rational Drug Development.

This review is focused on antisense and functional nucleic acid used for completely rational drug design and drug target assessment, aiming to reduce the time and money spent and increase the successful rate of drug development. Nucleic acids have unique properties that play two essential roles in drug development as drug targets and as drugs. Drug targets can be messenger, ribosomal, non-coding RNAs, ribozymes, riboswitches, and other RNAs. Furthermore, various antisense and functional nucleic acids can be valuable tools in drug discovery. Many mechanisms for RNA-based control of gene expression in both pro-and-eukaryotes and engineering approaches open new avenues for drug discovery with a critical role. This review discusses the design principles, applications, and prospects of antisense and functional nucleic acids in drug delivery and design. Such nucleic acids include antisense oligonucleotides, synthetic ribozymes, and siRNAs, which can be employed for rational antibacterial drug development that can be very efficient. An important feature of antisense and functional nucleic acids is the possibility of using rational design methods for drug development. This review aims to popularize these novel approaches to benefit the drug industry and patients.

Comprehensive in silico analyses of fifty-one uncharacterized proteins from Vibrio cholerae.

Due to the rise of multidrug-resistant strains of Vibrio cholerae and the recent cholera outbreaks in African and Asian nations, it is imperative to identify novel therapeutic targets and possible vaccine candidates. In this regard, this work primarily aims to identify and characterize new antigenic molecules using comparative RNA sequencing data and label-free proteomics data, carried out with essential GTPase cgtA knockdown and wild-type strain of V. cholerae. We identified hitherto 51 characterized proteins from high-throughput RNA-sequencing and proteomics data. This work involved the assessment of their physicochemical characteristics, subcellular localization, solubility, structures, and functional annotations. In addition, the immunoinformatic and reverse vaccinology technique was used to find new vaccine targets with high antigenicity, low allergenicity, and low toxicity profiles. Among the 51 proteins, 24 were selected based on their immunogenic profiles to identify B/T-cell epitopes. In addition, 20 prospective therapeutic targets were identified using virulence predictions and related investigations. Furthermore, two proteins, UniProt ID- Q9KRD2 and Q9KU58, with molecular weight of 92kDa and 12kDa, respectively, were chosen for cloning and expression towards in vitro biochemical characterization based on their range of expression patterns, high antigenic, low allergenic, and low toxicity properties. In conclusion, we believe that this study will reveal new facets and avenues for drug discovery and put us a step forward toward novel therapeutic interventions against the deadly disease of cholera.

Transcriptomic Profiling of OSCC Patients in an Indian Subset.

Tumor-specific biomarkers are needed for accomplishing antidote in early detection, as well as prognosis and designing therapeutic strategies. Comprehensive transcriptome profiling offers critical insights into the disease and reveal new avenue for drug discovery. Total 5 cancerous and histopathological normal tissue pairs of 5 OSCC patients included in the petite study. Transcriptome sequencing was performed using Roche's 454 sequencing platform followed by CLC Genomics Workbench was used to examine gene expression in OC development. A total 2082 genes were differentially expressed across all the five tumor-control pairs collected from the OC patients during the surgery. From these 1092 upregulated and 273 downregulated genes, whereas 717 genes were found to be non-significant. The genes with pvalue <0.05 and log2foldchange > 1 or log2foldchange < -1 were considered for further enrichment analysis. Topfunn was used for gene enrichment analysis to identify gene enrichment pathway analysis found some cancer related pathways such as TNF signaling, p53 signaling pathway, cGMP-PKG signaling pathway, Apelin signaling pathway and IL-17 signaling pathway were strikingly involved in proliferation and apoptosis of tumor cells. The PPI network construction was performed and identified 8 best protein interactions. The current study reports molecular biomarkers including INHBA, FJX1, OLR1, CDK2, IGHM, CXCL11, SH2D5 and FABP5 associated with cancer that can led to identify potential therapeutic targets for the better prognosis of the cancer patients. The signature candidate can be translated to clinical practice to increase early diagnostic accuracy.

Structural analogs of 2-(4-fluorophenyl)-6-methyl-3-(pyridin-4-yl)pyrazolo[1,5-a]pyridine for targeting Candida albicans non-essential stress kinase Yck2 through protein-ligand binding and dynamics analysis.

The rise in drug-resistant fungal infections poses a significant public health concern, necessitating the development of new antifungal therapies. We aimed to address this challenge by targeting a yeast casein kinase of Candida albicans for antifungal drug development. The compound library contained 589 chemical structures similar to the previously identified kinase inhibitor GW461484A. Through virtual screening, four compounds with the PubChem IDs 102583821, 12982634, 102487860, and 86260205 were selected based on their binding energies. Hydrophobic bonds and van der Waals interactions stabilised the docked complexes. Comprehensive interaction studies and a 200-nanosecond molecular dynamics simulation suggested that these molecules can maintain stable interactions with the target, as evidenced by satisfactory RMSD and RMSF values. The Rg-RMSD-based Free Energy Landscape of these complexes indicated thermodynamic stability due to the presence of conformers with global minima. These promising findings highlight the potential for developing novel antifungal therapies targeting Yck2 in C. albicans. Further experimental validation is required to assess the efficacy of these compounds as antifungal agents. This research provides a significant step towards combating antifungal resistance and opens up a new avenue for drug discovery.

Protein-Protein Docking Approach to GPCR Oligomerization.

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Clean Synthetic Strategies to Biologically Active Molecules from Lignin: A Green Path to Drug Discovery.

Deriving active pharmaceutical agents from renewable resources is crucial to increasing the economic feasibility of modern biorefineries and promises to alleviate critical supply-chain dependencies in pharma manufacturing. Our multidisciplinary approach combines research in lignin-first biorefining, sustainable catalysis, and alternative solvents with bioactivity screening, an in vivo efficacy study, and a structural-similarity search. The resulting sustainable path to novel anti-infective, anti-inflammatory, and anticancer molecules enabled the rapid identification of frontrunners for key therapeutic indications, including an anti-infective against the priority pathogen Streptococcus pneumoniae with efficacy in vivo and promising plasma and metabolic stability. Our catalytic methods provided straightforward access, inspired by the innate structural features of lignin, to synthetically challenging biologically active molecules with the core structure of dopamine, namely, tetrahydroisoquinolines, quinazolinones, 3-arylindoles and the natural product tetrahydropapaveroline. Our diverse array of atom-economic transformations produces only harmless side products and uses benign reaction media, such as tunable deep eutectic solvents for modulating reactivity in challenging cyclization steps.