RESUMEN

The Ayush sector has attained buoyant growth in the past decade as a science, public health, medicine, and industry. Artificial Intelligence, computational drug designing, and other combinatorial techniques could further accelerate the sector's growth. In this edition, we delve into the confluence of Ayurveda and technology, a theme that resonates profoundly in the contemporary healthcare and wellness landscape. The fusion of Ayurveda, an ancient system of medicine rooted in holistic well-being, with cutting-edge technology, is not just a paradigm shift but a necessary evolution in pursuing an integrated healthcare system where all systems have their defined, recognized, and respected contribution. Here, We are highlight one-such fusion initiative "Ayurinformatics Laboratory".

RESUMEN

This case study demonstrates how knowledge of degradation products together with predictions can establish a lean stability strategy using the accelerated predictive stability (APS) principles. Applying all available data for AZD4831, (R)-1-(2-(1-aminoethyl)-4-chlorobenzyl)-2-thioxo-2,3-dihydro-1H-pyrrolo[3,2-d]pyrimidin-4(5H)-one, a reliable predictive model was developed despite minor differences in technical batch tablet compositions. Early forced degradation studies were performed to map potential degradation pathways. The insights from these studies guided the design of an APS study, which in turn inform on a suitable clinical stability program, initial specification and shelf-life. The use of APS predictions of degradants as well as total impurities highlighted at an early stage, when designing the clinical stability program, the opportunity to identify which degradation product that would be shelf-life limiting. Hence, it was possible to guide the development stability activities and set an initial shelf-life of a tablet formulation. The presented study displays the importance of combining several sources of information in drug development, e.g., potential degradation pathways, accelerated stability, stability program design, metabolite data, and specification limits.

RESUMEN

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.

Asunto(s)

RESUMEN

The filamentous fungus Magnaporthe oryzae is widely recognized as a notorious plant pathogen responsible for causing rice blasts. With rapid advancements in molecular biology technologies, numerous regulatory mechanisms have been thoroughly investigated. However, most recent studies have predominantly focused on infection-related pathways or host defence mechanisms, which may be insufficient for developing novel structure-based prevention strategies. A substantial body of literature has utilized cryo-electron microscopy and X-ray diffraction to explore the relationships between functional components, shedding light on the identification of potential drug targets. Owing to the complexity of protein extraction and stochastic nature of crystallization, obtaining high-quality structures remains a significant challenge for the scientific community. Emerging computational tools such as AlphaFold for structural prediction, docking for interaction analysis, and molecular dynamics simulations to replicate in vivo conditions provide novel avenues for overcoming these challenges. In this review, we aim to consolidate the structural biological advancements in M. oryzae, drawing upon mature experimental experiences from other species such as Saccharomyces cerevisiae and mammals. We aim to explore the potential of protein construction to address the invasion and proliferation of M. oryzae, with the goal of identifying new drug targets and designing small-molecule compounds to manage this disease.

Asunto(s)

RESUMEN

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 µM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

Asunto(s)

RESUMEN

Hepatitis B Virus (HBV) remains a critical global health issue, with substantial morbidity and mortality. Current therapies, including interferons and nucleoside analogs, often fail to achieve complete cure or functional eradication. This review explores recent advances in anti-HBV agents, focusing on their innovative mechanisms of action. HBV entry inhibitors target the sodium taurocholate cotransporting polypeptide (NTCP) receptor, impeding viral entry, while nucleus translocation inhibitors disrupt key viral life cycle steps, preventing replication. Capsid assembly modulators inhibit covalently closed circular DNA (cccDNA) formation, aiming to eradicate the persistent viral reservoir. Transcription inhibitors targeting cccDNA and integrated DNA offer significant potential to suppress HBV replication. Immunomodulatory agents are highlighted for their ability to enhance host immune responses, facil-itating better control and possible eradication of HBV. These novel approaches represent significant advancements in HBV therapy, providing new strategies to overcome current treatment limitations. The development of cccDNA reducers is particularly critical, as they directly target the persistent viral reservoir, offering a promising pathway towards achieving a functional cure or complete viral eradication. Continued research in this area is essential to advance the effectiveness of anti-HBV therapies.

RESUMEN

COVID-19 has caused severe consequences in terms of public health and economy worldwide since its outbreak in December 2019. SARS-CoV-2 3C-like protease (3CLpro), crucial for the viral replications, is an attractive target for the development of antiviral drugs. In this study, several kinds of Michael acceptor warheads were utilized to hunt for potent covalent inhibitors against 3CLpro. Meanwhile, novel 3CLpro inhibitors with the P3-3,5-dichloro-4-(2-(dimethylamino)ethoxy)phenyl moiety were designed and synthesized which may form salt bridge with residue Glu166. Among them, two compounds 12b and 12c exhibited high inhibitory activities against SARS-CoV-2 3CLpro. Further investigations suggested that 12b with an acrylate warhead displayed potent activity against HCoV-OC43 (EC50 = 97 nM) and SARS-CoV-2 replicon (EC50 = 45 nM) and low cytotoxicity (CC50 > 10 µM) in Huh7 cells. Taken together, this study devised two series of 3CLpro inhibitors and provided the potent SARS-CoV-2 3CLpro inhibitor (12b) which may be used for treating coronavirus infections.

Asunto(s)

RESUMEN

Cyclic peptides have emerged as versatile scaffolds in drug discovery due to their stability and specificity. Here, we present the cPEPmatch webserver (accessible at https://t38webservices.nat.tum.de/cpepmatch/), an easy-to-use interface for the rational design of cyclic peptides targeting protein-protein interactions combined with a semi-quantitative evaluation of binding stability. This platform also offers access to a comprehensive database of cyclic peptide crystal structures. We demonstrate the webserver's utility through a series of case studies involving medically relevant protein systems, highlighting its potential to significantly advance drug discovery efforts.

RESUMEN

BACKGROUND: Obesity has emerged as a major health challenge globally in the last two decades. Dysregulated fatty acid metabolism and de novo lipogenesis are prime causes for obesity development which ultimately trigger other co-morbid pathological conditions thereby risking life longevity. Fatty acid metabolism and de novo lipogenesis involve several biochemical steps both in cytosol and mitochondria. Reportedly, the high catalytically active mitochondrial carbonic anhydrases (CAVA/CAVB) regulate the intercellular depot of bicarbonate ions and catalyze the rapid carboxylation of pyruvate and acetyl-co-A to acetyl-co-A and malonate respectively, which are the precursors of fatty acid synthesis and lipogenesis. Several in vitro and in vivo investigations indicate inhibition of mitochondrial carbonic anhydrase isoforms interfere in the functioning of pyruvate, fatty acid and succinate pathways. Targeting of mitochondrial carbonic anhydrase isoforms (CAVA/CAVB) could thereby modulate gluconeogenetic as well as lipogenetic pathways and pave way for designing of novel leads in the development pipeline of anti-obesity medications. METHODS: The present review unveils a diverse chemical space including synthetic sulphonamides, sulphamates, sulfamides and many natural bioactive molecules which selectively inhibit the mitochondrial isoform CAVA/CAVB with an emphasis on major state-of-art drug design strategies. RESULTS: More than 60% similarity in the structural framework of the carbonic anhydrase isoforms has converged the drug design methods towards the development of isoform selective chemotypes. While the benzene sulphonamide derivatives selectively inhibit CAVA/CAVB in low nanomolar ranges depending on the substitutions on the phenyl ring, the sulpamates and sulpamides potently inhibit CAVB. The virtual screening and drug repurposing methods have also explored many non-sulphonamide chemical scaffolds which can potently inhibit CAVA. CONCLUSION: The review could pave way for the development of novel and effective anti-obesity drugs which can modulate the energy metabolism.

RESUMEN

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Asunto(s)

RESUMEN

The increasing prevalence of antibiotic-resistant bacteria necessitates the exploration of novel therapeutic targets. Bacterial carbonic anhydrases (CAs) have been known for decades, but only in the past ten years they have garnered significant interest as drug targets to develop antibiotics having a diverse mechanism of action compared to the clinically used drugs. Significant progress has been made in the field in the past three years, with the validation in vivo of CAs from Neisseria gonorrhoeae, and vancomycin-resistant enterococci as antibiotic targets. This chapter compiles the state-of-the-art research on sulfonamide derivatives described as inhibitors of all known bacterial CAs. A section delves into the mechanisms of action of sulfonamide compounds with the CA classes identified in pathogenic bacteria, specifically α, ß, and γ classes. Therefore, the inhibitory profiling of the bacterial CAs with classical and clinically used sulfonamide compounds is reported and analyzed. Another section covers various other series of sulfonamide CA inhibitors studied for the development of new antibiotics. By synthesizing current research findings, this chapter highlights the potential of sulfonamide inhibitors as a novel class of antibacterial agents and paves the way for future drug design strategies.

Asunto(s)

RESUMEN

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

RESUMEN

BACKGROUND: Staphylococcus aureus is a widely distributed and highly pathogenic zoonotic bacterium. Sortase A represents a crucial target for the research and development of novel antibacterial drugs. OBJECTIVE: This study aims to establish quantitative structure-activity relationship models based on the chemical structures of a class of benzofuranene cyanide derivatives. The models will be used to screen new antibacterial agents and predict the properties of these molecules. METHOD: The compounds were randomly divided into a training set and a test set. A large number of descriptors were calculated using the software, and then the appropriate descriptors were selected to build the models through the heuristic method and the gene expression programming algorithm. RESULTS: In the heuristic method, the determination coefficient, determination coefficient of cross-validation, F-test, and mean squared error values were 0.530, 0.395, 9.006, and 0.047, respectively. In the gene expression programming algorithm, the determination coefficient and the mean squared error values in the training set were 0.937 and 0.008, respectively, while in the test set, they were 0.849 and 0.035. The results showed that the minimum bond order of a C atom and the relative number of benzene rings had a significant positive contribution to the activity of compounds. CONCLUSION: In this study, two quantitative structure-activity relationship models were successfully established to predict the inhibitory activity of a series of compounds targeting Staphylococcus aureus Sortase A, providing insights for further development of novel anti-Staphylococcus aureus drugs.

RESUMEN

BACKGROUND: Targeted protein degradation of neosubstrates plays a crucial role in hematological cancer treatment involving immunomodulatory imide drugs (IMiDs) therapy. Nevertheless, the persistence of inevitable drug resistance and hematological toxicities represents a significant obstacle to their clinical effectiveness. METHODS: Phenotypic profiling of a small molecule compounds library in multiple hematological cancer cell lines was conducted to screen for hit degraders. Molecular dynamic-based rational design and cell-based functional assays were conducted to develop more potent degraders. Multiple myeloma (MM) tumor xenograft models were employed to investigate the antitumor efficacy of the degraders as single or combined agents with standard of care agents. Unbiased proteomics was employed to identify multiple therapeutically relevant neosubstrates targeted by the degraders. MM patient-derived cell lines (PDCs) and a panel of solid cancer cell lines were utilized to investigate the effects of candidate degrader on different stage of MM cells and solid malignancies. Unbiased proteomics of IMiDs-resistant MM cells, cell-based functional assays and RT-PCR analysis of clinical MM specimens were utilized to explore the role of BRD9 associated with IMiDs resistance and MM progression. RESULTS: We identified a novel cereblon (CRBN)-dependent lead degrader with phthalazinone scaffold, MGD-4, which induced the degradation of Ikaros proteins. We further developed a novel potent candidate, MGD-28, significantly inhibited the growth of hematological cancer cells and induced the degradation of IKZF1/2/3 and CK1α with nanomolar potency via a Cullin-CRBN dependent pathway. Oral administration of MGD-4 and MGD-28 effectively inhibited MM tumor growth and exhibited significant synergistic effects with standard of care agents. MGD-28 exhibited preferentially profound cytotoxicity towards MM PDCs at different disease stages and broad antiproliferative activity in multiple solid malignancies. BRD9 modulated IMiDs resistance, and the expression of BRD9 was significant positively correlated with IKZF1/2/3 and CK1α in MM specimens at different stages. We also observed pronounced synergetic efficacy between the BRD9 inhibitor and MGD-28 for MM treatment. CONCLUSIONS: Our findings present a strategy for the multi-targeted degradation of Ikaros proteins and CK1α against hematological cancers, which may be expanded to additional targets and indications. This strategy may enhance efficacy treatment against multiple hematological cancers and solid tumors.

Asunto(s)

RESUMEN

Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and model exploitation might lead to analog identification with limited scaffold diversity. Here, we present ActiveDelta, an adaptive approach that leverages paired molecular representations to predict improvements from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 Ki benchmarking datasets. We show that both ActiveDelta implementations excel at identifying more potent inhibitors compared to the standard exploitative active learning implementations of Chemprop, XGBoost, and Random Forest. The ActiveDelta approach is also able to identify more chemically diverse inhibitors in terms of their Murcko scaffolds. Finally, deep models such as Chemprop trained on data selected through ActiveDelta approaches can more accurately identify inhibitors in test data created through simulated time-splits. Overall, this study highlights the large potential for molecular pairing approaches to further improve popular active learning strategies in low data regimes by enabling faster and more accurate identification of more diverse molecular hits against critical drug targets.

RESUMEN

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

Asunto(s)

RESUMEN

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Asunto(s)

RESUMEN

In 2023, the U.S. Food and Drug Administration has approved 29 small molecule drugs. These newly approved small molecule drugs possess the distinct scaffolds, thereby exhibiting diverse mechanisms of action and binding modalities. Moreover, the marketed drugs have always been an important source of new drug development and creative inspiration, thereby fostering analogous endeavors in drug discovery that potentially extend to the diverse clinical indications. Therefore, conducting a comprehensive evaluation of drug approval experience and associated information will facilitate the expedited identification of highly potent drug molecules. In this review, we comprehensively summarized the relevant information regarding the clinical applications, mechanisms of action and chemical synthesis of 29 small molecule drugs, with the aim of providing a promising structural basis and design inspiration for pharmaceutical chemists.

Asunto(s)

RESUMEN

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.