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)

Aprobación de Drogas , United States Food and Drug Administration , Estados Unidos , Humanos , Preparaciones Farmacéuticas/síntesis química , Preparaciones Farmacéuticas/química , Descubrimiento de Drogas , Bibliotecas de Moléculas Pequeñas/síntesis química

RESUMEN

In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound. In comparison to its predecessor, GalaxySagittarius, GalaxySagittarius-AF utilizes an enlarged structure database, incorporating curated AlphaFold model structures alongside their binding sites and ligands, predicted using an updated version of GalaxySite. GalaxySagittarius-AF covers a large human protein space compared to many other available computational target screening methods. The structure-based prediction method enhances the use of expanded structural information, differentiating it from other target prediction servers that rely on ligand-based methods. Additionally, the web server has undergone enhancements, operating two to three times faster than its predecessor. The updated report page provides comprehensive information on the sequence and structure of the predicted protein targets. GalaxySagittarius-AF is accessible at https://galaxy.seoklab.org/sagittarius_af without the need for registration.

Asunto(s)

Proteoma , Humanos , Proteoma/química , Proteoma/metabolismo , Ligandos , Bases de Datos de Proteínas , Sitios de Unión , Programas Informáticos , Biología Computacional/métodos , Conformación Proteica , Aprendizaje Profundo , Descubrimiento de Drogas/métodos , Modelos Moleculares , Proteínas/química , Proteínas/metabolismo

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)

Simulación del Acoplamiento Molecular , Programas Informáticos , Ligandos , Algoritmos , Descubrimiento de Drogas/métodos , Unión Proteica , Humanos , Proteínas/química , Proteínas/metabolismo , Interfaz Usuario-Computador

RESUMEN

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Asunto(s)

Descubrimiento de Drogas , Conformación Proteica , Descubrimiento de Drogas/métodos , Simulación del Acoplamiento Molecular , Unión Proteica , Simulación de Dinámica Molecular , Humanos , Inhibidores de Proteínas Quinasas/farmacología , Inhibidores de Proteínas Quinasas/química , Ligandos , Proteínas Quinasas/química , Proteínas Quinasas/metabolismo

RESUMEN

Peptides are valuable for therapeutic development, with multicyclic peptides showing promise in mimicking antigen-binding potency of antibodies. However, our capability to engineer multicyclic peptide scaffolds, particularly for the construction of large combinatorial libraries, is still limited. Here, we study the interplay of disulfide pairing between three biscysteine motifs, and designed a range of triscysteine motifs with unique disulfide-directing capability for regulating the oxidative folding of multicyclic peptides. We demonstrate that incorporating these motifs into random sequences allows the design of disulfide-directed multicyclic peptide (DDMP) libraries with up to four disulfide bonds, which have been applied for the successful discovery of peptide binders with nanomolar affinity to several challenging targets. This study encourages the use of more diverse disulfide-directing motifs for creating multicyclic peptide libraries and opens an avenue for discovering functional peptides in sequence and structural space beyond existing peptide scaffolds, potentially advancing the field of peptide drug discovery.

Asunto(s)

Cisteína , Disulfuros , Biblioteca de Péptidos , Disulfuros/química , Cisteína/química , Secuencias de Aminoácidos , Descubrimiento de Drogas/métodos , Secuencia de Aminoácidos , Péptidos/química , Péptidos/metabolismo , Péptidos Cíclicos/química , Péptidos Cíclicos/metabolismo , Unión Proteica , Humanos , Oxidación-Reducción , Pliegue de Proteína

RESUMEN

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Asunto(s)

Inteligencia Artificial , Descubrimiento de Drogas , Simulación del Acoplamiento Molecular , Descubrimiento de Drogas/métodos , Humanos , Canal de Sodio Activado por Voltaje NAV1.7/metabolismo , Canal de Sodio Activado por Voltaje NAV1.7/química , Unión Proteica , Cristalografía por Rayos X , Ligandos , Ubiquitina-Proteína Ligasas/metabolismo , Ubiquitina-Proteína Ligasas/química , Bibliotecas de Moléculas Pequeñas/química , Bibliotecas de Moléculas Pequeñas/farmacología , Evaluación Preclínica de Medicamentos/métodos

RESUMEN

Chikungunya fever is a mosquito-borne disease caused by Chikungunya virus (CHIKV). Treatment of CHIKV infections is currently supportive and does not limit viral replication or symptoms of persistent chronic arthritis. Although there are multiple compounds reported as antivirals active against CHIKV in vitro, there are still no effective and safe antivirals. Thus, active research aims at the identification of new chemical structures with antiviral activity. Here, we report the screen of the Pandemic Response Box library of small molecules against a fully infectious CHIKV reporter virus. Our screening approach successfully identified previously reported CHIKV antiviral compounds within this library and further expanded potentially active hits, supporting the use of reporter-virus-based assays in high-throughput screening format as a reliable tool for antiviral drug discovery. Four molecules were identified as potential drug candidates against CHIKV: MMV1634402 (Brilacidin) and MMV102270 (Diphyllin), which were previously shown to present broad-spectrum antiviral activities, in addition to MMV1578574 (Eravacycline), and the antifungal MMV689401 (Fluopicolide), for which their antiviral potential is uncovered here.

Asunto(s)

Antivirales , Fiebre Chikungunya , Virus Chikungunya , Ensayos Analíticos de Alto Rendimiento , Bibliotecas de Moléculas Pequeñas , Virus Chikungunya/efectos de los fármacos , Antivirales/farmacología , Antivirales/química , Fiebre Chikungunya/tratamiento farmacológico , Fiebre Chikungunya/virología , Humanos , Animales , Bibliotecas de Moléculas Pequeñas/farmacología , Ensayos Analíticos de Alto Rendimiento/métodos , Evaluación Preclínica de Medicamentos , Replicación Viral/efectos de los fármacos , Descubrimiento de Drogas , Chlorocebus aethiops , Células Vero

RESUMEN

Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery.

Asunto(s)

Aprendizaje Profundo , Diseño de Fármacos , Ligandos , Descubrimiento de Drogas/métodos , Humanos , Modelos Químicos , Polifarmacología , Proteínas/química , Proteínas/metabolismo

RESUMEN

Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis.

Asunto(s)

Dihidroorotato Deshidrogenasa , Diseño de Fármacos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH , Relación Estructura-Actividad Cuantitativa , Schistosoma mansoni , Schistosoma mansoni/efectos de los fármacos , Schistosoma mansoni/enzimología , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH/antagonistas & inhibidores , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH/química , Animales , Esquistosomiasis/tratamiento farmacológico , Ligandos , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/farmacología , Humanos , Antihelmínticos/farmacología , Antihelmínticos/química , Descubrimiento de Drogas , Esquistosomiasis mansoni/tratamiento farmacológico

RESUMEN

Decades-old project has new funding and a new set of compounds to test.

Asunto(s)

Descubrimiento de Drogas , Longevidad , Animales , Humanos , Longevidad/efectos de los fármacos , Ratones

RESUMEN

Controlling the formation and growth of ice is essential to successfully cryopreserve cells, tissues and biologics. Current efforts to identify materials capable of modulating ice growth are guided by iterative changes and human intuition, with a major focus on proteins and polymers. With limited data, the discovery pipeline is constrained by a poor understanding of the mechanisms and the underlying structure-activity relationships. In this work, this barrier is overcome by constructing machine learning models capable of predicting the ice recrystallisation inhibition activity of small molecules. We generate a new dataset via experimental measurements of ice growth, then harness predictive models combining state-of-the-art descriptors with domain-specific features derived from molecular simulations. The models accurately identify potent small molecule ice recrystallisation inhibitors within a commercial compound library. Identified hits can also mitigate cellular damage during transient warming events in cryopreserved red blood cells, demonstrating how data-driven approaches can be used to discover innovative cryoprotectants and enable next-generation cryopreservation solutions for the cold chain.

Asunto(s)

Criopreservación , Crioprotectores , Cristalización , Hielo , Crioprotectores/farmacología , Crioprotectores/química , Humanos , Criopreservación/métodos , Bibliotecas de Moléculas Pequeñas/farmacología , Bibliotecas de Moléculas Pequeñas/química , Aprendizaje Automático , Eritrocitos/efectos de los fármacos , Relación Estructura-Actividad , Descubrimiento de Drogas/métodos

RESUMEN

Decursin is a pyranocoumarin compounds which are rare secondary metabolic plant products, isolated from the roots of Angelica gigas (A. gigas). The native Korean species Angelica gigas Nakai (AGN) is widely used as a remedy for a variety of medical conditions including hematopoiesis, improving women's circulation, as sedatives, analgesics and tonic. It is unique because of the presence of substantial amounts of pyranocoumarins including decursin, decursinol, and decursinol angelate. In this review, we provide a comprehensive insight into the distribution, morphology, and chemical composition of A. gigas. A detailed discussion regarding the biological applications of decursin based on the literature retrieved from PubMed, ScienceDirect, Scopus, and Google Scholar from 2000 to the present has been discussed. Both in vitro and in vivo studies have demonstrated that decursin has potential neuroprotective, anti-inflammatory, anti-melanogenic, anti-angiogenic, antioxidant, and anti-visceral properties. Mechanistic findings establish its significance in regulating important signalling pathways, triggering apoptosis, and preventing metastasis in different cancer types. The review additionally addressed the isolation methods, biosynthesis, physiochemical characteristics, toxicity and pharmacokinetic profile of decursin. The present state of clinical studies including A. gigas is investigated, emphasizing its advancements and possibilities in the field of translational medicine.

Asunto(s)

Angelica , Benzopiranos , Butiratos , Descubrimiento de Drogas , Humanos , Butiratos/farmacología , Butiratos/química , Butiratos/uso terapéutico , Benzopiranos/farmacología , Benzopiranos/química , Benzopiranos/aislamiento & purificación , Angelica/química , Animales , Desarrollo de Medicamentos

RESUMEN

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

Asunto(s)

Simulación de Dinámica Molecular , Proteínas , Programas Informáticos , Ligandos , Proteínas/química , Proteínas/metabolismo , Bases de Datos de Proteínas , Modelos Moleculares , Conformación Proteica , Interfaz Usuario-Computador , Descubrimiento de Drogas/métodos

RESUMEN

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Asunto(s)

Algoritmos , Descubrimiento de Drogas , Método de Montecarlo , Descubrimiento de Drogas/métodos , Ligandos , Aprendizaje Profundo , Humanos

RESUMEN

Obesity is acknowledged as a significant risk factor for various metabolic diseases, and the inhibition of human pancreatic lipase (hPL) can impede lipid digestion and absorption, thereby offering potential benefits for obesity treatment. Anthraquinones is a kind of natural and synthetic compounds with wide application. In this study, the inhibitory effects of 31 anthraquinones on hPL were evaluated. The data shows that AQ7, AQ26, and AQ27 demonstrated significant inhibitory activity against hPL, and exhibited selectivity towards other known serine hydrolases. Then the structure-activity relationship between anthraquinones and hPL was further analysed. AQ7 was found to be a mixed inhibition of hPL through inhibition kinetics, while AQ26 and AQ27 were effective non-competitive inhibition of hPL. Molecular docking data revealed that AQ7, AQ26, and AQ27 all could associate with the site of hPL. Developing hPL inhibitors for obesity prevention and treatment could be simplified with this novel and promising lead compound.

Asunto(s)

Antraquinonas , Relación Dosis-Respuesta a Droga , Descubrimiento de Drogas , Inhibidores Enzimáticos , Lipasa , Páncreas , Relación Estructura-Actividad , Antraquinonas/farmacología , Antraquinonas/química , Antraquinonas/síntesis química , Lipasa/antagonistas & inhibidores , Lipasa/metabolismo , Humanos , Inhibidores Enzimáticos/farmacología , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/síntesis química , Estructura Molecular , Páncreas/enzimología , Simulación del Acoplamiento Molecular , Productos Biológicos/farmacología , Productos Biológicos/química , Productos Biológicos/síntesis química

RESUMEN

BACKGROUND: Cystic echinococcosis (CE) is a zoonotic disease caused by the larval stage of the dog tapeworm Echinococcus granulosus sensu lato (E. granulosus), with a worldwide distribution. The current treatment strategy for CE is insufficient. Limited drug screening models severely hamper the discovery of effective anti-echinococcosis drugs. METHODS: In the present study, using high-content screening technology, we developed a novel high-throughput screening (HTS) assay by counting the ratio of propidium iodide-stained dead protoscoleces (PSCs) to the total number of PSCs. In vitro and ex vivo cyst viability assays were utilized to determine the effect of drugs on cyst viability. RESULTS: Using the newly established HTS assay, we screened approximately 12,000 clinical-stage or The Food and Drug Administration (FDA)-approved small molecules from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library, as well as the LOPAC1280 and SelleckChem libraries, as a strategic approach to facilitate the drug discovery process. Initial screening yielded 173 compounds with anti-echinococcal properties, 52 of which demonstrated dose-response efficacy against E. granulosus PSCs in vitro. Notably, two agents, omaveloxolone and niclosamide, showed complete inhibition upon further validation in cyst and microcyst viability assays in vitro after incubation for 3 days, and in an ex vivo cyst viability assay using cysts isolated from the livers of mice infected with E. granulosus, as determined by morphological assessment. CONCLUSIONS: Through the development of a novel HTS assay and by repurposing libraries, we identified omaveloxolone and niclosamide as potent inhibitors against E. granulosus. These compounds show promise as potential anti-echinococcal drugs, and our strategic approach has the potential to promote drug discovery for parasitic infections.

Asunto(s)

Reposicionamiento de Medicamentos , Equinococosis , Echinococcus granulosus , Ensayos Analíticos de Alto Rendimiento , Echinococcus granulosus/efectos de los fármacos , Animales , Ensayos Analíticos de Alto Rendimiento/métodos , Equinococosis/tratamiento farmacológico , Equinococosis/parasitología , Ratones , Bibliotecas de Moléculas Pequeñas/farmacología , Evaluación Preclínica de Medicamentos , Antihelmínticos/farmacología , Descubrimiento de Drogas , Perros

RESUMEN

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo® from Bristol-Myers Squibb) and pembrolizumab (Keytruda® from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.

Asunto(s)

Inhibidores de Puntos de Control Inmunológico , Receptor de Muerte Celular Programada 1 , Humanos , Receptor de Muerte Celular Programada 1/antagonistas & inhibidores , Receptor de Muerte Celular Programada 1/inmunología , Inhibidores de Puntos de Control Inmunológico/uso terapéutico , Inhibidores de Puntos de Control Inmunológico/farmacología , Inteligencia Artificial , Descubrimiento de Drogas , Neoplasias/metabolismo , Neoplasias/tratamiento farmacológico , Neoplasias/inmunología , Unión Proteica

RESUMEN

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and STAT3 has emerged as an effective drug target for TNBC treatment. Herein, we employed a scaffold-hopping strategy of natural products to develop a series of naphthoquinone-furopiperidine derivatives as novel STAT3 inhibitors. The in vitro assay showed that compound 10g possessed higher antiproliferative activity than Cryptotanshinone and Napabucasin against TNBC cell lines, along with lower toxicity and potent antitumor activity in a TNBC xenograft model. Mechanistically, 10g could inhibit the phosphorylation of STAT3 and the binding affinity was determined by the SPR assay (KD = 8.30 µM). Molecule docking studies suggested a plausible binding mode between 10g and the SH2 domain, in which the piperidine fragment and the terminal hydroxy group of 10g played an important role in demonstrating the success of this evolution strategy. These findings provide a natural product-inspired novel STAT3 inhibitor for TNBC treatment.

Asunto(s)

Antineoplásicos , Productos Biológicos , Proliferación Celular , Simulación del Acoplamiento Molecular , Naftoquinonas , Piperidinas , Factor de Transcripción STAT3 , Neoplasias de la Mama Triple Negativas , Factor de Transcripción STAT3/antagonistas & inhibidores , Factor de Transcripción STAT3/metabolismo , Humanos , Neoplasias de la Mama Triple Negativas/tratamiento farmacológico , Neoplasias de la Mama Triple Negativas/patología , Naftoquinonas/farmacología , Naftoquinonas/química , Naftoquinonas/síntesis química , Naftoquinonas/uso terapéutico , Productos Biológicos/farmacología , Productos Biológicos/química , Productos Biológicos/síntesis química , Piperidinas/farmacología , Piperidinas/química , Piperidinas/síntesis química , Piperidinas/uso terapéutico , Animales , Femenino , Antineoplásicos/farmacología , Antineoplásicos/química , Antineoplásicos/síntesis química , Antineoplásicos/uso terapéutico , Línea Celular Tumoral , Proliferación Celular/efectos de los fármacos , Ratones , Relación Estructura-Actividad , Ratones Desnudos , Ensayos Antitumor por Modelo de Xenoinjerto , Descubrimiento de Drogas , Ratones Endogámicos BALB C , Ensayos de Selección de Medicamentos Antitumorales

RESUMEN

The pipeline of new antibiotics is insufficient to keep pace with the growing global burden of drug-resistant infections. Substantial economic challenges discourage private investment in antibiotic research and development (R&D), with a decline in the number of companies and researchers working in the field. Compounding these issues, many countries (from low income to high income) face a growing crisis of antibiotic shortages and inequitable access to existing and emerging treatments. This has led to an increasing role for public and philanthropic funding in supporting antibiotic R&D via the creation of nonprofit public-private partnerships, including Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator (CARB-X) and the Global Antibiotic Research and Development Partnership (GARDP), industry support for the AMR Action Fund, and pilot schemes to evaluate and reimburse antibiotics in innovative ways. Now is the time to raise the urgency, ambition and commitments of the world's leaders to fully support the antibiotic R&D ecosystem, incentivizing all sectors to conduct public health-driven antibiotic R&D and make effective antibiotics accessible to all who need them.

Asunto(s)

Antibacterianos , Antibacterianos/uso terapéutico , Humanos , Asociación entre el Sector Público-Privado , Salud Global , Desarrollo de Medicamentos , Investigación Biomédica , Descubrimiento de Drogas/economía

RESUMEN

Interest and research focusing on the design of novel pharmaceutical agents is always growing [...].

Asunto(s)

Descubrimiento de Drogas , Compuestos Heterocíclicos , Descubrimiento de Drogas/métodos , Humanos , Compuestos Heterocíclicos/farmacología , Compuestos Heterocíclicos/química , Compuestos Heterocíclicos/uso terapéutico