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1.
Methods Mol Biol ; 2576: 495-504, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36152212

RESUMO

A screening pool consisting of 617710 drug-like query molecules properly filtered from the ChEMBL database was employed for a ligand-based reverse screening toward the type 2 cannabinoid receptor (CB2) target. By using our recently developed PLATO polypharmacological web platform, 233 out of 617710 drug-like molecules were prioritized on the basis of the predicted bioactivity values, better than 0.2 µM with a probability of about 98%, toward the CB2 target. Building on these results, the occurrence of putative CB2-related targets was also investigated for prospective repurposing studies.


Assuntos
Polifarmacologia , Receptor CB2 de Canabinoide , Ligantes , Estudos Prospectivos , Receptores de Canabinoides
2.
Chem Biol Interact ; 368: 110239, 2022 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-36309139

RESUMO

Polypharmacology has become a new paradigm in drug discovery and plays an increasingly vital role in discovering multi-target drugs. In this context, multi-target drugs are a promising approach to treating polygenic diseases. Many in-silico prediction methods have been developed to screen active molecules acting on multiple targets. The relationship between the action of multiple targets and the drug's overall efficacy is significant for developing multi-target drugs. So, the prediction method for this relationship urgently needs to be developed. This paper introduces multi-target-based polypharmacology prediction (mTPP), an approach using virtual screening and machine learning to explore the relationship. To predict the activity of the potential hepatoprotective components, the data on the binding strength of a single ingredient with multiple targets and the proliferation rate of the compounds against acetaminophen (APAP)-induced injury L02 cells were all used to construct the mTPP model by Multi-layer Perceptron (MLP), Support Vactor Regression (SVR), Decision Tree Regressor (DTR), and Gradient Boost Regression (GBR) algorithms. Compared with MLP, SVR, and DTR algorithms, GBR algorithms showed the best performance with R2test = 0.73 and EVtest = 0.75. In addition, 20 candidates with potential effects against drug-induced liver injury (DILI) were predicted by the mTPP model. Furthermore, 2 of the 20 candidates, Chelerythrine and Biochanin A, were applied to evaluate the model's accuracy. The results showed that Chelerythrine and Biochanin A could improve the viability of APAP-induced injury cells. Thus, the mTPP model is hoped to help develop polypharmacology and discover multi-target drugs.


Assuntos
Acetaminofen , Polifarmacologia , Acetaminofen/farmacologia , Descoberta de Drogas/métodos , Aprendizado de Máquina , Algoritmos
3.
Yakugaku Zasshi ; 142(10): 1077-1082, 2022.
Artigo em Japonês | MEDLINE | ID: mdl-36184442

RESUMO

As the term polypharmacology suggests, there are multiple actions of small-molecule compounds. We proposed a decomposition and understanding concept that sheds light on the small effects in comparison to the large effects by decomposing these multiple effects. This concept was embodied by describing the effects of the compounds in a transcriptome profile, followed by factor analysis to extract latent variables as decomposed effects. Application of this approach to public datasets resulted in the inferences of compound effects consistent with existing knowledge such as gene ontologies and pathways. In one experimental validation, the potential inducibility of endoplasmic reticulum stress of several commercial drugs was detected by decomposition. Another study successfully discriminated the effects of a natural product and its derivatives despite their structural similarity. In the era of big data, it is important to infer conceptual elements composed of measurable elements as a higher layer than the given data of a specimen, which can expand our perception and understanding of the specimen. This review introduces an example of such a philosophy by applying it to the multiple effects of drugs to contribute to the understanding.


Assuntos
Produtos Biológicos , Polifarmacologia , Estresse do Retículo Endoplasmático
4.
Int J Mol Sci ; 23(16)2022 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-36012569

RESUMO

Since 1906, when Dr. Alois Alzheimer first described in a patient "a peculiar severe disease process of the cerebral cortex", people suffering from this pathology have been waiting for a breakthrough therapy. Alzheimer's disease (AD) is an irreversible, progressive neurodegenerative brain disorder and the most common form of dementia in the elderly with a long presymptomatic phase. Worldwide, approximately 50 million people are living with dementia, with AD comprising 60-70% of cases. Pathologically, AD is characterized by the deposition of amyloid ß-peptide (Aß) in the neuropil (neuritic plaques) and blood vessels (amyloid angiopathy), and by the accumulation of hyperphosphorylated tau in neurons (neurofibrillary tangles) in the brain, with associated loss of synapses and neurons, together with glial activation, and neuroinflammation, resulting in cognitive deficits and eventually dementia. The current competitive landscape in AD consists of symptomatic treatments, of which there are currently six approved medications: three AChEIs (donepezil, rivastigmine, and galantamine), one NMDA-R antagonist (memantine), one combination therapy (memantine/donepezil), and GV-971 (sodium oligomannate, a mixture of oligosaccharides derived from algae) only approved in China. Improvements to the approved therapies, such as easier routes of administration and reduced dosing frequencies, along with the developments of new strategies and combined treatments are expected to occur within the next decade and will positively impact the way the disease is managed. Recently, Aducanumab, the first disease-modifying therapy (DMT) has been approved for AD, and several DMTs are in advanced stages of clinical development or regulatory review. Small molecules, mAbs, or multimodal strategies showing promise in animal studies have not confirmed that promise in the clinic (where small to moderate changes in clinical efficacy have been observed), and therefore, there is a significant unmet need for a better understanding of the AD pathogenesis and the exploration of alternative etiologies and therapeutic effective disease-modifying therapies strategies for AD. Therefore, a critical review of the disease-modifying therapy pipeline for Alzheimer's disease is needed.


Assuntos
Doença de Alzheimer , Doença de Alzheimer/tratamento farmacológico , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides , Animais , Donepezila , Humanos , Memantina/uso terapêutico , Assistência Centrada no Paciente , Polifarmacologia
5.
Genes (Basel) ; 13(7)2022 07 21.
Artigo em Inglês | MEDLINE | ID: mdl-35886075

RESUMO

Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug-target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.


Assuntos
Doença Hepática Induzida por Substâncias e Drogas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Doença Hepática Induzida por Substâncias e Drogas/etiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia , Humanos , Farmacologia em Rede , Polifarmacologia
6.
Drug Discov Today ; 27(10): 103319, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35850431

RESUMO

Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.


Assuntos
Polifarmacologia , Proteoma , Descoberta de Drogas , Ligantes , Inibidores de Proteínas Quinases/farmacologia , Proteínas Quinases/metabolismo
7.
Int J Mol Sci ; 23(9)2022 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-35563636

RESUMO

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).


Assuntos
Descoberta de Drogas , Caça , Animais , Descoberta de Drogas/métodos , Ligantes , Compostos Organoplatínicos , Polifarmacologia
8.
J Chem Inf Model ; 62(10): 2600-2616, 2022 05 23.
Artigo em Inglês | MEDLINE | ID: mdl-35536589

RESUMO

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.


Assuntos
Inibidores de Proteínas Quinases , Proteínas Quinases , Sítios de Ligação , Humanos , Ligantes , Polifarmacologia , Inibidores de Proteínas Quinases/química , Inibidores de Proteínas Quinases/farmacologia , Proteínas Quinases/metabolismo
9.
Appl Biochem Biotechnol ; 194(10): 4511-4529, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35507249

RESUMO

Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.


Assuntos
COVID-19 , SARS-CoV-2 , Adenosina/análogos & derivados , Antivirais/química , Antivirais/farmacologia , COVID-19/tratamento farmacológico , Furina/genética , Humanos , Ligantes , Redes Neurais de Computação , Polifarmacologia , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/metabolismo , Receptor 7 Toll-Like
10.
J Ethnopharmacol ; 293: 115289, 2022 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-35427724

RESUMO

ETHNOPHARMACOLOGICAL RELEVANCE: Rhei Radix et Rhizoma, Lonicerae Japonicae Flos and Zingiberis Rhizoma was widely used in the treatment of inflammatory disease. The discovery of new multi-target compounds for new drug from the TCM was a possible direction. AIM OF THE STUDY: Multi-target compounds screening based on polypharmacology was an effective method. As an interdisciplinary field, polypharmacology screen multi-target compounds by various methods. So, a flexible screening framework to avoid the disadvantage of single methods is considered to have great significance. MATERIALS AND METHODS: The research propose a common framework called Traditional Chinese medicine target-effect relationship spectrum (TCM-TERS). TCM-TERS was constructed based on the pharmacophore and molecular docking models, which provided predicted activity by compounds screening. TCM-TERS merge the results of different models and visualize the targeted activity of each compounds. Then the TCM-TERS were analyzed by the analytic hierarchy process and active components were chosen by the contributing factors. The activity of components was verified on the RAW264.7 by RT-PCR. RESULTS: This article constructed TCM-TERS of Rhei Radix et Rhizoma, Lonicerae Japonicae Flos and Zingiberis Rhizoma with the COX-2, mPGES-1, 5-LOX and SPLA2-IIA. Seven compounds were chosen with multiple targeted activity based on the TCM-TERS, which showed remarkable activity in RT-PCR. CONCLUSION: The TCM-TERS was an efficient interdisciplinary method for drug discovery of the TCM, which provide a flexible method to the researcher that can screen specific compounds with multiple screening methods.


Assuntos
Medicamentos de Ervas Chinesas , Medicina Tradicional Chinesa , Anti-Inflamatórios/farmacologia , Anti-Inflamatórios/uso terapêutico , Medicamentos de Ervas Chinesas/farmacologia , Medicamentos de Ervas Chinesas/uso terapêutico , Simulação de Acoplamento Molecular , Polifarmacologia , Rizoma
11.
Cell Rep Med ; 3(1): 100492, 2022 01 18.
Artigo em Inglês | MEDLINE | ID: mdl-35106508

RESUMO

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.


Assuntos
Neoplasias/tratamento farmacológico , Polifarmacologia , Algoritmos , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Redes Neurais de Computação , Proteínas Quinases/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Transcrição Genética
12.
PLoS Comput Biol ; 18(2): e1009888, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-35213530

RESUMO

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as ß-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, ß-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the ß-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.


Assuntos
Aprendizado de Máquina , Polifarmacologia , Algoritmos
13.
Sci Rep ; 12(1): 3115, 2022 02 24.
Artigo em Inglês | MEDLINE | ID: mdl-35210493

RESUMO

Nuclear receptors (NR) are ligand-modulated transcription factors that regulate multiple cell functions and thus represent excellent drug targets. However, due to a considerable NR structural homology, NR ligands often interact with multiple receptors. Here, we describe a multiplex reporter assay (the FACTORIAL NR) that enables parallel assessment of NR ligand activity across all 48 human NRs. The assay comprises one-hybrid GAL4-NR reporter modules transiently transfected into test cells. To evaluate the reporter activity, we assessed their RNA transcripts. We used a homogeneous RNA detection approach that afforded equal detection efficacy and permitted the multiplex detection in a single-well format. For validation, we examined a panel of selective NR ligands and polypharmacological agonists and antagonists of the progestin, estrogen, PPAR, ERR, and ROR receptors. The assay produced highly reproducible NR activity profiles (r > 0.96) permitting quantitative assessment of individual NR responses. The inferred EC50 values agreed with the published data. The assay showed excellent quality ( = 0.73) and low variability ( = 7.2%). Furthermore, the assay permitted distinguishing direct and non-direct NR responses to ligands. Therefore, the FACTORIAL NR enables comprehensive evaluation of NR ligand polypharmacology.


Assuntos
Ligantes , Polifarmacologia/métodos , Receptores Citoplasmáticos e Nucleares/fisiologia , Bioensaio/métodos , Genes Reporter/efeitos dos fármacos , Humanos , Programas de Rastreamento/métodos , Ligação Proteica , Receptores Citoplasmáticos e Nucleares/efeitos dos fármacos , Receptores Citoplasmáticos e Nucleares/metabolismo
14.
ChemMedChem ; 17(6): e202100731, 2022 03 18.
Artigo em Inglês | MEDLINE | ID: mdl-35146935

RESUMO

The epigenetic reader BRD4 is involved in chromatin remodelling and transcriptional regulation, making it a promising therapeutic target. However, over the past decades, many BRD4 inhibitors that entered clinical trials were, in the main, unsatisfactory, due to some therapeutic limitations such as off-target effects and drug resistance. Combining a BRD4 inhibitor with another drug was expected to be an ideal option to overcome these hurdles and to improve therapeutic outcomes. However, such combination therapy could trigger toxicity caused by drug-drug interactions, complex pharmacokinetics, and additive effects. Recently, the application of dual-target drugs targeting BRD4 and other kinases has become an attractive approach to remedy the defects of a single BRD4 inhibitor. This review focuses on recent advances in the discovery of dual BRD4-kinase inhibitors, with an emphasis on their co-crystal structures and structure-activity relationships (SARs), as well as future perspectives in this field.


Assuntos
Antineoplásicos , Proteínas de Ciclo Celular , Neoplasias , Fatores de Transcrição , Antineoplásicos/farmacologia , Proteínas de Ciclo Celular/antagonistas & inibidores , Humanos , Neoplasias/tratamento farmacológico , Proteínas Nucleares , Polifarmacologia , Fatores de Transcrição/antagonistas & inibidores
15.
Cells ; 11(3)2022 01 29.
Artigo em Inglês | MEDLINE | ID: mdl-35159280

RESUMO

Polypharmacology breaks up the classical paradigm of "one-drug, one target, one disease" electing multitarget compounds as potential therapeutic tools suitable for the treatment of complex diseases, such as metabolic syndrome, psychiatric or degenerative central nervous system (CNS) disorders, and cancer. These diseases often require a combination therapy which may result in positive but also negative synergistic effects. The endocannabinoid system (ECS) is emerging as a particularly attractive therapeutic target in CNS disorders and neurodegenerative diseases including Parkinson's disease (PD), Alzheimer's disease (AD), Huntington's disease (HD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), stroke, traumatic brain injury (TBI), pain, and epilepsy. ECS is an organized neuromodulatory network, composed by endogenous cannabinoids, cannabinoid receptors type 1 and type 2 (CB1 and CB2), and the main catabolic enzymes involved in the endocannabinoid inactivation such as fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL). The multiple connections of the ECS with other signaling pathways in the CNS allows the consideration of the ECS as an optimal source of inspiration in the development of innovative polypharmacological compounds. In this review, we focused our attention on the reported polypharmacological examples in which FAAH and MAGL inhibitors are involved.


Assuntos
Doenças do Sistema Nervoso Central , Doenças Neurodegenerativas , Doenças do Sistema Nervoso Central/tratamento farmacológico , Endocanabinoides/metabolismo , Humanos , Monoacilglicerol Lipases/metabolismo , Doenças Neurodegenerativas/tratamento farmacológico , Polifarmacologia
16.
Pharmacol Res ; 176: 106055, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34990865

RESUMO

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.


Assuntos
Desenho de Fármacos , Polifarmacologia , Animais , Humanos
17.
J Chem Inf Model ; 62(2): 284-294, 2022 01 24.
Artigo em Inglês | MEDLINE | ID: mdl-35020376

RESUMO

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.


Assuntos
Polifarmacologia , Proteínas , Sítios de Ligação , Descoberta de Drogas/métodos , Humanos , Domínios Proteicos , Proteínas/química
18.
Mol Divers ; 26(3): 1675-1695, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34468898

RESUMO

Development of potential antitubercular molecules is a challenging task due to the rapidly emerging drug-resistant strains of Mycobacterium tuberculosis (M.tb). Structure-based approaches hold greater benefit in identifying compounds/drugs with desired polypharmacological profiles. These methods can be employed based on the knowledge of protein binding sites to identify the complementary ligands. In this study, polypharmacology guided computational drug repurposing approach was applied to identify potential antitubercular drugs. 20 important druggable protein targets in M.tb were considered from the target library of Molecular Property Diagnostic Suite-Tuberculosis (MPDSTB- http://mpds.neist.res.in:8084 ) for virtual screening. FDA approved drugs were collected, preprocessed and docked in the active sites of the 20 M.tb targets. The top 300 drug molecules from each target (20 × 300) were filtered-in and subsequently screened for possible antitubercular and antimycobacterial activity using PASS tool. Using this approach, 34 drugs with predicted antitubercular and anti-mycobacterial activity were identified along with good binding affinity against multiple M.tb targets. Interestingly, 21 out of the 34 identified drugs are antibiotics while 4 drug molecules (nitrofural, stavudine, quinine and quinidine) are non-antibiotics showing promising predicted antitubercular activity. Most of these molecules have the similar privileged antimycobacterial drugs scaffold. Further drug likeness properties were calculated to get deeper insights to M.tb lead molecules. Interestingly, it was also observed that the drugs identified from the study are under different stages of drug discovery (i.e., in vitro, clinical trials) for the effective treatment of various diseases including cancer, degenerative diseases, dengue virus infection, tuberculosis, etc. Krasavin et al., 2017 synthesized nitrofuran analogues with appreciable MICs (22-23 µM) against M.tb H37Rv. These experiments further add to the credibility of the drugs identified in this study (TB).


Assuntos
Mycobacterium tuberculosis , Tuberculose , Antituberculosos/química , Reposicionamento de Medicamentos , Humanos , Polifarmacologia , Tuberculose/tratamento farmacológico
19.
Br J Clin Pharmacol ; 88(2): 742-752, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34327724

RESUMO

AIMS: The aim of this study was to determine the differences and potential mechanistic rationale for observed adverse drug reactions (ADRs) between four approved PARP inhibitors (PARPi). METHODS: The Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profiles and NHS secondary care medicines database enabled the identification of suspected ADRs associated with the PARPi in the UK from launch to 2020. The polypharmacology of the PARPi were data-mined from several public data sources. RESULTS: The overall ADRs per 100 000 Rx identified across the four PARPi are statistically significant (χ2 test, P < .001). Rucaparib has the greatest relative suspected ADRs, which can be explained by its least clean kinome and physicochemical properties. The suspected gastrointestinal ADRs of rucaparib and niraparib can be ascribed to their kinase polypharmacology. Suspected blood and lymphatic system ADRs of PARPi can be linked to their high volume of distribution (Vd ). The thrombocytopenia rate of niraparib > rucaparib > olaparib tracked with the Vd trend. Hypertension is only associated with niraparib and could be explained by the therapeutically achievable inhibition of DYRK1A and/or transporters. Arrhythmia cases are potentially linked to the structural features of hERG ion-channel inhibition found in rucaparib and niraparib. Enhanced psychiatric/nervous disorders associated with niraparib can be interpreted from the diverse neurotransporter off-targets reported. CONCLUSIONS: Despite their similar mode of action, the differential polypharmacology of PARP inhibitors influences their ADR profile.


Assuntos
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Inibidores de Poli(ADP-Ribose) Polimerases , Humanos , Inibidores de Poli(ADP-Ribose) Polimerases/efeitos adversos , Polifarmacologia
20.
Cancer Res ; 82(3): 433-446, 2022 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-34903600

RESUMO

Tumor-associated macrophages (TAM) are an important component of the tumor microenvironment (TME) that can promote tumor progression, metastasis, and resistance to therapies. Although TAMs represent a promising target for therapeutic intervention, the complexity of the TME has made the study of TAMs challenging. Here, we established a physiologically relevant in vitro TAM polarization system that recapitulates TAM protumoral activities. This system was used to characterize dynamic changes in gene expression and protein phosphorylation during TAM polarization and to screen phenotypic kinase inhibitors that impact TAM programming. BMS-794833, a multitargeted compound, was identified as a potent inhibitor of TAM polarization. BMS-794833 decreased protumoral properties of TAMs in vitro and suppressed tumor growth in mouse triple-negative breast cancer models. The effect of BMS-794833 was independent of its primary targets (MET and VEGFR2) but was dependent on its effect on multiple signaling pathways, including focal adhesion kinases, SRC family kinases, STAT3, and p38 MAPKs. Collectively, these findings underline the efficacy of polypharmacologic strategies in reprogramming complex signaling cascades activated during TAM polarization. SIGNIFICANCE: A physiologically relevant in vitro system of TAM polarization uncovers signaling pathways that regulate polarization and identifies strategies to target macrophage reprogramming to suppress cancer growth.


Assuntos
Macrófagos/metabolismo , Polifarmacologia/métodos , Macrófagos Associados a Tumor/efeitos dos fármacos , Animais , Feminino , Humanos , Camundongos , Microambiente Tumoral
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