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2.
PLoS Negl Trop Dis ; 16(9): e0010645, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-36107859

RESUMO

We have a long-term vision to develop drug discovery research capacity within Ghana, to tackle unmet medical needs in Ghana and the wider West African region. However, there are several issues and challenges that need to be overcome to enable this vision, including training, human resource, equipment, infrastructure, procurement, and logistics. We discuss these challenges from the context of Ghana in this review. An important development is the universities and research centres within Ghana working together to address some of these challenges. Therefore, while there is a long way to go to fully accomplish our vision, there are encouraging signs.


Assuntos
Descoberta de Drogas , Gana , Humanos
3.
Biochem Biophys Res Commun ; 628: 68-75, 2022 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-36084553

RESUMO

PROTACs have emerged as a new class of drugs that can target the "undruggable" proteome by hijacking the ubiquitin proteasome system. Despite PROTACs' success, most current PROTACs interface with a limited number of E3 ligases, hindering their expansion to many challenging therapeutic uses. Currently, PROTAC drug discovery relies heavily on traditional Western blotting and reporter gene assays which are insensitive and prone to artifacts, respectively. New reliable methods to monitor true PROTAC function (i.e., ubiquitination and subsequent degradation of targets at physiological expression levels) without external tags are essential to accelerate the PROTAC discovery process and to address many unmet therapeutic areas. In this study, we developed a new high-throughput screening technology using "TUBEs" as ubiquitin-binding entities to monitor PROTAC-mediated poly-ubiquitination of native target proteins with exceptional sensitivity. As a proof of concept, targets including BRD3, Aurora A Kinase, and KRAS were used to demonstrate that ubiquitination kinetics can reliably establish the rank order potencies of PROTAC with variable ligands and linkers. PROTAC-treated cell lysates with the highest levels of endogenous target protein ubiquitination - termed "UbMax" - display excellent correlations with DC50 values obtained from traditional Western blots with the added benefits of being high throughput, providing improved sensitivity, and reducing technical errors.


Assuntos
Aurora Quinase A , Complexo de Endopeptidases do Proteassoma , Aurora Quinase A/metabolismo , Descoberta de Drogas/métodos , Peptídeos e Proteínas de Sinalização Intercelular , Ligantes , Complexo de Endopeptidases do Proteassoma/metabolismo , Proteólise , Proteoma/metabolismo , Proteínas Proto-Oncogênicas p21(ras)/metabolismo , Ubiquitina-Proteína Ligases/metabolismo , Ubiquitinação , Ubiquitinas/metabolismo
4.
Eur Rev Med Pharmacol Sci ; 26(17): 6014-6026, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-36111901

RESUMO

OBJECTIVE: Drug-target relationships provide the basis for network-based polypharmacology, and target deconvolution is a key step in phenotypic-screening based drug discovery. Due to the complexity of the mammalian proteomics and the often-limited affinity of the lead compound, it is challenging to identify the drug targets, especially when the goal is to identify all targets. This paper attempts to provide a brief and comprehensive introduction to the various methods in chemical proteomics for target deconvolution by categorizing them into two groups: the biochemical enrichment and the proteomics-screening methods. Moreover, a brief introduction of related Mass Spectrometry techniques is also provided, together with recent progress. MATERIALS AND METHODS: The data for this review were queried from Web of Science and PubMed, the keywords used were Drug targets, Target deconvolution, and Chemical Proteomics. A total of over 500 relevant articles, with a time limit from 1953 to 2022, were identified according to search strategy. Duplicate records and review articles were excluded by their titles and abstracts. Finally, we found about 120 articles matching our inclusion criteria, which covered representative research and reviews of various target discovery methods. RESULTS: Existing target discovery methods can be grouped into either biochemical enrichment or the proteomics-screening methods, with the recent emergence of a hybrid method combining these two such as lysine reactivity profiling. The advantage of the biochemical enrichment method is the ease of operation and the comprehensive target coverage. However, most biochemical enrichment methods require a high-affinity binding of the drug to the target proteins and cannot differentiate direct/indirect targets. The proteomics-screening methods do not require drug modification but have limited protein coverage, and most of them cannot differentiate direct/indirect targets. CONCLUSIONS: Although existing target discovery methods have greatly facilitated pharmacological research, each of these methods has advantages and disadvantages. New strategies/methods are needed to further improve both the coverage of the proteosome and the specificity.


Assuntos
Lisina , Proteômica , Animais , Descoberta de Drogas , Mamíferos , Espectrometria de Massas , Proteínas , Proteômica/métodos
5.
Front Cell Infect Microbiol ; 12: 933824, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36046742

RESUMO

Coronavirus disease 2019 (COVID-19) pandemic has killed huge populations throughout the world and acts as a high-risk factor for elderly and young immune-suppressed patients. There is a critical need to build up secure, reliable, and efficient drugs against to the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Bioactive compounds of Ashwagandha [Withania somnifera (L.) Dunal] may implicate as herbal medicine for the management and treatment of patients infected by SARS-CoV-2 infection. The aim of the current work is to update the knowledge of SARS-CoV-2 infection and information about the implication of various compounds of medicinal plant Withania somnifera with minimum side effects on the patients' organs. The herbal medicine Withania somnifera has an excellent antiviral activity that could be implicated in the management and treatment of flu and flu-like diseases connected with SARS-CoV-2. The analysis was performed by systematically re-evaluating the published articles related to the infection of SARS-CoV-2 and the herbal medicine Withania somnifera. In the current review, we have provided the important information and data of various bioactive compounds of Withania somnifera such as Withanoside V, Withanone, Somniferine, and some other compounds, which can possibly help in the management and treatment of SARS-CoV-2 infection. Withania somnifera has proved its potential for maintaining immune homeostasis of the body, inflammation regulation, pro-inflammatory cytokines suppression, protection of multiple organs, anti-viral, anti-stress, and anti-hypertensive properties. Withanoside V has the potential to inhibit the main proteases (Mpro) of SARS-CoV-2. At present, synthetic adjuvant vaccines are used against COVID-19. Available information showed the antiviral activity in Withanoside V of Withania somnifera, which may explore as herbal medicine against to SARS-CoV-2 infection after standardization of parameters of drug development and formulation in near future.


Assuntos
COVID-19 , Withania , Idoso , Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , Descoberta de Drogas , Humanos , Extratos Vegetais/farmacologia , Extratos Vegetais/uso terapêutico , SARS-CoV-2
6.
ACS Chem Biol ; 17(9): 2471-2482, 2022 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-36049119

RESUMO

Determining a molecule's mechanism of action is paramount during chemical probe development and drug discovery. The cellular thermal shift assay (CETSA) is a valuable tool to confirm target engagement in cells for a small molecule that demonstrates a pharmacological effect. CETSA directly detects biophysical interactions between ligands and protein targets, which can alter a protein's unfolding and aggregation properties in response to thermal challenge. In traditional CETSA experiments, each temperature requires an individual sample, which restricts throughput and requires substantial optimization. To capture the full aggregation profile of a protein from a single sample, we developed a prototype real-time CETSA (RT-CETSA) platform by coupling a real-time PCR instrument with a CCD camera to detect luminescence. A thermally stable Nanoluciferase variant (ThermLuc) was bioengineered to withstand unfolding at temperatures greater than 90 °C and was compatible with monitoring target engagement events when fused to diverse targets. Utilizing well-characterized inhibitors of lactate dehydrogenase alpha, RT-CETSA showed significant correlation with enzymatic, biophysical, and other cell-based assays. A data analysis pipeline was developed to enhance the sensitivity of RT-CETSA to detect on-target binding. RT-CETSA technology advances capabilities of the CETSA method and facilitates the identification of ligand-target engagement in cells, a critical step in assessing the mechanism of action of a small molecule.


Assuntos
Bioensaio , Descoberta de Drogas , Bioensaio/métodos , Descoberta de Drogas/métodos , Lactato Desidrogenases , Ligantes
7.
BMC Bioinformatics ; 23(1): 372, 2022 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-36100897

RESUMO

BACKGROUND: The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug-disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug-disease associations from a variety of sources of data. RESULTS: In order to identify potential drug-disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug-disease relations, drug-drug chemical similarity, drug-drug therapeutic similarity, disease-disease semantic similarity, and disease-disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information. CONCLUSION: 5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug-disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer's disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.


Assuntos
Inteligência Artificial , Reposicionamento de Medicamentos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos/métodos , Semântica
8.
Methods Mol Biol ; 2541: 89-104, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36083549

RESUMO

Libraries of DNA-encoded compounds (DELs) are a validated screening technology for drug discovery. Here we describe a library synthesis strategy that starts with a solid phase-bound, chemically very stable hexathymidine DNA sequence "hexT." Different heterocycle conjugates of the hexT oligonucleotide were synthesized from simple starting materials using metal or acid catalysts. The hexT conjugates were isolated, characterized, and ligated to coding DNA sequences.


Assuntos
Oligonucleotídeos , Bibliotecas de Moléculas Pequenas , Técnicas de Química Combinatória , DNA/química , DNA/genética , Descoberta de Drogas , Biblioteca Gênica , Oligonucleotídeos/química , Oligonucleotídeos/genética , Bibliotecas de Moléculas Pequenas/química
9.
Methods Mol Biol ; 2541: 155-164, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36083554

RESUMO

Affinity-based DNA-encoded library (DEL) selection is considered a powerful tool for small molecule drug discovery. Such selections are a multi-round process that involves incubation of a target protein with the DEL, capture of the protein and associated DEL compounds on a solid support, separation of bound molecules from the bulk DEL that is unbound, and recovery of bound DEL molecules. Each step is of great importance in order to achieve successful selections. Here we describe the selection process against a soluble target protein in both the immobilized and in-solution modes.


Assuntos
DNA , Bibliotecas de Moléculas Pequenas , Descoberta de Drogas , Bibliotecas de Moléculas Pequenas/metabolismo , Bibliotecas de Moléculas Pequenas/farmacologia
10.
Methods Mol Biol ; 2541: 165-172, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36083555

RESUMO

Compounds acting through irreversible covalent interactions with therapeutic targets represent an important mechanism of drug action. Here I describe a selection method for DNA-encoded libraries to discover irreversible covalent binders to target proteins. This method offers an enabling tool in drug discovery for therapeutic targets that may be undruggable for reversible inhibition.


Assuntos
DNA , Bibliotecas de Moléculas Pequenas , DNA/química , Descoberta de Drogas , Biblioteca Gênica , Proteínas , Bibliotecas de Moléculas Pequenas/farmacologia
12.
Med Oncol ; 39(12): 198, 2022 Sep 07.
Artigo em Inglês | MEDLINE | ID: mdl-36071299

RESUMO

Cancer has become the silent killer in less-developed countries and the most significant cause of morbidity worldwide. The accessible and frequently used treatments include surgery, radiotherapy, chemotherapy, and immunotherapy. Chemotherapeutic drugs traditionally involve using plant-based medications either in the form of isolated compounds or as scaffolds for synthetic drugs. To launch a drug in the market, it has to pass through several intricate steps. The multidrug resistance in cancers calls for novel drug discovery and development. Every year anticancer potential of several plant-based compounds and extracts is reported but only a few advances to clinical trials. The false-positive or negative results impact the progress of the cell-based anticancer assays. There are several cell-based assays but the widely used include MTT, MTS, and XTT. In this article, we have discussed various pitfalls and workable solutions.


Assuntos
Colorimetria , Neoplasias , Artefatos , Desenvolvimento de Medicamentos , Descoberta de Drogas , Humanos , Neoplasias/tratamento farmacológico
13.
BMC Bioinformatics ; 23(1): 367, 2022 Sep 07.
Artigo em Inglês | MEDLINE | ID: mdl-36071406

RESUMO

BACKGROUND: Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it is difficult to learn enough feature representation from tens of millions of compounds and hundreds of thousands of proteins only based on relatively limited labelled drug-target data. There are a large number of unknown drugs, which never appear in the labelled drug-target data. This is a kind of out-of-distribution problems in bio-medicine. Some recent studies adopted self-supervised pre-training tasks to learn structural information of amino acid sequences for enhancing the feature representation of proteins. However, the task gap between pre-training and DTA prediction brings the catastrophic forgetting problem, which hinders the full application of feature representation in DTA prediction and seriously affects the generalization capability of models for unknown drug discovery. RESULTS: To address these problems, we propose the GeneralizedDTA, which is a new DTA prediction model oriented to unknown drug discovery, by combining pre-training and multi-task learning. We introduce self-supervised protein and drug pre-training tasks to learn richer structural information from amino acid sequences of proteins and molecular graphs of drug compounds, in order to alleviate the problem of high variance caused by encoding based on deep neural networks and accelerate the convergence of prediction model on small-scale labelled data. We also develop a multi-task learning framework with a dual adaptation mechanism to narrow the task gap between pre-training and prediction for preventing overfitting and improving the generalization capability of DTA prediction model on unknown drug discovery. To validate the effectiveness of our model, we construct an unknown drug data set to simulate the scenario of unknown drug discovery. Compared with existing DTA prediction models, the experimental results show that our model has the higher generalization capability in the DTA prediction of unknown drugs. CONCLUSIONS: The advantages of our model are mainly attributed to two kinds of pre-training tasks and the multi-task learning framework, which can learn richer structural information of proteins and drugs from large-scale unlabeled data, and then effectively integrate it into the downstream prediction task for obtaining a high-quality DTA prediction in unknown drug discovery.


Assuntos
Descoberta de Drogas , Aprendizado de Máquina , Sistemas de Liberação de Medicamentos , Redes Neurais de Computação , Proteínas
14.
Curr Protoc ; 2(9): e544, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-36083100

RESUMO

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.


Assuntos
Antineoplásicos , Neoplasias , Antineoplásicos/farmacologia , Biomarcadores , Descoberta de Drogas , Humanos , Neoplasias/tratamento farmacológico , Estudos Prospectivos
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 1647-1650, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085941

RESUMO

Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely time-consuming and costly. In this study, we make an effort to explore the translation of CETSA features cross cell lines, i.e., known CETSA feature of a given protein in one cell line, can we automatically predict the CETSA feature of this protein in another cell line, and vice versa? Inspired by pix2pix and CycleGAN, which perform well on image-to-image translation cross various domains in computer vision, we propose a novel deep neural network model called CycleDNN for CETSA feature translation cross cell lines. Given cell lines A and B, the proposed CycleDNN consists of two auto-encoders, the first one encodes the CETSA feature from cell line A into Z in the latent space [Formula: see text], then decodes Z into the CETSA feature in cell line B., Similarly, the second one translates the CETSA feature from cell line B to cell line A through the latent space [Formula: see text]. In such a way, the two auto-encoders form a cyclic feature translation between cell lines. The reconstructed loss, cycle-consistency loss, and latent vector regularization loss are used to guide the training of the model. The experimental results on a public CETSA dataset demonstrate the effectiveness of the proposed approach.


Assuntos
Descoberta de Drogas , Redes Neurais de Computação , Linhagem Celular , Descoberta de Drogas/métodos , Proteínas , Projetos de Pesquisa
17.
Methods Cell Biol ; 172: 135-143, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36064220

RESUMO

The radiochemotherapy- or chemotherapy-induced stimulation of immunogenic cell death (ICD) affecting malignant cells ignites antitumor immune responses that are clinically relevant as they allow to achieve durable responses beyond treatment discontinuation. The mechanistic exploration of ICD and the discovery of agents and interventions that are endowed with the capacity to elicit ICD is of the utmost importance. Here, we describe an assay for the assessment of type I interferon (IFN) production, which is one of the salient features of ICD. Biosensor cells that express GFP under the control of the IFN-inducible MX dynamin like GTPase 1 (MX1) gene promoter are employed, and the fluorescent signal is assessed by automated microscopy. The described workflow is automation-friendly, rendering it compatible with high-throughput screening (HTS) for drug discovery.


Assuntos
Interferon Tipo I , Descoberta de Drogas , Ensaios de Triagem em Larga Escala , Morte Celular Imunogênica
18.
Nihon Yakurigaku Zasshi ; 157(5): 330-334, 2022.
Artigo em Japonês | MEDLINE | ID: mdl-36047147

RESUMO

With the remarkable scientific advances in stem cells technologies and culture systems, it is no longer a dream to construct in vitro cultured tissue/organ models that respond completely as if they were in vivo. The microphysiological system (MPS) is a symbol of the growing worldwide momentum to promote the use of in vitro methods. The development of MPS devices as a culture system itself has been almost completed both in Japan and overseas, thus, the focus from now on is on the construction of prediction systems and evaluation of their applicability in accordance with the specific requirements (context of use: CoU), for example, of the drug discovery process of pharmaceutical companies. A notable trend at this stage is the close communication between developers (companies and researchers), users, and regulatory authorities, and the organization of the European Organ on a Chip Society (EUROoCS) and the International MPS Summit involving these stakeholders has begun. It is strongly expected that academic studies on in vitro systems symbolized by MPS and their results for the evaluation and prediction of individual responses and their application in society will continue to advance, leading to the promotion of alternatives to animal experiments.


Assuntos
Descoberta de Drogas , Animais , Japão
19.
Nihon Yakurigaku Zasshi ; 157(5): 356-360, 2022.
Artigo em Japonês | MEDLINE | ID: mdl-36047153

RESUMO

G protein-coupled receptors (GPCRs) play pivotal roles in converting physicochemical stimuli due to environmental changes to intracellular responses. After ligand stimulation, many GPCRs are desensitized and then recycled or degraded through phosphorylation and ß-arrestin-dependent internalization, an important process to maintain protein quality control of GPCRs. However, it is unknown how GPCRs with low ß-arrestin sensitivity are controlled. Here we unmasked a ß-arrestin-independent GPCR internalization, named Redox-dependent Alternative Internalization (REDAI), focusing on ß-arrestin-resistant purinergic P2Y6 receptor (P2Y6R). P2Y6R is highly expressed in macrophage and pathologically contributes to the development of colitis in mice. Natural electrophiles including in functional foods induce REDAI-mediated P2Y6R degradation leading to anti-inflammation in macrophages. Prevention of Cys220 modification on P2Y6R resulted in aggravation of the colitis. These results strongly suggest that targeting REDAI on GPCRs will be a breakthrough strategy for the prevention and treatment of inflammatory diseases.


Assuntos
Arrestinas , Colite , Animais , Arrestinas/metabolismo , Colite/tratamento farmacológico , Descoberta de Drogas , Camundongos , Fosforilação , Receptores Acoplados a Proteínas G/metabolismo , beta-Arrestinas/metabolismo
20.
Nihon Yakurigaku Zasshi ; 157(5): 361-365, 2022.
Artigo em Japonês | MEDLINE | ID: mdl-36047154

RESUMO

Covalent drug forms a covalent bond with desease-related target proteins irreversibly inhibits their function. In order to develop a safe and non-toxic covalent drug, it is important to device new reaction chemistry that realizes a sufficient reactivity and high target selectivity for targeted protein under the complicated biological systems such as our body. Currently, new reaction chemistry is being actively developed all over the world to achieve excellent target selectivity of covalent drugs. In this essay, we intoroduce α-chlorofluoroacetamide and bicyclobutane amide as the new reactive groups for proteineous cysteine of targeted protein and their application to develop targeted covalent inhibitors for the treatment of cancer and infecsious deseases.


Assuntos
Cisteína , Descoberta de Drogas , Cisteína/metabolismo , Proteínas
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