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1.
Adv Exp Med Biol ; 1131: 27-72, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31646506

RESUMO

Ca2+, Na+ and K+- permeable ion channels as well as GPCRs linked to Ca2+ release are important drug targets. Accordingly, high-throughput fluorescence plate reader assays have contributed substantially to drug discovery efforts and pharmacological characterization of these receptors and ion channels. This chapter describes some of the basic properties of the fluorescent dyes facilitating these assay approaches as well as general methods for establishment and optimisation of fluorescence assays for ion channels and Gq-coupled GPCRs.


Assuntos
Bioensaio , Canais Iônicos , Receptores Acoplados a Proteínas-G , Animais , Bioensaio/tendências , Descoberta de Drogas , Corantes Fluorescentes/metabolismo , Humanos , Canais Iônicos/análise , Receptores Acoplados a Proteínas-G/análise
2.
Prog Chem Org Nat Prod ; 110: 1-35, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621009

RESUMO

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.


Assuntos
Produtos Biológicos/química , Biologia Computacional , Descoberta de Drogas , Química Farmacêutica , Modelos Moleculares
3.
Prog Chem Org Nat Prod ; 110: 37-71, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621010

RESUMO

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.


Assuntos
Produtos Biológicos/química , Biologia Computacional , Descoberta de Drogas , Química Farmacêutica
4.
Prog Chem Org Nat Prod ; 110: 73-97, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621011

RESUMO

Natural products have long played a leading role as direct source of drugs or as a means to inspire informed molecular design. Indeed, natural products have been biologically prevalidated as protein-binding motifs by millions of years of evolutionary pressure. Despite the tailored architectures, and the ever-growing chemistry toolbox to aid access such privileged structures, identifying the modes of action by which these molecules can be harnessed as therapeutics remains a major bottleneck in discovery chemistry. Herein, an overview of cheminformatics methods applied to the identification of modes of action of natural products is given, and a discussion of successful case studies is provided. A special focus is given to machine learning methods that may help to streamline the development of natural products into drug leads.


Assuntos
Produtos Biológicos/farmacologia , Biologia Computacional , Descoberta de Drogas , Química Farmacêutica
5.
Prog Chem Org Nat Prod ; 110: 143-175, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621013

RESUMO

Fragment-like natural products play a pivotal role in natural product research given their improved synthetic and computational tractability as well as commercial availability compared to more complex natural product structures. A multitude of computational tools have been developed to support the generation, analysis, and application of natural fragments for drug discovery and chemical biology research. In this contribution, the challenges and opportunities in such workflows are discussed and contextualized. Multiple successful applications and validations discussed herein attest to the relevance of natural fragments for drug discovery and the utility of machine learning and data science to support such endeavors.


Assuntos
Produtos Biológicos/química , Biologia Computacional , Descoberta de Drogas , Química Farmacêutica
6.
Prog Chem Org Nat Prod ; 110: 239-271, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621015

RESUMO

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.


Assuntos
Produtos Biológicos/farmacologia , Biologia Computacional , Modelos Moleculares , Química Farmacêutica , Descoberta de Drogas , Aprendizado de Máquina
7.
N Engl J Med ; 381(17): 1644-1652, 2019 10 24.
Artigo em Inglês | MEDLINE | ID: mdl-31597037

RESUMO

Genome sequencing is often pivotal in the diagnosis of rare diseases, but many of these conditions lack specific treatments. We describe how molecular diagnosis of a rare, fatal neurodegenerative condition led to the rational design, testing, and manufacture of milasen, a splice-modulating antisense oligonucleotide drug tailored to a particular patient. Proof-of-concept experiments in cell lines from the patient served as the basis for launching an "N-of-1" study of milasen within 1 year after first contact with the patient. There were no serious adverse events, and treatment was associated with objective reduction in seizures (determined by electroencephalography and parental reporting). This study offers a possible template for the rapid development of patient-customized treatments. (Funded by Mila's Miracle Foundation and others.).


Assuntos
Proteínas de Membrana Transportadoras/genética , Mutagênese Insercional , Lipofuscinoses Ceroides Neuronais/tratamento farmacológico , Lipofuscinoses Ceroides Neuronais/genética , Oligonucleotídeos Antissenso/uso terapêutico , Medicina de Precisão , Doenças Raras/tratamento farmacológico , Biópsia , Criança , Desenvolvimento Infantil , Descoberta de Drogas , Drogas em Investigação/uso terapêutico , Eletroencefalografia , Feminino , Humanos , Testes Neuropsicológicos , RNA Mensageiro , Convulsões/diagnóstico , Convulsões/tratamento farmacológico , Pele/patologia , Sequenciamento Completo do Genoma
9.
Expert Opin Ther Pat ; 29(11): 909-919, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31566022

RESUMO

Introduction: Myeloid cell leukemia-1 (MCL-1) is an anti-apoptotic member of the B-cell lymphoma-2 (BCL-2) family of proteins that regulates apoptosis. Elevated levels of MCL-1 contribute to tumorigenesis and resistance, not only to conventional chemotherapies but also to targeted therapies, including the BCL-2 selective inhibitor venetoclax. Accordingly, researchers in both the pharmaceutical industry and academia have been actively seeking MCL-1 inhibitors in the quest for new anti-cancer drugs. Areas covered: This review covers the patent literature on the discovery and development of small-molecule inhibitors of MCL-1 since 2017. Expert opinion: Pharmacologic inhibition of MCL-1's oncogenic activity has certainly come of age with the discovery of numerous inhibitors spanning a variety of chemotypes that selectively inhibit MCL-1 in the picomolar range and with on-target cell activity. Furthermore, seminal research by Servier has demonstrated for the first time that MCL-1 inhibition is tolerable in animal models of cancer, paving the way for the six Phase 1 clinical trials that are currently underway for hematological malignancies, among other cancers. After more than a decade of research, the hurdles and obstacles are mostly behind us, and uncovering the therapeutic impact of disrupting the protein-protein interactions of MCL-1 in humans is imminent.


Assuntos
Antineoplásicos/farmacologia , Apoptose/efeitos dos fármacos , Proteína de Sequência 1 de Leucemia de Células Mieloides/antagonistas & inibidores , Animais , Descoberta de Drogas/métodos , Humanos , Terapia de Alvo Molecular , Proteína de Sequência 1 de Leucemia de Células Mieloides/metabolismo , Neoplasias/tratamento farmacológico , Neoplasias/patologia , Patentes como Assunto
12.
Brain Nerve ; 71(9): 981-989, 2019 Sep.
Artigo em Japonês | MEDLINE | ID: mdl-31506400

RESUMO

The methodologies of computational drug discovery and drug repositioning (DR) based on biomolecular profile information are reviewed systematically. For big data drug discovery and DR, 1) methods of comparing gene expression profiles of the diseased state and drug-administered state to predict the effect and toxicity of the drug, 2) DR methods based on the disease network, and 3) prediction of drug effect based on the biological network analysis are described. For methods of AI-based drug discovery, virtual screening using AI and AI-based drug target exploration are reviewed.


Assuntos
Inteligência Artificial , Descoberta de Drogas , Reposicionamento de Medicamentos
13.
Adv Exp Med Biol ; 1167: 237-248, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31520359

RESUMO

In recent years, there has been growing interest in using Drosophila for drug discovery as it provides a unique opportunity to screen small molecules against complex disease phenotypes in a whole animal setting. Furthermore, gene-compound interaction experiments that combine compound feeding with complex genetic manipulations enable exploration of compound mechanisms of response and resistance to an extent that is difficult to achieve in other experimental models. Here, I discuss how compound screening and testing approaches reported in Drosophila fit into the current cancer drug discovery pipeline. I then propose a framework for a Drosophila-based cancer drug discovery strategy which would allow the Drosophila research community to effectively leverage the power of Drosophila to identify candidate therapeutics and push our discoveries into the clinic.


Assuntos
Antineoplásicos/farmacologia , Drosophila , Descoberta de Drogas , Neoplasias/tratamento farmacológico , Animais , Modelos Animais de Doenças , Técnicas Genéticas , Fenótipo
14.
Nihon Yakurigaku Zasshi ; 154(3): 143-150, 2019.
Artigo em Japonês | MEDLINE | ID: mdl-31527365

RESUMO

Quantitative systems pharmacology (QSP) is an emerging field of modeling technologies that describes the dynamic interaction between biological systems and drugs. Recently, QSP is increasingly being applied to pharmaceutical drug discovery and development, and used for various types of decision makings. In contrast to empirical and statistical models, QSP represents complex systems of human physiology by integrating comprehensive biological information, hence, it can address various purposes including target and/or disease-related biomarker identification, hypothesis testing, and prediction of clinical efficacy or toxicity. On the other hand, structures of QSP models become quite complicated with huge amount of biological components, therefore, close collaboration between pharmacologists having profound knowledge of biology and drug metabolism and pharmacokinetics (DMPK) scientists, experts of model building, is crucial for QSP development and implementation. This article introduces, from DMPK scientists to pharmacologists, main features of QSP and its applications in pharmaceutical industries, and discusses challenges and future perspectives for effective utilization in drug discovery and development.


Assuntos
Descoberta de Drogas/métodos , Modelos Biológicos , Farmacologia/métodos , Humanos , Farmacocinética , Projetos de Pesquisa
15.
Expert Opin Drug Metab Toxicol ; 15(9): 767-774, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31478386

RESUMO

Introduction: The phosphatidylinositide 3-kinase/AKT/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway has emerged as an important target in cancer therapy. Numerous PI3K/AKT/mTOR pathway inhibitors are extensively studied; some are used clinically, but most of these drugs are undergoing clinical trials. Potential adverse effects, such as severe hepatotoxicity and pneumonitis, have largely restricted the application and clinical significance of these inhibitors. A summary of mechanisms underlying the adverse effects is not only significant for the development of novel PI3K/AKT/mTOR inhibitors but also beneficial for the optimal use of existing drugs. Areas covered: We report a profile of the adverse effects, which we consider the class effects of PI3K/AKT/mTOR inhibitors. This review also discusses potential molecular toxicological mechanisms of these agents, which might drive future drug discovery. Expert opinion: Severe toxicities associated with PI3K/AKT/mTOR inhibitors hinder their approval and limit long-term clinical application of these drugs. A better understanding regarding PI3K/AKT/mTOR inhibitor-induced toxicities is needed. However, the mechanisms underlying these toxicities remain unclear. Future research should focus on developing strategies to reduce toxicities of approved inhibitors as well as accelerating new drug development. This review will be useful to clinical, pharmaceutical, and toxicological researchers.


Assuntos
Antineoplásicos/efeitos adversos , Descoberta de Drogas/métodos , Inibidores de Proteínas Quinases/efeitos adversos , Animais , Antineoplásicos/administração & dosagem , Antineoplásicos/farmacologia , Humanos , Fosfatidilinositol 3-Quinase/antagonistas & inibidores , Inibidores de Proteínas Quinases/administração & dosagem , Inibidores de Proteínas Quinases/farmacologia , Proteínas Proto-Oncogênicas c-akt/antagonistas & inibidores , Transdução de Sinais/efeitos dos fármacos , Serina-Treonina Quinases TOR/antagonistas & inibidores
17.
Curr Top Med Chem ; 19(16): 1436-1444, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31512997

RESUMO

Type 2 diabetes is a major health issue worldwide with complex metabolic and endocrine abnormalities. Hyperglycemia, defects in insulin secretion and insulin resistance are classic features of type 2 diabetes. Insulin signaling regulates metabolic homeostasis by regulating glucose and lipid turnover in the liver, skeletal muscle and adipose tissue. Major treatment modalities for diabetes include the drugs from the class of sulfonyl urea, Insulin, GLP-1 agonists, SGLT2 inhibitors, DPP-IV inhibitors and Thiazolidinediones. Emerging antidiabetic therapeutics also include classes of drugs targeting GPCRs in the liver, adipose tissue and skeletal muscle. Interestingly, recent research highlights several shared intermediates between insulin and GPCR signaling cascades opening potential novel avenues for diabetic drug discovery.


Assuntos
Diabetes Mellitus Tipo 2/tratamento farmacológico , Descoberta de Drogas , Hipoglicemiantes/farmacologia , Receptor de Insulina/antagonistas & inibidores , Receptores Acoplados a Proteínas-G/antagonistas & inibidores , Transdução de Sinais/efeitos dos fármacos , Animais , Diabetes Mellitus Tipo 2/metabolismo , Humanos , Hipoglicemiantes/química , Receptor de Insulina/metabolismo , Receptores Acoplados a Proteínas-G/metabolismo
19.
BMC Bioinformatics ; 20(1): 456, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492094

RESUMO

*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Peptídeos/uso terapêutico , Bases de Dados de Ácidos Nucleicos , Descoberta de Drogas
20.
Expert Opin Ther Pat ; 29(9): 689-702, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31402706

RESUMO

Introduction: Protein tyrosine phosphatase 1B (PTP1B) inhibition has been recommended as a crucial strategy to enhance insulin sensitivity in various cells and this fact is supported by human genetic data. PTP1B inhibitors improve the sensitivity of the insulin receptor and have the ability to cure insulin resistance-related diseases. In the latter years, targeting PTP1B inhibitors is being considered an attractive target to treat T2DM and therefore libraries of PTP1B inhibitors are being suggested as potent antidiabetic drugs. Areas covered: This review provides an overview of published patents from January 2015 to December 2018. The review describes the effectiveness of potent PTP1B inhibitors as pharmaceutical agents to treat type 2 diabetes. Expert opinion: Enormous developments have been made in PTP1B drug discovery which describes progress in natural products, synthetic heterocyclic scaffolds or heterocyclic hybrid compounds. Various protocols are being followed to boost the pharmacological effects of PTP1B inhibitors. Moreover these new advancements suggest that it is possible to get small-molecule PTP1B inhibitors with the required potency and selectivity. Furthermore, future endevours via an integrated strategy of using medicinal chemistry and structural biology will hopefully result in potent and selective PTP1B inhibitors as well as safer and more effective orally available drugs.


Assuntos
Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemiantes/farmacologia , Proteína Tirosina Fosfatase não Receptora Tipo 1/antagonistas & inibidores , Antígenos CD/metabolismo , Diabetes Mellitus Tipo 2/enzimologia , Desenho de Drogas , Descoberta de Drogas/métodos , Inibidores Enzimáticos/farmacologia , Humanos , Patentes como Assunto , Proteína Tirosina Fosfatase não Receptora Tipo 1/metabolismo , Receptor de Insulina/metabolismo
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