Frontiers in Drug Discovery最新文献

The role of physicochemical and topological parameters in drug design 物理化学和拓扑参数在药物设计中的作用

Frontiers in Drug Discovery Pub Date : 2024-07-09 DOI: 10.3389/fddsv.2024.1424402

Janki Darlami, Shweta Sharma

引用次数: 0

Editorial: Recent trends in anti-cancer drug discovery by in silico methods 社论：采用硅学方法发现抗癌药物的最新趋势

Frontiers in Drug Discovery Pub Date : 2024-05-15 DOI: 10.3389/fddsv.2024.1420267

Carmen Cerchia, Jose Correa Basurto, Angelo Lupo, Antonio Lavecchia

引用次数: 0

A review on dynamics of permeability-glycoprotein in efflux of chemotherapeutic drugs 化疗药物外流过程中渗透性糖蛋白的动力学综述

Frontiers in Drug Discovery Pub Date : 2024-04-11 DOI: 10.3389/fddsv.2024.1363364

Priyanka Rani, Pranabesh Mandal, Bikash Kumar Rajak, Durg Vijay Singh

{"title":"A review on dynamics of permeability-glycoprotein in efflux of chemotherapeutic drugs","authors":"Priyanka Rani, Pranabesh Mandal, Bikash Kumar Rajak, Durg Vijay Singh","doi":"10.3389/fddsv.2024.1363364","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1363364","url":null,"abstract":"Permeability-glycoprotein (P-gp) belongs to the ABS transporter protein family, with a high expression rate in cancerous cells. The substrate/inhibitors of the protein are structurally diverse, with no lucid mechanism of inhibition. There are two schools of thought on the inhibition mechanism: (i) P-gp inhibitors bind to the huge hydrophobic cavity between two Trans-Membrane Domains (TMDs), supported by ample literary proof and (ii) P-gp inhibitors bind to the vicinity of Nucleotide-Binding Sites (NBSs). Structural biologists have presented several experimental and theoretical structures of P-gp with bound nucleotides and inhibitors to explain the same. However, the available experimental P-gp structures are insufficient to address the catalytic transition path of mammalian P-gp in detail, thus the dynamics and mechanism by which drugs are effluxed is still unknown. Targeted Molecular Dynamics (targeted MD) could be used to minutely analyse and explore the catalytic transition inward open (IO) to outward open (OO) and relaxation path (OO to IO). Finally, analysis of targeted MD trajectory may help to explore different conformational states of Pg-p (reaction coordinate of catalytic transition/relaxation), efflux of compounds aided by the dynamics of Nucleotide Binding Domains/NBDs (ATP coupled process) and TMDs (peristalsis-like movement pushes the bound molecule). This review presents an understanding of the catalytic transition and dynamics of protein which provides insights at the efflux of chemotherapeutic drug using in cancer treatment.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A small molecule inhibitor of leucine carboxyl methyltransferase-1 inhibits cancer cell survival 亮氨酸羧基甲基转移酶-1 小分子抑制剂可抑制癌细胞存活

Frontiers in Drug Discovery Pub Date : 2024-04-11 DOI: 10.3389/fddsv.2024.1278163

O. A. Arosarena, A. S. Saribas, E. P. Papadopoulos

{"title":"A small molecule inhibitor of leucine carboxyl methyltransferase-1 inhibits cancer cell survival","authors":"O. A. Arosarena, A. S. Saribas, E. P. Papadopoulos","doi":"10.3389/fddsv.2024.1278163","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1278163","url":null,"abstract":"Reversible phosphorylation is the basis for signal transduction in eukaryotic cells, and this is tightly controlled by the complex interplay of kinases and phosphatases. Many malignancies are characterized by dysregulation of the delicate protein phosphorylation balance. The targeting of protein phosphatases has been gaining attention as their role in cancer development and progression has been elucidated. The protein phosphatase-2A (PP2A) family of phosphatases are the primary cellular serine/threonine phosphatases. PP2A heterotrimers containing the B55α (PR55α) regulatory subunit have been associated with oncogenic signaling, and B55 subunits are found exclusively in forms of PP2A in which the carboxyl terminus of the catalytic subunit (PP2Ac) is methylated. Methylation of PP2Ac is primarily mediated by leucine carboxyl methyltransferase-1 (LCMT-1). Demethylation is controlled by an esterase, PP2A methylesterase (PME-1). We tested two potential LCMT-1 small molecule inhibitors and found that methyl 4-methyl-2-[(2-methylbenzoyl)amino]-5-[[(3-methylphenyl)amino]carbonyl]-3-thiophenecarboxylate (henceforth referred to as Compound 2) significantly inhibited PP2Ac methylation in vitro (p = 0.0024), and in the MDA-MB-231 breast carcinoma (p = 0.0431) and Rosi melanoma (p = 0.0335) cell lines. Compound 2 significantly reduced survival in HEK-293, HS-5, MDA-MB-231 and Rosi cells; and constrained clonogenic colony formation in MCF7, MDA-MB-231 and Rosi cells. The LCMT-1inhibitor induced G0/G1 cell cycle arrest in Rosi cells (p = 0.0193) and induced apoptosis in MDA-MB-231 cells (p < 0.0001). Increased phosphorylation of the receptor-interacting serine/threonine protein kinase-1 (RIPK1) was detected in MDA-MB-231 (p = 0.0273) and Rosi cells (p = 0.0179) in response to treatment with Compound 2. These data add to the body of evidence pointing to LCMT-1 as an oncogenic target.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The (misleading) role of animal models in drug development 动物模型在药物研发中的（误导性）作用

Frontiers in Drug Discovery Pub Date : 2024-04-08 DOI: 10.3389/fddsv.2024.1355044

Thomas Hartung

引用次数: 1

Integrative computational approaches for discovery and evaluation of lead compound for drug design 发现和评估药物设计先导化合物的综合计算方法

Frontiers in Drug Discovery Pub Date : 2024-04-05 DOI: 10.3389/fddsv.2024.1362456

Utkarsha Naithani, Vandana Guleria

{"title":"Integrative computational approaches for discovery and evaluation of lead compound for drug design","authors":"Utkarsha Naithani, Vandana Guleria","doi":"10.3389/fddsv.2024.1362456","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1362456","url":null,"abstract":"In the drug discovery and development, the identification of leadcompoundsplaysa crucial role in the quest for novel therapeutic agents. Leadcompounds are the initial molecules that show promising pharmacological activity againsta specific target and serve as the foundation for drug development. Integrativecomputational approaches have emerged as powerful tools in expediting this complex andresource-intensive process. They enable the efficient screening of vast chemical librariesand the rational design of potential drug candidates, significantly accelerating the drugdiscoverypipeline. This review paper explores the multi-layered landscape of integrative computationalmethodologies employed in lead compound discovery and evaluation. These approaches include various techniques, including molecular modelling, cheminformatics, structure-based drug design (SBDD), high-throughput screening, molecular dynamics simulations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, anddrug-target interaction analysis. By revealing the critical role ofintegrative computational methods, this review highlights their potential to transformdrug discovery into a more efficient, cost-effective, and target-focused endeavour, ultimately paving the way for the development of innovative therapeutic agents to addressa multitude of medical challenges.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of machine learning to predict unbound drug bioavailability in the brain 应用机器学习预测非结合药物在大脑中的生物利用度

Frontiers in Drug Discovery Pub Date : 2024-04-04 DOI: 10.3389/fddsv.2024.1360732

J. F. Morales, M. E. Ruiz, Robert E. Stratford, Alan Talevi

{"title":"Application of machine learning to predict unbound drug bioavailability in the brain","authors":"J. F. Morales, M. E. Ruiz, Robert E. Stratford, Alan Talevi","doi":"10.3389/fddsv.2024.1360732","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1360732","url":null,"abstract":"Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confronting the bias towards animal experimentation (animal methods bias) 正视对动物实验的偏见（动物实验方法偏见）

Frontiers in Drug Discovery Pub Date : 2024-04-04 DOI: 10.3389/fddsv.2024.1347798

Catharine E. Krebs, Kathrin Herrmann

{"title":"Confronting the bias towards animal experimentation (animal methods bias)","authors":"Catharine E. Krebs, Kathrin Herrmann","doi":"10.3389/fddsv.2024.1347798","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1347798","url":null,"abstract":"Laws and policies are in place around the world to promote the replacement and reduction of nonhuman animals in science. These principles are rooted not just in ethical considerations for animals, but also in scientific considerations regarding the limitations of using nonhuman animals to model human biology, health, and disease. New nonanimal research approaches that use human biology, cells, and data to mimic complex human physiological states and therapeutic responses have become increasingly effective and accessible, replacing the use of animals in several applications, and becoming a crucial tool for biomedical research and drug development. Despite many advantages, acceptance of these new nonanimal methods has been slow, and barriers to their broader uptake remain. One such barrier is animal methods bias, the preference for animal-based methods where they are not necessary or where animal-free methods are suitable. This bias can impact research assessments and can discourage researchers from using novel nonanimal approaches. This article provides an introductory overview of animal methods bias for the general public, reviewing evidence, exploring consequences, and discussing ongoing mitigation efforts aimed at reducing barriers in the shift away from animal use in biomedical research and testing.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of first active compounds in drug discovery. how to proceed? 如何在药物发现过程中确定首批活性化合物？

Frontiers in Drug Discovery Pub Date : 2024-01-25 DOI: 10.3389/fddsv.2024.1342866

Stéphane Giraud

引用次数: 0