Expert Opinion on Drug Discovery最新文献_第6页

Hydrophobic tagging of small molecules: an overview of the literature and future outlook. 小分子的疏水标记：文献综述与未来展望。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-07-01 Epub Date: 2024-06-02 DOI: 10.1080/17460441.2024.2360416

Yang Zhou, Fan Zhou, Shujing Xu, Dazhou Shi, Dang Ding, Shuo Wang, Vasanthanathan Poongavanam, Kai Tang, Xinyong Liu, Peng Zhan

{"title":"Hydrophobic tagging of small molecules: an overview of the literature and future outlook.","authors":"Yang Zhou, Fan Zhou, Shujing Xu, Dazhou Shi, Dang Ding, Shuo Wang, Vasanthanathan Poongavanam, Kai Tang, Xinyong Liu, Peng Zhan","doi":"10.1080/17460441.2024.2360416","DOIUrl":"10.1080/17460441.2024.2360416","url":null,"abstract":"Introduction: Hydrophobic tagging (HyT) technology presents a distinct therapeutic strategy diverging from conventional small molecule drugs, providing an innovative approach to drug design. This review aims to provide an overview of the HyT literature and future outlook to offer guidance for drug design.Areas covered: In this review, the authors introduce the composition, mechanisms and advantages of HyT technology, as well as summarize the detailed applications of HyT technology in anti-cancer, neurodegenerative diseases (NDs), autoimmune disorders, cardiovascular diseases (CVDs), and other fields. Furthermore, this review discusses key aspects of the future development of HyT molecules.Expert opinion: HyT emerges as a highly promising targeted protein degradation (TPD) strategy, following the successful development of proteolysis targeting chimeras (PROTAC) and molecular glue. Based on exploring new avenues, modification of the HyT molecule itself potentially enhances the technology. Improved synthetic pathways and emphasis on pharmacokinetic (PK) properties will facilitate the development of HyT. Furthermore, elucidating the biochemical basis by which the compound's hydrophobic moiety recruits the protein homeostasis network will enable the development of more precise assays that can guide the optimization of the linker and hydrophobic moiety.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"799-813"},"PeriodicalIF":6.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phage display technology and its impact in the discovery of novel protein-based drugs 噬菌体展示技术及其对发现新型蛋白质药物的影响

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-18 DOI: 10.1080/17460441.2024.2367023

Catherine J. Hutchings, Aaron K. Sato

引用次数: 0

Artificial intelligence for small molecule anticancer drug discovery 人工智能发现小分子抗癌药物

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-18 DOI: 10.1080/17460441.2024.2367014

Lihui Duo, Yu Liu, Jianfeng Ren, Bencan Tang, Jonathan D. Hirst

引用次数: 0

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery. 了解结合自由能和动力学计算对现代药物发现的影响。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-09 DOI: 10.1080/17460441.2024.2349149

Victor A Adediwura, Kushal Koirala, Hung N Do, Jinan Wang, Yinglong Miao

{"title":"Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.","authors":"Victor A Adediwura, Kushal Koirala, Hung N Do, Jinan Wang, Yinglong Miao","doi":"10.1080/17460441.2024.2349149","DOIUrl":"10.1080/17460441.2024.2349149","url":null,"abstract":"Introduction: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.Areas covered: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (<math><mrow><msub><mi>k</mi><mrow><mi>off</mi></mrow></msub></mrow></math> and <math><mrow><msub><mi>k</mi><mrow><mi>on</mi></mrow></msub></mrow></math>) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.Expert opinion: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"671-682"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140897924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges with drug efficacy prediction of in vitro models of biofilms infecting cystic fibrosis airway. 囊性纤维化气道生物膜感染体外模型药效预测面临的挑战。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-07 DOI: 10.1080/17460441.2024.2350567

Ana Margarida Sousa, Maria Olívia Pereira

引用次数: 0

Innovative peptide architectures: advancements in foldamers and stapled peptides for drug discovery. 创新肽结构：折叠肽和钉肽在药物发现方面的进展。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-16 DOI: 10.1080/17460441.2024.2350568

Zhou Dongrui, Maho Miyamoto, Hidetomo Yokoo, Yosuke Demizu

{"title":"Innovative peptide architectures: advancements in foldamers and stapled peptides for drug discovery.","authors":"Zhou Dongrui, Maho Miyamoto, Hidetomo Yokoo, Yosuke Demizu","doi":"10.1080/17460441.2024.2350568","DOIUrl":"10.1080/17460441.2024.2350568","url":null,"abstract":"Introduction: Peptide foldamers play a critical role in pharmaceutical research and biomedical applications. This review highlights recent (post-2020) advancements in novel foldamers, synthetic techniques, and their applications in pharmaceutical research.Areas covered: The authors summarize the structures and applications of peptide foldamers such as α, β, γ-peptides, hydrocarbon-stapled peptides, urea-type foldamers, sulfonic-γ-amino acid foldamers, aromatic foldamers, and peptoids, which tackle the challenges of traditional peptide drugs. Regarding antimicrobial use, foldamers have shown progress in their potential against drug-resistant bacteria. In drug development, peptide foldamers have been used as drug delivery systems (DDS) and protein-protein interaction (PPI) inhibitors.Expert opinion: These structures exhibit resistance to enzymatic degradation, are promising for therapeutic delivery, and disrupt crucial PPIs associated with diseases such as cancer with specificity, versatility, and stability, which are useful therapeutic properties. However, the complexity and cost of their synthesis, along with the necessity for thorough safety and efficacy assessments, necessitate extensive research and cross-sector collaboration. Advances in synthesis methods, computational modeling, and targeted delivery systems are essential for fully realizing the therapeutic potential of foldamers and integrating them into mainstream medical treatments.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"699-723"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using DNA-encoded libraries of fragments for hit discovery of challenging therapeutic targets. 利用 DNA 编码的片段库发现具有挑战性的治疗靶点。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-16 DOI: 10.1080/17460441.2024.2354287

Guixian Zhao, Mengping Zhu, Yangfeng Li, Gong Zhang, Yizhou Li

{"title":"Using DNA-encoded libraries of fragments for hit discovery of challenging therapeutic targets.","authors":"Guixian Zhao, Mengping Zhu, Yangfeng Li, Gong Zhang, Yizhou Li","doi":"10.1080/17460441.2024.2354287","DOIUrl":"10.1080/17460441.2024.2354287","url":null,"abstract":"Introduction: The effectiveness of Fragment-based drug design (FBDD) for targeting challenging therapeutic targets has been hindered by two factors: the small library size and the complexity of the fragment-to-hit optimization process. The DNA-encoded library (DEL) technology offers a compelling and robust high-throughput selection approach to potentially address these limitations.Area covered: In this review, the authors propose the viewpoint that the DEL technology matches perfectly with the concept of FBDD to facilitate hit discovery. They begin by analyzing the technical limitations of FBDD from a medicinal chemistry perspective and explain why DEL may offer potential solutions to these limitations. Subsequently, they elaborate in detail on how the integration of DEL with FBDD works. In addition, they present case studies involving both de novo hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces.Expert opinion: The future of DEL-based fragment discovery may be promoted by both technical advances and application scopes. From the technical aspect, expanding the chemical diversity of DEL will be essential to achieve success in fragment-based drug discovery. From the application scope side, DEL-based fragment discovery holds promise for tackling a series of challenging targets.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"725-740"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. 您的另一项任务：通过机器学习预测小分子药物的药代动力学特性。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-10 DOI: 10.1080/17460441.2024.2348157

Davide Bassani, Neil John Parrott, Nenad Manevski, Jitao David Zhang

{"title":"Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.","authors":"Davide Bassani, Neil John Parrott, Nenad Manevski, Jitao David Zhang","doi":"10.1080/17460441.2024.2348157","DOIUrl":"10.1080/17460441.2024.2348157","url":null,"abstract":"Introduction: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.Areas covered: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.Expert opinion: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"683-698"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140897857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lessons learnt from machine learning in early stages of drug discovery. 从药物发现早期阶段的机器学习中汲取的经验教训。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-10 DOI: 10.1080/17460441.2024.2354279

Claudio N Cavasotto, Juan I Di Filippo, Valeria Scardino

引用次数: 0

New drug discovery strategies for the treatment of benznidazole-resistance in Trypanosoma cruzi, the causative agent of Chagas disease. 治疗南美锥虫病病原体克氏锥虫对苯并咪唑耐药性的新药研发战略。

IF 6.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2024-06-01 Epub Date: 2024-05-07 DOI: 10.1080/17460441.2024.2349155

Silvane Maria Fonseca Murta, Pedro Augusto Lemos Santana, Thibault Joseph William Jacques Dit Lapierre, André Berndt Penteado, Marissa El Hajje, Thabata Corazza Navarro Vinha, Daniel Barbosa Liarte, Mariana Laureano de Souza, Gustavo Henrique Goulart Trossini, Celso de Oliveira Rezende Júnior, Renata Barbosa de Oliveira, Rafaela Salgado Ferreira

{"title":"New drug discovery strategies for the treatment of benznidazole-resistance in Trypanosoma cruzi, the causative agent of Chagas disease.","authors":"Silvane Maria Fonseca Murta, Pedro Augusto Lemos Santana, Thibault Joseph William Jacques Dit Lapierre, André Berndt Penteado, Marissa El Hajje, Thabata Corazza Navarro Vinha, Daniel Barbosa Liarte, Mariana Laureano de Souza, Gustavo Henrique Goulart Trossini, Celso de Oliveira Rezende Júnior, Renata Barbosa de Oliveira, Rafaela Salgado Ferreira","doi":"10.1080/17460441.2024.2349155","DOIUrl":"10.1080/17460441.2024.2349155","url":null,"abstract":"Introduction: Benznidazole, the drug of choice for treating Chagas Disease (CD), has significant limitations, such as poor cure efficacy, mainly in the chronic phase of CD, association with side effects, and parasite resistance. Understanding parasite resistance to benznidazole is crucial for developing new drugs to treat CD.Areas covered: Here, the authors review the current understanding of the molecular basis of benznidazole resistance. Furthermore, they discuss the state-of-the-art methods and critical outcomes employed to evaluate the efficacy of potential drugs against T. cruzi, aiming to select better compounds likely to succeed in the clinic. Finally, the authors describe the different strategies employed to overcome resistance to benznidazole and find effective new treatments for CD.Expert opinion: Resistance to benznidazole is a complex phenomenon that occurs naturally among T. cruzi strains. The combination of compounds that inhibit different metabolic pathways of the parasite is an important strategy for developing a new chemotherapeutic protocol.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"741-753"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140876171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0