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Expert Opinion on Drug Discovery最新文献

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Inclisiran: the preclinical discovery and development of a novel therapy for the treatment of atherosclerosis. Inclisiran:治疗动脉粥样硬化的新型疗法的临床前发现和开发。
IF 6 2区 医学 Q1 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-05-28 DOI: 10.1080/17460441.2024.2360415
Donatos Tsamoulis, Loukianos S Rallidis, Constantine E Kosmas

Introduction: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality. Lipid lowering therapy (LLT) constitutes the cornerstone of ASCVD prevention and treatment. However, several patients fail to achieve therapeutic goals due to low treatment adherence or limitations of standard-of-care (SoC) LLTs. Inclisiran represents a pivotal low-density lipoprotein cholesterol (LDL-C) lowering agent aiming to address current unmet needs in LLT. It is the first available small interfering RNA (siRNA) LLT, specifically targeting PCSK9 mRNA and leading to post-transcriptional gene silencing (PTGS) of the PCSK9 gene.

Areas covered: Promising phase III trials revealed an ~ 50% reduction in LDL-C levels with subcutaneous inclisiran administration on days 1 and 90, followed by semiannual booster shots. Coupled with inclisiran's favorable safety profile, these findings led to its approval by both the EMA and FDA. Herein, the authors highlight the preclinical discovery and development of this agent and provide the reader with their expert perspectives.

Expert opinion: The evolution of gene-silencing treatments offers new perspectives in therapeutics. Inclisiran appears to have the potential to revolutionize ASCVD prevention and treatment, benefiting millions of patients. Ensuring widespread availability of Inclisiran, as well as managing additional healthcare costs that may arise, should be of paramount importance.

导言:动脉粥样硬化性心血管疾病(ASCVD)仍然是全球发病率和死亡率的主要原因。降脂治疗(LLT)是预防和治疗 ASCVD 的基石。然而,由于治疗依从性低或标准治疗(SoC)LLTs 的局限性,一些患者未能达到治疗目标。Inclisiran 是一种关键性的低密度脂蛋白胆固醇(LDL-C)降低药物,旨在解决目前低密度脂蛋白胆固醇治疗中尚未满足的需求。它是首个可用的小干扰 RNA(siRNA)LLT,特异性靶向 PCSK9 mRNA,导致 PCSK9 基因转录后基因沉默(PTGS):前景广阔的 III 期试验显示,在第 1 天和第 90 天皮下注射 inclisiran 后,低密度脂蛋白胆固醇(LDL-C)水平可降低约 50%,之后每半年注射一次加强针。这些研究结果加上 inclisiran 良好的安全性,使其获得了欧洲药品管理局(EMA)和美国食品和药物管理局(FDA)的批准。在此,作者重点介绍了这种药物的临床前发现和开发过程,并为读者提供了他们的专家观点:基因沉默疗法的发展为治疗提供了新的视角。Inclisiran似乎有可能彻底改变ASCVD的预防和治疗,使数百万患者受益。确保Inclisiran的广泛使用以及管理可能产生的额外医疗成本至关重要。
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引用次数: 0
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 Q1 PHARMACOLOGY & PHARMACY 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

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.

导言:疏水标记(HyT)技术是一种有别于传统小分子药物的独特治疗策略,为药物设计提供了一种创新方法。本综述旨在概述 HyT 文献和未来展望,为药物设计提供指导:在这篇综述中,作者介绍了 HyT 技术的组成、机制和优势,并总结了 HyT 技术在抗癌、神经退行性疾病(NDs)、自身免疫性疾病、心血管疾病(CVDs)等领域的详细应用。此外,本综述还讨论了 HyT 分子未来发展的关键方面:继成功开发蛋白水解靶向嵌合体(PROTAC)和分子胶之后,HyT成为一种极具前景的靶向蛋白质降解(TPD)策略。在探索新途径的基础上,对 HyT 分子本身进行修饰可能会增强这项技术。改进合成途径并重视药代动力学(PK)特性将促进 HyT 的开发。此外,阐明化合物的疏水分子招募蛋白平衡网络的生化基础,将有助于开发更精确的检测方法,从而指导连接体和疏水分子的优化。
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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 Q1 PHARMACOLOGY & PHARMACY Pub Date : 2024-06-18 DOI: 10.1080/17460441.2024.2367023
Catherine J. Hutchings, Aaron K. Sato
Phage display technology is a well-established versatile in vitro display technology that has been used for over 35 years to identify peptides and antibodies for use as reagents and therapeutics, a...
噬菌体展示技术是一种成熟的多功能体外展示技术,用于鉴定用作试剂和治疗剂的多肽和抗体已有 35 年的历史。
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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 Q1 PHARMACOLOGY & PHARMACY Pub Date : 2024-06-18 DOI: 10.1080/17460441.2024.2367014
Lihui Duo, Yu Liu, Jianfeng Ren, Bencan Tang, Jonathan D. Hirst
The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer tr...
从传统的细胞毒性化疗过渡到使用小分子抗癌药物进行癌症靶向治疗,提高了治疗效果。这种方法目前在癌症治疗中占主导地位。
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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 Q1 PHARMACOLOGY & PHARMACY 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

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 (koff and kon) 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.

导言:要进行合理的药物设计,了解受体与药物的结合过程和机制至关重要。随着超级计算技术的显著进步和方法上的突破,利用计算机模拟在原子水平上预测药物与受体相互作用的新时代已经来临:端点自由能计算方法,如分子力学/泊松玻尔兹曼表面积(MM/PBSA)或分子力学/广义玻恩表面积(MM/GBSA)、自由能扰动(FEP)和热力学积分(TI),常用于药物发现中的结合自由能计算。此外,动力学解离和结合速率常数(koff 和 kon)对药物的功能起着至关重要的作用。如今,分子动力学(MD)和增强采样模拟正越来越多地用于药物发现。在此,作者对用于药物结合自由能和动力学计算的计算技术进行了综述:由于对药物分子结合自由能和动力学速率的预测得到了改进,计算方法在药物发现和设计中的应用正在不断扩大。最近的微秒级增强采样模拟使准确捕捉配体的重复结合和解离成为可能,从而有助于更高效、更准确地计算配体结合自由能和动力学。
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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 Q1 PHARMACOLOGY & PHARMACY Pub Date : 2024-06-01 Epub Date: 2024-05-07 DOI: 10.1080/17460441.2024.2350567
Ana Margarida Sousa, Maria Olívia Pereira
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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 Q1 PHARMACOLOGY & PHARMACY Pub Date : 2024-06-01 Epub Date: 2024-05-16 DOI: 10.1080/17460441.2024.2350568
Zhou Dongrui, Maho Miyamoto, Hidetomo Yokoo, Yosuke Demizu

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.

简介:肽折叠体在药物研究和生物医学应用中发挥着至关重要的作用。这篇综述重点介绍了新型折叠器、合成技术及其在药物研究中应用的最新进展(2020 年以后):作者总结了α、β、γ-肽、碳氢叠层肽、脲型折叠剂、磺酸-γ-氨基酸折叠剂、芳香族折叠剂和类佩妥类等多肽折叠剂的结构和应用,这些折叠剂解决了传统多肽药物的难题。在抗菌方面,折叠酰胺在对抗耐药细菌的潜力方面取得了进展。在药物开发方面,多肽折叠物已被用作药物输送系统(DDS)和蛋白质-蛋白质相互作用(PPI)抑制剂:这些结构具有抗酶降解性,有望用于治疗给药,并以特异性、多功能性和稳定性破坏与癌症等疾病相关的关键 PPI,这些都是有用的治疗特性。然而,由于其合成过程复杂、成本高昂,而且必须进行全面的安全性和有效性评估,因此有必要开展广泛的研究和跨部门合作。合成方法、计算建模和靶向递送系统的进步对于充分发挥折叠剂的治疗潜力并将其纳入主流医疗方法至关重要。
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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 Q1 PHARMACOLOGY & PHARMACY 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

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.

导言:基于片段的药物设计(FBDD)针对具有挑战性的治疗靶点的有效性一直受到两个因素的阻碍:小规模的文库和片段到靶点优化过程的复杂性。DNA编码文库(DEL)技术提供了一种引人注目且稳健的高通量选择方法,有可能解决这些局限性:在这篇综述中,作者提出了一种观点,即 DEL 技术与 FBDD 的概念完全匹配,可促进命中发现。他们首先从药物化学的角度分析了FBDD的技术局限性,并解释了为什么DEL可以为这些局限性提供潜在的解决方案。随后,他们详细阐述了 DEL 与 FBDD 的整合工作原理。此外,他们还介绍了一些案例研究,包括新药发现和全配体发现,特别是针对具有广泛药物靶点界面的挑战性治疗靶点:基于 DEL 的片段发现的未来可能会受到技术进步和应用范围的双重推动。从技术层面来看,扩大 DEL 的化学多样性对于片段药物发现的成功至关重要。从应用范围来看,基于 DEL 的片段发现有望解决一系列具有挑战性的靶点。
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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 Q1 PHARMACOLOGY & PHARMACY 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

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.

导言:药代动力学(PK)特性预测对于药物发现和开发至关重要。机器学习(ML)模型利用统计模式识别来学习输入特征(如化学结构)和目标变量(如 PK 参数)之间的相关性,正越来越多地用于这一目的。为了将用于 PK 预测的 ML 模型嵌入工作流程并指导未来的发展,有必要深入了解这些模型的适用性、优势、局限性以及与其他方法的协同作用:这篇叙述性综述讨论了预测小分子 PK 参数的 ML 模型的设计和应用,特别是考虑到体外-体内外推法(IVIVE)和基于生理的药代动力学(PBPK)模型等既定方法。作者举例说明了这三种方法的应用场景,并强调了它们如何相互促进和补充。特别是,他们通过全面的文献综述,强调了应用机器学习进行 PK 预测的成就、技术水平和潜力:专家观点:机器学习模型经过精心设计、定期更新和合理使用,可以帮助用户优先选择具有良好 PK 特性的分子。知情的从业人员可以利用这些模型提高药物发现和开发过程的效率。
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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 Q1 PHARMACOLOGY & PHARMACY 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
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引用次数: 0
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Expert Opinion on Drug Discovery
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