Pub Date : 2024-07-01Epub Date: 2024-05-28DOI: 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.
{"title":"Inclisiran: the preclinical discovery and development of a novel therapy for the treatment of atherosclerosis.","authors":"Donatos Tsamoulis, Loukianos S Rallidis, Constantine E Kosmas","doi":"10.1080/17460441.2024.2360415","DOIUrl":"10.1080/17460441.2024.2360415","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"773-782"},"PeriodicalIF":6.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141156812","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}
Pub Date : 2024-07-01Epub Date: 2024-06-02DOI: 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.
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","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}
Pub Date : 2024-06-18DOI: 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...
{"title":"Phage display technology and its impact in the discovery of novel protein-based drugs","authors":"Catherine J. Hutchings, Aaron K. Sato","doi":"10.1080/17460441.2024.2367023","DOIUrl":"https://doi.org/10.1080/17460441.2024.2367023","url":null,"abstract":"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...","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":"2 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501074","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}
Pub Date : 2024-06-18DOI: 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...
{"title":"Artificial intelligence for small molecule anticancer drug discovery","authors":"Lihui Duo, Yu Liu, Jianfeng Ren, Bencan Tang, Jonathan D. Hirst","doi":"10.1080/17460441.2024.2367014","DOIUrl":"https://doi.org/10.1080/17460441.2024.2367014","url":null,"abstract":"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...","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":"24 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501075","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}
Pub Date : 2024-06-01Epub Date: 2024-05-09DOI: 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 ( and ) 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.
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","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}
Pub Date : 2024-06-01Epub Date: 2024-05-07DOI: 10.1080/17460441.2024.2350567
Ana Margarida Sousa, Maria Olívia Pereira
{"title":"Challenges with drug efficacy prediction of in vitro models of biofilms infecting cystic fibrosis airway.","authors":"Ana Margarida Sousa, Maria Olívia Pereira","doi":"10.1080/17460441.2024.2350567","DOIUrl":"10.1080/17460441.2024.2350567","url":null,"abstract":"","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"635-638"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140857673","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}
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.
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","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}
Pub Date : 2024-06-01Epub Date: 2024-05-16DOI: 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。
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Area covered: </strong>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 <i>de novo</i> hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces.</p><p><strong>Expert opinion: </strong>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.</p>","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}
Pub Date : 2024-06-01Epub Date: 2024-05-10DOI: 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。
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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 <i>in vitro-in vivo</i> 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.</p><p><strong>Expert opinion: </strong>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.</p>","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}
Pub Date : 2024-06-01Epub Date: 2024-05-10DOI: 10.1080/17460441.2024.2354279
Claudio N Cavasotto, Juan I Di Filippo, Valeria Scardino
{"title":"Lessons learnt from machine learning in early stages of drug discovery.","authors":"Claudio N Cavasotto, Juan I Di Filippo, Valeria Scardino","doi":"10.1080/17460441.2024.2354279","DOIUrl":"10.1080/17460441.2024.2354279","url":null,"abstract":"","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"631-633"},"PeriodicalIF":6.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140897858","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}