Expert Opinion on Drug Discovery最新文献_第9页

Advances in methicillin-resistant Staphylococcus aureus drug discovery: developments and challenges. 耐甲氧西林金黄色葡萄球菌药物发现的进展：发展和挑战。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2026-01-23 DOI: 10.1080/17460441.2026.2618787

Stephanos Vassilopoulos, Eleftherios Mylonakis

{"title":"Advances in methicillin-resistant Staphylococcus aureus drug discovery: developments and challenges.","authors":"Stephanos Vassilopoulos, Eleftherios Mylonakis","doi":"10.1080/17460441.2026.2618787","DOIUrl":"10.1080/17460441.2026.2618787","url":null,"abstract":"Introduction: Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe infections with excess mortality. Progress with traditional antibiotics has been incremental, while resistance, persistence, tolerance, and biofilm formation continue to erode effectiveness. Parallel advances in small-molecule discovery, long-acting lipoglycopeptides, next-generation β-lactams, and non-traditional modalities such as bacteriophage lysins have renewed interest in expanding therapeutic options, though transition from promising preclinical signals to clinical benefit remains challenging.Areas covered: A literature search was conducted using PubMed/MEDLINE, and Embase, for articles published from January 2010 through March 2025. This review synthesizes developments across: (i) agents in key clinical trials for invasive MRSA infection, emphasizing on trial designs, efficacy, and safety considerations; (ii) clinical study data with newer agents for MRSA skin infections and their potential application in invasive disease; (iii) preclinical pipelines including natural products, novel compounds, and other innovative antimicrobial strategies.Expert opinion: Among investigated agents, ceftobiprole, ceftaroline, dalbavancin, and exebacase represent promising options for invasive MRSA infections. The pipeline is further strengthened by novel classes and antimicrobial peptides, which show anti-MRSA activity and a low risk for resistance in preclinical models. Continued multidisciplinary collaboration and robust clinical trial infrastructure are essential to translate these advances into improved patient outcomes.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"231-244"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988996","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

The preclinical discovery and development of edaravone for the treatment of amyotrophic lateral sclerosis: what lessons have we learnt? 依达拉奉治疗肌萎缩性侧索硬化症的临床前发现和开发：我们吸取了什么教训？

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2026-01-29 DOI: 10.1080/17460441.2026.2619067

Tifenn Charbonnel, Elodie Richard, Adrien Dupuis, Maelys Palla, Patrick Vourc'h, Philippe Corcia, Yara Al Ojaimi, Hélène Blasco

{"title":"The preclinical discovery and development of edaravone for the treatment of amyotrophic lateral sclerosis: what lessons have we learnt?","authors":"Tifenn Charbonnel, Elodie Richard, Adrien Dupuis, Maelys Palla, Patrick Vourc'h, Philippe Corcia, Yara Al Ojaimi, Hélène Blasco","doi":"10.1080/17460441.2026.2619067","DOIUrl":"10.1080/17460441.2026.2619067","url":null,"abstract":"Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron loss, with limited therapeutic options. Among the few approved drugs, edaravone, a free radical scavenger developed originally for ischemic stroke, has attracted particular attention for its ability to counteract oxidative stress, a key driver of neurodegeneration. Its amphipathic structure and ability to cross the blood-brain barrier support its potential neuroprotective action.Areas covered: The authors discuss preclinical studies demonstrating edaravone's ability to reduce oxidative damage, preserve mitochondrial function, and modulate neuroinflammatory responses in ALS cellular and animal models. They discuss variations in dosage, timing, and disease models that produced heterogeneous results. In transgenic mice, edaravone may delay symptom onset and modestly extend survival, but these effects are inconsistent and often limited to early disease stages.Expert opinion: Clinically, edaravone provides modest benefits in a subset of patients, reflecting the translational gap between preclinical efficacy and clinical relevance. This case highlights broader challenges in ALS drug discovery, including limited model predictivity, methodological variability, and lack of patient stratification. The edaravone experience highlights key lessons for future neuroprotective approaches: the importance of standardized preclinical design, integration of human-based models, early pharmacokinetic validation, and biomarker-driven trials to advance precision neuroprotection in ALS.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"147-160"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017950","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

The promise of human bone marrow organoids for drug discovery and testing in myeloid and lymphoid cancers. 骨髓类器官在骨髓和淋巴细胞癌症药物发现和测试中的前景。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1080/17460441.2025.2601109

Nicki Panoskaltsis, Athanasios Mantalaris

引用次数: 0

Protein crystallization strategies in structure-based drug design. 基于结构的药物设计中的蛋白质结晶策略。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1080/17460441.2025.2605355

Margareta Ek, Linda Öster, Helena Käck, Tove Sjögren

{"title":"Protein crystallization strategies in structure-based drug design.","authors":"Margareta Ek, Linda Öster, Helena Käck, Tove Sjögren","doi":"10.1080/17460441.2025.2605355","DOIUrl":"10.1080/17460441.2025.2605355","url":null,"abstract":"Introduction: Efficient structure-based design requires robust protocols for generating protein-ligand complex structures to support iterative chemical optimization. However, developing reliable crystallization conditions suitable for drug discovery remains challenging, especially for novel targets and when working with diverse ligand classes.Areas covered: The review focuses on establishing robust crystallization workflows and providing solutions when standard methods prove inadequate for obtaining protein-ligand crystal structures. In addition to reviewing the literature for generic technical advances, the authors provide a comprehensive overview of project- and protein-specific approaches. To further substantiate their claims, the authors analyzed metadata from their proprietary structure collection, representing 20 years of crystallography supporting structure-based drug design. The authors provide two detailed examples showcasing rescue strategies in action.Expert opinion: Crystal structures will remain fundamental to structure-based drug design moving forward. Successful crystallization demands adaptable, multi-faceted strategies that systematically explore diverse protein variants and crystallization conditions. Future progress depends on integrating AI tools for construct design with project insights and robust experimental workflows. Success ultimately hinges on synergy between innovative problem-solving approaches and deep expertise in navigating this rapidly evolving landscape.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"201-217"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145818205","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

Understanding drug-target residence time and the implications on drug discovery. 了解药物靶点停留时间及其对药物发现的影响。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2026-01-21 DOI: 10.1080/17460441.2026.2619070

Hongli Liu, Haiyang Zhong, Dong Guo

{"title":"Understanding drug-target residence time and the implications on drug discovery.","authors":"Hongli Liu, Haiyang Zhong, Dong Guo","doi":"10.1080/17460441.2026.2619070","DOIUrl":"10.1080/17460441.2026.2619070","url":null,"abstract":"Introduction: High failure rates in clinical drug development are often attributed to inadequate therapeutic efficacy. While binding affinity has traditionally guided lead optimization, it reflects only the equilibrium state of drug-target interactions and often correlates poorly with in vivo pharmacological responses. This limitation has prompted growing interest in kinetic parameters that more accurately capture the dynamic nature of drug-target interactions.Areas covered: This review focuses on drug-target residence time (τ), defined as the reciprocal of the ligand dissociation rate constant (koff), which has emerged as a crucial determinant of drug efficacy. The authors discuss the impact of residence time on pharmacological outcomes, summarize factors influencing residence time, and outline experimental and computational approaches for its evaluation. This review is based on literature searches conducted using PubMed and Web of Science to identify articles published between the 2000 to 2025.Expert opinion: Integrating residence time and traditional binding affinity provides a more comprehensive framework for understanding drug-target interactions and guiding rational drug design. Optimizing residence time can enhance pharmacodynamic efficacy, improve target selectivity, and enhance safety. Accordingly, residence time is emerging as a key kinetic parameter in modern drug discovery.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"219-230"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009548","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

The expectations of in silico fragment-based drug design and future challenges. 基于硅片的药物设计的期望和未来的挑战。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2026-02-03 DOI: 10.1080/17460441.2026.2623154

Rupesh Chikhale

{"title":"The expectations of in silico fragment-based drug design and future challenges.","authors":"Rupesh Chikhale","doi":"10.1080/17460441.2026.2623154","DOIUrl":"10.1080/17460441.2026.2623154","url":null,"abstract":"Introduction: Fragment-based drug discovery (FBDD) employs the Grow-Merge-Link (GML) model to identify therapeutic compounds through a combination of experimental and computational techniques. Generative models facilitate compound design, predict interactions, and enhance chemical diversity.Areas covered: This perspective highlights recent FBDD developments, especially in silico methods where AI-ML accelerates discovery. Reinforcement learning optimizes properties, saving resources for targets like kinases and GPCRs. Generative chemistry enables de novo design, boosting diversity and IP, with pocket-aware design ensuring relevance and ADMET evaluation. Advances in VAEs and reinforcement learning speed up analogue creation and SAR by catalogue.Expert opinion: AI is transforming FBDD by automating compound design, predicting fragment-protein interactions, and expanding chemical diversity through deep learning, generative models, and reinforcement learning. These tools accelerate hit-to-lead processes, improve drug properties, and support multi-objective optimisation. AI enables fragment generation, pocket-specific design, and large-scale virtual screening, aiding the targeting of challenging proteins and modalities such as PROTACs and molecular glues. Larger fragment libraries enhance model training; although experimental validation remains key, AI reduces development time, improves accuracy, and broadens FBDD's scope.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"161-171"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104479","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

Deep learning in stroke therapeutics: drug repurposing and beyond. 中风治疗中的深度学习：药物再利用及其他。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-02-01 Epub Date: 2026-01-28 DOI: 10.1080/17460441.2026.2619641

Kit-Kay Mak, Bharath Chelluboina

{"title":"Deep learning in stroke therapeutics: drug repurposing and beyond.","authors":"Kit-Kay Mak, Bharath Chelluboina","doi":"10.1080/17460441.2026.2619641","DOIUrl":"10.1080/17460441.2026.2619641","url":null,"abstract":"Introduction: Deep learning is reshaping stroke research by accelerating drug repurposing amid heterogeneous pathology, narrow therapeutic windows, and poor translation. This review highlights current therapeutic challenges and emerging DL applications from preclinical modeling to clinical decision support.Area covered: This narrative review focuses on the application of DL in preclinical and clinical stroke research, with particular emphasis on their roles in drug discovery and repurposing, as well as the current limitations of these approaches. PubMed was searched for peer-reviewed studies using keywords related to drug repurposing, stroke, and computational approaches published between 2020 and 2025.Expert opinion: Given the global burden of stroke and limited therapeutic options, DL offers a timely solution by enabling accelerated drug repurposing and efficient drug development. Its ability to analyze high-dimensional data contributes to target identification, virtual screening, and drug repurposing that bridges translational gaps in stroke research. The approval of multiple AI-based diagnostic tools by regulatory bodies like the US FDA reflects growing clinical adoption. However, challenges remain in model interpretability, generalizability, and real-world validation.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"189-200"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997497","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

Small open reading frame-encoded peptides (SEPs) as hidden treasures: a review. 小的开放阅读框编码肽（sep）作为隐藏的宝藏：综述。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-01-01 Epub Date: 2026-01-04 DOI: 10.1080/17460441.2025.2603517

Xing Ren, Zhonghua Liu, Peng Zhang

{"title":"Small open reading frame-encoded peptides (SEPs) as hidden treasures: a review.","authors":"Xing Ren, Zhonghua Liu, Peng Zhang","doi":"10.1080/17460441.2025.2603517","DOIUrl":"10.1080/17460441.2025.2603517","url":null,"abstract":"Introduction: Small open reading frame-encoded peptides (SEPs) are short peptides translated from small open reading frames (sORFs) that were previously overlooked in genome annotations. SEPs have relatively small molecular sizes, fewer than 100 amino acids, some SEPs can be as short as a dozen amino acids. Recent studies have revealed their widespread presence across plants, animals, and microorganisms, as well as their diverse biological functions and potential applications.Areas covered: This review introduces the characteristics and biogenesis of SEPs, the processes and methods for their identification and validation, and their functional roles and target sites, highlighting the significant potential of SEPs in biological research and therapeutic development. Relevant literature was identified on PubMed (2010-2025) by searching for 'SEP,' 'sORF,' and 'Microprotein.'Expert opinion: The revolutionary advances in high-throughput omics technologies - particularly mass spectrometry and ribosome profiling - combined with computational prediction methods such as machine learning, have enabled the discovery of an increasing number of SEPs. Research on SEP is currently in a phase of rapid development, and this suggests that the field of peptide drugs may gain many promising molecular candidate.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"73-100"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145899730","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

Integrative computational approaches in the quest for novel Ebolavirus therapeutics. 寻求新型埃博拉病毒治疗的综合计算方法。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-01-01 Epub Date: 2025-12-23 DOI: 10.1080/17460441.2025.2605348

Sinosh Skariyachan, Vidya Niranjan, Anagha S Setlur, Swathi Vijayan, Denoj Sebastian

{"title":"Integrative computational approaches in the quest for novel Ebolavirus therapeutics.","authors":"Sinosh Skariyachan, Vidya Niranjan, Anagha S Setlur, Swathi Vijayan, Denoj Sebastian","doi":"10.1080/17460441.2025.2605348","DOIUrl":"10.1080/17460441.2025.2605348","url":null,"abstract":"Introduction: Ebolavirus (EBOV) is a highly pathogenic member of the Filoviridae family, causing severe hemorrhagic fever with high mortality rates and is persistent threat to global health with recurrent outbreaks. Although advances, including vaccine and monoclonal antibody therapeutic development, have improved survival chances, challenges such as cost and availability remain as well as potential viral evolution. Consequently, there is an urgent need for alternative, cost-effective and scalable therapeutics, which has driven interest in computational drug discovery as rapid response strategies.Area covered: This review synthesizes structural and functional insights into EBOV's key molecular targets and explores their roles in viral entry, replication, and immune evasion. The authors also discuss host-pathogen interactions as potential therapeutic routes of entry and present the most recent computational advances for hit identification and optimization. There is also coverage given to case studies highlighting successful in silico discovery efforts. Literature identified via PubMed, Scopus, and Web of Science were utilized to compose this manuscript with the authors focusing on on recently published studies, while also utilizing their own experience and insight.Expert opinion: While promising, translation of Ebola viral research remains limited by incomplete structural coverage, small and imbalanced datasets, and a gap between computational prediction and experimental validation. Hybrid in silico workflows are embedded within standardized, data-driven, and experimentally anchored pipelines, and are essential to bridge this gap. By uniting computational precision with laboratory validation, integrative strategies can accelerate the development of robust, deployable EBOV therapeutics and serve as a model for pandemic preparedness against other high-risk pathogens.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"117-131"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145818258","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

Utility of fluorinated α-amino acids in development of therapeutic peptides. 氟化α-氨基酸在治疗肽开发中的应用。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1080/17460441.2025.2601105

Paul Richardson

{"title":"Utility of fluorinated α-amino acids in development of therapeutic peptides.","authors":"Paul Richardson","doi":"10.1080/17460441.2025.2601105","DOIUrl":"10.1080/17460441.2025.2601105","url":null,"abstract":"Introduction: Fluorinated amino acids are at the focal point of two key current strategic areas within drug discovery being both important for the design/development of new small molecule drugs as well as having the potential to be exploited in the rapidly expanding area of peptide-based therapeutics. While the challenges of developing therapeutic peptides are relatively well understood and strategies have been developed to overcome these, the utilization of fluorinated amino acids for this purpose appears to be relatively sparse, particularly in progressing peptide derivatives into clinical development.Areas covered: This review discusses the applications of fluorinated amino acid (FAA) derivatives in modern drug discovery with a specific focus on their incorporation into therapeutic peptide derivatives, based on the currently available literature.Expert opinion: While only one naturally occurring and fluorinated amino acid (FAA) is known, the use of this class of compounds is prevalent in drug discovery campaigns. Synthetic advances have allowed ready access to a diverse range of compounds featuring this structural motif, and the bifunctional modularity of the amino and carboxylic acid moiety typically allows their ready incorporation into novel molecular entities through well-precedented chemistries. However, caution should be exercised when FAAs are utilized in solid-phase peptide synthesis (SPPS) as the electronegativity of the fluorine substation often mitigates the reactivity of the system leading to poor yields. Future efforts should look toward developing a more reliable understanding of the impact of these changes to enable a robust platform for therapeutic peptides to be designed based on the incorporation of fluorinated amino acids.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"19-48"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774043","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