Expert Opinion on Drug Discovery最新文献

Practical progress towards the development of recombinant antivenoms for snakebite envenoming. 蛇咬伤重组抗蛇毒血清研制的实际进展。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-04-29 DOI: 10.1080/17460441.2025.2495943

Stefanie K Menzies, Rohit N Patel, Stuart Ainsworth

{"title":"Practical progress towards the development of recombinant antivenoms for snakebite envenoming.","authors":"Stefanie K Menzies, Rohit N Patel, Stuart Ainsworth","doi":"10.1080/17460441.2025.2495943","DOIUrl":"10.1080/17460441.2025.2495943","url":null,"abstract":"Introduction: Snakebite envenoming is a neglected tropical disease that affects millions globally each year. In recent years, research into the potential production of recombinant antivenoms, formulated using mixtures of highly defined anti-toxin monoclonal antibodies, has rapidly moved from a theoretical concept to demonstrations of practical feasibility.Areas covered: This article examines the significant practical advancements in transitioning recombinant antivenoms from concept to potential clinical translation. The authors have based their review on literature obtained from Google Scholar and PubMed between September and November 2024. Coverage includes the development and validation of recombinant antivenom antibody discovery strategies, the characterization of the first broadly neutralizing toxin class antibodies, and recent translational proof-of-concept experiments.Expert opinion: The transition of recombinant antivenoms from a 'concept' to the current situation where high-throughput anti-venom mAb discovery is becoming routine, accompanied by increasing evidence of their broad neutralizing capacity in vivo, has been extraordinary. It is now important to build on this momentum by expanding the discovery of broadly neutralizing mAbs to encompass as many toxin classes as possible. It is anticipated that key demonstrations of whether recombinant antivenoms can match or surpass existing conventional polyvalent antivenoms in terms of neutralizing scope and capacity will be achieved in the next few years.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"799-819"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974503","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

A comprehensive update on the application of high-throughput fluorescence imaging for novel drug discovery. 高通量荧光成像技术在新药发现中的应用综述。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-05-05 DOI: 10.1080/17460441.2025.2499123

Michael Ronzetti, Anton Simeonov

{"title":"A comprehensive update on the application of high-throughput fluorescence imaging for novel drug discovery.","authors":"Michael Ronzetti, Anton Simeonov","doi":"10.1080/17460441.2025.2499123","DOIUrl":"10.1080/17460441.2025.2499123","url":null,"abstract":"Introduction: High-throughput fluorescence imaging (HTFI) is revolutionizing drug discovery by enabling rapid and precise detection of biological targets and cellular processes. Recent advances in fluorescence imaging technologies now provide unprecedented sensitivity, resolution, and throughput. Integration of artificial intelligence (AI) and machine learning (ML) into HTFI workflows significantly enhances data processing, aiding in hit identification, pattern recognition, and mechanistic understanding.Areas covered: This review outlines recent technological developments, integration strategies, and emerging applications of HTFI. It emphasizes HTFI's role in phenotypic screening, especially for complex diseases such as cancer, neurodegenerative disorders, and viral infections. Additionally, it highlights advances in 3D culture systems, organoids, and organ-on-a-chip technologies, which facilitate physiologically relevant testing, improved predictive accuracy, and translational potential, alongside innovative molecular probes and biosensors.Expert opinion: Despite its advancements, HTFI faces ongoing challenges, including data standardization, integration with multi-omics approaches, and scalability of advanced models. However, recent progress in organoid and 3D modeling technologies has enhanced the physiological relevance of HTFI assays, complemented by sophisticated AI and ML-driven data analysis techniques.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"785-797"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994776","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

Evaluating AutoGrow4 - an open-source toolkit for semi-automated computer-aided drug discovery. 评估AutoGrow4——一个用于半自动计算机辅助药物发现的开源工具包。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-05-02 DOI: 10.1080/17460441.2025.2499122

Davide Bassani, Matteo Pavan, Stefano Moro

{"title":"Evaluating AutoGrow4 - an open-source toolkit for semi-automated computer-aided drug discovery.","authors":"Davide Bassani, Matteo Pavan, Stefano Moro","doi":"10.1080/17460441.2025.2499122","DOIUrl":"10.1080/17460441.2025.2499122","url":null,"abstract":"Introduction: Drug discovery is a long and expensive process characterized by a high failure rate. To make this process more rational and efficient, scientists always look for new and better ways to design novel ligands for a target of interest. Among different approaches, de novo ones gained popularity in the last decade, thanks to their ability to efficiently explore the chemical space and their increasing reliability in generating high-quality compounds. Autogrow4 is open-source software for de novo drug design that generates ligands for a given target by exploiting a combination of a genetic algorithm and molecular docking calculations.Areas covered: In the present paper, the authors dissect this program's usefulness and limitations in generating new compounds from a pharmacodynamic and pharmacokinetic perspective. Specifically, this article examines all reported applications of the Autogrow code in the literature (as retrieved from the Scopus database) from the release of its first version in 2009 to the present.Expert opinion: In the hands of an expert molecular modeler, Autogrow4 is a useful tool for de novo ligand design. Its modular and open-source codebase offers many protocol customization features. The main downsides are limited control over the pharmacokinetic features of generated ligands and the bias toward high molecular weight compounds.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"711-720"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993254","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

Natural language processing in drug discovery: bridging the gap between text and therapeutics with artificial intelligence. 药物发现中的自然语言处理：用人工智能弥合文本和治疗之间的差距。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-04-30 DOI: 10.1080/17460441.2025.2490835

Christine Ann Withers, Amina Mardiyyah Rufai, Aravind Venkatesan, Santosh Tirunagari, Sebastian Lobentanzer, Melissa Harrison, Barbara Zdrazil

{"title":"Natural language processing in drug discovery: bridging the gap between text and therapeutics with artificial intelligence.","authors":"Christine Ann Withers, Amina Mardiyyah Rufai, Aravind Venkatesan, Santosh Tirunagari, Sebastian Lobentanzer, Melissa Harrison, Barbara Zdrazil","doi":"10.1080/17460441.2025.2490835","DOIUrl":"10.1080/17460441.2025.2490835","url":null,"abstract":"Introduction: The field of Natural Language Processing (NLP) within the life sciences has exploded in its capacity to aid the extraction and analysis of data from scientific texts in recent years through the advancement of Artificial Intelligence (AI). Drug discovery pipelines have been innovated and accelerated by the uptake of AI/Machine Learning (ML) techniques.Areas covered: The authors provide background on Named Entity Recognition (NER) in text - from tagging terms in text using ontologies to entity identification via ML models. They also explore the use of Knowledge Graphs (KGs) in biological data ingestion, manipulation, and extraction, leading into the modern age of Large Language Models (LLMs) and their ability to maneuver complex and abundant data. The authors also cover the main strengths and weaknesses of the many methods available when undertaking NLP tasks in drug discovery. Literature was derived from searches utilizing Europe PMC, ResearchRabbit and SciSpace.Expert opinion: The mass of scientific data that is now produced each year is both a huge positive for potential innovation in drug discovery and a new hurdle for researchers to overcome. Notably, methods should be selected to fit a use case and the data available, as each method performs optimally under different conditions.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"765-783"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001689","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

Use of pharmacokinetic versus pharmacodynamic endpoints to support human dose predictions: implications for rational drug design and early clinical development. 使用药代动力学和药效学终点来支持人体剂量预测：对合理药物设计和早期临床开发的影响。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-04-18 DOI: 10.1080/17460441.2025.2491670

Rui Li, Tristan S Maurer

{"title":"Use of pharmacokinetic versus pharmacodynamic endpoints to support human dose predictions: implications for rational drug design and early clinical development.","authors":"Rui Li, Tristan S Maurer","doi":"10.1080/17460441.2025.2491670","DOIUrl":"10.1080/17460441.2025.2491670","url":null,"abstract":"Introduction: The predicted human dose regimen of new chemical entities represents the most holistic and clinically relevant measure of drug-likeness upon which to base decisions in drug design and selection of candidate molecules for further development. Likewise, the predicted human dose regimen for efficacy and safety provides critical insight into clinical development planning. As such, human dose predictions are commonly generated in early stages of research and continually revisited as new data are generated through development.Areas covered: In this work, the authors illustrate scenarios where conventional approaches based on discrete pharmacokinetic metrics are inappropriate and propose a generalizable approach leveraging a predicted average pharmacodynamic effect rather than pharmacokinetic metrics. Preclinical and clinical data of a JAK inhibitor, tofacitinib, were used to illustrate the relative value of this approach to human dose prediction.Expert opinion: Due to the simplicity of implementation, pharmacokinetic-based approaches which target a discrete maximal, average, or minimum concentration have been widely used across the pharmaceutical industry. However, in emphasizing only one point on the overall exposure-time profile, such approaches can be misleading in terms of the expected pharmacodynamic effect. For future projections, the authors recommend using the average pharmacodynamic effect-based approach to calculate human efficacious dose.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"735-744"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062869","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

Are we missing a trick by not exploiting fruit flies in inflammation-led drug discovery for neurodegeneration? 在炎症导致的神经退行性疾病药物研发中，我们没有利用果蝇，是不是错过了一个技巧？

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-05-19 DOI: 10.1080/17460441.2025.2498675

Ray Price, Miguel Ramirez-Moreno, Amber Cooper, Rachita Singh, Yee Ming Khaw, Annastasiah Mudiwa Mhaka, Lovesha Sivanantharajah, Amrit Mudher

{"title":"Are we missing a trick by not exploiting fruit flies in inflammation-led drug discovery for neurodegeneration?","authors":"Ray Price, Miguel Ramirez-Moreno, Amber Cooper, Rachita Singh, Yee Ming Khaw, Annastasiah Mudiwa Mhaka, Lovesha Sivanantharajah, Amrit Mudher","doi":"10.1080/17460441.2025.2498675","DOIUrl":"10.1080/17460441.2025.2498675","url":null,"abstract":"Introduction: Alzheimer's disease (AD) remains a formidable challenge in neurodegeneration research, with limited therapeutic options despite decades of study. While Drosophila melanogaster has been instrumental in in modeling AD related Tau and amyloid beta toxicity, inflammation, a key driver of AD pathology, remains unexplored in fly models. Given the evolutionary conservation of innate immune pathways between flies and mammals, drosophila presents a powerful yet underutilized tool for inflammation led drug discovery in AD.Areas covered: This perspective highlights the relevance of Drosophila in studying neuroinflammatory processes, including microglial-like glial activation, systemic inflammation and gut-brain axis interactions. It further explores how fly models can be leveraged to screen anti-inflammatory compounds and dissect immune related genetic factors implicated in AD.Expert opinion: By integrating immune modulation in Drosophila-based drug discovery pipeline we can accelerate the identification of novel therapeutic strategies. Fully exploiting the potential of Drosophila in inflammation led drug screening may usher in a new era of AD therapeutics, bridging gaps between fundamental research and translational medicine.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"721-734"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077044","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

Advancing drug discovery with electrophysiological tools for lysosomal and organellar ion channels. 利用溶酶体和细胞器离子通道的电生理工具推进药物发现。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-05-13 DOI: 10.1080/17460441.2025.2505540

Niels Fertig, Alexandre Santinho

引用次数: 0

Momelotinib - a tale of trials, tribulations, transfusion independence, and triumph. 莫米洛替尼——一个关于考验、磨难、输血独立和胜利的故事。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-05-22 DOI: 10.1080/17460441.2025.2502021

Ruchi J Desai, Pankit Vachhani, Prithviraj Bose

{"title":"Momelotinib - a tale of trials, tribulations, transfusion independence, and triumph.","authors":"Ruchi J Desai, Pankit Vachhani, Prithviraj Bose","doi":"10.1080/17460441.2025.2502021","DOIUrl":"https://doi.org/10.1080/17460441.2025.2502021","url":null,"abstract":"Introduction: Momelotinib is a small molecule inhibitor of JAK1, JAK2, and ACVR1 that is approved by the FDA and EMA for adults patients with intermediate/high risk myelofibrosis (MF) and anemia. Inhibition of the JAK-STAT pathway has a well-established role in MF therapy, producing reductions in MF-related symptoms and spleen size. Inhibition of ACVR1 downregulates hepcidin production and improves anemia. The mechanism of action of momelotinib addresses three critical aspects of morbidity in MF, ith both spleen and symptom-directed therapy for both cytopenic and proliferative MF patients.Areas covered: Key milestones in the development of momelotinib and its regulatory approvals are reviewed here. Additionally, the efficacy, safety, and tolerability of momelotinib are discussed. The literature review is based on a comprehensive search of English language, peer-reviewed articles using PubMed and clinical trial information is taken from w ww. ClinicalTrials.gov. Studies from 1 January 2000, through 31 January 2025, were included.Expert opinion: The development of momelotinib represents an important breakthrough in MF therapy with spleen and symptom directed therapy with improvements in anemia and limited myelosuppression, facilitating dose intensity. Current and future research efforts for MF therapy are directed at development of newer, anemia-directed therapies including combinations with momelotinib.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":"20 6","pages":"699-709"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127050","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

Water in drug design: pitfalls and good practices. 药物设计中的水：陷阱和良好实践。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-06-01 Epub Date: 2025-04-27 DOI: 10.1080/17460441.2025.2497912

Balázs Zoltán Zsidó, Csaba Hetényi

{"title":"Water in drug design: pitfalls and good practices.","authors":"Balázs Zoltán Zsidó, Csaba Hetényi","doi":"10.1080/17460441.2025.2497912","DOIUrl":"10.1080/17460441.2025.2497912","url":null,"abstract":"Introduction: Structure-based drug design relies on optimizing drug-target interactions and blocking harmful pathophysiological events at the atomic level. Such events of the human body are modulated by water acting either as a medium or an individual partner in molecular interactions. A precise understanding of the modulatory mechanisms of water is essential for a successful drug design.Areas covered: The present review discusses different topographical and networking situations that result in radically different roles of water, a root of various pitfalls of drug design. The review surveys good practices for tackling the problems of determining water structure at atomic resolution. Techniques for quantifying the effects of bulk, networking, and individual water molecules on the stability of drug-target complexes are also discussed. The article is based on a literature search using the PubMed, Web of Science, and Google Scholar databases.Expert opinion: With advances in rapid computational algorithms and a better understanding of the physicochemical machinery of complex formation, theoretical approaches have resulted in elegant and cost-effective tools that fill the knowledge gaps left by the limited experimental methods. Overcoming the technical pitfalls of drug design, water transforms from a frustrating challenge into a handy tool for fine-tuning drug-target interactions.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"745-764"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985637","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

In silico design strategies for tubulin inhibitors for the development of anticancer therapies. 微管蛋白抑制剂的芯片设计策略用于抗癌治疗的发展。

IF 6 2区医学

Expert Opinion on Drug Discovery Pub Date : 2025-05-30 DOI: 10.1080/17460441.2025.2507384

Ahmed Kamal, Prasanna Anjaneyulu Yakkala, Lakshmi Soukya, Sajeli Ahil Begum

{"title":"In silico design strategies for tubulin inhibitors for the development of anticancer therapies.","authors":"Ahmed Kamal, Prasanna Anjaneyulu Yakkala, Lakshmi Soukya, Sajeli Ahil Begum","doi":"10.1080/17460441.2025.2507384","DOIUrl":"10.1080/17460441.2025.2507384","url":null,"abstract":"Introduction: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, necessitating the quest for safer alternatives.Area covered: The authors explore various aspects of computer-aided drug design (CADD) for tubulin inhibitors. The authors review various techniques like molecular docking, QSAR analysis, molecular dynamic simulations, and machine learning approaches for predicting drug efficacy and modern computational methods utilized in the design and discovery of agents with anticancer potential. This article is based on a comprehensive search of literature utilizing Scopus, PubMed, Google Scholar, and Web of Science, covering the period from 2018 to 2025.Expert opinion: CADD is crucial in the pursuit of new cancer treatments, particularly by merging computer algorithms with experimental data. CADD predicts small molecule activity against tubulin related targets, expediting drug candidate identification and optimization for enhanced efficacy with reduced toxicity. Challenges include limited predictive models and the need for sophisticated ones to capture complex interactions among targets and pathways. Despite relying on cancer cell line transcriptome profiles, CADD remains pivotal for future anticancer drug discovery efforts.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1-39"},"PeriodicalIF":6.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086069","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