Expert Opinion on Drug Discovery最新文献

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.

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.

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.

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.

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.

Introduction: The Zika virus (ZIKV) poses a significant public health threat due to its association with congenital Zika syndrome (CZS) and severe neurological disorders. Since its discovery, ZIKV has transitioned from sporadic outbreaks to a major epidemic in Brazil in 2015, which highlighted the urgent need for effective therapies, especially for vulnerable groups like pregnant women and newborns.

Areas covered: This review provides a comprehensive overview of recent advancements in ZIKV drug discovery and their current stage of development, with a particular focus on those tested in animal models from 2018 to date, excluding vaccine candidates. Repurposed drugs, such as molnupiravir and sofosbuvir, have shown the potential to inhibit viral replication and mitigate disease. Novel compounds targeting viral proteins and host-directed therapies are also discussed. Furthermore, advanced in vitro models, including brain organoids, have offered critical insights into therapeutic efficacy.

Expert opinion: Although some preclinical models have identified potential drugs ready for human translation, no protocol has yet been established for treating ZIKV infection. Currently, the treatment involves supportive care, managing symptoms, and preventing complications, especially for pregnant women. Ongoing research aims to develop specific antiviral therapies and vaccines; however, no such options are currently available.

Introduction: Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide. This disease is complex and heterogeneous, influenced by a variety of genetic, epigenetic, and environmental factors that drive CRC initiation and progression. Despite advances in therapeutic strategies, the five-year survival rate for metastatic CRC is alarmingly low. Traditional two-dimensional (2D) cell culture systems have been the foundation of cancer research, but their inability to replicate the complex tumor microenvironment (TME) limits their effectiveness.

Areas covered: This paper explores the evolution of CRC models, starting with the limitations of traditional 2D cell culture systems and the significant advancements offered by 3D models. Additionally, it highlights 3D bioprinting and on-chip CRC models, which have enhanced the ability to mimic in vivo conditions.

Expert opinion: The transition to advanced 3D models represents a pivotal shift in CRC research, offering considerable improvements over the established 2D models. These models hold promise for the development of patient-specific models that better mimic in vivo conditions. However, the inherent complexity of CRC continues to pose challenges in developing models that can fully capture the disease's multifaceted nature. This complexity and high costs associated with these technologies, along with the need for standardized protocols, pose significant challenges to their widespread adoption.

Introduction: Targeted protein degradation (TPD) is a cutting-edge technology that provides new avenues for drug discovery and development. PROteolysis TArgeting Chimeras (PROTACs) are the most established and advanced TPD strategy, enabling the selective degradation of disease-associated and 'undruggable' proteins of interest (POIs) by leveraging the cell's natural protein degradation machinery. To confirm that PROTAC-induced proximity drives protein degradation, target validation and ternary complex formation must be thoroughly assessed.

Areas covered: In this perspective, the authors detail some of the most widely used in silico, structural, in vitro, and in cellulo methods to validate PROTAC target engagement and ternary complex formation. Additionally, they discuss the growing use of PROTACs as chemical probes for novel target identification and validation.

Expert opinion: Target validation is essential in the PROTAC approach, and ongoing studies should prioritize confirming ternary complex formation using assays conducted under physiologically relevant cellular conditions. Proteomics analyses are among the most valuable tools for elucidating PROTAC mechanisms, selectivity, and outcomes. The authors are optimistic about the future of PROTACs in drug development and their use as probes to confirm target engagement. PROTAC technology holds vast opportunities for future exploration, offering significant potential to further both chemical and biological research.

Introduction: Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge.

Areas covered: This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area.

Expert opinion: Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.