Expert Opinion on Drug Discovery最新文献

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.

Introduction: In recent months, monkeypox (mpox) virus (MPXV) infections has grown to be a major worldwide concern. Cynomolgus monkeys, rhesus macaques, marmosets, and baboons are the nonhuman primate (NHP) models that provide the much needed means for developing new therapies against MPXV due to their genetic proximity to humans.

Area covered: In this review, the authors discuss epidemiology, transmission, clinical presentation, and the use of NHP in studying the treatment of MPXV over the past two decades on Google Scholar. NHP models have been widely used to evaluate the efficacy of antiviral drugs and antibodies, providing important information regarding immune responses and disease. NHPs continue to be an important mainstay in preclinical testing, enabling the optimization of the efficacy and safety of drugs, antibodies, and vaccines to accelerate the development of effective MPXV treatments for humans.

Expert opinion: The intravenous forms of medications like cidofovir, brincidofovir, and Vaccinia Immune Globulin (VIG) constitute the basis of MPXV therapy. Additionally, antibodies such as HAI, PN, and CF assess the efficacy of smallpox vaccination against MPXV in primates. This would help both the development of diagnostic tools and the optimization of vaccine strategies. Moreover, the similarities between MPXV and vaccinia or variola can play a role in developing targeted antiviral treatment methods.

Introduction: Proximity-inducing compounds promote protein-protein interactions by bringing proteins into close spatial alignment. Among them, targeted protein degradation (TPD) compounds are noteworthy for their potential to target previously 'undruggable' proteins. Native mass spectrometry (nMS), a technique that enables the detection of non-covalent interactions, is emerging as a key tool for studying compound-induced ternary complex formation.

Areas covered: This review highlights the use of nMS in unraveling the mechanisms of proximity-inducing compounds, focusing on all available studies published since 2020, identified through a PubMed database search. The authors explore how nMS helps investigate the efficacy and mechanisms of proteolysis-targeting chimeras (PROTACs) and molecular glues by capturing the binary and ternary complexes formed by E3 ligases, protein of interest (POI), and these molecules.

Expert opinion: nMS excels at analyzing intact protein complexes, providing real-time insights into interactions between E3 ligases, POIs, and proximity-inducing agents. This analysis helps understand the formation, stability, and dynamic nature of the complexes along with precise data on stoichiometry and binding affinities, which are crucial for selecting and refining effective degraders. The future of nMS in TPD research is promising, with potential applications in kinetic analysis, degrader screenings, and exploration of novel E3 ligases and target proteins.

Introduction: The advent of variant-specific disease-modifying drugs into clinical practice has provided remarkable benefits for people with cystic fibrosis (PwCF), a multi-organ life-limiting inherited disease. However, further efforts are needed to maximize therapeutic benefits as well as to increase the number of PwCF taking CFTR modulators.

Area covered: The authors discuss some of the key limitations of the currently available CFTR modulator therapies (e.g. adverse reactions) and strategies in development to increase the number of available therapeutics for CF. These include novel methods to accelerate theratyping and identification of novel small molecules and cellular targets representing alternative or complementary therapies for CF.

Expert opinion: While the CF therapy development pipeline continues to grow, there is a critical need to optimize strategies that will accelerate testing and approval of effective therapies for (ultra)rare CFTR variants as traditional assays and trials are not suitable to address such issues. Another major barrier that needs to be solved is the restricted access to currently available modulator therapies, which remains a significant burden for PwCF who are from racial and ethnic minorities and/or living in underprivileged regions.

Introduction: The journey from initial drug discovery to approval for respiratory diseases typically spans approximately 10.4 years and cost over $2.8 billion. This intricate process involves five stages: target identification, therapeutic molecule discovery, preclinical testing, clinical trials, and regulatory approval.

Areas covered: This review examines novel drug discovery strategies for chronic obstructive pulmonary disease (COPD), focusing on advanced in vitro models that replicate human lung conditions for accurate drug testing according to the following search string: discovery AND strategy AND COPD. It explores targeted molecular therapies, structure-based drug design, and drug repurposing approaches facilitated by computational analysis. The significance of personalized medicine in tailoring treatments for diverse COPDs is emphasized, highlighting the complexity of the disease and the necessity of these innovative methodologies to improve therapeutic outcomes.

Expert opinion: COPD remains a challenging area, with a significant unmet medical need. Despite previous efforts, few effective therapies exist. Innovative in vitro models, targeted molecular therapies, and drug repurposing strategies are showing promise. Emphasizing advanced preclinical models and repurposing existing drugs could transform treatment paradigms, promoting more effective therapies for complex diseases like COPD. These innovations hold potential for enhancing drug discovery efficiency, leading to personalized and precision medicine approaches.

Introduction: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs often leads to adverse drug reactions (ADRs) and therapeutic failure. As such, early prediction of such inhibitors is vital in drug development. In this context, the limitations of the traditional in vitro assays and QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques.

Areas covered: This narrative review presents the insights gained from the application of AI for predicting DME and DT inhibitors over the past decade. Several case studies demonstrate successful AI applications in enzyme-transporter interaction prediction, and the authors discuss workflows for integrating these predictions into drug design and regulatory frameworks.

Expert opinion: The application of AI in predicting DME and DT inhibitors has demonstrated significant potential toward enhancing drug safety and effectiveness. However, critical challenges involve the data quality, biases, and model transparency. The availability of diverse, high-quality datasets alongside the integration of pharmacokinetic and genomic data are essential. Lastly, the collaboration among computational scientists, pharmacologists, and regulatory bodies is pyramidal in tailoring AI tools for personalized medicine and safer drug development.

Introduction: Identifying treatments that can alter the natural history of amyotrophic lateral sclerosis (ALS) is challenging. For years, drug discovery in ALS has relied upon traditional approaches with limited success. Drug repurposing, where clinically approved drugs are reevaluated for other indications, offers an alternative strategy that overcomes some of the challenges associated with de novo drug discovery.

Areas covered: In this review, the authors discuss the challenge of drug discovery in ALS and examine the potential of drug repurposing for the identification of new effective treatments. The authors consider a range of approaches, from screening in experimental models to computational approaches, and outline some general principles for preclinical and clinical research to help bridge the translational gap. Literature was reviewed from original publications, press releases and clinical trials.

Expert opinion: Despite remaining challenges, drug repurposing offers the opportunity to improve therapeutic options for ALS patients. Nevertheless, stringent preclinical research will be necessary to identify the most promising compounds together with innovative experimental medicine studies to bridge the translational gap. The authors further highlight the importance of combining expertise across academia, industry and wider stakeholders, which will be key in the successful delivery of repurposed therapies to the clinic.

Introduction: Drug discovery is a complex and multifaceted process driven by scientific innovation and advanced technologies. Next-Generation Sequencing (NGS) platforms, encompassing both short-read and long-read technologies, have revolutionized the field by enabling the high-throughput and cost-effective analysis of DNA and RNA molecules. Continuous advancements in NGS-based technologies have enabled their seamless integration across preclinical and clinical workflows in drug discovery, encompassing early-stage drug target identification, candidate selection, genetically stratified clinical trials, and pharmacogenetic studies.

Area covered: This review provides an overview of the current and potential applications of NGS-based technologies in drug discovery and development process, including their roles in novel drug target identification, high-throughput screening, clinical trials, and clinical medication studies. The review is based on literature retrieval from the PubMed and Web of Science databases between 2018 and 2024.

Expert opinion: As technologies advance rapidly, NGS enhances accuracy and generates vast datasets. These datasets are extensively integrated with other heterogeneous data in systems biology and are mined using machine learning to extract significant insights, thereby driving progress in drug discovery.

Introduction: Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is often therapeutically challenging. While research has advanced our understanding of IBS pathophysiology, developing precise models to predict drug response and treatment outcomes remains a significant hurdle.

Areas covered: This perspective provides an overview of the use of animal models alongside cutting-edge technologies used to bring drugs from bench to bedside.Furthermore, the authors examine the progress and limitations of IBS modeling. The authors further discuss the challenges of traditional animal models and gives a spotlight to the potential of innovative technologies, such as organ-on-chip systems, computational models, and artificial intelligence (AI). These approaches intend to enhance both the understanding and treatment of IBS.

Expert opinion: Although animal models have been central to understanding IBS research, they have limitations. The future of IBS research resides in integrating organ-on-chip systems and utilizing modern technological developments, such as AI. These tools will enable the design of more effective treatment strategies and improve patients' overall well-being. To achieve this, collaboration between experts from various disciplines is essential to improve these models and guarantee their clinical application and reliability.

Introduction: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery faces growing challenges in terms of time, cost, and efficacy, there is a pressing need to integrate these emerging technologies to enhance the discovery process.

Areas covered: In this perspective, the authors explore the role of AI and ML in modern early drug discovery and discuss their application in drug target identification, compound screening, and biomarker discovery. This article is based on a thorough literature search using the PubMed database to identify relevant studies that highlight the use of AI/ML models in computational chemistry, systems biology, and data-driven approaches to drug development. Emphasis is placed on how these technologies address key challenges such as data integration, predictive performance, and cost-efficiency in the drug discovery pipeline.

Expert opinion: AI and ML have the potential to revolutionize early drug discovery by improving the accuracy and speed of identifying viable drug candidates. However, successful integration of these technologies requires overcoming challenges related to data quality, model interpretability, and the need for interdisciplinary collaboration.