Expert Opinion on Drug Discovery最新文献

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.

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.