Expert Opinion on Drug Discovery最新文献

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.

Introduction: The availability of well-characterized small and large animal models is critical for the discovery and development of new drugs that counter the negative health effects of unwanted, acute ionizing radiation exposures.

Area covered: This article discusses the opportunities and challenges of small and large animal models for the development and regulatory approval of novel drugs for acute radiation syndrome (ARS). Various animal models of ARS have been analyzed for both strengths and weaknesses relative to the development of drugs for ARS following the Food and Drug Administration (FDA) Animal Rule. This article is based on a search of literature utilizing PubMed, covering the period up to March 2025.

Expert opinion: Relative to large animal models, the rhesus macaque model is currently the most used and best characterized for translational relevance. Other large animal models (e.g. minipig) are currently used as well to evaluate other specific types of acute injury, such as cutaneous injuries. Due to the limited supply of rhesus macaques for studying radiation injury and countermeasure development, it is of some urgency to further characterize and consider the use of alternative models, especially large animal models, for advanced research and subsequent regulatory approval of ARS countering drugs.

Background: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).

Research design and methods: Structure and Ligand-based screening approaches were employed to evaluate 1.1 million molecules for FFAR4 agonistic activity. Eight promising candidates were selected based on their binding affinity, non-bonded interactions, and pharmacokinetic properties and subjected to 500 ns molecular dynamics simulations (MDS). The therapeutic efficacy of compounds was assessed through in vitro assays, including cell viability tests, glucose uptake analysis, and gene expression profiling.

Results: The analysis revealed several residues (VAL98, ARG99, ARG183, ARG22, ARG24, GLU43, and TRP305) that are essential for biological activity. Insights into the mechanistic contribution of amino acid residues located in the extracellular and intracellular loops of FFAR4 to ligand binding were obtained through MDS analysis. The binding energy values indicate a stronger binding affinity between the FFAR4 and hit molecules. In vitro experiments on selected compounds (Comp35, CompN1, CompN2, and diosmetin) confirmed their potential effects on insulin-stimulated glucose uptake, IR, inflammation, and diabetic pathways.

Conclusions: Comp35, diosmetin, CompN1, and CompN2 were found to be potential hit agonists and can be developed for therapy.

Introduction: Co-IP assays are well-established technologies widely applicated for investigating the mechanisms underlying protein-protein interactions and identifying protein-protein interaction modulators. These assays play an important role in elucidating the complex networks of protein interactions critical for cellular functions.

Areas covered: This review covers a technical protocol of standard Co-IP. The research contents and conclusions of Co-IP in protein-protein interactions and protein-protein interaction modulators are summarized. Finally, three derivations of Co-IP assays are introduced. Literature was surveyed from original publications, standard sources, PubMed and clinical trials through 14 April 2025.

Expert opinion: To perform Co-IP successfully, researchers must consider the selection of specific antibody, remission of nonspecific binding and detection limitations for transient or weak interactions. Co-IP assays offer several advantages over tandem affinity purification and pull-down methods, particularly in their applicability to primary cells. This allows for the study of PPIs in a natural cellular environment. Conventional Co-IP assays often struggle to detect weak or transient interactions and can suffer from nonspecific binding contamination. However, advancements in Co-IP techniques address these challenges, enhancing sensitivity and specificity, and enabling the detection of subtle interactions while distinguishing specific binding events. This makes Co-IP a powerful tool for exploring the dynamics of protein interactions in living systems.

Introduction: Artificial intelligence (AI) has emerged as a transformative tool in drug discovery, particularly in virtual screening (VS), a crucial initial step in identifying potential drug candidates. This article highlights the significance of AI in revolutionizing both ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches, streamlining and enhancing the drug discovery process.

Areas covered: The authors provide an overview of AI applications in drug discovery, with a focus on LBVS and SBVS approaches utilized in prospective cases where new bioactive molecules were identified and experimentally validated. Discussion includes the use of AI in quantitative structure-activity relationship (QSAR) modeling for LBVS, as well as its role in enhancing SBVS techniques such as molecular docking and molecular dynamics simulations. The article is based on literature searches on studies published up to March 2025.

Expert opinion: AI is rapidly transforming VS in drug discovery, by leveraging increasing amounts of experimental data and expanding its scalability. These innovations promise to enhance efficiency and precision across both LBVS and SBVS approaches, yet challenges such as data curation, rigorous and prospective validation of new models, and efficient integration with experimental methods remain critical for realizing AI's full potential in drug discovery.

Introduction: The success of antibiotics in the therapy of infectious diseases is overshadowed by almost inevitable emergence and dissemination of resistances toward these agents, which results in higher morbidity and mortality rates and increased costs. New strategies are now needed to both limit the risk of resistance and to discover new drugs that are efficacious.

Areas covered: This review investigates the resistance problems through evolutionary lenses to better understand and potentially design improved therapeutics for infectious diseases. Furthermore, it gives an overview of the evolutionary history of antibiotic resistance genes and antibiotic biosynthesis genes/clusters, the structures of natural resistomes, and the regulatory roles of antibiotics. The author utilized ScienceDirect, PubMed, Web of Science and Google Scholar using the article's keywords and their combinations to retrieve the most relevant and up-to-date information.

Expert opinion: Antibiotics and their corresponding resistances are ancient phenomena with their evolutionary timescales measured over a vast amount of time. Humans have also benefitted from access to, and the use of, a diverse range of antibiotics for many years also but have disrupted the balance by producing and using enormous amounts of antibiotics that have not existed before in natural ecosystems. This selective pressure has resulted in a tremendous expansion of resistomes. Future antibiotic discovery and development may need to pivot from exploiting extant antibiotic scaffolds and bacterial targets to reduce the risk of the rapid emergence of resistance from existing resistomes.

Introduction: Pyridones are six-membered, nitrogen-containing heterocycles, possessing two isomeric forms; these are 2-pyridones and 4-pyridones. Both pyridone rings display unique physicochemical properties including weak alkalinity and dual hydrogen-bond donor/acceptor propensities. These heterocyclic compounds are particularly underlined for their diverse biological effects, including their cytotoxicity activity as well as their antibacterial, antiviral, anti-inflammatory, and anti-fibrotic properties. This versatility has attracted remarkable interest and held promise for addressing the challenges of drug resistance.

Area covered: This review is the outcome of literature searches conducted on articles published between 2022 and 2025 across several major databases, including PubMed, Scopus, and Web of Science, using specific keywords concerning 'pyridone' and 'bioactivity.' It focuses on the identification of therapeutic targets, the process of molecular mechanisms, and the plausible modes of interaction and binding.

Expert opinion: Pyridones have been reported to exhibit a wide range of bioactivities by regulating critical signaling pathways that have a diverse influence on downstream gene expression, intracellular enzyme activity and cytoskeletal configuration. They are consequently used as privileged fragments in the design of biologically active molecules with promising application value in pharmaceutical chemistry. Further investigation will be required to enhance drug-like properties. Continuous progress in structure optimization and clinical trial results will help to provide a guideline for future drug candidate discovery.

Introduction: Psoriasis is a prevalent and widespread chronic immune disease and i s impacted by several variables. Although various medicines with diverse modes of operation have been licensed for the medical management of psoriasis, the ongoing investigation into its pathophysiological mechanisms, along with challenges related to administration and cost, has led to the increasing preference for new small molecule medications, namely janus kinase (JAK) and phosphodiesterase 4 (PDE4) inhibitors, in systemic therapy research.

Areas covered: This review takes a medicinal chemistry perspective to comprehensively explore the development as psoriasis therapy targets for small molecule inhibitors of JAK and PDE4. We describe the chemical space explored by medicinal chemists from 2010 to 2024, with particular emphasis on the importance of inhibitors with diverse scaffolds in studies of selectivity, potency and binding modes.

Expert opinion: Advancements in psoriasis treatment have shifted focus toward small-molecule drugs, such as JAK and PDE4 inhibitors, which offer advantages over biologics, including oral administration, improved cost-effectiveness, and reduced immunogenicity. Structural optimization based on receptor proteins and combination therapies further enhance drug performance and safety. Preclinical and clinical studies indicate that these strategies hold promise for developing more targeted, safer, and more effective treatments for psoriasis.

Introduction: Obesity is a major health crisis globally, with prevalence escalating significantly in recent decades. Obesity is not merely excessive weight but is associated with myriad health complications. Ensuring the translational effectiveness of pre-clinical obesity models is paramount, and the success of GLP-1 therapies has highlighted important benchmarks for guiding drug development.

Areas covered: The authors discuss the status of various animal models used for the development of anti-obesity drugs, with particular emphasis on rodent models and their validity of preclinical-to-clinical translation. They also highlight innovative animal model integration opportunities between obesity and other associated pathology. The article is based on literature searches using PubMed for content (up until February 2025).

Expert opinion: The effectiveness of GLP-1 therapies in treating type 2 diabetes and obesity presents an opportunity to evaluate the translational relevance of animal models of obesity. Due to their compelling safety profiles, GLP-1(s) are being tested in a wide range of obesity-associated diseases. Optimization of the mechanistic qualities in this drug class requires the incorporation of new endpoints beyond body weight, including lean mass preservation, cardiovascular health, and anti-inflammatory activities. Finally, we are compelled by the intersection of non-obesity disease models into an obesogenic framework to understand the combinatorial effects of obesity on these other disease indications, including heart failure, neurodegenerative diseases, and cancer.