Expert Opinion on Drug Discovery最新文献

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.

Introduction: The landscape of drug discovery is rapidly evolving, with natural products (NPs) playing a pivotal role in the development of novel therapeutics. Despite their historical significance, challenges persist in fully harnessing their potential in the development of modern medicine.

Areas covered: This perspective discusses the recent advances and opportunities in NP-based drug discovery. This includes exploration of the recently approved representative NP-derived drugs, innovative target identification strategies and advancements in hybrid NP molecules for addressing complex diseases. Moreover, the authors also discuss the role of NP-derived payloads in antibody-drug conjugates (ADCs) for targeted cancer therapy. This article is based on searches using the FDA and DrugBank database as well the Derwent Innovations Index from Web of Science between the period of 2017 to 2025.

Expert opinion: NPs remain vital to drug discovery, demonstrating adaptability in tackling complex medical challenges. Future efforts should focus on integrating advanced methodologies, such as artificial intelligence (AI), high-throughput screening, chemical biology, bioinformatics, gene regulation, the highly accurate non-labeling chemical proteomics approach to explore novel NPs targets. Emphasizing these developments will be crucial for maximizing the therapeutic potential of NPs in combating unmet medical needs.

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.

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.

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.