Expert Opinion on Drug Discovery最新文献

Introduction: Insomnia is a highly prevalent and clinically burdensome disorder that profoundly affects cognition, emotional regulation, cardiometabolic health, and neurodegenerative progression. Despite advances in understanding its neurobiology, current animal models fail to capture the chronic, heterogeneous, and comorbid nature of human insomnia, impeding progress in translational drug discovery.

Areas covered: This narrative review critically appraises genetic, environmental, pharmacological, and circuit-level models of insomnia, focusing on their translational relevance to drug discovery and is based on literature searches using PubMed and Scopus (2000-2025) where key systematic reviews were identified. The author also discusses how oversimplified paradigms and limited modeling of comorbidity constrain clinical applicability and highlight emerging tools - optogenetics, chemogenetics, CRISPR, wearable EEG, and AI - that enable high-resolution mapping of sleep - wake mechanisms.

Expert opinion: A paradigm shift toward integrated, multidimensional models is urgently needed to reflect the complexity of chronic insomnia better. Embedding these models into translational pipelines - through precision genetics, circuit manipulation, and AI-enhanced analytics - will accelerate mechanism-based drug discovery and support the development of durable, personalized treatments for this disabling and multifactorial disorder.

Introduction: Leishmaniasis is a devastating and complex parasitic disease caused by different species of protozoan members of the genus Leishmania. Unfortunately, available drugs are far from ideal and no vaccines are available. Under these circumstances, new effective antileishmanial drugs with reduced host toxicity and improved dosing protocols are urgently needed. The sterol biosynthesis pathway (SBP) is a promising focus for combating Leishmania infections. Thus, various strategies have been documented, such as drug repurposing, combined therapy, rational drug design, and the use of synergistic effects to develop the metallodrugs that can act on essential parasite targets.

Areas covered: This article reviews the critical enzymes participating in the ergostane-based sterol biosynthesis pathway (SBP) of Leishmania species, as well as recent progress in rational drug design, repurposing drugs, combined therapies, and the development of metallodrugs for use as antileishmanial agents. This review is based on literature searchers using SciFinder, Lens.org, Google Scholar, Web of Science, Pub Med, and DrugBank.

Expert opinion: The limited focus on human leishmaniasis has resulted in a shortfall in effective treatments for this parasitic disease. The post-squalene segment of the sterol biosynthetic pathway is a promising target for treating Leishmania infections, particularly effective drugs or metallodrugs that inhibit the CYP51 or 24-SMT enzymes.

Introduction: The first generation of approved T cell engagers (TCEs) showing promising efficacy in hematological and solid tumors relies on high binding affinity to CD3. Treatment of tumors with TCEs has been clinically associated with toxicities related to cytokine release syndrome (CRS). In addition to clinical strategies to mitigate CRS, antibody engineering efforts have been undertaken to generate TCEs with optimized therapeutic index. Strategies pursued in this context include affinity attenuation of CD3 binding arm, to achieve potent tumor cell killing with minimal cytokine secretion.

Areas covered: A literature search was conducted to identify peer-reviewed articles related to CD3 affinity and T cell engagers. Here, we provide an overview of the current state and recent developments in CD3 affinity-attenuation, both preclinically and clinically, with a focus on the challenges of developing TCEs with attenuated affinity to CD3 as well as identifying possible areas of improvement.

Expert opinion: CD3 affinity reduction can effectively lower cytokine levels preclinically; however, it is crucial to consider all factors influencing the mode of action of TCEs. Prioritizing the use of the most translatable preclinical models is essential to identify the right candidates for further development.

Introduction: GPCRs are targeted by nearly one-third of FDA-approved drugs and are therefore of great interest in drug discovery pursuits. Nuclear magnetic resonance (NMR) builds upon our understanding of the structural biology of GPCRs by helping to identify dynamic facets of ligand engagement, activation, and G protein coupling, often through the identification of an ensemble.

Areas covered: The basic facets of NMR spectroscopy and relaxation experiments (e.g. CPMG, WaterLOGSY, STD) are described as they pertain to the study of structure activity relationships (SAR), ligand-fragment interaction dynamics, and fragment-based drug discovery. This article is based on literature searches that have utilized ISI Web of Knowledge, MEDLINE, and Google Scholar.

Expert opinion: The structural biology of GPCRs and their associated complexes are rapidly advancing, particularly via cryoEM techniques which provide high-resolution structures and additional insights into minor states represented into the ensemble. Nevertheless, there is still an important niche for NMR methods to capture in terms of the delineation of detailed and physiologically representative ensembles of functional states and their associated dynamics.

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.