Expert Opinion on Drug Discovery最新文献

Introduction: Cancer cachexia (CC) is a multifactorial syndrome characterized by progressive weight loss, anorexia, and loss of skeletal muscle and fat mass, resulting in reduced quality of life and poor prognosis. Currently, there are no approved pharmacological treatments for CC, highlighting the urgent need for developing novel experimental models.

Area covered: This review covers recent advancements in preclinical models of CC, highlighting their implications for drug discovery and therapeutic development. The literature search was conducted in PubMed up to April 2025.

Expert opinion: CC remains clinically challenging and requires improved translational research and therapeutic strategies. Improved preclinical models, such as personalized patient-derived xenograft models incorporating patient-specific immune profiles and microbiota, hold promise for precision medicine. Identification of standardized extracellular vesicle (EV) derived biomarkers and effective targeting of EV signaling pathways are critical research directions. In addition, clinical validation of appetite regulators such as glucagon-like peptide-1 and growth differentiation factor-15, along with comprehensive approaches integrating diet, exercise, and targeted pharmacological interventions, will be pivotal. Finally, multidisciplinary collaboration is essential to translate these findings into meaningful therapies that will ultimately improve patient prognosis and quality of life.

Introduction: Follicular lymphoma (FL) is an indolent yet incurable subtype of non-Hodgkin lymphoma characterized by repeated relapses and diminishing responses with each treatment line. Although front-line chemoimmunotherapy achieves high initial response rates, a subset of patients - particularly those with early relapse (POD24) - experience poor outcomes and require alternative therapies. Tisagenlecleucel (tisa-cel), a CD19-directed chimeric antigen receptor (CAR) T-cell therapy, has emerged as a promising option for relapsed or refractory (r/r) FL, offering the potential for deep and durable remissions.

Areas covered: This review covers the scientific rationale, preclinical innovations, and clinical development of tisa-cel, from its origins in 2^nd-generation CAR-T engineering to its pivotal trials in hematologic malignancies. It is based on a literature search using PubMed, Embase, and conference abstracts from major hematology meetings from 1987 to April 2025. The paper deta ils the ELARA trial outcomes, subsequent long-term and real-world data, and the competitive landscape of third-line therapies for r/r FL.

Expert opinion: Tisa-cel has demonstrated high response rates and sustained remissions with a favorable safety profile in heavily pretreated FL, including high-risk populations such as those with POD24. While bispecific antibodies offer convenient outpatient administration, CAR-T cell therapy provides the potential for deep and durable remissions. The 4-1BB costimulatory domain used in tisa-cel and liso-cel is associated with a lower incidence of severe CRS and ICANS compared to CD28-based constructs. the field evolves, careful patient selection and head-to-head trials will be essential to refine therapeutic sequencing in r/r FL.

Introduction: In silico trials represent an unprecedented opportunity for ocular drug development. These trials not only promise significant reductions in costs and development timelines but also meaningful improvements in both patient safety and compliance.

Areas covered: This critical perspective gives discussion to the value of in silico trials for novel ocular drug discovery and development. Discussion includes the potential that these trials hold and the challenges that need to be addressed.

Expert opinion: The ophthalmic community stands at a critical juncture, where transitioning from traditional drug development paradigms to more integrative approaches, including computational methods, may profoundly reshape clinical practice. Nevertheless, there a several important limitations that must be overcome; these limitations include dependency on the quality and completeness of input data, accounting for complex biological systems, particularly in ophthalmology, and the variability in patient responses due to genetic, environmental, or lifestyle factors. The issue of silico model validation is also important, especially where the extensive real-world clinical data is not available for comparison. Another important concern is the limited regulatory acceptance of in silico trials to date while standardized guidelines and validation frameworks are still in development. All these issues will need to be addressed for future meaningful progression in the field.

Background: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein-protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein-ligand (PLI) and protein-protein interactions (PPI).

Research design and methods: The authors applied the protein-ligand interaction profiler, PLIP, to experimental and predicted structures and characterize drugs in clinical trials, which target PPI.

Results: PPIs generally involve more non-covalent interactions than PLI with overlapping interaction patterns and key binding site residues. In contrast to experimental structures, predicted structures fall short in accurately capturing interaction details at the interface.

Conclusion: Taken together, our analysis shows that PPIs and PLIs have sufficient commonalities to merit future work into computational screenings for drugs targeting PPIs. It will be key to further improve structure prediction, specifically for binding site details.

Introduction: Understanding the interactions between functional groups, ligands, molecular fragments, and whole molecules is critical in modern drug discovery. Key to this endeavor is the theoretical development of the fundamental inter-particle forces at play and their implementation in numerous software packages that allow the calculation of interaction energies at varying levels of theory ranging from the entirely classical at one extreme to the fully quantum mechanical at the other.

Areas covered: In this review, the authors consider the concept of an intermolecular potential energy function and its separation into short- and long-range regions. This is followed by a summary of the perturbation theory calculation of the electrostatic, induction, and dispersion energy shifts by expanding the charge distribution in terms of source multipole moments. Next, the authors outline the construction of a typical molecular force field and its parameterization; they also discuss the fundamental background of molecular dynamics (MD) simulations, their implementation in several well-known software packages and their deployment in modern computational drug discovery, including recent work with Artificial Intelligence and Machine Learning techniques. Papers cited by SSC were the result of a literature search conducted using PubMed and Google Scholar during Jan-July 2025 as well as from the authors' personal literature stock.

Expert opinion: While the underlying quantum mechanical theory of intermolecular forces is well-known, their accurate and reliable calculation for an ever-growing variety of increasingly complex systems mirrors the advances in computational hardware on which such simulations are performed. Coupled with emerging machine learning techniques, this allows for the rapid and efficient computer-aided discovery of potential new drug candidates, in the process revolutionizing research and development in both academia and industry.

Background: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeutic target for osteoporosis. However, there are currently no CTSK inhibitors available for clinical use.

Research design and methods: The authors employed a combination of deep learning approaches and experimental methods to identify novel CTSK inhibitors. Firstly, the authors utilized Chemprop to develop a predictive model for predicting CTSK inhibition. Subsequently, the top 100 predicted molecules were selected for experimental validation, with the most potent inhibitors chosen for further analysis, including enzyme kinetics, molecular docking, molecular dynamics simulations, and RANKL-induced osteoclastogenesis assays.

Results: The authors identified six compounds exhibiting concentration-dependent CTSK inhibitory effects, with Quercetin, γ-Linolenic acid (GLA), and Benzyl isothiocyanate (BITC) demonstrating the highest potency. Enzyme kinetics studies revealed that these inhibitors employ distinct mechanisms of CTSK inhibition. Molecular dynamics simulations further showed that Quercetin and BITC form stable interactions at the CTSK active site. Moreover, in-vitro studies demonstrated that Quercetin and GLA significantly inhibit RANKL-induced osteoclastogenesis in RAW264.7 cells.

Conclusions: This study led to the development of a deep learning model capable of predicting CTSK inhibitors and identified Quercetin, GLA, and BITC as promising candidates for the treatment of osteoporosis.

Introduction: Despite advances in antidepressant development, many patients with major depressive disorder (MDD) remain inadequately treated. Gepirone, a selective 5-HT1A agonist without reuptake inhibition, offers a novel mechanism potentially improving efficacy and tolerability over selective serotonin reuptake inhibitors (SSRIs) and earlier agents.

Areas covered: This case history describes gepirone's discovery and development, including its pharmacodynamic profile, preclinical data on pharmacology, mechanism of action, and effects on depressive-like behavior and anxiety, as well as early clinical findings on its safety and efficacy in major depressive disorder. The review draws on English peer-reviewed articles and trials (1983-2025) from major databases, including PubMed, Embase, and ClinicalTrials.gov.

Expert opinion: Although gepirone ER was approved due to evidence supporting clinical efficacy and favorable tolerability in MDD, its antidepressant effect size is modest relative to other monoamine-based antidepressants. It may offer particular benefit for patients who experience anxiety-related adverse effects on standard antidepressants and may be particularly useful in anxious depression or patients prioritizing tolerability. Approved by the U.S. Food and Drug Administration in 2023, withdrawn in 2024, the product will relaunch in late 2025. Future research should assess head-to-head efficacy, pharmacoeconomics, real-world outcomes, and its potential role in treatment-resistant depression.

Introduction: Advances in artificial intelligence (AI) have transformed the drug design and discovery process, introducing novel methods that can reduce costs, increase success rates, and shorten development timelines. However, due to the complexity and multifactorial nature of this process, no single AI approach is likely to be universally effective.

Areas covered: This review summarizes progress made over the past five years toward diverse drug development goals using AI tools. It also discusses the main challenges that inhibit the development and adoption of a broad AI solution in this field.

Expert opinion: Despite major advancements, AI fails to reach its full potential due to issues related to data quality, model complexity, computational costs, and organizational barriers. At present, the effectiveness of any AI approach heavily depends on its application. Ultimately, while the world strives for a general-purpose AI, no method in drug discovery can yet be considered universally applicable, and rather than relying on a one-size-fits-all solution, individual trade-offs and research objectives need to be carefully aligned to harness AI's potential in drug discovery.

Introduction: Carbohydrates are ubiquitous biomolecules that play indispensable roles in living systems, functioning in cellular communication, genetic information storage, cellular energy provision, structural support, host-pathogen interactions, and the biosynthesis of secondary metabolites such as antibiotics. Their inherent multifunctionality, stereochemical complexity, and natural affinity for binding specific proteins make them highly attractive scaffolds for drug discovery. Despite their biological significance, carbohydrate-based therapeutics remain underrepresented in the pharmacopoeia, comprising only a small fraction of approved drugs. This underutilization highlights the untapped potential of carbohydrates as sources of novel therapeutic agents with innovative mechanisms of action.

Areas covered: In this concise review, the authors summarize the current landscape of approved small-molecule drugs containing carbohydrate moieties and highlight recent advances in carbohydrate-based compounds with a wide spectrum of pharmacological activities, including antimicrobial, anticancer, antidiabetic, anti-inflammatory, neuroprotective, antiviral, and enzyme inhibitory effects.

Expert opinion: Carbohydrate-based therapeutics are transitioning from niche applications to mainstream drug discovery platforms and, as such, hold significant promise for generating future generations of pharmaceuticals. Consequently, the authors firmly advocate continued efforts in designing carbohydrate-derived drug candidates which are well positioned to deliver first or best-in-class drugs.

Introduction: Long COVID (LC), also known as post-acute COVID-19 syndrome (PASC), has emerged as a significant public health concern characterized by persistent symptoms following SARS-CoV-2 infection. This condition affects regardless of initial illness severity and can significantly impair daily functioning. Understanding the implications of LC is crucial, given that approximately 6.9 % of adults reported related symptoms in 2022, with increased prevalence among women and individuals of Hispanic descent. The pathogenesis of LC is multifactorial, involving mechanisms such as endothelial dysfunction, chronic inflammation, immune dysregulation, and potential viral persistence. The clinical manifestations include fatigue, cognitive impairment, musculoskeletal pain, and sleep disturbances. Current research emphasizes the importance of early antiviral interventions and vaccines to mitigate the risk of developing LC. Despite promising therapies like anti-inflammatory agents and metabolic enhancers, the lack of established biomarkers complicates diagnosis and treatment.

Areas covered: The authors provide an overview of the pathogenesis of LC and briefly review the currently available therapy. The authors then give their perspectives on how best future drug discovery efforts can be utilized to address the current demand for novel LC therapeutics to reduce the burden of this public health problem.

Expert opinion: Progress has been made in understanding the pathophysiology and potential treatment options, as well as in establishing reliable biomarkers for potential tailored strategies. Future research should prioritize both pharmacological and non-pharmacological interventions to enhance patient outcomes and quality of life. Addressing these challenges is essential for developing comprehensive care protocols for individuals affected by LC.