Expert Opinion on Drug Discovery最新文献

Introduction: Affinity selection mass spectrometry (AS-MS) is a powerful label-free technique for characterizing macromolecule-ligand interactions that has been used as a hit finding tool with significant success. Recent advances in MS and separation technology have positioned AS-MS to impact more areas of drug discovery.

Areas covered: This manuscript provides a brief historical review of AS-MS and the recently developed technologies that have enabled AS-MS. The report also provides examples and references for how AS-MS has been used for high-throughput screening (HTS) to DNA-encoded library (DEL) screening hit confirmation, Direct-to-Biology, and natural product screens. The references for this work were collected from a broad range of sources, including Google Scholar, Scopus, review articles identified via Google Scholar, and the internal AI resource at AbbVie Inc.

Expert opinion: AS-MS is a unique biophysical binding assay that does not rely on labels and can specifically detect binders from large pools of potential ligands based on molecular weight. There is still significant room for growth in areas of impact that will be driven by decreases in separation time and a move toward equilibrium conditions during separation. Increased use for driving rapid structure-activity relationships (SAR) has potential to decrease project cycle times in lead identification and optimization.

Introduction: Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe infections with excess mortality. Progress with traditional antibiotics has been incremental, while resistance, persistence, tolerance, and biofilm formation continue to erode effectiveness. Parallel advances in small-molecule discovery, long-acting lipoglycopeptides, next-generation β-lactams, and non-traditional modalities such as bacteriophage lysins have renewed interest in expanding therapeutic options, though transition from promising preclinical signals to clinical benefit remains challenging.

Areas covered: A literature search was conducted using PubMed/MEDLINE, and Embase, for articles published from January 2010 through March 2025. This review synthesizes developments across: (i) agents in key clinical trials for invasive MRSA infection, emphasizing on trial designs, efficacy, and safety considerations; (ii) clinical study data with newer agents for MRSA skin infections and their potential application in invasive disease; (iii) preclinical pipelines including natural products, novel compounds, and other innovative antimicrobial strategies.

Expert opinion: Among investigated agents, ceftobiprole, ceftaroline, dalbavancin, and exebacase represent promising options for invasive MRSA infections. The pipeline is further strengthened by novel classes and antimicrobial peptides, which show anti-MRSA activity and a low risk for resistance in preclinical models. Continued multidisciplinary collaboration and robust clinical trial infrastructure are essential to translate these advances into improved patient outcomes.

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron loss, with limited therapeutic options. Among the few approved drugs, edaravone, a free radical scavenger developed originally for ischemic stroke, has attracted particular attention for its ability to counteract oxidative stress, a key driver of neurodegeneration. Its amphipathic structure and ability to cross the blood-brain barrier support its potential neuroprotective action.

Areas covered: The authors discuss preclinical studies demonstrating edaravone's ability to reduce oxidative damage, preserve mitochondrial function, and modulate neuroinflammatory responses in ALS cellular and animal models. They discuss variations in dosage, timing, and disease models that produced heterogeneous results. In transgenic mice, edaravone may delay symptom onset and modestly extend survival, but these effects are inconsistent and often limited to early disease stages.

Expert opinion: Clinically, edaravone provides modest benefits in a subset of patients, reflecting the translational gap between preclinical efficacy and clinical relevance. This case highlights broader challenges in ALS drug discovery, including limited model predictivity, methodological variability, and lack of patient stratification. The edaravone experience highlights key lessons for future neuroprotective approaches: the importance of standardized preclinical design, integration of human-based models, early pharmacokinetic validation, and biomarker-driven trials to advance precision neuroprotection in ALS.

Introduction: Efficient structure-based design requires robust protocols for generating protein-ligand complex structures to support iterative chemical optimization. However, developing reliable crystallization conditions suitable for drug discovery remains challenging, especially for novel targets and when working with diverse ligand classes.

Areas covered: The review focuses on establishing robust crystallization workflows and providing solutions when standard methods prove inadequate for obtaining protein-ligand crystal structures. In addition to reviewing the literature for generic technical advances, the authors provide a comprehensive overview of project- and protein-specific approaches. To further substantiate their claims, the authors analyzed metadata from their proprietary structure collection, representing 20 years of crystallography supporting structure-based drug design. The authors provide two detailed examples showcasing rescue strategies in action.

Expert opinion: Crystal structures will remain fundamental to structure-based drug design moving forward. Successful crystallization demands adaptable, multi-faceted strategies that systematically explore diverse protein variants and crystallization conditions. Future progress depends on integrating AI tools for construct design with project insights and robust experimental workflows. Success ultimately hinges on synergy between innovative problem-solving approaches and deep expertise in navigating this rapidly evolving landscape.

Introduction: High failure rates in clinical drug development are often attributed to inadequate therapeutic efficacy. While binding affinity has traditionally guided lead optimization, it reflects only the equilibrium state of drug-target interactions and often correlates poorly with in vivo pharmacological responses. This limitation has prompted growing interest in kinetic parameters that more accurately capture the dynamic nature of drug-target interactions.

Areas covered: This review focuses on drug-target residence time (τ), defined as the reciprocal of the ligand dissociation rate constant (k_off), which has emerged as a crucial determinant of drug efficacy. The authors discuss the impact of residence time on pharmacological outcomes, summarize factors influencing residence time, and outline experimental and computational approaches for its evaluation. This review is based on literature searches conducted using PubMed and Web of Science to identify articles published between the 2000 to 2025.

Expert opinion: Integrating residence time and traditional binding affinity provides a more comprehensive framework for understanding drug-target interactions and guiding rational drug design. Optimizing residence time can enhance pharmacodynamic efficacy, improve target selectivity, and enhance safety. Accordingly, residence time is emerging as a key kinetic parameter in modern drug discovery.

Introduction: Fragment-based drug discovery (FBDD) employs the Grow-Merge-Link (GML) model to identify therapeutic compounds through a combination of experimental and computational techniques. Generative models facilitate compound design, predict interactions, and enhance chemical diversity.

Areas covered: This perspective highlights recent FBDD developments, especially in silico methods where AI-ML accelerates discovery. Reinforcement learning optimizes properties, saving resources for targets like kinases and GPCRs. Generative chemistry enables de novo design, boosting diversity and IP, with pocket-aware design ensuring relevance and ADMET evaluation. Advances in VAEs and reinforcement learning speed up analogue creation and SAR by catalogue.

Expert opinion: AI is transforming FBDD by automating compound design, predicting fragment-protein interactions, and expanding chemical diversity through deep learning, generative models, and reinforcement learning. These tools accelerate hit-to-lead processes, improve drug properties, and support multi-objective optimisation. AI enables fragment generation, pocket-specific design, and large-scale virtual screening, aiding the targeting of challenging proteins and modalities such as PROTACs and molecular glues. Larger fragment libraries enhance model training; although experimental validation remains key, AI reduces development time, improves accuracy, and broadens FBDD's scope.

Introduction: Deep learning is reshaping stroke research by accelerating drug repurposing amid heterogeneous pathology, narrow therapeutic windows, and poor translation. This review highlights current therapeutic challenges and emerging DL applications from preclinical modeling to clinical decision support.

Area covered: This narrative review focuses on the application of DL in preclinical and clinical stroke research, with particular emphasis on their roles in drug discovery and repurposing, as well as the current limitations of these approaches. PubMed was searched for peer-reviewed studies using keywords related to drug repurposing, stroke, and computational approaches published between 2020 and 2025.

Expert opinion: Given the global burden of stroke and limited therapeutic options, DL offers a timely solution by enabling accelerated drug repurposing and efficient drug development. Its ability to analyze high-dimensional data contributes to target identification, virtual screening, and drug repurposing that bridges translational gaps in stroke research. The approval of multiple AI-based diagnostic tools by regulatory bodies like the US FDA reflects growing clinical adoption. However, challenges remain in model interpretability, generalizability, and real-world validation.

Introduction: AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors.

Areas covered: The authors discuss the challenges associated with the traditional reductionist approach to drug discovery which relies on incomplete data for target validation and, specifically for small molecules, the expanse of chemical space providing potential candidates. The promise of AI is illustrated by both early success and failure stories. Lessons learned are provided at levels of realism, adoption and integration of AI within current Research and Development (R&D) organizational structures.

Expert opinion: The first decade of AI adoption in Big BioPharma has been characterized by genuine breakthroughs and sobering realities. While AI has delivered notable accelerations in hit identification and early-stage design, it has yet to fundamentally alter the success rates of late-stage clinical trials. The industry has learned that AI is neither a silver bullet nor a passing fad, though a critical and evolving component of modern R&D. By consolidating lessons from early adoption, the next decade may see AI truly shift the innovation frontier in global pharmaceutical discovery.

Introduction: Hit identification is a pivotal yet resource-intensive stage of early drug discovery, where large chemical libraries are screened to uncover compounds with target-specific activity. Traditional fluorescence- and luminescence-based high-throughput assays, while fast and automation-friendly, suffer from label-associated artifacts, limited biological relevance, and signal interference that can compromise data fidelity. These challenges, coupled with the growing scale of screening campaigns, have intensified the need for more robust and physiologically relevant label-free screening strategies.

Areas covered: This review highlights the emergence of label-free detection technologies as powerful alternatives for hit identification. By enabling direct measurement of biomolecular interactions or cellular responses without secondary reporters, these modalities reduce false positives, improve assay reliability, and enhance mechanistic insight. The authors also summarize their operating principles, recent applications, and practical considerations, emphasizing how label-free approaches can strengthen screening accuracy and accelerate early drug discovery.

Expert opinion: Label-free assays have rapidly advanced, offering real-time measurements, improved physiological relevance, and expanding throughput for early drug discovery. While these methods reduce artifacts and broaden target compatibility, challenges remain in validating biological relevance and managing complex kinetic data. Recent software innovations, including automated kinetic modeling and high-throughput data pipelines, are accelerating analysis and enhancing scalability.