Trends in pharmacological sciences最新文献

A central tenet of research articles is that they should accurately describe the experiments performed. Yet important aspects of experimental design and methods are sometimes omitted, precluding proper interpretation and follow-on studies. To remedy this, we urge researchers to adopt the CLEAR principle (Clarity, Evaluation, Assessment, Rigour) when reporting research.

Antibody-drug conjugates (ADCs) offer a promising approach for targeted cancer treatment. Progress, however, is constrained by the combinatorial complexity of design, toxicity and side effects, and variable clinical benefit across indications. Effective ADCs require rational matching of the antibody, linker, and payload to achieve stability in circulation and tumor-specific release, which makes development time- and cost-intensive. Artificial intelligence (AI) is shifting ADC development from empirical trial-and-error to data-driven, closed-loop engineering. By integrating sequence (for antibodies), structural, and molecular dynamics (MD) features of ADC components, AI models can accelerate target selection, conjugate optimization, and patient-response prediction. This review synthesizes advances in AI-driven ADC development across preclinical and clinical phases, highlights representative case studies and industry platforms, and outlines opportunities for AI-enabled next-generation ADCs.

Rare diseases (RDs) affect more than 400 million people worldwide, yet most patients remain undiagnosed or untreated due to delayed diagnosis and limited therapies. Artificial Intelligence (AI) offers powerful tools to address these unmet needs by integrating genomics, clinical, and imaging data to accelerate detection and therapeutic discovery. Nevertheless, most AI tools remain confined to proof of concept, exposing a persistent gap between algorithmic innovation and patient impact. Recent advances in generative models, federated learning (FL), and explainable AI (XAI) have begun to overcome barriers, such as data scarcity, privacy concerns, and biases. In this review, we highlight these developments and uniquely define the technical, ethical, and infrastructural priorities, including equity, Findable, Accessible, Interoperable, and Reusable (FAIR) data, and global coordination, required to translate AI for RDs into tangible clinical benefits.

Drugs that reprogram the cellular ubiquitin-proteasome system for removal of disease-causing proteins hold great promise as a new type of pharmacology. Small ubiquitin-related modifier (SUMO)-targeted ubiquitin ligases (StUbLs) are E3 ubiquitin ligases that mediate ubiquitylation of proteins primed by modification with SUMO. The antineoplastic drugs arsenic trioxide and fulvestrant stand out as examples for leveraging a SUMOylation-ubiquitylation cascade to inactivate the oncogenic fusion proteins PML-RARα and estrogen receptor α, respectively. However, approaches harnessing the StUbL system for targeting a broader spectrum of disease-relevant proteins are missing. Recent proof-of-concept studies indicate that proximity-inducing modalities can recruit aggregation-prone proteins to the StUbL machinery, potentially mitigating the formation of neurotoxic inclusions. We review new insights on StUbL-based therapeutics and reflect perspectives of reprogramming SUMO-StUbL signaling for use in oncology and neurology.

Dysregulated fibroblast growth factor receptor (FGFR) signaling - driven by amplifications, mutations, or fusions - represents a clinically validated oncogenic driver across diverse malignancies. Pan-FGFR-selective inhibitors (erdafitinib, pemigatinib, and futibatinib) have been developed in clinical practice. However, their therapeutic efficacy is substantially limited by inevitable on-target resistance mutations and toxicities via FGFR1/4 inhibition. Next-generation FGFR isoform-selective small-molecule inhibitors are emerging and represent active research frontiers. FGFR2-selective inhibitor lirafugratinib, FGFR3-selective inhibitors LOXO-435 and TYRA-300, FGFR2/3-selective inhibitor ABSK061, and FGFR4-selective inhibitors are in clinical development. Additionally, novel isoform-selective FGFR-targeting degraders, FGFR2b/FGFR3-selective antibodies, and de novo-designed 'c' isoform-selective proteins provide novel treatment strategies. This review provides an overview of the current FGFR-targeted therapeutics and limitations and evaluates next-generation inhibitor development to guide future research.

Many traditional psychostimulants used to treat neuropsychiatric disorders by targeting the dopamine transporter (DAT) exhibit a high potential of abuse. This necessitates the development of next-generation drugs with improved safety. Discovering atypical DAT inhibitors represent a promising strategy that may be facilitated by integration of structural pharmacology and artificial intelligence.

Cardiac fibrosis is a hallmark of cardiovascular and systemic diseases that arises in diverse pathological contexts such as inflammation, metabolic stress, and mechanical overload. Despite its clinical relevance, no FDA-approved therapies directly target cardiac fibrotic remodeling, highlighting persistent challenges in disease organization, model fidelity, and translational strategy. Recent advances in human induced pluripotent stem cell (iPSC)-derived models, engineered heart tissues, and in vivo systems have uncovered new fibrotic drivers, including immune-stroma crosstalk, metabolic reprogramming, and mechanotransduction, that are reshaping therapeutic development. This review synthesizes emerging molecular mechanisms, experimental models, and preclinical and clinical investigations of antifibrotic agents. Distinct from previous reviews, we emphasize cross-contextual alignment to support the development of precision antifibrotic therapy for cardiac fibrosis.

Camelid-derived nanobodies offer a promising alternative to conventional antibodies for the treatment of brain disorders. Their current development in models of schizophrenia or Alzheimer's disease highlights their strong therapeutic potential. Optimizing their delivery and ensuring their safety are major challenges, but their modularity and low immunogenicity make them promising candidates for neurotherapy.

Neurodegeneration arises from malfunctional intercellular interactions within the central nervous system (CNS). In a recent study, Frosch et al. identified a microglia-neuron enzyme delivery system the dysfunction of which drives Sandhoff disease, but which can be corrected by hematopoietic replacement therapy, thereby revealing new therapeutic opportunities for hereditary and sporadic neurodegenerative disorders.