Acta Pharmaceutica Sinica B

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Advances in selective targeting of serine hydrolases: A targeted covalent approach against hCES2A mitigates irinotecan toxicity in vivo

Acta Pharmaceutica Sinica B. 2025, 15(10): 5491-5492.
Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions

Fang Ren, Jing Wei, Qingxin Chen, Mengling Hu, Lu Yu, Jianing Mi, Xiaogang Zhou, Dalian Qin, Jianming Wu, Anguo Wu

Acta Pharmaceutica Sinica B. 2025, 15(9): 4327-4385.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.
Artificial intelligence in drug development for delirium and Alzheimer's disease

Ruixue Ai, Xianglu Xiao, Shenglong Deng, Nan Yang, Xiaodan Xing, Leiv Otto Watne, Geir Selbæk, Yehani Wedatilake, Chenglong Xie, David C. Rubinsztein, Jennifer E. Palmer, Bjørn Erik Neerland, Hongming Chen, Zhangming Niu, Guang Yang, Evandro Fei Fang

Acta Pharmaceutica Sinica B. 2025, 15(9): 4386-4410.

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.
A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer's disease therapeutic discovery

Yisheng Yang, Yizhu Diao, Lulu Jiang, Fanlu Li, Liye Chen, Ming Ni, Zheng Wang, Hai Fang

Acta Pharmaceutica Sinica B. 2025, 15(9): 4411-4426.

The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer's disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways—implicated in synaptic dysfunction and neuroinflammation—provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration.
AI-powered model for accurate prediction of MCI-to-AD progression

Ahmed Abdelhameed, Jingna Feng, Xinyue Hu, Fang Li, Sori Lundin, Paul E. Schulz, Cui Tao

Acta Pharmaceutica Sinica B. 2025, 15(9): 4427-4437.

Alzheimer's disease (AD) remains a formidable challenge in modern healthcare, necessitating innovative approaches for its early detection and intervention. This study aimed to enhance the identification of individuals with mild cognitive impairment (MCI) at risk of developing AD. Leveraging advances in computational power and the extensive availability of healthcare data, we explored the potential of deep learning models for early prediction using medical claims data. We employed a bidirectional gated recurrent unit (BiGRU) deep learning model for predictive modeling of MCI progression across various prediction intervals, extending up to five years post-initial MCI diagnosis. The performance of the BiGRU model was rigorously compared with several machine-learning model baselines to evaluate its efficacy. Using a robust cross-validation methodology, the BiGRU emerged as the top-performing model, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.833 (95% CI: 0.822, 0.843), an Area Under the Precision-Recall Curve (AUC-PR) of 0.856 (95% CI: 0.845, 0.867), and an F1-Score of 0.71 (95% CI: 0.694, 0.724) for a five-year prediction interval. The results indicate that BiGRU, utilizing longitudinal claims data, reliably predicts MCI-to-AD progression over a lengthy interval following the initial MCI diagnosis, offering clinicians a valuable tool for targeted risk identification and stratification.
Biased signaling via serotonin 5-HT₂A receptor: From structural aspects to in vitro and in vivo pharmacology

Michał K. Jastrzębski, Piotr Wójcik, Angelika Grudzińska, Giorgia Andreozzi, Tommaso Vetrò, Ayesha Asim, Akanksha Mudgal, Jakub Czapiński, Tomasz M. Wróbel, Damian Bartuzi, Katarzyna M. Targowska-Duda, Agnieszka A. Kaczor

Acta Pharmaceutica Sinica B. 2025, 15(9): 4438-4455.

G protein-coupled receptors (GPCRs) represent key drug targets, with approximately 30%-40% of all medications acting on these receptors. Recent advancements have uncovered the complexity of GPCR signaling, including biased signaling, which allows selective activation of specific intracellular pathways—primarily mediated by G proteins and β-arrestins. Among aminergic GPCRs, the serotonin 5-HT₂A receptor has garnered attention for its potential to generate therapeutic effects without adverse outcomes, such as hallucinations, through biased agonism. This review delivers a comprehensive overview of 5-HT₂A receptor-biased signaling and its significance in developing safer mental health therapeutics, particularly for depression and anxiety. We provide a critical evaluation of methodologies for assessing biased signaling, spanning from traditional radioligand binding assays to advanced biosensor technologies. Furthermore, we review structural studies and computational modeling that have identified key receptor residues modulating biased signaling. We also highlight novel biased ligands with selective pathway activation, presenting a promising avenue for developing targeted antidepressant therapies without psychedelic effects. Additionally, we explore the 5-HT₂A receptor's role in memory processes and stress response regulation. Ultimately, advancing our understanding of 5-HT₂A receptor-biased signaling could drive the development of next-generation GPCR-targeted therapies, maximizing therapeutic efficacy while minimizing side effects in psychiatric treatment.
Understanding the complexity of tumor-associated macrophages: Druggable and therapeutic insights

Acta Pharmaceutica Sinica B. 2025, 15(9): 4456-4475.

Macrophages are immune cells capable of exerting both pro-tumor and anti-tumor effects. Tumor-associated macrophages (TAMs) comprise a heterogeneous group of macrophages originating from monocytes and resident tissue macrophages. Their phenotypes and functions vary depending on factors such as tumor type, location, and stage. TAMs can promote tumor growth, angiogenesis, metastasis, immunosuppression, and drug resistance, or they can facilitate antigen presentation and immune activation, thereby contributing to tumor elimination. As such, TAMs are potential targets for cancer therapy, and various pharmacological strategies and clinic-approved drugs have been suggested to modulate their activity, recruitment, and depletion. However, the complexity and diversity of TAMs present significant challenges to understanding their roles and designing effective drug interventions. This review summarizes the current knowledge of TAMs, and drug development for TAMs as anti-tumor therapy targets, emphasizing the importance of single-cell omics technologies for characterizing TAM heterogeneity and identifying therapeutic opportunities. Additionally, it presents the latest clinical trials focused on TAM-targeted therapies and drugs. Collectively, this review discusses the therapeutic opportunities and challenges of TAM-targeted drug therapies and offers future perspectives and directions for advancing our understanding and manipulation of TAMs in drug development.
Immunoregulatory mechanisms in the aging microenvironment: Targeting the senescence-associated secretory phenotype for cancer immunotherapy

Acta Pharmaceutica Sinica B. 2025, 15(9): 4476-4496.

The aging microenvironment, as a key driver of tumorigenesis and progression, plays a critical role in tumor immune regulation through one of its core features—the senescence-associated secretory phenotype (SASP). SASP consists of a variety of interleukins, chemokines, proteases, and growth factors. It initially induces surrounding cells to enter a state of senescence through paracrine mechanisms, thereby creating a sustained inflammatory stimulus and signal amplification effect within the tissue microenvironment. Furthermore, these secreted factors activate key signaling pathways such as NF-κB, cGAS-STING, and mTOR, which regulate the expression of immune-related molecules (such as PD-L1) and promote the recruitment of immunosuppressive cells, including regulatory T cells and myeloid-derived suppressor cells. This process ultimately contributes to the formation of an immunosuppressive tumor microenvironment. Furthermore, the article explores potential anti-tumor immunotherapy strategies targeting SASP and its associated molecular mechanisms, including approaches to inhibit SASP secretion or eliminate senescent cells. Although these strategies have shown promise in certain tumor models, the high heterogeneity among tumor types may result in varied responses to SASP-targeted therapies. This highlights the need for further research into adaptive stratification and personalized treatment approaches. Targeting immune regulatory mechanisms in the aging microenvironment—particularly SASP—holds great potential for advancing future anti-tumor therapies.
The role of SARS-CoV-2 main protease in innate immune regulation: From molecular mechanisms to therapeutic implications

Acta Pharmaceutica Sinica B. 2025, 15(9): 4497-4510.

The main protease (Mpro) of SARS-CoV-2 plays a pivotal role in viral replication and immune evasion. Accumulating evidence highlights its significant role in suppressing innate immunity. In this review, we provide a comprehensive overview of how Mpro modulates host innate immune responses, including its interference with retinoic acid-inducible gene I (RIG-I)-like receptor (RLR) and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon gene (STING) signaling pathways, inhibition of interferon production, and disruption of inflammasome activities. As a protease, Mpro cleaves a variety of host proteins to attenuate antiviral innate immunity, a process dependent on its catalytic dyad (Cys145-His41), which is crucial for its proteolytic activity. Meanwhile, Mpro also exerts innate immune regulatory functions in a protease-independent manner. Notably, inhibitors targeting Mpro have demonstrated efficacy in restoring immune functions and suppressing viral replication, offering potential therapeutic strategies against SARS-CoV-2 infection.
Fibroblast activation protein targeting radiopharmaceuticals: From drug design to clinical translation

Acta Pharmaceutica Sinica B. 2025, 15(9): 4511-4542.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

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