Acta Pharmaceutica Sinica B

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Applications of AI/ML in accelerating the development of pulmonary drug delivery system

Acta Pharmaceutica Sinica B. 2026, 16(2): 686-708.

Artificial intelligence (AI) is a transformative technique for drug development, and it has been widely applied in pharmaceutical industry and academia. Pulmonary drug delivery systems (PDDS) are preferred for treating respiratory diseases due to their ability to provide localized and rapid action with fewer side effects. The integration of AI and Machine Learning (ML) has significantly accelerated the development of PDDS by enhancing both respiratory disease detection, and different stages during PDDS development. This paper provides an overview of the present landscape by literature analysis of the key areas of research. This review first introduces the fundamental principles of AI/ML and how they are applied in respiratory disease detection and diagnostics, highlighting FDA-approved software used in this field. Furthermore, we examine the role of AI in different stages during the development of PDDS, from identifying novel drug candidates to optimizing formulations and drug delivery mechanisms. The review also discusses regulatory and ethical considerations, along with existing challenges during AI-driven PDDS development. By addressing these key aspects, we provide insights into the revolutionary potential of AI/ML in advancing pulmonary drug delivery and improving therapeutic outcomes.
Artificial intelligence-guided design of lipid nanoparticles for mRNA delivery

Acta Pharmaceutica Sinica B. 2026, 16(2): 709-727.

Lipid nanoparticles (LNPs) hold significant potential for mRNA-based therapeutics, as evidenced by their successful use in SARS-CoV-2 mRNA vaccines. LNPs effectively protect and transport mRNA to target sites, thereby ensuring its stability and efficient transfection. Despite the progress, some challenges remain in the development of mRNA-LNP delivery systems, such as limited targeting specificity, the complexity of formulations, and the time-consuming and high-throughput screening process. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges, accelerating the design and optimization process of LNPs. AI-guided approaches can improve the efficiency of lipid structure and formulation screening by rapidly identifying key design parameters and employing predictive modeling to optimize LNP properties. The combination of AI and LNP technology offers significant advantages, including enabling the design of more personalized and precise delivery systems, streamlining the development process, and reducing the cost. This review discusses recent advancements in AI-guided mRNA-LNP delivery systems and highlights their potential to revolutionize mRNA therapeutics.
Anti-influenza drugs targeting trimeric RNA polymerase complex: From development to clinics

Yingjun Li, Jianfang Zhang, Fengming He, Cuiting Cao, Yangqing Zhan, Nanshan Zhong, Zifeng Yang

Acta Pharmaceutica Sinica B. 2026, 16(2): 728-745.

The rapid evolution of influenza viruses, driven by high mutation rates and cross-species transmission, underscores the importance of discovering antivirals with novel mechanisms of action and distinct resistance profiles. The influenza virus RNA polymerase, a highly conserved heterotrimeric complex, comprises polymerase basic protein 1 (PB1), polymerase basic protein 2 (PB2), and polymerase acidic protein (PA) in influenza A and B viruses, or polymerase 3 protein (P3) in influenza C and D viruses. This complex is essential for viral genome replication and transcription, rendering it a critical target for antiviral intervention. Over the past two decades, research on influenza polymerase (FluPol) has advanced from fundamental studies to drug development and clinical application. By 2025, six FluPol-targeting drugs have received regulatory approval: the PA inhibitors baloxavir marboxil, suraxavir marboxil, seloxavir marboxil, and pixavir marboxil; the PB1 inhibitor favipiravir; and the PB2 inhibitor onradivir, with several additional candidates progressing to clinical research. This review summarizes the structure and function of influenza polymerase and the mechanisms of action of different inhibitors, highlighting the discovery and clinical effectiveness of the newly approved FluPol-targeting drugs. It addresses the potential of FluPol inhibitors against highly pathogenic avian influenza and the challenges posed by resistance mutations.
Activating protein kinases to treat diseases: Current understanding and future challenges

Acta Pharmaceutica Sinica B. 2026, 16(2): 746-769.

Protein kinases, as one of the most important human enzymes, are signaling molecules that regulate almost all cell activities, including growth, cell division and metabolism. Dysfunction of these cellular pathways can lead to a variety of human diseases. Accumulating evidence on the down-regulation of key protein kinases in diseases has made a big progress. The down-regulation is related to cancer, heart disease, neurodegenerative diseases and other diseases. Thus, in this review, we defined the classifications of protein kinases and demonstrated the mechanisms of protein kinase activators in the treatment of human diseases, summarized the research progress of protein kinase activators, and further discussed the development progress of protein kinase activators in clinical stage. Accordingly, activation of protein kinases has become a crucial target for drug development. With the in-depth understanding of protein kinase functions and regulation mechanisms, the development of new protein kinase activators may continue to be a rapidly growing field, which will help to develop more accurate and effective targeted therapeutic strategies in the near future.
From concept to application: Exploring the evolution and potential of DUBTAC technology

Acta Pharmaceutica Sinica B. 2026, 16(2): 770-787.

Deubiquitinase-targeting chimeras (DUBTAC), as a highly promising emerging technology, can precisely remove ubiquitin chains from target proteins by recruiting deubiquitinases (DUBs), thereby enhancing the stability of the target proteins. Multiple functional proteins, such as the tumor suppressor proteins p53, RB, PTEN, upon stabilization by DUBTAC, can effectively restore or enhance their physiological functions, thus achieving therapeutic effects. Currently, the DUBTAC technology is still in its early stage of development, yet it has broad application prospects and represents a technological approach for developing various “undruggable” targets. This article delves into the design strategy of DUBTAC, and screens and recommends some candidate proteins with the potential to serve as drug targets. We aim to provide perspective in drug design, structural optimization, target selection, and related aspects.
Harnessing deep learning to accelerate the development of antibodies and aptamers

Acta Pharmaceutica Sinica B. 2026, 16(2): 788-801.

Artificial intelligence (AI) has revolutionized the design of antibodies and RNA aptamers, driving significant advancements in molecular therapeutics. In antibody design, AI enables accurate structure prediction and optimization of binding affinity, specificity, and stability, thereby accelerating the development of therapies targeting challenging antigens, such as those associated with viral infections and cancer. By integrating sequence and structural data, AI significantly reduces experimental costs and development timelines, streamlining the creation of next-generation antibody-based therapeutics. Similarly, AI has transformed RNA aptamer design, addressing long-standing challenges in structure prediction and binding optimization. AI-driven approaches allow for the rapid generation of aptamers with enhanced specificity, stability, and functional properties, expanding their potential applications in both therapeutics and diagnostics. These advancements offer scalable, cost-effective, and highly customizable solutions for precision medicine. As AI systems continue to evolve and integrate with experimental validation, they hold immense promise for developing more effective treatments for complex diseases, including cancer, autoimmune disorders, and viral infections. This marks the beginning of a new era in therapeutic innovation, where AI plays a pivotal role in addressing the challenges of modern medicine.
Deuterium modification of tyrosine kinase inhibitors contributes to reversing ferroptosis resistance through upregulation of aldehyde oxidase 1 in hepatocellular carcinoma

Acta Pharmaceutica Sinica B. 2026, 16(2): 802-819.

The susceptibility to ferroptosis partially determines the efficacy of tyrosine kinase inhibitors (TKIs) in hepatocellular carcinoma (HCC), exposing a mechanistic vulnerability that can be therapeutically exploited. The development of deuterated compounds is a promising strategy for the improvement of anti-tumor efficacy. Here, we identified HCC with higher level of ferroptosis-resistance exhibited insensitive to TKIs, which could be reversed by deuterated TKIs. Aldehyde oxidase 1 (AOX1) was screened as a critical gene mediating the responsiveness to deuterated TKIs-induced ferroptosis in HCC. The presence of a pyridyl tri-deuterated methanamide contributed to the upregulation of AOX1 in a structure-dependent manner, thereby promoting ferroptosis. Mechanistically, AOX1 inhibited sirtuin 6-mediated deacetylation of H3K9 and H3K56, leading to transcriptional activation of acyl-CoA synthetase long chain family member 5, which resulted in poly-unsaturated fatty acids hyperaccumulation-induced ferroptosis. Additionally, HCC with lower AOX1 expression conferred better efficacy to deuterated TKIs. In patient cohorts with HCC, those with lower AOX1 expression exhibited a more pronounced therapeutic response to deuterated sorafenib. Overall, the present study elucidates the mechanism by which deuterated TKIs reverse TKI resistance by promoting ferroptosis and suggests that AOX1 could serve as a biomarker to guide clinical decision-making for deuterated TKI treatment in HCC.
OTUD6A drives dopaminergic neuronal degeneration of Parkinson's disease through deubiquitinating ACTG1 in neuronal cells

Xia Zhao, Fan Chen, Li Xiong, Xiaoxia Xu, Ziyao Meng, Yu Deng, Qi Ai, Luyao Li, Qin Yu, Linjie Chen, Ruya Wang, Yiyu Ren, Wenhua Zheng, Jurui Wei, Houming Yu, Guang Liang

Acta Pharmaceutica Sinica B. 2026, 16(2): 820-835.

Parkinson's disease (PD) is a severe neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons. Emerging evidence suggests that deubiquitinating enzymes (DUBs), which regulate protein homeostasis through the cleavage of ubiquitin chains, play critical roles in PD pathogenesis. In this study, we discovered that a DUB, ovarian tumor deubiquitinase 6A (OTUD6A), was significantly upregulated in both PD patients and PD mouse models. Notably, OTUD6A deficiency effectively protected dopaminergic neurons from degeneration and improved motor deficits in both acute and chronic PD mouse models. Through comprehensive mass spectrometry analysis and co-immunoprecipitation assays, we identified that actin gamma 1 (ACTG1) serves as a key substrate of OTUD6A. Mechanistically, OTUD6A specifically interacts with the 8–181 aa domain of ACTG1 and preferentially cleaves K48-linked polyubiquitin chains, thereby enhancing ACTG1 protein stability in neuronal cells. The stabilized ACTG1 subsequently binds to p53 and facilitates its nuclear translocation, leading to the transcriptional activation of pro-apoptotic genes and promoting neuronal apoptosis. Collectively, our findings demonstrate that OTUD6A promotes dopaminergic neuron degeneration and PD progression by deubiquitinating and stabilizing ACTG1, which in turn activates a p53-dependent apoptotic pathway. These findings identify OTUD6A as a potential therapeutic target for PD intervention.
Gut microbiota drives the metabolic dysregulation in obesity-prone individuals by impairing GDCA-mediated activation of brown adipose thermogenesis and ileal GLP-1 secretion

Acta Pharmaceutica Sinica B. 2026, 16(2): 836-853.

Obesity-prone (OP) individuals exhibit an intrinsic predisposition to obesity and associated metabolic disorders, and early intervention in this population holds significant clinical value; however, the underlying mechanisms driving this susceptibility remain largely obscure. This study enrolled 46 OP subjects without diagnosed metabolic diseases and 35 healthy controls. Our findings revealed that, despite not reaching obesity diagnoses, OP subjects exhibited significant metabolic disturbances strongly associated with gut microbiota dysbiosis. They also displayed disturbed bile acid (BA) profiles, with depleted glycodeoxycholic acid (GDCA) identified as the most potent discriminator between the OP and healthy controls. Fecal microbiota transplantation (FMT) recapitulated metabolic dysfunction and BA pool remodeling, mediated by dysregulated hepatic expression of BA synthesis genes of Cyp8a1, Cyp7a1, and Cyp7b1. Notably, FMT-OP mice also phenocopied the diminished GDCA levels observed in OP subjects. GDCA supplementation in obese mice markedly improved body weight, hepatic steatosis, and metabolic dysfunction. Mechanistically, GDCA exerted anti-obesity effects by activating the TGR5 signaling, which enhanced brown adipose tissue (BAT) thermogenesis and stimulated ileal glucagon-like peptide-1 (GLP-1) secretion, thereby ameliorating obesity and associated metabolic dysregulation. Thus, these findings indicate that gut microbiota-driven dysregulation of BA signaling, particularly impaired TGR5 activation due to diminished GDCA, underlies glycolipid metabolic dysfunction in OP individuals.
The Kv4.3 channel on c-low threshold mechanoreceptors in DRG is a key mediator transforming tactile stimuli into nociceptive signals in chemotherapy-induced neuropathy

Acta Pharmaceutica Sinica B. 2026, 16(2): 854-878.

Oxaliplatin, a chemotherapeutic agent commonly used in colorectal cancer treatment, frequently induces chemotherapy-induced peripheral neuropathy (CIPN), with mechanical allodynia as a dose-limiting neurological complication. However, the precise pathophysiological mechanism underlying this sensory dysfunction remains inadequately elucidated. This study identifies Kv4.3 channel dysfunction in C-low threshold mechanoreceptors (C-LTMRs), a subset of tyrosine hydroxylase–positive (TH⁺) sensory neurons in the dorsal root ganglia (DRG), as the critical driver of oxaliplatin-induced mechanical allodynia. Using electrophysiological, pharmacological, and genetic approaches in mouse models, we have demonstrated that oxaliplatin selectively alters the firing pattern of C-LTMRs and enhances their excitability, particularly in response to low-intensity stimuli. This effect is mediated by Kv4.3 channel dysfunction within C-LTMRs, which underlies the pathological conversion of innocuous touch to pain. Critically, pharmacological inhibition or neuron-specific knockdown of Kv4.3 channels exacerbated mechanical allodynia, while Kv4.3 channel activation reversed neuronal hyperexcitability and alleviated oxaliplatin-induced mechanical allodynia. Thus, Kv4.3 dysfunction constitutes a core pathogenic mechanism of oxaliplatin-induced mechanical allodynia. Targeted enhancement of the Kv4.3 channel activity in C-LTMRs represents a promising precision analgesic strategy for this condition.

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