Advanced drug delivery reviews最新文献_第2页

Covalent chemistry in targeted protein degradation 靶向蛋白降解中的共价化学

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-01-12 DOI: 10.1016/j.addr.2026.115777

Jing Tan , Yuxin Liang , Shiqun Shao , Youqing Shen

Targeted protein degradation (TPD) has revolutionized drug discovery by enabling the selective removal of specific proteins within and outside cells through the cell’s natural degradation pathways. While various TPD modalities have demonstrated immense promise, the integration of covalent chemistry is rapidly emerging as a crucial approach to enhance target engagement, improve selectivity, and overcome limitations associated with non-covalent interactions. This review provides a comprehensive overview of the current landscape of covalent TPD and systematically explores how covalent chemistry advances the field of TPD. We first detail the diverse covalent modification strategies, reactive amino acid residues, and electrophilic warheads employed in the design of covalent ligands. Next, we discuss methodologies for covalent ligand discovery, including ligand-first and electrophile-first approaches. Finally, we highlight specific examples of covalent degraders across different TPD modalities, emphasizing their mechanisms of action and therapeutic potential. By integrating current knowledge and future directions, this review aims to provide insights for the rational design of next-generation covalent degraders and underscore their implications for the future of drug discovery.

靶向蛋白质降解（TPD）通过细胞的自然降解途径选择性去除细胞内外的特定蛋白质，从而彻底改变了药物的发现。虽然各种TPD模式已经显示出巨大的前景，但共价化学的整合正迅速成为增强靶标结合、提高选择性和克服非共价相互作用局限性的关键方法。本文综述了共价TPD的现状，并系统地探讨了共价化学如何推动TPD领域的发展。我们首先详细介绍了各种共价修饰策略、活性氨基酸残基和共价配体设计中使用的亲电弹头。接下来，我们讨论共价配体的发现方法，包括配体优先和亲电优先方法。最后，我们强调了共价降解物在不同TPD模式中的具体例子，强调了它们的作用机制和治疗潜力。通过整合现有知识和未来方向，本文旨在为下一代共价降解物的合理设计提供见解，并强调其对未来药物发现的意义。

{"title":"Covalent chemistry in targeted protein degradation","authors":"Jing Tan , Yuxin Liang , Shiqun Shao , Youqing Shen","doi":"10.1016/j.addr.2026.115777","DOIUrl":"10.1016/j.addr.2026.115777","url":null,"abstract":"<div><div>Targeted protein degradation (TPD) has revolutionized drug discovery by enabling the selective removal of specific proteins within and outside cells through the cell’s natural degradation pathways. While various TPD modalities have demonstrated immense promise, the integration of covalent chemistry is rapidly emerging as a crucial approach to enhance target engagement, improve selectivity, and overcome limitations associated with non-covalent interactions. This review provides a comprehensive overview of the current landscape of covalent TPD and systematically explores how covalent chemistry advances the field of TPD. We first detail the diverse covalent modification strategies, reactive amino acid residues, and electrophilic warheads employed in the design of covalent ligands. Next, we discuss methodologies for covalent ligand discovery, including ligand-first and electrophile-first approaches. Finally, we highlight specific examples of covalent degraders across different TPD modalities, emphasizing their mechanisms of action and therapeutic potential. By integrating current knowledge and future directions, this review aims to provide insights for the rational design of next-generation covalent degraders and underscore their implications for the future of drug discovery.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115777"},"PeriodicalIF":17.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing targeted protein degradation to potentiate cancer immunotherapy: from molecular mechanisms to delivery strategies 利用靶向蛋白降解增强癌症免疫治疗：从分子机制到递送策略

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-01-10 DOI: 10.1016/j.addr.2026.115776

Ye Liu , Ihsan Ullah , Youyong Yuan , Jun Wang

Cancer immunotherapy is limited by immune escape, which is driven by overexpression of immunosuppressive proteins in the tumor microenvironment (TME). Targeted Protein Degradation (TPD) technology, utilizing cellular machinery to eliminate specific proteins, offers a powerful strategy to overcome this resistance. However, the clinical translation of TPD degraders is critically hindered by formidable delivery challenges. Their inherent physicochemical properties result in poor oral bioavailability, difficulty crossing biological barriers, rapid metabolism, and insufficient tumor accumulation, preventing effective target engagement. This review focuses on the potential of TPD technology in combination with advanced drug delivery systems (DDS) to enhance cancer immunotherapy. We elaborate on how TPD reshapes the TME by degrading key immunomodulatory targets. Critically, this review provides an in-depth analysis of the major delivery bottlenecks currently limiting the efficacy of TPD degraders. Furthermore, it introduces advanced delivery strategies designed to overcome these obstacles, including nanocarriers, hydrogels, microneedles, and various stimuli-responsive delivery systems. Successfully overcoming these delivery obstacles is vital to unlocking the full therapeutic efficacy of TPD. Such progress holds promises for reprogramming immunosuppressive TME, overcoming resistance to existing immunotherapies, broadening the population of patients responsive to treatment, and ultimately delivering durable clinical benefits to more cancer patients.

肿瘤免疫治疗受到免疫逃逸的限制，免疫逃逸是由肿瘤微环境（TME）中免疫抑制蛋白过度表达驱动的。靶向蛋白质降解（TPD）技术，利用细胞机制来消除特定的蛋白质，提供了一个强大的策略来克服这种阻力。然而，TPD降解物的临床转化受到递送挑战的严重阻碍。其固有的物理化学性质导致口服生物利用度差，难以跨越生物屏障，代谢快，肿瘤蓄积不足，阻碍了有效的靶标结合。本文综述了TPD技术与先进的药物输送系统（DDS）结合在增强癌症免疫治疗中的潜力。我们详细阐述了TPD如何通过降解关键的免疫调节靶点来重塑TME。重要的是，这篇综述深入分析了目前限制TPD降解剂功效的主要递送瓶颈。此外，它还介绍了旨在克服这些障碍的先进递送策略，包括纳米载体、水凝胶、微针和各种刺激响应递送系统。成功克服这些递送障碍对于释放TPD的全部治疗效果至关重要。这一进展有望对免疫抑制性TME进行重编程，克服对现有免疫疗法的耐药性，扩大对治疗有反应的患者群体，并最终为更多癌症患者带来持久的临床益处。

{"title":"Harnessing targeted protein degradation to potentiate cancer immunotherapy: from molecular mechanisms to delivery strategies","authors":"Ye Liu , Ihsan Ullah , Youyong Yuan , Jun Wang","doi":"10.1016/j.addr.2026.115776","DOIUrl":"10.1016/j.addr.2026.115776","url":null,"abstract":"<div><div>Cancer immunotherapy is limited by immune escape, which is driven by overexpression of immunosuppressive proteins in the tumor microenvironment (TME). Targeted Protein Degradation (TPD) technology, utilizing cellular machinery to eliminate specific proteins, offers a powerful strategy to overcome this resistance. However, the clinical translation of TPD degraders is critically hindered by formidable delivery challenges. Their inherent physicochemical properties result in poor oral bioavailability, difficulty crossing biological barriers, rapid metabolism, and insufficient tumor accumulation, preventing effective target engagement. This review focuses on the potential of TPD technology in combination with advanced drug delivery systems (DDS) to enhance cancer immunotherapy. We elaborate on how TPD reshapes the TME by degrading key immunomodulatory targets. Critically, this review provides an in-depth analysis of the major delivery bottlenecks currently limiting the efficacy of TPD degraders. Furthermore, it introduces advanced delivery strategies designed to overcome these obstacles, including nanocarriers, hydrogels, microneedles, and various stimuli-responsive delivery systems. Successfully overcoming these delivery obstacles is vital to unlocking the full therapeutic efficacy of TPD. Such progress holds promises for reprogramming immunosuppressive TME, overcoming resistance to existing immunotherapies, broadening the population of patients responsive to treatment, and ultimately delivering durable clinical benefits to more cancer patients.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115776"},"PeriodicalIF":17.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145949869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Therapeutic gases as emerging treatments for oral diseases 治疗气体作为口腔疾病的新疗法

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-01-10 DOI: 10.1016/j.addr.2026.115774

Ann Badia , Jihyuk Yang , Sara Aliyeva , Yonghyun Choi , Jonghoon Choi , Tagbo H.R. Niepa

Oral delivery of gas-based therapies provides a targeted, minimally invasive approach to treating oral diseases. Conventional strategies, such as mechanical debridement, antibiotics, and surgical intervention, are limited by the inaccessibility of oral biofilms, the development of antimicrobial resistance, and challenges in promoting tissue regeneration. Therapeutic gases, including oxygen (O₂), ozone (O₃), nitrous oxide (N₂O), nitric oxide (NO), carbon monoxide (CO), carbon dioxide (CO₂), hydrogen (H₂), hydrogen sulfide (H₂S), and argon-based plasma, have emerged as promising options to address these challenges. Each gas exhibits distinct biological effects relevant to dental care, including antimicrobial properties, promotion of tissue healing and regeneration via angiogenesis and collagen synthesis, and anti-inflammatory benefits through modulation of oxidative stress and immune responses. Despite these advantages, significant barriers hinder clinical translation, such as dose control, toxicity at high concentrations, delivery limitations, and the high cost of specialized equipment. To address these challenges, research is advancing innovative delivery systems, such as gas-generating nanoplatforms, hydrogels, capsules, and nano-bubble water, that enable responsive release of the therapeutic gases within the oral environment. Future directions include developing safe, patient-friendly delivery technologies, expanding clinical trials, and establishing a transparent regulatory framework to fully realize the potential of gas-based therapies as effective adjuncts or alternatives to conventional dental treatments.

口服气体疗法为治疗口腔疾病提供了一种靶向性、微创性的方法。传统的策略，如机械清创、抗生素和手术干预，受到口腔生物膜难以接近、抗菌素耐药性的发展以及促进组织再生的挑战的限制。治疗气体，包括氧气（O2）、臭氧（O3）、氧化亚氮（N2O）、一氧化氮（NO）、一氧化碳（CO）、二氧化碳（CO2）、氢气（H2）、硫化氢（H2S）和氩基等离子体，已经成为解决这些挑战的有希望的选择。每种气体都表现出与牙科保健相关的不同生物效应，包括抗菌特性、通过血管生成和胶原合成促进组织愈合和再生，以及通过调节氧化应激和免疫反应而具有抗炎作用。尽管有这些优势，但临床转化仍存在重大障碍，如剂量控制、高浓度毒性、给药限制和专用设备的高成本。为了应对这些挑战，研究人员正在推进创新的输送系统，如产生气体的纳米平台、水凝胶、胶囊和纳米气泡水，这些系统能够在口腔环境中响应性地释放治疗气体。未来的发展方向包括开发安全、对患者友好的输送技术，扩大临床试验，建立透明的监管框架，以充分实现气体疗法作为传统牙科治疗的有效辅助或替代方案的潜力。

{"title":"Therapeutic gases as emerging treatments for oral diseases","authors":"Ann Badia , Jihyuk Yang , Sara Aliyeva , Yonghyun Choi , Jonghoon Choi , Tagbo H.R. Niepa","doi":"10.1016/j.addr.2026.115774","DOIUrl":"10.1016/j.addr.2026.115774","url":null,"abstract":"<div><div>Oral delivery of gas-based therapies provides a targeted, minimally invasive approach to treating oral diseases. Conventional strategies, such as mechanical debridement, antibiotics, and surgical intervention, are limited by the inaccessibility of oral biofilms, the development of antimicrobial resistance, and challenges in promoting tissue regeneration. Therapeutic gases, including oxygen (O<sub>2</sub>), ozone (O<sub>3</sub>), nitrous oxide (N<sub>2</sub>O), nitric oxide (NO), carbon monoxide (CO), carbon dioxide (CO<sub>2</sub>), hydrogen (H<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), and argon-based plasma, have emerged as promising options to address these challenges. Each gas exhibits distinct biological effects relevant to dental care, including antimicrobial properties, promotion of tissue healing and regeneration via angiogenesis and collagen synthesis, and anti-inflammatory benefits through modulation of oxidative stress and immune responses. Despite these advantages, significant barriers hinder clinical translation, such as dose control, toxicity at high concentrations, delivery limitations, and the high cost of specialized equipment. To address these challenges, research is advancing innovative delivery systems, such as gas-generating nanoplatforms, hydrogels, capsules, and nano-bubble water, that enable responsive release of the therapeutic gases within the oral environment. Future directions include developing safe, patient-friendly delivery technologies, expanding clinical trials, and establishing a transparent regulatory framework to fully realize the potential of gas-based therapies as effective adjuncts or alternatives to conventional dental treatments.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115774"},"PeriodicalIF":17.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biologics-device combinations: Enabling prolonged therapies in the posterior segment ocular disease 生物制剂-器械组合：延长后段眼病的治疗时间

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-01-09 DOI: 10.1016/j.addr.2026.115773

Shuqian Zhu , Jianjun Zhang , Xuling Jiang , Cheng Peng , Huiqin Liu , Feng Qian

Posterior segment ocular diseases (e.g., age-related macular degeneration and diabetic retinopathy, etc.) often necessitate frequent intravitreal (IVT) injections of biologics, due to the rapid drug clearance and formidable ocular barriers. While molecular engineering strategies and high-concentration protein formulations could extend the administration intervals to a certain extent, they are confronted with critical challenges, protein aggregation, high viscosity, and limited duration. This has spurred the development of innovative biologics-device combination products, which represent a paradigm shift towards prolonged therapy. This comprehensive review examines the latest advancements of these combination platforms, including refillable implants (e.g., SUSVIMO®), encapsulated cell technology (e.g., ENCELTO™), and recombinant adeno-associated virus (rAAV) vectors (e.g., LUXTURNA®). The progress in biologics - device combination technologies has significantly reduced the frequency of ocular injections. However, substantial hurdles, such as instability caused by material-biologics interactions, potential risks during the sterilization and manufacturing processes, safety risks, and the evolving regulatory landscape, still need to be addressed. Achieving a balance between the stability of biologics and advanced device design, enhancing long-term safety, and developing responsive smart systems with real-time monitoring and feedback capabilities remain crucial for the advancement of next-generation ophthalmic therapies.

后段眼病（如老年性黄斑变性、糖尿病性视网膜病变等）由于药物的快速清除和强大的眼屏障，往往需要频繁的玻璃体内注射生物制剂。虽然分子工程策略和高浓度蛋白质配方可以在一定程度上延长给药间隔，但它们面临着蛋白质聚集、高粘度和持续时间有限的关键挑战。这刺激了创新生物制剂-器械组合产品的发展，这代表了向延长治疗的范式转变。本综述综述了这些组合平台的最新进展，包括可再填充植入物（如SUSVIMO®）、封装细胞技术（如ENCELTO™）和重组腺相关病毒（rAAV）载体（如LUXTURNA®）。生物制剂与器械组合技术的进步显著降低了眼部注射的频率。然而，诸如材料与生物制剂相互作用引起的不稳定性、灭菌和生产过程中的潜在风险、安全风险以及不断变化的监管环境等实质性障碍仍然需要解决。实现生物制剂稳定性和先进设备设计之间的平衡，提高长期安全性，开发具有实时监测和反馈能力的响应性智能系统对于下一代眼科治疗的进步至关重要。

{"title":"Biologics-device combinations: Enabling prolonged therapies in the posterior segment ocular disease","authors":"Shuqian Zhu , Jianjun Zhang , Xuling Jiang , Cheng Peng , Huiqin Liu , Feng Qian","doi":"10.1016/j.addr.2026.115773","DOIUrl":"10.1016/j.addr.2026.115773","url":null,"abstract":"<div><div>Posterior segment ocular diseases (e.g., age-related macular degeneration and diabetic retinopathy, etc.) often necessitate frequent intravitreal (IVT) injections of biologics, due to the rapid drug clearance and formidable ocular barriers. While molecular engineering strategies and high-concentration protein formulations could extend the administration intervals to a certain extent, they are confronted with critical challenges, protein aggregation, high viscosity, and limited duration. This has spurred the development of innovative biologics-device combination products, which represent a paradigm shift towards prolonged therapy. This comprehensive review examines the latest advancements of these combination platforms, including refillable implants (e.g., SUSVIMO®), encapsulated cell technology (e.g., ENCELTO™), and recombinant adeno-associated virus (rAAV) vectors (e.g., LUXTURNA®). The progress in biologics - device combination technologies has significantly reduced the frequency of ocular injections. However, substantial hurdles, such as instability caused by material-biologics interactions, potential risks during the sterilization and manufacturing processes, safety risks, and the evolving regulatory landscape, still need to be addressed. Achieving a balance between the stability of biologics and advanced device design, enhancing long-term safety, and developing responsive smart systems with real-time monitoring and feedback capabilities remain crucial for the advancement of next-generation ophthalmic therapies.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115773"},"PeriodicalIF":17.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rational modification of PROTACs for tumor-selective protein degradation 合理修饰PROTACs用于肿瘤选择性蛋白降解

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-01-09 DOI: 10.1016/j.addr.2026.115775

Zhongliang Fu , Meichen Pan , Chunrong Yang , Hongwei Hou , Jinghong Li

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that hijack the ubiquitin-proteasome system to catalytically degrade pathogenic proteins. With the ability to target “undruggable” proteins and exert sustained pharmacological effects, PROTACs hold considerable promise for cancer therapy. However, achieving tumor-selective protein degradation remains a central challenge. This review outlines the application of PROTACs in cancer treatment and systematically summarizes emerging strategies to enhance tumor specificity. These approaches leverage hallmark features of tumors, distinctive surface biomarkers and a unique tumor microenvironment (TME), and are broadly categorized into two classes: active targeting, which employs tumor-selective ligands to enrich PROTACs in malignant cells; and conditionally activated strategies, where TME cues either selectively trigger PROTAC prodrugs or induce structural transformations in nanocarriers to enhance drug accumulation at the tumor site. By elucidating these mechanisms, we aim to bridge medicinal chemistry and intelligent nanomedicine, underpinning the tumor-selective protein degradation strategies and offering perspectives on future research directions to improve the biodistribution, safety, and therapeutic efficacy of next-generation PROTACs.

靶向蛋白水解嵌合体（Proteolysis-targeting chimeras, PROTACs）是一种异质双功能分子，它劫持泛素-蛋白酶体系统来催化降解致病性蛋白。由于能够靶向“不可药物”的蛋白质并发挥持续的药理作用，PROTACs在癌症治疗中具有相当大的前景。然而，实现肿瘤选择性蛋白降解仍然是一个核心挑战。本文综述了PROTACs在癌症治疗中的应用，并系统总结了提高肿瘤特异性的新策略。这些方法利用肿瘤的标志性特征、独特的表面生物标志物和独特的肿瘤微环境（TME），大致分为两类：主动靶向，利用肿瘤选择性配体富集恶性细胞中的PROTACs；以及条件激活策略，其中TME线索要么选择性地触发PROTAC前药，要么诱导纳米载体的结构转化，以增强肿瘤部位的药物积累。通过阐明这些机制，我们的目标是在药物化学和智能纳米医学之间建立桥梁，为肿瘤选择性蛋白质降解策略提供基础，并为未来的研究方向提供展望，以改善下一代PROTACs的生物分布、安全性和治疗效果。

{"title":"Rational modification of PROTACs for tumor-selective protein degradation","authors":"Zhongliang Fu , Meichen Pan , Chunrong Yang , Hongwei Hou , Jinghong Li","doi":"10.1016/j.addr.2026.115775","DOIUrl":"10.1016/j.addr.2026.115775","url":null,"abstract":"<div><div>Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that hijack the ubiquitin-proteasome system to catalytically degrade pathogenic proteins. With the ability to target “undruggable” proteins and exert sustained pharmacological effects, PROTACs hold considerable promise for cancer therapy. However, achieving tumor-selective protein degradation remains a central challenge. This review outlines the application of PROTACs in cancer treatment and systematically summarizes emerging strategies to enhance tumor specificity. These approaches leverage hallmark features of tumors, distinctive surface biomarkers and a unique tumor microenvironment (TME), and are broadly categorized into two classes: active targeting, which employs tumor-selective ligands to enrich PROTACs in malignant cells; and conditionally activated strategies, where TME cues either selectively trigger PROTAC prodrugs or induce structural transformations in nanocarriers to enhance drug accumulation at the tumor site. By elucidating these mechanisms, we aim to bridge medicinal chemistry and intelligent nanomedicine, underpinning the tumor-selective protein degradation strategies and offering perspectives on future research directions to improve the biodistribution, safety, and therapeutic efficacy of next-generation PROTACs.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115775"},"PeriodicalIF":17.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145949886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quality by Computational Design: Harnessing AI to Advance Rational Drug Development 计算设计的质量：利用人工智能促进合理药物开发

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2025-12-20 DOI: 10.1016/j.addr.2025.115764

Nannan Wang , Hao Zhong , Ping Xiong , Jinying Zhu , Defang Ouyang

The pharmaceutical Quality by Design (QbD) principle aims to reduce risk and improve efficiency across drug development lifecycle. However, QbD was originally established in an era preceding the widespread adoption of artificial intelligence (AI) and did not fully capture the potential of computational pharmaceutics. This gap is particularly pronounced in complex drug product development, where conventional QbD relies on empirical knowledge and labor-intensive experimentation. As a result, it struggles to accommodate multi-modal and multi-scale variables, and lacks sufficient flexibility, dynamic optimization capabilities, and the ability to perform clinically oriented inverse design. In recent years, advances in computational pharmaceutics have provided a new methodological foundation for drug development. In this context, we propose a novel paradigm, termed Quality by Computational Design (QbCD), which integrates computational pharmaceutics within the QbD framework to achieve mechanism-based and clinically guided formulation design. We first define the concept of QbCD, outline its essential components, implementation steps, and methodological strengths, and discuss relevant regulatory considerations. Building on this, we propose a practical QbCD implementation guideline to strengthen model credibility and ensure regulatory compliance. Subsequently, to establish the methodological foundation and demonstrate practical feasibility, we present the core techniques of QbCD, including AI, physical modeling, and in vivo modeling, and examine their applications across various stages of drug development. To further illustrate the practicality of QbCD, two representative cases are presented: a QbCD-enabled virtual development workflow for amorphous solid dispersions and a real-world implementation of QbCD in designing long-acting in situ gel injectables. Finally, we discuss future perspectives for QbCD, focusing on bridging the data gap, advancing methodological innovations, enhancing model credibility and regulatory compliance, and fostering a supportive scientific culture and ecosystem in computational pharmaceutics. These efforts aim to promote a more intelligent, efficient, and clinically aligned paradigm for rational drug development.

药品设计质量（QbD）原则旨在降低风险并提高整个药物开发生命周期的效率。然而，QbD最初是在人工智能（AI）广泛采用之前的时代建立的，并没有完全捕捉到计算药剂学的潜力。这一差距在复杂药物产品开发中尤为明显，传统的QbD依赖于经验知识和劳动密集型实验。因此，它难以适应多模态、多尺度变量，缺乏足够的灵活性、动态优化能力和面向临床的逆设计能力。近年来，计算药剂学的发展为药物开发提供了新的方法学基础。在这种背景下，我们提出了一种新的范式，称为计算设计质量（QbCD），它将计算药剂学整合到QbD框架中，以实现基于机制和临床指导的配方设计。我们首先定义了QbCD的概念，概述了其基本组成部分、实施步骤和方法优势，并讨论了相关的监管考虑。在此基础上，我们提出了切实可行的QbCD实施指南，以增强模型可信度并确保合规性。随后，为了建立方法学基础和论证实际可行性，我们介绍了QbCD的核心技术，包括人工智能、物理建模和体内建模，并研究了它们在药物开发各个阶段的应用。为了进一步说明QbCD的实用性，提出了两个代表性的案例：支持QbCD的非晶固体分散体的虚拟开发工作流和QbCD在设计长效原位凝胶注射剂中的实际实现。最后，我们讨论了QbCD的未来前景，重点是弥合数据差距，推进方法创新，提高模型可信度和法规遵从性，并在计算制药中培养支持性的科学文化和生态系统。这些努力的目的是促进一个更智能、更高效、更符合临床的合理药物开发模式。

{"title":"Quality by Computational Design: Harnessing AI to Advance Rational Drug Development","authors":"Nannan Wang , Hao Zhong , Ping Xiong , Jinying Zhu , Defang Ouyang","doi":"10.1016/j.addr.2025.115764","DOIUrl":"10.1016/j.addr.2025.115764","url":null,"abstract":"<div><div>The pharmaceutical Quality by Design (QbD) principle aims to reduce risk and improve efficiency across drug development lifecycle. However, QbD was originally established in an era preceding the widespread adoption of artificial intelligence (AI) and did not fully capture the potential of computational pharmaceutics. This gap is particularly pronounced in complex drug product development, where conventional QbD relies on empirical knowledge and labor-intensive experimentation. As a result, it struggles to accommodate multi-modal and multi-scale variables, and lacks sufficient flexibility, dynamic optimization capabilities, and the ability to perform clinically oriented inverse design. In recent years, advances in computational pharmaceutics have provided a new methodological foundation for drug development. In this context, we propose a novel paradigm, termed Quality by Computational Design (QbCD), which integrates computational pharmaceutics within the QbD framework to achieve mechanism-based and clinically guided formulation design. We first define the concept of QbCD, outline its essential components, implementation steps, and methodological strengths, and discuss relevant regulatory considerations. Building on this, we propose a practical QbCD implementation guideline to strengthen model credibility and ensure regulatory compliance. Subsequently, to establish the methodological foundation and demonstrate practical feasibility, we present the core techniques of QbCD, including AI, physical modeling, and <em>in vivo</em> modeling, and examine their applications across various stages of drug development. To further illustrate the practicality of QbCD, two representative cases are presented: a QbCD-enabled virtual development workflow for amorphous solid dispersions and a real-world implementation of QbCD in designing long-acting <em>in situ</em> gel injectables. Finally, we discuss future perspectives for QbCD, focusing on bridging the data gap, advancing methodological innovations, enhancing model credibility and regulatory compliance, and fostering a supportive scientific culture and ecosystem in computational pharmaceutics. These efforts aim to promote a more intelligent, efficient, and clinically aligned paradigm for rational drug development.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115764"},"PeriodicalIF":17.6,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic engineering approaches in stem and somatic cells for the generation of insulin-producing β-cells 干细胞和体细胞中产生胰岛素生成β细胞的基因工程方法

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2025-12-20 DOI: 10.1016/j.addr.2025.115766

Abiramy Jeyagaran , Katja Schenke-Layland

Cell replacement therapies hold great promise for the treatment of type 1 diabetes mellitus; however, the obtaining of sufficient transplantable β-cells limits the availability of this treatment option. The generation of β-cells from human pluripotent stem cells or other somatic cells through classical differentiation, forward programming, or transdifferentiation approaches offers an alternative source of therapeutic β-cells for the treatment of type 1 diabetes mellitus. Through increasing understanding of pancreatic and β-cell development, transcription factors neurogenin 3 (NGN3), pancreas/duodenum homeobox protein 1 (PDX1), and MAF BZIP Transcription Factor A (MAFA) have been identified to be crucial for glucose-responsive insulin secretion of adult β-cells. In this review, we address and discuss recent advances in transdifferentiation approaches using these three markers for the timely generation of mature β-cells, and the insights they provide on cell development and plasticity.

细胞替代疗法对治疗1型糖尿病有很大的希望；然而，获得足够的可移植β细胞限制了这种治疗选择的可用性。通过经典分化、正向编程或转分化方法，从人类多能干细胞或其他体细胞中产生β细胞，为治疗1型糖尿病提供了另一种治疗性β细胞来源。随着对胰腺和β细胞发育的进一步了解，转录因子神经原素3 （NGN3）、胰腺/十二指肠同源盒蛋白1 （PDX1）和MAF BZIP转录因子A （MAFA）已被确定对成人β细胞的葡萄糖反应性胰岛素分泌至关重要。在这篇综述中，我们讨论了利用这三种标记及时生成成熟β细胞的转分化方法的最新进展，以及它们对细胞发育和可塑性提供的见解。

引用次数: 0

Engineering tissue patterning in human stem cell-based embryo models 人类干细胞胚胎模型的工程组织模式

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2025-12-20 DOI: 10.1016/j.addr.2025.115765

Ella G. Lambert , Sara Romanazzo , Peter L.H. Newman , Kristopher A. Kilian

Human embryonic development is challenging to study in vitro as animal models inadequately represent human biology, while use of natural human embryos is both ethically and technically limited. Stem cell-based embryo models (SCBEMs) have emerged as a powerful alternative, enabling faithful recapitulation of early human development. However, current approaches predominantly rely on stochastic self-organisation with globally delivered signals, producing variable and often non-recapitulative structures. This review addresses this gap by introducing the first engineering-anchored taxonomy of human SCBEMs, systematically organizing the literature by their underlying technical platform rather than biological outcome alone. We demonstrate how five key engineering approaches – micropatterning, biomaterials, microwells, microfluidics, and dynamic culture – constrain morpho-and-histogenic patterning to determine developmental fidelity. We identify metabolic constraints limiting current models to ∼1 mm diameter as the primary bottleneck and demonstrate how vascular engineering and perfusion systems offer solutions. Finally, we propose standardisation metrics linking technical parameters to biological outcomes and establish an ethical framework defined by engineering choices.

人类胚胎发育的体外研究具有挑战性，因为动物模型不能充分代表人类生物学，而天然人类胚胎的使用在伦理和技术上都受到限制。基于干细胞的胚胎模型（SCBEMs）已经成为一种强大的替代方案，能够忠实地再现早期人类发育。然而，目前的方法主要依赖于具有全局传递信号的随机自组织，产生可变且通常是非概括的结构。本综述通过引入第一个以工程为基础的人类方案分类法来解决这一差距，系统地根据其潜在的技术平台而不是单独的生物学结果组织文献。我们展示了五种关键的工程方法——微模式、生物材料、微孔、微流体和动态培养——如何约束形态和组织结构模式来确定发育保真度。我们确定代谢限制将当前模型限制在1毫米直径为主要瓶颈，并展示血管工程和灌注系统如何提供解决方案。最后，我们提出了将技术参数与生物学结果联系起来的标准化指标，并建立了一个由工程选择定义的伦理框架。

{"title":"Engineering tissue patterning in human stem cell-based embryo models","authors":"Ella G. Lambert , Sara Romanazzo , Peter L.H. Newman , Kristopher A. Kilian","doi":"10.1016/j.addr.2025.115765","DOIUrl":"10.1016/j.addr.2025.115765","url":null,"abstract":"<div><div>Human embryonic development is challenging to study in vitro as animal models inadequately represent human biology, while use of natural human embryos is both ethically and technically limited. Stem cell-based embryo models (SCBEMs) have emerged as a powerful alternative, enabling faithful recapitulation of early human development. However, current approaches predominantly rely on stochastic self-organisation with globally delivered signals, producing variable and often non-recapitulative structures. This review addresses this gap by introducing the first engineering-anchored taxonomy of human SCBEMs, systematically organizing the literature by their underlying technical platform rather than biological outcome alone. We demonstrate how five key engineering approaches – micropatterning, biomaterials, microwells, microfluidics, and dynamic culture – constrain morpho-and-histogenic patterning to determine developmental fidelity. We identify metabolic constraints limiting current models to ∼1 mm diameter as the primary bottleneck and demonstrate how vascular engineering and perfusion systems offer solutions. Finally, we propose standardisation metrics linking technical parameters to biological outcomes and establish an ethical framework defined by engineering choices.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115765"},"PeriodicalIF":17.6,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery 小数据，大挑战：机器和深度学习策略用于数据有限的药物发现

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2025-12-18 DOI: 10.1016/j.addr.2025.115762

Nazreen Pallikkavaliyaveetil , Sriram Chandrasekaran

A critical bottleneck limiting the potential of Machine Learning (ML) and Deep Learning (DL) models within the drug discovery and development (DDD) pipeline is the scarcity of high-quality experimental data. Limited data is not an anomaly but an inherent characteristic of the DDD process. Significant financial costs, time, and confidentiality concerns limit the scale of available datasets. Applying standard ML and DL algorithms directly to these small datasets presents substantial challenges. Traditional ML models remain constrained by their dependence on handcrafted features and limited ability to capture complex biological relationships. In contrast, DL algorithms that assume data abundance are prone to overfitting and poor generalization when trained on small datasets. The small data problem thus represents a fundamental constraint that shapes the practical utility and trustworthiness of AI applications in DDD. While prior reviews have surveyed the broad landscape of AI and ML in drug discovery, a significant gap exists concerning the small data challenge across the DDD pipeline. Addressing this challenge requires adapting DL methods that typically assume data abundance, while also extending traditional ML approaches that, although well-suited to small data, remain limited in their representational capacity. This review addresses this gap by surveying key drug discovery tasks, highlighting the prevalence of limited data, and synthesizing both traditional ML methods and advanced DL strategies tailored to these contexts. By integrating methodological advances with task-specific applications, the review outlines current approaches and identifies opportunities for advancing robust, interpretable, and generalizable AI in drug discovery.

在药物发现和开发（DDD）管道中限制机器学习（ML）和深度学习（DL）模型潜力的一个关键瓶颈是缺乏高质量的实验数据。有限的数据不是异常，而是DDD过程的固有特征。重大的财务成本、时间和机密性问题限制了可用数据集的规模。将标准的ML和DL算法直接应用于这些小数据集具有实质性的挑战。传统的机器学习模型仍然受到依赖手工制作的特征和捕获复杂生物关系的有限能力的限制。相比之下，假设数据丰富的DL算法在小数据集上训练时容易过度拟合和泛化不良。因此，小数据问题代表了影响DDD中人工智能应用的实际效用和可信度的基本约束。虽然之前的评论已经调查了药物发现中人工智能和机器学习的广泛前景，但在DDD管道的小数据挑战方面存在显着差距。解决这一挑战需要适应通常假设数据丰富的DL方法，同时也扩展传统的ML方法，这些方法虽然非常适合小数据，但在表示能力方面仍然有限。这篇综述通过调查关键的药物发现任务，突出有限数据的普遍性，以及综合传统的机器学习方法和针对这些背景量身定制的高级深度学习策略来解决这一差距。通过将方法上的进步与特定任务的应用相结合，本综述概述了当前的方法，并确定了在药物发现中推进稳健、可解释和可推广的人工智能的机会。

{"title":"Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery","authors":"Nazreen Pallikkavaliyaveetil , Sriram Chandrasekaran","doi":"10.1016/j.addr.2025.115762","DOIUrl":"10.1016/j.addr.2025.115762","url":null,"abstract":"<div><div>A critical bottleneck limiting the potential of Machine Learning (ML) and Deep Learning (DL) models within the drug discovery and development (DDD) pipeline is the scarcity of high-quality experimental data. Limited data is not an anomaly but an inherent characteristic of the DDD process. Significant financial costs, time, and confidentiality concerns limit the scale of available datasets. Applying standard ML and DL algorithms directly to these small datasets presents substantial challenges. Traditional ML models remain constrained by their dependence on handcrafted features and limited ability to capture complex biological relationships. In contrast, DL algorithms that assume data abundance are prone to overfitting and poor generalization when trained on small datasets. The small data problem thus represents a fundamental constraint that shapes the practical utility and trustworthiness of AI applications in DDD. While prior reviews have surveyed the broad landscape of AI and ML in drug discovery, a significant gap exists concerning the small data challenge across the DDD pipeline. Addressing this challenge requires adapting DL methods that typically assume data abundance, while also extending traditional ML approaches that, although well-suited to small data, remain limited in their representational capacity. This review addresses this gap by surveying key drug discovery tasks, highlighting the prevalence of limited data, and synthesizing both traditional ML methods and advanced DL strategies tailored to these contexts. By integrating methodological advances with task-specific applications, the review outlines current approaches and identifies opportunities for advancing robust, interpretable, and generalizable AI in drug discovery.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115762"},"PeriodicalIF":17.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The growing impact of machine learning on drug formulation science 机器学习对药物配方科学的影响越来越大

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2025-12-12 DOI: 10.1016/j.addr.2025.115761

Zeqing Bao , Steven Huynh , Frantz Le Devedec , Jo Nguyen , Christine Allen

Machine learning (ML) has increasingly been adopted in drug formulation science to support more efficient, data-driven drug development strategies. A growing number of studies have demonstrated the promise of ML tools across various aspects of drug formulation science, including both preformulation studies and formulation optimization. Building on these foundational efforts, more advanced data collection and ML techniques are now being integrated, driving innovation and expanding the scope of ML applications in the field. To better understand the trend of breakthroughs in this area, this review examines relevant works published in the past decade, identifying key trends, core applications, and emerging techniques in ML-driven drug delivery. Representative studies are highlighted as examples to illustrate the evolving landscape and practical implementations of these technologies. Furthermore, this review explores forward-looking perspectives, highlighting the convergence of ML with the increasing openness of regulatory bodies, the integration of organoid models, and the advancement of experimental automation.

机器学习（ML）越来越多地应用于药物配方科学，以支持更高效、数据驱动的药物开发策略。越来越多的研究已经证明了ML工具在药物配方科学的各个方面的前景，包括预配方研究和配方优化。在这些基础工作的基础上，现在正在整合更先进的数据收集和机器学习技术，推动创新并扩大机器学习在该领域的应用范围。为了更好地了解这一领域的突破趋势，本文回顾了过去十年发表的相关著作，确定了机器学习驱动给药的关键趋势、核心应用和新兴技术。有代表性的研究被强调为例子，以说明这些技术的发展前景和实际实现。此外，本文还探讨了前瞻性的观点，强调了随着监管机构的日益开放，类器官模型的整合以及实验自动化的进步，机器学习的融合。

{"title":"The growing impact of machine learning on drug formulation science","authors":"Zeqing Bao , Steven Huynh , Frantz Le Devedec , Jo Nguyen , Christine Allen","doi":"10.1016/j.addr.2025.115761","DOIUrl":"10.1016/j.addr.2025.115761","url":null,"abstract":"<div><div>Machine learning (ML) has increasingly been adopted in drug formulation science to support more efficient, data-driven drug development strategies. A growing number of studies have demonstrated the promise of ML tools across various aspects of drug formulation science, including both preformulation studies and formulation optimization. Building on these foundational efforts, more advanced data collection and ML techniques are now being integrated, driving innovation and expanding the scope of ML applications in the field. To better understand the trend of breakthroughs in this area, this review examines relevant works published in the past decade, identifying key trends, core applications, and emerging techniques in ML-driven drug delivery. Representative studies are highlighted as examples to illustrate the evolving landscape and practical implementations of these technologies. Furthermore, this review explores forward-looking perspectives, highlighting the convergence of ML with the increasing openness of regulatory bodies, the integration of organoid models, and the advancement of experimental automation.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115761"},"PeriodicalIF":17.6,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0