Advanced drug delivery reviews最新文献_第3页

Overcoming barriers and shaping the future: Challenges and innovations in nucleic acid therapies for Glioblastoma 克服障碍，塑造未来：胶质母细胞瘤核酸治疗的挑战与创新

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI: 10.1016/j.addr.2025.115759

Alaa Zam , Nadia Rouatbi , Adam A. Walters , Khuloud T. Al-Jamal

Glioblastoma (GBM) is the most aggressive and treatment-resistant primary brain tumor in adults. Conventional therapies offer limited benefit due to the tumor's heterogeneity, invasive nature, and the presence of the blood–brain barrier, which restricts therapeutic access. Nucleic acid (NA)-based therapies, including small interfering RNA, microRNA, antisense oligonucleotides, splice-switching oligonucleotides, and CRISPR-based systems, have emerged as promising tools to modulate oncogenic pathways and overcome resistance mechanisms at the genetic level. However, effective delivery remains the primary challenge in translating these therapies into clinical success. This review examines the current landscape of NA-based strategies for GBM, with a focus on innovative delivery systems designed to navigate biological barriers and enhance therapeutic precision. We highlight clinical progress made with nanocarrier platforms such as liposomes, lipid nanoparticles, and exosome-based systems, and evaluate their safety, specificity, and delivery efficiency. Additionally, we discuss the most promising preclinical advances, including multifunctional, targeted, and stimuli-responsive carriers, that demonstrate strong potential for clinical translation. Our analysis underscores that the therapeutic efficacy of NA approaches in GBM is inseparable from the sophistication of their delivery platforms. Moving forward, the integration of rationally designed carriers with gene-targeted payloads holds the key to unlocking the full potential of precision medicine in GBM.

胶质母细胞瘤（GBM）是成人中最具侵袭性和治疗耐药性的原发性脑肿瘤。由于肿瘤的异质性、侵袭性和血脑屏障的存在，限制了治疗途径，传统疗法的疗效有限。基于核酸（NA）的治疗，包括小干扰RNA、微RNA、反义寡核苷酸、剪接开关寡核苷酸和基于crispr的系统，已经成为在遗传水平上调节致癌途径和克服耐药机制的有前途的工具。然而，有效的递送仍然是将这些疗法转化为临床成功的主要挑战。本文综述了目前基于na的GBM治疗策略，重点关注旨在克服生物屏障和提高治疗精度的创新给药系统。我们强调了纳米载体平台（如脂质体、脂质纳米颗粒和外泌体系统）的临床进展，并评估了它们的安全性、特异性和递送效率。此外，我们还讨论了最有希望的临床前进展，包括多功能、靶向和刺激反应性载体，这些载体显示出强大的临床转化潜力。我们的分析强调NA入路治疗GBM的疗效与其输送平台的复杂性密不可分。展望未来，整合合理设计的载体与基因靶向有效载荷是释放GBM精准医疗全部潜力的关键。

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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 : 2026-02-01 Epub 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。

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引用次数: 0

GMP-compliant manufacturing of iPSC-derived therapeutic cell products: Technologies, applications, risks and limitations ipsc衍生治疗细胞产品的gmp合规生产：技术、应用、风险和限制

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-12-02 DOI: 10.1016/j.addr.2025.115744

Alexandra Haase, Arjang Ruhparwar, Ulrich Martin

The development of induced pluripotent stem cells (iPSCs) has transformed the field of regenerative medicine. However, to use iPSCs for therapeutic applications, iPSC-based products must be produced under Good Manufacturing Practice (GMP) conditions. This process involves reprogramming somatic cells, characterizing and banking iPSC lines, introducing therapeutic transgenes if necessary, and scaling up cell expansion and differentiation for clinical use. This review provides an overview of the relevant regulatory authorities and relevant regulations in the US, Europe, and Japan. It also discusses the current challenges and opportunities in producing GMP-compliant iPSCs. These challenges include the need for defined culture media, as well as developing all the required GMP-compliant processes, such as reprogramming, establishing iPSC clones, and manufacturing processes that lead to the final advanced therapy medicinal product (ATMP). For autologous products in particular, this can be complicated by cell line-specific variation of proliferation velocity and differentiation biases. The review also discusses attempts to develop automated closed systems. It emphasizes the importance of ensuring the sterility, identity, (epi)genetic integrity, and functionality of the final cell products to guarantee the safety and the efficacy of iPSC-based therapies. However, the need for reproducibility, rigorous quality control and safety requirements has resulted in high regulatory hurdles and extremely high costs, which often prevent the initiation of clinical trials. Overcoming these challenges will enable iPSCs to play an integral role in future medicine and offer new treatment options for various diseases.

诱导多能干细胞（iPSCs）的发展已经改变了再生医学领域。然而，要将ipsc用于治疗应用，基于ipsc的产品必须在良好生产规范（GMP）条件下生产。这一过程包括对体细胞进行重编程，鉴定和储存iPSC系，必要时引入治疗性转基因，扩大细胞扩增和分化以供临床使用。本综述概述了美国、欧洲和日本的相关监管机构和相关法规。它还讨论了生产符合gmp的iPSCs的当前挑战和机遇。这些挑战包括需要明确的培养基，以及开发所有必需的符合gmp的流程，例如重编程，建立iPSC克隆，以及导致最终先进治疗药物产品（ATMP）的制造流程。特别是对于自体产物，这可能会因细胞系特异性增殖速度和分化偏差的变化而变得复杂。该评论还讨论了开发自动化封闭系统的尝试。它强调了确保最终细胞产品的无菌性、身份、（epi）遗传完整性和功能的重要性，以确保基于ipsc的治疗的安全性和有效性。然而，对可重复性、严格的质量控制和安全要求的需要导致了很高的监管障碍和极高的成本，这往往阻碍了临床试验的开展。克服这些挑战将使多能干细胞在未来医学中发挥不可或缺的作用，并为各种疾病提供新的治疗选择。

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引用次数: 0

Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools 利用胶体药物聚集给药：从使用计算工具的承诺到预测

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.addr.2025.115758

Kai V. Slaughter , Xiang Olivia Li , Molly S. Shoichet

Colloidal drug aggregates are amorphous nanoparticles formed by the self-assembly of hydrophobic small molecule drugs. They can be leveraged as drug-rich nanoparticle formulations for drug delivery. However, it is difficult to predict which drugs will form colloidal aggregates, which stabilizers will be effective, and what the in vivo fate of the nanoparticles will be. These challenges can be addressed, in part, with computational tools including artificial intelligence such as machine learning. Molecular dynamics simulations have been used to improve our understanding of the intermolecular forces that govern the assembly of colloidal drug aggregates. Several predictive tools exist to identify aggregators, but these are typically used to eliminate aggregators from screening libraries rather than design drug delivery formulations. Colloidal drug aggregates require stabilizers to prevent particle growth and precipitation. Computational analyses have been used to predict which colloidal drug aggregators can be stabilized by a particular small molecule excipient and to identify drug-stabilizer pairs. Successful stabilization has enabled colloidal drug aggregate evaluation for applications such as nanomedicine and sustained release. Additionally, certain colloid-forming drugs can be useful for co-delivery of nucleic acids. In future studies, computational tools can be developed to predict the biological activity of colloidal drug aggregates, building upon other approaches currently used for lipid nanoparticles and other modalities. Ultimately, leveraging computational strategies to improve the design of colloidal drug aggregates can help realize the potential of this high drug-loading delivery platform.

胶体药物聚集体是由疏水小分子药物自组装形成的无定形纳米颗粒。它们可以作为富含药物的纳米颗粒配方用于药物输送。然而，很难预测哪些药物会形成胶体聚集体，哪些稳定剂会有效，以及纳米颗粒在体内的命运如何。这些挑战可以部分地通过包括机器学习等人工智能在内的计算工具来解决。分子动力学模拟已被用于提高我们对控制胶体药物聚集体组装的分子间力的理解。有几种预测工具可以识别聚合器，但这些工具通常用于从筛选库中消除聚合器，而不是设计给药配方。胶体药物聚集体需要稳定剂来防止颗粒生长和沉淀。计算分析已经被用来预测哪些胶体药物聚集剂可以被特定的小分子赋形剂稳定，并识别药物稳定剂对。成功的稳定使胶体药物聚集体的应用评估，如纳米医学和缓释。此外，某些形成胶体的药物可用于核酸的共递送。在未来的研究中，可以开发计算工具来预测胶体药物聚集体的生物活性，以目前用于脂质纳米颗粒和其他模式的其他方法为基础。最终，利用计算策略来改进胶体药物聚集体的设计可以帮助实现这种高载药递送平台的潜力。

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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 : 2026-02-01 Epub 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。

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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 : 2026-02-01 Epub 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。

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引用次数: 0

Microbial heterogeneity-mediated treatment evasion and the potential for engineered live biotherapeutic products 微生物异质性介导的治疗逃避和工程活生物治疗产品的潜力

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.addr.2025.115740

Eli G. Cytrynbaum , Megan N. McClean

Microbial populations exhibit both genetic and non-genetic heterogeneity. In the clinical context, this heterogeneity is of concern as it provides subsets of microbial populations with enhanced immune evasion abilities and antimicrobial resistance. Fungal pathogens are of special concern as fungal diseases and antifungal resistance are increasing worldwide and similarities between eukaryotic cells make it challenging to identify targets that are toxic to fungi without also harming the human host. Engineered live biotherapeutic products (eLBPs) could provide an alternative and complementary approach to manipulating and treating heterogeneous fungal populations due to their potential to provide localized delivery to the affected site, continuous long-term treatment, environmental sensing, and delivery of therapeutics specific to virulent or drug-resistant organisms. However, the development of eLBPs targeting fungi remains limited.

This review assesses our current understanding of genetic and non-genetic microbial heterogeneity and how this impacts treatment strategies particularly for pathogenic fungi. We focus on Candida yeasts, specifically Candida albicans, as Candida species are the most common opportunistic fungal pathogens. We review the current scope and potential of eLBPs to address heterogeneous and rising fungal infections.

微生物种群表现出遗传和非遗传异质性。在临床环境中，这种异质性值得关注，因为它提供了具有增强的免疫逃避能力和抗菌素耐药性的微生物种群亚群。真菌病原体受到特别关注，因为真菌疾病和抗真菌耐药性在世界范围内日益增加，真核细胞之间的相似性使得鉴定对真菌有毒而又不伤害人类宿主的靶标具有挑战性。工程活生物治疗产品（elbp）可以提供一种替代和补充的方法来操纵和治疗异质真菌群体，因为它们有可能提供局部递送到受影响的部位，持续的长期治疗，环境传感，以及递送针对有毒或耐药生物的治疗药物。然而，针对真菌的elbp的开发仍然有限。这篇综述评估了我们目前对遗传和非遗传微生物异质性的理解，以及这如何影响治疗策略，特别是对致病真菌。我们专注于念珠菌酵母菌，特别是白色念珠菌，因为念珠菌是最常见的机会性真菌病原体。我们回顾了elbp目前的范围和潜力，以解决异质性和上升的真菌感染。

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引用次数: 0

Gas-based therapeutics and delivery platforms in cancer immunotherapy 肿瘤免疫治疗中的气体疗法和输送平台

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-12-06 DOI: 10.1016/j.addr.2025.115746

Van-Anh Thi Nguyen , Chieh-Cheng Huang , Yunching Chen

Gas-based therapeutics are emerging as a promising strategy in cancer immunotherapy. Small gaseous signaling molecules such as nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and oxygen (O₂) efficiently penetrate tumor tissues and modulate diverse immune pathways. These therapeutic gases can relieve tumor hypoxia, enhance immune cell infiltration, induce immunogenic cancer cell death, and suppress immunosuppressive signaling within the tumor microenvironment (TME). Therefore, they potentiate immune checkpoint blockade and other immunotherapies while overcoming key barriers to immune evasion. Despite this promise, the clinical translation of gas-based therapies faces significant challenges, including short half-lives, systemic toxicity, and lack of spatiotemporal control. To address these limitations, a variety of delivery platforms have been developed—from nanocarriers and injectable hydrogels to inhalable and oral prodrug formulations and stimuli-responsive systems—that enable safe, tumor-targeted, and controlled release of therapeutic gases. Such engineered strategies maximize antitumor efficacy while minimizing off-target effects. This review highlights the immunomodulatory roles of therapeutic gases, examines state-of-the-art delivery technologies, and discusses how these advances lay the foundation for precision gas immunotherapy to unlock the clinical potential of gaseous immunomodulators in cancer treatment.

基于气体的治疗方法正在成为一种有前景的癌症免疫治疗策略。小的气体信号分子如一氧化氮（NO）、一氧化碳（CO）、硫化氢（H2S）和氧（O2）有效地渗透肿瘤组织并调节多种免疫途径。这些治疗气体可以缓解肿瘤缺氧，增强免疫细胞浸润，诱导免疫原性癌细胞死亡，抑制肿瘤微环境（tumor microenvironment， TME）内的免疫抑制信号。因此，它们增强了免疫检查点封锁和其他免疫疗法，同时克服了免疫逃避的关键障碍。尽管前景光明，但气体疗法的临床转化面临着重大挑战，包括半衰期短、全身毒性和缺乏时空控制。为了解决这些限制，已经开发了各种递送平台-从纳米载体和可注射水凝胶到可吸入和口服前药制剂和刺激反应系统-使治疗气体的安全，肿瘤靶向和控制释放。这种工程策略最大限度地提高了抗肿瘤效果，同时最大限度地减少了脱靶效应。这篇综述强调了治疗气体的免疫调节作用，检查了最先进的输送技术，并讨论了这些进步如何为精确气体免疫治疗奠定基础，以释放气体免疫调节剂在癌症治疗中的临床潜力。

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引用次数: 0

Guiding design and performance of nonviral nucleic acid delivery vehicles via machine learning 通过机器学习指导非病毒核酸运载工具的设计和性能

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-11-24 DOI: 10.1016/j.addr.2025.115739

John A. Hutchinson , Sidharth Panda , Plinio D. Rosales , Janey P. Sowada , Miles S. Willis , Michael C. Leyden , Prodromos Daoutidis , Theresa M. Reineke

Machine Learning (ML) techniques have enabled the advancement of many technologies throughout the pharmaceutical industry, especially for drug discovery. One of the most rapidly growing technologies within the pharmaceutical space is gene therapy, with twenty six FDA-approved genetic medicines and over three thousand treatments currently undergoing clinical trials. A key challenge in the successful employment of gene therapy is effective nucleic acid delivery, and nonviral delivery vectors provide a cost-effective and highly customizable solution to this challenge. However, the vast design space also poses a large challenge for traditional development, which relies heavily on iterative trial-and-error and costly in vivo and in vitro experiments. This review identifies key ML techniques and discusses how these approaches have been utilized to improve the design of nonviral nucleic acid delivery vehicles. ML has the potential to radically transform the design space for nucleic acid therapies, like it has already done in drug discovery and drug formulations. This potential is being realized in research and has already led to the advent of several commercial enterprises seeking to build full end-to-end platforms for rapidly decreasing development time for new gene therapies.

机器学习（ML）技术使制药行业的许多技术取得了进步，特别是在药物发现方面。基因治疗是制药领域发展最快的技术之一，目前有26种fda批准的基因药物和3000多种治疗方法正在进行临床试验。成功使用基因疗法的一个关键挑战是有效的核酸递送，而非病毒递送载体为这一挑战提供了一种成本效益高且高度可定制的解决方案。然而，巨大的设计空间也给传统的开发带来了巨大的挑战，传统的开发严重依赖于反复的试错和昂贵的体内和体外实验。这篇综述确定了关键的ML技术，并讨论了如何利用这些方法来改进非病毒核酸递送载体的设计。ML有可能从根本上改变核酸疗法的设计空间，就像它已经在药物发现和药物配方中所做的那样。这种潜力正在研究中得到实现，并且已经导致一些商业企业寻求建立完整的端到端平台，以快速缩短新基因疗法的开发时间。

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引用次数: 0

Engineering tumor spatial heterogeneity in vitro 体外工程肿瘤空间异质性

IF 17.6 1区医学 Q1 PHARMACOLOGY & PHARMACY

Advanced drug delivery reviews

Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI: 10.1016/j.addr.2025.115757

Changchong Chen , Zixuan Zhao , Dong Hua Seah , Kenny Zhuoran Wu , Senthilkumar Mohanaselvi , Eliza Li Shan Fong

Spatial heterogeneity is a fundamental feature of the tumor microenvironment, characterized by structured variations in cellular composition, phenotypic states, extracellular matrix (ECM) organization, and biochemical and biophysical gradients. These spatial patterns shape tumor evolution, modulate immune infiltration, and underlie resistance to therapy. Advances in spatial transcriptomics and multiplex imaging have revealed dynamic and region-specific niches, such as hypoxic cores, immune-excluded zones, and fibroblast-dense invasive fronts, that correlate with clinical outcomes. However, most in vitro models fail to capture this architectural complexity. Recent engineering technologies, including 3D bioprinting, organoid assembloids, organ-on-a-chip systems, and ECM-mimetic scaffolds, now enable controlled reconstruction of tumor spatial organization and microregional heterogeneity. These technologies allow integration of patient-derived cells, tunable matrix environments, and spatially defined signaling to mimic in vivo pathophysiology. When integrated with spatial transcriptomics and proteomics, these models enable mechanistic exploration of microregional tumor biology, evaluation of therapeutic responses, and investigation of immunotherapy resistance. This review integrates our current understanding of spatial heterogeneity in cancer with enabling engineering strategies to guide future developments in tumor biology and therapeutic innovation.

空间异质性是肿瘤微环境的一个基本特征，其特征是细胞组成、表型状态、细胞外基质（ECM）组织以及生化和生物物理梯度的结构性变化。这些空间模式塑造肿瘤的演变，调节免疫浸润，并对治疗产生抵抗。空间转录组学和多重成像技术的进步揭示了与临床结果相关的动态和区域特异性生态位，如缺氧核心、免疫排斥区和成纤维细胞密集侵入前沿。然而，大多数体外模型无法捕获这种架构复杂性。最近的工程技术，包括3D生物打印、类器官组装体、器官芯片系统和模拟ecm支架，现在可以控制肿瘤空间组织和微区域异质性的重建。这些技术允许整合患者来源的细胞、可调节的基质环境和空间定义的信号来模拟体内病理生理。当与空间转录组学和蛋白质组学相结合时，这些模型可以对微区域肿瘤生物学进行机制探索，评估治疗反应，并研究免疫治疗耐药性。这篇综述将我们目前对癌症空间异质性的理解与工程策略结合起来，以指导肿瘤生物学和治疗创新的未来发展。

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引用次数: 0