Advanced drug delivery reviews最新文献

{"title":"Technology for Nucleic Acid Delivery in the Treatment of Hematological Malignancies","authors":"Saba Abbasi Dezfouli ,&nbsp;Hasan Uludağ ,&nbsp;Mohammad Nasrullah ,&nbsp;Amarnath Praphakar Rajendran ,&nbsp;Remant K.C.","doi":"10.1016/j.addr.2026.115780","DOIUrl":"10.1016/j.addr.2026.115780","url":null,"abstract":"<div><div>Blood (hematological) cancers display a wide spectrum of etiologies that can be attributed to specific molecular and chromosomal changes. While the uncontrolled proliferation of blood cells could be controlled to some degree by conventional anti-neoplastic agents, advanced therapies are needed to enhance the chances of survival. Nucleic acid therapeutics offer a great promise in combating blood cancers; they could be tailored to address the root cause of the diseases and can be deployed on their own or in combination with clinical drugs to achieve superior outcomes. In this review, we summarize the technology of delivering nucleic acids for the treatment of blood cancers. We start with the review of common types of hematological malignancies, highlighting the molecular pathology behind the malignancies. We then articulate the spectrum of nucleic acids promising for therapy as well as their critical features for delivery and securing efficacious outcomes. Since it is well recognized that the critical challenge is deploying nucleic acids safely in a clinical setting, we focus on the more-predictable, leading synthetic carriers promising for delivery of nucleic acids in clinics. We emphasize synthetic carriers that form supramolecular complexes with nucleic acids, resulting in nanoparticulate formulations, as well as approaches to derivatize the nucleic acids to make them suitable for cellular uptake and targeted delivery. We then summarize highly promising attempts to tackle blood cancers using new approaches, emphasizing microRNA-mediated gene regulation and the CRISPR-based gene editing approaches. These new approaches are interrogated especially from the perspective of delivery technology, with the purpose of designing improved delivery systems. We conclude with the authors' perspective on the future of nucleic acid therapeutics for the treatment of blood cancers, providing authors' perspectives for significant advances in the field.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115780"},"PeriodicalIF":17.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995264","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}

{"title":"Synergies between data science methods and innovative drug delivery technologies","authors":"David A. Winkler","doi":"10.1016/j.addr.2026.115783","DOIUrl":"10.1016/j.addr.2026.115783","url":null,"abstract":"<div><div>Most areas of science and technology and beyond are undergoing an almost unprecedented rate of change, driven largely by the rapid growth in automation and robotics, computational power, and AI and machine learning algorithms. Many areas of science and medicine have adopted these technologies or are on a steep learning curve to do so in the short to medium term. Drug delivery systems that are very important for optimising therapeutic efficacy, patient compliance, and amelioration of side-effects are similarly undergoing a quiet revolution in modalities. However, drug delivery systems are arguably lagging many other scientific and biomedical fields in applying informatics, physics-based computational design and simulation approaches, and AI and machine learning to design, optimisation, and simulation of drug delivery systems. Here I review studies in which selected computational methods have been employed for these purposes, aiming to highlight their potential to accelerate the provision of more effective drug delivery systems and to identify modalities in which the benefits of these computational methods have not been achieved at all, or at least sub-optimally. The aim is to focus on less well-addressed existing and emerging drug delivery systems and to provide a perspective on what needs to be done, what could be done better, and where the synergistic partnership between computational/AI methods and contemporary drug delivery system may lead in the future.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115783"},"PeriodicalIF":17.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071496","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}

{"title":"Therapeutic gases as emerging treatments for oral diseases","authors":"Ann Badia ,&nbsp;Jihyuk Yang ,&nbsp;Sara Aliyeva ,&nbsp;Yonghyun Choi ,&nbsp;Jonghoon Choi ,&nbsp;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 (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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115774"},"PeriodicalIF":17.6,"publicationDate":"2026-03-01","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}

{"title":"Overcoming barriers and shaping the future: Challenges and innovations in nucleic acid therapies for Glioblastoma","authors":"Alaa Zam ,&nbsp;Nadia Rouatbi ,&nbsp;Adam A. Walters ,&nbsp;Khuloud T. Al-Jamal","doi":"10.1016/j.addr.2025.115759","DOIUrl":"10.1016/j.addr.2025.115759","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115759"},"PeriodicalIF":17.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717923","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}

{"title":"Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery","authors":"Nazreen Pallikkavaliyaveetil ,&nbsp;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":"2026-02-01","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}

{"title":"The growing impact of machine learning on drug formulation science","authors":"Zeqing Bao ,&nbsp;Steven Huynh ,&nbsp;Frantz Le Devedec ,&nbsp;Jo Nguyen ,&nbsp;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":"2026-02-01","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}

{"title":"Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools","authors":"Kai V. Slaughter ,&nbsp;Xiang Olivia Li ,&nbsp;Molly S. Shoichet","doi":"10.1016/j.addr.2025.115758","DOIUrl":"10.1016/j.addr.2025.115758","url":null,"abstract":"<div><div>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 <em>in vivo</em> 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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115758"},"PeriodicalIF":17.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717893","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}

{"title":"GMP-compliant manufacturing of iPSC-derived therapeutic cell products: Technologies, applications, risks and limitations","authors":"Alexandra Haase,&nbsp;Arjang Ruhparwar,&nbsp;Ulrich Martin","doi":"10.1016/j.addr.2025.115744","DOIUrl":"10.1016/j.addr.2025.115744","url":null,"abstract":"<div><div>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, (<em>epi</em>)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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115744"},"PeriodicalIF":17.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657300","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}

{"title":"Quality by Computational Design: Harnessing AI to Advance Rational Drug Development","authors":"Nannan Wang ,&nbsp;Hao Zhong ,&nbsp;Ping Xiong ,&nbsp;Jinying Zhu ,&nbsp;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":"2026-02-01","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}

{"title":"Microbial heterogeneity-mediated treatment evasion and the potential for engineered live biotherapeutic products","authors":"Eli G. Cytrynbaum ,&nbsp;Megan N. McClean","doi":"10.1016/j.addr.2025.115740","DOIUrl":"10.1016/j.addr.2025.115740","url":null,"abstract":"<div><div>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.</div><div>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 <em>Candida</em> yeasts, specifically <em>Candida albicans</em>, as <em>Candida</em> species are the most common opportunistic fungal pathogens. We review the current scope and potential of eLBPs to address heterogeneous and rising fungal infections.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115740"},"PeriodicalIF":17.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611319","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}