Pub Date : 2025-12-20DOI: 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.
{"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}
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.
{"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}
Pub Date : 2025-12-12DOI: 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.
{"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}
Pub Date : 2025-12-12DOI: 10.1016/j.addr.2025.115760
Ainara Salgado-Pascual , Sara Zalba , Juan José Lasarte , Maria J. Garrido
Pancreatic cancer, particularly ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal subtypes due to late diagnosis, the absence of early biomarkers, and the presence of a complex tumor microenvironment (TME). This TME is characterized by a dense desmoplastic stroma, hypovascularization, immunosuppression, and an acidic extracellular pH. All of these factors hinder the delivery and efficacy of conventional therapies, especially in advanced stages.
Nanoparticles (NPs), including liposomes, polymeric micelles, albumin-bound particles and lipid nanoparticles, have emerged as a promising tool for overcoming TME barriers, and enhance drug delivery in tumor while minimizing systemic toxicity. NPs can exploit mechanisms such as the Enhanced Permeability and Retention (EPR) effect and active targeting. Clinically approved NPs such as Nab-Paclitaxel and liposomal Irinotecan have demonstrated improved pharmacokinetics and therapeutic benefits in PDAC. Furthermore, ongoing clinical trials are exploring novel NP-based strategies such as gene delivery, radiosensitization, immunomodulation and ferroptosis induction. Despite these promising advances, significant challenges remain, including poor tumor penetration, heterogeneity in EPR, and immune recognition of NPs leading to their clearance from bloodstream before reaching the tumor. Innovative solutions such as biomimetic coatings, stimuli-responsive systems and personalized formulations, are being evaluated to enhance NP performance. Standardization of NP characterization and data reporting are essential to accelerating clinical translation. The integration of artificial intelligence and machine learning could further optimize NP design and patient stratification. Overall, nanotechnology represents a crucial frontier of research for developing more effective and personalized pancreatic cancer treatments.
{"title":"Emerging nanoparticle-based therapies for pancreatic cancer: Current clinical landscape","authors":"Ainara Salgado-Pascual , Sara Zalba , Juan José Lasarte , Maria J. Garrido","doi":"10.1016/j.addr.2025.115760","DOIUrl":"10.1016/j.addr.2025.115760","url":null,"abstract":"<div><div>Pancreatic cancer, particularly ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal subtypes due to late diagnosis, the absence of early biomarkers, and the presence of a complex tumor microenvironment (TME). This TME is characterized by a dense desmoplastic stroma, hypovascularization, immunosuppression, and an acidic extracellular pH. All of these factors hinder the delivery and efficacy of conventional therapies, especially in advanced stages.</div><div>Nanoparticles (NPs), including liposomes, polymeric micelles, albumin-bound particles and lipid nanoparticles, have emerged as a promising tool for overcoming TME barriers, and enhance drug delivery in tumor while minimizing systemic toxicity. NPs can exploit mechanisms such as the Enhanced Permeability and Retention (EPR) effect and active targeting. Clinically approved NPs such as Nab-Paclitaxel and liposomal Irinotecan have demonstrated improved pharmacokinetics and therapeutic benefits in PDAC. Furthermore, ongoing clinical trials are exploring novel NP-based strategies such as gene delivery, radiosensitization, immunomodulation and ferroptosis induction. Despite these promising advances, significant challenges remain, including poor tumor penetration, heterogeneity in EPR, and immune recognition of NPs leading to their clearance from bloodstream before reaching the tumor. Innovative solutions such as biomimetic coatings, stimuli-responsive systems and personalized formulations, are being evaluated to enhance NP performance. Standardization of NP characterization and data reporting are essential to accelerating clinical translation. The integration of artificial intelligence and machine learning could further optimize NP design and patient stratification. Overall, nanotechnology represents a crucial frontier of research for developing more effective and personalized pancreatic cancer treatments.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115760"},"PeriodicalIF":17.6,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731034","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}
Pub Date : 2025-12-11DOI: 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.
{"title":"Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools","authors":"Kai V. Slaughter , Xiang Olivia Li , 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":"2025-12-11","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}
Pub Date : 2025-12-10DOI: 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.
{"title":"Overcoming barriers and shaping the future: Challenges and innovations in nucleic acid therapies for Glioblastoma","authors":"Alaa Zam , Nadia Rouatbi , Adam A. Walters , 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":"2025-12-10","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}
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.
{"title":"Engineering tumor spatial heterogeneity in vitro","authors":"Changchong Chen , Zixuan Zhao , Dong Hua Seah , Kenny Zhuoran Wu , Senthilkumar Mohanaselvi , Eliza Li Shan Fong","doi":"10.1016/j.addr.2025.115757","DOIUrl":"10.1016/j.addr.2025.115757","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115757"},"PeriodicalIF":17.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731927","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}
Pub Date : 2025-12-10DOI: 10.1016/j.addr.2025.115755
Xinyu Gou , Shi He , Bilan Wang , Lingli Zhang , Yongzhong Cheng , Xiang Gao
Tumor drug resistance is a major challenge in cancer treatment, as traditional chemotherapeutic agents and small molecule inhibitors often become ineffective in targeting tumors due to drug resistance. Proteolysis Targeting Chimeras (PROTAC) technology, as a novel protein degradation method, provides a new insight into overcoming drug resistance in tumors with the assistance of nanodelivery systems. PROTAC is able to degrade rather than merely inhibit tumor-associated proteins, thus avoiding drug resistance caused by gene mutations, protein overexpression and conformational changes, demonstrating significant advantages in overcoming tumor resistance. First, PROTAC eliminates the biological activity of the target protein by directly degrading it, thus overcoming the limitation of traditional inhibitors, which are susceptible to mutations of the structure and activity of the target protein. Second, PROTAC molecules are highly versatile and flexible, and can target proteins that are difficult to target with conventional drugs, including enzymatically inactive proteins, transcription factors and oncogenic protein complexes. In addition, PROTAC technology, with the booster of nanodelivery systems, can effectively improve solubility and bioavailability, enhance targeting and delivery efficiency while improving its stability, and can be combined with other therapeutic methods to further enhance the therapeutic effect. The versatility of PROTAC makes it a highly promising option for overcoming tumor drug resistance, and their effectiveness has been validated in a variety of cancers, including breast cancer, prostate cancer, and leukemia. In this paper, we will review the recent progress of PROTAC technology in overcoming tumor drug resistance and briefly summarize the advantages and challenges of PROTAC technology combined with nanodelivery system, hoping to provide valuable references for researchers in related fields.
{"title":"Integrating PROTAC-based targeted protein degradation with nanodelivery systems to overcome cancer therapeutic resistance","authors":"Xinyu Gou , Shi He , Bilan Wang , Lingli Zhang , Yongzhong Cheng , Xiang Gao","doi":"10.1016/j.addr.2025.115755","DOIUrl":"10.1016/j.addr.2025.115755","url":null,"abstract":"<div><div>Tumor drug resistance is a major challenge in cancer treatment, as traditional chemotherapeutic agents and small molecule inhibitors often become ineffective in targeting tumors due to drug resistance. Proteolysis Targeting Chimeras (PROTAC) technology, as a novel protein degradation method, provides a new insight into overcoming drug resistance in tumors with the assistance of nanodelivery systems. PROTAC is able to degrade rather than merely inhibit tumor-associated proteins, thus avoiding drug resistance caused by gene mutations, protein overexpression and conformational changes, demonstrating significant advantages in overcoming tumor resistance. First, PROTAC eliminates the biological activity of the target protein by directly degrading it, thus overcoming the limitation of traditional inhibitors, which are susceptible to mutations of the structure and activity of the target protein. Second, PROTAC molecules are highly versatile and flexible, and can target proteins that are difficult to target with conventional drugs, including enzymatically inactive proteins, transcription factors and oncogenic protein complexes. In addition, PROTAC technology, with the booster of nanodelivery systems, can effectively improve solubility and bioavailability, enhance targeting and delivery efficiency while improving its stability, and can be combined with other therapeutic methods to further enhance the therapeutic effect. The versatility of PROTAC makes it a highly promising option for overcoming tumor drug resistance, and their effectiveness has been validated in a variety of cancers, including breast cancer, prostate cancer, and leukemia. In this paper, we will review the recent progress of PROTAC technology in overcoming tumor drug resistance and briefly summarize the advantages and challenges of PROTAC technology combined with nanodelivery system, hoping to provide valuable references for researchers in related fields.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115755"},"PeriodicalIF":17.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717895","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}
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.
{"title":"Gas-based therapeutics and delivery platforms in cancer immunotherapy","authors":"Van-Anh Thi Nguyen , Chieh-Cheng Huang , Yunching Chen","doi":"10.1016/j.addr.2025.115746","DOIUrl":"10.1016/j.addr.2025.115746","url":null,"abstract":"<div><div>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 (H<sub>2</sub>S), and oxygen (O<sub>2</sub>) 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.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"229 ","pages":"Article 115746"},"PeriodicalIF":17.6,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689331","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}
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