Pub Date : 2026-03-23DOI: 10.1016/j.jconrel.2026.114859
Boris Sevarika, Adèle Couvidou, Dana Aldawod, Margarita C. Dinamarca, Scott McNeil
Efficient intracellular delivery of therapeutic enzymes remains a central limitation of protein-based therapies. In lysosomal storage disorders such as Pompe disease, classical enzyme replacement therapy (ERT) is particularly constrained by inefficient delivery to skeletal muscle, limited cellular uptake, and immunogenicity. Here, we introduce a lipid nanoparticle (LNP) platform designed for charge-mediated enzyme encapsulation, enabling high enzyme loading (up to 100 enzymes per LNP) while preserving catalytic activity. Developed enzyme-loaded LNPs achieve efficient delivery to muscular tissues and significantly increase cellular uptake compared with free enzyme administration. High particle-level enzyme loading allows each internalization event to deliver a large enzymatic payload, reducing the number of uptake events required per cell and resulting in up to 15-fold higher intracellular enzyme activity in muscle cells without cytotoxicity. Structurally, enzymes are internally sequestered within multilamellar vesicular structures rather than exposed on the particle surface, overcoming limitations of earlier low-yield or adsorption-based approaches and achieving encapsulation efficiencies of up to 60%. Upon repeated dosing in mice, the encapsulated enzymes exhibit markedly reduced immunogenicity, with more than a 5-fold decrease in anti-drug antibody formation and elimination of antibody-mediated infusion-associated reactions. Together, these results demonstrate that encapsulation-driven enhancement of tissue delivery, cellular uptake, and intracellular enzyme availability directly addresses key limitations of classical ERT. The scalable and biocompatible LNP platform, therefore, provides a generalizable framework for improving enzyme therapeutics for lysosomal storage disorders.
{"title":"Charge-driven lipid nanoparticle encapsulation of enzymes for enhanced ERT with reduced immunogenicity","authors":"Boris Sevarika, Adèle Couvidou, Dana Aldawod, Margarita C. Dinamarca, Scott McNeil","doi":"10.1016/j.jconrel.2026.114859","DOIUrl":"https://doi.org/10.1016/j.jconrel.2026.114859","url":null,"abstract":"Efficient intracellular delivery of therapeutic enzymes remains a central limitation of protein-based therapies. In lysosomal storage disorders such as Pompe disease, classical enzyme replacement therapy (ERT) is particularly constrained by inefficient delivery to skeletal muscle, limited cellular uptake, and immunogenicity. Here, we introduce a lipid nanoparticle (LNP) platform designed for charge-mediated enzyme encapsulation, enabling high enzyme loading (up to 100 enzymes per LNP) while preserving catalytic activity. Developed enzyme-loaded LNPs achieve efficient delivery to muscular tissues and significantly increase cellular uptake compared with free enzyme administration. High particle-level enzyme loading allows each internalization event to deliver a large enzymatic payload, reducing the number of uptake events required per cell and resulting in up to 15-fold higher intracellular enzyme activity in muscle cells without cytotoxicity. Structurally, enzymes are internally sequestered within multilamellar vesicular structures rather than exposed on the particle surface, overcoming limitations of earlier low-yield or adsorption-based approaches and achieving encapsulation efficiencies of up to 60%. Upon repeated dosing in mice, the encapsulated enzymes exhibit markedly reduced immunogenicity, with more than a 5-fold decrease in anti-drug antibody formation and elimination of antibody-mediated infusion-associated reactions. Together, these results demonstrate that encapsulation-driven enhancement of tissue delivery, cellular uptake, and intracellular enzyme availability directly addresses key limitations of classical ERT. The scalable and biocompatible LNP platform, therefore, provides a generalizable framework for improving enzyme therapeutics for lysosomal storage disorders.","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":"27 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502265","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}
Formulation design is constrained by scarce and heterogeneous experimental data, which limits the accuracy and generalizability of conventional AI models. Here, we introduce a physics-based machine learning (PBML) approach that integrates physics-based modeling with data-driven learning to improve drug formulation development. Our approach predicts key formulation properties across two different systems, including physical stability of amorphous solid dispersions (ASDs) and molecular hygroscopicity. For ASDs, molecular dynamics (MD)-derived descriptors that explicitly encode non-covalent interactions (drug-polymer interaction energies, hydrogen-bond networks) and mobility (diffusion coefficients) markedly outperform empirical experimental parameters on the same dataset, improving generalization to unseen APIs under grouped cross-validation (75.2% vs. 66.1%). For hygroscopicity, hygroscopic/nonhygroscopic labels were first assigned based on MD simulation results. A classification model was then trained using these MD-derived labels and validated to have strong performance on external experimental datasets (accuracy = 0.967; F1-score = 0.957; AUC-ROC = 0.931). Across both systems, the synergy between MD-derived descriptors and TabPFN (Tabular Prior-data Fitted Network) performs well on limited formulation datasets. In addition, SHapley Additive exPlanations (SHAP) analyses align feature importance with known experimental mechanisms. For instance, stronger drug-polymer attraction and lower API mobility stabilize ASDs, while higher surface polarity and electrostatic potential (ESP) variance drive hygroscopicity, thereby improving interpretability at the representation level. Overall, our PBML framework provides a data-efficient and mechanism-grounded approach that can enhance decision-making in formulation design. This approach has the potential to extend the utility of limited datasets in drug formulation design and to reduce experimental burden.
配方设计受到实验数据稀缺和异质性的限制，限制了传统人工智能模型的准确性和可泛化性。在这里，我们介绍了一种基于物理的机器学习（PBML）方法，该方法将基于物理的建模与数据驱动的学习相结合，以改善药物配方开发。我们的方法预测了两种不同体系的关键配方特性，包括非晶固体分散体（asd）的物理稳定性和分子吸湿性。对于自闭症谱系障碍，明确编码非共价相互作用（药物-聚合物相互作用能、氢键网络）和迁移率（扩散系数）的分子动力学（MD）衍生描述符在同一数据集上的表现明显优于经验实验参数，在分组交叉验证下提高了对未见api的泛化（75.2% vs 66.1%）。对于吸湿性，首先根据MD模拟结果分配吸湿性/非吸湿性标签。然后使用这些md衍生的标签训练分类模型，并验证其在外部实验数据集上具有较强的性能（准确率 = 0.967;F1-score = 0.957;AUC-ROC = 0.931）。在这两个系统中，md衍生描述符和TabPFN（表列先验数据拟合网络）之间的协同作用在有限的配方数据集上表现良好。此外，SHapley加性解释（SHAP）分析将特征的重要性与已知的实验机制结合起来。例如，更强的药物-聚合物吸引力和更低的API迁移率稳定了asd，而更高的表面极性和静电电位（ESP）方差驱动了吸湿性，从而提高了表征层面的可解释性。总体而言，我们的PBML框架提供了一种数据高效和基于机制的方法，可以增强配方设计中的决策。这种方法有可能扩展有限数据集在药物配方设计中的效用，并减少实验负担。
{"title":"Physics-based machine learning for enhanced drug formulation development","authors":"Hao Zhong, Ping Xiong, Nannan Wang, Kunda Li, Ruifeng Wang, Yiyang Wu, Defang Ouyang","doi":"10.1016/j.jconrel.2026.114860","DOIUrl":"https://doi.org/10.1016/j.jconrel.2026.114860","url":null,"abstract":"Formulation design is constrained by scarce and heterogeneous experimental data, which limits the accuracy and generalizability of conventional AI models. Here, we introduce a physics-based machine learning (PBML) approach that integrates physics-based modeling with data-driven learning to improve drug formulation development. Our approach predicts key formulation properties across two different systems, including physical stability of amorphous solid dispersions (ASDs) and molecular hygroscopicity. For ASDs, molecular dynamics (MD)-derived descriptors that explicitly encode non-covalent interactions (drug-polymer interaction energies, hydrogen-bond networks) and mobility (diffusion coefficients) markedly outperform empirical experimental parameters on the same dataset, improving generalization to unseen APIs under grouped cross-validation (75.2% vs. 66.1%). For hygroscopicity, hygroscopic/nonhygroscopic labels were first assigned based on MD simulation results. A classification model was then trained using these MD-derived labels and validated to have strong performance on external experimental datasets (accuracy = 0.967; F1-score = 0.957; AUC-ROC = 0.931). Across both systems, the synergy between MD-derived descriptors and TabPFN (Tabular Prior-data Fitted Network) performs well on limited formulation datasets. In addition, SHapley Additive exPlanations (SHAP) analyses align feature importance with known experimental mechanisms. For instance, stronger drug-polymer attraction and lower API mobility stabilize ASDs, while higher surface polarity and electrostatic potential (ESP) variance drive hygroscopicity, thereby improving interpretability at the representation level. Overall, our PBML framework provides a data-efficient and mechanism-grounded approach that can enhance decision-making in formulation design. This approach has the potential to extend the utility of limited datasets in drug formulation design and to reduce experimental burden.","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":"80 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502003","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 : 2026-03-23DOI: 10.1016/j.jconrel.2026.114861
Anna Pietrella, Irene Paris, Claudia Migliorini, Marco Morelli, Andrea Carpentieri, Pietro Matricardi, Chiara Di Meo, Rosanna Papa
{"title":"Hyaluronan and gellan nanohydrogels exhibit an unexpected activity in hampering Staphylococcus epidermidis biofilm","authors":"Anna Pietrella, Irene Paris, Claudia Migliorini, Marco Morelli, Andrea Carpentieri, Pietro Matricardi, Chiara Di Meo, Rosanna Papa","doi":"10.1016/j.jconrel.2026.114861","DOIUrl":"https://doi.org/10.1016/j.jconrel.2026.114861","url":null,"abstract":"","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":"1 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502007","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}
Coacervation phenomena in the biological world can be categorized as intracellular and extracellular coacervation. Intracellular coacervation has inspired the formation of membrane-free coacervate droplets and the development of droplet-based drug delivery systems, whereas the extracellular coacervation observed in marine mussels has motivated the creation of numerous coacervate-derived hydrogels. Coacervate-derived hydrogels, formed via liquid-liquid phase separation and subsequent gelation, represent a promising class of biomaterials, particularly for advanced drug delivery. Given the current lack of systematic reviews on coacervate-derived hydrogels, this review systematically elucidated their formation mechanism, characterization techniques, material systems (including natural/synthetic polymers, peptides, and inorganic components), and diverse forms (e.g., injectable, powder-based, stimuli-responsive). We particularly highlighted their exceptional potential in drug delivery, leveraging their high loading capacity, gentle encapsulation that preserves bioactivity, and tunable release kinetics in response to physiological stimuli. Beyond drug delivery, we also discussed their broad applications in bioadhesives, tissue engineering, 3D printing, and artificial tissue construction. Furthermore, this review discussed the current challenges faced by coacervate-derived hydrogels, including in vivo stability, precise control over drug release, long-term biosafety, and clinical translation. It also provided perspectives on future research directions, aiming to promote the further development and application of these materials in precision medicine and regenerative medicine.
{"title":"Liquid-liquid phase separation-driven coacervate-derived hydrogels and their biological applications","authors":"Jiao Zhang, Qian Hu, Qi Xie, Jingwen Liu, Wenke Lu, Xuewu Song, Liyun Xing, Li Kong, Conglian Yang, Zhiping Zhang","doi":"10.1016/j.jconrel.2026.114856","DOIUrl":"https://doi.org/10.1016/j.jconrel.2026.114856","url":null,"abstract":"Coacervation phenomena in the biological world can be categorized as intracellular and extracellular coacervation. Intracellular coacervation has inspired the formation of membrane-free coacervate droplets and the development of droplet-based drug delivery systems, whereas the extracellular coacervation observed in marine mussels has motivated the creation of numerous coacervate-derived hydrogels. Coacervate-derived hydrogels, formed via liquid-liquid phase separation and subsequent gelation, represent a promising class of biomaterials, particularly for advanced drug delivery. Given the current lack of systematic reviews on coacervate-derived hydrogels, this review systematically elucidated their formation mechanism, characterization techniques, material systems (including natural/synthetic polymers, peptides, and inorganic components), and diverse forms (e.g., injectable, powder-based, stimuli-responsive). We particularly highlighted their exceptional potential in drug delivery, leveraging their high loading capacity, gentle encapsulation that preserves bioactivity, and tunable release kinetics in response to physiological stimuli. Beyond drug delivery, we also discussed their broad applications in bioadhesives, tissue engineering, 3D printing, and artificial tissue construction. Furthermore, this review discussed the current challenges faced by coacervate-derived hydrogels, including in vivo stability, precise control over drug release, long-term biosafety, and clinical translation. It also provided perspectives on future research directions, aiming to promote the further development and application of these materials in precision medicine and regenerative medicine.","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":"29 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502002","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 : 2026-03-22DOI: 10.1016/j.jconrel.2026.114851
M. Neumann, D. Kožinec, D. Hène, L. van Uden, K. Schneeberger, L.J.W. van der Laan, J. Drost, G.G. Slaats, M.C. Verhaar, T. Vermonden
{"title":"Dynamic matrix remodeling in boronate ester hydrogels for 3D organoid cultures","authors":"M. Neumann, D. Kožinec, D. Hène, L. van Uden, K. Schneeberger, L.J.W. van der Laan, J. Drost, G.G. Slaats, M.C. Verhaar, T. Vermonden","doi":"10.1016/j.jconrel.2026.114851","DOIUrl":"https://doi.org/10.1016/j.jconrel.2026.114851","url":null,"abstract":"","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":"3 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495646","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：