Pub Date : 2026-02-06DOI: 10.1016/j.addr.2026.115793
Guangli Hu, Mikolaj Milewski, Yogita Krishnamachari, Adam Procopio, W. Peter Wuelfing, Lei Zhu, Sachin Mittal, Jason Cheung, Jeffrey Givand, Rubi Burlage, Allen Templeton, Hanmi Xi, Yongchao Su, Nicole Buist
Subcutaneous (SC) administration is often a preferred approach for biologic therapeutics, offering enhanced convenience, improved patient adherence, and reduced healthcare costs compared to traditional intravenous (IV) infusion. The growing demand for high-dose subcutaneous formulations (HiSubQ), particularly for drugs requiring large doses, has driven advancements and innovations in formulation, manufacturing, device development, and analytical characterization. However, HiSubQ development faces challenges such as protein instability, high viscosity, and complex manufacturing processes. Addressing these hurdles requires innovative protein engineering, formulation strategies, advanced drug delivery devices, and high-resolution analytical tools to ensure stability, injectability, and bioperformance. A strong interdisciplinary collaboration across formulation, device, bioperformance, and analytics is required to drive such innovation. This review provides an overview of SC drug development, emphasizing key advancements in formulation design, biopharmaceutic considerations, device integration, and analytical characterization. We propose tactics and high-level roadmaps that can enable the development of patient-centric solutions to meet the rising demand for SC biologics.
{"title":"High concentration subcutaneous biological drug products: challenges and advancements","authors":"Guangli Hu, Mikolaj Milewski, Yogita Krishnamachari, Adam Procopio, W. Peter Wuelfing, Lei Zhu, Sachin Mittal, Jason Cheung, Jeffrey Givand, Rubi Burlage, Allen Templeton, Hanmi Xi, Yongchao Su, Nicole Buist","doi":"10.1016/j.addr.2026.115793","DOIUrl":"https://doi.org/10.1016/j.addr.2026.115793","url":null,"abstract":"Subcutaneous (SC) administration is often a preferred approach for biologic therapeutics, offering enhanced convenience, improved patient adherence, and reduced healthcare costs compared to traditional intravenous (IV) infusion. The growing demand for high-dose subcutaneous formulations (HiSubQ), particularly for drugs requiring large doses, has driven advancements and innovations in formulation, manufacturing, device development, and analytical characterization. However, HiSubQ development faces challenges such as protein instability, high viscosity, and complex manufacturing processes. Addressing these hurdles requires innovative protein engineering, formulation strategies, advanced drug delivery devices, and high-resolution analytical tools to ensure stability, injectability, and bioperformance. A strong interdisciplinary collaboration across formulation, device, bioperformance, and analytics is required to drive such innovation. This review provides an overview of SC drug development, emphasizing key advancements in formulation design, biopharmaceutic considerations, device integration, and analytical characterization. We propose tactics and high-level roadmaps that can enable the development of patient-centric solutions to meet the rising demand for SC biologics.","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"177 1","pages":""},"PeriodicalIF":16.1,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122131","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 : 2026-01-31DOI: 10.1016/j.addr.2026.115792
Claudia Muñoz Villaescusa, Diana van der Ven, Miguel A. Quetzeri-Santiago, David Fernandez Rivas
{"title":"A strategic guide of techniques for biomedical and tissue engineering applications to measure mechanical properties of soft matter, eye and skin","authors":"Claudia Muñoz Villaescusa, Diana van der Ven, Miguel A. Quetzeri-Santiago, David Fernandez Rivas","doi":"10.1016/j.addr.2026.115792","DOIUrl":"https://doi.org/10.1016/j.addr.2026.115792","url":null,"abstract":"","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"8 1","pages":""},"PeriodicalIF":16.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095684","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 : 2026-01-28DOI: 10.1016/j.addr.2026.115783
David A. Winkler
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.
{"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":"2026-01-28","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}
Pub Date : 2026-01-25DOI: 10.1016/j.addr.2026.115784
Sanjay Konagurthu , Dineli T.S. Ranathunga , Stephanie Buchanan , Nairuti Milan Mehta , Tom Reynolds
It is well-known that drug development is challenging and a time- and resource-intensive endeavor. Historically, it has relied heavily on trial-and-error, empirical approaches that yield a low probability of success. Despite continuous efforts to improve efficiency across the development stages the overall success rate from clinical trial initiation to market approval remains low. In response to these challenges, in-silico predictive modeling and simulations are becoming indispensable tools for accelerating and de-risking the drug product development process. These computational methods use simulated and real-world data to guide decision-making across the entire development pipeline. Notably, these tools are now gaining widespread acceptance not only in discovery but also across the delivery and formulation stages of drug development. Advances in artificial intelligence (AI) and machine learning (ML) are proving transformative, enabling rapid analysis of large datasets and the development of predictive models that enhance classification, prediction, and optimization capabilities across the drug product development process. This review provides an overview of the various in-silico predictive modeling and simulation techniques for drug product development, emphasizing the use of AI/ML, and their applications in drug delivery. We highlight their role in improving drug performance, manufacturability, stability, safety, and overall success from clinical development through commercialization.
{"title":"The predictive edge: modeling and simulation in drug product development","authors":"Sanjay Konagurthu , Dineli T.S. Ranathunga , Stephanie Buchanan , Nairuti Milan Mehta , Tom Reynolds","doi":"10.1016/j.addr.2026.115784","DOIUrl":"10.1016/j.addr.2026.115784","url":null,"abstract":"<div><div>It is well-known that drug development is challenging and a time- and resource-intensive endeavor. Historically, it has relied heavily on trial-and-error, empirical approaches that yield a low probability of success. Despite continuous efforts to improve efficiency across the development stages the overall success rate from clinical trial initiation to market approval remains low. In response to these challenges, in-silico predictive modeling and simulations are becoming indispensable tools for accelerating and de-risking the drug product development process. These computational methods use simulated and real-world data to guide decision-making across the entire development pipeline. Notably, these tools are now gaining widespread acceptance not only in discovery but also across the delivery and formulation stages of drug development. Advances in artificial intelligence (AI) and machine learning (ML) are proving transformative, enabling rapid analysis of large datasets and the development of predictive models that enhance classification, prediction, and optimization capabilities across the drug product development process. This review provides an overview of the various in-silico predictive modeling and simulation techniques for drug product development, emphasizing the use of AI/ML, and their applications in drug delivery. We highlight their role in improving drug performance, manufacturability, stability, safety, and overall success from clinical development through commercialization.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115784"},"PeriodicalIF":17.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047854","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 : 2026-01-22DOI: 10.1016/j.addr.2026.115782
Rebecca I. Sienel , Nikolaus Plesnila
Central nervous system (CNS) injuries—such as stroke, traumatic brain injury, and perinatal hypoxia—trigger complex secondary cascades involving oxidative stress, inflammation, and apoptosis that limit recovery and therapeutic efficacy. Recent advances in medical gas delivery offer a novel, multifaceted approach to modulate these pathological processes. Gases including hydrogen, nitric oxide, carbon monoxide, xenon, and argon demonstrate potent neuroprotective, anti-inflammatory, and vasomodulatory properties in preclinical models. This review synthesizes current evidence on gas-based interventions across CNS pathologies, elucidates their molecular mechanisms, and evaluates translational challenges related to timing, dosing, and delivery technologies. Gas therapeutics represent a promising frontier in neurocritical care with potential to transform outcomes in otherwise intractable neurological injuries.
{"title":"Medicinal gases for treating central nervous system injuries","authors":"Rebecca I. Sienel , Nikolaus Plesnila","doi":"10.1016/j.addr.2026.115782","DOIUrl":"10.1016/j.addr.2026.115782","url":null,"abstract":"<div><div>Central nervous system (CNS) injuries—such as stroke, traumatic brain injury, and perinatal hypoxia—trigger complex secondary cascades involving oxidative stress, inflammation, and apoptosis that limit recovery and therapeutic efficacy. Recent advances in medical gas delivery offer a novel, multifaceted approach to modulate these pathological processes. Gases including hydrogen, nitric oxide, carbon monoxide, xenon, and argon demonstrate potent neuroprotective, anti-inflammatory, and vasomodulatory properties in preclinical models. This review synthesizes current evidence on gas-based interventions across CNS pathologies, elucidates their molecular mechanisms, and evaluates translational challenges related to timing, dosing, and delivery technologies. Gas therapeutics represent a promising frontier in neurocritical care with potential to transform outcomes in otherwise intractable neurological injuries.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115782"},"PeriodicalIF":17.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032813","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 : 2026-01-22DOI: 10.1016/j.addr.2026.115781
Helena Ros, Natasha Chan, Michael T. Cook, David Shorthouse
The optimisation of drug delivery systems is a complex, multidimensional challenge involving the interplay of formulation composition, process parameters, and biological performance. Traditional empirical and statistical approaches are increasingly limited by the high dimensionality, nonlinearity, and multi-objective nature of modern drug delivery problems. In this review, we explore how artificial intelligence (AI) and machine learning (ML) are transforming formulation science by enabling data-driven, adaptive, and efficient optimisation strategies. We provide a conceptual and practical overview of ML-guided optimisation workflows, including surrogate modelling, Bayesian optimisation, active learning, and multi-objective optimisation. Key challenges such as data scarcity, experimental throughput, and model interpretability are discussed. Applications across diverse delivery modalities, including solid oral dosage forms, lipid nanoparticles, biologics, and long-acting injectables, are critically examined, highlighting how ML can accelerate formulation development, reduce experimental burden, and uncover novel design spaces. We conclude by outlining future directions for integrating AI into pharmaceutical R&D, with a focus on the emergence of self-driving laboratories. This review aims to equip drug delivery scientists with the foundational knowledge and practical tools to harness AI and ML in the rational design and optimisation of advanced drug delivery systems.
{"title":"Artificial intelligence and machine learning guided optimization in drug delivery","authors":"Helena Ros, Natasha Chan, Michael T. Cook, David Shorthouse","doi":"10.1016/j.addr.2026.115781","DOIUrl":"https://doi.org/10.1016/j.addr.2026.115781","url":null,"abstract":"The optimisation of drug delivery systems is a complex, multidimensional challenge involving the interplay of formulation composition, process parameters, and biological performance. Traditional empirical and statistical approaches are increasingly limited by the high dimensionality, nonlinearity, and multi-objective nature of modern drug delivery problems. In this review, we explore how artificial intelligence (AI) and machine learning (ML) are transforming formulation science by enabling data-driven, adaptive, and efficient optimisation strategies. We provide a conceptual and practical overview of ML-guided optimisation workflows, including surrogate modelling, Bayesian optimisation, active learning, and multi-objective optimisation. Key challenges such as data scarcity, experimental throughput, and model interpretability are discussed. Applications across diverse delivery modalities, including solid oral dosage forms, lipid nanoparticles, biologics, and long-acting injectables, are critically examined, highlighting how ML can accelerate formulation development, reduce experimental burden, and uncover novel design spaces. We conclude by outlining future directions for integrating AI into pharmaceutical R&D, with a focus on the emergence of self-driving laboratories. This review aims to equip drug delivery scientists with the foundational knowledge and practical tools to harness AI and ML in the rational design and optimisation of advanced drug delivery systems.","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"190 1","pages":""},"PeriodicalIF":16.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014291","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 : 2026-01-16DOI: 10.1016/j.addr.2026.115780
Saba Abbasi Dezfouli , Hasan Uludağ , Mohammad Nasrullah , Amarnath Praphakar Rajendran , Remant K.C.
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.
{"title":"Technology for Nucleic Acid Delivery in the Treatment of Hematological Malignancies","authors":"Saba Abbasi Dezfouli , Hasan Uludağ , Mohammad Nasrullah , Amarnath Praphakar Rajendran , 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":"2026-01-16","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}
Pub Date : 2026-01-15DOI: 10.1016/j.addr.2026.115778
Hye Jin Lee, Yunxuan Xie, Colin F. Greineder, Peter M. Tessier
Oligonucleotide therapeutics, including antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), have gained increasing attention as a novel modality for gene-targeted interventions for central nervous system (CNS) disorders, particularly in the context of rare and inherited neurological conditions. By correcting pathogenic abnormalities in gene splicing or expression, oligonucleotide therapeutics offer a combination of extreme specificity and disease-modifying or even curative effects. However, achieving robust delivery to the CNS after systemic administration remains a significant challenge due to the presence of the blood-brain barrier and the intrinsic physicochemical limitations of oligonucleotide therapeutics, such as their large molecular size, high charge, and susceptibility to enzymatic degradation. Peptide-, antibody-, and lipid-based conjugates have emerged as versatile strategies for CNS oligonucleotide delivery, offering distinct advantages in molecular recognition, tunability, biocompatibility, and structural uniformity. Here, we review emerging design principles for engineering peptide, antibody, and lipid conjugates to enhance binding affinity, target selectivity, pharmacokinetics, and pharmacodynamics of oligonucleotide therapeutics for CNS applications. We also discuss how engineered delivery platforms have the potential to improve therapeutic efficacy across a spectrum of neurological disorders, from rare hereditary syndromes to highly prevalent neurodegenerative diseases.
{"title":"Bioconjugates for improved delivery of oligonucleotide therapeutics to the central nervous system","authors":"Hye Jin Lee, Yunxuan Xie, Colin F. Greineder, Peter M. Tessier","doi":"10.1016/j.addr.2026.115778","DOIUrl":"https://doi.org/10.1016/j.addr.2026.115778","url":null,"abstract":"Oligonucleotide therapeutics, including antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), have gained increasing attention as a novel modality for gene-targeted interventions for central nervous system (CNS) disorders, particularly in the context of rare and inherited neurological conditions. By correcting pathogenic abnormalities in gene splicing or expression, oligonucleotide therapeutics offer a combination of extreme specificity and disease-modifying or even curative effects. However, achieving robust delivery to the CNS after systemic administration remains a significant challenge due to the presence of the blood-brain barrier and the intrinsic physicochemical limitations of oligonucleotide therapeutics, such as their large molecular size, high charge, and susceptibility to enzymatic degradation. Peptide-, antibody-, and lipid-based conjugates have emerged as versatile strategies for CNS oligonucleotide delivery, offering distinct advantages in molecular recognition, tunability, biocompatibility, and structural uniformity. Here, we review emerging design principles for engineering peptide, antibody, and lipid conjugates to enhance binding affinity, target selectivity, pharmacokinetics, and pharmacodynamics of oligonucleotide therapeutics for CNS applications. We also discuss how engineered delivery platforms have the potential to improve therapeutic efficacy across a spectrum of neurological disorders, from rare hereditary syndromes to highly prevalent neurodegenerative diseases.","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"269 1","pages":"115778"},"PeriodicalIF":16.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993315","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 : 2026-01-14DOI: 10.1016/j.addr.2026.115779
Brian S. Wong , Jing Ling , Yongchao Su , Dan Fu
The presence of subvisible particles in protein-based pharmaceutics is a critical quality attribute that is highly regulated due to potential risks to product stability, quality, bioavailability, and patient safety. While numerous analytical technologies have been developed to measure and analyze these particles, optical characterization methods are widely used for their simplicity, robustness, and versatility. Selecting the appropriate technique from a vast array of optical spectroscopy and imaging methods can be overwhelming, but it is crucial for successful characterization. For example, compendial methods such as light obscuration are most commonly used but can underestimate particle counts and are unable to provide chemical identification. This review article aims to provide a comprehensive comparison of optical particle characterization techniques, detailing their physical principles, applications, strengths, and weaknesses. We evaluate methods based on elastic light scattering, flow-based imaging, particle tracking, and vibrational spectroscopy. We highlight the inherent trade-off between analytical throughput and information content, aiming to guide the rational selection of analytical tools for the comprehensive characterization of subvisible particles in protein therapeutics.
{"title":"Optical imaging and spectroscopic characterization of subvisible particles in protein therapeutics","authors":"Brian S. Wong , Jing Ling , Yongchao Su , Dan Fu","doi":"10.1016/j.addr.2026.115779","DOIUrl":"10.1016/j.addr.2026.115779","url":null,"abstract":"<div><div>The presence of subvisible particles in protein-based pharmaceutics is a critical quality attribute that is highly regulated due to potential risks to product stability, quality, bioavailability, and patient safety. While numerous analytical technologies have been developed to measure and analyze these particles, optical characterization methods are widely used for their simplicity, robustness, and versatility. Selecting the appropriate technique from a vast array of optical spectroscopy and imaging methods can be overwhelming, but it is crucial for successful characterization. For example, compendial methods such as light obscuration are most commonly used but can underestimate particle counts and are unable to provide chemical identification. This review article aims to provide a comprehensive comparison of optical particle characterization techniques, detailing their physical principles, applications, strengths, and weaknesses. We evaluate methods based on elastic light scattering, flow-based imaging, particle tracking, and vibrational spectroscopy. We highlight the inherent trade-off between analytical throughput and information content, aiming to guide the rational selection of analytical tools for the comprehensive characterization of subvisible particles in protein therapeutics.</div></div>","PeriodicalId":7254,"journal":{"name":"Advanced drug delivery reviews","volume":"230 ","pages":"Article 115779"},"PeriodicalIF":17.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145987776","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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