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Transformative Impact of Nanocarrier‐Mediated Drug Delivery: Overcoming Biological Barriers and Expanding Therapeutic Horizons

Kim, Minhye, Shin, Myeongyeon, Zhao, Yaping, Ghosh, Mrinmoy, Son, Young‐Ok·2024·Small Science
DOI 10.1002/smsc.202400280PMC PMC11935121
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Abstract

Advancing therapeutic progress is centered on developing drug delivery systems (DDS) that control therapeutic molecule release, ensuring precise targeting and optimal concentrations. Targeted DDS enhances treatment efficacy and minimizes off‐target effects, but struggles with drug degradation. Over the last three decades, nanopharmaceuticals have evolved from laboratory concepts into clinical products, highlighting the profound impact of nanotechnology in medicine. Despite advancements, the effective delivery of therapeutics remains challenging because of biological barriers. Nanocarriers offer a solution with a small size, high surface‐to‐volume ratios, and customizable properties. These systems address physiological and biological challenges, such as shear stress, protein adsorption, and quick clearance. They allow targeted delivery to specific tissues, improve treatment outcomes, and reduce adverse effects. Nanocarriers exhibit controlled release, decreased degradation, and enhanced efficacy. Their size facilitates cell membrane penetration and intracellular delivery. Surface modifications increase affinity for specific cell types, allowing precise treatment delivery. This study also elucidates the potential integration of artificial intelligence with nanoscience to innovate future nanocarrier systems.

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Figures

Figure 1 Common biopolymers used in organic nanocarriers. This figure illustrates the diverse array of common biopolymers utilized in the formulation of organic nanocarriers for drug delivery applications. Biopolymers play a critical role in enhancing the stability, biocompatibility, and functionality of nanocarrier systems. Several types of biopolymers, including polymeric NPs, lipid‐based carriers, dendrimers, nanoemulsions, and nanomicelles are highlighted. Each biopolymer offers unique characteristics and functionalities that contribute to their effectiveness in drug delivery. Created with BioRender.com.
Figure 1 Common biopolymers used in organic nanocarriers. This figure illustrates the diverse array of common biopolymers utilized in the formulation of organic nanocarriers for drug delivery applications. Biopolymers play a critical role in enhancing the stability, biocompatibility, and functionality of nanocarrier systems. Several types of biopolymers, including polymeric NPs, lipid‐based carriers, dendrimers, nanoemulsions, and nanomicelles are highlighted. Each biopolymer offers unique characteristics and functionalities that contribute to their effectiveness in drug delivery. Created with BioRender.com.CC BY · PubMed Central
Figure 2 The diagram illustrates the structure of LPNPs, highlighting the interaction between lipids and polymers. A) The structure of a liposome features an aqueous core surrounded by a bilayer membrane. This membrane consists of hydrophobic tails forming the interior of the bilayer, while hydrated hydrophilic heads stabilize the outer and inner surfaces. Polymersomes, which are synthetic counterparts to liposomes, are composed of amphiphilic block copolymer membranes. Although both carriers share many similar properties, polymersomes offer greater versatility and improved stability. Adapted with permission.[ 238 ] Copyright 2014, Elsevier. B)(a) The illustration shows LPNPs created through interactions with (b–d) different polymers, where the lipids (depicted in yellow) are enveloped by a “belt” of polymer (depicted in green). Different polymers can be used to create this polymer–lipid combination, with each polymer influencing the NP's properties, such as its stability, drug release profile, and interaction with biological systems. Hydrophilic groups are highlighted in blue, and hydrophobic groups are highlighted in red. [ 239 ] C) (a) Conformational adaptation at the polymer/lipid domain boundary in hybrid vesicles is expected when domain formation occurs, allowing the components to adjust and integrate within distinct regions. (b) In contrast, the absence of conformational adaptation between the polymer and lipid boundaries results in a homogeneous mixture, where the components are evenly distributed without distinct domain separation.[ 88 ] D) Hybrid lipid/polymer vesicle systems, which incorporate the lipid 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) and amphiphilic triblock copolymers composed of poly(ethylene glycol)–poly(dimethylsiloxane)–poly(ethylene glycol) (PEG–PDMS–PEG), are studied to understand how hydrophilic blocks impact vesicle morphology. The hydrophilic mass fraction of the amphiphilic polymer plays a significant role in influencing the membrane thickness, size polydispersity, and lamellarity of these polymer/lipid hybrid vesicles. Changes in the hydrophilic mass fraction can lead to variations in how the vesicles form, including their structural uniformity and stability, making this a crucial factor in the design and application of such hybrid systems. Adapted with permission.[ 92 ] Copyright 2022, ACS. E) The schematic view of a hybrid polymer/lipid membrane with lateral phase separation typically shows a membrane where distinct regions or “domains” of polymers and lipids coexist within the same membrane. Adapted with permission.[ 93 ] Copyright 2022, Elsevier.
Figure 2 The diagram illustrates the structure of LPNPs, highlighting the interaction between lipids and polymers. A) The structure of a liposome features an aqueous core surrounded by a bilayer membrane. This membrane consists of hydrophobic tails forming the interior of the bilayer, while hydrated hydrophilic heads stabilize the outer and inner surfaces. Polymersomes, which are synthetic counterparts to liposomes, are composed of amphiphilic block copolymer membranes. Although both carriers share many similar properties, polymersomes offer greater versatility and improved stability. Adapted with permission.[ 238 ] Copyright 2014, Elsevier. B)(a) The illustration shows LPNPs created through interactions with (b–d) different polymers, where the lipids (depicted in yellow) are enveloped by a “belt” of polymer (depicted in green). Different polymers can be used to create this polymer–lipid combination, with each polymer influencing the NP's properties, such as its stability, drug release profile, and interaction with biological systems. Hydrophilic groups are highlighted in blue, and hydrophobic groups are highlighted in red. [ 239 ] C) (a) Conformational adaptation at the polymer/lipid domain boundary in hybrid vesicles is expected when domain formation occurs, allowing the components to adjust and integrate within distinct regions. (b) In contrast, the absence of conformational adaptation between the polymer and lipid boundaries results in a homogeneous mixture, where the components are evenly distributed without distinct domain separation.[ 88 ] D) Hybrid lipid/polymer vesicle systems, which incorporate the lipid 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) and amphiphilic triblock copolymers composed of poly(ethylene glycol)–poly(dimethylsiloxane)–poly(ethylene glycol) (PEG–PDMS–PEG), are studied to understand how hydrophilic blocks impact vesicle morphology. The hydrophilic mass fraction of the amphiphilic polymer plays a significant role in influencing the membrane thickness, size polydispersity, and lamellarity of these polymer/lipid hybrid vesicles. Changes in the hydrophilic mass fraction can lead to variations in how the vesicles form, including their structural uniformity and stability, making this a crucial factor in the design and application of such hybrid systems. Adapted with permission.[ 92 ] Copyright 2022, ACS. E) The schematic view of a hybrid polymer/lipid membrane with lateral phase separation typically shows a membrane where distinct regions or “domains” of polymers and lipids coexist within the same membrane. Adapted with permission.[ 93 ] Copyright 2022, Elsevier.CC BY · PubMed Central
Figure 3 Hybrid structures combining polymers and lipids in nanocarrier design. This figure illustrates the concept of hybrid structures that combine polymers and lipids into a single entity, offering significant potential as versatile and efficient drug delivery carriers. Hybridization of polymers and lipids results in nanocarriers with enhanced properties, including improved stability, biocompatibility, and drug‐loading capacity. The figure depicts the structural composition of these hybrid nanocarriers, highlighting the integration of the polymer chains and lipid bilayers. Created with BioRender.com.
Figure 3 Hybrid structures combining polymers and lipids in nanocarrier design. This figure illustrates the concept of hybrid structures that combine polymers and lipids into a single entity, offering significant potential as versatile and efficient drug delivery carriers. Hybridization of polymers and lipids results in nanocarriers with enhanced properties, including improved stability, biocompatibility, and drug‐loading capacity. The figure depicts the structural composition of these hybrid nanocarriers, highlighting the integration of the polymer chains and lipid bilayers. Created with BioRender.com.CC BY · PubMed Central
Figure 4 Biological barriers to effective drug delivery. This figure illustrates the major biological barriers that pose challenges to the effective delivery of therapeutics during drug development. These barriers include the BBB, skin layers, intercellular barrier, TME, and epithelial barrier. Each barrier presents unique challenges for drug delivery to target tissues, such as restricting the passage of molecules or altering drug distribution within the body. Created with BioRender.com.
Figure 4 Biological barriers to effective drug delivery. This figure illustrates the major biological barriers that pose challenges to the effective delivery of therapeutics during drug development. These barriers include the BBB, skin layers, intercellular barrier, TME, and epithelial barrier. Each barrier presents unique challenges for drug delivery to target tissues, such as restricting the passage of molecules or altering drug distribution within the body. Created with BioRender.com.CC BY · PubMed Central
Figure 5 BBB regulation and targeted drug delivery across the barrier. A) Schematic representation of the BBB structure, showing brain capillary endothelial cells in association with astrocytes and pericytes.[ 110 ] B) Diagram illustrating various mechanisms for crossing the BBB, such as passive diffusion, carrier‐mediated transport, receptor‐mediated transcytosis, and AMT.[ 108 ] C) Influence of material properties on the adsorption, distribution, and clearance of DDSs following systemic administration, highlighting factors such as particle size, surface charge, and hydrophobicity.[ 240 ] D) The various biomolecules and their conjugation to the vesicle as BBB‐targeted carriers, including antibodies, peptides, and small molecules The various biomolecules and their conjugation to the vesicle as BBB‐targeted carriers, including antibodies, peptides, and small molecules. Adapted with permission.[ 241 ] Copyright 2022, Elsevier. E) Schematic depiction of the primary pharmacokinetic principles for transporter‐mediated drug delivery to the brain, including drug, prodrug, or nanocarrier transport across the BBB and brain parenchymal cellular barrier.[ 242 ] F) Illustration of receptor‐mediated drug delivery utilizing ligand‐conjugated NPs targeted to the brain, showing the interaction between the ligand and specific receptors on the BBB.[ 108 ] Figures are reproduced with permission.
Figure 5 BBB regulation and targeted drug delivery across the barrier. A) Schematic representation of the BBB structure, showing brain capillary endothelial cells in association with astrocytes and pericytes.[ 110 ] B) Diagram illustrating various mechanisms for crossing the BBB, such as passive diffusion, carrier‐mediated transport, receptor‐mediated transcytosis, and AMT.[ 108 ] C) Influence of material properties on the adsorption, distribution, and clearance of DDSs following systemic administration, highlighting factors such as particle size, surface charge, and hydrophobicity.[ 240 ] D) The various biomolecules and their conjugation to the vesicle as BBB‐targeted carriers, including antibodies, peptides, and small molecules The various biomolecules and their conjugation to the vesicle as BBB‐targeted carriers, including antibodies, peptides, and small molecules. Adapted with permission.[ 241 ] Copyright 2022, Elsevier. E) Schematic depiction of the primary pharmacokinetic principles for transporter‐mediated drug delivery to the brain, including drug, prodrug, or nanocarrier transport across the BBB and brain parenchymal cellular barrier.[ 242 ] F) Illustration of receptor‐mediated drug delivery utilizing ligand‐conjugated NPs targeted to the brain, showing the interaction between the ligand and specific receptors on the BBB.[ 108 ] Figures are reproduced with permission.CC BY · PubMed Central
Figure 6 Vascular normalization mechanisms, associated signaling pathways, and the role of drug‐loaded NPs. A) Vascular normalization can be achieved through various strategies, including targeting VEGF signaling, Ang–Tie signaling, phospholipase C (PLC), phosphoinositide 3‐kinase (PI3K), angiopoietin 2 (Ang2), and epidermal growth factor receptor (EGFR) pathways in endothelial cells, as well as disrupting oncogenic signaling in cancer cells. Immune checkpoint blockade also induces vascular normalization by activating CD8+ or CD4+ T cells and promoting eosinophil accumulation. Key abbreviations: AAT (antiangiogenic therapy), IFP (interstitial fluid pressure), CAF (cancer‐associated fibroblast), ECM, VEGF (VEGFR (VEGF receptor).[ 174 ] B) (a) Drug‐loaded NPs can suppress hypoxic tumor growth by targeting and reprogramming abnormal tumor vasculature. They also can convert M2 macrophages into M1 phenotypes, which reactivates the immune response, further inhibiting tumor growth. Additionally, these NPs can activate mature DCs, leading to the activation of B cells via T cells, and the production of antibodies that target and eliminate tumor cells. By stimulating these pathways, NPs can counteract the inhibitory effects of myeloid‐derived suppressor cells on mature DCs and the suppressive effects of regulatory T cells (Tregs) on interferon‐gamma (INF‐γ), ultimately resulting in tumor cell death. (b) TAMs can inhibit tumor vasculature through M1 macrophages and interleukin‐12 (IL‐12). However, hypoxic conditions can induce the conversion of TAM1 into TAM2, which promotes tumor vasculature through interleukin‐10 (IL‐10). Drug‐loaded NPs can prevent this TAM1 to TAM2 conversion, thereby reducing tumor growth. Additionally, siRNA‐loaded NPs can specifically suppress TAM2 activity.[ 243 ] Figures are Reproduced with permission.
Figure 6 Vascular normalization mechanisms, associated signaling pathways, and the role of drug‐loaded NPs. A) Vascular normalization can be achieved through various strategies, including targeting VEGF signaling, Ang–Tie signaling, phospholipase C (PLC), phosphoinositide 3‐kinase (PI3K), angiopoietin 2 (Ang2), and epidermal growth factor receptor (EGFR) pathways in endothelial cells, as well as disrupting oncogenic signaling in cancer cells. Immune checkpoint blockade also induces vascular normalization by activating CD8+ or CD4+ T cells and promoting eosinophil accumulation. Key abbreviations: AAT (antiangiogenic therapy), IFP (interstitial fluid pressure), CAF (cancer‐associated fibroblast), ECM, VEGF (VEGFR (VEGF receptor).[ 174 ] B) (a) Drug‐loaded NPs can suppress hypoxic tumor growth by targeting and reprogramming abnormal tumor vasculature. They also can convert M2 macrophages into M1 phenotypes, which reactivates the immune response, further inhibiting tumor growth. Additionally, these NPs can activate mature DCs, leading to the activation of B cells via T cells, and the production of antibodies that target and eliminate tumor cells. By stimulating these pathways, NPs can counteract the inhibitory effects of myeloid‐derived suppressor cells on mature DCs and the suppressive effects of regulatory T cells (Tregs) on interferon‐gamma (INF‐γ), ultimately resulting in tumor cell death. (b) TAMs can inhibit tumor vasculature through M1 macrophages and interleukin‐12 (IL‐12). However, hypoxic conditions can induce the conversion of TAM1 into TAM2, which promotes tumor vasculature through interleukin‐10 (IL‐10). Drug‐loaded NPs can prevent this TAM1 to TAM2 conversion, thereby reducing tumor growth. Additionally, siRNA‐loaded NPs can specifically suppress TAM2 activity.[ 243 ] Figures are Reproduced with permission.CC BY · PubMed Central
Figure 7 Strategies for overcoming abnormal vasculature barriers in the TME. This figure illustrates the three primary strategies developed to address the abnormal vasculature barriers encountered within the TME and facilitate the administration of nanoagents. Normalization and promotion: This involves interventions to normalize the tumor vasculature to improve blood flow and vessel integrity. Promoting the restoration of a more regular vascular network enhances the delivery of nanoagents to tumor tissues. Permeabilization or disruption: This strategy focuses on temporarily permeabilizing or disrupting the abnormal tumor vasculature to facilitate nanoagent penetration into tumor tissues. By creating transient openings in the vascular barrier, this approach allows for improved delivery of therapeutic agents to the TME. Tumor‐associated platelets inhibition: This strategy targets tumor‐associated platelets, which contribute to tumor growth and metastasis by promoting angiogenesis and thrombosis. By inhibiting the activity of these platelets, this approach aims to prevent abnormal vasculature formation and enhance the efficacy of nanoagent delivery to tumors. Created with BioRender.com.
Figure 7 Strategies for overcoming abnormal vasculature barriers in the TME. This figure illustrates the three primary strategies developed to address the abnormal vasculature barriers encountered within the TME and facilitate the administration of nanoagents. Normalization and promotion: This involves interventions to normalize the tumor vasculature to improve blood flow and vessel integrity. Promoting the restoration of a more regular vascular network enhances the delivery of nanoagents to tumor tissues. Permeabilization or disruption: This strategy focuses on temporarily permeabilizing or disrupting the abnormal tumor vasculature to facilitate nanoagent penetration into tumor tissues. By creating transient openings in the vascular barrier, this approach allows for improved delivery of therapeutic agents to the TME. Tumor‐associated platelets inhibition: This strategy targets tumor‐associated platelets, which contribute to tumor growth and metastasis by promoting angiogenesis and thrombosis. By inhibiting the activity of these platelets, this approach aims to prevent abnormal vasculature formation and enhance the efficacy of nanoagent delivery to tumors. Created with BioRender.com.CC BY · PubMed Central
Figure 8 Nanocarriers for targeted DDS to the TME. A) Nano‐DDSs can selectively target TME of melanoma through modifications such as protein/peptide conjugation, antibody‐based targeting, carbohydrate surface decorating, or polymer coating. These carriers can then release their payloads into the TME, exerting antitumor effects. Adapted with permission. [ 244 ] Copyright 2021, Elsevier. B) Nano carrier‐based smart DDS overcomes the barriers in TME and releases drugs in cancer cells. Solid tumors induce high expression of ECM molecules (collagens, proteoglycans, hyaluronic acid, and laminins), which become complex and disordered, altering the characteristics of the TME. The ECM acts as a physical barrier, reducing the delivery of therapeutics, nutrients, and immune cells to solid tumors, leading to poorer prognosis. Created with BioRender.com.[ 245 , 246 ] C) The figure illustrates the biological barriers within the TME that affect nanodrug delivery, along with strategies for TME remodeling to enhance tumor penetration. (a) Techniques such as vasculature normalization, permeabilization, disruption, and blocking tumor‐associated platelets are employed to overcome barriers to transvascular transport. (b) Strategies for disrupting the ECM or inhibiting its synthesis are also depicted. Additionally, the regulation of (c) CAFs and (d) TAMs through depletion, reeducation, or utilization is shown to facilitate deeper infiltration of nanodrugs within the tumor stroma. Adapted with permission.[ 177 ] Copyright 2017, Elsevier. Distribution of chitosan NPs (CNPs) in mice bearing transplanted tumors. Time‐dependent, noninvasive in vivo near‐infrared fluorescence (NIRF) imaging was performed on tumor‐bearing mice following the injection of NIRF dye‐labeled CNPs (200 μg μL−1 head). The distribution of CNPs across these various tumor models reflects the diverse characteristics of the TMEs associated with clinical tumors.[ 247 ] Figures are Reproduced with permission.
Figure 8 Nanocarriers for targeted DDS to the TME. A) Nano‐DDSs can selectively target TME of melanoma through modifications such as protein/peptide conjugation, antibody‐based targeting, carbohydrate surface decorating, or polymer coating. These carriers can then release their payloads into the TME, exerting antitumor effects. Adapted with permission. [ 244 ] Copyright 2021, Elsevier. B) Nano carrier‐based smart DDS overcomes the barriers in TME and releases drugs in cancer cells. Solid tumors induce high expression of ECM molecules (collagens, proteoglycans, hyaluronic acid, and laminins), which become complex and disordered, altering the characteristics of the TME. The ECM acts as a physical barrier, reducing the delivery of therapeutics, nutrients, and immune cells to solid tumors, leading to poorer prognosis. Created with BioRender.com.[ 245 , 246 ] C) The figure illustrates the biological barriers within the TME that affect nanodrug delivery, along with strategies for TME remodeling to enhance tumor penetration. (a) Techniques such as vasculature normalization, permeabilization, disruption, and blocking tumor‐associated platelets are employed to overcome barriers to transvascular transport. (b) Strategies for disrupting the ECM or inhibiting its synthesis are also depicted. Additionally, the regulation of (c) CAFs and (d) TAMs through depletion, reeducation, or utilization is shown to facilitate deeper infiltration of nanodrugs within the tumor stroma. Adapted with permission.[ 177 ] Copyright 2017, Elsevier. Distribution of chitosan NPs (CNPs) in mice bearing transplanted tumors. Time‐dependent, noninvasive in vivo near‐infrared fluorescence (NIRF) imaging was performed on tumor‐bearing mice following the injection of NIRF dye‐labeled CNPs (200 μg μL−1 head). The distribution of CNPs across these various tumor models reflects the diverse characteristics of the TMEs associated with clinical tumors.[ 247 ] Figures are Reproduced with permission.CC BY · PubMed Central
Figure 9 Role of AI in biomedical research. This figure highlights the pivotal role of AI in driving biomedical research and revolutionizing healthcare. AI significantly advances biomedical research by unlocking new insights, accelerating discovery processes, and facilitating personalized healthcare approaches. Key applications include AI‐based disease diagnostics and prediction: A) Developing a smartphone‐based computing program for diagnosing middle ear disease and providing medical suggestions. Adapted with permission.[ 248 ] Copyright 2022, Elsevier. B) AI in echocardiography.[ 249 ] C) AI‐assisted genetic analysis for mutation and target identification, particularly in functional genomics AI‐assisted genetic analysis for mutation and target identification, particularly in functional genomics.[ 250 ] D) AI models for variant detection and prioritization of disease‐causing variants in different genomic regions.[ 251 ] E) AI‐assisted biomedical imaging and processing, enhancing tasks such as preprocessing, image analysis, and interpretation, integrated with ML.[ 252 ] F) AI‐based data interception and model prediction.[ 253 ] G) AI‐assisted targeted drug delivery, Created with BioRender.com.[ 231 ] H) AI‐based early heath prediction.[ 254 ] I) AI‐assisted diagnostics, including AI‐based cough diagnosis systems that encompass every stage with data exchange capabilities for researchers and medical doctors.[ 255 ] J) AI‐assisted therapeutics in precision medicine, particularly in evaluating cancer treatments.[ 256 ] Figures are reproduced with permission. Created with BioRender.com.
Figure 9 Role of AI in biomedical research. This figure highlights the pivotal role of AI in driving biomedical research and revolutionizing healthcare. AI significantly advances biomedical research by unlocking new insights, accelerating discovery processes, and facilitating personalized healthcare approaches. Key applications include AI‐based disease diagnostics and prediction: A) Developing a smartphone‐based computing program for diagnosing middle ear disease and providing medical suggestions. Adapted with permission.[ 248 ] Copyright 2022, Elsevier. B) AI in echocardiography.[ 249 ] C) AI‐assisted genetic analysis for mutation and target identification, particularly in functional genomics AI‐assisted genetic analysis for mutation and target identification, particularly in functional genomics.[ 250 ] D) AI models for variant detection and prioritization of disease‐causing variants in different genomic regions.[ 251 ] E) AI‐assisted biomedical imaging and processing, enhancing tasks such as preprocessing, image analysis, and interpretation, integrated with ML.[ 252 ] F) AI‐based data interception and model prediction.[ 253 ] G) AI‐assisted targeted drug delivery, Created with BioRender.com.[ 231 ] H) AI‐based early heath prediction.[ 254 ] I) AI‐assisted diagnostics, including AI‐based cough diagnosis systems that encompass every stage with data exchange capabilities for researchers and medical doctors.[ 255 ] J) AI‐assisted therapeutics in precision medicine, particularly in evaluating cancer treatments.[ 256 ] Figures are reproduced with permission. Created with BioRender.com.CC BY · PubMed Central
Figure 10 The diagram illustrates the role of AI integrated across various stages of biomedical research, highlighting its impact and challenges. AI‐driven workflows are increasingly explored in digital pathology, drug discovery, and development, showcasing significant potential in personalized care, clinical decision support systems, early disease detection, disease monitoring, and dynamic drug dosing. However, the successful application of AI in these areas also faces challenges, such as the need for high‐quality large datasets, managing the overwhelming influx of data (data deluge), and addressing data drift, which can affect the accuracy and reliability of AI models. These challenges may slow progress and delay the clinical implementation of AI‐driven solutions in healthcare. Created with BioRender.com.
Figure 10 The diagram illustrates the role of AI integrated across various stages of biomedical research, highlighting its impact and challenges. AI‐driven workflows are increasingly explored in digital pathology, drug discovery, and development, showcasing significant potential in personalized care, clinical decision support systems, early disease detection, disease monitoring, and dynamic drug dosing. However, the successful application of AI in these areas also faces challenges, such as the need for high‐quality large datasets, managing the overwhelming influx of data (data deluge), and addressing data drift, which can affect the accuracy and reliability of AI models. These challenges may slow progress and delay the clinical implementation of AI‐driven solutions in healthcare. Created with BioRender.com.CC BY · PubMed Central

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Metadata

Authors
Kim, M., Shin, M., Zhao, Y., Ghosh, M., & Son, Y.
Venue
Small Science
Publisher
Wiley
Published
2024-09-17
Volume
4
Issue
11
ISSN
2688-4046
DOI
10.1002/smsc.202400280
PMC
PMC11935121
Cited by
97
References
18
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
National Research Foundation of Korea (2020R1A2C2004128, RS‐2023‐00208776, 2019R1A6A1A10072987, 2022H1D3A2A02053110, 2023RIS‐009, 2020R1A6C101A188)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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CoChat. (2026). Citation check: Transformative Impact of Nanocarrier‐Mediated Drug Delivery: Overcoming Biological Barriers and Expanding Therapeutic Horizons. https://app.cochat.ai/papers/10.1002/smsc.202400280
The paper
Kim, M., Shin, M., Zhao, Y., Ghosh, M., & Son, Y. (2024). Transformative Impact of Nanocarrier‐Mediated Drug Delivery: Overcoming Biological Barriers and Expanding Therapeutic Horizons. Small Science. https://doi.org/10.1002/smsc.202400280