Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review

Nanoparticle-Based Approaches in the Diagnosis and Treatment of Brain Tumors

Glioblastomas are highly invasive brain tumors among perilous diseases. They are characterized by their fast proliferation and delayed detection that render them a significant focal point for medical research endeavors within the realm of cancer. Among glioblastomas, Glioblastoma multiforme (GBM) is the most aggressive and prevalent malignant brain tumor. For this, nanomaterials such as metallic and lipid nanoparticles and quantum dots have been acknowledged as efficient carriers. These nano-materials traverse the blood-brain barrier (BBB) and integrate and reach the necessary regions for neuro-oncology imaging and treatment purposes. This paper provides a thorough analysis on nanoparticles used in the diagnosis and treatment of brain tumors, especially for GBM.

Epidemiology of malignant brain tumors in Genova, Italy. 1993-2017

We present an updated analysis on the incidence of primary brain tumors in the Metropolitan Area of Genova, the capital of the northwestern Italian region Liguria.The number of cases and incidence rates for all malignant brain tumors, glioblastoma, malignant brain tumors other than glioblastoma, and brain tumors not otherwise specified were calculated for each of seven three-year and one-eighth four-year periods in which the quarter century 1993-2017 was divided. The rates were age-adjusted (AAR) using the 2013 European standard population, presented per 100,000 person-years and the average percentage change over the three-year period was calculated.The number of cases of all malignant brain tumors and glioblastoma was higher in males than in females in each three-year period and in the entire quarter century 1993-2017. During the latter, the average three-year percentage change in AARs for all brain tumors was minimal [0.6 (95% C.I. = -1.0/2.1) %] while for glioblastoma there was a change of 5.3 (95% C.I. = -0.4/11.3) %. The partially concurrent decline in the incidence rates of malignant brain tumors other than glioblastoma or not otherwise specified suggests that the observed increase in the incidence rate of glioblastoma during 1993-2017 may have been at least partially linked to the improvement during the same period in sensitivity and specificity of the diagnosis of glioblastoma, depleting the reservoirs of other malignant or unspecified brain tumors. Research into possibly increased environmental risk factors (e.g., population exposure to ionizing radiation) for glioblastoma in Genova remains warranted.

Exploring Extracellular Vesicle Surface Protein Markers Produced by Glioblastoma Tumors: A Characterization Study Using In Vitro 3D Patient-Derived Cultures

BACKGROUND/OBJECTIVES

Glioblastoma (GBM) is an aggressive brain cancer with limited treatment options. Extracellular vesicles (EVs) derived from GBM cells contain important biomarkers, such as microRNAs, proteins, and DNA mutations, which are involved in tumor progression, invasion, and resistance to treatment. Identifying surface markers on these EVs is crucial for their isolation and potential use in noninvasive diagnosis. This study aimed to use tumor-derived explants to investigate the surface markers of EVs and explore their role as diagnostic biomarkers for GBM.

METHODS

Tumor explants from nine GBM patients without IDH1/IDH2 mutations or 1p-19q co-deletion were cultured to preserve both tumor viability and cytoarchitecture. EVs were collected from the tumor microenvironment using differential centrifugation, filtration, and membrane affinity binding. Their surface protein composition was analyzed through multiplex protein assays. RNA-Seq data from TCGA and GTEx datasets, along with in silico single-cell RNA-seq data, were used to assess EV surface biomarker expression across large GBM patient cohorts.

RESULTS

The in vitro model successfully replicated the tumor microenvironment and produced EVs with distinct surface markers. Biomarker analysis in large datasets revealed specific expression patterns unique to GBM patients compared with healthy controls. These markers demonstrated potential as a GBM-specific signature and were correlated with clinical data. Furthermore, in silico single-cell RNA-seq provided detailed insights into biomarker distribution across different cell types within the tumor.

CONCLUSIONS

This study underscores the efficacy of the tumor-derived explant model and its potential to advance the understanding of GBM biology and EV production. A key innovation is the isolation of EVs from a model that faithfully mimics the tumor's original cytoarchitecture, offering a deeper understanding of the cells involved in EV release. The identified EV surface markers represent promising targets for enhancing EV isolation and optimizing their use as diagnostic tools. Moreover, further investigation into their molecular cargo may provide crucial insights into tumor characteristics and evolution.

Advancements in nanotheranostics for glioma therapy

Gliomas are brain tumors mainly derived from glial cells that are difficult to treat and cause high mortality. Radiation, chemotherapy, and surgical excision are the conventional treatments for gliomas. Patients who have surgery or have undergone chemotherapy for glioma treatment have poor prognosis with tumor recurrence. In particular, for glioblastoma, the 5-year average survival rate is 4-7%, and the median survival is 12-18 months. A number of issues hinder effective treatment such as, poor surgical resection, tumor heterogeneity, insufficient drug penetration across the blood-brain barrier, multidrug resistance, and difficulties with drug specificity. Nanotheranostic-mediated drug delivery is becoming a well-researched consideration, and an efficient non-invasive method for delivering chemotherapeutic drugs to the target area. Theranostic nanomedicines, which incorporate therapeutic drugs and imaging agents for personalized therapies, can be used for preventing overdose of non-responders. Through the identification of massive and complicated information from next-generation sequencing, machine learning enables for precise prediction of therapeutic outcomes and post-treatment management for patients with cancer. This article gives a thorough overview of nanocarrier-mediated drug delivery with a brief introduction to drug delivery challenges. In addition, this assessment offers a current summary of preclinical and clinical research on nanomedicines for gliomas. In the future, nanotheranostics will provide personalized treatment for gliomas and other treatable cancers.

Perspective: Raman spectroscopy for detection and management of diseases affecting the nervous system

Raman spectroscopy (RS) is used increasingly for disease detection, including diseases of the nervous system (CNS). This Perspective presents RS basics and how it has been applied to disease detection. Research that focused on using a novel Raman-based technology-Rametrix® Molecular Urinalysis (RMU)-for systemic disease detection is presented, demonstrated by an example of how the RS/RMU technology could be used for detection and management of diseases of the CNS in companion animals.

Impact of RANGAP1 SUMOylation on Smad4 nuclear export by bioinformatic analysis and cell assays

Small Ubiquitin-like Modifier (SUMOylation) regulates a variety of cellular activities, and its dysregulation has been associated with glioma etiology. The aim of this research was to clarify the function of SUMOylation-related genes in glioma and determine relevant prognostic markers. The Cancer Genome Atlas (TCGA) Glioma and GSE16011 datasets were analyzed through bioinformatics to identify Ran GTPase activating protein 1 (RANGAP1) as the hub gene for further study. Experimental validation consisted of quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB), and immunoprecipitation (IP) to evaluate RANGAP1 expression, function, and interaction with SUMO1. To assess the role of RANGAP1 knockdown and SUMOylation in glioma cells, various assays were conducted, including cell proliferation, migration, invasion, and apoptosis. In addition, cell cycle analysis and immunofluorescence were performed. Through bioinformatics, RANGAP1 was identified as a crucial prognostic gene for glioma. Experimental studies confirmed the downregulation of RANGAP1 in glioma cells and verified that RANGAP1 repair impedes tumor growth. When it comes to RANGAP1 silencing, it enhanced cell proliferation, invasion and migration. Additionally, SUMO1 was identified as a specific SUMO molecule coupled to RANGAP1, affecting the location of Sma and Mad related protein 4 (Smad4) in the nucleocytoplasm and the transforming growth factor (TGF)-β/Smad signaling pathway. The functional impact of RANGAP1 SUMOylation on cell proliferation and migration was further confirmed through experiments using a SUMOylation-impairing mutation (K524R). Our findings suggest that RANGAP1 may be a potential prognostic marker in gliomas and could play a role in regulating cell proliferation, migration, and invasion. SUMOylation of RANGAP1 is responsible for regulating the TGF-β/Smad signaling pathway, which is crucial for the progression of tumors. Further investigations and experiments are necessary to confirm these results.

Optic Nerve Glioblastoma with Optic Chiasm Involvement: A Case Report and a Brief Literature Review

Background: optic nerve glioblastoma is an uncommon pathology. The optic chiasm, optic tract, or optic nerves are possible places from which the tumor can originate. Most of the neuroimaging findings are nonspecific. To confirm the diagnosis, a biopsy is required. A delay to the treatment plan for optic nerve glioblastoma results in poor patient survival rates. Case report: a 68-year-old woman with an uncomplicated medical history presented with exophthalmos, deteriorating eyesight, and partial loss of vision. Using radiological data together with postoperative histopathological and histochemical analysis, optic nerve glioblastoma, IDH-wildtype, with optic chiasm involvement was diagnosed. Conclusion: optic nerve glioblastoma is a rare and aggressive form of cancer that affects the optic nerve, leading to significant vision impairment and potentially life-threatening complications. Treatment options are restricted and difficult because of the location and nature of the condition; surgery, radiation therapy, and chemotherapy are frequently needed as part of a multidisciplinary approach.

Pivotal Role of FBXW4 in Glioma Progression and Prognosis

Backgrounds

Glioma stands as one of the most formidable brain tumor types, with patient outcomes remaining bleak even in the face of advancements in treatment modalities. FBXW4, a constituent of the F-box and WD repeat domain-containing protein family, is recognized for its participation in diverse cellular activities, including those related to tumor dynamics. Yet, the therapeutic relevance and specific role of FBXW4 in the context of glioma are not well defined. This study aims to elucidate the functional dynamics and significance of FBXW4 in glioma cases.

Methods

This research undertook a comprehensive analysis of FBXW4's expression patterns and clinical relevance in glioma by harnessing data from the TCGA and GTEx databases.

Results

The investigation revealed a distinct downregulation of FBXW4 in glioma tissues compared to normal brain counterparts, with a pronounced correlation between FBXW4 levels and disease severity. Intriguingly, FBXW4 expression inversely related to WHO tumor grades, with the most advanced grade IV gliomas exhibiting the lowest FBXW4 levels, whereas grade II tumors demonstrated the highest. Cases presenting with IDH1/2 mutations or 1p/19q codeletions were also associated with elevated FBXW4 levels. Furthermore, diminished FBXW4 expression aligned with an increased risk of mortality.

Conclusions

The findings suggest that FBXW4 holds promise as a prognostic marker and a potential therapeutic avenue in glioma management. Nonetheless, future research is imperative to decode the intricate signaling pathways involving FBXW4 and to understand its broader clinical ramifications in glioma treatment paradigms.

Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection

Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide.

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

Objective

Telomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.

Methods

This was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.

Results

Among the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.

Conclusion

The LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.

Optimizing Glioma Resection Outcomes: A Systematic Review of Intraoperative Magnetic Resonance Imaging Guidance in Neurosurgery

This systematic review evaluates the efficacy of intraoperative magnetic resonance imaging (iMRI) in enhancing glioma resection outcomes within neurosurgical procedures. Given the complexity and variability of gliomas, achieving precise and safe resections is challenging, necessitating the use of advanced imaging techniques like iMRI. This technology provides real-time, high-resolution insights during surgery, allowing for adaptations based on surgical dynamics and brain shifts. Our comprehensive search across multiple databases selected five significant studies that collectively demonstrate the beneficial impact of iMRI. These studies highlight its role in significantly improving the extent of tumor resection and suggest potential enhancements in both immediate and long-term patient outcomes. The findings indicate that iMRI facilitates more aggressive yet safe resections, particularly in high-risk glioma cases. However, the implementation of iMRI in clinical practice requires careful consideration of training, resource allocation, and the potential variability in outcomes due to study design heterogeneity. Future research should focus on randomized controlled trials to better understand the cost-effectiveness and long-term benefits of iMRI, promoting its wider adoption in neurosurgical settings.

Advances in porphyrins and chlorins associated with polysaccharides and polysaccharides-based materials for biomedical and pharmaceutical applications

Over the last decade, the convergence of advanced materials and innovative applications has fostered notable scientific progress within the biomedical and pharmaceutical fields. Porphyrins and their derivatives, distinguished by an extended conjugated π-electron system, have a relevant role in propelling these advancements, especially in drug delivery systems, photodynamic therapy, wound healing, and (bio)sensing. However, despite their promise, the practical clinical application of these macrocycles is hindered by their inherent challenges of low solubility and instability under physiological conditions. To address this limitation, researchers have exploited the synergistic association of porphyrins and chlorins with polysaccharides by engineering conjugated systems and composite/hybrid materials. This review compiles the principal advances in this growing research field, elucidating fundamental principles and critically examining the applications of such materials within biomedical and pharmaceutical contexts. Additionally, the review addresses the eventual challenges and outlines future perspectives for this poignant research field. It is expected that this review will serve as a comprehensive guide for students and researchers dedicated to exploring state-of-the-art materials for contemporary medicine and pharmaceutical applications.

Gold nanomakura: nanoarchitectonics and their photothermal response in association with carrageenan hydrogels

Photothermal conversion of light into heat energy is an intrinsic optical property of metal nanoparticles when irradiated using near-infrared radiation. However, the impact of size and shape on the photothermal behaviour of gold nanomakura particles possessing optical absorption within 600-700 nm as well as on incorporation in hydrogels is not well reported. In this study, nanomakura-shaped anisotropic gold nanoparticles (AuNMs) were synthesized via a surfactant-assisted seed-mediated protocol. Quaternary cationic surfactants having variable carbon tail length (n = 16, 14, 12) were used as capping for tuning the plasmon peak of gold nanomakura within a 600-700 nm wavelength. The aspect ratio as well as anisotropy of synthesized gold nanomakura can influence photothermal response upon near-infrared irradiation. The role of carbon tail length was evident via absorption peaks obtained from longitudinal surface plasmon resonance analysis at 670, 650, and 630 nm in CTAB-AuNM, MTAB-AuNM, and DTAB-AuNM, respectively. Furthermore, the impact of morphology and surrounding milieu of the synthesized nanomakuras on photothermal conversion is investigated owing to their retention of plasmonic stability. Interestingly, we found that photothermal conversion was exclusively assigned to morphological features (i.e., nanoparticles of higher aspect ratio showed higher temperature change and vice versa irrespective of the surfactant used). To enable biofunctionality and stability, we used kappa-carrageenan- (k-CG) based hydrogels for incorporating the nanomakuras and further assessed their photothermal response. Nanomakura particles in association with k-CG were also able to show photothermal conversion, depicting their ability to interact with light without hindrance. The CTAB-AuNM, MTAB-AuNM, and DTAB-AuNM after incorporation into hydrogel beads attained up to ≈17.2, ≈17.2, and ≈15.7 °C, respectively. On the other hand, gold nanorods after incorporation into k-CG did not yield much photothermal response as compared to that of AuNMs. The results showed a promising platform to utilize nanomakura particles along with kappa-carrageenan hydrogels for enabling usage on nanophotonic, photothermal, and bio-imaging applications.

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.

Prostate-Specific Membrane Antigen Positron Emission Tomography Oncological Applications beyond Prostate Cancer in Comparison to Other Radiopharmaceuticals

BACKGROUND

Prostate-specific membrane antigen (PSMA) is a type II transmembrane glycoprotein overexpressed on the surface of tumor cells in most of the patients affected by prostate adenocarcinoma (PCa). However, PSMA expression has also been demonstrated in the endothelial cells of newly formed vessels of various solid tumors, suggesting a role for PSMA in neoangiogenesis. In this scenario, gallium-68 (68Ga) or fluoro-18 (18F)-labeled PSMA positron emission tomography (PET) may play a role in tumors other than PCa, generally evaluated employing other radiopharmaceuticals targeting different pathways. This review aims to investigate the detection rate of PSMA-PET compared to other radiopharmaceuticals (especially [18F]FDG) in non-prostate tumors to identify patients who may benefit from the use of such a theragnostic agent.

METHODS

We performed a bibliographic search on three different databases until February 2024 using the following terms: "positron emission tomography", "PET", "PET/CT", "Prostate-specific membrane antigen", "PSMA", "non-prostate", "not prostate cancer", "solid tumor", "FDG", "Fluorodeoxyglucose", "FAPi", "FET", "MET", "DOPA", "choline", "FCH", "FES", "DOTATOC", "DOTANOC", and "DOTATATE". Only original articles edited in English with at least 10 patients were included.

RESULTS

Out of a total of 120 articles, only 25 original articles comparing PSMA with other radiotracers were included in this study. The main evidence was demonstrated in renal cell carcinoma, where PSMA showed a higher detection rate compared to [18F]FDG PET/CT, with implications for patient management. PSMA PET may also improve the assessment of other entities, such as gliomas, in defining regions of early neoangiogenesis. Further data are needed to evaluate the potential role of PSMA-PET in triple-negative breast cancer as a novel therapeutic vascular target. Finally, unclear applications of PSMA-PET include thyroid and gastrointestinal tumors.

CONCLUSIONS

The present review shows the potential use of PSMA-labeled PET/CT in solid tumors beyond PCa, underlining its value over other radiopharmaceuticals (mainly [18F]FDG). Prospective clinical trials with larger sample sizes are crucial to further investigate these possible clinical applications.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Biosensor-Enhanced Organ-on-a-Chip Models for Investigating Glioblastoma Tumor Microenvironment Dynamics

Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC models, providing a comprehensive platform for investigating glioblastoma tumor microenvironment dynamics. The applications of this combined approach in studying glioblastoma dynamics are critically scrutinized, emphasizing its potential to bridge the gap between simplistic models and the intricate in vivo conditions. Furthermore, the article discusses the implications of biosensor-enhanced OOC models in elucidating the dynamic features of the tumor microenvironment, encompassing cell migration, proliferation, and interactions. By furnishing real-time insights, these models significantly contribute to unraveling the complex biology of glioblastoma, thereby influencing the development of more accurate diagnostic and therapeutic strategies.

Diffuse Midline Gliomas: Challenges and New Strategies in a Changing Clinical Landscape

Diffuse intrinsic pontine glioma (DIPG) was first described by Harvey Cushing, the father of modern neurosurgery, a century ago. Since then, the classification of this tumor changed significantly, as it is now part of the broader family of diffuse midline gliomas (DMGs), a heterogeneous group of tumors of midline structures encompassing the entire rostro-caudal space, from the thalamus to the spinal cord. DMGs are characterized by various epigenetic events that lead to chromatin remodeling similarities, as two decades of studies made possible by increased tissue availability showed. This new understanding of tumor (epi)biology is now driving novel clinical trials that rely on targeted agents, with finally real hopes for a change in an otherwise unforgiving prognosis. This biological discovery is being paralleled with equally exciting work in therapeutic drug delivery. Invasive and noninvasive platforms have been central to early phase clinical trials with a promising safety track record and anecdotal benefits in outcome.

HSA-ZW800-PEG for Enhanced Optophysical Stability and Tumor Targeting

Small molecule fluorophores often face challenges such as short blood half-life, limited physicochemical and optical stability, and poor pharmacokinetics. To overcome these limitations, we conjugated the zwitterionic near-infrared fluorophore ZW800-PEG to human serum albumin (HSA), creating HSA-ZW800-PEG. This conjugation notably improves chemical, physical, and optical stability under physiological conditions, addressing issues commonly encountered with small molecules in biological applications. Additionally, the high molecular weight and extinction coefficient of HSA-ZW800-PEG enhances biodistribution and tumor targeting through the enhanced permeability and retention effect. The unique distribution and elimination dynamics, along with the significantly extended blood half-life of HSA-ZW800-PEG, contribute to improved tumor targetability in both subcutaneous and orthotopic xenograft tumor-bearing animal models. This modification not only influences the pharmacokinetic profile, affecting retention time and clearance patterns, but also enhances bioavailability for targeting tissues. Our study guides further development and optimization of targeted imaging agents and drug-delivery systems.