Nanoparticles approaches in neurodegenerative diseases diagnosis and treatment

Asefy, Zahra; Hoseinnejhad, Sirus; Ceferov, Zaker

doi:10.1007/s10072-021-05234-x

Cited by 34 publications

(12 citation statements)

References 67 publications

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“…By 2040, the World Health Organization (WHO) forecast that due to the aging of the population, neurodevelopmental disorder (NDD) will overtake cancer as the second most deadly human disease [ 53 ] . Aging is an important risk factor for NDD, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD).…”

Section: Applications Of 3d Printing In Medical Conditions Associated...mentioning

confidence: 99%

Applications of 3D printing in aging

Ma¹,

J²,

Wang³

et al. 2024

IJB

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Aging is inevitable, and how to age healthily is a key concern. Additive manufacturing offers many solutions to this problem. In this paper, we first briefly introduce various 3D printing technologies commonly used in the biomedical field, particularly in aging research and aging care. Next, we closely examine aging-related health conditions of nervous system, musculoskeletal system, cardiovascular system, and digestive system with a focus on the application of 3D printing in these fields, including the creation of in vitro models and implants, production of drugs and drug delivery systems, and fabrication of rehabilitation and assistive medical devices. Finally, the opportunities, challenges, and prospects of 3D printing in the field of aging are discussed.

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Section: Applications Of 3d Printing In Medical Conditions Associated...mentioning

confidence: 99%

Applications of 3D printing in aging

Ma¹,

J²,

Wang³

et al. 2024

IJB

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“…Although the precise mechanisms driving NDs are not fully elucidated, researchers suggest a multifaceted interplay involving genetic, epigenetic, and environmental factors. Presently, there are no established treatments capable of slowing, halting, or preventing the progression of any NDs [1,2].…”

Section: Introductionmentioning

confidence: 99%

NANO.PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study

He,

Nader,

Abarrategi

et al. 2024

Preprint

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Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising NDDS and NP candidates and to address the few data in the literature. IF-process was carried out between 4403 NDDS assays and 260 cytotoxicity NP assays conducting a dataset of 500000 cases. The optimal IFPTML identified was the DT algorithm, demonstrating satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained AUROC scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. On the other hand, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (hexadecyltrimethylammonium bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500000 prediction dataset was created involving n(NP cores)=5 vs. n(cell lines) =53 vs. n(NP shapes) =5 vs. n(NP coats) =16 vs. n(drugs) =123. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. Specifically, the experiment revealed that the cell line Lycopersicon esculentum showed promise for ecotoxicity studies across various coating systems. In contrast, Danio rerio cell lines (embryos, juveniles, and adults) showed lower predictive values, suggesting less favorable candidates. MacGowan volume was notably relevant for CTAB, PS, and PEG as coating agents, excluding PVA. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDS compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

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“…N2D3Ss have the ability to protect NDDs from chemical and enzymatic degradation, direct the active compound towards the target site with a substantial reduction of toxicity for the adjacent tissues, and help the NDDs to pass physiological barriers, increasing bioavailability without resorting to high dosages [ 5 , 11 ]. Therefore, researchers are studying and developing new treatment approaches that use N2D3Ss for diagnosis and treatment [ 12 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

He,

Segura Abarrategi,

Bediaga

et al. 2024

Beilstein J. Nanotechnol.

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Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood–brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

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Nanoparticles approaches in neurodegenerative diseases diagnosis and treatment

Cited by 34 publications

References 67 publications

Applications of 3D printing in aging

Applications of 3D printing in aging

NANO.PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

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