He Shan, Nader Karam, Abarrategi Julen Segura, Bediaga Harbil, Nocedo-Mena Deyani, Ascencio Estefania, Casanola-Martin Gerardo M, Castellanos-Rubio Idoia, Insausti Maite, Rasulev Bakhtiyor, Arrasate Sonia, González-Díaz Humberto
Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA.
Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
J Nanobiotechnology. 2024 Jul 23;22(1):435. doi: 10.1186/s12951-024-02660-9.
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, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown 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 Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (FeO_A and FeO_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (FeO_A and FeO_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium 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 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. 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.
神经退行性疾病涉及神经元的进行性死亡。由于溶解性、生物利用度以及穿越血脑屏障（BBB）的问题，传统治疗方法往往面临困境。生物医学领域的纳米颗粒（NPs）作为向中枢神经系统输送神经退行性疾病药物（NDDs）的载体，正受到越来越多的关注。在此，我们引入了计算分析和实验分析。在计算研究中，使用了一种特定的IFPTML技术，该技术结合了信息融合（IF）+微扰理论（PT）+机器学习（ML）来选择最有前景的纳米颗粒神经元疾病药物递送（N2D3）系统。对于IFPTML模型在纳米科学中的应用，使用了NANO.PTML。在4403个NDDs检测和260个细胞毒性NP检测之间进行了IF过程，构建了一个包含500,000个案例的数据集。最优的IFPTML是决策树（DT）算法，在训练集（375k/75%）和验证集（125k/25%）中，其特异性值分别为96.4%和96.2%，敏感性值分别为79.3%和75.7%，表现令人满意。此外，DT模型在训练和验证系列中的受试者操作特征曲线下面积（AUROC）得分分别为0.97和0.96，突出了其在分类任务中的有效性。在实验部分，通过油酸铁（FeOl）前体的热分解合成了两种NPs样品（FeO_A和FeO_B），并采用不同方法对其结构进行了表征。此外，为了使合成的疏水性NPs（FeO_A和FeO_B）可溶于水，使用了两亲性的十六烷基三甲基溴化铵（CTAB）分子。因此，为了对更广泛的NP系统变体进行研究，使用IFPTML-DT模型进行了一个实验性的说明性模拟实验。为此，创建了一组500,000个预测数据集。该实验的结果突出了某些NANO.PTML系统作为有前景的进一步研究候选对象。NANO.PTML方法有潜力加速实验研究，并为各种NP和NDDs化合物提供初步见解，是耗时的试错程序的一种有效替代方法。
