He Shan, Segura Abarrategi Julen, Bediaga Harbil, Arrasate Sonia, González-Díaz Humberto
Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain.
Beilstein J Nanotechnol. 2024 May 15;15:535-555. doi: 10.3762/bjnano.15.47. eCollection 2024.
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.
神经退行性疾病的特征是神经元细胞死亡缓慢进展。传统的药物治疗策略常常失败，原因在于药物溶解度差、生物利用度低以及无法有效透过血脑屏障。因此，开发新型神经退行性疾病药物（NDDs）迫在眉睫。纳米颗粒（NP）系统在将NDDs输送到中枢神经系统方面越来越受到关注。然而，由于NP与NDD化合物的组合数量众多以及涉及的各种检测方法，发现有效的纳米颗粒神经元疾病药物递送系统（N2D3Ss）具有挑战性。人工智能/机器学习（AI/ML）算法有潜力通过预测最有前景的用于检测的NDD和NP候选物来加速这一过程。尽管如此，与已检测的NDDs相比，关于N2D3S活性的报告数据相对有限，这使得AI/ML分析具有挑战性。在这项工作中，采用了结合信息融合（IF）、微扰理论（PT）和机器学习（ML）的IFPTML技术来应对这一挑战。最初，我们将来自ChEMBL的4403个NDD检测和来自期刊文章的260个NP细胞毒性检测融合到一个统一的数据集中。通过重采样过程，生成了三个新的工作数据集，每个数据集包含500,000个案例。我们利用线性判别分析（LDA）以及人工神经网络（ANN）算法，如多层感知器（MLP）和深度学习网络（DLN），构建线性和非线性IFPTML模型。IFPTML-LDA模型在训练案例（>375,000）中的灵敏度（Sn）和特异性（Sp）值分别在70%至73%范围内，在验证案例（>125,000）中的值分别在70%至80%范围内。相比之下，IFPTML-MLP和IFPTML-DLN在训练和验证系列中的Sn和Sp值均在85%至86%范围内。此外，IFPTML-ANN模型的受试者工作特征曲线下面积（AUROC）约为0.93至0.95。这些结果表明，IFPTML模型可作为神经科学药物递送系统设计中的有价值工具。
