Topalian Romain, Kavallaris Leo, Rosenau Frank, Mavoungou Chrystelle
Institute for Applied Biotechnology, Biberach University of Applied Sciences, Karlstraße 6-11, 88400 Biberach, Germany.
Institute of Pharmaceutical Biotechnology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
ACS Pharmacol Transl Sci. 2025 Feb 27;8(3):762-773. doi: 10.1021/acsptsci.4c00643. eCollection 2025 Mar 14.
The development of nasal drug delivery systems requires advanced analytical techniques and tools that allow for distinguishing between the nose-to-brain epithelial tissues with better precision, where traditional bioanalytical methods frequently fail. In this study, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is coupled to machine learning (ML) and deep learning (DL) techniques to discriminate effectively between epithelial tissues. The primary goal of this work was to develop Safe-by-Design models for intranasal drug delivery using ex vivo pig tissues experiment, which were analyzed by way of ML modeling. We compiled an ATR-FTIR spectral data set from olfactory epithelium (OE), respiratory epithelium (RE), and tracheal tissues. The data set was used to train and test different ML algorithms. Accuracy, sensitivity, specificity, and F1 score metrics were used to evaluate optimized model performance and their abilities to identify specific spectral signatures relevant to each tissue type. The used feedforward neural network (FNN) has shown 0.99 accuracy, indicating that it had performed a discrimination with a high level of trueness estimates, without overfitting, unlike the built support vector machine (SVM) model. Important spectral features detailing the assignment and site of two-dimensional (2D) protein structures per tissue type were determined by the SHapley Additive exPlanations (SHAP) value analysis of the FNN model. Furthermore, a denoising autoencoder was built to improve spectral quality by reducing noise, as confirmed by higher Pearson correlation coefficients for denoised spectra. The combination of spectroscopic analysis with ML modeling offers a promising strategy called, Safe-by-Design, as a monitoring strategy for intranasal drug delivery systems, also for designing the analysis of tissue for diagnosis purposes.
鼻腔给药系统的发展需要先进的分析技术和工具，以便更精确地区分鼻脑上皮组织，而传统的生物分析方法常常在此失效。在本研究中，衰减全反射傅里叶变换红外（ATR-FTIR）光谱与机器学习（ML）和深度学习（DL）技术相结合，以有效区分上皮组织。这项工作的主要目标是利用体外猪组织实验开发用于鼻腔给药的设计安全模型，并通过ML建模进行分析。我们收集了来自嗅上皮（OE）、呼吸上皮（RE）和气管组织的ATR-FTIR光谱数据集。该数据集用于训练和测试不同的ML算法。使用准确率、灵敏度、特异性和F1分数指标来评估优化模型的性能及其识别与每种组织类型相关的特定光谱特征的能力。所使用的前馈神经网络（FNN）显示出0.99的准确率，这表明它进行了具有高度真实估计的区分，且没有过拟合，这与构建的支持向量机（SVM）模型不同。通过FNN模型的SHapley加性解释（SHAP）值分析，确定了详细描述每种组织类型二维（2D）蛋白质结构的归属和位点的重要光谱特征。此外，构建了一个去噪自动编码器，通过降低噪声来提高光谱质量，去噪光谱的皮尔逊相关系数更高证实了这一点。光谱分析与ML建模的结合提供了一种名为“设计安全”的有前景的策略，作为鼻腔给药系统的监测策略，也用于设计用于诊断目的的组织分析。
