Clinical Pharmacy Department, Faculty of Pharmacy, Beni-suef University, Beni-suef, Egypt.
Pharmaceutics Department, Faculty of Pharmacy, Beni-suef University, Beni-suef, Egypt; Pharmaceutics Department, Faculty of Pharmacy, Taif University, Taif, Saudi Arabia.
Pulm Pharmacol Ther. 2018 Jun;50:62-71. doi: 10.1016/j.pupt.2018.04.005. Epub 2018 Apr 7.
Substituting nebulisers by another, especially in non-invasive ventilation (NIV), involves many process-variables, e.g. nebulizer-type and fill-volume of respirable-dose, which might affect patient optimum-therapy. The aim of the present work was to use neural-networks and genetic-algorithms to develop performance-models for two different nebulizers.
In-vitro, ex-vivo and in-vivo models were developed using input-variables including nebulizer-type [jet nebulizer (JN) and vibrating mesh nebulizer (VMN)] fill-volumes of respirable dose placed in the nebulization chamber with an output-variable e.g. average amount reaching NIV patient. Produced models were tested and validated to ensure effective predictivity and validity in further optimization of nebulization process.
Data-mining produced models showed excellent training, testing and validation correlation-coefficients. VMN showed high nebulization efficacy than JN. JN was affected more by increasing the fill-volume. The optimization process and contour-lines obtained for in-vivo model showed increase in pulmonary-bioavailability and systemic-absorption with VMN and 2 mL fill-volumes.
Modeling of aerosol-delivery by JN and VMN using different fill-volumes in NIV circuit was successful in demonstrating the effect of different variable on dose-delivery to NIV patient. Artificial neural networks model showed that VMN increased pulmonary-bioavailability and systemic-absorption compared to JN. VMN was less affected by fill-volume change compared to JN which should be diluted to increase delivery.
在无创通气（NIV）中，用另一种雾化器替代原有的雾化器，涉及许多过程变量，例如雾化器类型和可吸入剂量的填充量，这些变量可能会影响患者的最佳治疗效果。本研究旨在使用神经网络和遗传算法来开发两种不同雾化器的性能模型。
采用体外、离体和体内模型，输入变量包括雾化器类型[射流雾化器（JN）和振动网孔雾化器（VMN）]和放置在雾化室中的可吸入剂量的填充量，输出变量为例如到达 NIV 患者的平均量。所产生的模型经过测试和验证，以确保在进一步优化雾化过程中具有有效的预测性和有效性。
数据挖掘产生的模型显示出出色的训练、测试和验证相关系数。VMN 的雾化效果优于 JN。增加填充量会对 JN 产生更大的影响。体内模型的优化过程和等高线显示，VMN 和 2ml 填充量可增加肺部生物利用度和全身吸收。
使用不同填充量的 JN 和 VMN 在 NIV 回路中对气溶胶输送进行建模，成功地证明了不同变量对 NIV 患者剂量输送的影响。人工神经网络模型显示，与 JN 相比，VMN 增加了肺部生物利用度和全身吸收。与 JN 相比，VMN 受填充量变化的影响较小，需要稀释以增加输送量。
