Mendyk Aleksander, Tuszyński Paweł K, Polak Sebastian, Jachowicz Renata
Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
Drug Des Devel Ther. 2013;7:223-32. doi: 10.2147/DDDT.S41401. Epub 2013 Mar 27.
The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR.
Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique.
The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations.
It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2-4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.
本研究的目的是基于体外溶出曲线以及剂型的定量和定性组成作为协变量，开发一种广义的体外-体内关系（IVIVR）模型。在药物制剂开发的早期阶段，当尚无体内结果且无法建立经典的体外-体内相关性（IVIVC）/IVIVR时，这样的模型将有很大帮助。
使用化学信息学软件计算药物物质（即活性药物成分）和辅料的分子描述符。数据从文献中收集。使用人工神经网络作为建模工具。训练过程采用10折交叉验证技术进行。
数据库最初包含93种制剂和307个输入，后来在敏感性分析过程中限制为28个。四个最佳模型被引入人工神经网络集成。对于37.6%的制剂准确预测了完整的体内曲线。
已经表明，人工神经网络可以成为在各种制剂的综合广义模型中构建IVIVR的有效预测工具。由于经典的IVIVC/IVIVR是针对2-4种制剂且使用单一活性药物成分进行的，此处描述的方法的独特之处在于它将各种活性药物成分和剂型纳入单个模型。因此，无需体内数据即可获得初步的IVIVC/IVIVR，而使用当前的IVIVC/IVIVR程序是不可能的。
