Deep learning for in vitro prediction of pharmaceutical formulations

Abstract

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

Biomarker Application for Precision Medicine in Stroke

“…With the amount of data gathered, machine-learning technology could foster the development of prognostic and diagnostic biomarkers and provide a solid mechanistic understanding of the dynamics relative to clinical course. Along that line, the U.S. Food and Drug Administration (FDA) announced an effort to develop a regulatory framework for medical artificial intelligence (AI) algorithms that reflects the fact that these tools are continuously learning and evolving from experience gained in real-world clinical use [68,69]. This biomarker-powered, self-learning engine may ultimately transform healthcare.…”

Digital Pharmaceutical Sciences

“…There are different types of neural network architectures in DL, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected feed-forward networks, which have been comprehensively discussed elsewhere (44). DL has become very popular and has gained interest in diverse research areas of pharmaceutical research such as in pharmaceutical formulation development (45), drug discovery (46), and drug repurposing (47). Their predictability and generalization performance are generally better than that of other machine learning methods, such as SVMs and RFs (45).…”

Application of interpretable artificial neural networks to early monoclonal antibodies development

“…Artificial Neural Networks have been extensively reviewed in the literature, and they have been used successfully in the pharmaceutical industry. [12][13][14][15][16][17][18][19][20][21][30][31][32][33][34][35][36] The various applications of ANNs relevant to the pharmaceutical field are classification or pattern recognition, prediction and modeling. Theoretical details can be found elsewhere.…”

“…In recent years, this method has been applied in the pharmaceutical research area for different purposes. [12][13][14][15][16][17][18][19] Supervised ANNs were used as an alternative to response surface methodology 20 while unsupervised networks are an alternative to principal component analysis. Analysis of design of experiments is also possible by ANNs.…”