A Novel Artificial Intelligence System in Formulation Dissolution Prediction

Abstract

Artificial neural network (ANN) techniques are widely used to screen the data and predict the experimental result in pharmaceutical studies. In this study, a novel dissolution result prediction and screen system with a backpropagation network and regression methods was modeled. For this purpose, 21 groups of dissolution data were used to train and verify the ANN model. Based on the design of input data, the related data were still available to train the ANN model when the formulation composition was changed. Two regression methods, the effective data regression method (EDRM) and the reference line regression method (RLRM), make this system predict dissolution results with a high accuracy rate but use less database than the orthogonal experiment. Based on the decision tree, a data screen function is also realized in this system. This ANN model provides a novel drug prediction system with a decrease in time and cost and also easily facilitates the design of new formulation.

Revisiting Supersaturation of a Biopharmaceutical Classification System IIB Drug: Evaluation via a Multi-Cup Dissolution Approach and Molecular Dynamic Simulation

“…AI can bypass the limitations of current mathematical models and predict in vivo absorption based on big data, including the previous dissolution and properties of the components and excipients. On the other hand, with the progress of organoids, many intestinal organoid models have been developed and used to study intestinal physiology and develop drugs [33], including drug absorption-related nephrotoxicity [34] and drug absorption-related pharmacokinetics and pharmacodynamics [35]. Hence, there is a small gap for microfluidic chip-based intestinal organoids to be an advanced alternative to instrumental models.…”

Retracted: A Novel Artificial Intelligence System in Formulation Dissolution Prediction

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”