Artificial Neural Networks: Functioningandapplications in Pharmaceutical Industry

Abstract

Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

“…Pharmaceutical applications of ANNs include drug design, optimizing preformulation and formulation of various dosage forms, modelling in vitro-in vivo correlations, interpreting analytical data, determining the relationship between the chemical structure and biological activity of a substance (quantitative structure-activity relationship) as well as pharmacotoxicological, pharmacokinetic and pharmacodynamic studies (76,88). ANN has been adopted to predict the release rate from lignin-containing aspirin tablets (89) and the characteristics of buccoadhesive pharmaceutical wafers containing loratadine (90).…”