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A Critical Assessment of the Application of Advanced Data Analytics in Inhaled and Nasal Product Development

Ganley W, Doherty N, Jones GP, Ashworth I, Di Memmo F.

Respiratory Drug Delivery 2022. Volume , 2022: 187-196.

Abstract:

Artificial intelligence (AI) and machine learning (ML) algorithms have the potential to transform the way that many industries operate by supporting key decision-making processes with predictions and extracting patterns in complex data sets. The approach however is not a panacea. There are a number of challenges that must be addressed including gathering input data, selecting the correct model, interpreting the models we build and ensuring that they are used ethically before AI/ML becomes commonplace in pharmaceutical development.

The technology and tooling that could benefit the development of orally inhaled and nasal drug products (OINDPs) exists already. It is therefore our responsibility as an industry to acquire the necessary skills, identify the problems best served by these technologies and decide where AI/ML methods fit into decision making processes. This article presents an appraisal of the use of AI/ML methods in the development of OINDPs; how they compare to established methods such as statistics and mechanistic modeling, their potential for improving the pharmaceutical development process and the challenges associated with their application. The article closes with an example application using AI/ML methods to expedite the development of a nasal drug product.