ELKE WIESER¹, STEFAN PAULI²
1.VTU Österreich, Wien, Austria
2.VTU Engineering Schweiz AG, Muttenz, Switzerland

Abstract

This article examines the integration of Artificial Intelligence (AI) in pharmaceutical manufacturing, particularly in regard to Good Manufacturing Practice (GMP) standards. While AI offers significant potential, challenges remain in regulation and validation. Regulatory bodies like the FDA and European authorities are working on incorporating AI into GxP processes, but specific guidelines are still in development. The report highlights recent advancements, including a proposed validation framework and examples of successful AI applications in GxP-compliant production. Overall, AI adoption in pharmaceutical manufacturing is advancing, but it should be approached cautiously to maintain product quality and patient safety.

CONCEPTS AND DEFINITION OF ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) generally refers to machines that partially replicate human intelligence in an artificial manner. A fitting quote from Elaine Rich in 1983 defines AI as “the study of how to make computers do things at which, at the moment, people are better.” AI encompasses a variety of technologies, including machine learning (ML) algorithms that learn independently from collected data. These range from simpler methods like linear regression or random forests to more complex ones such as neural networks. The latter connect neurons in a network modeled after the structure of the human brain, linking one neuron (nerve cell) to a network of other neurons. Large neural networks are referred to as deep learning when they excel in processing images, text, or speech, although they require a significant amount of data and computational power. Therefore, deep learning is the closest approximation to the brain, although even here, much is simplified or adapted since our brain cannot be replicated one-to-one. Neurotransmitters in the human brain, such as dopamine, are not simulated, and the cells ...