Thesis

Application of deep generative models for the design of pharmaceutical manufacturing processes

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17214
Person Identifier (Local)
  • 202155680
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Designing processes for pharmaceutical product manufacturing is a complex and resource-intensive task. With increasing research costs and quality standards, the pharmaceutical industry seeks innovative technologies to enhance productivity and maintain competitiveness. While a variety of tools exist in the process design domain for optimizing conditions or selecting materials, options for guiding the selection of manufacturing operations remain limited. In this context, deep generative models (DGMs) emerge as a promising approach. DGMs, known for learning the probability distribution of data, have gained popularity for their ability to generate realistic examples, commonly applied in text and image generation. In drug discovery, DGMs have successfully generated new active substances with desirable properties. However, their application in the manufacturing space remains unexplored. These models have the potential to assist in operation selection and experimental targeting, thereby reducing development time. This thesis aims to investigate the applicability of DGMs in pharmaceutical manufacturing process design, developing DGMs capable of generating plausible sequences of operations for product manufacturing, taking input information about the target product. A significant challenge in developing DGMs is the requirement for large datasets. To address this, two datasets were constructed using natural language processing (NLP) applied to primary and secondary manufacturing data extracted from patents. The primary processing dataset comprises over 385K manufacturing processes, while the secondary processing dataset includes approximately 9K procedures for various dosage forms and active ingredients. The study involved training and comparing several architectures based on generative adversarial networks (GAN) and variational autoencoder (VAE) using different metrics. Real and generated sequences were contrasted manually to evaluate how closely the model outputs resemble typical manufacturing sequences. This research contributes to the exploration of DGMs’ application in pharmaceutical manufacturing, offering insights into their potential for operation selection and product development. In the end, DGMs were successfully trained and their potential for the generation of plausible sequences was demonstrated. A survey assessed by a panel of experts yielded that the models generated sequences at least as good as the actual procedures in 38% of occasions for the primary domain. While this shows the potential of generative modelling in this field, it also remarks there is room for improvement to make it applicable in real-world scenarios.
Advisor / supervisor
  • Brown, Cameron
  • Johnston, Blair
Resource Type
DOI
Date Created
  • 2024

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