Machine Reading Comprehension and Expert System technologies for social innovation in the drug excipient selection process

Abstract: The growth of the global population together with several unpredicted crises such as political, health, and financial, create an environment of uncertainty in which social innovations can be developed to offer stability in people’s lives and create new business development opportunities for the benefit of the economy and the society. One of the undoubted rights of every human being is access to affordable medical treatment. However, the costs and time needed for research and development on new or specialized drugs are not often covered by governmental budgets and initiatives that could make such medicines accessible to all who needed them. Private companies invest tremendous amounts and expect returns on their investments. This gap, between the availability of a drug and its accessibility, created the social need for a generic drug market and the inspiration for advanced innovations to serve it. Research indicates that the price of brand-name drugs can drop up to 80% after the commercialization of a new generic which has the same action and can potentially replace them. The global generic drug market worth is expected to increase from $311.8 billion in 2021 to $442.3 billion in 2026. Excipients represent a market value of $4 billion, accounting for 0.5% of the total pharmaceutical market. The global market of AI was estimated at 43.1 billion in 2020 and is predicted to reach $228.3 billion by 2026 with a 32.7 % CAGR. On the other hand, the revenues of the AI Health market are projected to grow from $6.9 billion in 2021 to $67.4 billion in 2027 reaching $120.2 billion by 2028 with a CAGR of 45.3%.The choice of excipients in drug development is a critical and time-consuming process. Currently, excipients are chosen based on the route of administration, physicochemical characteristics, place of action, and the type of release of the active ingredient. The process involves many quality control tests on the drug such as fragility, dissolution, disintegration, dosage uniformity, and stability, which are repeated when the excipient changes. This laborious and time-consuming process considers a massive number of existing excipients categorized into different functional groups used for different purposes.This paper addresses this challenge and introduces an approach to resolve it using Artificial Intelligence for social innovation in the formulation development industry. Specifically, the paper presents an Expert system (ES) based software architecture to facilitate assess and utilize drug-excipient relationship data scattered in various forms of documentation and/or scientific literature. The inference engine of the ES operates with rule base and case-based reasoning powered by Machine Reading Comprehension (MRC) and Natural Language Processing (NLP) technologies that populate and enrich the knowledge base. The MRC and NLP technologies interpret existing drug formulations and propose potential new drug formulations, based on its physicochemical characteristics.According to research results, the time to introduce a generic drug can be reduced by 30% if there is an indicative formulation to start the process. The eight months gained can be used to market the product. This is a significant amount of time that reduces research and development costs, reduces the time to market, and increases productivity and operations efficiency. The research conducted is based on an extensive literature review, primary research with surveys and interviews but also with the analysis of several case studies to indicate the need for the proposed technology and support the system architecture design. Furthermore, the paper presents the pre and post-condition for adopting such technology, highlights research limitations, and identifies areas of further research to be conducted for the optimization of the technology and its contribution to the global economy and society.

Keywords: Excipient, Drug, Formulation, Artificial Intelligence, Expert Systems, Pharmaceutical, Innovation, Technology.

DOI: 10.54941/ahfe1003273

Cite this paper:

​