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Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting
Journal article   Open access   Peer reviewed

Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting

Yiwen Shi, Ping Ren, Jing Wang, Biao Han, Taha ValizadehAslani, Felix Agbavor, Yi Zhang, Meng Hu, Liang Zhao and Hualou Liang
Journal of biomedical informatics, v 148, pp 104533-104533
Dec 2023
DOI: https://doi.org/10.1016/j.jbi.2023.104533
PMID: 37918623
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://arxiv.org/pdf/2306.16275View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Benchmarking Drugs, Generic Language Natural Language Processing
Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment, which provides the basis of recommendations for fasting and fed bioequivalence studies to guide the pharmaceutical industry for developing generic drug products. However, manual summarization of food effect from extensive drug application review documents is time-consuming. Therefore, there is a need to develop automated methods to generate food effect summary. Recent advances in natural language processing (NLP), particularly large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability with regard to the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach,iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the iterative prompting process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. Taken together, these results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of the PSG assessment cycle and promoting generic drug product development.

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19 Record Views
24 citations in Web of Science
19 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Interdisciplinary Applications
Medical Informatics
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