How can natural language processing help model informed drug development?: a review

Lay Summary One of the biggest problems in healthcare fields is that a large amount of medical data remains unstructured (eg, text, image, signal, etc.) and untapped after it is created. Natural language processing (NLP) has been leveraged in recent years to extract relevant information out of unstructured data. NLP is an artificial intelligence technique to process and analyze human-generated spoken or written data. This review focuses on current NLP applications in the field of drug discovery and development. It provides a comprehensive overview of NLP in model informed drug development (MIDD) which involves quantitative models for decision-making in drug development. Researchers utilize NLP to mine data from previously untapped sources. This aims to increase the efficiency of the drug development process. We also highlight the technical aspects of various tools utilized to develop the currently existing NLP models. We provide information on various easily accessible resources which can be deployed to develop an NLP model for MIDD applications. Lastly, this article gives insights into potential opportunities that currently exist to expand and carry NLP in MIDD forward. Objective To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement. Materials and Methods Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified. Results NLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications. Discussion Challenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD. Conclusions This review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.