Applications of Advanced Natural Language Processing for Clinical Pharmacology.

Natural language processing (NLP) is a branch of artificial intelligence, which combines computational linguistics, machine learning, and deep learning models to process human language. Although there is a surge in NLP usage across various industries in recent years, NLP has not been widely evaluated and utilized to support drug development. To demonstrate how advanced NLP can expedite the extraction and analyses of information to help address clinical pharmacology questions, inform clinical trial designs, and support drug development, three use cases are described in this article: (1) dose optimization strategy in oncology, (2) common covariates on pharmacokinetic (PK) parameters in oncology, and (3) physiologically-based PK (PBPK) analyses for regulatory review and product label. The NLP workflow includes (1) preparation of source files, (2) NLP model building, and (3) automation of data extraction. The Clinical Pharmacology and Biopharmaceutics Summary Basis of Approval (SBA) documents, US package inserts (USPI), and approval letters from the US Food and Drug Administration (FDA) were used as our source data. As demonstrated in the three example use cases, advanced NLP can expedite the extraction and analyses of large amounts of information from regulatory review documents to help address important clinical pharmacology questions. Although this has not been adopted widely, integrating advanced NLP into the clinical pharmacology workflow can increase efficiency in extracting impactful information to advance drug development.

CD43 is an adverse prognostic factor in newly diagnosed multiple myeloma.

Currently, the expression pattern and prognostic value of CD43 expression in multiple myeloma (MM) remain unknown. 109 newly diagnosed MM patients were recruited and CD43 expression was determined by multiparameter flow cytometry, of which 77 (70.6%) were CD43 positive. Patients with positive CD43 expression were more likely to present with, hemoglobin < 85 g/L (p = 0.008), International Staging System (ISS) stage III (p = 0.044), 13q14 deletion (p = 0.034) and more monoclonal plasma cells (p = 0.003). Patients with CD43 positive had significantly poor treatment response (p = 0.021), progression-free survival (PFS) (p = 0.012), and overall survival (OS) (p = 0.023) than those without CD43. The poorer prognosis of CD43-positive patients was retained in multivariate analysis (p = 0.005 for PFS; p = 0.013 for OS). Our study indicated that CD43 was an independent adverse prognostic factor in multiple myeloma.