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.

Population Pharmacokinetic Analysis of the BTK Inhibitor Zanubrutinib in Healthy Volunteers and Patients With B-Cell Malignancies.

Zanubrutinib is a potent, second-generation Bruton's tyrosine kinase inhibitor that is currently being investigated in patients with B-cell malignancies and recently received accelerated approval in the United States for treatment of relapsed/refractory mantle cell lymphoma. The objective of this analysis was to develop a population pharmacokinetic (PK) model to characterize the PKs of zanubrutinib and identify the potential impact of intrinsic and extrinsic covariates on zanubrutinib PK. Data across nine clinical studies of patients with B-cell malignancies and data of healthy volunteers (HVs) were included in this analysis, at total daily doses ranging from 20 to 320 mg. In total, 4,925 zanubrutinib plasma samples from 632 subjects were analyzed using nonlinear mixed-effects modeling. Zanubrutinib PKs were adequately described by a two-compartment model with sequential zero-order then first-order absorption, and first-order elimination. A time-dependent residual error model was implemented in order to better capture the observed maximum concentration variability in subjects. Baseline alanine aminotransferase and health status (HVs or patients with B-cell malignancies) were identified as statistically significant covariates on the PKs of zanubrutinib. These factors are unlikely to be clinically meaningful based on a sensitivity analysis. No statistically significant differences in the PKs of zanubrutinib were observed based on age, sex, race (Asian, white, and other), body weight, mild or moderate renal impairment (creatinine clearance ≥ 30 mL/minute as estimated by Cockcroft-Gault), baseline aspartate aminotransferase, bilirubin, tumor type, or use of acid-reducing agents (including proton pump inhibitors). These results support that no dose adjustment is considered necessary based on the aforementioned factors.