QSAR Models at the US FDA/NCTR

Abstract

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

The influence of drug properties and host factors on delayed onset of symptoms in drug‐induced liver injury

“…Drug property information was retrieved from the Liver Toxicity Knowledge Base database developed and maintained at the US Food and Drug Administration's National Center for Toxicological Research . This knowledge base accumulates comprehensive drug property information on US‐marketed pharmaceuticals including various physicochemical, pharmacokinetic and toxicological properties.…”

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach

“…Drug property information was retrieved from the Liver Toxicity Knowledge Base (LTKB) database developed and maintained at the US Food and Drug Administration's National Center for Toxicological Research (Chen 2013;Hong 2016). This knowledge base accumulates comprehensive drug property information on US-marketed pharmaceuticals.…”

Characterization of the metabolism of eupalinolide A and B by carboxylesterase and cytochrome P450 in human liver microsomes

“…A QSAR model is established based on structural characteristics and properties of known compounds and is used to predict metabolism of compounds with similar structures under the assumption that molecules with similar structures likely exhibit similar biochemical properties ( Wu et al, 2013 ). The FDA as well as the Registration, Evaluation and Authorization of Chemicals issued by the European Union consider QSAR predictions to be highly reliable ( Gertrudes et al, 2012 ; Hong et al, 2016 ). Molecular docking, another computational approach, has become widely used in the early stage of drug development to simulate interactions among proteins and ligands for the purposes of elucidating compound enzymatic metabolism ( Kaur et al, 2019 ; Kazmi et al, 2019 ).…”