Elsevier, a global information analytics business specializing in science and health, has entered into a Collaborative Research and Development Agreement (CRADA) with the National Center for Toxicological Research (NCTR) at the Food and Drug Administration (FDA). Effective October 8, 2019, NCTR and Elsevier began collaborating on a project to conduct research for the development of a predictive drug-induced liver injury (DILI) algorithm by integrating the rule-of-two model and preclinical data from the literature and the summary basis of FDA and European Medicines Agency (EMA) approval documents collected in Elsevier’s PharmaPendium database.
DILI is a leading cause of attrition of compounds in drug development and one of the two most frequent causes for drug withdrawals, restrictions and project terminations. Further, analysis of over 500 withdrawals revealed that, of the drugs withdrawn due to toxicity, 21 percent were attributed to hepatic toxicity, thus making it the leading cause for toxicity-related drug withdrawals.
DILI is also a leading reason for regulatory actions involving investigational and approved medications and a leading cause of acute liver failure in the United States.
“The predictive toxicology space is becoming a growing area of interest as we seek to understand how to improve clinical outcomes without risking the safety of patients,” said Guenther Kurapkat, Senior Vice President, Life Science Solutions, Elsevier. “This CRADA will strengthen the collaborative relationship between Elsevier and the FDA and paves a path forward for future projects that seek to address key pain points for the industry, which continues to be challenged with rising R&D costs and low success rates.”
A major problem in drug development is the frequency of adverse hepatic reactions induced by new molecular entities (NMEs), mainly in oncology. Hepatotoxicity occurs in one-third of patients treated with a protein kinase inhibitor, with fatal outcomes reported for pazopanib, sunitinib and regorafenib. Ten percent of patients treated with immune checkpoint inhibitors, notably ipilimumab, may also develop liver injury with high rates of recurrent liver injury upon challenge.
The oncology population that is treated with multiple drugs is more likely to have multiple comorbidities and comedications and is therefore at risk for hepatotoxicity.
The FDA issues detailed recommendations in drug labels as to liver test monitoring intervals and stopping rules. As such, mitigating DILI risk as early as possible during drug development has significant implications for lowering R&D costs and, more importantly, improving patient safety, mostly in oncology.
The DILI algorithm could be used independently or integrated in a way for the use of assessing DILI in the preclinical phase of drug development and could be utilized by the FDA when DILI issues arise during various stages of the regulatory review process.