Pharmacometric Models in Drug Development: FDA
FDA says it supports increasing use of pharmacometric models in drug development because once validated against independent empirical data they allow it to better understand drug effects in individuals and populations who were not part of the clinical trials and to explore additional real-world scenarios. “For example, a model that incorporated information about the impact of the kidney or liver function on the clearance of a certain drug could be used to reasonably predict drug exposure in patients experiencing kidney impairment or prone to drug-drug interactions,” an agency posting says. “By pursuing research into pharmacometric models and by advancing a general framework for assessing their credibility, CDER has been able to offer guidance to sponsors on the use of modeling approaches, thereby fostering drug development broadly and promoting an increase in the number of regulatory submissions that use pharmacometric approaches.”
CDER researchers have recently examined how methods that use artificial neural networks can be applied to modeling problems. “Specifically, CDER scientists have developed a model based on a kind of recurrent neural network to simulate the time course of a [pharmacodynamic (PD)] response that is not directly related to the drug concentration, but rather that develops latently, according to complex biological intermediate steps,” the posting says.
The CDER research has demonstrated that simulated pharmacokinetic/pharmacodynamic data can be analyzed with a machine learning algorithm. It suggests that with further research, neural network-based models “may complement traditional PK/PD models in the area of highly complex PK/PD data analysis and possibly facilitate development of predictive models with improved accuracy,” the posting says.