Case Studies of Categorical Data-Derived Adjustment Factors

Investigations were performed on representative compounds from five different therapeutic classes to evaluate the use of categorical data-derived adjustment factors to account for interindividual variability. The five classes included antidepressants, angiotensin converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDS), cholesterol lowering agents, and antibiotics. Each of the case studies summarized the mode of action of the class responsible for both the therapeutic and adverse effects and the key pharmacodynamic (PD) and pharmacokinetic (PK) parameters that determine the likelihood of these responses for individual compounds in the class. For each class, an attempt was made to identify the key factors that determine interindividual variability and whether there was a common basis to establish a categorical default adjustment factor that could be applied across the class (or at least across specific subclasses within the class). Linking the PK and PD parameters to the critical e...