Assessing Similarity to Existing Drugs to Decide Whether to Continue Drug Development

Developing a drug requires large investments, over many years, with dramatic increases in development costs at later stages. Thus, one wants to make a No Go decision on a compound early, unless evidence continues to suggest that the project will ultimately be successful, so that resources can be focused on the most promising compounds to benefit patients. Instead of predicting the probability of success of a Phase III study, our approach to this decision uses the Phase II study results to assess similarity of the novel compound to existing drugs that are classified by different decision categories, such as a clear Go decision (e.g., a clearly effective drug), a (unfortunately common) Not Sure decision (e.g., a potentially useful but not outstanding drug), and a clear No Go decision (e.g., a clearly not effective drug). We describe how this modeling can be done using both individual and binary endpoints and how results can be combined for several different endpoints. Potential extensions of the method are also discussed.