Seeking beneficial drug-drug combinations (DDCs) from real-world evidence is an emerging topic in phenotypic drug discovery. With sophisticated algorithms, the numbers of DDC hypotheses generated often reach to tens of thousands. However, due to limited resources, only a few, top-ranking hypotheses are selected for experimental validations. Often, researchers start from the topmost DDC pairs and work their way down until they find a pair with successful validation. While this is an established way of performing validations, there still exists room to improve. Here, we present a systematic approach to perform a secondary analysis on the DDCs to streamline the validation procedure. Specifically, we propose a method in which we search for additional patterns in terms of chemical classes and biological target interactions of the drug pair. Using 78,345 DDC hypotheses generated from the FDA Adverse Event Reporting System (FAERS) data in our prior study, we demonstrate how the proposed analysis can reveal additional biochemical and mechanical insights of drug interactions that can streamline experimental validation.