Introduction We developed a framework for identifying and mitigating adverse interactions in multi-morbid patients managed according to multiple clinical practice guidelines (CPGs)1. The framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management scenarios. It handles many complexities of CPGs (e.g. time-based interactions) and also considers patient preferences2. One limitation is its inability to capture hierarchical dependencies between concepts at different levels of granularity. This limitation results in a very detailed specification of secondary knowledge. In this work we address this shortcoming by expanding the FOL-based knowledge representation to handle hierarchical representations of drug classes. Hierarchical Representation of Drug Classes Our expanded representation describes hierarchical relationships between drug classes using a logical biconditional a ↔ b, where a is a single predicate drugClass(c), and the consequent b is a disjunction of several such predicates corresponding to more specific drug classes. A biconditional is powerful because it is transitive and can be infinitely nested. To identify drugs from a given class we use an implication a → b, where a is also a single drugClass(c), and b is a set of terms corresponding to specific drugs. Consider the class of anticoagulants consisting of vitamin-k antagonists and novel oral anticoagulants (NOACs), and specific drugs for each class: drugClass(anticoag) ↔ drugClass(vitamin k) ∨ drugClass(NOAC) drugClass(vitamin k) → {warfarin, atromentin, phenindione} drugClass(NOAC) → {dabigatran, rivaroxaban, apixaban, edoxaban, betrixaban}