Experimentally derived biomimetic chromatographic descriptors for drug-induced phospholipidosis liability prediction.

Background and purpose: Drug-induced phospholipidosis (PLD) is a complex intracellular liability commonly associated with the lysosomal accumulation of cationic amphiphilic drugs and remains an important concern in drug development because of its implications for safety, intracellular disposition, and compound prioritization. Although cationic amphiphilicity, membrane interactions, and lysosomal trapping are established features of PLD, experimentally accessible descriptors that capture PLD-relevant membrane affinity remain valuable for early-stage risk assessment. This study aimed to evaluate whether biomimetic chromatographic descriptors can improve the prediction and interpretation of PLD liability by providing experimentally derived information on membrane affinity and protein-binding-related distributional behavior. Experimental approach: A curated dataset of 65 compounds with experimentally reported PLD responses was analyzed using multiple linear regression and ordinal classification approaches. PLD potency was expressed as pEC₅₀, while ordinal models classified compounds into non-inducers, weak/moderate inducers, and strong inducers. Membrane affinity was quantified using immobilized artificial membrane chromatography, expressed as CHI IAM, while protein-related interactions were represented by human serum albumin affinity, log Key results: CHI IAM-containing models showed strong predictive performance for PLD potency, with external predictivity reaching Q Conclusion: Biomimetic chromatographic descriptors provide experimentally derived, mechanistically interpretable surrogates for assessing early-stage PLD liability. CHI IAM offers a practical approximation of membrane-affinity behavior relevant to lysosomal phospholipid accumulation and PLD risk. Protein-binding descriptors may provide complementary information related to distributional phenotypes, but their added value appears less consistent than that of membrane affinity. This approach extends beyond conventional lipophilicity-based QSAR by incorporating experimentally measured distribution-relevant properties and may support compound prioritization during drug discovery.