Model-based prediction of nanoparticle and dissolved form ratios using total concentration data: a case study of SNB-101 - Park, Jinha; Bae, Soo Hyeon; Jeon, Sangil; Park, Young Hwan; Lee, Dong Cheol; Han, Seunghoon (Frontiers in Pharmacology, 2025)
IntroductionIrinotecan (CPT-11), a topoisomerase I inhibitor, serves as a prodrug for SN-38, its active metabolite with significantly higher cytotoxic potency. Despite its clinical efficacy, irinotecan’s therapeutic potential is limited by low fraction of conversion to SN-38, inefficient tumor targeting, and dose-limiting toxicities such as diarrhea and neutropenia. Nanoparticle-based formulations, such as SNB-101, offer a promising solution by encapsulating irinotecan and SN-38, enhancing solubility, improving drug delivery efficiency, and reducing systemic toxicity through tumor-specific accumulation via the enhanced permeability and retention (EPR) effect.MethodsThis study aimed to develop a pharmacokinetic (PK) model to differentiate between nanoparticle (NP) and dissolved (S) forms of irinotecan and SN-38 using total plasma concentration data from a Phase I clinical trial of SNB-101 (NCT04640480). The 11-compartment model incorporated prior knowledge of dissolved irinotecan PK and newly observed clinical data to characterize NP-to-S transitions and their respective contributions to total drug exposure.ResultsResults revealed that SNB-101 is predominantly predicted to deliver SN-38 in its nanoparticle form, with NP-SN-38 contributing over 80% of total SN-38 exposure. The high exposure to NP-SN-38 correlated with reduced systemic toxicity compared to conventional irinotecan formulations, despite significantly increased total SN-38 levels.DiscussionThis reduced exposure to dissolved SN-38 and irinotecan likely underpins the favorable safety profile observed in dose-escalation studies. This model-based approach underscores the utility of nanoparticle formulations in improving drug delivery and highlights the importance of distinguishing between NP and S forms for accurate efficacy and toxicity predictions. The framework may provide a useful tool for optimizing dose selection and accelerating the clinical development of nanoparticle-based therapeutics.