Model-based prediction of nanoparticle and dissolved form ratios using total concentration data: a case study of SNB-101

This 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.

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Publication Details

Journal
Frontiers in Pharmacology
Published
2025-08-21
DOI
https://doi.org/10.3389/fphar.2025.1556618
Primary Topic
Cancer therapeutics and mechanisms
Type
article
Field-Weighted Citation Impact
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article

Model-based prediction of nanoparticle and dissolved form ratios using total concentration data: a case study of SNB-101

Sangil Jeon, Seunghoon Han, Jong‐Eun Park, Soo Hyeon Bae et al.
Frontiers in Pharmacology
Cancer therapeutics and mechanisms
article

Model-based prediction of nanoparticle and dissolved form ratios using total concentration data: a case study of SNB-101

Sangil Jeon, Seunghoon Han, Jong‐Eun Park, Soo Hyeon Bae, Dong Cheol Lee, Young Hwan Park
article en

Abstract

This 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.

Frontiers in PharmacologyVol. 16
Korea Research Institute of Bioscience and Biotechnology (KR), Catholic University of Korea (KR)
Openalex Percentile: Top 81%
Cancer therapeutics and mechanisms
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Model-based prediction of nanoparticle and dissolved form ratios using total concentration data: a case study of SNB-101 — Sangil Jeon, Seunghoon Han, et al. · Frontiers in Pharmacology (2025) | TGRS Research Map | TGRS