PII-102 - PREDICTING THE EXPOSURE OF IRINOTECAN AND ITS ACTIVE METABOLITE (SN-38) IN CANCER PATIENTS USING PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING.

A. Rababah¹, K. Denic², J. Reid³, S. Neuhoff⁴; ¹Mayo Clinic, Division of Oncology Research, Mayo Clinic- Rochester, MN, USA, ²Travere Therapeutics, San Diego, CA 92130, USA, San Diego, CA, USA, ³Mayo Clinic, Rochester, MN, USA, ⁴Certara UK, Sheffield, UK.

Background: Irinotecan is used in the treatment of adult and pediatric cancers. It acts as a prodrug of SN-38 which has an over 100-fold greater cytotoxic activity. The metabolic and pharmacokinetic behavior of irinotecan is complex. Clinical patient data are highly variable in the efficacy, toxicity, and pharmacokinetic profile of the drug. To better address safety and efficacy concerns, an in vitro-to-in vivo extrapolation (IVIVE) linked physiologically-based pharmacokinetic model (PBPK) was developed for irinotecan and it’s metabolite, SN-38.

Methods: Clinical data were used in combination with measured physicochemical and blood-binding data to inform the IVIVE-PBPK model for irinotecan and SN-38. The former being eliminated by CYP3A, esterases, hepatic uptake, and renal clearance; while the latter being eliminated by UGT1A1 metabolism, biliary and renal clearance. A middle-out approach was used to build the parent model for a population of adult cancer patients based on literature data. The default Sim-Cancer population available within the Simcyp Simulator (V23) was used. The irinotecan metabolism was described by incorporating CYP3A kinetics, and the corresponding fraction metabolised was verified using an interaction with ketoconazole. The SN-38 metabolite was generated via CES1 and CES2 metabolism. Independent intravenously administered irinotecan studies were used for verification, including data from a Phase I trial that incorporated >30 subjects with available UGT1A1 genotype data.

Results: The model predicted the plasma concentration time profiles of irinotecan in good agreement with observed data from several clinical studies covering a dose range of 75-400 mg/m2. The SN-38 model recovered observed plasma concentration time profiles from individual patients, which reflected the sensitivity to the metabolite synthesis (via CES1, CES2) relative to its elimination (UGT1A1, biliary and renal clearance).

Conclusion: We developed an IVIVE-PBPK model that predicts the pharmacokinetic behavior of irinotecan and SN-38 in an adult cancer population. The model allows evaluation of ‘What if?’ scenarios to understand differences in between-subject variabilities of the active metabolite SN-38. This understanding may lead to an increase in individualized treatment for irinotecan.