Anyue Yin (1,2)*, Hester Ettaieb (3)*, Jesse J. Swen (1,2), Liselotte van Deun (3), Thomas M.A. Kerkhofs (3), Robert J.H.M van der Straaten (1), Eleonora P.M. van der Kleij-Corssmit (4), Hans Gelderblom (5), Michiel Kerstens (6), Richard A. Feelders (7), Marelise Eekhoff (8), Henri Timmers (9), Antonio D’Avolio (10), Jessica Cusato (10), Henk-Jan Guchelaar (1,2), Harm R. Haak (3,11,12), Dirk Jan A.R. Moes (1,2) * These authors contributed equally
(1) Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands, (2) Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, the Netherlands, (3) Department of Internal Medicine, Department of Endocrinology, Máxima Medical Centre, Eindhoven/Veldhoven, the Netherlands. (4) Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Centre, Leiden, the Netherlands. (5) Department of Medical Oncology, Leiden University Medical Centre, Leiden, the Netherlands. (6) Department of Endocrinology, University of Groningen, University Medical Centre Groningen, the Netherlands. (7) Department of Internal Medicine, Division of Endocrinology, Erasmus Medical Centre, Rotterdam, the Netherlands. (8) Department of Internal Medicine, Division of Endocrinology, VU Medical Centre, Amsterdam, the Netherlands. (9) Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Centre, Nijmegen, the Netherlands. (10) Department of Medical Sciences, Unit of Infectious Diseases, Amedeo di Savoia Hospital, University of Turin, Turin, Italy. (11) Department of Internal Medicine, Division of General Internal Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands. (12) CAPHRI School for Public Health and Primary Care, Ageing and Long-Term Care, Maastricht, the Netherlands
Introduction: Mitotane, a highly lipophilic compound with an extremely long half-life, is the only agent approved for treatment of adrenocortical carcinoma (ACC)[1]. To ensure treatment efficacy and avoid toxicity, mitotane plasma concentration is advised to be maintained between the therapeutic range of 14-20 mg/L[1], which requires therapeutic drug monitoring (TDM). However, the lack of ability to predict mitotane plasma concentrations may result in a suboptimal time period to reach the therapeutic window or unexpected toxicity[2].
Objectives: We aim to develop a population pharmacokinetics (PK) model to characterize and predict the drug concentration of mitotane in ACC patients. Subsequently, we aim to identify covariates that affect mitotane clearance and thereby facilitate mitotane dose optimization and individualization for ACC patients.
Methods: Routine mitotane TDM trough concentration data, as well as a limited amount of intensive sampling data, was collected retrospectively from patients diagnosed with ACC from the Dutch Adrenal Network.
Population PK modelling analysis was performed with NONMEM (version 7.4.1). Data below LLOQ (<4%) was omitted. Inter-occasion variability (IOV) of apparent systematic clearance (CL/F) was included and every 200 days period was defined as an occasion. Absorption rate constant was first estimated based on the data of patients who contributed drug absorption information and then fixed to analyses the full dataset.
The effects of potential covariates on parameters were evaluated. Lean body weight (LBW) of a patient was calculated with James function and the fat amount (FAT) was estimated as body weight minus LBW. DNA samples were analyzed using DMETTM plus array[3] (Affymetrix UK Ltd), and SNPs with call rate ≥ 97% and minor allele frequency ≥ 0.1 were included. Association between genotypes and ETA of CL/F was first assessed with R software (version 3.4.1), with ANOVA test or t-test which depended on the number of genotype groups. Subsequently, the effect of SNPs that were identified to be potentially related to mitotane clearance (p < 0.05) and other potential covariates on CL/F and apparent distribution volume (V/F) were explored. Stepwise covariate modelling (SCM) function implemented with Perl-Speaks-NONMEM was applied[4]. Both a forward inclusion (p < 0.05, degree of freedom=1) and a backward elimination process (p < 0.01, degree of freedom=1) were performed. After evaluating the model, simulations were performed to identify optimal treatment regimens for different patients.
Results: A 2-compartment model with first-order absorption and elimination best described the 881 concentration data points collected from 48 patients. Of the investigated SNPs, 11 SNPs, located in the genes CYP2C18, CYP2C19, SLCO1B1, SLCO1B3, VKORC1, and UGT1A6 were found to be potentially related to mitotane clearance. After SCM, LBW, genotypes of SLCO1B1 (rs4149057), CYP2C19*2, and SLCO1B3 (I233M) were identified to influence CL/F of mitotane significantly, which decreased the CV% of CL/F from 67.0% to 43.4%. FAT was identified to influence the central V/F significantly. The predictability and stability of the model were confirmed to be acceptable by VPC and Bootstrap. The starting dose of an individual patient was identified by making the simulated mitotane concentration (PRED) at the 98th day reach 14 mg/L. Starting by increasing the dose by 0.5g every 21 days until the target was reached or increasing by 1.5g after 126 days for patients still not reaching the target by then was demonstrated to shorten the period required to reach the target while limiting the risk of toxicity. Assuming TDM will be performed every 14 days and dose will be adjusted 7 days after blood collection, simulation results showed that increasing mitotane dose by 1.5g if the concentration < 14 mg/L, keeping dose same if the concentration is within 14-18mg/L, decreasing dose by 1g if the concentration reached 18-20 mg/L, and decreasing dose by 3g if the concentration > 20mg/L was a good protocol to maintain mitotane concentration within 14-20mg/L.
Conclusions: A 2-compartment model was demonstrated to well characterize mitotane concentrations from ACC patients. LBW, genotypes of SLCO1B1 (rs4149057), CYP2C19*2, and SLCO1B3 (I233M) were identified to affect mitotane CL/F and FAT was identified to affect the central V/F. An optimal treatment schedule was developed by simulation with the final model.
References:
[1] Paragliola, R.M., et al., Role of Mitotane in Adrenocortical Carcinoma – Review and State of the art. Endocrine Oncology, 2018.
[2] Kerkhofs, T.M., et al., Development of a pharmacokinetic model of mitotane: toward personalized dosing in adrenocortical carcinoma. Ther Drug Monit, 2015. 37(1): p. 58-65.
[3] Arbitrio, M., et al., DMET (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine. Oncotarget, 2016. 7(33): p. 54028-54050.
[4] Jonsson, E.N. and M.O. Karlsson, Automated covariate model building within NONMEM. Pharmaceutical Research, 1998. 15(9): p. 1463-1468.
Reference: PAGE 28 (2019) Abstr 9018 [www.page-meeting.org/?abstract=9018]
Poster: Drug/Disease Modelling - Oncology