Zheng Liu1,2,3, Jennifer H Martin1, Winston Liauw4, Sue-Anne McLachlan5, Emma Link6,7, Anetta Matera6, Michael Thompson8, Michael Jefford7,9, Rod J Hicks7,8, Carleen Cullinane7,10, Athena Hatzimihalis10, Ian Campbell7,11, Simone Crowley12, Phillip J Beale13, Christos S Karapetis14, Timothy Price15, Mathew E Burge16, Michael Michael17,18. 1. School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia. 2. Clinical Pharmacology, Department of Medicine, The Royal Children's Hospital Melbourne, Melbourne, Australia. 3. Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia. 4. Department of Medical Oncology, St. George's Hospital, Sydney, Australia. 5. Department of Medical Oncology, St. Vincent's Hospital, Melbourne, Australia. 6. Biostatistics and Clinical Trials Centre, Peter MacCallum Cancer Centre, Melbourne, Australia. 7. Department of Oncology, Sir Peter MacCallum, University of Melbourne, Melbourne, Australia. 8. Department of Nuclear Medicine, Peter MacCallum Cancer Centre, Melbourne, Australia. 9. Department of Medical Oncology, Peter MacCallum Cancer Centre, 305 Grattan Street, Melbourne, VIC, 3000, Australia. 10. Translational Research Laboratory, Peter MacCallum Cancer Centre, Melbourne, Australia. 11. Victorian Breast Cancer Research Cooperative (VBCRC) Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Australia. 12. Previously Victorian Breast Cancer Research Cooperative (VBCRC) Cancer Genetics Laboratory, The Murdoch Children's Research Institute, The Royal Children's Hospital, Peter MacCallum Cancer Centre), MelbourneMelbourne, Australia. 13. Department of Medical Oncology, Concord and Royal Prince Alfred Hospital, Sydney, Australia. 14. Department of Medical Oncology, Flinders Medical Centre, Flinders Centre for Innovation in Cancer, Adelaide, Australia. 15. Department of Medical Oncology, The Queen Elizabeth Hospital, Adelaide, Australia. 16. Department of Medical Oncology, Royal Brisbane and Women's Hospital, Brisbane, Australia. 17. Department of Oncology, Sir Peter MacCallum, University of Melbourne, Melbourne, Australia. Michael.Michael@petermac.org. 18. Department of Medical Oncology, Peter MacCallum Cancer Centre, 305 Grattan Street, Melbourne, VIC, 3000, Australia. Michael.Michael@petermac.org.
Abstract
BACKGROUND: Body surface area (BSA)-based dosing of irinotecan (IR) does not account for its pharmacokinetic (PK) and pharmacodynamic (PD) variabilities. Functional hepatic nuclear imaging (HNI) and excretory/metabolic/PD pharmacogenomics have shown correlations with IR disposition and toxicity/efficacy. This study reports the development of a nonlinear mixed-effect population model to identify pharmacogenomic and HNI-related covariates that impact on IR disposition to support dosage optimization. METHODS: Patients had advanced colorectal cancer treated with IR combination therapy. Baseline blood was analysed by Affymetrix DMET™ Plus Array and, for PD, single nucleotide polymorphisms (SNPs) by Sanger sequencing. For HNI, patients underwent 99mTc-IDA hepatic imaging, and data was analysed for hepatic extraction/excretion parameters. Blood was taken for IR and metabolite (SN38, SN38G) analysis on day 1 cycle 1. Population modelling utilised NONMEM version 7.2.0, with structural PK models developed for each moiety. Covariates include patient demographics, HNI parameters and pharmacogenomic variants. RESULTS: Analysis included (i) PK data: 32 patients; (ii) pharmacogenomic data: 31 patients: 750 DMET and 22 PD variants; and (iii) HNI data: 32 patients. On initial analysis, overall five SNPs were identified as significant covariates for CLSN38. Only UGT1A3_c.31 T > C and ABCB1_c.3435C > T were included in the final model, whereby CLSN38 reduced from 76.8 to 55.1%. CONCLUSION: The identified UGT1A3_c.31 T > C and ABCB1_c.3435C > T variants, from wild type to homozygous, were included in the final model for SN38 clearance.
BACKGROUND: Body surface area (BSA)-based dosing of irinotecan (IR) does not account for its pharmacokinetic (PK) and pharmacodynamic (PD) variabilities. Functional hepatic nuclear imaging (HNI) and excretory/metabolic/PD pharmacogenomics have shown correlations with IR disposition and toxicity/efficacy. This study reports the development of a nonlinear mixed-effect population model to identify pharmacogenomic and HNI-related covariates that impact on IR disposition to support dosage optimization. METHODS: Patients had advanced colorectal cancer treated with IR combination therapy. Baseline blood was analysed by Affymetrix DMET™ Plus Array and, for PD, single nucleotide polymorphisms (SNPs) by Sanger sequencing. For HNI, patients underwent 99mTc-IDA hepatic imaging, and data was analysed for hepatic extraction/excretion parameters. Blood was taken for IR and metabolite (SN38, SN38G) analysis on day 1 cycle 1. Population modelling utilised NONMEM version 7.2.0, with structural PK models developed for each moiety. Covariates include patient demographics, HNI parameters and pharmacogenomic variants. RESULTS: Analysis included (i) PK data: 32 patients; (ii) pharmacogenomic data: 31 patients: 750 DMET and 22 PD variants; and (iii) HNI data: 32 patients. On initial analysis, overall five SNPs were identified as significant covariates for CLSN38. Only UGT1A3_c.31 T > C and ABCB1_c.3435C > T were included in the final model, whereby CLSN38 reduced from 76.8 to 55.1%. CONCLUSION: The identified UGT1A3_c.31 T > C and ABCB1_c.3435C > T variants, from wild type to homozygous, were included in the final model for SN38 clearance.
Authors: Michael Michael; Mick Thompson; Rod J Hicks; Paul L Mitchell; Andrew Ellis; Alvin D Milner; Julia Di Iulio; Andrew M Scott; Volker Gurtler; Janelle M Hoskins; Stephen J Clarke; Niall C Tebbut; Kian Foo; Michael Jefford; John R Zalcberg Journal: J Clin Oncol Date: 2006-08-08 Impact factor: 44.544
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