Comparison of an assumption‐free Bayesian approach with Optimal Sampling Schedule to a maximum a posteriori Approach for Personalizing Cyclophosphamide Dosing

Variable metabolism, dose‐dependent efficacy, and a narrow therapeutic target of cyclophosphamide (CY) suggest that dosing based on individual pharmacokinetics (PK) will improve efficacy and minimize toxicity. Real‐time individualized CY dose adjustment was previously explored using a maximum a posteriori (MAP) approach based on a five serum–PK sampling in patients with hematologic malignancy undergoing stem cell transplantation. The MAP approach resulted in an improved toxicity profile without sacrificing efficacy. However, extensive PK sampling is costly and not generally applicable in the clinic. We hypothesize that the assumption‐free Bayesian approach (AFBA) can reduce sampling requirements, while improving the accuracy of results.

[1]  Population Pharmacokinetics.  Guidance for Industry Population Pharmacokinetics , 1999 .

[2]  F. Appelbaum,et al.  Conditioning regimen-dependent disposition of cyclophosphamide and hydroxycyclophosphamide in human marrow transplantation patients. , 1996, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  Venkat Venkatasubramanian,et al.  High fidelity mathematical model building with experimental data: A Bayesian approach , 2008, Comput. Chem. Eng..

[4]  Ene I Ette,et al.  Population Pharmacokinetics I: Background, Concepts, and Models , 2004, The Annals of pharmacotherapy.

[5]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[6]  T. Gooley,et al.  Cyclophosphamide metabolism, liver toxicity, and mortality following hematopoietic stem cell transplantation. , 2003, Blood.

[7]  Karline Soetaert,et al.  Solving Differential Equations in R , 2012 .

[8]  Dirk Eddelbuettel,et al.  Rcpp: Seamless R and C++ Integration , 2011 .

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  M O Karlsson,et al.  A mechanism-based pharmacokinetic-enzyme model for cyclophosphamide autoinduction in breast cancer patients. , 1999, British journal of clinical pharmacology.

[11]  Gintaras V. Reklaitis,et al.  Pharmacokinetic Based Design of Individualized Dosage Regimens Using a Bayesian Approach , 2011 .

[12]  D. Salinger,et al.  Personalized Dosing of Cyclophosphamide in the Total Body Irradiation–Cyclophosphamide Conditioning Regimen: A Phase II Trial in Patients With Hematologic Malignancy , 2009, Clinical pharmacology and therapeutics.

[13]  P. Vicini,et al.  Diminishing the risk of nonrelapse mortality in hematopoietic stem cell transplantation: Prediction of exposure to the cyclophosphamide metabolite Carboxyethylphosphoramide mustard , 2004, Clinical pharmacology and therapeutics.

[14]  Brian R. Phillips,et al.  Real-time Dose Adjustment of Cyclophosphamide in a Preparative Regimen for Hematopoietic Cell Transplant: A Bayesian Pharmacokinetic Approach , 2006, Clinical Cancer Research.

[15]  A. Mallet.  A maximum likelihood estimation method for random coefficient regression models , 1986 .

[16]  Timothée Poisot,et al.  The digitize Package: Extracting Numerical Data from Scatterplots , 2011, R J..

[17]  Gintaras V. Reklaitis,et al.  A Decomposition Strategy for the Variational Inference of Complex Systems , 2016, Technometrics.

[18]  Andrew D. Martin,et al.  MCMCpack: Markov chain Monte Carlo in R , 2011 .

[19]  Gintaras V. Reklaitis,et al.  A Stochastic Optimization approach for the design of Individualized Dosage Regimens , 2013 .