Model-based individualized treatment of chemotherapeutics: Bayesian population modeling and dose optimization

Devaraj Jayachandran, José Laínez-Aguirre, Ann Rundell, Terry Vik, Robert Hannemann, Gintaras Reklaitis, Doraiswami Ramkrishna

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian populationmodeling approach to develop a pharmacologicalmodel for 6-MPmetabolismin humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited.With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

Original languageEnglish (US)
Article numbere0133244
JournalPLoS One
Volume10
Issue number7
DOIs
StatePublished - Jul 30 2015

Fingerprint

thiopurine methyltransferase
6-Mercaptopurine
thiopurine S-methyltransferase
pharmacogenomics
Pharmacogenetics
dosage
drugs
Pharmaceutical Preparations
Population
nucleotides
Phenotype
phenotype
therapeutics
Pediatrics
Bayes Theorem
inflammatory bowel disease
autoimmune diseases
Immune System Diseases
Prodrugs
Model predictive control

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Jayachandran, D., Laínez-Aguirre, J., Rundell, A., Vik, T., Hannemann, R., Reklaitis, G., & Ramkrishna, D. (2015). Model-based individualized treatment of chemotherapeutics: Bayesian population modeling and dose optimization. PLoS One, 10(7), [e0133244]. https://doi.org/10.1371/journal.pone.0133244

Model-based individualized treatment of chemotherapeutics : Bayesian population modeling and dose optimization. / Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami.

In: PLoS One, Vol. 10, No. 7, e0133244, 30.07.2015.

Research output: Contribution to journalArticle

Jayachandran, D, Laínez-Aguirre, J, Rundell, A, Vik, T, Hannemann, R, Reklaitis, G & Ramkrishna, D 2015, 'Model-based individualized treatment of chemotherapeutics: Bayesian population modeling and dose optimization', PLoS One, vol. 10, no. 7, e0133244. https://doi.org/10.1371/journal.pone.0133244
Jayachandran, Devaraj ; Laínez-Aguirre, José ; Rundell, Ann ; Vik, Terry ; Hannemann, Robert ; Reklaitis, Gintaras ; Ramkrishna, Doraiswami. / Model-based individualized treatment of chemotherapeutics : Bayesian population modeling and dose optimization. In: PLoS One. 2015 ; Vol. 10, No. 7.
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