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Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

Jayachandran D, Laínez-Aguirre J, Rundell A, Vik T, Hannemann R, Reklaitis G, Ramkrishna D - PLoS ONE (2015)

Bottom Line: Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population.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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical Engineering, Purdue University, 480 Stadium Mall Way, West Lafayette, IN, 47907, United States of America.

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 population modeling approach to develop a pharmacological model for 6-MP metabolism in 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.

No MeSH data available.


Related in: MedlinePlus

Evolution of information as a function of time and parameter set determined via optimal DoE technique.
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pone.0133244.g007: Evolution of information as a function of time and parameter set determined via optimal DoE technique.

Mentions: Table 3 shows the error associated with all the parameters. Any parameter with less than 2% of the error associated with the most sensitive parameter will be regarded as less sensitive and hence fixed at the population mean for all individual patients. The most sensitive parameter and hence the highest error involved was found to be kcm. Taking this as the reference error, the error involved was less than 2% for all parameters, except kme. However, as mentioned before, the correlation between kcm and kme is 0.96. Bayesian estimation with only kcm and kme as estimable parameters (other parameters were fixed at population mean) confirmed this trend and is shown in Fig 6. As a result, it suffices to estimate only kcm and fix all other parameters at the population mean for new patients. Consequently, the experimental design was formulated with the objective of improving the precision of parameter estimation for kcm. Fig 7 shows the evolution of Fisher’s information as a function of time and parameter sets. From the figure, the maximum information is made available towards the steady state of the model. Concentration densities simulated with population prior also pointed that the maximum variation in the concentration distribution resulted when the drug concentrations are higher. However, it is not prudent to wait until the steady state to gather the data and identify the new patient. Hence, by compromising about 5% of the maximum information, 35th day was determined as the optimal time to collect blood sample to measure the 6-TGN concentration.


Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

Jayachandran D, Laínez-Aguirre J, Rundell A, Vik T, Hannemann R, Reklaitis G, Ramkrishna D - PLoS ONE (2015)

Evolution of information as a function of time and parameter set determined via optimal DoE technique.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4520687&req=5

pone.0133244.g007: Evolution of information as a function of time and parameter set determined via optimal DoE technique.
Mentions: Table 3 shows the error associated with all the parameters. Any parameter with less than 2% of the error associated with the most sensitive parameter will be regarded as less sensitive and hence fixed at the population mean for all individual patients. The most sensitive parameter and hence the highest error involved was found to be kcm. Taking this as the reference error, the error involved was less than 2% for all parameters, except kme. However, as mentioned before, the correlation between kcm and kme is 0.96. Bayesian estimation with only kcm and kme as estimable parameters (other parameters were fixed at population mean) confirmed this trend and is shown in Fig 6. As a result, it suffices to estimate only kcm and fix all other parameters at the population mean for new patients. Consequently, the experimental design was formulated with the objective of improving the precision of parameter estimation for kcm. Fig 7 shows the evolution of Fisher’s information as a function of time and parameter sets. From the figure, the maximum information is made available towards the steady state of the model. Concentration densities simulated with population prior also pointed that the maximum variation in the concentration distribution resulted when the drug concentrations are higher. However, it is not prudent to wait until the steady state to gather the data and identify the new patient. Hence, by compromising about 5% of the maximum information, 35th day was determined as the optimal time to collect blood sample to measure the 6-TGN concentration.

Bottom Line: Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population.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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical Engineering, Purdue University, 480 Stadium Mall Way, West Lafayette, IN, 47907, United States of America.

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 population modeling approach to develop a pharmacological model for 6-MP metabolism in 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.

No MeSH data available.


Related in: MedlinePlus