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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

Optimal 6-MP dosing and corresponding optimal 6-TGN concentration profile for two representative patients.The red region is the 95% CR of concentration optimized for the group. Green region is 95% CR back calculated until the 6-TGN measurement is taken. Black region is the optimized profile with patient-specific model after 6-TGN measurement on the 35th day. Blue and pink stems represent 6-MP doses before and after 6-TGN measurement respectively. The blue dashed line designates the concentration target. See text for details.
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pone.0133244.g009: Optimal 6-MP dosing and corresponding optimal 6-TGN concentration profile for two representative patients.The red region is the 95% CR of concentration optimized for the group. Green region is 95% CR back calculated until the 6-TGN measurement is taken. Black region is the optimized profile with patient-specific model after 6-TGN measurement on the 35th day. Blue and pink stems represent 6-MP doses before and after 6-TGN measurement respectively. The blue dashed line designates the concentration target. See text for details.

Mentions: Dose optimization is performed using the robust MPC strategy to achieve a therapeutically effective 6-TGN concentration. All dosing calculations are based on 15 days sampling horizon with 75 days treatment window as patients visit the clinic every two weeks. 50 parameter sets are sampled either from the group prior or patient-specific distribution. The combined error for all the parameters, weighted by their corresponding likelihood, was minimized within the MPC optimization. Clinical studies recommend a therapeutic 6-TGN concentration of 235–400 pmol/8x108 RBCs for an effective management of both efficacy and toxicity [47,48]. Hence, we optimized 6-MP input with a target 6-TGN concentration of 300 pmol/8x108 RBCs. Fig 9 shows the optimal 6-MP input together with resultant 6-TGN concentration for patients who had different response in relation to their respective groups. As mentioned earlier, without the patient-specific model until day 35, the dose is optimized based on the enzyme activity. The red region shows the 95% CR of concentration optimized for an enzyme activity group. When the patient-specific model is obtained with a measurement on the 35th day, the optimized region shifts based on the drug concentration measured and reaches the target with narrow CR (shown in black). The CR in green shows the back calculation with the same dose as that of group optimum but with patient-specific model. The patient in subplot A had a lower reaction rate in relation to the group to which he/she belonged and hence when the actual measurement becomes available the dose had to be increased to push the 6-TGN concentration higher. The opposite is true for the patient in subplot B. Dose inputs for different patients suggest that the dosage varied as much as 200% and as low as 25% of the standard dose, which is not uncommon in the clinical practice. Although there are obvious and significant differences between the standard and optimized 6-MP usage, the merit of dose optimization should be viewed from the maximization of therapeutic benefits rather than the reduction of drug input as the cost of drug is only a fraction of the overall healthcare spending.


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)

Optimal 6-MP dosing and corresponding optimal 6-TGN concentration profile for two representative patients.The red region is the 95% CR of concentration optimized for the group. Green region is 95% CR back calculated until the 6-TGN measurement is taken. Black region is the optimized profile with patient-specific model after 6-TGN measurement on the 35th day. Blue and pink stems represent 6-MP doses before and after 6-TGN measurement respectively. The blue dashed line designates the concentration target. See text for details.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0133244.g009: Optimal 6-MP dosing and corresponding optimal 6-TGN concentration profile for two representative patients.The red region is the 95% CR of concentration optimized for the group. Green region is 95% CR back calculated until the 6-TGN measurement is taken. Black region is the optimized profile with patient-specific model after 6-TGN measurement on the 35th day. Blue and pink stems represent 6-MP doses before and after 6-TGN measurement respectively. The blue dashed line designates the concentration target. See text for details.
Mentions: Dose optimization is performed using the robust MPC strategy to achieve a therapeutically effective 6-TGN concentration. All dosing calculations are based on 15 days sampling horizon with 75 days treatment window as patients visit the clinic every two weeks. 50 parameter sets are sampled either from the group prior or patient-specific distribution. The combined error for all the parameters, weighted by their corresponding likelihood, was minimized within the MPC optimization. Clinical studies recommend a therapeutic 6-TGN concentration of 235–400 pmol/8x108 RBCs for an effective management of both efficacy and toxicity [47,48]. Hence, we optimized 6-MP input with a target 6-TGN concentration of 300 pmol/8x108 RBCs. Fig 9 shows the optimal 6-MP input together with resultant 6-TGN concentration for patients who had different response in relation to their respective groups. As mentioned earlier, without the patient-specific model until day 35, the dose is optimized based on the enzyme activity. The red region shows the 95% CR of concentration optimized for an enzyme activity group. When the patient-specific model is obtained with a measurement on the 35th day, the optimized region shifts based on the drug concentration measured and reaches the target with narrow CR (shown in black). The CR in green shows the back calculation with the same dose as that of group optimum but with patient-specific model. The patient in subplot A had a lower reaction rate in relation to the group to which he/she belonged and hence when the actual measurement becomes available the dose had to be increased to push the 6-TGN concentration higher. The opposite is true for the patient in subplot B. Dose inputs for different patients suggest that the dosage varied as much as 200% and as low as 25% of the standard dose, which is not uncommon in the clinical practice. Although there are obvious and significant differences between the standard and optimized 6-MP usage, the merit of dose optimization should be viewed from the maximization of therapeutic benefits rather than the reduction of drug input as the cost of drug is only a fraction of the overall healthcare spending.

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