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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

A general framework for model-based individualized dosing of chemotherapeutic drugs.Pharmacokinetic and pharmacodynamic models are formulated based on underlying physiology. With extensive data from a large cohort of patients, a population model is formulated based on the Bayesian approach. A few measurements from a new patient, collected at optimal time points, enables the adaptation of the population model to an individual behavior. Patient models are used to optimize the dose based on model predictive control to maintain the drug concentration within the therapeutic window. In this work, only pharmacokinetic aspects of 6-MP are considered.
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pone.0133244.g001: A general framework for model-based individualized dosing of chemotherapeutic drugs.Pharmacokinetic and pharmacodynamic models are formulated based on underlying physiology. With extensive data from a large cohort of patients, a population model is formulated based on the Bayesian approach. A few measurements from a new patient, collected at optimal time points, enables the adaptation of the population model to an individual behavior. Patient models are used to optimize the dose based on model predictive control to maintain the drug concentration within the therapeutic window. In this work, only pharmacokinetic aspects of 6-MP are considered.

Mentions: There is a growing body of literature that acknowledges this state of affairs and suggests the need for tailoring the dose regimen based on a patient’s genetic and phenotypic make-up [8,23–25]. Given the dynamic nature of physiological responses, it has to be an ongoing process rather than a ‘study-and-adopt’ approach. In other words, following a detailed analysis and accumulation of information during the study phase, a minimum of information must be obtained from each new patient to adapt the approach to the patient before making predictions and dose optimization. Given the significant limitation on continuous monitoring in the clinical settings, a robust model-based in silico approach, adaptable to individual patients, is indispensable. A recent report by the National Academy of Engineering and the Institute of Medicine highlights the potential of such engineering approaches, consummated through a partnership between healthcare professionals and engineers, in patient focused health care delivery [25]. Hence, these factors form the thrust of this manuscript and are summarized in Fig 1. In section 2, we describe the model and methodologies used and provide some important results in section 3. Finally in section 4, we conclude with discussion on the impact, constraints in clinical implementation and possible extension.


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)

A general framework for model-based individualized dosing of chemotherapeutic drugs.Pharmacokinetic and pharmacodynamic models are formulated based on underlying physiology. With extensive data from a large cohort of patients, a population model is formulated based on the Bayesian approach. A few measurements from a new patient, collected at optimal time points, enables the adaptation of the population model to an individual behavior. Patient models are used to optimize the dose based on model predictive control to maintain the drug concentration within the therapeutic window. In this work, only pharmacokinetic aspects of 6-MP are considered.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0133244.g001: A general framework for model-based individualized dosing of chemotherapeutic drugs.Pharmacokinetic and pharmacodynamic models are formulated based on underlying physiology. With extensive data from a large cohort of patients, a population model is formulated based on the Bayesian approach. A few measurements from a new patient, collected at optimal time points, enables the adaptation of the population model to an individual behavior. Patient models are used to optimize the dose based on model predictive control to maintain the drug concentration within the therapeutic window. In this work, only pharmacokinetic aspects of 6-MP are considered.
Mentions: There is a growing body of literature that acknowledges this state of affairs and suggests the need for tailoring the dose regimen based on a patient’s genetic and phenotypic make-up [8,23–25]. Given the dynamic nature of physiological responses, it has to be an ongoing process rather than a ‘study-and-adopt’ approach. In other words, following a detailed analysis and accumulation of information during the study phase, a minimum of information must be obtained from each new patient to adapt the approach to the patient before making predictions and dose optimization. Given the significant limitation on continuous monitoring in the clinical settings, a robust model-based in silico approach, adaptable to individual patients, is indispensable. A recent report by the National Academy of Engineering and the Institute of Medicine highlights the potential of such engineering approaches, consummated through a partnership between healthcare professionals and engineers, in patient focused health care delivery [25]. Hence, these factors form the thrust of this manuscript and are summarized in Fig 1. In section 2, we describe the model and methodologies used and provide some important results in section 3. Finally in section 4, we conclude with discussion on the impact, constraints in clinical implementation and possible extension.

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