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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

Comparison of 95% CR predicted using different information on biological chain-of-response for two representative patients.The black region represents the population. The gray region shows the prediction when only TPMT enzyme activity is measured. The red region shows the 95% CR predicted when just one measurement of 6-TGN is available (the solid dot).
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pone.0133244.g008: Comparison of 95% CR predicted using different information on biological chain-of-response for two representative patients.The black region represents the population. The gray region shows the prediction when only TPMT enzyme activity is measured. The red region shows the 95% CR predicted when just one measurement of 6-TGN is available (the solid dot).

Mentions: Another important objective of this work is to evaluate the impact of measuring various entities in biological chain-of-response on the uncertainty in predicting variables used for therapeutic drug monitoring and optimization. Despite the attractiveness of the DNA sequencing and gene expression profiling in characterizing patients, the more downstream variables, such as enzyme activity and active metabolite concentration, provide a much more robust indication of underlying response and thus helping to minimize the uncertainty. For this purpose, the individual parameter distributions are sampled to represent various measurements viz. whole population, groups based on TPMT genotype, groups based on TPMT enzyme activity and individual patient 6-TGN concentration measured at a single time point. Two TPMT genotype groups were formed based on enzyme activity. A TPMT enzyme activity of less than 10 U/ml/hr. is deemed as TPMTH/TPMTL (heterozygous) and an activity of more than 10 U/ml/hr. is deemed as TPMTH/TPMTH (homozygous-High) [31]. There are no TPMTL/TPMTL (homozygous-Low) patients found in this study. As Fig 8 shows, the black region is the 95% CR of concentration for the whole population included in this study. Without any attempt to collect genetic/phenotypic measurements from a patient, the most that can be aspired for is that the concentration will fall in the range of 50–650 pmol/8x108 RBCs. This is indeed a reflection of the current status of clinical practice. Obviously, such uncertainties would preclude the possibility of dose individualization. The confidence regions for both the TPMT genotype groups were no different from this population CR. The gray region shows the 95% CR for the group having a TPMT activity of 15–17 U/ml/hr. With the measurement of a slightly downstream marker, the CR is narrower compared to the CR predicted for the population. However, when the 6-TGN concentration is measured and the model is adapted subsequently, the 95% CR is much narrower with notably lower uncertainty. With this patient-specific model, making accurate prediction of drug concentration at hand, one can venture into dose optimization strategies to direct the drug concentration into a desired region that maximizes the efficacy and minimizes the side-effects.


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)

Comparison of 95% CR predicted using different information on biological chain-of-response for two representative patients.The black region represents the population. The gray region shows the prediction when only TPMT enzyme activity is measured. The red region shows the 95% CR predicted when just one measurement of 6-TGN is available (the solid dot).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0133244.g008: Comparison of 95% CR predicted using different information on biological chain-of-response for two representative patients.The black region represents the population. The gray region shows the prediction when only TPMT enzyme activity is measured. The red region shows the 95% CR predicted when just one measurement of 6-TGN is available (the solid dot).
Mentions: Another important objective of this work is to evaluate the impact of measuring various entities in biological chain-of-response on the uncertainty in predicting variables used for therapeutic drug monitoring and optimization. Despite the attractiveness of the DNA sequencing and gene expression profiling in characterizing patients, the more downstream variables, such as enzyme activity and active metabolite concentration, provide a much more robust indication of underlying response and thus helping to minimize the uncertainty. For this purpose, the individual parameter distributions are sampled to represent various measurements viz. whole population, groups based on TPMT genotype, groups based on TPMT enzyme activity and individual patient 6-TGN concentration measured at a single time point. Two TPMT genotype groups were formed based on enzyme activity. A TPMT enzyme activity of less than 10 U/ml/hr. is deemed as TPMTH/TPMTL (heterozygous) and an activity of more than 10 U/ml/hr. is deemed as TPMTH/TPMTH (homozygous-High) [31]. There are no TPMTL/TPMTL (homozygous-Low) patients found in this study. As Fig 8 shows, the black region is the 95% CR of concentration for the whole population included in this study. Without any attempt to collect genetic/phenotypic measurements from a patient, the most that can be aspired for is that the concentration will fall in the range of 50–650 pmol/8x108 RBCs. This is indeed a reflection of the current status of clinical practice. Obviously, such uncertainties would preclude the possibility of dose individualization. The confidence regions for both the TPMT genotype groups were no different from this population CR. The gray region shows the 95% CR for the group having a TPMT activity of 15–17 U/ml/hr. With the measurement of a slightly downstream marker, the CR is narrower compared to the CR predicted for the population. However, when the 6-TGN concentration is measured and the model is adapted subsequently, the 95% CR is much narrower with notably lower uncertainty. With this patient-specific model, making accurate prediction of drug concentration at hand, one can venture into dose optimization strategies to direct the drug concentration into a desired region that maximizes the efficacy and minimizes the side-effects.

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