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A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080.

Keizer RJ, Gupta A, Mac Gillavry MR, Jansen M, Wanders J, Beijnen JH, Schellens JH, Karlsson MO, Huitema AD - J Pharmacokinet Pharmacodyn (2010)

Bottom Line: Modeling was performed in NONMEM, and direct and indirect response PK-PD models were evaluated.A previously developed PK model was used.This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute, Slotervaart Hospital, Amsterdam, The Netherlands. ron.keizer@slz.nl

ABSTRACT
Hypertension and proteinuria are commonly observed side-effects for anti-angiogenic drugs targeting the VEGF pathway. In most cases, hypertension can be controlled by prescription of anti-hypertensive (AH) therapy, while proteinuria often requires dose reductions or dose delays. We aimed to construct a pharmacokinetic-pharmacodynamic (PK-PD) model for hypertension and proteinuria following treatment with the experimental VEGF-inhibitor E7080, which would allow optimization of treatment, by assessing the influence of anti-hypertensive medication and dose reduction or dose delays in treating and avoiding toxicity. Data was collected from a phase I study of E7080 (n = 67), an inhibitor of multiple tyrosine kinases, among which VEGF. Blood pressure and urinalysis data were recorded weekly. Modeling was performed in NONMEM, and direct and indirect response PK-PD models were evaluated. A previously developed PK model was used. An indirect response PK-PD model described the increase in BP best, while the probability of developing proteinuria toxicity in response to exposure to E7080, was best described by a Markov transition model. This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.

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Related in: MedlinePlus

VPC of the Markov transition model for proteinuria grades (split in two to avoid overlap of the prediction intervals). The grey areas show the 90% prediction interval for the % of patients experiencing PU, while the dotted line represents the model predicted median. Solid lines are observed % of proteinuria grades
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Fig3: VPC of the Markov transition model for proteinuria grades (split in two to avoid overlap of the prediction intervals). The grey areas show the 90% prediction interval for the % of patients experiencing PU, while the dotted line represents the model predicted median. Solid lines are observed % of proteinuria grades

Mentions: A VPC (Fig. 3) of the Markov model showed that on a population level, the model predicted the % of patients experiencing the PU grades just as good as the proportional odds model. However, a predictive check for all transitions of the Markov model, shown in Fig. 4, showed much better agreement between observed and predicted number of transitions between toxicity grades than the proportional odds model. For almost all transitions, the observed number was within the 90% (PI) of the model. Therefore the Markov model was chosen as final model for describing PU toxicities.


A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080.

Keizer RJ, Gupta A, Mac Gillavry MR, Jansen M, Wanders J, Beijnen JH, Schellens JH, Karlsson MO, Huitema AD - J Pharmacokinet Pharmacodyn (2010)

VPC of the Markov transition model for proteinuria grades (split in two to avoid overlap of the prediction intervals). The grey areas show the 90% prediction interval for the % of patients experiencing PU, while the dotted line represents the model predicted median. Solid lines are observed % of proteinuria grades
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2921067&req=5

Fig3: VPC of the Markov transition model for proteinuria grades (split in two to avoid overlap of the prediction intervals). The grey areas show the 90% prediction interval for the % of patients experiencing PU, while the dotted line represents the model predicted median. Solid lines are observed % of proteinuria grades
Mentions: A VPC (Fig. 3) of the Markov model showed that on a population level, the model predicted the % of patients experiencing the PU grades just as good as the proportional odds model. However, a predictive check for all transitions of the Markov model, shown in Fig. 4, showed much better agreement between observed and predicted number of transitions between toxicity grades than the proportional odds model. For almost all transitions, the observed number was within the 90% (PI) of the model. Therefore the Markov model was chosen as final model for describing PU toxicities.

Bottom Line: Modeling was performed in NONMEM, and direct and indirect response PK-PD models were evaluated.A previously developed PK model was used.This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute, Slotervaart Hospital, Amsterdam, The Netherlands. ron.keizer@slz.nl

ABSTRACT
Hypertension and proteinuria are commonly observed side-effects for anti-angiogenic drugs targeting the VEGF pathway. In most cases, hypertension can be controlled by prescription of anti-hypertensive (AH) therapy, while proteinuria often requires dose reductions or dose delays. We aimed to construct a pharmacokinetic-pharmacodynamic (PK-PD) model for hypertension and proteinuria following treatment with the experimental VEGF-inhibitor E7080, which would allow optimization of treatment, by assessing the influence of anti-hypertensive medication and dose reduction or dose delays in treating and avoiding toxicity. Data was collected from a phase I study of E7080 (n = 67), an inhibitor of multiple tyrosine kinases, among which VEGF. Blood pressure and urinalysis data were recorded weekly. Modeling was performed in NONMEM, and direct and indirect response PK-PD models were evaluated. A previously developed PK model was used. An indirect response PK-PD model described the increase in BP best, while the probability of developing proteinuria toxicity in response to exposure to E7080, was best described by a Markov transition model. This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.

Show MeSH
Related in: MedlinePlus