Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring

Netterberg, Ida; Nielsen, Elisabet I.; Friberg, Lena E.; Karlsson, Mats O.

doi:10.1007/s00280-017-3366-x

Cited by 22 publications

(29 citation statements)

References 41 publications

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“…Important model parameters are the drug effect parameter “Slope” and the pretreatment baseline neutrophil concentration “Circ0.” For inference, neutrophil concentrations were considered on a log‐scale at time points

t = 0, 3, \dots, 21

days postdose, see Section for full details. This simulation study aims to demonstrate the limitations of MAP estimation for a model frequently used in MIPD for TDM . Because recursive data processing and decision‐support gain in relevance for long‐term monitoring, we performed a simulation study for multiple cycle therapy with paclitaxel using the NLME model in ref.…”

Section: Methodsmentioning

confidence: 99%

“…We investigated different characteristics

T (\cdot)

of the neutropenia time‐course related to risk and recovery . Depending on the nadir (i.e., minimal neutrophil concentration), different grades of neutropenia are distinguished, see Figure .…”

Section: Methodsmentioning

confidence: 99%

“…Only if too many samples carry an almost negligible weight and the total weight is limited to only a few samples (weight degeneracy), a resampling is performed. We used a criterion based on the effective sample size (7) θ MAP n = arg max θ p(θ|y 1:n ).…”

Section: Pfmentioning

confidence: 99%

“…This simulation study aims to demonstrate the limitations of MAP estimation for a model frequently used in MIPD for TDM. 7,21 Because recursive data processing and decision-support gain in relevance for long-term monitoring, we performed a simulation study for multiple cycle therapy with paclitaxel using the NLME model in ref. 9.…”

Section: Bayesian Da In Individualized Chemotherapymentioning

confidence: 99%

“…In the context of chemotherapy‐induced neutropenia—the most frequent dose‐limiting side effect for cytotoxic anticancer drugs with substantial decrease of neutrophil granulocytes and, thus, potentially life‐threatening fever and infections—we demonstrate the clear benefits of uncertainty quantification compared with purely MAP‐based predictions (e.g., ref. 7) using the gold‐standard model for neutropenia . Further, we compare the full Bayesian approaches regarding quality of uncertainty quantification and computational runtime for multiple cycle chemotherapy .…”

mentioning

confidence: 99%

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Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Maier

Hartung

Wiljes

et al. 2020

CPT Pharmacom & Syst Pharma

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An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.

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t = 0, 3, \dots, 21

Section: Methodsmentioning

confidence: 99%

“…We investigated different characteristics

T (\cdot)

of the neutropenia time‐course related to risk and recovery . Depending on the nadir (i.e., minimal neutrophil concentration), different grades of neutropenia are distinguished, see Figure .…”

Section: Methodsmentioning

confidence: 99%

Section: Pfmentioning

confidence: 99%

Section: Bayesian Da In Individualized Chemotherapymentioning

confidence: 99%

mentioning

confidence: 99%

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Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Maier

Hartung

Wiljes

et al. 2020

CPT Pharmacom & Syst Pharma

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Model‐Based Prediction of Irinotecan‐Induced Grade 4 Neutropenia in Cancer Patients: Influence of Incorporating Germline Genetic Factors in the Model

Karas,

Mathijssen,

van Schaik

et al. 2024

Clin Pharma and Therapeutics

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Neutropenia is the major dose‐limiting toxicity of irinotecan‐based therapy. The objective of this study was to assess whether inclusion of germline genetic variants into a population pharmacokinetic/pharmacodynamic model can improve prediction of irinotecan‐induced grade 4 neutropenia and identify novel variants of clinical value. A semimechanistic population pharmacokinetic/pharmacodynamic model was used to predict neutrophil response over time in 197 patients receiving irinotecan. Covariate analysis was performed for demographic/clinical factors and 4,781 genetic variants in 84 drug response‐ and toxicity‐related genes to identify covariates associated with neutrophil response. We evaluated the predictive value of the model for grade 4 neutropenia reflecting different clinical scenarios of available data on identified demographic/clinical covariates, baseline and post‐treatment absolute neutrophil counts (ANCs), individual pharmacokinetics, and germline genetic variation. Adding 8 genetic identified covariates (rs10929302 (UGT1A1), rs1042482 (DPYD), rs2859101 (HLA‐DQB3), rs61754806 (NR3C1), rs9266271 (HLA‐B), rs7294 (VKORC1), rs1051713 (ALOX5), and ABCB1 rare variant burden) to a model using only baseline ANCs improved prediction of irinotecan‐induced grade 4 neutropenia from area under the receiver operating characteristic curve (AUC‐ROC) of 50–64% (95% confidence interval (CI), 54–74%). Individual pharmacokinetics further improved the prediction to 74% (95% CI, 64–84%). When weekly ANC was available, the identified covariates and individual pharmacokinetics yielded no additional contribution to the prediction. The model including only ANCs at baseline and at week 1 achieved an AUC‐ROC of 78% (95% CI, 69–88%). Germline DNA genetic variants may contribute to the prediction of irinotecan‐induced grade 4 neutropenia when incorporated into a population pharmacokinetic/pharmacodynamic model. This approach is generalizable to drugs that induce neutropenia and ultimately allows for personalized intervention to enhance patient safety.

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Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology

Maier

Hartung

Kloft

et al. 2021

CPT Pharmacom & Syst Pharma

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Model‐informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model‐informed dosing tables or are based on maximum a posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient‐specific information. We propose three novel approaches for MIPD using Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose‐limiting side effect in anticancer chemotherapy. These approaches have the potential to substantially reduce the incidence of life‐threatening grade 4 and subtherapeutic grade 0 neutropenia compared with existing approaches. We further show that RL allows to gain further insights by identifying patient factors that drive dose decisions. Due to its flexibility, the proposed combined DA‐RL approach can easily be extended to integrate multiple end points or patient‐reported outcomes, thereby promising important benefits for future personalized therapies.

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Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring

Cited by 22 publications

References 41 publications

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Model‐Based Prediction of Irinotecan‐Induced Grade 4 Neutropenia in Cancer Patients: Influence of Incorporating Germline Genetic Factors in the Model

Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology

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