Multivariate Exact Discrepancy: A New Tool for PK/PD Model Evaluation

Baklouti, Sarah; Comets, Emmanuelle; Gandia, Peggy; Concordet, Didier

doi:10.1007/s40262-023-01296-6

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…While we chose models to represent the typical Caucasian patient population, it is important to note that the results may not generalize to special populations, such as pediatric patients or those taking co-medications. Models for such analyses should ideally be selected to accurately represent the target population [ 39 ]. Finally, this study evaluated the prediction of exposure based on a single occasion.…”

Section: Discussionmentioning

confidence: 99%

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework

Centanni,

Reijnhout,

Thijs

et al. 2024

Clin Pharmacokinet

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Background Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). Methods Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. Results Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. Conclusions For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

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Section: Discussionmentioning

confidence: 99%

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework

Centanni,

Reijnhout,

Thijs

et al. 2024

Clin Pharmacokinet

View full text Add to dashboard Cite

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Vancomycin population pharmacokinetic models: Uncovering pharmacodynamic divergence amid clinicobiological resemblance

Gandia,

Chaiben,

Fabre

et al. 2024

CPT Pharmacom & Syst Pharma

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Vancomycin is an antibiotic used for severe infections. To ensure microbiological efficacy, a ratio of AUC/MIC ≥400 is recommended. However, there is significant interindividual variability in its pharmacokinetic parameters, necessitating therapeutic drug monitoring to adjust dosing regimens and ensure efficacy while avoiding toxicity. Population pharmacokinetic (PopPK) models enable dose personalization, but the challenge lies in the choice of the model to use among the multitude of models in the literature. We compared 18 PopPK models created from populations with the same sociodemographic and clinicobiological characteristics. Simulations were performed for a 47 years old man, weighing 70 kg, with an albumin level of 35.5 g/L, a creatinine clearance of 100 mL/min, an eGFR of 106 mL/min/1.73 m2, and receiving an intravenous infusion of 1 g × 2/day of VCM over 1 h for 48 h. Simulations of time–concentration profiles revealed differences, leading us to determine the probability of achieving microbiological efficacy (AUC/MIC ≥ 400) with each model. Depending on some models, a dose of 1 g × 2/day is required to ensure microbiological efficacy in over 90% of the population, while with the same dose other models do not exceed 10% of the population. To ensure that 90% of the patients are correctly exposed, a dose of vancomycin ranging from 0.9 g × 2/day to 2.2 g × 2/day is necessary a priori depending on the chosen model. These differences raise an issue in choosing a model for performing therapeutic drug monitoring using a PopPK model with or without Bayesian approach. Thus, it is fundamental to evaluate the impact of these differences on both efficacy/toxicity.

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Multivariate Exact Discrepancy: A New Tool for PK/PD Model Evaluation

Cited by 2 publications

References 13 publications

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework

Vancomycin population pharmacokinetic models: Uncovering pharmacodynamic divergence amid clinicobiological resemblance

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