Parametric Approaches in Population Pharmacokinetics

Guidi, Monia; Csajka, Chantal; Buclin, Thierry

doi:10.1002/jcph.1633

Cited by 24 publications

(23 citation statements)

References 112 publications

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“…To interpret the residual variability, knowledge about the assay parameters is key: For a good model, the magnitude of the residual error should lie within the margin of the analytical error. To account for interindividual variability, both parametric 41 and nonparametric approaches 42 are used. The main difference is the use of a defined distribution of PK parameters in the case of the parametric approaches, whereas in the case of nonparametric approaches so‐called support points are estimated from the clinical data, which do not make distribution assumptions.…”

Section: From Therapeutic Drug Monitoring To Model‐informed Precisionmentioning

confidence: 99%

“…The main difference is the use of a defined distribution of PK parameters in the case of the parametric approaches, whereas in the case of nonparametric approaches so‐called support points are estimated from the clinical data, which do not make distribution assumptions. For a detailed review of either approach, the reader is referred to two comprehensive reviews 41,42 . More research, beyond simulation studies, is necessary to compare the predictive performance of both approaches in real‐world data when future drug exposure is predicted in the context of MIPD.…”

Section: From Therapeutic Drug Monitoring To Model‐informed Precisionmentioning

confidence: 99%

“…For a detailed review of either approach, the reader is referred to two comprehensive reviews. 41,42 More research, beyond simulation studies, is necessary to compare the predictive performance of both approaches in real-world data when future drug exposure is predicted in the context of MIPD.…”

Section: Model-informed Precision Dosing (Mipd)mentioning

confidence: 99%

See 2 more Smart Citations

From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics

Wicha

Märtson

Nielsen

et al. 2021

Clin Pharma and Therapeutics

170

159

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(ISAP), the PK/PD study group of the European Society of Clinical Microbiology, Infectious Diseases (EPASG) Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended.

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Section: From Therapeutic Drug Monitoring To Model‐informed Precisionmentioning

confidence: 99%

Section: From Therapeutic Drug Monitoring To Model‐informed Precisionmentioning

confidence: 99%

See 1 more Smart Citation

From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics

Wicha

Märtson

Nielsen

et al. 2021

Clin Pharma and Therapeutics

170

159

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“…More details on the strengths and limitations of each method are provided in the 2 previously mentioned papers on P and NP methods 1,2 …”

Section: General Questions On Both Approachesmentioning

confidence: 99%

“…In 2 separate articles, we have reviewed the characteristics of parametric (P) and nonparametric (NP) approaches in population pharmacokinetics (popPK) 1,2 . The aim of this third article is to answer some frequently asked questions that are raised by scholars, clinicians, and researchers interested in popPK and model‐informed precision dosing (MIPD).…”

mentioning

confidence: 99%

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts’ Discussion on Use, Strengths, and Limitations

Goutelle

Woillard

Buclin

et al. 2021

The Journal of Clinical Pharma

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Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model‐informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area.

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Population Pharmacokinetic Models for Direct Oral Anticoagulants: A Systematic Review and Clinical Appraisal Using Exposure Simulation

Terrier

Gaspar

Guidi

et al. 2022

Clin Pharma and Therapeutics

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Available data have shown an association between direct oral anticoagulant (DOAC) plasma concentration and clinical, particularly bleeding, events. Factors that may influence DOAC plasma concentration are therefore the focus of particular attention. Population pharmacokinetic (PopPK) analyses can help in identifying such factors while providing predictive models. The main aim of the present study was to identify all the PopPK models to date for the four most frequently used DOACs (dabigatran, apixaban, rivaroxaban, and edoxaban). The secondary aim was to use these models to simulate different DOAC plasma concentration–time profiles in relevant clinical scenarios. The results of our model‐based simulations confirm the clinical relevance of the known major factors influencing DOAC exposure and support the current approved dose adaptation, at least for atrial fibrillation. They also highlight how the accumulation of covariates, not currently considered for dose adaptation due to their seemingly minor influence on DOAC exposure, lead to supratherapeutic blood concentrations and could thus enhance the risk of major bleeding. The present results therefore question DOAC dose adaptation in the presence of these covariates, such as drug–drug interaction or genotypes, alongside the known existing covariates. As the overall effect of accumulation of several covariates could be difficult to apprehend for the clinicians, PopPK modeling could represent an interesting approach for informed precision dosing and to improve personalized prescription of DOACs.

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Parametric Approaches in Population Pharmacokinetics

Cited by 24 publications

References 112 publications

From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics

From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts’ Discussion on Use, Strengths, and Limitations

Population Pharmacokinetic Models for Direct Oral Anticoagulants: A Systematic Review and Clinical Appraisal Using Exposure Simulation

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