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Related Concept Videos

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Related Experiment Video

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Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated

Cyril Leven1,2, Anne Coste3, Camille Mané1

  • 1Department of Biochemistry and Pharmaco-Toxicology, Brest University Hospital, 29200 Brest, France.

Pharmaceutics
|February 26, 2022
PubMed
Summary

Posologyr, a new open-source R package, offers reliable Bayesian methods for individualizing drug doses. Its performance validation shows excellent accuracy in parameter estimation and acceptable bias in dose adjustments for precision medicine.

Keywords:
Bayesian dosingMaximum A Posterioriclinical pharmacokineticsdosage individualizationtherapeutic drug monitoring

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Area of Science:

  • Pharmacometrics
  • Computational Biology
  • Drug Development

Background:

  • Model-informed precision dosing (MIPD) enhances therapeutic drug monitoring.
  • Open-source software for MIPD is currently limited.
  • Posologyr is a novel R package designed for Bayesian parameter estimation and dose individualization.

Purpose of the Study:

  • To validate the performance of the Posologyr R package for Bayesian individual parameter estimation.
  • To benchmark Posologyr against established software for dose individualization.
  • To assess the accuracy and reliability of Posologyr for clinical applications.

Main Methods:

  • Benchmarking Posologyr's estimation functions against reference software (e.g., NONMEM, Monolix).
  • Utilizing 35 population pharmacokinetic models with 4,000 simulated subjects per model.
  • Comparing Maximum A Posteriori (MAP) estimates with NONMEM post hoc estimates.
  • Evaluating full posterior distributions against Monolix conditional distribution estimates.

Main Results:

  • Posologyr's MAP estimation demonstrated excellent performance in 98.7% of cases.
  • Bias in dosage adjustment proposals, using full posterior distributions, was acceptable in 97% of cases.
  • Median bias for dosage adjustments was found to be 0.65%.

Conclusions:

  • Posologyr is a validated, high-performing open-source tool for Bayesian dose individualization.
  • The package demonstrates strong accuracy and reliability, suitable for developing future precision dosing applications.
  • Posologyr addresses the need for accessible, robust software in model-informed precision dosing.