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Related Concept Videos

Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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 relationship...
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations01:15

Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations

Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...

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Related Experiment Video

Updated: Jun 30, 2026

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
08:38

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS

Published on: November 8, 2015

A Clustering-Based Grey Modeling Approach for Tacrolimus Concentration Prediction Under Sparse Therapeutic Drug

Zhiyi Xu1, Yidan Mu2, Rongrong Tian1

  • 1School of Mathematics and Statistics, Wuhan University of Technology, Wuhan, China.

Therapeutic Drug Monitoring
|June 29, 2026
PubMed
Summary

A new framework using clustered nonlinear grey Bernoulli models improves tacrolimus concentration prediction after liver transplantation, even with sparse patient data. This data-efficient approach enhances therapeutic drug monitoring accuracy.

Keywords:
density-based spatial clustering of applications with noisegrey prediction modeltacrolimustherapeutic drug monitoring

Related Experiment Videos

Last Updated: Jun 30, 2026

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
08:38

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS

Published on: November 8, 2015

Area of Science:

  • Pharmacology
  • Biostatistics
  • Transplantation Medicine

Background:

  • Tacrolimus monitoring post-liver transplant faces challenges due to patient variability and irregular data.
  • Conventional pharmacokinetic and data-intensive models struggle with sparse, irregularly sampled concentration data.

Purpose of the Study:

  • To develop a novel hierarchical prediction framework for tacrolimus trough concentrations.
  • To address limitations in therapeutic drug monitoring caused by sparse and irregular data.

Main Methods:

  • A framework integrating density-based spatial clustering with a self-memory algorithm-based nonlinear grey Bernoulli model (SA-NGBM) was developed.
  • Patients were stratified into subgroups using clinical indicators, with cluster-specific SA-NGBM models calibrated on representative patients.
  • The framework was validated on retrospective data from 129 liver transplant recipients and an independent cohort of 60 patients.

Main Results:

  • The clustered SA-NGBM framework significantly reduced prediction error from 41.4% to 21.2% in the development cohort.
  • In the validation cohort, mean absolute relative prediction error decreased from 56.8% to 27.3% for the largest subgroup.
  • Accurate predictions were achieved using longitudinal data from only 4 representative patients.

Conclusions:

  • The clustered SA-NGBM framework offers a data-efficient, stratified modeling strategy to reduce prediction error in sparse therapeutic drug monitoring.
  • Further refinement and prospective validation are necessary for clinical implementation of this approach.