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Related Concept Videos

Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

438
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
438
Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

248
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...
248
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

286
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.
286
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

854
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...
854
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

54
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
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Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

230
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
230

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Model-Informed Precision Dosing: Conceptual Framework for Therapeutic Drug Monitoring Integrating Machine Learning

Jennifer Le1,2, Hien N Le1, Giang Nguyen1

  • 1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.

Journal of Personalized Medicine
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

Model-informed precision dosing (MIPD) integrated with AI and EHRs enhances drug safety and efficacy in population health. This approach offers real-time dosing adjustments, improving outcomes, especially for vulnerable patients.

Keywords:
artificial intelligencemachine learningmodel-informed precision dosingpatient safetypharmacokineticspopulation healthpopulation pharmacokinetics

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Area of Science:

  • Pharmacology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Traditional therapeutic drug monitoring has limitations including manual interpretation and steady-state sampling requirements.
  • Model-informed precision dosing (MIPD) offers a promising solution for drug safety and efficacy within population health informatics.
  • This study explores MIPD integration with AI, ML, and EHRs for enhanced precision dosing.

Purpose of the Study:

  • To explore the integration of MIPD within population health informatics.
  • To evaluate the potential of AI/ML and EHRs in enhancing precision dosing.
  • To identify implications for vulnerable populations.

Main Methods:

  • Searched PubMed and Embase for peer-reviewed studies (1958-2024).
  • Included studies on MIPD, population health, and AI/ML for individualized dosing.
  • Focused on critically-ill, geriatric, and pediatric populations.

Main Results:

  • MIPD using Bayesian methods is a scalable precision medicine innovation.
  • AI/ML combined with EHRs enables real-time, precise dosing adjustments.
  • Potential benefits include improved patient safety, optimized outcomes, and reduced costs, particularly for vulnerable groups.

Conclusions:

  • Further research is needed to define, implement, and evaluate practical AI/ML applications in MIPD.
  • Developing standards and identifying suitable drugs are crucial for advancing personalized medicine.
  • Collaboration among professionals and secure data management are essential for successful implementation.