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Related Concept Videos

Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

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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...
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Dosage Regimens: Partial Pharmacokinetic Parameters01:01

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It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
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Dosage Regimens: Designs and Approaches01:28

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Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Dosage Regimen Designs: Nomograms and Tabulations01:23

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Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
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Near-optimal Individualized Treatment Recommendations.

Haomiao Meng1, Ying-Qi Zhao2, Haoda Fu3

  • 1Department of Mathematical Sciences, Binghamton University, State University of New York, Binghamton, NY 13902, USA.

Journal of Machine Learning Research : JMLR
|August 2, 2021
PubMed
Summary

This study introduces Alternative Individualized Treatment Recommendations (A-ITR) for precision medicine, offering multiple treatment options per patient. Developed within the outcome weighted learning framework, A-ITR enhances treatment selection flexibility.

Keywords:
angle-based classificationindividualized treatment recommendationreproducing kernel Hilbert spaceset-valued classificationstatistical learning theory

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Area of Science:

  • * Precision Medicine
  • * Machine Learning
  • * Biostatistics

Background:

  • * Individualized Treatment Recommendation (ITR) aims to personalize treatment selection based on patient characteristics.
  • * Existing ITR methods often provide a single optimal treatment, limiting patient choice.
  • * There is a need for flexible recommendation systems offering multiple treatment alternatives.

Purpose of the Study:

  • * To develop methods for Alternative Individualized Treatment Recommendations (A-ITR).
  • * To provide a set of near-optimal, alternative treatment options for patients.
  • * To enhance the flexibility of precision medicine frameworks.

Main Methods:

  • * Formulation of ITR as a weighted classification problem.
  • * Development of two novel A-ITR methods within the Outcome Weighted Learning (OWL) framework.
  • * Utilizing simulation studies and real-world data analysis.

Main Results:

  • * Demonstrated the utility of the proposed A-ITR framework through simulations and a Type 2 diabetes patient dataset.
  • * Established the consistency of the developed A-ITR estimation methods.
  • * Provided an upper bound for the risk associated with the estimated recommendations.

Conclusions:

  • * The A-ITR framework offers a more flexible approach to personalized treatment selection.
  • * The proposed methods are statistically sound and practically applicable.
  • * An R package 'aitr' is available for implementing these A-ITR methods.