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Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

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A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
Dose Size and Dosing Frequency: Determination Methods01:21

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Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...
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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...
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...
Radiation: Applications01:17

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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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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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Related Experiment Video

Updated: Jun 9, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Toward universal dose prediction: A multi-scale, multi-objective framework for sequential boost radiotherapy.

Austen Matthew Maniscalco1, Xinran Zhong1, Sean Domal1

  • 1Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Medical Physics
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

A new multi-plan deep learning framework accurately predicts radiation therapy doses across sequential plans, improving organ sparing and reducing planning time. This approach enhances accuracy for cumulative dose distributions in complex radiotherapy cases.

Keywords:
Jacobian descentartificial intelligenceboostdeep learningdose predictionmulti‐planmulti‐scalemulti‐taskradiation therapyradiotherapysequentialuniversal

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Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources
05:18

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Sequential boost radiotherapy (RT) presents challenges in dose allocation across multiple plans while protecting organs at risk (OARs).
  • Current dose prediction models are limited to single plans, complicating sequential boost RT planning.
  • The iterative optimization process for OAR sparing in sequential RT is time-intensive.

Purpose of the Study:

  • To propose a multi-plan dose prediction framework for sequential boost RT that models individual and cumulative plan doses.
  • To integrate the full RT course context for more efficient optimization objective establishment.
  • To develop a versatile dose prediction approach adaptable to various RT scenarios.

Main Methods:

  • A U-Net-based Hybrid Convolutional Neural Network (CNN) was developed to predict dose distributions for each plan and the plan-sum.
  • The model incorporates pooling layers, skip connections, and a transformer bottleneck for global context.
  • Training utilized a multi-objective loss function (MSE and MS-SSIM) with Jacobian descent on a site-agnostic dataset of 64 patients.

Main Results:

  • The multi-plan model demonstrated statistically significant improvements over a single-plan model in both plan dose and plan-sum dose distributions.
  • Key metrics showed lower Mean Absolute Error (MAE/Rx) and higher Structural Similarity Index Measure (SSIM) for the multi-plan approach.
  • Specifically, MAE/Rx for plan-sum dose was 1.146 ± 0.174% vs. 1.525 ± 0.188% (p < 0.001), and SSIM was 0.972 ± 0.006 vs. 0.960 ± 0.006 (p < 0.001).

Conclusions:

  • The proposed multi-plan dose prediction framework enhances accuracy and consistency by considering cumulative dose requirements across the full RT course.
  • This approach streamlines treatment planning for sequential boost RT, offering clinicians a more accurate dose allocation strategy.
  • The framework provides a foundation for a universal RT dose prediction model applicable to diverse treatment sites and fractionation schemes.