Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Dosage Regimen: Individualization

Dosage Regimen: Individualization

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...

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

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 (CHF).

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

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...

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

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...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Advancing child health: forecasting the next great research achievements.

Pediatric research·2026

Same author

Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data.

PloS one·2026

Same author

Clinical Model-Informed Precision Dosing Consult Service for Accelerating Personalized Medication in Pediatric Patients.

Clinical pharmacology and therapeutics·2026

Same author

Evolving language of pediatric anxiety in electronic health records.

JAMIA open·2026

Same author

Sequencing and health data resource of children of African ancestry.

Genetics in medicine : official journal of the American College of Medical Genetics·2026

Same author

A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text.

Communications medicine·2026

Same journal

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

Biomedical informatics insights·2019

Same journal

LEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different Populations.

Biomedical informatics insights·2019

Same journal

A Genome Model to Explain Major Features of Neurodevelopmental Disorders in Newborns.

Biomedical informatics insights·2019

Same journal

Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules.

Biomedical informatics insights·2019

Same journal

Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

Biomedical informatics insights·2019

Same journal

Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.

Biomedical informatics insights·2019

See all related articles

Search research articles

Related Experiment Videos

Personalizing Drug Selection Using Advanced Clinical Decision Support.

John Pestian¹, Malik Spencer, Pawel Matykiewicz

¹Clinical Linguistic Group, Division of Biomedical Informatics. Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229. john.pestian@cchmc.org .

Biomedical Informatics Insights

|November 10, 2009

Summary

This summary is machine-generated.

This study introduces CHRISTINE, a pharmacogenetics clinical decision support system. It optimizes drug therapy for epilepsy and ADHD by integrating clinical and genetic data in pediatric patients.

Related Experiment Videos

Area of Science:

Pharmacogenetics
Clinical Decision Support Systems
Neurocognitive Computing

Background:

Pediatric academic medical centers face challenges in optimizing drug therapy for complex conditions.
Personalized medicine requires integrating diverse patient data for treatment selection.

Purpose of the Study:

To describe the development of an advanced pharmacogenetics clinical decision support system (CDSS) for pediatric patients.
To integrate clinical and genetic data for optimizing drug therapy in epilepsy and ADHD treatment.

Main Methods:

Development of the CHRISTINE system at a leading pediatric academic medical center.
Combination of clinical data with genetic information to guide therapeutic choices.
Application of neurocognitive computing principles within the CDSS framework.

Main Results:

Successful development of the CHRISTINE system, a novel pharmacogenetics CDSS.
Demonstrated potential for optimizing drug selection in pediatric epilepsy and ADHD.
Integration of clinical and genetic data for enhanced treatment personalization.

Conclusions:

The CHRISTINE system represents an advancement in pharmacogenetics clinical decision support.
Neurocognitive computing offers a valuable approach for integrating complex patient data.
This approach has the potential to improve therapeutic outcomes for pediatric patients with neurological disorders.