2022
DOI: 10.1002/psp4.12801
|View full text |Cite
|
Sign up to set email alerts
|

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Abstract: Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
6
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
1

Relationship

2
4

Authors

Journals

citations
Cited by 10 publications
(6 citation statements)
references
References 25 publications
0
6
0
Order By: Relevance
“…Its benefits are many for numerous existing and novel therapeutic areas but also for subpopulations often viewed as outside the mainstream development target population, including pediatrics and people with rare diseases. 19,20,36,48 These benefits inform not only key decision criteria (study population, indication, study design, and sampling scheme) but also the basis for clinical use (e.g., dosing timing and adjustments) and prescribing information (therapeutic window, contraindications, DDI potential, etc.). In pregnant women, for whom targeted clinical investigation is expected to be a continual challenge, we must leverage in silico techniques as much as possible, especially when they are well vetted against RWD sources or data from prospective clinical evaluation.…”
Section: Discussionmentioning
confidence: 99%
See 3 more Smart Citations
“…Its benefits are many for numerous existing and novel therapeutic areas but also for subpopulations often viewed as outside the mainstream development target population, including pediatrics and people with rare diseases. 19,20,36,48 These benefits inform not only key decision criteria (study population, indication, study design, and sampling scheme) but also the basis for clinical use (e.g., dosing timing and adjustments) and prescribing information (therapeutic window, contraindications, DDI potential, etc.). In pregnant women, for whom targeted clinical investigation is expected to be a continual challenge, we must leverage in silico techniques as much as possible, especially when they are well vetted against RWD sources or data from prospective clinical evaluation.…”
Section: Discussionmentioning
confidence: 99%
“…QSP is an important component of a model‐based drug development paradigm adopted by many within the pharmaceutical industry. Its benefits are many for numerous existing and novel therapeutic areas but also for subpopulations often viewed as outside the mainstream development target population, including pediatrics and people with rare diseases 19,20,36,48 . These benefits inform not only key decision criteria (study population, indication, study design, and sampling scheme) but also the basis for clinical use (e.g., dosing timing and adjustments) and prescribing information (therapeutic window, contraindications, DDI potential, etc.).…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…These approaches have been increasingly applied in the drug development process to inform optimal therapeutic strategies or identify novel therapeutic targets. [66][67][68] Aghamiri et al 69 an excellent overview of applications integrating ML and QSP. As highlighted in this review, ML algorithms possess the capability to handle big data from disparate sources, which is promising for supporting complex QSP platforms in being computationally efficient while making accurate predictions related to disease and drug mechanisms and response.…”
Section: Approaches For Quantitative Systems Pharmacologymentioning
confidence: 99%