2015
DOI: 10.1371/journal.pone.0119575
|View full text |Cite
|
Sign up to set email alerts
|

Quantitative Structure-Property Relationship (QSPR) Modeling of Drug-Loaded Polymeric Micelles via Genetic Function Approximation

Abstract: Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug loading capacity of polymeric micelles using the genetic function approximation (GFA) algorithm. These models were furth… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
29
0
1

Year Published

2017
2017
2023
2023

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 35 publications
(30 citation statements)
references
References 50 publications
(39 reference statements)
0
29
0
1
Order By: Relevance
“…For example, Wu et al . reported a series of quantitative structure‐property relationships (QSPR) models to evaluate the content of doxorubicin in polymer micelles, but since these models are only for doxorubicin development, it is difficult to generalize to other drugs . In addition, screening the drug carrier by computational simulation requires professional computational software and computational simulation researchers, which is often very expensive.…”
Section: Background and Originality Contentmentioning
confidence: 99%
“…For example, Wu et al . reported a series of quantitative structure‐property relationships (QSPR) models to evaluate the content of doxorubicin in polymer micelles, but since these models are only for doxorubicin development, it is difficult to generalize to other drugs . In addition, screening the drug carrier by computational simulation requires professional computational software and computational simulation researchers, which is often very expensive.…”
Section: Background and Originality Contentmentioning
confidence: 99%
“…A good and acceptable QSAR model must obey the following criteria: regression-coefficient (R 2 ) and adjusted regression-coefficient (R 2 adj) values close to one. The Cross validated regression-coefficient (Q 2 cv ) > 0.5, R 2 − Q 2 cv ≤ 0.3, R 2 pred ≥ 0.6, and N test ≥ 5 [33,37,38]. The generated QSAR model satisfied the criteria and therefore acceptable statistically.…”
Section: Qsar Model and Validationmentioning
confidence: 83%
“…GFA algorithm, selecting the basic functions genetically, developed better models than those made using stepwise regression methods. And then, the models were estimated using the "lack of fit" (LOF), which was measured using a slight variation of the original Friedman formula, so that best model received the best fitness score [10].…”
Section: Methodsmentioning
confidence: 99%