Generalized Additive Mixed-Models for Pharmacology Using Integrated Discrete Multiple Organ Co-Culture

Autor: Stephanie Cole, Russell M. Dorsey, Harry Salem, Lamont Booker, Janna S. Madren-Whalley, Thomas Ingersoll, Albert P. Li
Jazyk: angličtina
Rok vydání: 2016
Předmět:
0301 basic medicine
lcsh:Medicine
Pharmacology
Toxicology
Pathology and Laboratory Medicine
Mice
Mathematical and Statistical Techniques
0302 clinical medicine
Animal Cells
Medicine and Health Sciences
lcsh:Science
Connective Tissue Cells
Staining
Multidisciplinary
Mathematical model
Mathematical Models
Linear model
Drugs
Cell Staining
Liver
Connective Tissue
030220 oncology & carcinogenesis
Physical Sciences
Coculture Technique
Cellular Types
Anatomy
Statistics (Mathematics)
Research Article
Mixed model
Biology
Research and Analysis Methods
Models
Biological
03 medical and health sciences
3T3-L1 Cells
Confidence Intervals
Animals
Humans
Cyclophosphamide
Independence (probability theory)
lcsh:R
Biology and Life Sciences
Cell Biology
Fibroblasts
Coculture Techniques
Nuclear Staining
Maxima and minima
Cell staining
Biological Tissue
030104 developmental biology
Specimen Preparation and Treatment
Hepatocytes
IdMOC
lcsh:Q
Mathematics
Zdroj: PLoS ONE, Vol 11, Iss 4, p e0152985 (2016)
PLoS ONE
ISSN: 1932-6203
Popis: Integrated Discrete Multiple Organ Co-culture (IDMOC) is emerging as an in-vitro alternative to in-vivo animal models for pharmacology studies. IDMOC allows dose-response relationships to be investigated at the tissue and organoid levels, yet, these relationships often exhibit responses that are far more complex than the binary responses often measured in whole animals. To accommodate departure from binary endpoints, IDMOC requires an expansion of analytic techniques beyond simple linear probit and logistic models familiar in toxicology. IDMOC dose-responses may be measured at continuous scales, exhibit significant non-linearity such as local maxima or minima, and may include non-independent measures. Generalized additive mixed-modeling (GAMM) provides an alternative description of dose-response that relaxes assumptions of independence and linearity. We compared GAMMs to traditional linear models for describing dose-response in IDMOC pharmacology studies.
Databáze: OpenAIRE
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