scholarly journals Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis

2017 ◽  
Vol 6 (10) ◽  
pp. 686-694 ◽  
Author(s):  
SM Sheikh Ghadzi ◽  
MO Karlsson ◽  
MC Kjellsson
Keyword(s):  
Glucose Tolerance ◽  
Simulation Study ◽  
Model Based ◽  
Glucose Tolerance Tests ◽  
Model Based Analysis

scholarly journals Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10681
Author(s):  
Jake Dickinson ◽  
Marcel de Matas ◽  
Paul A. Dickinson ◽  
Hitesh B. Mistry
Keyword(s):  
Simulation Study ◽  
Statistical Power ◽  
Tumour Growth ◽  
Empirical Approach ◽  
Series Data ◽  
Ratio Test ◽  
Data Set ◽  
Model Based ◽  
Patient Derived Xenograft ◽  
Model Based Analysis

Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

scholarly journals Analysis of patient derived xenograft studies in Oncology drug development: impact on design and interpretation of future studies

2019 ◽  
Author(s):  
Jake Dickinson ◽  
Marcel de Matas ◽  
Paul A Dickinson ◽  
Hitesh Mistry
Keyword(s):  
Time Series ◽  
Drug Development ◽  
Simulation Study ◽  
Statistical Power ◽  
T Test ◽  
Model Based ◽  
Patient Derived Xenograft ◽  
Oncology Drug Development ◽  
Paired T Test ◽  
Model Based Analysis

AbstractBackgroundPreclinical Oncology drug development is heavily reliant on xenograft studies to assess the anti-tumour effect of new compounds. Patient derived xenograft (PDX) have become popular as they may better represent the clinical disease, however variability is greater than in cell-line derived xenografts. The typical approach of analysing these studies involves performing an un-paired t-test on the mean tumour volumes between the treated and control group at the end of the study. This approach ignores the time-series and may result in false conclusions, especially when considering the increased variability of PDX studies.AimTo test the hypothesis that a model-based analysis provides increased power than analysis of final day volumes and to provide insights into more efficient PDX study designs.MethodsData was extracted from tumour xenograft time-series data from a large publicly available PDX drug treatment database released by Novartis. For all 2-arm studies the percent tumour growth inhibition (TGI) at two time-points, day 10 and day 14 was calculated. For each study, the effect of treatment was calculated using an un-paired t-test and also a model-based analysis using the likelihood ratio-test. In addition a simulation study was also performed to assess the difference in power between the two data-analysis approaches for different levels of TGI for PDX or standard cell-line derived xenografts (CDX).ResultsThe model-based analysis had greater statistical power than the un-paired t-test approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25 percent whereas the un-paired t-test approach required at least 50 percent TGI. These findings were confirmed within the simulation study performed which also highlighted that CDX studies require less animals than PDX studies which show the equivalent level of TGI.ConclusionThe analysis of 59 2-arm PDX studies highlighted that taking a model-based approach gave increased statistical power over simply performing an un-paired t-test on the final study day. Importantly the model-based approach was able to detect smaller size of effect compared to the un-paired t-test approach is which maybe common of such studies. These findings were confirmed within simulated studies which also highlighted the same sample size used for CDX studies would lead to inadequately powered PDX studies. Application of a model-based analysis should allow studies to use less animals and run experiments for a shorter period thus providing effective insight into compound anti-tumour activity

STUDIES IN CONGENITAL GENERALIZED LIPODYSTROPHY (SEIP-BERARDINELLI SYNDROME)

1973 ◽  
Vol 72 (3) ◽  
pp. 475-494 ◽  
Author(s):  
Svein Oseid
Keyword(s):  
Insulin Resistance ◽  
Glucose Tolerance ◽  
Normal Glucose Tolerance ◽  
Juvenile Form ◽  
Congenital Generalized Lipodystrophy ◽  
Glucose Levels ◽  
Normal Glucose ◽  
Glucose Tolerance Tests ◽  
The One ◽  
Adipose Organ

ABSTRACT Six cases of congenital generalized lipodystrophy have been studied at different ages from infancy to adolescence with regard to glucose tolerance, insulin secretion, and insulin sensitivity. During the first few years of life there is normal glucose tolerance. The fasting immuno-reactive insulin (IRI) levels are either slightly elevated or normal. The IRI response to glucose is exaggerated and prolonged, at least from the third year of life. Some degree of insulin resistance is already present in infancy. From the age of 8–10 years glucose tolerance decreases rapidly. The fasting IRI levels are usually grossly elevated, while fasting plasma glucose levels are only moderately elevated or normal. The IRI responses to oral and iv administered glucose, and to tolbutamide are exaggerated; the insulinogenic indices are high. Cortisone primed glucose tolerance tests become abnormal. Insulin resistance is marked, and increases with age. After cessation of growth at approximately 12 years of age, frank diabetes with fasting hyperglycaemia and diabetic glucose tolerance curves developed in the one patient followed beyond this age. Her fasting IRI was increased, but there was a poor IRI response to glucose stimulation, suggesting a partial exhaustion of the β-cells. Her initial IRI response to tolbutamide was still good, but not as brisk as in the younger patients. This type of diabetes is quite different from the juvenile form, and also from the diabetes of older age. It may be causally related to the lack of an adequate adipose organ necessary for the disposal of excesses of glucose, or possibly related to another anti-insulin mechanism.

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