On the Use of Historical Control Data in Pre-Clinical Safety Studies

Maringwa, John; Faes, Christel; Aerts, Marc; Geys, Helena; Teuns, Greet; Poel, Bart; Bijnens, Luc

doi:10.1080/10543400701216355

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…Although it is common to focus on findings from one specific study, since most toxicity studies are conducted in genetically homogeneous animal strains, historical control data (HCD) from previous studies can be helpful in interpreting results, particularly if the findings occur infrequently and the studies are all conducted by the same testing laboratory. 12,13 This study was conducted at Charles River Laboratories. Dosing of the F0-generation Crl: CD-1 mice was initiated in December 2009 and the final scheduled sacrifice of F1-generation mice occurred in February 2010.…”

Section: Historical Control Data Reportmentioning

confidence: 99%

Addendum to Iwai and Hoberman (2014)—Reassessment of Developmental Toxicity of PFHxA in Mice

et al. 2019

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This article presents a supplemental data analysis and evaluation of the findings from an oral (gavage) combined developmental and perinatal/postnatal reproduction toxicity study of the ammonium salt of perfluorohexanoic acid (CASRN: 21615-47-4) in Crl: CD-1(ICR) mice. The original study has been cited as supporting a lowest-observed-adverse-effects level of 175 mg/kg/d and no-observed-adverse-effects level of 35 mg/kg/d for developmental effects from perfluorohexanoic acid (PFHxA, CASRN: 307-24-4) in mice. The statistical analysis reported in 2014 was accurate in terms of quantifying statistical significance within phase 2 of the study. However, given the low incidence of findings, the purpose of this article is to extend the analysis and interpretation of findings by pooling the control group information from both phases of the same study, comparing the study findings to the incidence rates for stillbirths and postpartum viability for this species and strain of mouse observed for similar studies conducted by the same laboratory, and evaluating data on the incidence and range of spontaneous eye abnormalities reported in the literature. Based on this supplemental evaluation, the original study supports a NOAEL of 175 mg/kg/d for PFHxA in mice, which is a factor of 5-fold higher than previously reported. Furthermore, to the extent that this study may be considered in the selection of a point of departure for PFHxA in mice, it is noted that 175 mg/kg/d for maternal exposure is an unbounded NOAEL for developmental effects, meaning that the study did not establish a dose at which developmental effects may occur.

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Section: Historical Control Data Reportmentioning

confidence: 99%

Addendum to Iwai and Hoberman (2014)—Reassessment of Developmental Toxicity of PFHxA in Mice

et al. 2019

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“…resulting in over‐dispersion) as a means of monitoring stability in laboratory conditions. Models for binomial data that include historical control groups as random effects (Maringwa et al , ) can improve the estimate for the true response proportion of controls (this is in the generalized linear mixed‐models framework). These models can also allow for over‐dispersion, a common feature of binomial data, and the additional historical controls will improve the estimate of over‐dispersion.…”

Section: When Can Historical Controls Be Used?mentioning

confidence: 99%

Reducing sample size in experiments with animals: historical controls and related strategies

Kramer

Font

2015

Biol Rev

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Reducing the number of animal subjects used in biomedical experiments is desirable for ethical and practical reasons. Previous reviews of the benefits of reducing sample sizes have focused on improving experimental designs and methods of statistical analysis, but reducing the size of control groups has been considered rarely. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. subjects used as controls in similar previous experiments. Using example data from published reports, we describe how to incorporate information from historical controls under a range of assumptions that might be made in biomedical experiments. Assuming more similarities between historical and current controls yields higher savings and allows the use of smaller current control groups. We conducted simulations, based on typical designs and sample sizes, to quantify how different assumptions about historical controls affect the power of statistical tests. We show that, under our simulation conditions, the number of current control subjects can be reduced by more than half by including historical controls in the analyses. In other experimental scenarios, control groups may be unnecessary. Paying attention to both the function and to the statistical requirements of control groups would result in reducing the total number of animals used in experiments, saving time, effort and money, and bringing research with animals within ethically acceptable bounds.

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“…The use of historical data is not a new phenomenon, and it has been investigated in the literature. In, 3 they construct a mixed-effects model including historical controls and conclude that this approach can be useful in regard to increasing precision, given a degree of similarity between the studies. Sufficient similarity between historical and current control data is also a breaking point for.…”

Section: Introductionmentioning

confidence: 99%

“…9 This is clearly an important factor for incorporating historical data successfully, which we also investigate carefully in the 15 year series of hyperinsulinemic glucose clamp studies used for this article. Mostly in the literature, only historical controls are incorporated in the model (an approach used in both 3 and 9 ), but due to our large amount of historical insulin analogue data and the structure of our model, we can benefit from constructing a mixed-effects model including both the historical control rats (most importantly) but also the historical insulin analogues in order to increase precision even further. In, 11 see also references therein, they incorporate the historical data using a Bayesian approach, and in 5 several Bayesian methods to include historical information are compared, where they conclude that it is important to estimate and allow for the heterogeneity between trials in the analysis.…”

Section: Introductionmentioning

confidence: 99%

Applying Historical Data in a Nonlinear Mixed-Effects Model Can Reduce the Number of Control Rats Required for Calculation of The Relative Potency of Insulin Analogues

Nielsen

Andersen

Brand

et al. 2021

Preprint

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This paper examines how to reduce the number of control animals in preclinical hyperinsulemic glucose clamp studies if we make use of information on historical studies. A data set consisting of 59 studies in rats to investigate new insulin analogues for diabetics, collected by Novo Nordisk in the years 2000 to 2015, is analysed. A simulation experiment is performed based on a carefully built nonlinear mixed-effects model including historical information, comparing results (for the relative log- potency) with the standard approach ignoring previous studies. We find that by including historical information in the form of the mixed-effects model proposed, we can to remove between 23% and 51% of the control rats in the two studies looked closely upon to get the same level of precision on the relative log-potency as in the standard analysis. How to incorporate the historical information in the form of the mixed-effects model is discussed, where both a meta approach as well as a Bayesian approach are suggested. The conclusions are similar for the two approaches, and therefore, we conclude that the inclusion of historical information is beneficial in regard to using fewer control rats.

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On the Use of Historical Control Data in Pre-Clinical Safety Studies

Cited by 9 publications

References 12 publications

Addendum to Iwai and Hoberman (2014)—Reassessment of Developmental Toxicity of PFHxA in Mice

Addendum to Iwai and Hoberman (2014)—Reassessment of Developmental Toxicity of PFHxA in Mice

Reducing sample size in experiments with animals: historical controls and related strategies

Applying Historical Data in a Nonlinear Mixed-Effects Model Can Reduce the Number of Control Rats Required for Calculation of The Relative Potency of Insulin Analogues

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