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In pre-clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right-skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi-Poisson assumption or the negative-binomial distribution, we propose prediction intervals for overdispersed count data to be used as HCL. Variable baseline quantities are accounted for by offsets. Furthermore, we provide a bootstrap calibration algorithm that accounts for the skewed distribution and achieves equal tail probabilities. Comprehensive Monte-Carlo simulations assessing the coverage probabilities of eight different methods for HCL calculation reveal, that the bootstrap calibrated prediction intervals control the type-1-error best. Heuristics traditionally used in control charts (e.g., the limits in Shewhart c- or u-charts or the mean ± 2 SD) fail to control a pre-specified coverage probability. The application of HCL is demonstrated based on data from the Ames assay and for numbers of relapses of multiple sclerosis patients. The proposed prediction intervals and the algorithm for bootstrap calibration are publicly available via the R package predint., (© 2024 The Author(s). Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Fungal specialized metabolites are known for their potent biological activities, among which tropolone sesquiterpenoids (TS) stand out for their diverse bioactivities. Here, we report cytotoxic and proliferation inhibitory effects of the recently discovered TS compounds 4-hydroxyxenovulene B and 4-dihydroxy norpycnidione, and the structurally related 4-hydroxy norxenovulene B and xenovulene B. Inhibition of metabolic activity after TS treatment was observed in Jurkat, PC-3 and FAIK3-5 cells, whereas MDA-MB-231 cells were unresponsive to treatment. Structurally similar epolones were shown to induce erythropoietin (EPO). Therefore, FAIK3-5 cells, which can naturally produce EPO, were applied to test the compounds in this regard. While no effect on EPO production in FAIK3-5 cells could be demonstrated, effects on their proliferation, viability, and morphology were observed depending on the presence of tropolone moieties in the molecules. Our study underlines the importance of relevant cell models for bioactivity testing of compounds with unknown mechanisms of action., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The alkaline comet assay is frequently used as in vivo follow-up test within different regulatory environments to characterize the DNA-damaging potential of different test items. The corresponding OECD Test guideline 489 highlights the importance of statistical analyses and historical control data (HCD) but does not provide detailed procedures. Therefore, the working group "Statistics" of the German-speaking Society for Environmental Mutation Research (GUM) collected HCD from five laboratories and >200 comet assay studies and performed several statistical analyses. Key results included that (I) observed large inter-laboratory effects argue against the use of absolute quality thresholds, (II) > 50% zero values on a slide are considered problematic, due to their influence on slide or animal summary statistics, (III) the type of summarizing measure for single-cell data (e.g., median, arithmetic and geometric mean) may lead to extreme differences in resulting animal tail intensities and study outcome in the HCD. These summarizing values increase the reliability of analysis results by better meeting statistical model assumptions, but at the cost of information loss. Furthermore, the relation between negative and positive control groups in the data set was always satisfactorily (or sufficiently) based on ratio, difference and quantile analyses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

For reporting toxicology studies, the presentation of historical control data and the validation of the concurrent control group with respect to historical control limits have become requirements. However, many regulatory guidelines fail to define how such limits should be calculated and what kind of target value(s) they should cover. Hence, this manuscript is aimed to give a brief review on the methods for the calculation of historical control limits that are in use as well as on their theoretical background. Furthermore, this manuscript is aimed to identify open issues for the use of historical control limits that need to be discussed by the community. It seems that, even after 40 years of discussion, more issues remain open than solved, both, with regard to the available methodology as well as its implementation in user-friendly software. Since several of these topics equally apply to several research fields, this manuscript is addressed to all relevant stakeholders who deal with historical control data obtained from toxicological studies, regardless of their background or field of research., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In vitro transdifferentiation of patient-derived mesenchymal stem/stromal cells (MSCs) into neurons is of special interest for treatment of neurodegenerative diseases. Although there are encouraging studies, little is known about physiological modulations during this transdifferentiation process. Here, we focus on the analysis of gap junction dependent cell-cell communication and the expression pattern of gap junction-building connexins during small molecule-induced neuronal transdifferentiation of human bone marrow-derived MSCs. During this process, the MSC markers CD73, CD90, CD105, and CD166 were downregulated while the neuronal marker Tuj1 was upregulated. Moreover, the differentiation protocol used in the present study changed the cellular morphology and physiology. The MSCs evolved from a fibroblastoid morphology towards a neuronal shape with round cell bodies and neurite-like processes. Moreover, depolarization evoked action potentials in the transdifferentiated cells. MSCs expressed mRNAs encoding Cx43 and Cx45 as well as trace levels of Cx26, Cx37- and Cx40 and allowed transfer of microinjected Lucifer yellow. The differentiation protocol increased levels of Cx26 (mRNA and protein) and decreased Cx43 (mRNA and protein) while reducing the dye transfer. Cx36 mRNA was nearly undetectable in all cells regardless of treatment. Treatment of the cells with the gap junction coupling inhibitor carbenoxolone (CBX) only modestly altered connexin mRNA levels and had little effect on neuronal differentiation. Our study indicates that the small molecule-based differentiation protocol generates immature neuron-like cells from MSCs. This might be potentially interesting for elucidating physiological modifications and mechanisms in MSCs during the initial steps of differentiation towards a neuronal lineage., (Copyright © 2020 Dilger, Neehus, Grieger, Hoffmann, Menssen and Ngezahayo.)

Bioassays are highly standardized trials for assessing the impact of a chemical compound on a model organism. In that context, it is standard to compare several treatment groups with an untreated control. If the same type of bioassay is carried out several times, the amount of information about the historical controls rises with every new study. This information can be applied to predict the outcome of one future control using a prediction interval. Since the observations are counts of success out of a given sample size, like mortality or histopathological findings, the data can be assumed to be binomial but may exhibit overdispersion caused by the variability between historical studies. We describe two approaches that account for overdispersion: asymptotic prediction intervals using the quasi-binomial assumption and prediction intervals based on the quantiles of the beta-binomial distribution. Both interval types were α-calibrated using bootstrap methods. For an assessment of the intervals coverage probabilities, a simulation study based on various numbers of historical studies and sample sizes as well as different binomial proportions and varying levels of overdispersion was run. It could be shown that α-calibration can improve the coverage probabilities of both interval types. The coverage probability of the calibrated intervals, calculated based on at least 10 historical studies, was satisfactory close to the nominal 95%. In a last step, the intervals were computed based on a real data set from the NTP homepage, using historical controls from bioassays with the mice strain B6C3F1., (© 2019 John Wiley & Sons, Ltd.)