The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.

Automated Spot Counting in Microbiology

Biological samples are routinely analyzed for microbe concentration. The samples are diluted, loaded onto established host cell cultures, and incubated. If infectious agents are present in the samples, they form circular spots that do not contain the host cells. Each spot is assumed to be originated from a single microbial unit such as a bacterial colony forming unit or viral plaque forming unit. The undiluted sample concentration is estimated by counting the spots and back-calculating. Counting the number of spots by trained technicians is currently the gold standard but it is laborious, subjective, and hard to scale. This paper presents a new automated algorithm for spot counting, Localized and Sequential Thresholding (LoST). Validation studies showed that LoST performance was comparable with manual counting and outperformed several existing tools on images with overlapping spots. The LoST algorithm employs sequential thresholding through a two-stage segmentation and borrows information across all images from the same dilution series to fine-tune the count and identify right censoring. The algorithm increases the efficiency of the spot counting and the quality of the downstream analysis, especially when coupled with an appropriate statistical serial dilution model to enhance the undiluted sample concentration estimation procedure.

Measures of spatial heterogeneity in the liver tissue micro-environment as predictive factors for fibrosis score

The organization and interaction between hepatocytes and other hepatic non-parenchymal cells plays a pivotal role in maintaining normal liver function and structure. Although spatial heterogeneity within the tumor micro-environment has been proven to be a fundamental feature in cancer progression, the role of liver tissue topology and micro-environmental factors in the context of liver damage in chronic infection has not been widely studied yet. We obtained images from 110 core needle biopsies from a cohort of chronic hepatitis B patients with different fibrosis stages according to METAVIR score. The tissue sections were immunofluorescently stained and imaged to determine the locations of CD45 positive immune cells and HBsAg-negative and HBsAg-positive hepatocytes within the tissue. We applied several descriptive techniques adopted from ecology, including Getis-Ord, the Shannon Index and the Morisita-Horn Index, to quantify the extent to which immune cells and different types of liver cells co-localize in the tissue biopsies. Additionally, we modeled the spatial distribution of the different cell types using a joint log-Gaussian Cox process and proposed several features to quantify spatial heterogeneity. We then related these measures to the patient fibrosis stage by using a linear discriminant analysis approach. Our analysis revealed that the co-localization of HBsAg-negative hepatocytes with immune cells and the co-localization of HBsAg-positive hepatocytes with immune cells are equally important factors for explaining the METAVIR score in chronic hepatitis B patients. Moreover, we found that if we allow for an error of 1 on the METAVIR score, we are able to reach an accuracy of around 80%. With this study we demonstrate how methods adopted from ecology and applied to the liver tissue micro-environment can be used to quantify heterogeneity and how these approaches can be valuable in biomarker analyses for liver topology.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp³, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule

The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two-armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization.

Bayesian pooling versus sequential integration of small preclinical trials: a comparison within linear and nonlinear modeling frameworks

Bayesian sequential integration is an appealing approach in drug development, as it allows to recursively update posterior distributions as soon as new data become available, thus considerably reducing the computation time. However, preclinical trials are often characterized by small sample sizes, which may affect the estimation process during the first integration steps, particularly when complex PK-PD models are used. In this case, sequential integration would not be practicable, and trials should be pooled together. This work is aimed at comparing simple Bayesian pooling with sequential integration through a simulation study. The two techniques are compared under several scenarios using linear as well as nonlinear models. The results of our simulation study encourage the use of Bayesian sequential integration with linear models. However, in the case of nonlinear models several caveats arise. This paper outlines some important recommendations and precautions in that respect.

Testing of acetaminophen in support of the international multilaboratory in vivo rat Pig-a assay validation trial

Acetaminophen, a nonmutagenic compound as previously concluded from bacteria, in vitro mammalian cell, and in vivo transgenic rat assays, presented a good profile as a nonmutagenic reference compound for use in the international multilaboratory Pig-a assay validation. Acetaminophen was administered at 250, 500, 1,000, and 2,000 mg·kg-1 ·day-1 to male Sprague Dawley rats once daily in 3 studies (3 days, 2 weeks, and 1 month with a 1-month recovery group). The 3-Day and 1-Month Studies included assessments of the micronucleus endpoint in peripheral blood erythrocytes and the comet endpoint in liver cells and peripheral blood cells in addition to the Pig-a assay; appropriate positive controls were included for each assay. Within these studies, potential toxicity of acetaminophen was evaluated and confirmed by inclusion of liver damage biomarkers and histopathology. Blood was sampled pre-treatment and at multiple time points up to Day 57. Pig-a mutant frequencies were determined in total red blood cells (RBCs) and reticulocytes (RETs) as CD59-negative RBC and CD59-negative RET frequencies, respectively. No increases in DNA damage as indicated through Pig-a, micronucleus, or comet endpoints were seen in treated rats. All positive controls responded as appropriate. Data from this series of studies demonstrate that acetaminophen is not mutagenic in the rat Pig-a model. These data are consistent with multiple studies in other nonclinical models, which have shown that acetaminophen is not mutagenic. At 1,000 mg·kg-1 ·day-1 , Cmax values of acetaminophen on Day 28 were 153,600 ng/ml and 131,500 ng/ml after single and repeat dosing, respectively, which were multiples over that of clinical therapeutic exposures (2.6-6.1 fold for single doses of 4,000 mg and 1,000 mg, respectively, and 11.5 fold for multiple dose of 4,000 mg) (FDA 2002). Data generated were of high quality and valid for contribution to the international multilaboratory validation of the in vivo Rat Pig-a Mutation Assay.

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

Models for zero-inflated, correlated count data with extra heterogeneity: when is it too complex?

Statistical analysis of count data typically starts with a Poisson regression. However, in many real-life applications, it is observed that the variation in the counts is larger than the mean, and one needs to deal with the problem of overdispersion in the counts. Several factors may contribute to overdispersion: (1) unobserved heterogeneity due to missing covariates, (2) correlation between observations (such as in longitudinal studies), and (3) the occurrence of many zeros (more than expected from the Poisson distribution). In this paper, we discuss a model that allows one to explicitly take each of these factors into consideration. The aim of this paper is twofold: (1) investigate whether we can identify the cause of overdispersion via model selection, and (2) investigate the impact of a misspecification of the model on the power of a covariate. The paper is motivated by a study of the occurrence of drug-induced arrhythmia in beagle dogs based on electrocardiogram recordings, with the objective to evaluate the effect of potential drugs on the heartbeat irregularities. Copyright © 2016 John Wiley & Sons, Ltd.

GLMM approach to study the spatial and temporal evolution of spikes in the small intestine

Mixed models can be applied in a wide range of settings. Probably, they are most commonly used to handle grouping in the data. In addition, mixed models can be used for smoothing purposes as well. When dealing with non-normal data, the use of smoothing methods within the generalized linear mixed models (GLMM) framework is less familiar. We explore the use of GLMM for smoothing purposes in both spatial and longitudinal dimensions. The methodology is illustrated by analysis of spike potentials in the small intestine of different cats. Spatio-temporal models that use two-dimensional smoothing splines across the spatial dimension and random effects to account for the correlations during successive slow-waves are developed. A major advantage of the mixed-model approach is that it can handle smoothing together with grouping (or other types of correlations) in a unified model. In this way, areas with high spike incidence compared with other areas can be detected. Also, the temporal and spatial characteristics of spikes during successive slow-waves can be identified.

Use of fractional polynomials for dose-response modelling and quantitative risk assessment in developmental toxicity studies

Developmental toxicity studies are designed to assess the potential adverse effects of an exposure on developing fetuses. Safe dose levels can be determined using dose-response modelling. To this end, it is important to investigate the effect of misspecifying the dose-response model on the safe dose. Since classical polynomial predictors are often of poor quality, there is a clear need for alternative specifications of the predictors, such as fractional polynomials. By means of simulations, we will show how fractional polynomial predictors may resolve possible model misspecifications and may thus yield more reliable estimates of the benchmark doses.

Detecting influential observations in a model-based cluster analysis

Finite mixture models have been used to model population heterogeneity and to relax distributional assumptions. These models are also convenient tools for clustering and classification of complex data such as, for example, repeated-measurements data. The performance of model-based clustering algorithms is sensitive to influential and outlying observations. Methods for identifying outliers in a finite mixture model have been described in the literature. Approaches to identify influential observations are less common. In this paper, we apply local-influence diagnostics to a finite mixture model with known number of components. The methodology is illustrated on real-life data.

A clustering algorithm for multivariate longitudinal data

Latent growth modeling approaches, such as growth mixture models, are used to identify meaningful groups or classes of individuals in a larger heterogeneous population. But when applied to multivariate repeated measures computational problems are likely, due to the high dimension of the joint distribution of the random effects in these mixed-effects models. This article proposes a cluster algorithm for multivariate repeated data, using pseudo-likelihood and ideas based on k-means clustering, to reveal homogenous subgroups. The algorithm was demonstrated on an electro-encephalogram dataset set quantifying the effect of psychoactive compounds on the brain activity in rats.

Flexible modelling of simultaneously interval censored and truncated time-to-event data

This paper deals with the analysis of data from a HET-CAM(VT) experiment. From a statistical perspective, such data yield many challenges. First of all, the data are typically time-to-event like data, which are at the same time interval censored and right truncated. In addition, one has to cope with overdispersion as well as clustering. Traditional analysis approaches ignore overdispersion and clustering and summarize the data into a continuous score that can be analysed using simple linear models. In this paper, a novel combined frailty model is developed that simultaneously captures all of the aforementioned statistical challenges posed by the data.

Carcinogenicity in rats of the SGLT2 inhibitor canagliflozin

The carcinogenicity potential of canagliflozin, an inhibitor of SGLT2, was evaluated in a 2-year rat study (10, 30, and 100 mg/kg). Rats showed an increase in pheochromocytomas, renal tubular tumors, and testicular Leydig cell tumors. Systemic exposure multiples at the highest dose relative to the maximum clinical dose were 12- to 21-fold. Pheochromocytomas and renal tubular tumors were noted in both sexes at 100 mg/kg. Leydig cell tumors were observed in males in all dose groups and were associated with increased luteinizing hormone levels. Hyperplasia was increased in the adrenal medulla at 100 mg/kg, but only a limited increase in simple tubular hyperplasia was observed in the kidney of males at 100 mg/kg. Hyperostosis occurred and was accompanied by substantial effects on calcium metabolism, including increased urinary calcium excretion and decreased levels of calcium regulating hormones (1,25-dihydroxyvitamin D and parathyroid hormone). A separate study with radiolabeled calcium confirmed that increased urinary calcium excretion was mediated via increased calcium absorption from the gastrointestinal tract. It was hypothesized that, at high doses, canagliflozin might have inhibited glucose absorption in the intestine via SGLT1 inhibition that resulted in glucose malabsorption, which increased calcium absorption by stimulating colonic glucose fermentation and reducing intestinal pH. Pheochromocytomas and adrenal medullary hyperplasia were attributed to altered calcium homeostasis, which have a known relationship in the rat. In conclusion, Leydig cell tumors were associated with increased luteinizing hormone levels and pheochromocytomas were most likely related to glucose malabsorption and altered calcium homeostasis. Renal tubular tumors may also have been linked to glucose malabsorption.

Recommendations on the statistical analysis of the Comet assay

In 2010, the Statisticians in the Pharmaceutical Industry (PSI) Toxicology Special Interest Group met to discuss the design and analysis of the Comet assay. The Comet assay is one potential component of the package of safety studies required by regulatory bodies. As these studies usually involve a three-way nested experimental design and as the distribution of the measured response is usually either lognormal or lognormal plus a point mass at zero, the analysis is not straightforward. This has led to many different types of analysis being proposed in the literature, with several different methods applied within the pharmaceutical industry itself. This article summarises the PSI Toxicology Group's discussions and recommendations around these issues.

A unified approach to multi-item reliability

The reliability of multi-item scales has received a lot of attention in the psychometric literature, where a myriad of measures like the Cronbach's α or the Spearman-Brown formula have been proposed. Most of these measures, however, are based on very restrictive models that apply only to unidimensional instruments. In this article, we introduce two measures to quantify the reliability of multi-item scales based on a more general model. We show that they capture two different aspects of the reliability problem and satisfy a minimum set of intuitive properties. The relevance and complementary value of the measures is studied and earlier approaches are placed in a broader theoretical framework. Finally, we apply them to investigate the reliability of the Positive and Negative Syndrome Scale, a rating scale for the assessment of the severity of schizophrenia.

Modeling spatial learning in rats based on Morris water maze experiments

The Morris water maze, developed by Morris (J Neurosci Methods 1984: 11: 47-60), is a behavioral experiment designed to test the spatial memory. When repeating the experiment several times, the changes in time (latency) and distance (path) taken to reach the platform are indicators for the learning and memory abilities of the rat. In juvenile toxicity studies, it is of interest to test whether dosing juvenile rats with some compound of interest has an effect on its learning ability. The traditional analysis uses non-parametric tests to check for a possible dose-effect. However, due to the many tests performed, this approach lacks power. Here, an alternative method is proposed, accounting for the longitudinal design of the study, the right-censoring of observations when animals did not find the platform and the correlation between the time and distance taken to reach the platform.

Effects of phosphodiesterase 10 inhibition on striatal cyclic AMP and peripheral physiology in rats

Phosphodiesterases (PDEs) form a family of enzymes involved in the hydrolysis of cyclic adenosine and guanosine monophosphate (cAMP and cGMP). PDE10A is a member of this family that is almost exclusively expressed in the striatum. Increasing cAMP/cGMP levels via inhibition of PDE10A is under consideration as a novel therapeutic avenue in the discovery of antipsychotics. Papaverine has been used as a pharmacological tool to establish the possible clinical use of PDE10A inhibitors as antipsychotics. Papaverine is known to increase cAMP levels in striatum and to decrease blood pressure, body temperature and locomotor activity after systemic administration. In this study, the effects of papaverine are compared to those of a more specific PDE10A inhibitor MP10. Papaverine raised striatal cAMP levels with hypothermia, hypoactivity and decreased cardiovascular responses. The more selective MP10 had significantly less effects on body temperature and cardiovascular functions, but reduced locomotor activity to a similar extend as papaverine.

Analysis of cross-over designs with serial correlation within periods using semi-parametric mixed models

The use of semi-parametric mixed models has proven useful in a wide variety of settings. Here, we focus on the application of the methodology in the particular case of a cross-over design with relatively long sequences of repeated measurements within each treatment period and for each subject. Other than an overall measure of the difference between each one of the experimental groups and the control group, specific time point comparisons may also be of interest. To that effect, we propose the use of flexible semi-parametric mixed models, enabling the construction of simulation-based simultaneous confidence bands. The bands take into account both between- and within-subject variabilities, while simultaneously correcting for multiple time point comparisons. Owing to the relatively long sequences of measurements per subject, the presence of serially correlated errors is anticipated and investigated. We illustrate how several formulations of semi-parametric mixed models can be fitted and the construction of simulation-based simultaneous confidence bands using SAS PROC MIXED.

Application of penalized splines in analyzing neuronal data

Neuron experiments produce high-dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in the temporal trend of a single neuron. An approach to investigate the maximal firing rate, based on the penalizedspline model is proposed. Determination of the time of maximal firing rate is based on non-linear optimization of the objective function with the corresponding confidence intervals constructed based on the first-order derivative function. To distinguish between the curves from different experimental conditions in a moment-by-moment sense, bias adjusted simulation-based simultaneous confidence bands leading to global inference in the time domain are constructed. The bands are an extension of the approach proposed by Ruppert et al. (2003). These methods are in a second step extended towards the analysis of a population of neurons via a marginal or population-averaged model.

A high-dimensional joint model for longitudinal outcomes of different nature

In repeated dose-toxicity studies, many outcomes are repeatedly measured on the same animal to study the toxicity of a compound of interest. This is only one example in which one is confronted with the analysis of many outcomes, possibly of a different type. Probably the most common situation is that of an amalgamation of continuous and categorical outcomes. A possible approach towards the joint analysis of two longitudinal outcomes of a different nature is the use of random-effects models (Models for Discrete Longitudinal Data. Springer Series in Statistics. Springer: New York, 2005). Although a random-effects model can easily be extended to jointly model many outcomes of a different nature, computational problems arise as the number of outcomes increases. To avoid maximization of the full likelihood expression, Fieuws and Verbeke (Biometrics 2006; 62:424-431) proposed a pairwise modeling strategy in which all possible pairs are modeled separately, using a mixed model, yielding several different estimates for the same parameters. These latter estimates are then combined into a single set of estimates. Also inference, based on pseudo-likelihood principles, is indirectly derived from the separate analyses. In this paper, we extend the approach of Fieuws and Verbeke (Biometrics 2006; 62:424-431) in two ways: the method is applied to different types of outcomes and the full pseudo-likelihood expression is maximized at once, leading directly to unique estimates as well as direct application of pseudo-likelihood inference. This is very appealing when interested in hypothesis testing. The method is applied to data from a repeated dose-toxicity study designed for the evaluation of the neurofunctional effects of a psychotrophic drug. The relative merits of both methods are discussed.

Modulation of group II metabotropic glutamate receptor (mGlu2) elicits common changes in rat and mice sleep-wake architecture

Compiling pharmacological evidence implicates metabotropic glutamate mGlu(2) receptors in the regulation of emotional states and suggests positive modulators as a novel therapeutic approach of Anxiety/Depression and Schizophrenia. Here, we investigated subcutaneous effects of the metabotropic glutamate mGlu(2/3) agonist (LY354740) on sleep-wake architecture in rat. To confirm the specific effects on rapid eye movement (REM) sleep were mediated via metabotropic glutamate mGlu(2) receptors, we characterized the sleep-wake cycles in metabotropic glutamate mGlu(2) receptor deficient mice (mGlu(2)R(-/-)) and their arousal response to LY354740. We furthermore examined effects on sleep behavior in rats of the positive allosteric modulator, biphenyl-indanone A (BINA) alone and in combination with LY354740 at sub-effective doses. LY354740 (1, 3 and 10 mg/kg) dose-dependently suppressed REM sleep and prolonged its onset latency. Metabotropic glutamate mGlu(2)R(-/-) and their wild type (WT) littermates exhibited similar spontaneous sleep-wake phenotype, while LY354740 (10 mg/kg) significantly affected REM sleep variables in WT but not in the mutant. In rats, BINA (1, 3, 10, 20, 40 mg/kg) dose-dependently suppressed REM sleep, lengthened its onset latency and slightly enhanced passive waking. Additionally, combined treatment elicited a synergistic action on REM sleep variables. Our findings show common changes of REM sleep variables following modulation of metabotropic glutamate mGlu(2) receptor and support an active role of this receptor in the regulation of REM sleep. The synergistic action of BINA on LY354740's effects on sleep pattern implies that positive modulators would tune the endogenous glutamate tone suggesting potential benefit in the treatment of psychiatric disorders, in which REM sleep overdrive is manifested.

Classification of sporadic Creutzfeldt-Jakob disease based on clinical and neuropathological characteristics

Creutzfeldt-Jakob disease (CJD) is a rare and fatal neurodegenerative disease of unknown cause. Patients are usually aged between 50 and 75 and typical clinical features include rapidly progressive dementia associated with myoclonus and a characteristic electroencephalographic pattern. Neuropathological examination reveals cortical spongiform change, hence the term 'spongiform encephalopathy'. Several statistical techniques were applied to classify patients with sporadic CJD (sCJD), based on clinical and neuropathological investigation. We focus on the classification of neuropathologically confirmed sCJD patients. In order to obtain a classification rule that correctly classifies this type of patients and at the same time controls the overall error rate, we apply several classification techniques, which in general, produce comparable results. The boosting method produces the best results and the variable 14-3-3 protein in cerebrospinal fluid plays the most important role in the prediction of neuropathologically confirmed sCJD.

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

A number of methods to formally incorporate historical control information in pre-clinical safety evaluation studies have been proposed in literature. However, it remains unclear when one should use historical data. Focusing on the logistic-normal model, we investigate situations where historical studies may prove to be useful. Aspects of estimation (precision and bias) and testing (power) for treatment effect are investigated under different conditions such as the number of historical control studies, the degree of homogeneity amongst them, the level of treatment effect and different control rates. The possibility to use a selected subset of historical control studies is also explored.