The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the "ARRIVE Essential 10," which constitutes the minimum requirement, and the "Recommended Set," which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration (E&E) document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0.

Improving the reproducibility of biomedical research is a major challenge. Transparent and accurate reporting is vital to this process; it allows readers to assess the reliability of the findings and repeat or build upon the work of other researchers. The ARRIVE guidelines (Animal Research: Reporting In Vivo Experiments) were developed in 2010 to help authors and journals identify the minimum information necessary to report in publications describing in vivo experiments. Despite widespread endorsement by the scientific community, the impact of ARRIVE on the transparency of reporting in animal research publications has been limited. We have revised the ARRIVE guidelines to update them and facilitate their use in practice. The revised guidelines are published alongside this paper. This explanation and elaboration document was developed as part of the revision. It provides further information about each of the 21 items in ARRIVE 2.0, including the rationale and supporting evidence for their inclusion in the guidelines, elaboration of details to report, and examples of good reporting from the published literature. This document also covers advice and best practice in the design and conduct of animal studies to support researchers in improving standards from the start of the experimental design process through to publication.

What exactly is 'N' in cell culture and animal experiments?

Biologists determine experimental effects by perturbing biological entities or units. When done appropriately, independent replication of the entity-intervention pair contributes to the sample size (N) and forms the basis of statistical inference. If the wrong entity-intervention pair is chosen, an experiment cannot address the question of interest. We surveyed a random sample of published animal experiments from 2011 to 2016 where interventions were applied to parents and effects examined in the offspring, as regulatory authorities provide clear guidelines on replication with such designs. We found that only 22% of studies (95% CI = 17%-29%) replicated the correct entity-intervention pair and thus made valid statistical inferences. Nearly half of the studies (46%, 95% CI = 38%-53%) had pseudoreplication while 32% (95% CI = 26%-39%) provided insufficient information to make a judgement. Pseudoreplication artificially inflates the sample size, and thus the evidence for a scientific claim, resulting in false positives. We argue that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide concrete examples of in vitro, ex vivo, and in vivo experimental designs.

Training in experimental design and statistics is essential: Response to Jordan.

This Formal Comment responds to Jordan et al., and stresses that if scientific findings are to be robust, training in experimental design and statistics is critical to ensure that research questions, design considerations, and analyses are aligned.

The ARRIVE guidelines 2.0: updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the 'ARRIVE Essential 10,' which constitutes the minimum requirement, and the 'Recommended Set,' which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

Predicting Drug-Induced Liver Injury with Bayesian Machine Learning.

Drug induced liver injury (DILI) can require significant risk management in drug development and on occasion can cause morbidity or mortality, leading to drug attrition. Optimizing candidates preclinically can minimize hepatotoxicity risk, but it is difficult to predict due to multiple etiologies encompassing DILI, often with multifactorial and overlapping mechanisms. In addition to epidemiological risk factors, physicochemical properties, dose, disposition, lipophilicity, and hepatic metabolic function are also relevant for DILI risk. Better human-relevant, predictive models are required to improve hepatotoxicity risk assessment in drug discovery. Our hypothesis is that integrating mechanistically relevant hepatic safety assays with Bayesian machine learning will improve hepatic safety risk prediction. We present a quantitative and mechanistic risk assessment for candidate nomination using data from in vitro assays (hepatic spheroids, BSEP, mitochondrial toxicity, and bioactivation), together with physicochemical (cLogP) and exposure (Cmaxtotal) variables from a chemically diverse compound set (33 no/low-, 40 medium-, and 23 high-severity DILI compounds). The Bayesian model predicts the continuous underlying DILI severity and uses a data-driven prior distribution over the parameters to prevent overfitting. The model quantifies the probability that a compound falls into either no/low-, medium-, or high-severity categories, with a balanced accuracy of 63% on held-out samples, and a continuous prediction of DILI severity along with uncertainty in the prediction. For a binary yes/no DILI prediction, the model has a balanced accuracy of 86%, a sensitivity of 87%, a specificity of 85%, a positive predictive value of 92%, and a negative predictive value of 78%. Combining physiologically relevant assays, improved alignment with FDA recommendations, and optimal statistical integration of assay data leads to improved DILI risk prediction.

The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the "ARRIVE Essential 10," which constitutes the minimum requirement, and the "Recommended Set," which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the "ARRIVE Essential 10," which constitutes the minimum requirement, and the "Recommended Set," which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

Integrated in vitro models for hepatic safety and metabolism: evaluation of a human Liver-Chip and liver spheroid.

Drug-induced liver injury remains a frequent reason for drug withdrawal. Accordingly, more predictive and translational models are required to assess human hepatotoxicity risk. This study presents a comprehensive evaluation of two promising models to assess mechanistic hepatotoxicity, microengineered Organ-Chips and 3D hepatic spheroids, which have enhanced liver phenotype, metabolic activity and stability in culture not attainable with conventional 2D models. Sensitivity of the models to two hepatotoxins, acetaminophen (APAP) and fialuridine (FIAU), was assessed across a range of cytotoxicity biomarkers (ATP, albumin, miR-122, α-GST) as well as their metabolic functionality by quantifying APAP, FIAU and CYP probe substrate metabolites. APAP and FIAU produced dose- and time-dependent increases in miR-122 and α-GST release as well as decreases in albumin secretion in both Liver-Chips and hepatic spheroids. Metabolic turnover of CYP probe substrates, APAP and FIAU, was maintained over the 10-day exposure period at concentrations where no cytotoxicity was detected and APAP turnover decreased at concentrations where cytotoxicity was detected. With APAP, the most sensitive biomarkers were albumin in the Liver-Chips (EC50 5.6 mM, day 1) and miR-122 and ATP in the liver spheroids (14-fold and EC50 2.9 mM, respectively, day 3). With FIAU, the most sensitive biomarkers were albumin in the Liver-Chip (EC50 126 µM) and miR-122 (15-fold) in the liver spheroids, both on day 7. In conclusion, both models exhibited integrated toxicity and metabolism, and broadly similar sensitivity to the hepatotoxicants at relevant clinical concentrations, demonstrating the utility of these models for improved hepatotoxicity risk assessment.

A multi-scale convolutional neural network for phenotyping high-content cellular images.

MOTIVATION: Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. RESULTS: Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs. AVAILABILITY AND IMPLEMENTATION: The network specifications and solver definitions are provided in Supplementary Software 1. CONTACT: william_jose.godinez_navarro@novartis.com or xian-1.zhang@novartis.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A call for transparent reporting to optimize the predictive value of preclinical research.

The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.

Large-scale phenotyping links adult hippocampal neurogenesis to the reaction to novelty.

The discovery of adult-born neurons in the hippocampus has triggered a wide range of studies that link the new neurons to various behavioral functions. However, the role of new neurons in behavior is still equivocal. Conflicting results may be due to the difficulty in manipulating neurogenesis without off-target effects as well as the statistical approach used, which fail to account for neurogenesis-independent effects of experimental manipulations on behavior. In this study, we apply a more comprehensive statistical and conceptual approach. Instead of between-group analyses, we consider the within-group relationships between neurogenesis and behavior (ANCOVA and mediation analysis) in a large-scale experiment, in which distinct age- (3 and 5 months) and strain- (DBA and C57) related differences in basal levels of neurogenesis in mice are compared with a large number (∼1,500) of behavioral read outs. The analysis failed to detect any association between anxiety and motor impulsivity with neurogenesis. However, within-group adult hippocampal neurogenesis is associated with the reaction to novelty. Specifically, more neurogenesis is associated with a longer latency to explore and a lower frequency of exploratory actions, overall indicative of a phenotype where animals with more neurogenesis were slower to explore a novel environment. This effect is observed in 5-months-old, but not in 3-months-old mice of both strains. An association between the reaction to novelty and adult neurogenesis can have a major impact on results from previous studies using classical behavioral experiments, in which animals are tested in a--for the animal--novel experimental set-up. The neurogenesis-novelty association found here is also a necessary link in the relation that has been suggested to exist between neurogenesis and psychiatric disorders marked by a failure to cope with novelty.

Sleep deficits but no metabolic deficits in premanifest Huntington's disease.

OBJECTIVE: Huntington disease (HD) is a fatal autosomal dominant, neurodegenerative condition characterized by progressively worsening motor and nonmotor problems including cognitive and neuropsychiatric disturbances, along with sleep abnormalities and weight loss. However, it is not known whether sleep disturbances and metabolic abnormalities underlying the weight loss are present at a premanifest stage. METHODS: We performed a comprehensive sleep and metabolic study in 38 premanifest gene carrier individuals and 36 age- and sex-matched controls. The study consisted of 2 weeks of actigraphy at home, 2 nights of polysomnography and multiple sleep latency tests in the laboratory, and body composition assessment using dual energy x-ray absorptiometry scanning with energy expenditure measured over 10 days at home by doubly labeled water and for 36 hours in the laboratory by indirect calorimetry along with detailed cognitive and clinical assessments. We performed a principal component analyses across all measures within each studied domain. RESULTS: Compared to controls, premanifest gene carriers had more disrupted sleep, which was best characterized by a fragmented sleep profile. These abnormalities, as well as a theta power (4-7Hz) decrease in rapid eye movement sleep, were associated with disease burden score. Objectively measured sleep problems coincided with the development of cognitive, affective, and subtle motor deficits and were not associated with any metabolic alterations. INTERPRETATION: The results show that among the earliest abnormalities in premanifest HD is sleep disturbances. This raises questions as to where the pathology in HD begins and also whether it could drive some of the early features and even possibly the pathology.

Chronic early life stress alters developmental and adult neurogenesis and impairs cognitive function in mice.

Early life stress (ES) increases vulnerability to psychopathology and impairs cognition in adulthood. These ES-induced deficits are associated with lasting changes in hippocampal plasticity. Detailed information on the neurobiological basis, the onset, and progression of such changes and their sex-specificity is currently lacking but is required to tailor specific intervention strategies. Here, we use a chronic ES mouse model based on limited nesting and bedding material from postnatal day (P) 2-9 to investigate; (1) if ES leads to impairments in hippocampus-dependent cognitive function in adulthood and (2) if these alterations are paralleled by changes in developmental and/or adult hippocampal neurogenesis. ES increased developmental neurogenesis (proliferation and differentiation) in the dentate gyrus (DG) at P9, and the number of immature (NeurD1(+)) cells migrating postnatally from the secondary dentate matrix, indicating prompt changes in DG structure in both sexes. ES lastingly reduced DG volume and the long-term survival of developmentally born neurons in both sexes at P150. In adult male mice only, ES reduced survival of adult-born neurons (BrdU/NeuN(+) cells), while proliferation (Ki67(+)) and differentiation (DCX(+)) were unaffected. These changes correlated with impaired performance in all learning and memory tasks used here. In contrast, in female mice, despite early alterations in developmental neurogenesis, no lasting changes were present in adult neurogenesis after ES and the cognitive impairments were less prominent and only apparent in some cognitive tasks. We further show that, although neurogenesis and cognition correlate positively, only the hippocampus-dependent functions depend on changes in neurogenesis, whereas cognitive functions that are not exclusively hippocampus-dependent do not. This study indicates that chronic ES has lasting consequences on hippocampal structure and function in mice and suggests that male mice are more susceptible to ES than females. Unraveling the mechanisms that underlie the persistent ES-induced effects may have clinical implications for treatments to counteract ES-induced deficits.

A retrospective analysis of hand tapping as a longitudinal marker of disease progression in Huntington's disease.

BACKGROUND: Current clinical assessments of motor function in Huntington's Disease (HD) rely on subjective ratings such as the Unified Huntington's Disease Rating scale (UHDRS). The ability to track disease progression using simple, objective, inexpensive, and robust measures would be beneficial. METHODS: One objective measure of motor performance is hand-tapping. Over the last 14 years we have routinely collected, using a simple device, the number of taps made by the right and left hand over 30 seconds in HD patients attending our NHS clinics. RESULTS: Here we report on a longitudinal cohort of 237 patients, which includes patients at all stages of the disease on a wide range of drug therapies. Hand tapping in these patients declines linearly at a rate of 5.1 taps per year (p < 0.0001; 95% CI = 3.8 to 6.3 taps), and for each additional year of age patients could perform 0.9 fewer taps (main effect of age: p = 0.0007; 95% CI = 0.4 to 1.4). Individual trajectories can vary widely around this average rate of decline, and much of this variation could be attributed to CAG repeat length. Genotype information was available for a subset of 151 patients, and for each additional repeat, patients could perform 5.6 fewer taps (p < 0.0001; 95% CI = 3.3 to 8.0 taps), and progressed at a faster rate of 0.45 fewer taps per year (CAG by time interaction: p = 0.008; 95% CI = 0.12 to 0.78 taps). In addition, for each unit decrease in Total Functional Capacity (TFC) within individuals, the number of taps decreased by 6.3 (95% CI = 5.4 to 7.1, p < 0.0001). CONCLUSIONS: Hand tapping is a simple, robust, and reliable marker of disease progression. As such, this simple motor task could be a useful tool by which to assess disease progression as well therapies designed to slow it down.

Causal mediation analysis: How to avoid fooling yourself that X causes Y.

The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.

Ditching the norm: Using alternative distributions for biological data analysis.

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for.

Improving basic and translational science by accounting for litter-to-litter variation in animal models.

BACKGROUND: Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or "litter effects", can influence the results in addition to the experimental factors of interest. Furthermore, sometimes an experimental treatment can only be applied to whole litters rather than to individual offspring. An example is the valproic acid (VPA) model of autism, where VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. RESULTS: A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p<0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. CONCLUSIONS: Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature.

Optimal experimental design for efficient toxicity testing in microphysiological systems: A bone marrow application.

Introduction: Microphysiological systems (MPS; organ-on-a-chip) aim to recapitulate the 3D organ microenvironment and improve clinical predictivity relative to previous approaches. Though MPS studies provide great promise to explore treatment options in a multifactorial manner, they are often very complex. It is therefore important to assess and manage technical confounding factors, to maximise power, efficiency and scalability. Methods: As an illustration of how MPS studies can benefit from a systematic evaluation of confounders, we developed an experimental design approach for a bone marrow (BM) MPS and tested it for a specified context of use, the assessment of lineage-specific toxicity. Results: We demonstrated the accuracy of our multicolour flow cytometry set-up to determine cell type and maturity, and the viability of a "repeated measures" design where we sample from chips repeatedly for increased scalability and robustness. Importantly, we demonstrated an optimal way to arrange technical confounders. Accounting for these confounders in a mixed-model analysis pipeline increased power, which meant that the expected lineage-specific toxicities following treatment with olaparib or carboplatin were detected earlier and at lower doses. Furthermore, we performed a sample size analysis to estimate the appropriate number of replicates required for different effect sizes. This experimental design-based approach will generalise to other MPS set-ups. Discussion: This design of experiments approach has established a groundwork for a reliable and reproducible in vitro analysis of BM toxicity in a MPS, and the lineage-specific toxicity data demonstrate the utility of this model for BM toxicity assessment. Toxicity data demonstrate the utility of this model for BM toxicity assessment.

Expression profiles of metabolic enzymes and drug transporters in the liver and along the intestine of beagle dogs.

Beagle dogs are widely used in preclinical pharmacokinetic, safety, and formulation studies. However, little is known about intestinal and hepatic distribution of major enzymes and transporters involved in oral absorption and presystemic drug metabolism. We characterized mRNA levels of CYP3A12, CYP3A26, CYP2D15, UGT1A6, ABCB1 (MDR1), ABCC1 (MRP1), ABCG2 (BCRP), SLC15A1 (PEPT1), and SLC22A1 (OCT1) in dog liver and along the intestine by real-time quantitative reverse transcription-polymerase chain reaction. Tissue protein levels of CYP2D15, MDR1, and PEPT1 were obtained by Western blot. Gene distribution and expression variability was statistically described by a generalized additive mixed model smoothing function and correspondence analysis. Results were compared with the expression pattern known for the human orthologs. Hepatic mRNA levels for metabolic enzymes were generally higher than those for membrane transporters, whereas in the intestine the opposite was observed. Hepatic mRNA levels followed the order CYP2D15 > UGT1A6 ≈ CYP3A26 > ABCB1 ≈ SLC15A1 ≈ SLC22A1 > ABCG2 > ABCC1 ≈ CYP3A12. Along the gut, the genes were differentially distributed with greatest expression in duodenum/upper jejunum (ABCG2), middle jejunum (ABCB1 and SLC15A1), or in cecum/colon (ABCC1 and CYP2D15). CYP3A12, CYP3A26, SLC22A1, and UGT1A6 had a rather uniform expression. Intestinal mRNA profiles of CYP2D15, ABCB1, and SLC15A1 correlated with the respective protein levels. Canine CYP3A12/26, CYP2D15, and ABCB1 colonic distributions differed from those of human orthologs, whereas UGT1A6, ABCC1, ABCG2, SLC15A1, and SLC22A1 were comparable to those of humans in both small and large intestine. We aim to apply these data to better interpret pharmacokinetic studies in dogs with respect to their human relevance.