Drug development is currently hampered by high attrition rates; many developed treatments fail during clinical testing. Part of the attrition may be due to low animal-to-human translational success rates; so-called “translational failure”. As far as we know, no systematic overview of published translational success rates exists. The following research question was examined: “What is the observed range of the animal-to-human translational success (and failure) rates within the currently available empirical evidence?”. We searched PubMed and Embase on 16 October 2017. We included reviews and all other types of “umbrella”-studies of meta-data quantitatively comparing the translational results of studies including at least two species with one being human. We supplemented our database searches with additional strategies. All abstracts and full-text papers were screened by two independent reviewers. Our scoping review comprises 121 references, with various units of measurement: compound or intervention (k = 104), study/experiment (k = 10), and symptom or event (k = 7). Diagnostic statistics corresponded with binary and continuous definitions of successful translation. Binary definitions comprise percentages below twofold error, percentages accurately predicted, and predictive values. Quantitative definitions comprise correlation/regression (r2) and meta-analyses (percentage overlap of 95% confidence intervals). Translational success rates ranged from 0 to 100%. The wide range of translational success rates observed in our study might indicate that translational success is unpredictable; i.e. it might be unclear upfront if the results of primary animal studies will contribute to translational knowledge. However, the risk of bias of the included studies was high, and much of the included evidence is old, while newer models have become available. Therefore, the reliability of the cumulative evidence from current papers on this topic is insufficient. Further in-depth “umbrella”-studies of translational success rates are still warranted. These are needed to evaluate the probabilistic evidence for predictivity of animal studies for the human situation more reliably, and to determine which factors affect this process.

Systematic Reviews (SRs) hold promise for implementing the 3Rs in animal sciences: they can retrieve available alternative models, help refining experiments, and identify insufficiencies, or an excess of, scientific knowledge on a particular topic. Unfortunately, SRs can be labour- and time-intensive, especially the reference screening and data extraction phases. Fortunately, there are several software tools available that help make screening faster and easier. However, it is not always clear which features the tools offer. Therefore, a feature analysis was performed to compare different reference screening tools as objectively as possible. This analysis enables researchers to select the most appropriate tool for their needs.Fifteen different tools were compared: CADIMA, Covidence, DistillerSR, Endnote, Endnote using Bramer’s method, EROS, HAWC, Microsoft Excel, Excel using VonVille’s method, Microsoft Word, Rayyan, RevMan, SyRF, SysRev.com, and SWIFT Active Screener. Their support of 21 features was tested. Features were categorised as mandatory, desirable, and optional.DistillerSR, Covidence, and SWIFT Active Screener are the tools that support all mandatory features. These tools are preferred for screening references, but none of them are free. The best scoring free tool is Rayyan, which lacks one mandatory function: distinct title/abstract and full-text phases. The lowest scoring tools are those not specifically designed for SRs, like Microsoft Word and Endnote. Their use can only be advised for small and simple SRs.A well-informed selection of SR screening tools will benefit review quality and speed, which can contribute to the advancement of the 3Rs in animal studies.

Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market). Evidence Stream 1: A systematic literature review of in vivo studies on rosiglitazone or troglitazone was conducted (PROSPERO registration CRD42018112353). Evidence Stream 2: in vitro data on troglitazone and rosiglitazone were retrieved from the US EPA ToxCast database. Evidence Stream 3: troglitazone- and rosiglitazone-related DILI cases were retrieved from WHO Vigibase. All three evidence stream analyses were conducted according to evidence-based methodologies and performed according to pre-registered protocols. Evidence Stream 1: 9288 references were identified, with 42 studies included in analysis. No reported biomarker for either drug indicated a strong hazard signal in either preclinical animal or human studies. All included studies had substantial limitations, resulting in “low” or “very low” certainty in findings. Evidence Stream 2: Troglitazone was active in twice as many in vitro assays (129) as rosiglitazone (60), indicating a strong signal for more off-target effects. Evidence Stream 3: We observed a fivefold difference in both all adverse events and liver-related adverse events reported, and an eightfold difference in fatalities for troglitazone, compared to rosiglitazone. In summary, published animal and human trials failed to predict troglitazone’s potential to cause severe liver injury in a wider patient population, while in vitro data showed marked differences in the two drugs’ off-target activities, offering a new paradigm for reducing drug attrition in late development and in the market. This investigation concludes that death and disability due to adverse drug reactions may be prevented if mechanistic information is deployed at early stages of drug development by pharmaceutical companies and is considered by regulators as a part of regulatory submissions.

Disruption of the monoaminergic system, e.g. by sleep deprivation (SD), seems to promote certain diseases. Assessment of monoamine levels over the circadian cycle, during different sleep stages and during SD is instrumental to understand the molecular dynamics during and after SD. To provide a complete overview of all available evidence, we performed a systematic review. A comprehensive search was performed for microdialysis and certain monoamines (dopamine, serotonin, noradrenaline, adrenaline), certain monoamine metabolites (3,4-dihydroxyphenylacetic acid (DOPAC), 5-hydroxyindoleacetic acid (5-HIAA)) and a precursor (5-hydroxytryptophan (5-HTP)) in PubMed and EMBASE. After screening of the search results by two independent reviewers, 94 publications were included. All results were tabulated and described qualitatively. Network-meta analyses (NMAs) were performed to compare noradrenaline and serotonin concentrations between sleep stages. We further present experimental monoamine data from the medial prefrontal cortical (mPFC). Monoamine levels varied with brain region and circadian cycle. During sleep, monoamine levels generally decreased compared to wake. These qualitative observations were supported by the NMAs: noradrenaline and serotonin levels decreased from wakefulness to slow wave sleep and decreased further during Rapid Eye Movement sleep. In contrast, monoamine levels generally increased during SD, and sometimes remained high even during subsequent recovery. Decreases during or after SD were only reported for serotonin. In our experiment, SD did not affect any of the mPFC monoamine levels. Concluding, monoamine levels vary over the light-dark cycle and between sleep stages. SD modifies the patterns, with effects sometimes lasting beyond the SD period.

Systematic reviews are important tools in animal research, but the ever-increasing number of studies makes retrieval of all relevant publications challenging. Search filters aid in retrieving as many animal studies as possible. In this paper we provide updated and expanded versions of the SYRCLE animal filters for PubMed and Embase. We provide the Embase filter for both Embase.com and via Ovid. Furthermore, we provide new animal search filters for Web of Science (WoS) and APA PsycINFO via psycnet.apa.org and via Ovid. Compared with previous versions, the new filters retrieved 0.5–47.1% (19 references for PubMed, 837 for WoS) more references in a real-life example. All filters retrieved additional references, comprising multiple relevant reviews. A random sample from WoS found at least one potentially relevant primary study. These animal search filters facilitate identifying as many animal studies as possible while minimising the number of non-animal studies.