Table 1 Minimum criteria that need to be fulfilled/considered before starting a preclinical confirmatory multicenter trial. Best practices are based on existing (reporting) guidelines and sketch the ideal situation. However, there can be practical limitation that hinder e.g., blinding or randomization

Internal Validity

Blinding

Concealment of group allocation from one or more investigator(s) involved in a preclinical study

Blinded outcome assessment

Blinding of treatment allocation, experiment(s), outcome assessment and analyses

Experiments in which the treatment allocation is directly linked to an obvious phenotypic difference from the start of the experiment (e.g. genetically modified mice with different fur colors)

Randomization

Using chance methods to allocate subjects to intervention and/or treatment according to a clearly defined probability distribution

Completely randomized [13]

Block design and stratification within known (not post-hoc) important predicting strata (like bodyweight)

Social transfer of e.g. pain may limit randomization options [19, 20]

Inclusion/Exclusion

Differentiate between animal attrition or drop-out and (data) outlier management

Clearly a priori defined inclusion/exclusion criteria

Reporting of drop-out rate and/or animal attrition

If data points are removed, it must be performed before unblinding according to a pre-defined protocol

Report full datasets and report all excluded animals with reason

Inclusion/exclusion criteria can be based on animal welfare (severity assessment and human endpoint), on scientific outcome (e.g. three times SD) or on characteristics of the model (genotype, phenotype, stage of disease)

Outcome

Primary outcome needs to be clearly defined (measurement unit and time point) and disease relevant (as defined involving a clinician)

Primary and secondary outcomes are clearly defined

Quality Management/ Assurance

Including standardization (and harmonization) of protocols

Protocols /work instructions and/or standard operating procedures in place

Measures to assure quality of methods and models are defined (e.g. baseline measures across laboratories)

Harmonization of protocols across laboratories prior to the multicenter study (identification of differences)

Training of experimenters

Different regulatory requirements regarding animal welfare in multi-center studies performed across different legal jurisdictions

Claim specification

Knowledge claim specification

Preregistration including specification of hypotheses (knowledge claims) and criteria for acceptance/ rejection

preclinicaltrials.eu

animalstudyregistry.org osf.io

Statistical methods

Need to be defined in advance (which methods are to be performed and which assumptions been made) including sample size calculation

Preregistration [21];

Registered reports [22]

Reach out to statistical consultants if needed

Reliability

Consistency in a measurement

Sufficient number of animals to assess the clinically or biologically meaningful effect and its associated uncertainty to inform sample size calculations

Increase sample size via within-lab replication to estimate effect size with adequate precision

Within-lab replication can happen in parallel or across time (preferred)

Translational Validity

Extent to which a scientific finding can be translated from preclinical to clinical (human) contexts

Animal model is relevant for disease and reflects some of its characteristics

Indicating context of relevance (diagnostic manuals and categorical criteria or transdiagnostic approaches)

Be aware of model limitations!

Include clinically relevant biomarker(s) and/or diagnostics

For medicinal product: biodistribution and/or bioavailability

Animal model is highly relevant and carries many disease characteristics

And/or perform experiment using different (animal or human cell based) models/ tissue with complementary characteristics (Triangulation)

Experiments focusing on e.g. mechanistic understanding that do not aim directly at clinical translation