In vitro, in vivo, ex vivo, ADME – we’ve got you covered with our Experiment app

Many data management tools lack good support for the actual workflows going on in the labs. It’s common practice to analyze the data in external applications, such as Excel, and only upload the final results to the data management platform. This makes it very hard to re-analyze and compare old and new experiments.

Below you’ll find a description of each of the major areas we cover with our Experiment App.

In vitro standard plate based experiments

With the standard in vitro application we offer data support for the classic/early binding and efficacy assays where standard conditions and plate layouts are used for the initial assessment of the activity.

The data management aspect of these types of experiments are either “just” calculations of %Inhibition or – for the IC50 experiments – a curvefit algorithm and a visualisation of the curve. Subsequent manipulation of the curve axes as well as tagging/removal of the outliers and a re-fit is needed as part of the QA and interpretation.

  • Upload raw data / reader files from plate readers
  • Visualise in relevant plate layout view
  • Curvefit and display of curves
  • Manipulate the individual curves – bogus outliers
  • Compare compound by visualising curves together
  • Import other/old data sets and compare across experiments

In vivo experiments with animals divided in different dosing groups

Many data management tools lack good support for the in vivo experiments and hence the scientists in this area often use excel and then just upload the ED50 results or other calculated output to the data systems and hence reducing these to data storage without any ability for later re-analysis and comparing.

The grit42 platform offers full in vivo data support where the animals are maintained in the relevant dosing groups with the data linked to the relevant individual animal in the group. Descriptive statistics on the group level is an integral part of the application and the user can add the different stats values – calculated in the background – by a click of a button. Relevant box plots of the dosing groups with SD’s are created automatically and other experiment data – ie with other doses – can be imported and compared with the current new data.

  • Visualise animals in the experiment in relevant (dosing) groups
  • Enter or upload data per animal
  • Calculate relevant descriptive stats parameters in the background
  • Visualise group data in box plots with SD
  • Import other experiments/data sets and compare in box plots

In vitro plate based ADME, safety and other non-standard experiments

When the research projects – and the initial hits – move forward from the initial standard in vitro evaluation the next step is often more advanced and non-standard plate based assays where conditions and plate layouts etc change more often and depends on the nature of the projects.

The raw data is similar to the standard screening data – reader files etc – but often origins from other type of equipment. After upload of the data the relevant analysis can still be a sigmoidal curve but often also includes other types of maths/stats and visualisations in order to enable relevant interpretations of the data.

  • Upload raw data / reader files from plate readers
  • Visualise in relevant non-standard plate layout view
  • Run relevant math/stats analysis on the data in the background

In vivo safety and ADME experiments

The ADME and safety tox areas have entered the drug discovery process late compared to the classical in vivo studies and was then often forced to “deliver a number” or similar interpretation into the data systems to enable easier use for the lead selection process. Their data analysis needs have therefore historically not been supported very well. It has been a combination of speciality data creation tools (equipment) and analysis in excel.

But the in vivo part of these areas have very similar data and analysis needs as the standard in vivo areas with data grouped and visualised according to the dosing groups. The subsequent data analysis, plots and descriptive stats might vary somewhat from in vivo but is in general still math and stats values calculated per group and/or compound and compared.

  • Visualise animals in the experiment in relevant (dosing) groups
  • Enter or upload data per animal
  • Calculate relevant descriptive stats parameters in the background
  • Visualise group data in box plots with SD
  • Import other experiments/data sets and compare in box plots

Clinical research data from anonymized patients

When the research projects are successful – and hence deliver a new drug candidate – the next steps in the value chain are scale up of the compound synthesis and long term tox studies before the drug finally move into clinical development and testing.

When data from the clinical studies are received it is relevant to go back and review if the results from the pharmacological in vivo studies – that let to the selection of the candidate – are validated in the clinic in order to make sure the animal models translate well to humans.

Our experiment centric approach enables us to support this endeavour where (often fully anonymised) clinical research data is uploaded to the grit42 platform and there analysed, visualised and compared to similar relevant animal based data.

  • Import and store anonymized data from clinical studies
  • Make bioinformatics analysis on the data directly in the platform, programmatically via our API or import data directly into your local R via our R-client
  • Compare/translate relevant makers from animal studies to clinical human studies


Below a couple of extra screen shots from our new version.

Example of box plot of data from in vivo study with animals in dosing groups. Here the plot has been enlarged while data is hidden for a better view of the plot.

Example of a SAR table showing a window with the underlying individual results behind the one value shown in the table. From here it is also possible to request a new result (experiment) of the compound.

Our browse feature where users can search across the different types of experiments in the database and view the data on the right hand side. This enables questions like “Show me all the experiments where compound = XYZ and specie = rat” etc. This is done by opening the filter option.

Showing an ADME dataset with Clearance and half life results

After carefully evaluating several compound management service providers, we selected grit42. We found their comprehensive inventory management platform ideal to keep track of our compounds and assays, in an intelligent and user-friendly way. In addition, they also focus on capturing experiment data including metadata, which allows us to combine the compound library information with the in vitro profiling data from the various assays run under the DK-OPENSCREEN umbrella. This fits perfectly with our future ambition of becoming the leading Danish screening hub, linking academic institutions and private organisations within pre-clinical drug discovery.

Director of DK-OPENSCREEN and Professor at the Technical University of Denmark (DTU), Mads Hartvig Clausen