How informatics and data management can transform the digital laboratory


Preclinical development is the combination of numerous specific scientific domains. It often encompasses:

  • Pharmacology (PD - both in-vitro e.g. plate-based and in-vivo e.g. in life) 
  • Drug metabolism (what happens to the drug when it is metabolized in the body 
  • Pharmacokinetics (PK - what happens to the drug when in the body, where it goes and at what concentration for how long • Bioanalysis (specific analytical techniques used to estimate the drug concentration and metabolite concentration in specific tissues) 
  • Formulations (how best to take the API and formulate this into a drug product (a pill, injectable, inhaled aerosol) and 
  • Safety/toxicology (analysis of the effects of the drug on specific organs and safety concerns e.g. cardiac safety, liver toxicity, etc.)

When it comes to informatics and data management, each of these domains is characterized by specific requirements due to their diverse scientific needs, which we will discuss later in this white paper.

However, they all produce critical elements of data and supporting information that is required for investigational new drug (IND) and new drug application (NDA) submissions and further regulatory approval.

Every domain produces critical data and this data and contextual information should be captured and managed in the most effective and robust manner. This also means that, given this data is required for regulatory submission, the speed and efficiency of how this data is collected is also of great importance.

Data needs to be accessible as quickly as possible, without compromising on the quality of the data. This is what we describe as a ‘data value chain’. It links all of the domains together, at the data level.

These key drivers are tractable problems that can, to a certain degree, be addressed with informatics and data management applications. However, they are not just solvable with software and data management – the complementary laboratory and scientific processes need to be optimized at the same time. But, why? Because automating an inefficient lab process does not add as much value to the business as automating an extremely efficient and optimized lab process.

So, when thinking about optimizing the holistic preclinical development process, we must address both the data management facets and the laboratory/scientific process at the same time. This is not new information, but it needs to be kept in mind when choosing what to do and when to do it with respect to an informatics/ lab process optimization strategy. Many of our customers have been through this process – but many will honestly say that they could have done things better with hindsight.

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