How to validate your bioassays for effective early drug discovery research
Bioassays are an essential tool for early drug discovery. They allow researchers to identify the few promising compounds that have the potential to become precursors of new medicines. In previous blogs, we’ve explored the key principles of bioassay design and practicalities of bioassay construction. Here we look at how you can carry out bioassay validation to confirm that your bioassay is fit-for-purpose.
Read on to find out more about the key guidelines and statistical rules you should follow to ensure your bioassays are reproducible and consistent. These will help you make data-driven decisions about the compounds that could make or break the success of your drug discovery research.
How to quality control your bioassays
The success of your drug discovery research is reliant on the robustness of your assays, to ensure that your decisions about the suitability of your compounds are based on the highest quality data. As such, it is crucial for your bioassays to have undergone rigorous quality control procedures. These include:
1. Setting up sufficient control wells
Controls differ depending on the bioassay you’re running, but you should always include both positive and negative controls in equal numbers to ensure that the statistical analysis is not skewed in one direction or another.
2. Using an appropriate pharmacological reference or standard compound
To enhance your bioassay’s robustness, use a pharmacological tool or reference compound. This will allow you to measure the reproducibility of your bioassay, as well as inter-assay variation independently of the controls.
3. Applying key statistical parameters
Statistics are an important tool to determine whether the compounds you test have a significant effect when compared to the controls. Several parameters need to be considered:
- Signal-to-background (S:B) or signal-to-noise (S:N) – S:B is a measure of the ratio of the mean of the high (stimulated/positive) controls to the mean of the low (background) controls. S:N is calculated as the ratio of the maximum mean signal to the minimum mean signal dividing by the standard deviation of the assay.
- Assay variability – measured using the standard deviation of the control wells to calculate the ‘coefficient of variation’.
- Z prime (Z’), Z-factor and Z-score – each method provides a statistically relevant value that determines the reproducibility of your assay controls. This takes into consideration the standard deviation of both the positive and negative controls, along with the assay window and the activity of any compound outside of a null effect across all samples on the plate.
To ensure your assay is hitting the quality control mark, download our free eBook to learn more about applying these parameters to your bioassays.
How to identify a ‘hit’ in early drug discovery
In the search for that crucial active, or ‘hit’, compound or biologic that interacts with your target biology, you need to be sure that what you think is a ‘hit’ is actually a ‘hit’. Otherwise, you risk basing your decisions about whether to progress your compounds on inaccurate data.
So, how do you define a ‘hit’? Most compounds you screen will have activity centred around zero – the null effect. An active compound has an activity distinct from the background noise of the assay. A ‘hit’ is a compound that consistently shows the same activity in a statistically significant way.
How to pharmacologically validate your assay
To validate your results, screen compounds with known activities (ideally with a range of potencies), along with control samples on the same day and different days. This will increase the confidence you have in your assay to detect active compounds in a consistent manner.
What else can you do to revitalise your bioassay development?
The quest for that all-important compound that has the potential to become a new medicine is far from straightforward. Quality control and bioassay validation are essential steps in the process of building a reproducible and fit-for-purpose bioassay to ensure you are producing the highest quality data.
You may find that your bioassay development is hindered by challenges that are difficult to resolve. These issues are more common than you might think! So, to keep your research moving forward, take a look at some of the frequently asked questions in bioassay development to help you overcome barriers to success.