The Assay Technology Trends Transforming Drug Discovery
List Sep 20, 2018 | by Laura Elizabeth Mason, Science Writer, Technology Networks
So, you don’t get the results you were expecting, but why not? Could it be the underlying science – or could it be your choice of assay?
Choosing the right assay for your drug discovery research is crucial. It must be fit-for-purpose, otherwise you can end up with irrelevant, variable or misleading results, which ultimately impact the progress of your drug development pipeline.
As questions become increasingly complex – models do too. As researchers focus their attention on more complex heterogeneous diseases, more complex and biologically relevant assays must be developed and implemented, to be able to answer more sophisticated questions.
Here we highlight 6 technology trends – with the potential to improve productivity and cost-effectiveness in drug discovery – if they are implemented correctly!
1. Cell Models are Becoming More Physiologically Relevant
Immortalized and animal-derived models are becoming a thing of the past. Thanks to advances in stem cell technology, models using differentiated, human cells are being adopted, that more accurately reflect how a drug may behave inside the human body. CRISPR/Cas9 gene editing has enabled researchers to develop improved models of disease, that can then be used alongside ‘normal’ unedited cells, that have an equivalent genetic background.
As more and more human stem cell-derived models and precision-engineered cell-lines become commercially available, their accessibility has increased, and cost has significantly reduced.
2. Reviving Phenotypic Cell-based Assays
Not too long ago, it seemed that the use of phenotypic screening in drug discovery was being ‘phased out’ as target-based approaches began to take center stage. However, lately there has been a resurgence of interest in the use of phenotypic cell-based assays. Retrospective studies, indicating that they are indeed more fruitful at discovering efficacious small molecule drugs in comparison to target-centric approaches has led to their revival.
Improved cell models coupled with better detection technologies and the enhanced ability to miniaturize assays has led to greater adoption of phenotypic approaches.
3. Predictive Cell Toxicity Assays: The Earlier the Better
Toxicity and efficacy – the two key factors that determine whether a drug candidate will fail or succeed. Late-stage failures can result in drug developers losing huge amounts of money. Detecting toxicities early in the development process - where cost of failure is significantly lower is certainly preferable.
Therefore, rather than waiting to assess the toxicity profile at the preclinical stage, companies are screening compounds using cell-based in vitro assays at earlier stages of the pipeline.
4. Kinetic Measurements: An Additional Dimension to Assays
Kinetic data offers great insight into a drug’s mechanism of action (MOA). Increased interest in kinases as potential drug targets has meant researchers have diverted their attention towards advancing assays that measure enzyme kinetics and has in turn led to the arrival of affordable kits.
Measuring the kinetics of cell behaviors and particular markers has enabled researchers to identify novel target classes.
5. Optimize Assay Performance Using the Design of Experiments (DOE) Methodology
Optimization becomes more and more challenging as assay models become increasingly complex. The ‘design of experiments (DOE)’ methodology is a statistical experimental design method for determining cause and effect relationships. DOE approaches are aiding the optimization of assays by helping researchers pinpoint the most influential assay parameters as well as the specific variables that can impact performance.
Although DOE approaches require a significant number of experimental combinations, that must be conducted with precision, to determine accurate information, it is becoming easier as automation capabilities continue to advance.
6. Data Doesn’t Have to be Daunting: Integrate and Standardize
Unlocking the power of data in drug discovery. As drug discovery processes become increasingly automated, the volume of data you are able to generate has skyrocketed. Whilst the ability to collate data from multiple sources is exciting – as it gives you a more comprehensive picture of your drug candidate and help you make more informed decisions – it can be also be a daunting process. But, it doesn’t have to be as long as you integrate and standardize.
Embrace and exploit the informatics and AI expertise available to you. These tools are indispensable when it comes to processing data as efficiently, and painlessly, as possible.
A more in-depth review of recent assay technology trends can be found here.
For most laboratory workflows, you’re only as good as your starting material. This is particularly true for next-generation sequencing (NGS). Library quality is all-important in ensuring you receive high quality data.
Whilst each platform and each type of NGS experiment will require its own optimization, there are several tips and tricks to preparing NGS libraries that hold true across all applications, which we've complied in this handy list.