Expert view: Screening tools for better lead characterisation
Predictiveness is one of the key factors for success in the drug discovery workflow. Many compounds fail at late stages due to lack of predictive data or the discovery of unwanted side effects.
A combination of early discovery tools for better characterisation and predictive data can help researchers working on the next generation of therapeutics to better understand their lead candidate. This allows researchers to react quickly and prevent failures from reaching the clinical pipeline.
Genome editing tools like the CRISPR-Cas9 system are integral in identifying new targets involved in the biological and genetic mechanisms responsible for disease. Tools like the Sanger Arrayed Lentiviral CRISPR Library can be used to screen both the whole mouse and human genome and can help determine which gene might be involved in a particular pathway.
After choosing the correct target, the next step is developing a predictive cell model for screening purposes. Custom cell engineering is therefore of utmost importance in screening potential candidates and assuring that researchers can select a new molecular entity with the best safety and efficacy properties.
Coupling a robust and predictive cell model with compound libraries, such as the MyriaScreen Diversity collection or LOPAC, will not only ensure that your lead compound will have drug-like properties but that off-target effects are minimised.
Using robust genetic engineering and screening tools will not only lead to a stronger drug pipeline, but will also ensure continuity in the workflow and guarantee that only the leads with the highest potential will make their way to clinical trials.