Expert view: Effective coupling of assays and cell models

Assay robustness tends to be the favoured descriptor when a particular assay can cope with minute changes in the sample, equipment, or operator. The more robust an assay, the more predictive data it ultimately yields. The industry’s greatest challenge for assay development has always been using those robust assays to characterise and define the most highly predictive cell models…

Using a high-quality assay coupled with a predictive cell line, researchers can generate the level of sensitivity, specificity, reproducibility, and accuracy that their screening programmes need to find the strongest lead candidates. With late-phase drug attrition rates typically used to measure R&D efficiency and biotech/pharma success, stronger methods are necessary in order to ensure that only the most viable and successful candidates make it through to clinical trials. The need to reduce attrition rates isn’t a new idea, but with new technologies the approaches for it are changing quickly.

Characterising cell lines

Assay systems such as the SmCxProTM are providing the sensitivity needed in order to characterise cell lines that provide weak/noisy signals and false negatives or positives, while protein-protein interaction assays like Duolink® help probe for the network connectivity necessary in a fully functional cell line. These assays, combined with gene editing tools such as ZFN and CRISPR for accurate engineering of cell lines, have enabled researchers to push through only the strongest of lead candidates. While the NMEs being screened now won’t make it to trials for another decade, we know that the challenges in assay development are continuously being addressed with the advent of new technologies. Whether an assay is phenotypic or genotypic, proteomic or functional, the assay itself is only as robust as the cell model used and the ultimate challenge has always been to find the right coupling of the two.

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