Reducing failure at the most expensive preclinical stage of pharmaceutical development
Lead optimization means the identification of new molecular entities and refinement of their structure to improve potency and pharmacokinetics while reducing off-target effects.
Traditionally involving in vitro and in vivo ADMET studies and data modeling methods such as QSARs, this stage consumes the largest share of finances in preclinical development.
But today, data can be leveraged to help empower lead optimization. In fact, the combination of high-quality, accessible data and powerful in silico profiling solutions has proven to accurately predict the potential of lead compounds.
That means that researchers can now query a broad number of potential NME structures in less time. In silico methods also help pharmaceutical companies greatly decrease their reliance on animal studies. And finally, it is less expensive than traditional methods and can provide greater coverage of compounds.
In this article, the value of higher-quality data, better in silico tools and improved data management is discussed in terms of the considerable potential for lead optimization.
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