With global sales of monoclonal antibodies (mAbs) reaching $168.70 billion in 2021 and continuing to rise at an estimated annual growth rate of 11.5%1, one major issue is that they are still very expensive, reflecting the high costs associated with development and manufacture. For example, the price of treatment with Humira®, which was approved by the Federal Drug Administration (FDA) in 2002, is around $38,000 per year. In the case of the more recently approved KEYTRUDA®, where this rises to more than $100,000 per year, they are not accessible for many patients even in developed nations. mAb-based therapies are expensive to develop and manufacture than small molecule based drugs, with costs typically ranging between $95 and $200 per gram.
Biopharmaceutical companies looking to produce more affordable mAb therapies are therefore searching for ways to reduce their cost of goods (CoGs). One strategy is to select high affinity, high titer lead candidates which do not have potential development and manufacturability issues. Taking into account developability and manufacturability during the selection process enables the identification of lead candidates having a better fit to existing manufacturing platforms, reducing the likelihood of having to spend time solving technical issues and developing additional steps during process development, ultimately leading to shorter timelines and lower development and manufacturing costs. Further, liabilities which could degrade the therapeutic efficacy can be reduced, resulting in longer shelf life and improved serum stability.
To effectively perform optimal lead selection and sequence optimization requires an array of computational and high-throughput empirical workflows to enable early evaluation of antibody sequences for potential liabilities and to devise methods for their repair. Biopharmaceutical companies often lack these capabilities and therefore need to partner with client focused, technology driven specialists who are aware that the primary objective for their client is to generate results from toxicology and first-in-human (FIH) studies as rapidly and inexpensively as possible.
