The first biologics drug, humanized insulin (5.8 kDa), became available in 1982 following the advent of biotechnology, and it marked a new era in pharmaceutical industry. Modern advances in biotechnology permit large-scale syntheses of biologics in a more or less cost-effective manner. Having once started with large peptides and recombinant proteins, biologics nowadays include a wide range of other entities, such as antibodies, monoclonal antibodies, and more recently, nanobodies and related objects, soluble receptors, recombinant DNA, antibody-drug conjugates (ADCs), fusion proteins, immunotherapeutics, and synthetic vaccines.
(Edited version of this post originally appeared in Forbes)
“It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change” -- Leon C. Megginson
Last year brought about new hope and even more hype around the idea of applying artificial intelligence (AI) for “revolutionizing” drug discovery research -- via machines being able to “learn” chemistry and biology from vast amounts of experimental data to propose potent drug candidates, accurately predict their properties and possible toxicity risks. It is supposed to dramatically minimize failures in clinical trials -- saving R&D budgets, time, and most importantly, lives of patients.
Investments in artificial intelligence (AI) for drug discovery are surging. Big Pharmas are throwing big bucks at AI. Sanofi signed a 300 Million dollars deal with the Scottish AI startup Exscentia, and GSK did the same for 42 Million dollars. Also, the Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery.
In this craze, lots of pharma and biotech decision-makers wonder whether they should jump on the bandwagon, or wait and see.