Being under ever-increasing pressure to compete in a challenging economic and technological environment, pharmaceutical and biotech companies must continually innovate in their R&D programmes to stay ahead of the game.
External innovations come in different forms and originate in different places -- from university labs, to privately held venture capital-backed startups and contract research organizations (CROs). Let’s get to reviewing some of the most influential research trends which will be “hot” in 2018 and beyond, and summarize some of the key players driving innovations.
Last year BioPharmaTrend summarized several important trends affecting biopharmaceutical industry, namely: an advancement of various aspects of gene editing technologies (mainly, CRISPR/Cas9); a fascinating growth in the area of immuno-oncology (CAR-T cells); an increasing focus on microbiome research; a deepening interest in precision medicine; some important advances in antibiotics discovery; a growing excitement about artificial intelligence (AI) for drug discovery/development; a controversial but rapid growth in the area of medical cannabis; and continuous focus of pharma on engaging in R&D outsourcing models to access innovations and expertise.
Below is a continuation of this review with several more active areas of research added to the list, and some extended commentaries on the trends outlined above -- where relevant.
1. Adoption of Artificial Intelligence (AI) by pharma and biotech
With all the hype around AI nowadays, it is hard to surprise anybody with this trend in pharmaceutical research. However, it should be noted that AI-driven companies really start getting traction with big pharma and other leading life science players, with lots of research partnerships and collaborative programs -- here is a list of key deals so far, and here is a brief review of some notable activity in the “AI for drug discovery” space over the last several months.
A potential of AI-based tools is now explored at all stages of drug discovery and development -- from research data mining and assisting in target identification and validation, to helping come up with novel lead compounds and drug candidates, and predicting their properties and risks. And finally, AI-based software is now able to assist in planning chemical synthesis to obtain compounds of interest. AI is also applied to planning pre-clinical and clinical trials and analyzing biomedical and clinical data.
Beyond target-based drug discovery, AI is applied in other research areas, for instance, in phenotypic drug discovery programs -- analyzing data from high content screening methods.
With a major focus of AI-driven startups on small molecule drug discovery, there is also an interest in applying such technologies for biologics discovery and development.
2. Expanding chemical space for drug discovery explorations
A vital part of any small molecule drug discovery program is hit exploration -- identification of those starting point molecules which would embark on a journey towards successful medications (rarely they survive this journey, though) -- via numerous optimization, validation, and testing stages.
The key element of hit exploration is the access to an expanded and chemically diverse space of drug-like molecules to choose candidates from, especially, for probing novel target biology. Given that existing compound collections at the hands of pharma were built in part based on the small molecule designs targeting known biological targets, new biological targets require new designs and new ideas, instead of recycling excessively the same chemistry.
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Topics: Industry Trends