The significant reduction in sequencing time and costs for omics (genomic, transcriptomic, metabolomic, proteomic, etc) has enabled widespread usage of this sequenced data to create an “open source” approach to science and R&D.
The digitization of biology allows us to convert biological data and information into digital information that can be stored, analyzed and processed by computers. Software has enabled us to communicate this language of biology to make this data more accessible to researchers worldwide, accelerate drug development, improve clinical trial matching, personalize medicines and more.
Breakdown by R&D source of late-stage pipeline
Innovation in the modern day era comes from corporations acquiring companies where innovation is happening. This trend has enabled greater opportunities for TechBio companies to develop innovative platforms that license out assets to pharma, tech giants, payers, retail pharmacies and more.
Computation and prediction have outpaced experimentation. Consider that in the world of protein sequence prediction, AlphaFold2 expanded their database 200x from ~1M to 200M+ structures compared with the <200K structures that have been experimentally resolved. TechBio has the potential to dramatically increase our understanding of biology and apply it in new ways.
Our increased understanding of biology will fuel a future of in-vivo biology, where drugs can be personalized to an individual. From gene editing to discovering novel targets, we now have novel biology that can be the catalyst for a whole new applied world.
Estimated median expense on new FDA approved drugs (2009–2018)
On average, it costs about $1.3 billion to bring a new drug to market; in the field of oncology and immunology drugs, this average cost is more than double, or about $2.8 billion. TechBio aims to reduce these skyrocketing costs by applying AI and engineering across the drug development continuum from novel target identification to preclinical disease modeling, clinical trial patient recruitment and beyond.
Annual storage in petabytes per year (2025 projection)
Cost-effective data storage will enable us to utilize the growing amount of biological data being generated and shared. Genomics data is expected to require up to 40,000 petabytes of storage each year by 2025, compared to X (fka Twitter) which is estimated to require 17 petabytes a year or all the data collected on astronomy (1,000 petabytes a year).
“I am optimistic that Excision’s technology and approach using CRISPR could potentially cure infectious diseases and bring much needed therapies to those in need around the world”