Tech-Enabled Biology: Pioneering a New Era in Biotechnology

 The first biotech revolution began 50 years ago when molecular biologists used DNA engineering to introduce a foreign genetic sequence into a bacteria and successfully produce a protein not encoded by the host genome. This revolutionary moment enabled a new era of scientific research that has radically advanced our understanding of how cells function in health and disease. It also opened the door to wholly new classes of therapies (recombinant proteins, monoclonal antibodies, targeted small molecules, gene and cell therapies, and gene editing) that have improved health outcomes for millions of patients.

Despite the transformative power of the first biotech revolution, traditional biopharmaceutical drug development paradigms continue to face significant R&D hurdles even after decades of advancement. There is a less than 10% attrition rate of therapies that make it to clinical trials and a roughly 9% success rate from Phase I to FDA approval, significant obstacles to translating molecular biology discoveries into the therapies needed to address the unmet medical needs of millions of people. These inefficiencies have resulted in billions of dollars wasted on failed R&D projects and patients being enrolled in clinical trials of investigational therapies from which they were unlikely to benefit. Obstacles persist even after product approval due to challenges in understanding how best to deploy novel therapies in real-world settings outside the highly defined patient populations evaluated in clinical trials.




Getting beyond these bottlenecks requires a new approach to integrating biology and technology, led by advanced artificial intelligence (AI) and machine learning (ML) paradigms. Just as biologists used DNA engineering to catalyze the first biotech revolution, data scientists can engineer biology utilizing computation, enabling a new era of compute-enabled biotechnology companies. Technology-forward biotech — or tech-enabled bio — companies are driving tremendous advances in human health by structuring, analyzing, and extrapolating data from disparate sources to identify novel drug targets, design therapies optimized for safety and efficacy, enable novel diagnostic and prognostic tools, and identify patients most likely to benefit from a particular treatment. Equally important, these vast data sets have the power to radically reduce the time and cost of developing novel therapies and improve their use in real-world settings by allowing corporate and clinical decisions to be based on millions of real-world data points rather than predefined data inputs. This benefits patients, payers, and companies, and their investors.


The benefits of digitalizing life science R&D workflows, including wet lab experiments, high-throughput compound screening, animal models, and extensive clinical trials, cannot be overstated. These fragmented workflows contribute significantly to the time, cost, and risk bottlenecks that have long plagued traditional drug development and treatment strategies. The new era of full-stack compute-enabled bio companies automating, optimizing, and connecting these siloed workflows and enabling the transformation of previously disparate data into actionable insights will drive incredible advances in human health.


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