The Biotech to Techbio Shift: The Next Generation of Data-Enabled Scientific Breakthroughs

And its immense potential in revolutionizing clinical trial design, drug development, and precision medicine

Krish Ramadurai
Apr 27 · 5 min read

Over the past year, the COVID-19 pandemic has shed increasing awareness on the importance of the biotechnology sector and its pivotal role in ushering novel discoveries in research and development (R&D), treatments, and cures.

While indeed the notion of biotechnology’s importance in both the global economy and human health has never been more apparent, to the keen observer, one fascinating element has been the shift of “Biotech” becoming “Techbio.” The traditional biotech R&D process has been marred by bottlenecks and redundancies spanning data collection, analysis, and extrapolation, as well as wet-lab benchwork. However, over the past decade, a powerful yet subtle tangential shift in how scientific data is harnessed, processed, and translated into functional insights has occurred.

In the healthcare and life sciences space, there has been a massive amount of data created and procured from healthcare institutions and research labs worldwide. With these vast troves of data, only a finite amount has been cleaned, labeled, and structured to generate functional clinical and scientific insights to improve current standards of clinical care and develop novel discoveries.

However, these conventional methods begin to fail when the data becomes multivariate and heterogeneous. Oftentimes data such as electronic health records (EHRs) include multiple variables, including diagnoses and comorbidities for patients. This is true for drug development and clinical trials, in which utilizing heterogeneous vital for developing more efficacious drugs and trial protocols. Techbio companies are not simply biotechnology companies with a functional computational approach added on, but rather built from the ground up utilizing bespoke machine learning (ML), artificial intelligence (AI), and deep neural networks (DNN) with proprietary data sets. These Techbio companies have the functional capacity to analyze complex, heterogeneous information to create stratified genotypic and phenotypic patient groups. This holds immense potential in revolutionizing clinical trial design, drug development, and precision medicine.

These applications span a plethora of unique application domains, including structural genomics, proteomics, pharmacogenetics, synthetic biology, and biomanufacturing. Furthermore, computational approaches have been deployed in healthcare for managing clinical trial protocol design, patient enrollment, and clinical workflow process automation for practitioners. AI/ML/DNN can significantly reduce costs while simultaneously enhancing the functional utility and value generation of bioprocess outputs. Techbio companies such as Strateos and Insilico Medicine have utilized computational approaches to accelerate drug discovery and synthetic biology research. Insilico Medicine recently ushered a paradigm shift in drug development by developing the world’s first AI-designed drug for Idiopathic Pulmonary Fibrosis to go to the clinic. The company designed the drug from scratch in less than 18 months for $2 million.

Also, computation has been able to harbor thousandfold increases in hit-to-lead rates, an order of magnitude that would have been thought to be impossible only ten years ago. This process ultimately reduces the cost and failure rate of assets, representing a shift in the traditional drug discovery model that has been plagued by high rates of failure and unsustainable costs. Furthermore, Techbio companies such as Strateos lead a new era of automation in life science R&D workflows. Strateos, in conjunction with Eli Lilly, have developed a robotic cloud laboratory that can compress a three-and-a-half-year drug discovery cycle into less than 12 months. The company combines state-of-the-art ML, robotics, and automated biological synthesis to allow scientists and researchers to design and validate drug molecules from anywhere in the world at unparalleled speed, reproducibility, and cost savings. Perhaps one of the most exciting underlying elements is capturing and extrapolating novel biological data harnessed from the automation of these experiments and synthetic biology research. Strateos’ virtual infrastructure allows the company to capture experimental data on a continual learning basis to refine and accelerate the next set of experiments continuously. Thus, the laboratory system continuously optimizes the next set of adaptive experimental protocols creating further enhanced R&D processes while simultaneously mitigating time-intensive manual benchwork.

The boom in AI startups has led investors to be more scrupulous in defining the real-world functional utility of artificial intelligence, machine learning, and neural networks in creating realized actual value for customers and institutions. An algorithm is only as good as the data that it is trained on. Thus garnering access to high-quality, structured, and labeled data represents a significant bottleneck for integrating AI/ML/DNN. The future is, however, very bright for Techbio companies. The ability to utilize human data and ex-vivo processes for therapeutic testing, screening, and development will accelerate novel treatments and cures to market faster with the promise to dramatically improve the current standard of human health globally.

Krish Ramadurai is a Partner and Healthcare & Life Sciences Principal at Harmonix Fund — an early-stage venture capital fund investing at the intersection of healthcare, life sciences, and deep technology. Harmonix deploys evidence-based approaches to analyze, accelerate, and scale the science-driven breakthroughs of tomorrow, today. Furthermore, he is a multi-published scientific author and former researcher at the world’s top think tanks and academic research institutes, including Harvard University’s Belfer Center for Science and International Affairs, the Taubman Center State and Local Government, and the Massachusetts Institute of Technology. He earned an MBA in healthcare and strategy from Washington University in St. Louis, a master’s degree in biology from Harvard University, and a bachelor’s degree in integrative biology and economics with a chemistry minor at the University of Illinois at Urbana-Champaign.

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