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The Intersection of Computational and Biological Sciences: A New Era in Biopharma

For nearly half a century, the biopharma / life sciences lifecycle has followed a familiar path. From (i) drug discovery to (ii) clinical trials, and (iii) commercial launch and distribution, the pace of change and innovation has been minimal. That is, until the moment when DeepMind’s AlphaFold AI model was published in 2021, revolutionizing the ability to predict protein folding. This breakthrough has condensed what used to take months or even years of trial and error into mere hours. Today, with AI dominating headlines, it’s clear that biopharma stands as one of the industries set to undergo profound transformation. The convergence of computational and biological sciences offers exciting possibilities, from heightened programmability and complexity to enhanced efficacy. In this post, we delve into this intersection, while highlighting a few of the innovators leading the way.

Drug Discovery: Pioneering New Frontiers
At the highly attended All In Summit in LA last week, Dr. Nicole Paulk outlined the eras, or “modalities,” of modern medicine. We align with her perspective on these modalities and aim to showcase how AI is leading groundbreaking advancements in each one:

Chemical Medicine / Small Molecules are the pills available over-the-counter at pharmacies. Given their longstanding role in traditional medicine, AI/ML presents numerous opportunities for optimizing existing drugs, designing new ones, and predicting their effectiveness at an unprecedented pace. This promises a revolutionary shift in traditional medicine. Leading companies such as Hexagon Bio, Enveda Biosciences, Montai, and Terray Therapeutics are spearheading this transformation.

Protein Medicine / Biologics category encompasses insulin, antibodies (e.g. Humira, used to treat inflammation caused by arthritis, Chron’s disease, and more), and vaccines. Looking forward, AI offers the potential for highly targeted treatments, minimizing potential side effects. This advancement holds significant promise, particularly in cancer therapy. Leading the charge in this field are companies like Big Hat Biosciences, Alloy Therapeutics, and Upstream Bio.

Living Medicines / Gene and Cell Therapies represent the cutting edge of medical innovation. These therapies hold the potential to cure diseases and have already shown promise in reversing conditions such as pediatric Parkinson’s, eradicating malaria, and restoring sight. Computational and engineering principles are crucial to living medicine formation, with many incorporating “engineered logic circuits” that activate the therapy if and when specific conditions are met, allowing for maximum precision. Given this computational nature, AI/ML plays a pivotal role in advancing these therapies, enabling novel approaches, and identifying new targets. Tessera Therapeutics leads the way, utilizing AI/ML to pioneer “gene writing,” a method for precise DNA creation. Other trailblazing companies, including Sana Biotechnology, Beam Therapeutics, and Kite Pharma, have long been pioneers in driving the frontier of gene and cell therapies.

 Clinical Trials: Ready for a Reboot
As AI reshapes drug development timelines and efficiency across various modalities, the next phase in the biopharma process ripe for AI disruption is clinical trials. Currently, clinical trials exhibit a success rate of less than 10% and on average take 8-10 years to complete. The research arms of major pharmaceutical companies grapple with stringent patient eligibility criteria and burdensome trial protocols, contributing to extended timelines and reduced success rates.

AI has the power to facilitate a paradigm shift in this inefficient process. Pioneering companies like Paradigm are utilizing technology in the trial recruitment and management process to champion equitable access and enhance patient engagement. Others are seeking to upend trial design itself. Unlearn.AI, for example, leverages AI/ML to fortify control groups with synthetic patient data, reducing the necessary size of placebo groups, expediting trials, and saving costs.

A Unified BioPharma Ecosystem
Big Pharma companies such as Amgen, Novartis, Eli Lilly, and Merck, all acknowledge the transformative impact of AI in biopharma. On average, they each invested $10 billion in R&D last year, a sizeable portion of which supported AI integrations and partnerships with innovative startups. These enormous investment figures have fostered the emergence of multiple startups with valuations surpassing $10 billion over the past three years. All stakeholders, from drug discovery startups to big pharma, share a collective objective: to streamline clinical trials, enhance success rates, and propel advancements in life sciences. This collective objective was emphasized in last year’s Montgomery Summit’s BioPharma panel, which featured all stakeholders – big pharma, drug discovery firms, and clinical trial companies – working towards this shared goal.

Generate Biomedicines, a March Capital portfolio company, exemplifies a unified biopharma ecosystem. They use ML/AI for groundbreaking drug discovery and therapeutic development in collaboration with partners like Amgen and MD Anderson. Generate deploys a mutually beneficial platform approach, applying advanced ML to novel therapeutics to help advance R&D at Amgen and MD Anderson, while also tapping into Amgen’s and MD Anderson’s strategic data and distribution capabilities. This symbiotic relationship has yielded 17 active programs to-date across different modalities (small molecule, antibody, gene therapy, etc.), two of which are active clinical programs. The company’s Series C funding round, which included new investors like NVIDIA and Amgen, was recently announced.

We firmly believe that AI technology is set to revolutionize the biopharma industry. Many biotech firms now generate more data daily than even the most successful startups. Over the next 5-10 years, we foresee a surge in experimentation and discovery driven by AI, potentially reshaping the landscape of approved drugs. For instance, the swift adoption of technologies like CRISPR CAS9 and CART cell therapies has become commonplace in modern labs, surpassing even the rapid pace of Moore’s law. This unprecedented pace of technological advancement in biopharma is propelling us into a new era at the intersection of computational and biological sciences, driven by AI. The journey has just commenced, and the coming decade holds the potential for transformative progress that will benefit the health of all.