Written by: Behzad Mahdavi - Ginkgo Bioworks
Last September, the FDA approved Skysona, a gene therapy for a rare, lethal brain disease in children. That’s the good news. The bad news? A one-time infusion to potentially save a child will cost $3 million. It’s one of the most expensive drugs in the world.
Having spent my career in biopharma manufacturing and commercialization, I viscerally understand the high cost of developing and manufacturing cell and gene therapies. It’s a complex, exacting business that requires extensive regulation, top talent, complex manufacturing technologies, testing and trials. These fundamentals will not change, and I don’t believe they should. But change is necessary somewhere—it shouldn’t cost $3 million to save a life.
Peter Marks, the director of the FDA’s Center for Biologics Evaluation and Research, recently characterized therapeutics reimbursement as the “800-pound gorilla in the room,” underscoring how critical cost management is for the future of cell and gene therapy adoption. He’s right.
However, we already have the tools to dramatically change how we develop these groundbreaking treatments. We can now intervene at the discovery stage and address the underlying causes of a therapy’s high cost. How? By deploying advanced biology optimization for therapies in their real manufacturing conditions. If the cost is the 800-pound gorilla in the room, then the opportunity to overcome it is the 8,000-pound elephant hidden in the bioreactor.
Drug development has two basic parts: discovery and manufacturing. These efforts are largely siloed and, traditionally, require vastly different areas of expertise, technological platforms, and investment. Discovery focuses on finding treatments that are safe and efficacious compared to current standards of care and bringing them through clinical trials to approval submission. Manufacturing is about providing that treatment at a scale and cost so it is available to patients while generating an attractive ROI to compensate drug developers for their years of upfront costs.
Today, drug discovery processes and manufacturing are sequential. The bottlenecks in manufacturability—the point in the treatment chain that shapes so much of the cost structure—are worked out long after the biological components of the treatment are locked down through trials and approvals. When a drug reaches the manufacturing stage, it is already too late to engage the elephant.
Let’s talk about what we mean by the elephant opportunity: advanced biological optimization. The therapeutic design space is massive. Traversing it has traditionally been costly and treacherous, or downright impossible. In that design space, however, lies a set of ‘hits’ for pharmaceutical developers. Hits are a group of compounds with similar efficacy and safety. Some hits will have better manufacturability while others are much more effective against a therapeutic target. The golden ticket comes when compounds are quite successful in both criteria.
Here’s the key: optimization of one will often result in improvements on the other. More effective compounds can be, net-net, manufactured more quickly per effective dose because less is needed to reach therapeutic efficacy.
Limiting our toolkit to traditional process manufacturing and operational optimization offers, at best, only incremental improvement. Instead, we should take the opportunity to disrupt the status quo by leveraging cutting-edge research techniques to optimize the efficiency of our compounds. By integrating discovery and optimal manufacturability from the beginning, we can dramatically improve output, speed to market, cost, and availability.
Why has this type of optimization been overlooked until now? Biological optimization is an extremely complex process that can require designing, building, testing, and analyzing millions of robust and complex combinatorial experiments in a relatively short time. As a result, the pharma industry has largely not taken advantage of this opportunity and has instead focused on speed-to-market. In many cases, this has also meant ignoring the roots of manufacturing costs and the challenges of large-scale production. This has led to biologically unoptimized therapies—efficient over current standards but far short of the potential best hits in their class.
Today, we have platforms that can engage the elephant directly and bring down the cost of drugs. At Ginkgo Bioworks we can run workflows consisting of highly automated, high-throughput, biological experiments in our lab space, which we call our Foundry. By running millions of iterative design–build–test–learn experiments, we are working to support the rapid prototyping, optimization, and development of advanced therapies in real manufacturing conditions.
Effective biological optimization means that we do not explore the design space anew with each project. Instead, when we deploy our bioinformatics capabilities, AI-driven protein design, or high-throughput screening on massive genetic libraries, we iterate our knowledge from our previous efforts. In the same way that computer engineers develop a codebase for new programs, we have combined proprietary and public databases to create one of the largest biological codebases on the planet. Simply put, a platform approach allows partners to do the work they would traditionally outsource to CROs and CDMOs under one roof, unlocking the enormous potential of integration at the very start of drug development. While companies traditionally outsource to CROs what they don’t want to do in-house, we’re seeing companies work with us to access capabilities and scale they don’t have in-house.
Using these approaches, we have been able to show significant improvements in campaigns designed to increase manufacturability. In our work with Aldevron, for example, we optimized and improved the manufacturability of an important and difficult-to-make enzyme in mRNA vaccines, vaccinia capping enzyme, by 10X. This was a significant breakthrough. Consider your own capital expenses and output. How much would a tenfold increase in manufacturability save you and your customers?
Combining approaches like these would allow pharmaceutical developers to simultaneously discover new hits and test them for manufacturability in assays that mimic realistic large-scale manufacturing conditions. We’re excited to move beyond the days of reaching the end of discovery and approval only to be hamstrung on manufacturability.
It’s time to engage the 8,000-pound elephant and use the power of biology at scale to optimize discovery and manufacturing. This is our opportunity to deliver for drug developers, CDMOs, stakeholders, and most importantly, the patients served by pharmaceutical development.
Dr. Behzad Mahdavi is the senior vice president of biopharma manufacturing and life sciences tools at Ginkgo Bioworks. This article represents his views and does not necessarily reflect the views of Ginkgo Bioworks.