Bridging Discovery and CMC with Asimov's Rapid Pools

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The rapid growth of complex antibody modalities—including bispecifics, multispecifics, Fc-fusions, and emerging formats—has widened the gap between discovery-stage expression systems and manufacturability. Transient expression, still widely used for screening and optimization, often fails to reveal architecture-driven liabilities such as chain imbalance, mispairing, instability, aggregation, or poor effective yield. As a result, promising molecular formats may be prematurely deprioritized based on incomplete expression data, or advanced with hidden risks that surface only during cell line development and CMC.

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Asimov’s Rapid Pools platform addresses this challenge by enabling high-throughput generation of stable CHO expression pools using a new high-efficiency transposase, CHO-K1 GS host, and model-guided vector design. By providing early, CLD-relevant expression and assembly data, RapidPools allow protein engineers to directly evaluate the manufacturability of complex architectures during the design phase. This enables informed sequence, vector, and chain-balancing optimizations that can derisk differentiated formats that might otherwise be dropped, while more accurately identifying designs unlikely to scale.This presentation will highlight two customer case studies.

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The first demonstrates how a Rapid Pools campaign showed that the top transient performers were not always the top stable pool performers when evaluated on titer and critical quality attributes, changing which leads advanced.
The second showcases a molecule rescue campaign, where design-driven expression limitations were diagnosed and corrected through targeted sequence and vector redesign, allowing a challenging molecule to progress to lead candidate selection.

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Highlights

Virtual Private Network (VPN): Users connect to the cluster, provide some credentials and are then able to access internal tools.

Single Sign-On: A tool like Kerberos allows you to use the same account across various components.

Home-grown user accounts: You implement an authentication system and users have a separate username/password for your computing infrastructure.

Asimov, the synthetic biology company building a full-stack platform to program living cells, announced today it has been awarded a contract as part of the Defense Advanced Research Projects Agency (DARPA) Automating Scientific Knowledge Extraction (ASKE) opportunity.

Through ASKE, Asimov will work to develop a physics-based artificial intelligence (AI) design engine for biology. The goal of the initiative is to improve the reliability of programming complex cellular behaviors.

“To achieve truly predictive engineering of biology, we require dramatic advances in computer-aided design. Machine learning will be critical to bridge genome-scale experimental data with computational models that accurately capture the underlying biophysics. As genetically engineered systems grow in complexity, they become difficult for humans to design and understand. For simple genetic systems with only a couple of genes, synthetic biologists typically use high-throughput screening and basic optimization algorithms. But to engineer more complex applications in health, materials, and manufacturing, we need radically new algorithms to intelligently design the DNA and simulate cell behavior.”

Alec Nielsen, Phd, Asimov CEO

Over the past 50 years, DARPA has been a world leader in spurring innovation across the field of AI, including statistical-learning and rule-based approaches. We are proud to work with DARPA to advance the state-of-the-art in AI-assisted genetic engineering.

Asimov’s founders previously built a hybrid genetic engineering and computer-aided design platform called Cello to program logic circuit behaviors in cells. The ASKE opportunity will seek to support an ambitious expansion in the types of biological behaviors that can be engineered.
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Asimov’s approach will leverage “multi-omics” cellular measurements, structured biological metadata, and novel AI architectures that combine deep learning, reinforcement learning, and mechanistic modeling. Over the past year, the company has ramped up hiring in experimental synthetic biology, machine learning, and data science to accelerate development of their genetic design platform.

Highlights

Headering 3

DARPA recently announced a multi-year investment of $2B into innovative artificial intelligence research called the AI Next campaign. A part of this wide-ranging AI strategy is DARPA’s Artificial Intelligence Exploration program, which was developed to help expeditiously move pioneering AI research from idea to exploration in fewer than 90 days. DARPA’s ASKE opportunity is part of this program and is focused on developing AI technologies that can reason over rich models of complex systems.

“Over the past 50 years, DARPA has been a world leader in spurring innovation across the field of AI, including statistical-learning and rule-based approaches. We are proud to work with DARPA to advance the state-of-the-art in AI-assisted genetic engineering.”

Alec Nielsen, PhD, Asimov CEO