Lotte Biologics joined hands with U.S. artificial intelligence (AI)-based cell line development corporations ASIMOV to unveil a next-generation biopharmaceutical development and production model. The two companies highlighted their next-generation contract development and manufacturing organization (CDMO) competitiveness by presenting a case in which they completed the entire process—from the DNA stage to GMP (good manufacturing practice) drug substance production—in about 8.5 months through a collaboration system that operates the entire workflow from cell line development to clinical drug production as if it were a single team.
Lotte Biologics and ASIMOV gave a joint presentation at BioUSA on the 22nd (local time) in San Diego, California. Angela Oh, director of manufacturing science and technology (MSAT) at Lotte Biologics, and Imroz Gagas, ASIMOV's vice president of global commercial sales, spoke on the theme "Eliminating partner complexity in development & manufacturing."
The biopharmaceutical market has recently been reorganizing around high-difficulty modalities such as bispecific antibodies, Antibody-Drug Conjugate (ADC), and antibody-oligonucleotide conjugates (AOC). As complex molecules increase, development difficulty has risen, while pressure is growing to accelerate market entry. As a result, securing both development speed and productivity has emerged as a core task for the industry.
Director Angela Oh said, "New drug developers are under pressure to move up the first-in-human (FIH) timeline, but at the same time, CMC (chemistry, manufacturing and controls) requirements are rising," adding, "Considering the schedule, expense, and likelihood of success, an approach that identifies risks early and manages them in an integrated manner is needed."
She continued, "In conventional biopharmaceutical development, cell line development (CLD), process development (PD), and GMP production are often carried out sequentially by different organizations, which can cause schedule delays, rework, and quality risks during tech transfer," adding, "The two companies built an 'orchestrated approach' from the outset that makes joint decisions, rather than relying on simple handoffs."
ASIMOV designs vectors optimized for the characteristics of candidate substances through its AI and Synthetic Biology-based cell line development platform CHO Edge. Unlike the conventional industry practice of applying similar platform vectors to different molecules such as antibodies, fusion proteins, and bispecifics, it uses a Machine Learning algorithm to design molecule-specific customized vectors.
Vice President Gagas said, "The industry has long applied a single platform to a variety of molecules, but that does not guarantee optimal outcomes," adding, "ASIMOV designs vectors from scratch to match molecular characteristics and uses an approach that simultaneously optimizes protein expression and quality."
The core of the companies' collaboration model is that cell line development, process development, and GMP production are not carried out sequentially but are parallelized as much as possible.
Under the conventional approach, a CDMO could begin process development only after about 18 weeks, when the final clone was secured and material was received. In contrast, ASIMOV provided Lotte Biologics with a high-productivity cell pool-based research cell bank (RCB) about seven weeks after transfection. In the eighth week, it supplied early look material produced in a bioreactor, enabling early initiation of downstream process development and analytical method development.
Lotte Biologics used this material to conduct upstream process confirmation, downstream process optimization, and analytical method development in parallel. At the same time, it also advanced production of materials for toxicology studies (pool-to-tox), establishment of a master cell bank (MCB), viral clearance validation, and procurement of raw and auxiliary materials in a parallel, not sequential, manner.
Oh said, "By securing process characteristics and quality data from the early stages, we were able to improve Production yield and optimize quality," adding, "We also secured raw and auxiliary materials early, reducing supply chain risks and moving up GMP production readiness."
The two companies also operated a joint milestone system, mock tech transfer, and an integrated quality system. At each development stage, they aligned key quality attributes (PQA) and process parameters in advance to minimize rework and schedule delays that can occur during tech transfer.
As a result, they completed the process from the DNA stage to GMP drug substance production in about 8.5 months. GMP production began about seven months after the project started, and they also secured a high-productivity cell line with an 8–12 g/L level during the early process development stage.
The companies expect that this model can be applied not only to monoclonal antibodies but also to bispecific antibodies, Antibody-Drug Conjugate (ADC), fusion proteins, and other complex next-generation modalities.
Oh said, "In a biopharmaceutical development environment of increasing complexity, there are limits to optimizing individual steps alone," adding, "When the entire process is organically connected, development time can be shortened and the likelihood of success can be increased."
She added, "We will continue to expand differentiated one-stop development and manufacturing services to help global clients move up their market entry timelines."