Monoclonal antibodies (mAbs) have been a huge success story in the biopharmaceutical industry. They have changed the landscape of biologicals and offered therapies for previously untreatable diseases. Although mAbs have been on the market since the mid 1980s, they have become increasingly sophisticated over the past decades. This also holds true for the technologies enabling the identification, design, preclinical and manufacturing processes of monoclonal antibodies.
While the traditional hybridoma technology is still broadly used for the creation of mAbs, novel technologies such as immune antibody libraries or fully humanoid antibody libraries have emerged in recent years. Moreover, researchers can today leverage a range of powerful approaches to optimize the mAb creation process, e.g., high-throughput screening, state-of-the-art sequencing, AI / machine learning or analytics. This, in turn, speeds up the development cycle, allows for precise selection of the desired mAb properties and ultimately reduces failures of monoclonal antibodies at advanced development stages.
But how to choose from the vast range of technologies for antibody development? This challenge was discussed at Evotec´s recent Innovation Week in a session titled "Translate your idea into a product: AI -driven antibody discovery at Evotec".
The session covered Evotec´s capabilities for supporting all activities across the R&D continuum, i.e.
- Discovery and optimization of novel antibodies for specific disease targets,
- Evaluation of lead antibodies for disease efficacy and safety and
- Process development and manufacturing.
"In many cases, it is not the question of using either one or another technology," says Barbara Bachler-Konetzki, Group Leader In Vitro Pharmacology at Evotec. "Depending on the desired properties of the monoclonal antibody, combining several technologies will do the trick. Therefore, it is important to have access to a comprehensive repertoire of leading-edge, synergistic technologies."
For state-of-the-art monoclonal antibody development, Evotec has established a unique one-stop-shop from target identification to IND. This includes a broad technology platform as well as unparalleled expertise in drug development and even manufacturing, including latest advances in artificial intelligence and machine learning such as generative adversarial networks (GAN) to create synthetic realistic outcomes by machine learning (J.HAL).
The resulting integrated biologics platform is called J.DESIGN and integrates molecular, process and manufacturing design.
The example of Evotec´s internal SARS-CoV-2 campaign shows how its proprietary J.HAL technology can be used to identify antibodies effectively blocking the SARS-CoV-2 infection pathway by binding to SARS spike protein, effectively neutralizing the infectivity across several SARS-CoV-2 strains. The subsequent in silico sequence analysis informs about mAb properties and engineering opportunities to reach the desired properties re immunogenicity, stability etc. Further steps, e.g., sequence and stability optimization, improve manufacturability and yields or pharmacokinetics (PK).
For mAb development, Evotec pursues a translational approach, i.e., leveraging its extensive expertise in various therapeutic areas to facilitate the setup of disease-relevant biological assays. Among others, this allows for PK/PD characterization of biologics early in the R&D process as well as the prediction of downstream in vivo efficacy and demonstration of target engagement. Moreover, Evotec’s pre-clinical department offers the full range of in vitro and in vivo GLP and non-GLP pre-clinical evaluation studies to assess the safety profile of the drug candidate. In addition, Evotec has established several sophisticated manufacturing facilities worldwide to reduce the risk of downstream attrition and delay.
This unparalleled infrastructure and translational expertise put Evotec in a unique position to conduct leading-edge, integrated monoclonal antibody development programs all the way from target identification to manufacturing.
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