Antibody Discovery: Developing Therapeutic Antibodies For Treating Various Diseases
Antibody Discovery: Developing Therapeutic Antibodies For Treating Various Diseases
The development of monoclonal antibody technology in the 1970s laid the foundation for the rapid progress in antibody discovery that was to follow

The development of monoclonal antibody technology in the 1970s laid the foundation for the rapid progress in antibody discovery that was to follow. George Kohler and Cesar Milstein were the first to develop a technique to mass-produce monoclonal antibodies in 1975. They fused mouse spleen cells from an immunized mouse with mouse myeloma cells to generate monoclonal antibody-secreting hybridomas. This breakthrough allowed scientists to isolate single antibody-producing cell lines that could churn out unlimited quantities of identical antibodies.


Hybridoma technology proved transformative for biomedical research. However, breakthrough therapies involving humanized or fully human
Antibody Discovery had to wait, as the first monoclonal antibodies were fully murine in origin. This limited their clinical efficacy and caused immune reactions in humans. During the 1980s and 1990s, researchers worked on techniques like CDR-grafting to reduce the murineness of monoclonal antibodies.

Phage Display Drives Advances In Antibody Discovery 

The development of phage display technology in the 1980s enabled a revolutionary new approach to discovery. George Smith pioneered the use of bacteriophage to display antibody fragments on their surfaces. This allowed large libraries of antibodies to be rapidly screened and selected based on binding to target antigens. 

Phage display completely transformed discovery from a hybridoma-driven process to a versatile in vitro selection technique. Antibody libraries could now be constructed against any antigen without requiring animal immunization. This paved the way for human and humanized antibodies that did not cause immune reactions. It also facilitated the discovery of antibodies against targets that were difficult to immunize against, like soluble cytokines.

Today, phage display is the dominant platform for discovery thanks to its power and versatility. Large naïve and immunized libraries can be easily constructed and screened. Multiple rounds of selection enable isolation of antibodies with exquisite affinity and specificity for various targets. Phage display also accelerated humanization and affinity maturation workflows once lead antibodies were isolated.

Therapeutic Antibodies Emerge As Best-Selling Biologics

By the late 1990s and early 2000s, the successes of therapeutic MAbs like Remicade, Humira and Avastin validated monoclonal antibodies as a new class of drugs. Remicade was the first fully human antibody and ushered in the era of effective treatments for autoimmune diseases. Targets in oncology were also validated with blockbuster MAbs like Herceptin and Avastin proving highly efficacious.


The commercial success of these early biologics spurred massive  investments into discovery and development. Virtually all big pharmaceutical and biotech companies built extensive pipelines of novel antibody therapies through the 2000s and beyond. Today, dominate the biologics landscape with over 80 FDA-approved mAbs across various disease areas. Global MAb  revenue crossed $100 billion by 2020, with several blockbusters amassing over $10 billion in annual sales.

Latest Advancements In Antibody Technologies

Antibody discovery and engineering platforms continue advancing rapidly to meet new challenges. Researchers are improving phage and yeast display systems to isolate antibodies against membrane proteins and complex multi-subunit targets. New technologies like mRNA display enable discovery of antibodies without the use of phages or host cells.

Structure-guided antibody design is integrating crystal structures, epitope bins and modeling to drive affinity maturation. Platforms like AnimAb and Anthropic harness machine learning to computationally design tailored therapeutic antibodies de novo. Emerging modalities include bispecifics, antibody drug conjugates (ADCs), CAR-Ts and other multifunctional formats to enable novel mechanisms of action.

Deep learning and AI are augmenting traditional discovery workflows. IBM’s Watson for Drug Discovery uses natural language processing and machine learning to identify potential antibody targets and antigens. DeepMind’s AlphaFold has revolutionized structure prediction, aiding in engineering and optimization of antibody properties. Ongoing advancements aim to make discovery and optimization more predictable, scalable and cost-effective than traditional methods alone.

After initial breakthroughs in hybridoma technology, monoclonal antibodies emerged as viable therapeutics following key innovations in humanization, phage display and downstream development. Rigorous validation and blockbuster successes in disease areas established MAbs as the dominant biologics modality. Ongoing progress continues pushing boundaries through novel platforms, structural design and incorporation of artificial intelligence. With more antibodies targeted at complex diseases and new modalities, the future potential of this class of therapeutics remains immense. 

 Get more insights on this topic:  https://www.marketwebjournal.com/antibody-discovery-a-journey-towards-diagnosing-and-treating-diseases/

About Author:

Priya Pandey is a dynamic and passionate editor with over three years of expertise in content editing and proofreading. Holding a bachelor's degree in biotechnology, Priya has a knack for making the content engaging. Her diverse portfolio includes editing documents across different industries, including food and beverages, information and technology, healthcare, chemical and materials, etc. Priya's meticulous attention to detail and commitment to excellence make her an invaluable asset in the world of content creation and refinement. (LinkedIn - https://www.linkedin.com/in/priya-pandey-8417a8173/)

 

*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it

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