Antibody Industry Trends

The FDA has revoked Emergency Use Authorizations (EUAs) for multiple COVID-19 monoclonal antibody (mAb) therapies, including those developed by Eli Lilly, AstraZeneca, Vir Biotechnology, and Regeneron. This decision reflects the challenges of targeting rapidly mutating viruses like SARS-CoV-2, which

Antibody drugs offer targeted and effective therapies for inflammatory conditions such as chronic diseases like rheumatoid arthritis, Crohn's disease, and psoriasis. By specifically binding to pro-inflammatory cytokines or their receptors, monoclonal antibodies help modulate the immune response,

Biparatopic antibodies (bpAbs) are bispecific antibodies which bind distinct, non-overlapping epitopes on an antigen. This unique binding mode allows for superior affinity and specificity, promotes antagonism, locks target conformation, and results in higher-order target clustering. The antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking.

As antibody-drug development accelerates, ensuring consistent binding affinity, stability, and specificity is critical to improving treatment outcomes. Recent advances in antibody sequencing, artificial intelligence, and high-throughput screening have bolstered efforts to produce reliable antibody candidates more efficiently.

This month, two high-profile deals for bispecific antibodies targeting the PD-1/PD-L1 and VEGF pathways were announced. The DealsOn November 14, Merck announced its licensing agreement with LaNova Medicines for LM-299, an investigational PD-1/VEGF bispecific antibody. Merck is committing up to

Respiratory Syncytial Virus (RSV) is a leading cause of severe respiratory infections, particularly in infants, the elderly, and immunocompromised individuals. Despite its global prevalence, there has historically been a lack of effective therapeutic options for high-risk populations.

Discover the latest insights on antibody drug reliability, addressing aggregation, impurities, and stability challenges in biomedical research for improved outcomes.

This week, computationally designed antibody therapeutics have been high on the news, leveraging advanced deep learning and AI-driven methodologies to accelerate the discovery and optimization of antibodies.

This year’s Nobel prize in chemistry was awarded last month for using computationally designing novel proteins and predicting structures! The first half of the prize was awarded to John Jumper and Demis Hassabis, who developed AlphaFold, an artificial intelligence (AI) program that can predict a protein’s shape and function from its chemical sequence.

Anti-amyloid antibodies are designed to target and remove amyloid-beta plaques in the brain, a hallmark of Alzheimer’s disease. These plaques are toxic protein clusters believed to disrupt communication between neurons, leading to cell death and cognitive decline. By binding to amyloid-beta, these antibodies aim to neutralize or clear the plaques, potentially slowing disease progression.

Recently, we have seen exciting developments in the antibody-drug conjugate (ADC) space, from cloud computing to radioisotopes.

This past week, the Biomedical Advanced Research and Development Authority (BARDA) has provided funding for several antibody-related drug development programs. BARDA is a U.S. government agency within the Department of Health and Human Services (HHS) that funds and supports the development of medical countermeasures to protect against public health threats, such as pandemics, bioterrorism, and other emergencies.