Novel Antibodies

Dual-Targeting Bispecific Antibodies(BsAbs), Nanobodies and Antibody drug conjugates in Modern Therapeutics

Role of Novel Antibodies in Therapeutics

Novel antibodies, such as bispecific antibodies, nanobodies, and antibody-drug conjugates (ADCs), are characterized by multispecific targeting, increased potency and stability, and non-natural effector functions, contributing to broader therapeutic benefits. Nanobodies serve as precise targeted vehicles, ADCs act as potent therapeutic agents, and bispecific antibodies stand out as versatile therapeutic tools. Explore the next- generation of antibody therapeutics for advanced medical applications.

While novel antibodies hold immense potential for personalized medicine and targeted therapies, there are still several challenges associated with their development and use:


  • Limited Effector Functions
  • Targeting Intracellular Antigens
  • Engineering Multivalency

Antibody Drug Conjugates:

  • Drug-Linker Stability and Controlled Release
  • Payload Selection and Optimization
  • Tissue Penetration

Bispecific Antibodies:

  • Optimizing target engagement
  • Immunogenicity and Immune Response
  • Engineering and Stability

Diverse Antibody based therapeutics reshaping the drug landscape

Representing innovative class of drugs including Bispecific monoclonal antibodies, Antibody drug conjugates and Nanobodies

Bispecific Monoclonal Antibodies

Bispecific monoclonal antibodies can simultaneously bind to two different types of antigens or two different epitopes on the same antigen. Various types of bispecific antibodies include Bispecific T-cell Engagers, Tandem diabodies, Dual-affinity re-targeting proteins, Bispecific killer engagers (BiKEs), etc.

Example of FDA approved drugs:
Blincyto (blinatumomab) is approved for the treatment of acute lymphocytic leukemia.

Antibody Drug Conjugates

ADCs are Targeted Trojan horses of the immune system. It is a tripartite entity made up of mAB, a cytotoxic drug and a linker connecting the antibody and drug. Leveraging the mAB’s exquisite specificity, ADCs bind to the target and release the drug, leading to targeted cellular destruction.

Example of FDA approved drugs: Adcetris (brentuximab vedotin) is used for relapsed or refractory Hodgkin lymphoma and systemic anaplastic large cell lymphoma.


Nanobodies are single-domain heavy chain-only antibodies. Their compact size provides access to tight spaces, enhancing tissue penetration, stability, and solubility. Different types of nanobodies include single-domain VHHS, bivalent, and multivalent nanobodies.

Example of FDA approved drugs:
Caplacizumab (Cablivi) is a nanobody, approved for treating acquired thrombotic thrombocytopenic purpura (TTP), a rare and life-threatening blood disorder.

Aganitha’s Point of View

Next-Generation Antibodies: Tailoring Novel Antibodies for Precise and Potent Therapies using AI, ML and in silico tools

Camelid-specific Sequence Analysis

Utilizing camelid-specific antibody sequence databases specially VHH to provide valuable insights into the sequence diversity and common motifs in natural camelid antibodies, guiding the design/virtual screening of synthetic nanobodies.


VHH Framework Optimization

Utilizing ML models to optimize the framework regions(VHH domains) for enhanced stability and solubility, which are critical due to the absence of the light chain in nanobodies.

CDR Loop Refinement

Utilizing specialized computational approaches such as diffusion based models to specifically model and refine the CDR loops of nanobodies, which are crucial for antigen binding.

Aggregation Control

Streamlining the identification and modification of aggregation-prone regions to ensure stability, solubility, and efficacy of antibodies. Thus, facilitating early screening and targeted mutagenesis for enhancing the expression and solubility of antibodies.

Implementing In Silico Affinity Maturation

Employing in silico affinity maturation, and utilizing Generative AI , ML and computational methods to enhance the binding affinity of antibody candidates to their targets, a critical step in effective therapeutic development.

Ensemble Approach for Binding Affinity Prediction

Employing an in silico ensemble approach, utilizing a variety of computational techniques to predict the efficacy of molecule-target interactions, essential for successful therapeutic development.

Advancing Molecular Modeling Techniques

Utilizing physics-based and diffusion algorithms for advanced molecular modeling, the platform efficiently deciphers complex interactions of bispecific antibodies, nanobodies for optimizing their therapeutic efficacy and ensures targeted precision of ADCs.

Conducting Ongoing Developability Assessments

Using Generative AI with Developability constraints for generating candidates. Continually conducting comprehensive developability assessments to ensure antibody candidates exhibit effective binding capabilities and are suitable for safe and efficient drug development.

Enhancing Antibody Design with AI

Integrating Generative AI and machine learning to enhance and streamline the antibody design process, surpassing traditional methodologies for enabling predictive and precise antibody engineering.

Ensemble Approach for Binding Affinity Prediction

Incorporating computational virtual screening, a rapid technique for evaluating a library of antibody sequences and structures to  identify potential candidates for further development.

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