Abstract:Affinity maturation is often a necessary step for the development of potent therapeutic molecules. Many different diversification strategies have been used for antibody affinity maturation, including error-prone PCR, chain shuffling, and targeted complementary-determining region (CDR) mutation. Although effective, they can negatively impact antibody stability or alter epitope recognition. Moreover, they do not address the presence of sequence liabilities, such as glycosylation, asparagine deamidation, aspartat… Show more
“…Figure 13). Liability motifs are short amino acid sequences, which may negatively impact various aspects of antibody developability when present in their CDRs ( 79 ). Developable germlines represent a group of human immunoglobulin genes (V H and V L ), which have been suggested to harbor favorable biophysical properties ( 80 , 81 ).…”
Section: Resultsmentioning
confidence: 99%
“…Figure 1A), and the human-engineered datasets (humanized mouse antibodies: Kymouse, patented antibody dataset: PAD, and therapeutic monoclonal antibodies: mAbs). In (A), the x-axis refers to amino acid sequence liability motifs as identified by Teixeira and colleagues ( 79 ). The figure is segmented into columns (sequence liability type) and rows (antibody species and chain type).…”
Designing effective monoclonal antibody (mAb) therapeutics face a significant challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibody repertoires may provide valuable guidance for antibody selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These knowledge gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DPs, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine learning (ML) tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work charts a roadmap to evaluate the plasticity of antibody developability, providing a more rational perspective for therapeutic mAb design.
“…Figure 13). Liability motifs are short amino acid sequences, which may negatively impact various aspects of antibody developability when present in their CDRs ( 79 ). Developable germlines represent a group of human immunoglobulin genes (V H and V L ), which have been suggested to harbor favorable biophysical properties ( 80 , 81 ).…”
Section: Resultsmentioning
confidence: 99%
“…Figure 1A), and the human-engineered datasets (humanized mouse antibodies: Kymouse, patented antibody dataset: PAD, and therapeutic monoclonal antibodies: mAbs). In (A), the x-axis refers to amino acid sequence liability motifs as identified by Teixeira and colleagues ( 79 ). The figure is segmented into columns (sequence liability type) and rows (antibody species and chain type).…”
Designing effective monoclonal antibody (mAb) therapeutics face a significant challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibody repertoires may provide valuable guidance for antibody selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These knowledge gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DPs, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine learning (ML) tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work charts a roadmap to evaluate the plasticity of antibody developability, providing a more rational perspective for therapeutic mAb design.
“…Annotation of sequence liabilities is a widespread practice across early-stage antibody discovery and engineering. Entire phage libraries can be designed as liability-free [ 36 ] and much effort is exerted to engineer these out of individual sequences during the lead optimization stages [ 11 , 37 , 38 ].…”
Antibody-based therapeutics must not undergo chemical modifications that would impair their efficacy or hinder their developability. A commonly used technique to de-risk lead biotherapeutic candidates annotates chemical liability motifs on their sequence. By analyzing sequences from all major sources of data (therapeutics, patents, GenBank, literature, and next-generation sequencing outputs), we find that almost all antibodies contain an average of 3–4 such liability motifs in their paratopes, irrespective of the source dataset. This is in line with the common wisdom that liability motif annotation is over-predictive. Therefore, we have compiled three computational flags to prioritize liability motifs for removal from lead drug candidates: 1. germline, to reflect naturally occurring motifs, 2. therapeutic, reflecting chemical liability motifs found in therapeutic antibodies, and 3. surface, indicative of structural accessibility for chemical modification. We show that these flags annotate approximately 60% of liability motifs as benign, that is, the flagged liabilities have a smaller probability of undergoing degradation as benchmarked on two experimental datasets covering deamidation, isomerization, and oxidation. We combined the liability detection and flags into a tool called Liability Antibody Profiler (LAP), publicly available at lap.naturalantibody.com. We anticipate that LAP will save time and effort in de-risking therapeutic molecules.
“…48 Interestingly, similar designed library approaches can also be used for simultaneous affinity maturation and removal of sequence liabilities from lead antibody candidates. 50 Figure 1. Approaches to discover antibody candidates with fewer physicochemical liabilities.…”
“…Even experimentally, methods of affinity maturation are routinely used to optimize binding affinity, but this often comes at the expenses of other properties that underpin developability potential, including stability, solubility and polyreactivity. 50 , 73 , 77 , 78 Multi-trait co-optimization has been compared to solving a Rubik’s cube, where each face represents one biophysical property. Changing one face will affect other faces, often detrimentally, and solving a single face is much simpler than completing the puzzle.…”
Section: Emerging In Silico Tools To Support Discovery and Selectionmentioning
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
