A single donor is sufficient to produce a highly functional in vitro antibody library

Abstract

Antibody complementarity determining region diversity has been considered to be the most important metric for the production of a functional antibody library. Generally, the greater the antibody library diversity, the greater the probability of selecting a diverse array of high affinity leads. According to this paradigm, the primary means of elevating library diversity has been by increasing the number of donors. In the present study we explored the possibility of creating an in vitro antibody library from a single healthy individual, showing that the number of lymphocytes, rather than the number of donors, is the key criterion in the production of a diverse and functional antibody library. We describe the construction of a high-quality phage display library comprising 5 × 109 human antibodies by applying an efficient B cell extraction protocol from a single donor and a targeted V-gene amplification strategy favoring specific antibody families for their improved developability profiles. Each step of the library generation process was followed and validated by next generation sequencing to monitor the library quality and diversity. The functionality of the library was tested using several therapeutically relevant targets for which a vast number of different antibodies with desired biophysical properties were obtained.

Drug-like antibodies with high affinity, diversity and developability directly from next-generation antibody libraries

“…Our previous experience using the same methods with other libraries shows that a diversity >10 8 unique HCDR3s is anticipated when measured by NovaSeq. 40 Analysis of the HCDR3 sequence length revealed a normal distribution with a mode at 14 amino acids, consistent with the human repertoire ( Figure 6(c )).…”

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

“… 5 Libraries can aim to combat this by selecting for genes with known favorable characteristics using native heavy and light chains for improved specificity. 44–46 Limitations to natural-repertoire approaches also include the inherently biased nature, meaning diverse antibodies may be missed owing to sequence space restrictions. Nevertheless, available sequence space might not be as constrained as previously expected, as multiple clinical-stage therapeutics have high sequence-identity matches in naturally sourced antibody repertoires.…”

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

“…In summary, emerging high-throughput experimental assays that are capable of generating large (developability-adjusted) antibody-antigen binding data in the order of 10 4 –10 5 have begun to unlock the potential of ML for the prediction of antibody-antigen binding. 3 , 176 , 183 , 184 However, for the prediction or generation of paratope-epitope pairs on the sequence level without any structure-aided encoding, much larger data at a higher resolution may still be necessary, as previously suggested by us. 54 …”

“… 320 One experimental solution that may contribute to improving the overall percentage of candidates with a fitting developability profile prior to computation and optimization is the development of developability-optimized screening libraries. 183 An intriguing computational solution to optimizing the number of mAb candidates with respect to multiple design parameters is the combination of ML models trained on data from different experimental campaigns. 324 …”