Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions

ABSTRACT

The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

KEYWORDS:

• Bayesian optimization
• bispecific
• HCAb
• machine learning
• multispecific
• nanobody
• VHH

Acknowledgments

We would like to thank members of LabGenius for the scientific discussion of the review. In particular, we would like to thank Lucy Shaw, Tonya Frolov and Leonard Wossnig for detailed comments and careful review of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.