Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions
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 qua...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2024.2341443 |
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| _version_ | 1832576522473439232 |
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| author | Michael Mullin James McClory Winston Haynes Justin Grace Nathan Robertson Gino van Heeke |
| author_facet | Michael Mullin James McClory Winston Haynes Justin Grace Nathan Robertson Gino van Heeke |
| author_sort | Michael Mullin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-01baed59c39a4b3285f77233b47c5e3e |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-01baed59c39a4b3285f77233b47c5e3e2025-01-31T04:19:38ZengTaylor & Francis GroupmAbs1942-08621942-08702024-12-0116110.1080/19420862.2024.2341443Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutionsMichael Mullin0James McClory1Winston Haynes2Justin Grace3Nathan Robertson4Gino van HeekeLabGenius, London, UKLabGenius, London, UKLabGenius, London, UKLabGenius, London, UKLabGenius, London, UKThe 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.https://www.tandfonline.com/doi/10.1080/19420862.2024.2341443Bayesian optimizationbispecificHCAbmachine learningmultispecificnanobody |
| spellingShingle | Michael Mullin James McClory Winston Haynes Justin Grace Nathan Robertson Gino van Heeke Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions mAbs Bayesian optimization bispecific HCAb machine learning multispecific nanobody |
| title | Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions |
| title_full | Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions |
| title_fullStr | Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions |
| title_full_unstemmed | Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions |
| title_short | Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions |
| title_sort | applications and challenges in designing vhh based bispecific antibodies leveraging machine learning solutions |
| topic | Bayesian optimization bispecific HCAb machine learning multispecific nanobody |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2024.2341443 |
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