A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers
The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | mAbs |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2022.2138092 |
| _version_ | 1798016134518145024 |
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| author | James T. Heads Sebastian Kelm Kerry Tyson Alastair D. G. Lawson |
| author_facet | James T. Heads Sebastian Kelm Kerry Tyson Alastair D. G. Lawson |
| author_sort | James T. Heads |
| collection | DOAJ |
| description | The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method. |
| first_indexed | 2024-04-11T15:45:57Z |
| format | Article |
| id | doaj.art-ae4bb49c558444769fdeb77ccc96bfe5 |
| institution | Directory Open Access Journal |
| issn | 1942-0862 1942-0870 |
| language | English |
| last_indexed | 2024-04-11T15:45:57Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj.art-ae4bb49c558444769fdeb77ccc96bfe52022-12-22T04:15:31ZengTaylor & Francis GroupmAbs1942-08621942-08702022-12-0114110.1080/19420862.2022.2138092A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffersJames T. Heads0Sebastian Kelm1Kerry Tyson2Alastair D. G. Lawson3UCB Pharma, 208 Bath Road, Slough SL1 3WE, UKUCB Pharma, 208 Bath Road, Slough SL1 3WE, UKUCB Pharma, 208 Bath Road, Slough SL1 3WE, UKUCB Pharma, 208 Bath Road, Slough SL1 3WE, UKThe propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.https://www.tandfonline.com/doi/10.1080/19420862.2022.2138092Developabilityaggregationpredictionhydrophobicitychargeantibody |
| spellingShingle | James T. Heads Sebastian Kelm Kerry Tyson Alastair D. G. Lawson A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers mAbs Developability aggregation prediction hydrophobicity charge antibody |
| title | A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers |
| title_full | A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers |
| title_fullStr | A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers |
| title_full_unstemmed | A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers |
| title_short | A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers |
| title_sort | computational method for predicting the aggregation propensity of igg1 and igg4 p mabs in common storage buffers |
| topic | Developability aggregation prediction hydrophobicity charge antibody |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2022.2138092 |
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