Monoclonal antibodies (nanobody comparator) are full-size antibody therapeutics used as traditional immunotargeting agents, acting through highly specific antigen binding but constrained by bulkiness, high price, immune response risk, and difficulty reaching tumors. They remain a backbone of treatment in relapsed refractory multiple myeloma, where antibody-based therapies are central to clinical management. Their large size can also cause low tumor permeability and uneven biodistribution, which has motivated interest in smaller alternatives such as nanobodies. Recent work has also incorporated mAbs into a multi-modal half-life prediction framework in mice, using AlphaFold-derived structural features, protein language model embeddings, and XGBoost models to prioritize candidates. Overall, the literature highlights both their established therapeutic importance and the ongoing effort to improve pharmacokinetics and tumor delivery.
Relapsed-refractory multiple myeloma
- Antibody-based therapeutics were described as one of the backbones of treatment for relapsed refractory multiple myeloma in a clinical review of RRMM therapies. (PMID:41842719)
- The review emphasized the central role of mAbs in current RRMM treatment strategies, underscoring their established therapeutic utility. (PMID:41842719)
Delivery, pharmacokinetics, and tumor penetration
- Traditional immunotargeting agents were noted to face limitations including bulkiness, price, immune response, and poor tumor penetration. (PMID:41930712)
- Full-size antibodies were described as successful but limited by large size, low tumor permeability, and uneven biodistribution. (PMID:41936638)
- The comparison with nanobody-based approaches highlights why smaller formats are being explored as alternatives to full-size mAbs. (PMID:41936638; PMID:41930712)
Half-life prediction and computational modeling
- mAbs were included as an antibody class in a half-life prediction framework built from experimental data in C57BL/6 mice. (PMID:41996252)
- The study used AlphaFold-based structural properties and protein language model embeddings as feature representations for antibody modeling. (PMID:41996252)
- XGBoost models were trained to predict mAb half-lives, supporting computational prioritization of candidates with favorable pharmacokinetics. (PMID:41996252)