Bispecific Antibody Use in Patients With Lymphoma and Multiple Myeloma.
Adam Phillip Gordon BraunSushanth GouniAstrid E PullesPaolo StratiMonique C MinnemaLihua E BuddePublished in: American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting (2024)
This article endeavors to navigate the clinical journey of bispecific antibodies (BsAbs), from elucidating common toxicities and management strategies to examining novel agents and broadening access in community health care. These drugs, commonly through T-cell activation, result in shared adverse events such as cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome. Variations in target antigens and designs, however, might introduce unique toxicities for different BsAbs, warranting specific management approaches. Recent US Food and Drug Administration approvals of BsAbs targeting CD3 + T cells linked to CD20 for non-Hodgkin lymphoma and to B-cell maturation antigen or GPRC5D for multiple myeloma have transformed the treatment landscape for hematologic malignancies. Emerging new agents promise further enhancement and safety, exploring novel antigen targets, innovative structures such as trispecific antibodies, and the engagement of diverse immune cells. Simultaneously, the expansion of BsAbs into community practices is underway, demanding a multifaceted strategy that encompasses educational initiatives, operational adaptations, and collaborative frameworks. This ensures comprehensive treatment access, allowing every patient, irrespective of geographical or socioeconomic status, to benefit from these advancements in cancer therapy.
Keyphrases
- healthcare
- multiple myeloma
- cancer therapy
- case report
- mental health
- single cell
- drug administration
- primary care
- drug delivery
- quality improvement
- dendritic cells
- machine learning
- immune response
- high resolution
- diffuse large b cell lymphoma
- social media
- stem cells
- regulatory t cells
- risk assessment
- cell therapy
- deep learning
- human health