Review examines engineered antibodies for brain-targeted rabies immunotherapy, indicating potential advancements in treatment after symptom onset.
Rabies remains one of the clearest therapeutic paradoxes in infectious diseases: it is largely preventable before neuroinvasion, yet once clinical symptoms appear, mortality approaches 100%. This sharp transition reflects more than delayed diagnosis alone. Wild-type rabies virus reaches and spreads within the central nervous system under conditions of relative immune silence, while the blood–brain barrier (BBB) severely restricts the entry of circulating immune effectors, including virus-neutralizing antibodies. As a result, conventional immunotherapy, although highly effective in post-exposure prophylaxis, performs poorly after symptom onset because it no longer reaches the relevant compartment. This review examines whether engineered antibodies can overcome that limitation and provide a realistic path toward brain-targeted rabies immunotherapy. We first outline why symptomatic rabies remains refractory to standard immune intervention, emphasizing the combined roles of viral immune evasion and BBB-mediated anatomical exclusion. We then review recent proof-of-concept studies showing that antibody-based rescue after central nervous system invasion is biologically plausible, particularly when antibodies are delivered directly into the central nervous system (CNS), retain Fc-dependent immune activity, or are modified to improve BBB penetration. Building on these findings, we discuss key design principles for next-generation therapeutics, including epitope breadth, resistance to viral escape, Fc tuning, and delivery modules based on peptide shuttles or receptor-mediated transcytosis platforms such as TfR1- and CD98hc-targeted systems. Finally, we highlight the major translational barriers that still separate experimental rescue from clinical therapy, including the narrow therapeutic window, model limitations, safety concerns, ethical issues, implementation constraints, and the need for standardized endpoints.
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Sun et al. (2026) studied this question.
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