Back

CD28-Selective Inhibition Prolongs Non-Human Primate Kidney Transplant Survival

Lovasik, B. P.; Kim, S. C.; Higginbotham, L.; Wakwe, W.; Mathews, D. V.; Breeden, C. P.; Farris, A. B.; Larsen, C. P.; Ford, M. L.; Nadler, S.; Adams, A. B.

2023-05-05 immunology
10.1101/2023.05.03.539333 bioRxiv
Show abstract

Costimulation blockade using belatacept results in improved renal function after kidney transplant as well as decreased likelihood of death/graft loss and reduced cardiovascular risk; however, higher rates and grades of acute rejection have prevented its widespread clinical adoption. Treatment with belatacept blocks both positive (CD28) and negative (CTLA-4) T cell signaling. CD28-selective therapies may offer improved potency by blocking CD28-mediated costimulation while leaving CTLA-4 mediated coinhibitory signals intact. Here we test a novel domain antibody directed at CD28 (anti-CD28 dAb (BMS-931699)) in a non-human primate kidney transplant model. Sixteen macaques underwent native nephrectomy and received life-sustaining renal allotransplantation from an MHC-mismatched donor. Animals were treated with belatacept alone, anti-CD28 dAb alone, or anti-CD28 dAb plus clinically relevant maintenance (MMF, Steroids) and induction therapy with either anti-IL-2R or T cell depletion. Treatment with anti-CD28 dAb extended survival compared to belatacept monotherapy (MST 187 vs. 29 days, p=0.07). The combination of anti-CD28 dAb and conventional immunosuppression further prolonged survival to MST [~]270 days. Animals maintained protective immunity with no significant infectious issues. These data demonstrate CD28-directed therapy is a safe and effective next-generation costimulatory blockade strategy with a demonstrated survival benefit and presumed advantage over belatacept by maintaining intact CTLA-4 coinhibitory signaling.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.