# Computational Drug Repositioning for Antibody Mediated Renal Allograft Rejection

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $800,454

## Abstract

ABSTRACT
Despite advances in kidney transplantation (KT) with improved 1-year graft survival to >90%, KT attrition is
unchanged due to persistent antibody mediated rejection (ABMR). Given limited ABMR treatment options, the
outcome is persistent KT inflammation and accelerated KT loss. As subtypes of rejection are diverse in their
pathogenesis and treatment response, the need for precise treatments based on molecular basis of rejection is
well-founded. We propose to develop and apply a drug repurposing pipeline to ABMR expression profiles to
query publicly available drug-gene expression databases, to identify single and combination drug repurposing
candidates for ABMR (Aim 1). Our preliminary, published studies support the successful analyses for ABMR
with identification of publically available histological and molecular stable (control) and ABMR renal allograft
datasets, to be integrated with single-cell RNASeq analysis experiments that will be conducted on curated ABMR
renal allograft biopsies. We will use transcriptomic-based computational drug-repurposing to identify potential
new single agent and combination therapeutics for the treatment of ABMR, based on expression reversal,
leveraging public and newly generated single cell data. We will further determine the kinetics and the mechanism
of action of promising drugs or drug combinations in human cells and ABMR tissue, ex vivo (Aim 2). Promising
single agent and combination agents will be further validated for efficacy for treatment or prevention of ABMR in
vivo, in a pre-clinical, established, rodent renal allograft model of ABMR (Aim 3).
Our approach is highly significant because we will investigate unique and novel cell-specific transcriptomic
profiles, underlying biological processes and signaling pathways in ABMR and these data will be analyzed with
a drug repurposing pipeline to discover novel single agents and combination therapies that can be used as a
personalized approach to management of ABMR. The in vivo experiments will take these studies to validation of
compound or combination of compound efficacy for reversing or preventing ABMR. The successful completion
of these studies can propose new, FDA approved drugs, that could be repurposed for improving long term
outcomes in kidney transplant recipients, by reducing ABMR injury, extend graft and patient survival.

## Key facts

- **NIH application ID:** 10982244
- **Project number:** 1R01AI180118-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** MARINA SIROTA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $800,454
- **Award type:** 1
- **Project period:** 2024-06-28 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982244

## Citation

> US National Institutes of Health, RePORTER application 10982244, Computational Drug Repositioning for Antibody Mediated Renal Allograft Rejection (1R01AI180118-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10982244. Licensed CC0.

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