# Mathematical modeling for optimal control of BK virus infection in kidney transplant recipients

> **NIH NIH R21** · DUKE UNIVERSITY · 2024 · $145,910

## Abstract

BK virus (BKV) infection and nephropathy is a major cause of organ loss following kidney transplantation. There
are no effective antivirals for BKV, and standard clinical practice is to reduce immunosuppression, which raises
the risk of allograft rejection. There is no consensus on how to safely reduce immunosuppression, and treatment
of BKV viremia varies by the attending physician and transplant center. To address this gap in our knowledge
for how to optimally manage kidney transplant recipients with BKV viremia, we propose to use mathematical
modeling and optimal control theory to develop software-guided management of immunosuppression
personalize for individual patients. To translate these models and algorithms to clinical reactive, we need to
calibrate and validate them on longitudinal data of kidney transplant recipients with BKV viremia. We propose to
utilize NIAID-funded Clinical Trials in Organ Transplantation (CTOT) data sets available in ImmPort, together
with pediatric and adult CTOT kidney transplant data not currently in ImmPort, to build the largest longitudinal
BKV monitoring data set and use it to calibrate our models. We build this proposed work on our published
mathematical model of BKV viremia, which accurately models BKV proliferation and infection of kidney cells, the
elicitation of anti-viral and allo-specific cytotoxic T cells that damage kidney cells and reduce graft function, and
the non-replenishment of damaged kidney cells resulting in rising creatinine levels. We propose to extend and
refine the model to fit the longitudinal clinical data from CTOT kidney transplant recipients with BKV viremia more
accurately. To better utilize the extensive patient data from the CTOT studies, we also propose an innovative
approach to build a more accurate BKV model by learning the equations describing immune regulation directly
from data. Learning will be performed using a neural network emulator, which can approximate arbitrarily
complex mathematical functions. This machine learning approach has the advantage of being more flexible and
using ancillary data sources that allow a greater degree of personalized model characterization. The CTOT data
will be used to inform immune response modeling and to validate the initial model refinements as well as assist
us in further model development. We then layer receding horizon control (RHC) algorithms, also known as model
predictive control, on top of the mathematical models, to provide adaptive guidance on optimal
immunosuppression doses customized to individual patients. The calibration of these models is critical, and a
critical component of our proposal is the use advanced statistical methods to estimate model parameters from
sparse longitudinal data and to perform sensitivity analysis. Finally, the proposal benefits immensely from the
collaboration of three experienced clinicians who manage kidney transplant recipients, who will provide domain
expertise, help with the interpretation of d...

## Key facts

- **NIH application ID:** 10861036
- **Project number:** 5R21AI169170-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** JANICE MARIE MCCARTHY
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $145,910
- **Award type:** 5
- **Project period:** 2023-06-06 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861036

## Citation

> US National Institutes of Health, RePORTER application 10861036, Mathematical modeling for optimal control of BK virus infection in kidney transplant recipients (5R21AI169170-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10861036. Licensed CC0.

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