Reparameterized multiobjective control of BCG immunotherapy

Sep 24, 2026

Reparameterized multiobjective control (RMC) is proposed to determine an optimal Bacillus Calmette–Guérin (BCG) drug dosing regimen for bladder cancer. The method performs constrained optimization using a nonlinear bladder cancer model with impulsive drug instillation, aiming to balance therapeutic efficacy against constraints. Performance is compared against Koopman model predictive control (MPC) and is assessed through numerical simulations. Results demonstrate efficient elimination of cancerous cells and suggest the control framework can generalize to other model-based treatment designs.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Background and clinical motivation

Burden of bladder cancer and need for effective regimens

Role and mechanism of BCG immunotherapy

Challenges in dosing design

Control-theoretic framework

Feedback control and dynamic systems

Nonlinear dynamics in systems pharmacology

Need for optimal dosing under constraints

Proposed method and evaluation

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Reparameterized multiobjective control of BCG immunotherapy Rongting Yue1,2* & Abhishek Dutta1,2 Bladder cancer is a cancerous disease that mainly affects elder men and women. The immunotherapy that uses Bacillus of Calmette and Guerin (BCG) effectively treats bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG relies on drug dosing, and the design of an optimal BCG regimen is an open question. In this study, we propose the reparameterized multiobjective control (RMC) approach for seeking an optimal drug dosing regimen and apply it to the design of BCG treatment. This approach utilizes constrained optimization based on a nonlinear bladder cancer model with impulsive drug instillation. We compare the performance of RMC with Koopman model predictive control (MPC) and validate the efficacy of optimal BCG dosing regimens through numerical simulations, demonstrating the efficient elimination of cancerous cells. The proposed control framework holds the potential for generalization to other model-based treatment designs. Cancer is a significant global public health concern and a leading cause of mortality. Bladder cancer, a subtype of cancer, accounts for approximately 7.6% of cancer-related deaths in men and 2.2% in women, based on the 5-year average annual percent change (2015)1. The high mortality rate associated with bladder cancer necessitates the development of highly effective treatment regimens. Chemotherapy and immunotherapy are two commonly recommended approaches for different stages of bladder cancer due to their efficacy2,3. While chemotherapy directly targets and kills cancer cells, immunotherapy enhances the body’s immune system to combat neoplastic diseases. One widely used immunotherapy for early-stage bladder cancer is Bacillus Calmette–Guérin (BCG) . BCG is highly effective, with a success rate of approximately 90% . It involves the instillation of live BCG into the affected area to activate lymphocytes and stimulate an antitumor response from the immune system4. The regulation ofT lymphocytes, such as CD4+ and CD8+, plays a crucial role in cancer immunotherapy by directing antigen-specific cytotoxicity5. Following instillation, BCG attaches to urothelial cells and is internalized by bladder cancer cells. The cancer cells release cytokines and regulate molecules such as MHC II and ICAM-1 to present processed antigens, thereby activating cytotoxic T-cells. This immune response mediates the cytotoxicity of immune cells, leading to the elimination of cancer cells4. BCG treatment regimens have been studied for decades. Though BCG is therapeutically effective for bladder cancer, the dosing regimen of BCG is dedicated and needs to be designed carefully to ensure its efficacy. On the one hand, high doses of drugs result in the fast elimination of cancerous cells, while high drug toxicity and severe side effects may cause undesired harmful effects to patients. On the other hand, low drug doses will result in a great loss in therapeutic efficacy. In bladder cancer treatment, a high dose of BCG could lower the efficacy of cytokines induction and lead to the termination of treatment due to severe side effects, including BCG sepsis6. The clinical characteristics of different BCG can be found in a literature search7. Thus, there are constraints on drug doses due to patients’ health conditions, and how to design the optimal doses and duration for patients indifferent cancerous stages is an open question. Designing optimal dosing regimens for BCG treatment is challenging due to constraints and nonlinearity in the treatment model. Control theory is essential for effective disease management strategies, ensuring precise control of treatment interventions. Control theory focuses on dynamic systems and aims to regulate their behavior to align the system’s state with a desired reference8. A control sys

FAQs

What problem does the study address in BCG immunotherapy?

It targets the open question of designing an optimal BCG dosing regimen, since drug performance depends on dosing while toxicity and side effects impose constraints.

How does the proposed reparameterized multiobjective control (RMC) approach work?

RMC uses constrained optimization based on a nonlinear bladder cancer model with impulsive drug instillation to search for an optimal dosing regimen.

What method is used for comparison and how is efficacy validated?

RMC is compared with Koopman model predictive control (MPC), and efficacy of optimal BCG dosing regimens is validated via numerical simulations showing efficient elimination of cancerous cells.

Sep 24, 2026

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