PCOMPBIOL-D-26-00091

In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol

PLOS Computational Biology

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PLOS Computational Biology

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At this stage, the following Authors/Authors require contributions: Adrianne L Jenner, Robyn P Araujo, Christine E Engeland, Johannes P. W. Heidbuechel, Noa Levi, and Guy Ungerechts. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewer #1: The manuscript addresses the emerging role of bispecific T-cell engagers (BiTEs) and their integration within oncolytic viral platforms in clinical practice. The study explores a novel therapeutic strategy in which BiTEs are encoded within oncolytic measles virus vectors (MV-BiTE), enabling localized production of immune-activating agents directly within tumour sites. This approach aims to enhance T-cell-mediated cytotoxicity against cancer cells. Recognising the challenge of translating preclinical efficacy into heterogeneous human populations, the authors develop an in silico clinical trial based on a system of ordinary differential equations. By incorporating inter-patient variability derived from tumour volume data and baseline measurements from a Phase II clinical trial, the model provides predictive insights into treatment response. Notably, the study identifies effector T-cell killing rates and BiTE pharmacokinetics as primary drivers of heterogeneity and suggests that modified dosing regimens may improve outcomes in non-responding patients. Overall, the work highlights the potential of in silico trials to inform clinical translation and optimise therapeutic design.

The authors are to be commended for the quality of the work, which is already presented in a form very close to publication standard. The manuscript is clearly structured, with a logical organisation into well-defined sections that facilitate readability. The exposition is precise and accessible, and the supplementary material is व्यव thoughtfully organised, allowing the reader to efficiently locate methodological details and supporting information. Furthermore, the mathematical framework is, for the most part, well-suited to the biological problem under investigation. The study demonstrates a high level of biological relevance and, importantly, embodies what should be the role of applied mathematics in biomedical research, actively contributing to hypothesis generation and potentially informing clinical decision-making.

That said, I would like to raise several minor points that, if addressed, could further strengthen the manuscript:

- Limited characterisation of T-cell dynamics (activation, expansion, exhaustion)

The current model appears to simplify T-cell behaviour by focusing primarily on cytotoxic activity, without explicitly accounting for key immunological processes such as activation thresholds, clonal expansion, and functional exhaustion. These processes are known to play a critical role in shaping the efficacy and sustainability of anti-tumour immune responses, particularly in the context of chronic antigen exposure typical of cancer. The omission of such mechanisms may limit the biological interpretability and predictive reliability of the model. For instance, T-cell exhaustion could significantly alter long-term treatment outcomes, especially under repeated dosing schedules. Expanding the model to include additional compartments or state variables representing these processes, or, at minimum, discussing their potential impact and justification for their exclusion, would enhance the robustness of the biological conclusions.

- Use of MATLAB and implications for reproducibility

The implementation of the computational framework in MATLAB, while standard in many applied mathematics contexts, may limit accessibility and reproducibility, particularly for researchers without access to proprietary software. Given the increasing emphasis on open science, the use of open-source alternatives (such as Python-based ecosystems or Julia) could facilitate broader validation and reuse of the model. At the very least, the authors might consider providing detailed documentation or exploring options for sharing code in a more accessible format to improve reproducibility.

-Deterministic choice of initial conditions

The selection of initial conditions (e.g., line 258 and related sections) appears somewhat deterministic, which may not fully reflect the variability inherent in biological systems and patient populations. Since the study explicitly aims to capture inter-individual heterogeneity, a more thorough justification of these choices would be valuable. In particular, it would be useful to clarify whether sensitivity analyses were conducted with respect to initial conditions, and how robust the model predictions are to such variations. Incorporating stochastic elements or distributions for initial states could further align the modelling framework with the variability observed in clinical settings.

-Potential inconsistency in dosing interpretation

The manuscript states that “lower-dosage injections given more frequently were predicted to reduce the tumour size most significantly,” and that “more frequent injections were optimal.” However, this is followed by the claim that “having periods of time between injections is more likely to be successful than giving large bolus injections close together.” This latter statement appears, at least superficially, to introduce some ambiguity. If frequent administration is optimal, it would be helpful to clarify what is meant by “periods of time between injections,” and how this differs from the concept of frequent dosing. Is the key factor the avoidance of high-dose clustering, rather than frequency per se? A more precise formulation of this point would improve clarity and prevent potential misinterpretation.

In summary, this is a strong and well-executed study that makes a meaningful contribution to the field of mathematical oncology and immunotherapy modelling. Addressing the points above would further enhance its clarity, reproducibility, and biological depth.

Reviewer #2: Please see the attached document.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes:  Khaphetsi Joseph Mahasa

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Submitted filename: PCOMPBIOL-D-26-00091_reviewer_Mahasa.pdf

Dear Dr. Jenner,

We are pleased to inform you that your manuscript 'In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol' has been provisionally accepted for publication in PLOS Computational Biology.

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PLOS Computational Biology

Amber Smith

Section Editor

PLOS Computational Biology

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