PCOMPBIOL-D-25-02336

Modeling phage therapy

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1: The authors build upon an existing mouse model of phage therapy that incorporates immune system synergy, extending it to a human context. The new model includes the use of phage cocktails and accounts for the potential of bacteria to independently evolve resistance to all cocktail components. The modeling approach and parameterization are thoughtfully designed and presented with meticulous clarity, making the work easy to follow and reproduce. Well-parameterized human models are critical for advancing phage therapy development, and this manuscript represents a valuable and timely contribution. I have mostly minor comments, listed below.

Model:

Would cocktails still always be advantageous if phages might interfere with each other during infection?

L146: the latent period varies widely between phages and environments, how important is this assumption for the model results?

L 330: Wouldn’t the argument here be similar to the one later in the manuscript where more infectious phages lead to easier resistance evolution? A higher number of phages from the beginning would lead to a similar effect, meaning that the phage number does matter (arguing for a lower initial phage dose).

L 345: While probably outside of this manuscript, it would be nice to also consider would partial cross-resistance in a future version of the model.

Antibiotics were included as a potential death rate for bacteria but they could also have an influence on phage numbers and infectivity through bacterial physiology and killing of phage-infected hosts.

L421: I am confused as to how the adsorption rate a is related to the infectivity vectors beta_j

Figures:

Fig.1 It might be a bit confusing t only see the sensitive bacteria appear in panel C. Maybe a red S could be written next to the total bacterial line as well to make clear that they are just not visible. And this could be mentioned in the legend. d) The difference between the three lines is hard to see, especially for the neutrophils, maybe changing the line types could help.

Fig.2 it feels like switching a and b would allow a more logical ordering

Fig.4 B11 seems to have the same colour as N?

Fig.5 It would be helpful to show the administration of cocktails 1 and 2 more clearly in the figure.

Discussion: The results could be linked back more to previous findings on phage cocktail efficacy, e.g. https://doi.org/10.1099/mic.0.001110, https://doi.org/10.1128/AEM.00757-12 and phage dosing, e.g. https://doi.org/10.1093/cid/ciad516

Writing:

While it is very helpful to have a very detailed description of each model component and parametrization step, I am wondering if it would be helpful to separate them out a bit more into Methods and Results parts to improve the flow. E.g. 2.1 seems less of a result than a detailed methods section and could probably be given as a much shorter version in the Results section and an extended one in the Methods.

L 228: dB/dT = 0?

L482: something seems weird about this sentence

Reviewer #2: This is a very clear contribution that makes a compelling case for several key take-home messages. The authors have carefully justified their modelling assumptions throughout, and gone to great lengths to find and explore realistic parameter values. I particularly enjoyed the straight-forward "what have we learned" summaries on the way through the paper. The contribution is a model for writing up results with a clear eye to what we can (and cannot) reliably infer from mathematical models.

Minor points:

1. It would be helpful to the reader to mention that the dN/dt equation will be coming later, when the S-R-P system is introduced (perhaps I just missed this). So around eqns 1-3 since (1) depends on N, or around eqns 10-13.

2. I recognize that the SI is already substantial. Nonetheless I found the paper somewhat overly detailed in the parameter estimate section. Table 1 and the associated text, for example, are of interest to readers who are *very* close to this work, whereas the figures for example will be of quite broad interest. The Discussion and Introduction are nicely aimed for broad appeal. Perhaps a little more of the parameter estimation could be directed to the SI, with a broader readership in mind for the main paper?

3. Clarify that the A_j are constants?

4. Figure 3 should be re-arranged such that panels (a) and (b) are in a column, followed by (c & d), then (e & f). Or re-arrange as (3x2) rather than (2x3).

Typos, etc:

1. line 219 "These data"

2. atto-fox: I'm very familiar with this phenomenon, but is it sufficiently well-known for your intended readership? Maybe there's an atto-fox reference you could give for the new reader (eg. grad student who has no idea what this might mean)?

3. line 682, double negative "Not all ... need not"

Reviewer #3: Review of Boer et al. “Modeling phage therapy”

The manuscript by Boer and colleagues is a highly thorough consideration of the bridge between theoretical population dynamic simulations and real-world mouse and patient data. First, the authors develop an ODE model, based on a previously published model by Roach and colleagues, which accounts for the role of the host response (neutrophils) in the dynamics of a single phage treatment of one bacterial population which can evolve resistance. This adapted model is parametrized and ground-truthed against the same mouse data as the Roach model. Next, the authors attempt to retrospectively explain patient outcomes during phage therapy by further updating their model to account for multiple phages and numerous bacterial states. Finally, the authors interpret their simulations to make suggestions about how to successfully employ phage for therapy. Overall, this manuscript represents a strong and important contribution to the field of phage therapy and more generally to the quantitative modeling of antimicrobial treatment and is a good fit for publication in PLOS Computation Biology. That being said, I do have suggestions that the authors should consider in improving their manuscript prior to acceptance.

Major Comments:

1. While the authors use the common variables in the field of phage modeling, they are not defined until Table S2. In contrast the states are defined in the text in the first paragraph of the results. This manuscript has the potential to be of interest to mathematical biologists that do not work with phage; thus, the authors should define the variables prior to using them, most likely with a table similar to S2.

2. The authors intend to employ this model generally to phage therapy, thus they should perform a sensitivity analysis for at least some critical parameters which are likely to vary such as b, µ (outside of the context of the multiphage model), k, and the CBC threshold. This analysis also opens up further interpretation by the authors as to what makes a good phage (e.g., if we see the model is very sensitive to a phage specific parameter, then we should consider selecting phage to optimize for that parameter).

3. A more direct and thorough consideration of the limitations of the application of their model (critically, the things their model does not consider) should be brought up in the discussion. This section should be suited for those in the field who are not as familiar with mathematical and computer-simulation modelling.

4. The authors should also expand the second to last paragraph of the discussion to more thoroughly consider what sort of empirical data would further the field based on the predictions of their modelling. The authors should consider both experimental in vitro and clinical data that would be useful. It would not be inappropriate to suggest specific experiments to either further support or reject the predictions of their model.

5. While the authors plausibly explain the biology (phage defense systems) of why the pretreatment resistance frequency is so high in the Patterson case, how can they explain the biology of the incredibly high mutation rate which they use in Figures 5 and 6 (versus the more reasonable rate used in Figure 3)?

Minor Comments:

1. While the manuscript certainly does model phage therapy, the title feels a bit ambitious as if it were the definitive manuscript on modeling phage therapy. A slightly more descriptive title should be considered.

2. On my printed copy of the manuscript, the line thickness varies substantially between figures which makes the later figures (e.g., Figure 5 and Figure S3) difficult to interpret.

3. I’d suggest another term instead of “Guessed” for the assumed parameter values in Table S2.

4. The ReadMe for the Zenodo data is not particularly helpful but for recreating the figures.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Dear Prof. De Boer,

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James Faeder

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PLOS Computational Biology

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