Dear Prof. Dr. Roussos,

Thank you very much for submitting your manuscript "PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Dina Schneidman

Software Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The paper presents PDKit, a software tool aimed at collecting and analysing data related to Parkinson’s Disease and derived from wearables and behavioural monitoring. The last step of the tool is to provide a prediction of the severity of the disease with respect to standard clinical evaluations.

The paper can be classified as a technical paper, describing the main software modules composing the tool and the general features of each module, presenting only some simple application examples.

The presented results derive from previous studies on big datasets collected during experimental campaigns. These results have been already published by the authors in dedicated papers.

The main novelty of the paper is the presentation of the software architecture of the tool, which is also available to researchers and it has been already dowloaded several times. However, the description of the tool lacks of specific examples of its applications, for example referring to the experimental results presented in Section “Results”. The authors claim that they provide specific examples and use cases in notebooks, available for download, and in the Read-the-Docs files, but not describing in detail this part in the paper, it reduces the novel contribution of the paper.

In addition, a more detailed description of the advantages of using the tool, for example from the medical users (in terms of usability and acceptance) would provide an important added-value for the paper. For all these reasons, I suggest the authors to review the paper including these missing parts.

Reviewer #2: This manuscript describes a tool kit developed by the authors with “to facilitate the development and open sharing of novel digital biomarkers for PD and hence help address the current lack of algorithmic and

model transparency ».

This goal is extremely worthwhile for PD and other diseases, where digital biomarkers and digital clinical outcome assessment are becoming increasingly widely used, often without well characterised performance or transparent algorithms. And the point is well made that this is a barrier to acceptance of these methods by regulators.

The content is very interesting, but I think readers may find it hard to follow the flow of the paper, as the results section that involves results obtained by processing data from CUSSP, seems out of place, without proper method or discussion of the results. These results are from quite a large data volume, and I was surprised to see so little comment on them and no reference in the conclusions.

I would suggest a refinement of the structure to address this concern, including expanding these results, having a clear description of how the toolkit enabled this analysis, plus discussion ad reference in conclusions, so that readers can get an example of the application of the tookit to generate novel results.

More minor comments.

Line 139: “One approach suggests that features employed for symptom assessment should reflect biomedical intuition based on clinical experience, with the opposing view exhorting the advantages of a purely

data-driven approach”. A patient rather than clinical experience perspective should also be mentioned her, see FDA patient focused drug development programmes.

Line 162. Given the focus on regulatory issues, please clarify some of the terminology, and in particular the difference between Biomarker and Clinical outcome assessments (see various references on the EMA and FDA web site https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tool-ddt-qualification-programs). The digital technology that are the focus of this paper might be either biomarkers or clinical outcome assessments, but more likely the latter.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Derek Hill

Figure Files:

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Dear Prof. Dr. Roussos,

We are pleased to inform you that your manuscript 'PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Dina Schneidman

Software Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors addressed all the reviewers' comments providing detailed motivations. The paper is ready for publication.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No