Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

Round 1

Reviewer 1 Report

The present paper present an overview of recent development and applications of physiologically-based pharmacokinetic modelling of nanomaterials. I must admit that I am more familiar with the structure-descriptor development, rather than with their applications. Anyway, this is a thoroughly prepared review, well-written and worth publishing. I would recommend acceptance of the manuscript.

Author Response

We thank the reviewer for the comments

Reviewer 2 Report

Dear Editor,

This manuscript concerns a very interesting review regarding physiologically-based pharmacokinetic (PBPK) modelling of nanomaterials. Several different methodologies and techniques are presented for all main processes related to PBPK, including absorption, distribution, metabolism and elimination (ADME) of nanomaterials in living organisms.

The review describes in detail the historical and theoretical foundations of the various models. Since this is a review article I would propose Authors in the revise version to consider the following issues:

1) The basic mass balance differential equations for most of the processes described PBPK models are presented, but not for metabolism.

What I mostly found missing is a clear presentation of the parameters need to be estimated for each model, either via “top-down” or “bottom-up” methodologies. A corresponding table with all model parameters and the kind of data required, either from experiments or from other simulations, to estimate them would be very useful.

2) A very broad range of different nanomaterials (NMs) used in the PBPK processes. Again it would be useful to discuss the main differences between the various NMs. For example, differences in average size, size distribution, shape, functionalization, etc. could be very important on determining their interaction with biological tissues and fluids. Such info can be added in Table 1 as well.

3)  Another area that is missing in this review article is the (possible) coupling of the continuum PBPK models with more detailed simulation approaches, such as molecular simulations in the atomic, or in the mesoscopic level. For example, the encapsulation and interaction of NMs with the cells are important for many PBPK models. Is it possible to derive the corresponding model parameters from more detailed molecular simulations?

4) The part concerning the validation and the sensitivity analysis of the models is rather limited. I would propose Authors to describe in more detail the different methods, e.g. frequentist vs Bayesian like approaches, adding the required equations as well. This could help for the reader to understand how the various model parameters can be validated and which are the moist important for the final predictions.

5) Last but not least, I would recommend Authors to revise the final “overall assessment and conclusion” section, in order to present in a more clear way the main challenges and open problems in the field.

Minor:

-- Page numbering and section numbering should be thoroughly checked.

Author Response

In response to the queries below, the following were addressed in the paper:

1. Paragraph 5 and equation 7 have been added on page 11. A table for each model may not be realistic since each model requires numerous parameters. However, in order to address this concern, a new paragraph has been added on pages 16-17 to present a summary of required parameters.
2. Table 1 has been revised to include the sizes and functionalization of NMs for each model may not be realistic since each model.
3. We thank the reviewer for this comment but somehow it was not clear as to the exact request stated.
4. Section 4 of the paper Model evaluation and Validation has been revised to present a more robust discussion on sensitivity analysis and validation. Moreover, paragraphs 2 and 3 on page 3 have been revised to present a further discussion on probabilistic analysis as well as frequentist and Bayesian analyses.
5. A paragraph was added to present additional challenges in the field.

Reviewer 3 Report

The review is interesting and surely covers an important field of actual and future computational research. Moreover some improvements will be necessary for improving this valuable manuscript.

english grammar should be moderately improved

PBPK should be defined in the abstract and check if other acronyms were not define through the manuscript

The validation section should be improved by discussing the performances and if the proposed models are applicable. In fact, could be of interest to read if for the validated methods the validation (experimental or in silico) assured a good way for considering the model predictive. Moreover, in Table 1 not for all the validated methods were reported if they are predictive based on the considered validation. In this section the authors should consider to insert a personal point of view in which they discuss if the actual validation methods are fine or an improvement and in which way is needed.

The mentioned comment is the main comment for the review. In fact, the authors should critically analyzed the selected experiments. I strongly suggest to insert a paragraph in which will be reported the strengths and weaknesses of the proposed approaches selected by the authors. Moreover a further paragraph should be added reporting a future perspectives and possible direction always based on the point of view of the authors. Actually is difficult to highlight the mentioned tasks.

In my opinion these paragraphs are necessary to improve the review article.

Author Response

In the penultimate paragraph of the conclusion, we have discussed how lack of publicly available software of the PBPK models hampers independent validation. In that regard, the models have been reported to be validated and their predictive performance touted as acceptable without undergoing independent verification. What we have done in this paper, however, is to critique the models. We have in the end proposed more robust methods for model validation.