Modeling osteoporosis to design and optimize pharmacological therapies comprising multiple drug types
Curation statements for this article:-
Curated by eLife
Evaluation Summary:
This paper will be of interest to the pharmacology community with interest in available drug treatments for osteoporosis and how to optimize these. The key findings of the paper are based on in silico results and indicate that combined drug treatments may be more efficient in treatment of osteoporosis. This could have a significant impact on clinical management of osteoporosis patients.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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Abstract
For the treatment of postmenopausal osteoporosis, several drug classes with different mechanisms of action are available. Since only a limited set of dosing regimens and drug combinations can be tested in clinical trials, it is currently unclear whether common medication strategies achieve optimal bone mineral density gains or are outperformed by alternative dosing schemes and combination therapies that have not been explored so far. Here, we develop a mathematical framework of drug interventions for postmenopausal osteoporosis that unifies fundamental mechanisms of bone remodeling and the mechanisms of action of four drug classes: bisphosphonates, parathyroid hormone analogs, sclerostin inhibitors, and receptor activator of NF-κB ligand inhibitors. Using data from several clinical trials, we calibrate and validate the model, demonstrating its predictive capacity for complex medication scenarios, including sequential and parallel drug combinations. Via simulations, we reveal that there is a large potential to improve gains in bone mineral density by exploiting synergistic interactions between different drug classes, without increasing the total amount of drug administered.
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Author Response:
We fully agree that there are more detailed theoretical descriptions of both bone mineral density and the pharmacokinetics of the considered drugs and we are aware of the details of these submodels. We deliberately chose a simplified description to keep the model computationally manageable and reduce the number of free parameters to a minimum. In our opinion, the precision with which the model can capture clinical data in even complex scenarios demonstrates that such a simplified approach is warranted. That is why we regard these model features as simplifications rather than weaknesses. We would also be happy to explain the rationale behind these simplifications in more detail in the revised manuscript.
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Evaluation Summary:
This paper will be of interest to the pharmacology community with interest in available drug treatments for osteoporosis and how to optimize these. The key findings of the paper are based on in silico results and indicate that combined drug treatments may be more efficient in treatment of osteoporosis. This could have a significant impact on clinical management of osteoporosis patients.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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Public Review:
This paper reports on the development of a mechanistic model of bone remodeling that accounts for key regulatory factors of the remodeling process which control bone cell numbers. The model is used to simulate osteoporosis and a variety of combined drug treatments. A number of drug treatments were implemented in a pseudo pharmacokinetic fashion. The model was first calibrated on a large number of experimental data sets. Subsequently the model was tested/validated using complementary experimental data sets. Simulation results show that the model is able to predict a significant number of experimental data sets. In a further step, a variety of different combined drug therapies were tested in order to identify an optimum combination. The authors concluded that this computational modeling framework has great potential for …
Public Review:
This paper reports on the development of a mechanistic model of bone remodeling that accounts for key regulatory factors of the remodeling process which control bone cell numbers. The model is used to simulate osteoporosis and a variety of combined drug treatments. A number of drug treatments were implemented in a pseudo pharmacokinetic fashion. The model was first calibrated on a large number of experimental data sets. Subsequently the model was tested/validated using complementary experimental data sets. Simulation results show that the model is able to predict a significant number of experimental data sets. In a further step, a variety of different combined drug therapies were tested in order to identify an optimum combination. The authors concluded that this computational modeling framework has great potential for future use in order to optimize combined dosing regimen.
The paper is very well written and the methods, and results are clearly described. Also, the authors provided all the source codes for their simulation results to be reproducible. The mathematical model was well described and the accompanying figure helped identifying action of different regulatory mechanisms and drug actions.
Some weaknesses of the paper are the following:
Formulation of the equation for BMD: was simply assumed to be the product of bone density and mineral content. Particularly, the latter function is formulated in a very phenomenological way. There are more rigorous, materials science based, ways to formulate bone mineralization.
Pharmacokinetic (PK) formulations of drugs: the representation of drug concentration for the different drugs is a simplification. Generally, PK models need to be used to provide values of drug concentrations in the bone compartment which interact with the remodeling process. The bisphosphonate PK model might be more complex due to the absorption of the drug into the bone matrix and dissolution of the drug during bone resorption.
The discussion section was rather unconventional, as no links/comparisons with existing literature were made. However, given that the essential computational modeling results are on combined drug treatments that have not been tested experimentally nor with other computational simulations, this is ok.
The authors demonstrated that use of a mechanistic bone remodeling model combined with different drug actions allows to explore optimal treatment regimens including combined drug therapies for osteoporosis. The results clearly showed that some drug combinations lead to significantly higher BMD gains than others.
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