In silico investigations of the anti-catabolic effects of pamidronate and denosumab on multiple myeloma-induced bone disease

Dynamics of Bone Cell Interactions and Differential Responses to PTH and Antibody-Based Therapies

We propose a mathematical model describing the dynamics of osteoblasts and osteoclasts in bone remodeling. The goal of this work is to develop an integrated modeling framework for bone remodeling and bone cell signaling dynamics that could be used to explore qualitatively combination treatments for osteoporosis in humans. The model has been calibrated using 57 checks from the literature. Specific global optimization methods based on qualitative objectives have been developed to perform the model calibration. We also added pharmacokinetics representations of three drugs to the model, which are teriparatide (PTH(1-34)), denosumab (a RANKL antibody) and romosozumab (a sclerostin antibody), achieving excellent goodness-of-fit of human clinical data. The model reproduces the paradoxical effects of PTH on the bone mass, where continuous administration of PTH results in bone loss but intermittent administration of PTH leads to bone gain, thus proposing an explanation of this phenomenon. We used the model to simulate different categories of osteoporosis. The main attributes of each disease are qualitatively well captured by the model, for example changes in bone turnover in the disease states. We explored dosing regimens for each disease based on the combination of denosumab and romosozumab, identifying adequate ratios and doses of both drugs for subpopulations of patients in function of categories of osteoporosis and the degree of severity of the disease.

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

OBJECTIVE

Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib.

MATERIALS AND METHODS

We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was "Robust Multi-array analysis" normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis.

RESULTS

Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways.

CONCLUSION

The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.

In silico investigations of potential anabolic treatments in multiple myeloma-induced bone disease

No anabolic drugs are currently approved to treat multiple myeloma (MM)-induced bone disease and the anti-MM agent bortezomib exhibits the anabolic effects in the clinic. In this study, we focus on investigating potential anabolic treatments of MM-induced bone disease using our previously proposed MM-bone model, with the goal for clarifying the underlying molecular/cellular mechanisms. Firstly, a variety of virtual drug treatments are explored by the parametric study to clarify the anabolic-related molecular/cellular mechanisms. The real drug (i.e., bortezomib) treatments are further examined by developing an integrated model with bortezomib to validate the clarified anabolic-related molecular/cellular mechanisms. The simulated responses to the bortezomib treatments that are validated by the clinical data are consistent with the simulated responses to the virtual drug treatments. Our study clarifies that the anabolic effects in the treatment of MM-induced bone disease are associated with promoting the differentiation of bone marrow stromal cells (BMSC) and inhibiting the apoptosis of active osteoblasts, while promoting the differentiation of osteoblast precursors is instead suggested to be associated with the anti-catabolic effects. Compared with the individual anabolic therapies, the anabolic therapies that promote the differentiation of BMSC in combination with the anti-MM/anti-catabolic therapies are found to induce a greater increase in the bone volume, while the anabolic therapies that inhibit the apoptosis of active osteoblasts in combination with the anti-MM/anti-catabolic therapies induce a lower increase in the bone volume. The simulations also suggest that the direct inhibition of bortezomib on the osteoclast activity is probably a redundant mechanism.