Cherif, A. | bioRxiv Science

bioRxiv2021

Modeling osteoporosis to design and optimize pharmacologic therapies comprising multiple drug types

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 (PTH) analogs, sclerostin inhibitors and receptor activator of NF-{kappa}B ligand (RANKL) 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.

pharmacology and toxicology↗

bioRxiv2021

In silico screening of TMPRSS2 SNPs that affect its binding with SARS-CoV2 spike protein and directly involved in the interaction affinity changes

In this paper, we used in silico analysis to shed light on the possible interaction between TMPRSS2 and SARS-CoV2 spike (S) protein by examining the role of TMPRSS2 single nucleotide polymorphisms (SNPs) in relation with susceptibility and inter-individual variability of SARS-CoV2 infection. First, we used molecular docking of human TMPRSS2 protein to predict the binding site of TMPRSS2, especially the TMPRSS2 link loops, in order to assess the effect TMPRSS2 SNPs. The latter lead to missense variants on the interaction between TMPRSS2 and SARS-CoV2 S protein. In a second step, we further refine our analysis by performing a structure-function analysis of the complexes using PyMol software, and finally by MD simulations to validate the as-obtained results. Our findings show that 17 SNPs among the 692 natural TMPRSS2 coding variants are in positions to influence the binding of TMPRSS2 with the viral S protein. All of them give more important interaction energy as assessed by docking. Among the 17 SNPs, four missense variants E389A, K392Q, T393S and Q438E lead to "directly increasing" the interaction affinity and 2 missense variants R470I and Y416C cause it "directly decreasing". The R470I and Y416C present in African and American population, respectively. While the other 4 SNP variants (E389A; K392Q; T393S and Q438E) are present only in the European population, which could link the viral infection susceptibility to demographic, geographic and genetic factors.

bioinformatics↗