Blocking the ZZ domain of sequestosome1/p62 suppresses myeloma growth and osteoclast formation in vitro and induces dramatic bone formation in myeloma-bearing bones in vivo.

We reported that p62 (sequestosome 1) serves as a signaling hub in bone marrow stromal cells (BMSCs) for the formation of signaling complexes, including NFκB, p38MAPK and JNK, that are involved in the increased osteoclastogenesis and multiple myeloma (MM) cell growth induced by BMSCs that are key contributors to multiple myeloma bone disease (MMBD), and demonstrated that the ZZ domain of p62 (p62-ZZ) is required for BMSC enhancement of MMBD. We recently identified a novel p62-ZZ inhibitor, XRK3F2, which inhibits MM cell growth and BMSC growth enhancement of human MM cells. In the current study, we evaluate the relative specificity of XRK3F2 for p62-ZZ, characterize XRK3F2's capacity to inhibit growth of primary MM cells and human MM cell lines, and test the in vivo effects of XRK3F2 in the immunocompetent 5TGM1 MM model. We found that XRK3F2 induces dramatic cortical bone formation that is restricted to MM containing bones and blocked the effects and upregulation of tumor necrosis factor alpha (TNFα), an osteoblast (OB) differentiation inhibitor that is increased in the MM bone marrow microenvironment and utilizes signaling complexes formed on p62-ZZ, in BMSC. Interestingly, XRK3F2 had no effect on non-MM bearing bone. These results demonstrate that targeting p62 in MM models has profound effects on MMBD.

New QSAR prediction models derived from GPCR CB2-antagonistic triaryl bis-sulfone analogues by a combined molecular morphological and pharmacophoric approach.

In order to build quantitative structure-activity relationship (QSAR) models for virtual screening of novel cannabinoid CB2 ligands and hit ranking selections, a new QSAR algorithm has been developed for the cannabinoid ligands, triaryl bis-sulfones, using a combined molecular morphological and pharmacophoric search approach. Both pharmacophore features and shape complementarity were considered using a number of molecular descriptors, including Surflex-Sim similarity and Unity Query fit, in addition to other molecular properties such as molecular weight, ClogP, molecular volume, molecular area, molecular polar volume, molecular polar surface area and dipole moment. Subsequently, partial least squares regression analyses were carried out to derive QSAR models linking bioactivity and the descriptors mentioned, using a training set of 25 triaryl bis-sulfones. Good prediction capability was confirmed for the best QSAR model by evaluation against a test set of a further 20 triaryl bis-sulfones. The pharmacophore and molecular shape-based QSAR scoring function now established can be used to predict the biological properties of virtual hits or untested compounds obtained from ligand-based virtual screenings.

Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions.

Quantitative structure-activity relationship (QSAR) studies are useful computational tools often used in drug discovery research and in many scientific disciplines. In this study, a robust fragment-similarity-based QSAR (FS-QSAR) algorithm was developed to correlate structures with biological activities by integrating fragment-based drug design concept and a multiple linear regression method. Similarity between any pair of training and testing fragments was determined by calculating the difference of lowest or highest eigenvalues of the chemistry space BCUT matrices of corresponding fragments. In addition to the BCUT-similarity function, molecular fingerprint Tanimoto coefficient (Tc) similarity function was also used as an alternative for comparison. For validation studies, the FS-QSAR algorithm was applied to several case studies, including a dataset of COX2 inhibitors and a dataset of cannabinoid CB2 triaryl bis-sulfone antagonist analogues, to build predictive models achieving average coefficient of determination (r(2)) of 0.62 and 0.68, respectively. The developed FS-QSAR method is proved to give more accurate predictions than the traditional and one-nearest-neighbour QSAR methods and can be a useful tool in the fragment-based drug discovery for ligand activity prediction.