Odanacatib (ODN) is a selective cathepsin K inhibitor that acts as an anti-resorptive agent to treat osteoporosis. ODN is also found effective in reducing the effect of severe periodontitis. The interaction between ODN and human serum albumin (HSA) was investigated using spectroscopic, microscopic, and in silico approaches to characterize their binding. The fluorescence intensity of HSA increased upon the addition of increasing concentrations of ODN accompanied by blueshift in the fluorescence spectrum, which suggested hydrophobic formation around the microenvironment of the fluorophores upon ODN binding. A moderate binding affinity was obtained for ODN-HSA binding, with binding constant (Ka) values of ∼104 M-1. Circular dichroism results suggested that the overall secondary and tertiary structures of HSA were both only slightly altered upon ODN binding. The surface morphology of HSA was also affected upon ODN binding, showing aggregate formation. Drug displacement and molecular docking results revealed that ODN preferably binds to site III in subdomain IB of HSA, while molecular dynamics simulations indicated formation of a stable protein complex when site III was occupied by ODN. The ODN-HSA complex was mainly stabilized by a combination of hydrogen bonding, hydrophobic interactions, and van der Waals forces. These findings provide additional information to understand the interaction mechanism of ODN in blood circulation and may help in future improvements on the adverse effects of ODN.
Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.