DESIGN AND IN VITRO EVALUATION OF ACRIVASTINE AS ORODISPERSIBLE TABLET USING DIRECT COMPRESSION METHOD.

OBJECTIVE: The aim: This study aimed to develop mouth-dissolving tablets of Acrivastine, an antihistamine medication, in order to increase its oral bioavailability. PATIENTS AND METHODS: Materials and methods: Different super disintegrants, such as crospovidone, croscarmellose sodium, and sodium starch glycolate, were used to make Acrivastine oral dispersible tablets (ODTs). These super disintegrants were utilized in various concentrations. The formulation (F3) with 6% w/w crospovidone had a fast disintegration time (less than 30 seconds) and practically total drug release within 10 minutes. All of the formulations were made using the direct compression method and proper diluents, binders, and lubricants. Fourier transform infrared spectroscopy (FTIR) tests were used to investigate the drug-ex¬cipient interaction, and all formulations demonstrated improved drug-excipient compatibility. RESULTS: Results: The average weight of all formulations was between 175 and 180 mg. All formulations' hardness and friability were within acceptable ranges. Direct compression tablets had a hardness of 3.2 to 4 kg/cm2. All formulations were determined to have a friability of less than 1.0%. For oral dissolving tablets, the in vitro disintegration time is critical, and this time preferred to be < 60 seconds. The results also showed that crospovidone disintegrated after 24 seconds and sodium starch glycolate disintegrated in 40 seconds in vitro. CONCLUSION: Conclusions: When compared to croscarmellose sodium and sodium starch glycolate, crospovidone performs better as a super disintegrant. In comparison to other formula, tablets breakdown in the mouth in 30 seconds and have a maximum in vitro drug release time in 1-3 minutes.

Evaluating predictive performance of network biomarkers with network structures.

Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts.

Combining multiple perspective as intelligent agents into robust approach for biomarker detection in gene expression data.

Locating exceptional, abnormal or unusual trends in gene expression data to identifying disease biomarkers is the vital problem tackled in this paper. We developed a comprehensive framework that incorporates different perspectives each realised by an agent. Each agent applies its method to analyse the gene expression data and to come up with some candidate genes as potential cancer biomarkers. Further, gene enrichment, protein interaction, and miRNA regulation are given weight; they are used to confirm the discoveries by the major agents. We conducted experiments on two data sets; the obtained results are very encouraging with a high classification rate.

A closer look at "social" boundary genes reveals knowledge to gene expression profiles.

As social network analysis is gaining popularity in modeling real world problems, the task of applying the social network model concepts and notions to biological data is still one of the most attractive research problems to be addressed. According, our work described in this paper focuses on a particular set of genes that reside on the community boundaries in gene co-expression networks. Stemmed from community mining problem in social networks, peripheries of communities (i.e., boundaries) can be used to aid certain biological analysis. The proposed method consists of three parts: 1) Finding communities of gene co-expression networks through clustering. 2) Analyzing stability of community structures by Monte Carlo method. 3) Designing of dynamic adoption of boundaries using geometric convexity. We validated our findings using breast cancer gene expression data from various studies. Our approach contributes to the new branch of applying social network mechanisms in biological data analysis, leading to new data mining strategies implied by witnessing social behaviors in gene expression analysis.