Cyclodextrin-Based Arsenal for Anti-Cancer Treatments.

Anti-cancer drugs are mostly limited in their use due to poor physicochemical and biopharmaceutical properties. Their lower solubility is the most common hurdle limiting their use upto their potential. In the recent years, the cyclodextrin (CD) complexation have emerged as existing approach to overcome the problem of poor solubility. CD-based nano-technological approaches are safe, stable and showed well in vivo tolerance and greater payload for encapsulation of hydrophobic drugs for the targeted delivery. They are generally chosen due to their ability to get self-assembled to form liposomes, nanoparticles, micelles and nano-sponges etc. This review paper describes a birds-eye view of the various CD-based nano-technological approaches applied for the delivery of anti-cancer moieties to the desired target such as CD based liposomes, niosomes, niosoponges, micelles, nanoparticles, monoclonal antibody, magnetic nanoparticles, small interfering RNA, nanorods, miscellaneous formulation of anti-cancer drugs containing CD. Moreover, the author also summarizes the various shortcomings of such a system and their way ahead.

Modelling and forecasting Positive and Negative Syndrome Scale scores to achieve remission using time series analysis.

OBJECTIVES: Schizophrenia is a chronic mental condition. The objective of this study is to apply time series modelling to Positive and Negative Syndrome Scale scores of outpatients with schizophrenia, observed at regular intervals of time, and hence forecast the number of visits required to reach remission. METHODS: A retrospective data of outpatients diagnosed with chronic paranoid-type schizophrenia were extracted from the records of outpatient department of a tertiary hospital in New Delhi, India. Autoregressive integrated moving average (ARIMA) and ARIMAX models (ARIMA with explanatory variable as Clinical Global Impression Severity scale) are fitted to the data. The best fit models are employed to forecast the number of visits required to reach remission for the outpatients who did not achieve remission by the end of study. Prediction accuracy of the two models is compared using mean absolute percentage error and mean absolute deviation. RESULTS: The ARIMA (1, 2, 1) and ARIMAX (1, 2, 1) models are identified to be suitable models after a series of statistical tests. CONCLUSIONS: ARIMA and ARIMAX models are suitable to predict number of visits required to reach remission. Further, ARIMAX model performed better than ARIMA model.