Oral administration is the most convenient route among various routes of drug delivery as it offers high patient compliance. However, the poor aqueous solubility and poor enzymatic/metabolic stability of drugs are major limitations in successful oral drug delivery. There are several approaches to improve problems related to hydrophobic drugs. Among various approaches, nanotechnology based drug delivery system has potential to overcome the challenges associated with the oral route of administration. Novel drug delivery systems are available in many areas of medicine. The application of these systems in the treatment of hypertension continues to broaden. The present review focuses on various nanocarriers available in oral drug administration for improving solubility profile, dissolution, and consequently bioavailability of hydrophobic antihypertensive drugs.

Most of the empirical evaluations of active learning approaches in the literature have focused on a single classifier and a single performance measure. We present an extensive empirical evaluation of common active learning baselines using two probabilistic classifiers and several performance measures on a number of large datasets. In addition to providing important practical advice, our findings highlight the importance of overlooked choices in active learning experiments in the literature. For example, one of our findings shows that model selection is as important as devising an active learning approach, and choosing one classifier and one performance measure can often lead to unexpected and unwarranted conclusions. Active learning should generally improve the model's capability to distinguish between instances of different classes, but our findings show that the improvements provided by active learning for one performance measure often came at the expense of another measure. We present several such results, raise questions, guide users and researchers to better alternatives, caution against unforeseen side effects of active learning, and suggest future research directions.

No abstract

No abstract