Anubha Gupta

To define and differentiate relevant aspects of blood-brain barrier transport and distribution in order to aid research methodology in brain drug delivery. Pharmacokinetic parameters relative to the rate and extent of brain drug delivery are described and illustrated with relevant data, with special emphasis on the unbound, pharmacologically active drug molecule. Drug delivery to the brain can be comprehensively described using three parameters: Kp,uu (concentration ratio of unbound drug in brain to blood), CLin (permeability clearance into the brain), and Vu,brain (intra-brain distribution). The permeability of the blood-brain barrier is less relevant to drug action within the CNS than the extent of drug delivery, as most drugs are administered on a continuous (repeated) basis. Kp,uu can differ between CNS-active drugs by a factor of up to 150-fold. This range is much smaller than that for log BB ratios (Kp), which can differ by up to at least 2,000-fold, or for BBB permeabilities, which span an even larger range (up to at least 20,000-fold difference). Methods that measure the three parameters Kp,uu, CLin, and Vu,brain can give clinically valuable estimates of brain drug delivery in early drug discovery programmes.

Although natural gums and their derivatives are used widely in pharmaceutical dosage forms, their use as biodegradable polymeric materials to deliver bioactive agents has been hampered by the synthetic materials. These natural polysaccharides do hold advantages over the synthetic polymers, generally because they are nontoxic, less expensive, and freely available. Natural gums can also be modified to have tailor-made materials for drug delivery systems and thus can compete with the synthetic biodegradable excipients available in the market. In this review, recent developments in the area of natural gums and their derivatives as carriers in the sustained release of drugs are explored.

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.