Further exploring rm2 metrics for validation of QSPR models

Nonionic surfactant vesicular systems for effective drug delivery—an overview

Oligonucleotide therapy: An emerging focus area for drug delivery in chronic inflammatory respiratory diseases.

The Need for Drugs and Drug Delivery Systems.- Overview of Controlled Release Mechanisms.- Hydrophobic Polymers of Pharmaceutical Significance.- Hydrogels.- Biodegradable Polymers in Drug Delivery Systems.- Diffusion Controlled Drug Delivery Systems.- Swelling Controlled Drug Delivery Systems.- Degradable Polymeric Carriers for Parenteral Controlled Drug Delivery Systems.- Porous Systems.- Targeted Delivery Using Biodegradable polymeric Nanoparticles.- Liposomes in Drug Delivery.- Receptor Mediated Delivery Systems for Cancer Therapeutics.- Biological Rhythms, Drug Delivery, and Chronotherapeutics.- Site Specific Controlled Release for Cardiovascular Disease - Translational Direction.- Drug Delivery Systems to Fight Cancer.- Applications of Vaccine Delivery in Infectious Diseases.- Tissue Engineering in Drug Delivery.- The Shaping of Controlled Release Drug Product Development by Emerging Trends in the Commercial, Regulatory and Political Macro-Environment

Fundamentals and applications of controlled release drug delivery

Niosomes from 80s to present: the state of the art.

Over the past several years, treatment of infectious diseases and immunisation has undergone a revolutionary shift. With the advancement of biotechnology and genetic engineering, not only a large number of disease-specific biological have been developed, but also emphasis has been made to effectively deliver these biologicals. Niosomes are vesicles composed of non-ionic surfactants, which are biodegradable, relatively nontoxic, more stable and inexpensive, an alternative to liposomes. This article reviews the current deepening and widening of interest of niosomes in many scientific disciplines and, particularly its application in medicine. This article also presents an overview of the techniques of preparation of niosome, types of niosomes, characterisation and their applications.

Niosome: A future of targeted drug delivery systems.

Drug delivery system based on chronobiology--A review.

Quantitative structure-activity relationship (QSAR) studies have been carried out on indolyl aryl sulfones, a class of novel HIV-1 non-nucleoside reverse transcriptase inhibitors, using physicochemical, topological and structural parameters along with appropriate indicator variables. The statistical tools used were linear methods (e.g., stepwise regression analysis, partial least squares (PLS), factor analysis followed by multiple regression (FA-MLR), genetic function approximation combined with multiple linear regression (GFA-MLR) and GFA followed by PLS or G/PLS and nonlinear method (artificial neural network or ANN). In case of physicochemical parameters, GFA-MLR generated the best Equation (n = 97, R(2) = 0.862, Q(2) = 0.821). Using topological parameters, the best Equation (based on leave-one-out Q(2)) was obtained with stepwise regression technique (n = 97, R(2) = 0.867, Q(2) = 0.811). When topological and physicochemical parameters were used in combination, statistical quality increased to a great extent (n = 97, R(2) = 0.891, Q(2) = 0.849 from stepwise regression). Furthermore, the whole dataset had been divided into test (25% of whole dataset) and training (remaining 75%) sets. Models were developed based on the training set and predictive potential of such models was checked from the test set. The selection of the training set was based on K-means clustering of the standardized descriptors (topological and physicochemical). In this case also the best results were obtained with stepwise regression (n = 72, R(2) = 0.906, Q(2) = 0.853) but external predictive capacity of this model ([image omitted] ) was inferior to the model developed from GFA-MLR technique (R(2) = 0.883, Q(2) = 0.823, [image omitted] ). However, the squared regression coefficient between observed activity and predicted activity values of the test set compounds for the best linear model, i.e., GFA-MLR (r(2) = 0.736) was lower in comparison to the best nonlinear model developed using artificial neural network (r(2) = 0.781). Thus, based on external validation, the ANN models were superior to the linear models. The predictive potential of the best linear Equation (stepwise regression model) was superior to that of the previously published CoMFA (Q(2) = 0.81, SDEP(Test) = 0.89) on the same data set (Ragno R. et al., J Med Chem 2006, 49, 3172-3184). Furthermore, the physicochemical parameter based models also supported the previous observations based on docking (Ragno R. et al., J Med Chem 2005, 48, 213-223).

https://www.tandfonline.com/doi/pdf/10.1080/14756360701811379

Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones

Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives

Quantitative structure-activity relationship (QSAR) studies have been performed on piperidine derivatives (n = 119) as CCR5 antagonists. The whole data set was divided into a training set (75% of the dataset) and a test set (remaining 25%) on the basis of K-means clustering technique. Models developed from the training set were used to assess the predictive potential of the models using test set compounds. Initially classical type QSAR models were developed using structural, spatial, electronic, physicochemical and/or topological parameters using statistical methods like stepwise regression, partial least squares (PLS) and factor analysis followed by multiple linear regression (FA-MLR). Using topological and structural parameters, FA-MLR provided the best equation based on internal validation (Q2 = 0.514) but the best externally validated model was obtained with PLS ( = 0.565). When structural, physicochemical, spatial and electronic descriptors were used, the best Q2 value (0.562) was obtained from the s...

Asim Sattwa Mandal

Papers

Niosome: A future of targeted drug delivery systems.

Drug delivery system based on chronobiology--A review.

Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones

Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives

Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools.