Novel thermoresponsive assemblies of co-grafted natural and synthetic polymers for water purification

Water contamination and its purification are a global problem. The current approach to purify water is reduction of impurities to acceptable levels. One of the ways to achieve this is by use of water-soluble polymers that extract organic and metallic contaminants, from water. This paper presents a blend of composite polymers that eliminates both the contaminants simultaneously by the principle of adsorption at lower critical solution temperature. These composite polymers have been synthesized by grafting poly(N,N-diethylacrylamide), poly(N-isopropylacrylamide) and poly(N-vinylcaprolactam) on-to the natural polymer chitosan or its derivatives, giving smart graft polymeric assemblies (GPAs). One of the graft polymers, GPA-2, exhibits excellent adsorption properties able to remove metal ions like cadmium, cobalt, copper, lead, iron and also organic impurities like chlorophenol and phthalic anhydride. Studies reveal that 6 mg/ml GPA-2 is able to effect a 100% removal of organic impurities – chlorophenol (50 ppm) and phthalic anhydride (70 ppm) – from water, while complete removal of the heavy metal ions (Cu+2, Co+2 and Cd+2) together at 30 ppm concentration has been achieved with 7.5 mg/ml GPA-2. The reduction in level of impurities along with recyclability and reproducibility in the elimination spectrum makes these assemblies promising materials in water treatment.</jats:p

<i>In silico</i> optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a ‘parallel progression approach to drug design’ applied to β-blockers

<p>The present work exploits the potential of <i>in silico</i> approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein ‘parallel’ information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the ‘parallel progression approach’. This approach utilizes molecular modeling tools <i>viz.</i> hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log <i>V</i>_d) and clearance (log Cl), which together influence the half-life (<i>t</i>_1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant p<i>K</i>_i have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and p<i>K</i>_i values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through <i>in silico</i> methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the ‘parallel progression approach’ helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.</p

Quantifying ligand–receptor interactions for gorge-spanning acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease

<div><p>There is a need for continued development of acetylcholinesterase (AChE) inhibitors that could prolong the life of acetylcholine in the synaptic cleft and also prevent the aggregation of amyloid peptides associated with Alzheimer’s disease. The lack of a 3D-QSAR model which specifically deconvulates the type of interactions and quantifies them in terms of energies has motivated us to report a CoRIA model vis-à-vis the standard 3D-QSAR methods, CoMFA and CoMSIA. The CoRIA model was found to be statistically superior to the CoMFA and CoMSIA models and it could efficiently extract key residues involved in ligand recognition and binding to AChE. These interactions were quantified to gauge the magnitude of their contribution to the biological activity. In order to validate the CoRIA model, a pharmacophore map was first constructed and then used to virtually screen public databases, from which novel scaffolds were cherry picked that were not present in the training set. The biological activities of these novel molecules were then predicted by the CoRIA, CoMFA, and CoMSIA models. The hits identified were purchased and their biological activities were measured by the Ellman’s method for AChE inhibition. The predicted activities are in unison with the experimentally measured biological activities.</p></div