Sulfamide derivatives with selective carbonic anhydrase VII inhibitory action.

A set of N,N'-disubstituted sulfamides and sodium cyclamate have been tested for their inhibitory action against six isoforms of carbonic anhydrase (CA, EC 4.2.1.

Inhibition pattern of sulfamide-related compounds in binding to carbonic anhydrase isoforms I, II, VII, XII and XIV.

A set of sulfamides and sulfamates were synthesized and tested against several isoforms of carbonic anhydrase: CA I, CA II, CA VII, CA XII and CA XIV. The biological assays showed a broad range of inhibitory activity, and interesting results were found for several compounds in terms of activity (Ki <1μm) and selectivity: some aromatic sulfamides are active against CA I, CA II and/or CA VII; while they are less active in CA XII and CA XIV. On the other hand, bulky sulfamides are selective to CA VII.

Affinity of sulfamates and sulfamides to carbonic anhydrase II isoform: experimental and molecular modeling approaches.

Sixteen aromatic and aliphatic sulfamides and sulfamates were synthesized and tested in their inhibition to carbonic anhydrase CAII activity. The weaker inhibition pattern shown by sulfamides as compared to sulfamates is interpreted in this research by means of molecular modeling techniques, including known inhibitors (topiramate and its sulfamide cognate) in the analysis. The results nicely explain the origin of the inhibitory activity, which is not only related to positive interactions of the ligand with the active site residues but also to the solvation pattern characteristic of each ligand.

A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening.

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect.