Biological test models

Hansch recommended a lateral validation of QSAR results, i.e., the comparison of models of closely related series of compounds in one biological test system (cf. equations 16 and 17) or trie comparison of the QSAR models derived for one series of compounds in several related biological test models (e.g., serine and cysteine proteases). If all models are of comparable quality, and if they show similar regression coefficients of the physicochemical terms, the results can be accepted. However, in most cases the required effort will be too large to do this routinely. In addition, even closely related enzymes or receptors may have significantly different binding sites. 

A 5-(methylthio)methyl-substituted derivative (cis-28) of ( + )-3-PPP has been reported [91]. The background to the study was the structural similarity between pergolide (29) and (cis-28). However, the biological testing of (cis-28) showed that it is inactive in vivo (GBL model), while an in vitro assay (inhibition of tyrosine hydroxylation) showed (cis-28) to be equipotent to racemic 3-PPP itself. These results indicate that the steric bulk in (cis-28) is not compatible with potent DA receptor interaction. However, since compound (cis-28) was not resolved, there is a possibility that one or both of the enantiomers of (cis-28) might have antagonistic properties [89]. 

In general, the biological evaluation of hypericin in various test models is limited by its poor water solubility. It was shown in in vitro as well as in vivo studies (18,78) that the water solubility of hypericin was remarkably enhanced in the presence of procyanidins or flavonol glycosides of SJW extract. In a recent pharmacokinetic study in rats, it was shown that procyanidin B2 as well as hyperoside increased the oral bioavailability of hypericin by approximately 58% (B2) and 34% (hyperoside) (Fig. 5) (19). Procyanidin B2 and hyperoside had a different influence on the plasma kinetics of hypericin median maximal plasma levels of hypericin were detected after 360 minutes (Cmav 8.6 ng/mL) for B2, and after 150 minutes... 

A set of molecules that rank high after this process would be synthesized and subject to biological tests, i.e., in vitro enzymatic assay or binding affinity experiments, in order to confirm design rationales. Simultaneously, X-ray co-crystal structures of these ligands in complex with the target are to be determined to further corroborate modeling results. Positive results from such approaches are decisive for selection of next set of compounds for synthesis and the future directions of lead optimization. 

Computer modelling has reduced the need to synthesize every analogue of a lead compound. It is also often used retrospectively to confirm the information derived from other sources. Combinatorial chemistry, which originated in the field of peptide chemistry, has now been expanded to cover other areas. The term covers a group of related techniques for the simultaneous production of large numbers of compounds for biological testing. Consequently, it is used for structure action studies and to discover new lead compounds. The procedures may be automated. 

As an example, we can consider the discovery and development of an antiviral drug. Inhibitors of the HIV protease, an enzyme essential for the maturation of the virus, can potentially cure HIV-infected people. The discovery process may consist of finding samples able to inhibit the viral aspartic protease over a certain threshold, while having little or no effect on another protease of the same class, such as pepsin. The hits will be submitted to further biological tests to identify leads, patentable compounds that are capable, for example, of inhibiting viral replication in cellular models. 

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