No abstract

Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to discriminate about 7 out of 10 true hits from a random database of 990 ligands. The use of consensus lists common to two or three scoring functions clearly enhances hit rates among the top 5% scorers from 10% (single scoring) to 25-40% (double scoring) and up to 65-70% (triple scoring). However, in all tested cases, no clear relationships could be found between docking and ranking accuracies. Moreover, predicting the absolute binding free energy of true hits was not possible whatever docking accuracy was achieved and scoring function used. As the best docking/consensus scoring combination varies with the selected target and the physicochemistry of target-ligand interactions, we propose a two-step protocol for screening large databases: (i) screening of a reduced dataset containing a few known ligands for deriving the optimal docking/consensus scoring scheme, (ii) applying the latter parameters to the screening of the entire database.

The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).

We have recently proposed a macromolecular prodrug strategy for improved cancer chemotherapy based on two features (Kratz, F.; et al. J. Med. Chem 2000, 43, 1253-1256.): (a) rapid and selective binding of thiol-reactive prodrugs to the cysteine-34 position of endogenous albumin after intravenous administration and (b) release of the albumin-bound drug in the acidic environment at the tumor site due to the incorporation of an acid-sensitive bond between the drug and the carrier. To investigate this therapeutic strategy in greater depth, four (maleinimidoalkanoyl)hydrazone derivatives of doxorubicin were synthesized differing in the length of the aliphatic spacer (1, -(CH(2))(2)-; 2, -(CH(2))(3)-; 3, -(CH(2))(5)-; 4, -(CH(2))(7)-). The albumin-binding doxorubicin prodrugs, especially the (6-maleimidocaproyl)hydrazone derivative of doxorubicin (3), are rapidly and selectively bound to the cysteine-34 position of endogenous albumin. 3 was distinctly superior to the parent compound doxorubicin in three animal tumor models (RENCA, MDA-MB 435, and MCF-7) with respect to antitumor efficacy and toxicity.