Caterina Bissantz

ADVERTISEMENT RETURN TO ISSUEPerspectiveNEXTADDITION / CORRECTIONThis article has been corrected. View the notice.A Medicinal Chemist's Guide to Molecular InteractionsCaterina Bissantz, Bernd Kuhn, and Martin Stahl*View Author Information Discovery Chemistry, F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland*To whom correspondence should be addressed. Phone: +41616888421. Fax: +41616888421. E-mail: [email protected]Cite this: J. Med. Chem. 2010, 53, 14, 5061–5084Publication Date (Web):March 26, 2010Publication History Received27 January 2010Published online26 March 2010Published inissue 22 July 2010https://doi.org/10.1021/jm100112jCopyright © 2010 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views105668Altmetric-Citations1107LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (6 MB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Aromatic compounds,Group 17 compounds,Ligands,Molecules,Noncovalent interactions Get e-Alerts

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).

Abstract Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a major global pathogen with limited treatment options 1 . No new antibiotic chemical class with activity against A. baumannii has reached patients in over 50 years 1 . Here we report the identification and optimization of tethered macrocyclic peptide (MCP) antibiotics with potent antibacterial activity against CRAB. The mechanism of action of this molecule class involves blocking the transport of bacterial lipopolysaccharide from the inner membrane to its destination on the outer membrane, through inhibition of the LptB 2 FGC complex. A clinical candidate derived from the MCP class, zosurabalpin (RG6006), effectively treats highly drug-resistant contemporary isolates of CRAB both in vitro and in mouse models of infection, overcoming existing antibiotic resistance mechanisms. This chemical class represents a promising treatment paradigm for patients with invasive infections due to CRAB, for whom current treatment options are inadequate, and additionally identifies LptB 2 FGC as a tractable target for antimicrobial drug development.

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.