1. Novel Dihydrofolate Reductase Inhibitors. Structure- Based versus Diversity-Based Design and High- Throughput Synthesis and Screening Pierre C. Wyss, Paul Gerber, Peter G. Hartman, Christian Hubschwerlen, Hans Locher, Hans-Peter Marty, Martin Stahl F. Hoffman-La Roche Ltd, Basel Switzerland J. Med. Chem. 2003, 46, 2304 Kiran-20060214

2. The spread of antibiotic resistance has led to a renewed / ongoing search for new targets and new antibacterial compounds DHFR plays a key role in the synthesis of amino acids and purines. DHFR null bacteria are not viable Bacteria are gram +ve or gram –ve depending on whether they stain purple or pink with the Gram stain Boils, pimples, strep throat, ear infections, nosocomial infections, pneumonia etc. are caused bacteria Trimethoprim (TMP) was already in clinical use at the time this work was done Hoffmann-La Roche had RO-64-5781 in hand, it was 190,000 times more potent than TMP but it was not druggable. IC50: concentration required to suppress 50% of bacterial growth MIC: lowest concentration that will inhibit bacterial growth 100% after overnight incubation (IC100) Biology

3. Goal and methods Design novel DHFR inhibitors with good drug-like properties Two methods evaluated: - Structure-based library design Crystal structures for DHFR known Potent compounds known - Diversity-based library design Known potent compounds had high molecular weights, were highly plasma protein bound and showed poor solubility Design compounds of a different structural class and reduced molecular weight Selectivity - Inhibit bacterial DHFRs but not human DHFR

4. Basic idea for library design Replace the greasy hydrophobic piece (red), keep the diaminopyrimidine piece (blue)

5. Testing synthetic feasibility - Compound 6 is good practical chemistry, is a key intermediate for library synthesis - Reactions could be done on 0.35 - 0.7  M scale - Purification by HPLC not required - 1392 compounds made in library format

6. Paradigm for structure-based virtual screening - Diaminopyrimidine is an ideal fit for a narrow pocket in the active site: “needle” - Docking would be done with the constraint of a fixed position for the needle fragment N N complexed with S. aureus DHFR

7. Homology model for structure-based screening Sa: TMP-sensitive S. aureus DHFR Hum: human DHFR Spn: TMP-sensitive S. pneumoniae DHFR Sp1: TMP-resistant S. pneumoniae DHFR - No significant induced fit effects can be observed, assume receptor is rigid for docking experiments - High similarity at the active site, assume that use of a single crystal structure will be sufficient for screening against all targets

8. Structure-based compound selection: method 9948 secondary amines retrieved as virtual reagents Single 3D conformations were generated using Corina The protonation states were adjusted, the nitrogen on the aminomethyl substituent was not protonated The single crystal structure of DHFR from TMP-sensitive S. aureus complexed with RO-62-6091 was used Library members were docked with a fixed position (taken from the crystal structure) for the 2,4-diaminopyrimidine piece FlexX was used for docking experiments, hydrogen bonds formed at solvent- exposed regions of the enzyme were penalized.

9. Structure-based compound selection: results FlexX produced docking results for about half of the library For each compound the solution with the highest binding energy was selected 252 of the 300 top-ranked candidates were synthesized (LIBRARY 1) 150 candidates were selected randomly from compounds for which no docking solution could be found. Another 150 lowest-ranked compounds were selected 269 of these 300 compounds were synthesized (LIBRARY 2)

10. Some observations from FlexX docking Hits from LIBRARY 1 were more active against Spn DHFR even though the crystal structure of Sa DHFR had been used i.e. “the power of virtual screening lies in its ability to filer out undesirable compounds rather than identifying specific active ones” –FlexX does not find docking solutions for compounds with more than one bulky substituent at the aminomethyl nitrogen. –FlexX discards very small molecules and compounds with long flexible moieties. –FlexX assigns high ranks to conformationally restricted flat rigid polycyclic motifs: an additional 370 compounds (LIBRARY 4)

11. The library of 9448 virtual reagents was clustered according to chemical similarity - Compounds were superimposed in pairs at the newly formed C-N bond - The amine substituent was rotated. Conformers with maximum volume and H-bond-donor and H-bond-acceptor overlap were generated. - The list of pairwise similarity scores was used for clustering in a binary tree using a complete linkage algorithm -- two clusters are combined only if all members of the first are within the distance threshhold of all members of the second -- Each iteration reduces the number of clusters by one Results: About 500 compounds were needed to adequately represent the chemical space of the library, a set of 501 compounds was synthesized (LIBRARY 3) Diversity-based compound selection: method & results

12. Experimental validation and results Primary high throughput screen against Sa DHFR and Spn DHFR at 10  M, select compounds that show >50% inhibition Screen at 25  M for antibacterial activity Also test for thymidine antagonism and activity in the presence of 10% human serum

13. IC50s (  M) against Spn DHFR for hits from Library 1 0.075 1.1 0.01 0.044 0.007 0.012 0.021 0.0098 0.006 0.045 0.002 0.004

14. Selectivity for hits from Library 1 - Except for compound 11, all the compounds were more active against Spn/Sp1 - Only 10 and 13 showed selectivity for bacterial DHFR over Human DHFR compared to TMP - The R isomer of 9 helped identify a new cleft in the enzyme

15. Crystal Structure of (R)-9 N N

16. Summary Overall, the structure-based method gave better results Potent and selective inhibitors of wild type and TMP-resistant S. pneumoniae DHFR were identified The new class of inhibitors had features for better physicochemical properties

17. Discussion The same set of 9948 amines was used for both libraries. But the diversity library gave a poor hit rate. Can that be improved? Is there enough information now available to attempt virtual prediction of selectivity (human vs bacterial DHFR?)

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