Novel Dihydrofolate Reductase Inhibitors. Structure-Based versus Diversity-Based Design and High-Throughput Synthesis and Screening

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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
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Biology

• 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)

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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

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Basic idea for library design

• Replace the greasy hydrophobic piece (red), keep
  the diaminopyrimidine piece (blue)

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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 mM scale -
Purification by HPLC not required - 1392
compounds made in library format
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Paradigm for structure-based virtual screening
N
N
complexed with S. aureus DHFR
- 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
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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
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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.

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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)

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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)

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Diversity-based compound selection method
results

• 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)

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Experimental validation and results

• Primary high throughput screen against Sa DHFR
  and Spn DHFR at 10 mM, select compounds that show
  gt50 inhibition
• Screen at 25 mM for antibacterial activity
• Also test for thymidine antagonism and activity
  in the presence of 10 human serum

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IC50s (mM) against Spn DHFR for hits from Library
1
0.006
0.075
0.045
1.1 0.01
0.002
0.044 0.007
0.004
0.012 0.021 0.0098
0.004
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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
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Crystal Structure of (R)-9
N
N
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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

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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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