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Fragment Based-GQSAR for GPCR Studies

Presenter:Kundan B. IngaleApplication Scientistkundani@vlifesciences.com

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• Mediate biological signalling in health/disease

• Commercially validated - 40% of top 100 drugs

• < 2% of proteins in PDB

• Difficult to crystallize or too big for NMR

• Other Issues

• Non-alpha helices

• Loops may contain other secondary structures and domains

• Bias towards TM proteins that are easy to crystallize

• Energetics of TM proteins not completely understood (polar interactions or van der Waals interactions play role in role in helix-helix interaction)

GPCR…

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Challenges in Multi-target ligand design for GPCRs

• Design of Multi Targeted ligands:

• Advantages over single drug for single target

• Ligands that simultaneously bind to 5HT1A and 5HTT have shown good promise in treatment of major depression

• Can structure based method be used ?

• Are Ligand based methods useful ?

• Shape Based Comparisons ?

• Pharmacophore based ?

• QSAR ?

• What Next ..?

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Key elements of GQSAR

Where is GQSAR useful

•Lead optimization by using site specific clues from GQSAR model •Scaffold hopping by choosing

• groups/fragments satisfying descriptor

• ranges of actives in the dataset•Novel library generation along with predicted activity of ligands

• Alignment independent fragment based QSAR modeling

• Conformer independent method• GQSAR models generation for both

congeneric and non-congeneric data• Provides site specific clues• Patent pending method

Group QSAR: For lead optimization

Publication references

• QSAR Combi Science 2009, 28:36–51• J Mol Graph Mod 2010;28:683-694

Fig: GQSAR Workflow

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Dataset for GQSAR

• BindingDB database: 162 molecules (5HT1A receptor and 5HTT Inhibitory activities)

• Biological activity: Binding affinity data (Ki nM)

ClassNumber of molecules

Activity (Ki nM) Min Max

C1 (Piperidine) 69

HT1A 0.91 3200.00HTT 0.24 9006.00

C2(Piperazine) 56

HT1A 0.12 475.00HTT 1.30 3900.00

C3(non ring N atom) 26

HT1A 2.00 1470.00HTT 0.50 4700.00

C4(1,2,3,6-

tetrahydropyridine) 8

HT1A 10.90 92.60

HTT 19.80 387.00C5

(azabicyclo[3.2.1]oct-3-ene) 3HT1A 127.70 357.00HTT 8.50 33.00

NO

OH

NH

CH3

S

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Representative Molecules

NO

O

O NH

F

NO

O

OCH3

N

N

O

NH

CH3O

Piperazine (C2)

Non Ring Nitrogen (C3) 1,2,3,6-tetrahydropyridine (C4)

azabicyclo[3.2.1]oct-3-ene (C5)

Piperidine (C1)

NH

NH

O

NH

O

F

Fragmentation Pattern

Fragment R1 (aromatic region): aromatic ring connected with the core of the molecule i.e. fragment R2

Fragment R2 (anchor region): substituent present in the center of the molecule

Fragment R3 (flexible region): substituent connected to other end of the fragment R2

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Relationship between 5HT1A and 5HTT inhibition

Design and optimize molecules for multi-target activity

r2 = 0.051

Fig: Scatter Plot of pKi_5HT1A Vs pKi_5HTT

Data Processing and Model building

• Biological Activity: negative logarithm of binding affinity i.e. pKi (nM)

• Descriptors: 2D group based descriptors and their squared terms

• Training set: 93 molecules ( From scaffold C2-C5)

• Test set : 69 molecules (From scaffold : C1)

• GQSAR enables identification of common set of descriptors influencing the binding of ligands to both the targets

• GQSAR model: (without Fragment Interaction Descriptors)

• 62% (r2 = 0.620) of variation in the 5HT1A activity

• 49 % (r2 = 0.490) of variation in the 5HTT activity

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GQSAR Model

• Fragment Interaction Descriptors:

• Example : R1_slogp*R2_smr: Product of log P of fragment R1 and molar refraction of fragment R2.

• GQSAR model: (with Fragment Interaction Descriptors)

• 71% (r2 = 0.710) of variation in the 5HT1A activity • 83% (r2 = 0.830) of variation in the 5HTT activity

Fig: Contribution Plot for descriptors in GQSAR equation

Model Representation

Model Validation

• Test Set : 69 molecules (chemical class not present in the training set).

• Model Applicability Domain Check: 50 molecules out of 69

• Prediction Correctness: molecules predicted within ±1 log units

• 5HT1A: 46 (92%),

• 5HTT: 40 (80%)

• Prediction accuracy: > 80% with a new scaffold

Aromatic region (R1) Descriptors: R1-4pathClusterCount (6.33, 1.8)*

Anchor region (R2) Descriptors: R2-PSAExclPandS (2.62, 2.92)*

Flexible region (R3) Descriptors:

R3-4pathClusterCount^2 (-1.47, -1.11)*

R3-SssNHE-index^2 (-4.39, -3.73)*

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Model Interpretation

* Figures in bracket indicate contribution of descriptor towards 5HTT and 5HT1A respectively

↑ Branched substitution

↑ Polar surface

↓ Branched Substitution↓ H-don N atom attached to 2 heavy atoms pKi (5HTT): -0.97;

pKi(5HT1A) : 0.04

N

N

O

NH

CH3O

N

CH3

OOCH3

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• Aromatic (R1) and Flexible (R3) regions interaction descriptors:

• R3-4pathClusterCount*R1-T==2 (-19.63, -8.22)

• R3-smr*R1-T==6 (-6.83, -14.75)

Model Interpretation (Interaction Descriptors)

* Figures in bracket indicate contribution of descriptor towards 5HTT and 5HT1A respectively

↓sp2 atoms separated by 2 bonds

↓ Branched substitution

↓sp2 atoms separated by 6 bonds

↓ Molar refractivity

pKi (5HTT): -0.59; pKi(5HT1A) : -0.3

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Summary and Conclusion

• GQSAR method can be successfully applied to non-congeneric series of molecules

• With GQSAR, one can identify common set of descriptors that influence the multi-targeted activities of ligands

• GQSAR method provides site specific clues for Lead optimization

• GQSAR method can be effectively used to design Multi Targeted ligands

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References

• GQSAR is patented by VLife Sciences Technologies Pvt. Ltd.

• References:

• "Group Based QSAR (G-QSAR) : Mitigating Interpretation Challenges in QSAR”, QSAR & Combinatorial Science, 28(1),36–51(2009)

• "A Comprehensive Structure-Activity Analysis of Protein Kinase B-alpha (Akt1) Inhibitors“, Journal of Molecular Graphics and Modelling, (2010) doi: 10.1016/j.jmgm.2010.01.007

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