It includes -Drug discovery process -Trajectory of computer aided drug design -Data source -Drug design for benign prostatic hyperplasia -Target identification -Ligand design -Docking score -Result and conclusion #CADD #Drug research #prostrate cancer #new research panel #advance research #computer aided drug design

1. SURVIVAL OF THE FITTEST
HYGIA COLLEGE OF PHARMACY, LUCKNOW,UP
Presented by:

2. CONTENT
• Drug Discovery Process
• Trajectory of Computer Aided Drug Design
• Data Source
• Drug Design for Benign Prostatic Hyperplasia
• Target Identification (Structure Based Drug Design)
• Ligand Design
• Docking Score
• Result & Conclusion

3. Process of Drug Discovery & Development - Failure Rate at each Step
Reference: U. Nielsch, U. Fuhrmann, S. Jaroch New approaches to drug discovery Springer Nature, New York (2016).
Duxin Sun, We Gao,” Why 90% of clinical drug development fails and how to improve it? “Acta Pharmaceutica Sinica B,Volume 12, Issue 7, July 2022, Pages 3049-3062.

4. TRAJECTORY OF COMPUTER AIDED DRUG DESIGN

5. LIGAND BASED DRUG DESIGN
Provides crucial insights into the nature of drug-target
interactions and predictive models suitable for lead
compound optimization in the absence of 3D
structures of potential drug targets.
Structure-based drug design is the design and
optimization of a chemical structure, based on available
protein target information, with the goal of identifying a
compounds suitable for biological testing, leading to a
drug candidate..
STRUCTURE BASED DRUG DESIGN

6. Data Source
Chemical Databases Website link Protein Databases Website link
Zinc https://zinc.docking.org/ PDB https://www.rcsb.org/
PubChem https://pubchem.ncbi.nlm.nih.gov/ PIR https://proteininformationresource.or
g/
DrugBank https://go.drugbank.com/ Swiss-Prot https://www.ebi.ac.uk/uniprot/
ChemBank https://pubchem.ncbi.nlm.nih.gov/so
urce/ChemBank
SCOP https://scop.mrc-lmb.cam.ac.uk/
eMolecules https://www.emolecules.com/
Reference: https://canterbury.libguides.com/chem/journal-articles

7. Data Source
Reference: https://canterbury.libguides.com/chem/journal-articles

8. Drug Design for Benign Prostatic Hyperplasia
Dihydrotestosterone (DTH) is causative factor in progression of
BENIGN PROSTATIC HYPERPLASIA (BHP).
Inhibition of 5AR is the treatment for controlling the BPH by diminishing the concentration
of DHT in the prostate and is expected to improve the pathology of disease.
Action of 5-alpha reductase a membrane bound NADPH dependant enzyme
Benign prostatic hyperplasia (BPH), a common condition of males over the age of 50 is characterized by
enlargement of the prostate and results in urinary obstructions.

9. PDB ID: 7BW1
Leu16, Leu19, Ala20, Val23, Lys25, Pro26, Ser27,
Tyr29, Gly30, Lys31, Ala40, Trp44, Gln47, Glu48, His81,
Tyr82, Arg85, Tyr89, Asn93, Arg94, Gly95, Arg96, Tyr98,
Leu102, Arg105, Gly106, Phe109, Cys110, Asn151,
Asp155, Leu158, Arg159, Leu161, Arg162, Ser168,
Tyr169, Arg170, Pro172, Phe177, Ser181, Gly182,
Asn184, Phe185, Glu188, Trp192, Phe207, Ser211,
Phe214, Leu215, Leu217, Arg218, His221, His222,
Tyr226.
Active Pocket of 5αR-2
Residues present in the Pocket
Pocket Surface area (Å2 ) Volume (Å3 )
1 829.083 648.171
In-silico 5-Alpha reductase activity prediction using SBDD
Reference: www.rcsb.org

10. Treatment ejaculatory dysfunction,
breast enlargement, insulin resistance
kidney dysfunction and other metabolic
dysfunctions.
FINASTERIDE 5-alpha reductase inhibitor (5ARI)
DUTASTERIDE
DESIGN of 5-Alpha reductase Inhibitors ND-1 to ND-6
Clinically Approved
Reference: Dhingra N,”Computer-Aided Drug Design and Development: An Integrated Approach”, 1-15. http://dx.doi.org/10.5772/intechopen.105003

11. Docking Score of Novel
5ARIs
S. No. Compounds D-score
1. ND-1 −66.23
2. ND-2 −62.87
3. ND-3 −74.91
4. ND-4 −60.68
5. ND-5 −59.75
6. ND-6 −62.79
7. FN (Finasteride) −57.09
Order of Docking Score
ND3 > ND1 > ND2 > ND6 > ND4 and ND5
ND-3 Highest D-score of −74.91 than FN
Reference drug finasteride (FN) known to possess an
affinity for the 5AR receptor is included in the
docking studies for comparing the docking results.
Reference: Dhingra N,”Computer-Aided Drug Design and Development: An Integrated Approach”, 1-15. http://dx.doi.org/10.5772/intechopen.105003

12. Result
The docking score has been observed in order ND3 > ND1 > ND2 > ND6 > ND4 and ND5.
Similar docking behavior has been observed among all the synthesized compounds in comparison to
FN, by interacting hydrophobically with common amino acid residues THR175A, ALA59A, ALA63A,
SER471A, and ALA264A.
Additional aromatic interactions have also been observed only in ND-6 for amino acid HIS268A. Among
all the newly synthesized derivatives ND-1 to ND-6, ND-3 has been found to bound best with the 5AR
receptor affording the highest D-score of −74.91 than FN. This high score of −74.91 can be attributed
to its strong hydrogen bonds between NO2 at the p-position of the benzene ring with amino acid residue
GLY241A and GLY245A.

13. CADD CONCLUSION
• Cost Effective
• Less Time Consuming
• Accelerate therapeutic Development
• ADMET determination decreases the chances of failure.
• Avoid High Failure Rates.

14. Thank you