Drug Discovery Today: Fighting TB with Technology

1. Desktop Drug Discovery and Development
rational drug discovery
computer-aided drug design (CADD)
computational drug design
computer-aided molecular design (CAMD)
computer-aided molecular modeling (CAMM)
in silico drug design
computer-aided rational drug design
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jbbillones KeyNotes
Junie B. Billones, Ph.D.
Department of Physical Sciences and Mathematics
College of Arts and Sciences and
Institute of Pharmaceutical Sciences
National Institutes of Health
University of the Philippines Manila
The Health Sciences Center
AKA

2. !
jbbillones KeyNotes
Discovery by ‘trial and error’
mold
Alexander Fleming (1928) Penicillium notatum
Penicillin - first miracle drug Amoxicillin (1972)

3. Bovet (1937) conducted over
1000 expts to come up with
first antihistamine.
Laboratory Chemicals Histamine
!
jbbillones KeyNotes
Discovery by ‘trial and error’
The Antihistamines
Diphenhydramine (1943) Chlorpheniramine (1950)
an SSRI too! (1969)
Promethazine (1940s)

4. !
jbbillones KeyNotes
Drug Discovery and Development
http://thirusaba.blogspot.com
5000 workers, USD 800 M, 12 years

5. Our Approach: Rational Drug Discovery
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jbbillones KeyNotes
Rational Drug Discovery
Kapetanovic, IM. Chemico-Biological Interactions 171 (2008) 165–176

6. !
jbbillones KeyNotes
Rational Drug Discovery
http://thirusaba.blogspot.com

7. !
jbbillones KeyNotes
Rational Drug Discovery
Tang et al. (2006) Drug Discovery Today: Technologies, 3(3), 307.
Disease-related
genomics
Target
identification
Target
validation
Lead
discovery
Lead
optimization
Preclinical
tests
Clinical
trials
Computer-Aided Drug Discovery
- Reverse docking
- Bioinformatics
- Protein structure
prediction
- Target
druggability
- Library design
- Docking Scoring
- De novo design
- Pharmacophore
- Target flexibiity
- QSAR
- Structure-based
optimization
- In silico ADMET
prediction
- Physiologically-based
pharmacokinetic
(PBPK) simulations

8. !
jbbillones KeyNotes
Target Identification and Validation

9. Li et al, PLoS One, 5(7) 2010
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jbbillones KeyNotes
Protein Target Prediction
DrugCIPHER
For a query chemical, each protein in the PPI network (genome-wide) is assigned
three concordance scores based on the different regression models. The protein
with large concordance scores is hypothesized to be the target proteins.

10. !
jbbillones KeyNotes
Lead Discovery

11. !
http://www.proxychem.com
jbbillones KeyNotes
Lead Optimization

12. !
(cell/enzyme)
jbbillones KeyNotes
Preclinical Tests

13. Protein Structure
Known Unknown
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jbbillones KeyNotes
Strategies in Lead Discovery
http://thirusaba.blogspot.com
Structure-
Based Design
Ligand-
Based Design
De Novo
Design
Library
Design
HTS
Unknown Known
Ligand Structure

14. !
jbbillones KeyNotes
Protein Structure-Based Drug Design

15. !
jbbillones KeyNotes
Protein Structure Prediction

16. Example of a Forcefield
How do we calculate the energy of a
!
http://alexandrutantar.wordpress.com
jbbillones KeyNotes
conformation?

17. !
jbbillones KeyNotes
Ligand Structure Optimization

18. !
jbbillones KeyNotes
Pharmacophore Generation
Receptor-based Pharmacophore
Pharmacophore
- the spat ial
arrangement of
chemical groups
that determine
its activity

19. Pharmacophore Generation
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jbbillones KeyNotes
Ligand-based Pharmacophore
Niu et al. (2012) Chemical Biology and Drug Design, 79(6), 972.

20. !
jbbillones KeyNotes
Virtual Screening

21. Energy component methods
- based on the assumption that the free energy of
binding interaction can be decomposed into a sum
of individual contributions:
(e.g., LUDI,ChemScore, GOLD, AutoDock)
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jbbillones KeyNotes
Knowledge-based scoring
functions
- using statistics for observed interatomic
contact frequencies and or distances in a
large database of structures
(e.g., PMF, DrugScore, SmoG, Bleep)
Example:
Molecular Docking

22. Virtual Screening Results
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jbbillones KeyNotes
Rank-ordered
list of hits
#1
#2
#3
#4

23. Product of Structure-based RDD
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jbbillones KeyNotes
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enough memory to open the image, or the image may have been
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the red x still appears, you may have to delete the image and then
insert it again.
Nelfinavir in the active site of HIV-1 protease:
AIDS drug nelfinavir (brand name Viracept) is one of
the drugs on the market that can be traced directly to
computer-aided structure-based methods.

24. Drugs derived from structure-based approaches
Capoten Captopril ACE Hypertension 1981 Bristol-
!
Myers
Squibb
jbbillones KeyNotes
Trusopt Dorzolamide Carbonic
anhydrase
Glaucoma 1995 Merck
Viracept Nelfinavir HIV protease HIV/ AIDS 1999 Agouron
(Pfizer)
and Lilly
Tamiflu Oseltamivir Neuraminidase Influenza 1999 Gilead and
Roche
Gleevec Imatinib BCR- Abl Chronic
myelogenous
leukaemia
2001 Novartis

25. !
jbbillones KeyNotes
De Novo Drug Design
A. Binding site comprising
three binding pockets
B. Crystallographic screening
locates molecular
fragments that bind to one,
two or all three pockets
C. A lead compound is
designed by organizing all
three fragments around a
core template
D. Growing out of a single
fragment

26. !
jbbillones KeyNotes
De Novo Drug Design
Growing
Linking

27. !
jbbillones KeyNotes
Quantitative Structure-Activity Relationship
QSAR
Biological activity = (0D + 1D + 2D + 3D + 4D)
(IC50, Ki, MIC) molecular properties

28. !
jbbillones KeyNotes
Quantitative Structure-Activity Relationship
0D 1D 2D 3D 4D
atom count
molecular
weight
sum of atomic
properties
fragment
counts
topological
descriptors
geometrical
atomic
coordinates
energy grid
combination
of atomic
coordinates
and sampling
of
conformations
e.g.
# of OH
# of NH
e.g.
Weiner index
Harrary index
Over 4000 descriptors can be calculated by Dragon software

29. !
jbbillones KeyNotes
Quantitative Structure-Activity Relationship

30. !
jbbillones KeyNotes
QSAR Study of Curcuminoids

31. Current Rational Drug Discovery Efforts in UP
Computer-Aided Discovery of Compounds
for the Treatment of Tuberculosis
Billones, JB* et al. (EIDR 2012-2016)
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jbbillones KeyNotes
in the Philippines
5 million
compounds
Vistual Screening
Molecular Docking
De Novo elaboration
Chemical synthesis
Bioassay
Pantothenate synthetase
(involved in synthesis of Vit B5 for growth)
FtsZ
(involved in bacterial cell division)
lipB
(involved in cofactor synthesis,
Essential for growth)
menB
(involved in synthesis of Vit K2 for growth)

32. MTB PutativeDrug Targets
Mtb Target
Enzymes
LipB BioA Ldt

33. Lipoate Protein Ligase B (LipB)
catalyzes the biosynthesis of lipoate, a
cofactor responsible for the activation of
key enzymes in the Mtb metabolic
pathway (Spalding et al. 2010)
Mtb has no known back-up mechanism
that can take over the role of LipB in its
metabolic machinery (Rawal et al. 2010)
lipB knockout model fails to grow
significantly up-regulated in MDR-TB
patients (Rachmann et al. 2005)

34. Structure-based Screening
(A) Defined binding sphere (red) on
the binding site of LipB. (B) Structure-based
pharmacophore model based
on the defined binding site of LipB.
(A) Three dimensional structure of lipoate protein ligase B
(LipB). (B) Molecular overlay of downloaded protein
structure (blue) and prepared protein structure (pink);
(RMSD = 0.71 Å).
Billones et al. Orient. J. Chem., 29(4), 1457-1468 (2013)

35. Virtual Screening against LipB
In silico
ADMET filters
19 compounds Virtual Screening
(rigid > flexible > docking)
131 compounds
5,347,140 compounds
For
cytotoxicity
assay

36. Compound 5
Database I
Natural Compounds
Compound 1
Database I
Compound 2
Database I
The structures are concealed in accordance with patent rules.
Compound 3
Database A
Compound 4
Database A

37. Semi-Synthetic Compounds
Compound 6
Database A
Compound 7
Database A
Compound 8
Databse A
Compound 9
Database A
The structures are concealed in accordance with patent rules.

38. Synthetic Compounds
Compound 10
Database Z
Compound 11
Database D
Compound 12
Database D Compound 13
Database E
The structures are concealed in accordance with patent rules.

39. In Silico ADMET Evaluation
• Absorption
• Distribution
• Metabolism
• Excretion
• Hepatotoxicity
ADMET
Cheng Susnow and Dixon, 2003, and Dixon, 2003)
• Carcinogenicity
• Mutagenicity
• Developmental Toxicity
• Irritancy
• Skin sensitivity
• Aerobic Biodegradability
• etc.
TOPKAT
Enslein K, Gombar V, Blake B, 1994

40. ADMET Properties
Compound Carcinogenicity Mutagenicity
Developmental
Toxicity
Potential
Absorption Solubility
CYP2D6
Inhibition
Plasma Protein
Binding
Hepatotoxicity
NSC68342 1.000 0 1.000* Low absorption
Optimum
solubility
Inhibitor Binding is >90% Toxic
NSC96317 1.000* 0 0
Very low
absorption
Good solubility Non-inhibitor Binding is <90% Toxic
NSC118483 1.000* 0 0.998
Very low
absorption
Yes, optimal
solubility
Non-inhibitor Binding is >90% Non-toxic
NSC118476 1.000 0 1.000
Very low
absorption
Yes, optimal
solubility
Non-inhibitor Binding is <90% Toxic
NSC118473 0 0 0.959*
Very low
absorption
Yes, optimal
solubility
Non-inhibitor Binding is >95% Toxic
NSC164080 0 0 0.204
Good
absorption
Yes, good
solubility
Non-inhibitor Binding is >90% Toxic
NSC211851 0 0 0.001
Very low
absorption
No, too soluble Non-inhibitor Binding is <90% Toxic
NSC227190 0.999 0.265 1.000+
Very low
absorption
Yes, good
solubility
Non-inhibitor Binding is >95% Toxic
NSC245342 0.001 1.000 1.000+
Very low
absorption
Yes, good
solubility
Non-inhibitor Binding is >95% Toxic
TOPKAT VALUES: 0 – 0.29: Low probability; 0.30 – 0.69: Indeterminate; 0.70 – 1.00: High Probability; *Within Optimum Prediction Space (OPS) and OPS limit, and the probability value can be
accepted with confidence; +Outside of OPS but within OPS limit

41. Next Step: Cytoxicity Assay

42. Next Step: Synthesis of Lead Variants

44. !
jbbillones KeyNotes
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For queries:
jbbillones@up.edu.ph