drug discovery today: fighting tb with technology

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!jbbillones KeyNotes

Desktop Drug Discovery and Development

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

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

AKA

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Discovery by ‘trial and error’

Alexander Fleming (1928) Penicillium notatum

mold

Amoxicillin (1972)Penicillin - first miracle drug

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Discovery by ‘trial and error’

The Antihistamines

Diphenhydramine (1943) Chlorpheniramine (1950) an SSRI too! (1969)

Promethazine (1940s)

Laboratory Chemicals Histamine

Bovet (1937) conducted over 1000 expts to come up with first antihistamine.

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Drug Discovery and Development

http://thirusaba.blogspot.com

5000 workers, USD 800 M, 12 years

http://thirusaba.blogspot.com/

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Rational Drug Discovery

Kapetanovic, IM. Chemico-Biological Interactions 171 (2008) 165–176

Our Approach: Rational Drug Discovery

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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

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Target Identification and Validation

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Protein Target PredictionDrugCIPHER

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.

Li et al, PLoS One, 5(7) 2010

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Lead Discovery

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http://www.proxychem.com

Lead Optimization

http://www.proxychem.com/

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(cell/enzyme)

Preclinical Tests

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Strategies in Lead Discovery


Structure- Based Design

Ligand- Based Design

De Novo Design

Library Design HTS

Protein StructureKnown Unknown

Kno

wn

Unk

now

n

Liga

nd S

truc

ture


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Protein Structure-Based Drug Design

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Protein Structure Prediction

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http://alexandrutantar.wordpress.com

How do we calculate the energy of a conformation?

Example of a Forcefield

http://alexandrutantar.wordpress.com/

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Ligand Structure Optimization

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Pharmacophore GenerationReceptor-based Pharmacophore

Pharmacophore - t he spa t i a l arrangement of chemical groups that determine its activity

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Ligand-based Pharmacophore

Niu et al. (2012) Chemical Biology and Drug Design, 79(6), 972.

Pharmacophore Generation

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Virtual Screening

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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)

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)

Example:

Molecular Docking

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Rank-ordered list of hits

Virtual Screening Results

#1

#2

#3

#4

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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.

Product of Structure-based RDD

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Capoten Captopril ACE Hypertension 1981 Bristol-Myers Squibb

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

Drugs derived from structure-based approaches

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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

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De Novo Drug Design

Grow

ing

Linking

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Quantitative Structure-Activity Relationship

QSAR

Biological activity = (0D + 1D + 2D + 3D + 4D) (IC50, Ki, MIC) molecular properties

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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

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QSAR Study of Curcuminoids

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Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis

in the Philippines

Current Rational Drug Discovery Efforts in UP

Vistual Screening

5 million compounds

Molecular DockingDe 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)

Billones, JB* et al. (EIDR 2012-2016)

Mtb Target Enzymes

LipB BioA Ldt

MTB PutativeDrug Targets

significantly up-regulated in MDR-TB patients (Rachmann et al. 2005)

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

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)

5,347,140 compounds

131 compounds 19 compounds

Virtual Screening (rigid > flexible > docking)

In silico ADMET filters

For cytotoxicity

assay

Virtual Screening against LipB

Compound 5 Database I

Natural Compounds


The structures are concealed in accordance with patent rules.


Compound 3 Database A


Semi-Synthetic Compounds



Compound 8 Databse A



Synthetic Compounds

Compound 10 Database Z

Compound 11 Database D

Compound 12 Database D Compound 13

Database E


•  Absorption •  Distribution •  Metabolism •  Excretion •  Hepatotoxicity

ADMET

•  Carcinogenicity •  Mutagenicity •  Developmental Toxicity •  Irritancy •  Skin sensitivity •  Aerobic Biodegradability •  etc.

TOPKAT

In Silico ADMET Evaluation

Enslein K, Gombar V, Blake B, 1994

Cheng and Dixon, 2003) Susnow and Dixon, 2003,

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


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


NSC245342 0.001 1.000 1.000+ Very low

absorption

Yes, good


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

Next Step: Cytoxicity Assay

Next Step: Synthesis of Lead Variants

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