Transcript Slide 1
Hierarchic Informational Technology for Effective Molecular Design of Drug Agents
From Science to Business Workshop 11-12 October 2006, Kyiv
Victor E. Kuz’min +380-48-7225127 [email protected]
A.V. Bogatsky Phys. – Chem. Institute NAS of Ukraine Odessa
Talk outline
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Introduction Problem Description & Market Need Solution of Problem The Principle Steps of QSAR Brief HIT4QSAR description The Principle Steps of HIT4QSAR Experimental results 1 – 4 The unique and overwhelming HIT4QSAR Advantages of HIT4QSAR Relatively Competitive Approaches and Models advantages The comparative analysis of efficacy of HIT4QSAR Competitive Matrix Stage of development of HIT4QSAR Targeted Market Segment Opportunity for joint work Contact information
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Proprietary information statement • The technology material presented in this talk is available for licensing or joint product development.
• None of the slides contain any confidential or proprietary information which would prevent patenting the technology.
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Introduction
"
There are
10 180 10 18
likely drugs, possible compounds,
10 7
known compounds,
10 6
commercially available compounds,
10 6
compounds in corporate databases,
10 4
compounds in drug databases,
10 3
commercial drugs and
10 2
profitable drugs "
A. Weininger J. Chem. Inf. Comput. Sci ., 37, 138 (1997)
5 Problem Description & Market Need
Ongoing 3-4 years 1 year 6-8 years 2-3 years Timeline in a drug discovery project COMPOUNDS DISCOVERY 10,000-20,000 compounds SAFETY TESTING
DRUG DISCOVERY
PREPARED IND SUBMISSION 1,000 compounds CLINICAL DEVELOPMENT Metabolism & Pharmacokinetics Formulation Research Process Development Clinical Phase(I, II, III) Toxicology Ooms, F. Curr. Med. Chem. 2000, 7, 141-158 DRUG SUBMISSION 10 compounds 1400 1 compound 1200 •How to decrease the set of the explored compounds?
•How to accelerate the drug discovery process? •How to reduce financial expenditure?
1000 800 600 400 200 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
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Solution of Problem
QSAR
Quantitative Structure – Activity Relationship STRUCTURE ACTIVITY RELATIONSHIP Software for QSAR HIT4QSAR – Hierarchical Informational Technology for QSAR (PCI NAS, Odessa, Ukraine) CoMFA - Comparative Molecular Field Analysis (Tripos, USA) CoMSiA - Comparative Molecular Similarity Analysis (Tripos, USA) HQSAR - Holographic QSAR (Tripos, USA) EVA - Eigenvalue Analysis (Tripos, USA) CODESSA – Comprehensive Descriptors for Structural and Statistical Analysis (SemiChem, USA) Cerius 2 - QSAR software (Accelrys , USA) EMMA – Effective Modelling of Molecular Activity (MSU, Russia) DRAGON - QSAR software (MU, Italy)
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Training set
Structure 2 3 … … O N Obs.
activity 2.2
3.0
4.1
… O 0.5
… O
Calculation of the structural parameters
S 1 S 2 S 3 … S M 1.2
1.3
1.7
… 1.9
2.7
3.3
… O 1 2.8
3.4
4.8
0.1
… … 2 8 … … … … 2.1
-2.1
… 10
The Principle Steps of QSAR
QSAR
A = f ( S 1 ,S 2 ,S 3 ,…,S M )
Verification of model Test set
1 Structure Obs.
activity 2.1
Pred.
activity 2.5
2 OH Cl 3.2
3 O … … NH 2 4.0
… 2.6
4.2
… Structures of new compounds Selection of structural parameters promotes the activity
Prediction
1 2 Structure CH 2 Obs.
activity ?
NH ?
Pred.
activity 2.6
5.0
3 H N … … NH … ?
6.0
…
Molecular design
H N H C NH 2 O NH 2 O O H C NH 2 O NH 2 O
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Brief
HIT4QSAR
description
1D 2D 3D Molecular composition С 3 H 7 O 2 N Structural formula
H H 3 C O NH 2 OH
Space structure (single conformation) 4D Set of the conformers
Molecules ……… m i ……… Training set Activity ……… A i ……… Simplex representation of molecular structure Weight parameters for atoms Charge Lipofilicity Polarizability Informational Field H-Bond
The principle steps of HIT4QSAR
New molecules Prediction Predicted activity Statistical characteristics R – correlation coefficient Q – cross-validation coefficient QSAR A i =f(S i1 , S i2 , …q i1 , q i2 , …) Test set Molecules ……… Activity ……… m j ……… A j ……… Selection of molecular fragments that determine the Activity Molecular Design of New Perspective Compounds with High Activity Structure parameters calculation Local parameters (Quantitative of simplexes) m i → S i1 , S i2 , … Fourier transformation Integral parameters m i → q i1 , q i2 , … Estimation of contribution to interaction “molecule-biological target” Hypotheses about mode of of Biological Activity
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O N N H H N H H N N O LgTID 50 =0.2 O H 2 C N H H N O H 2 C COONa LgTID 50 =0 COONa H 2 N C H 2 LgTID 50 =0.05 COOH O O C 2 H 5 OH N N C 2 H 5 OH O O LgTID 50 =1.66 O (CH 2 ) 5 N H N O H N (CH 2 ) 5 COONa NaOOC LgTID 50 =0.25 O N H O O O O LgTID 50 =-1.5 O Me N Me O (CH 2 ) 5 N H H N (CH 2 ) 5 COONa NaOOC LgTID 50 =0.25 HOOC COOH H 3 C N CH 3 LgTID 50 =1.25
Experimental results 1 ANALYSIS OF THE ANTI-INFLUENZA ACTIVITY (LgTID 50 ) Training set
H 3 C O O (CH 2 ) 5 N H H N (CH 2 ) 5 COOH HOOC LgTID 50 =-0.45 O O O H 3 C N CH 3 LgTID 50 =0.1 O CH 3 O (CH 2 ) 5 N H N O H N (CH 2 ) 5 COOH HOOC LgTID 50 =-0.25 O O H 2 N N H H 3 C N CH 3 LgTID 50 =-0.25 N H NH 2 O H 2 C N H H N O H 2 C OH COOH LgTID 50 =0.72 COOH OH N N O LgTID 50 =5.33
Statistical characteristics of QSAR models
QSAR models R 2 Q 2 H 2 N LgTID 50 =0.15 COOH CH LgTID 50 =3.5 3 NH 2 O H C N H N H CH 3 N O H C CH 3 LgTID 50 =2.5 O O O N C 2 H 4 C 2 H 4 O O O LgTID 50 =2 H 2 N LgTID 50 =0.75 COONa N N H OH LgTID 50 =-5 N H O H N N N N N LgTID 50 =2.8 H N H 5 C 2 OOC O O N N O O LgTID 50 =1.66 COOC 2 H 5 H 2 O C N H N H N O H 2 C H 2 NOOC O O N N O O LgTID 50 =-0.75 O O COONH 2 H 3 C H 3 C N CH 3 LgTID 50 =0.33 CH 3 O O COOH COOH LgTID 50 =0.71 O O N O NH 2 LgTID 50 =1.3 H 2 N O N N N O H O LgTID 50 =6.25 OH OH H O NaOOC H 3 C NH 2 LgTID 50 =4.75 COONa N CH 3 LgTID 50 =0.33 N COOH LgTID 50 =2.2 2D 4D 3D 0.968
0.939
0.978
0.943
0.980
0.961
11 Experimental results 2 Color-coding of molecular fragments with standpoint of their influence on the activity
- enhance the activity - decrease the activity
12 Experimental results 3 The relative influence of molecular fragments on value of activity,
lgTID 50 Enhance the activity
O N N N O O O 2.5
OH N 2.5
2.0
H 2 N H 3 C C 0.5
1.5
-CH 2 -CH 2 -NH -0.5
1.4
Decrease the activity
-CH 2 -COOH -0.25
-CO-NH -0.2
2
13 Molecular design of structures with potentially high anti-influenza activity
OH N OH N Structure O O 356-3054 H O N OH N N 324-2381 OH O N O 241-938 N O O O OH Observed Predicted 5.1 5.6 3 5.6 6.0 2.4 n.d. 5.5 O O 470-4121 HOOC N OH N n.d. 6.1 OH 460-5083 O N H Me H N O N O Me O H N N H 547-7129 n.d. 5.1
14 Experimental results 4
The relative influence of some physical and chemical factors into the activity estimated from the HIT4QSAR models
10% 35% 25% 11% 19% Dispersionic Electrostatic Hydrofobic Shape of molecule Other
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The unique and overwhelming
HIT4QSAR
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advantages simplex representation are:
of molecular structure (
SiRMS
),
Fourier transformation
of structure parameters spectrum, characteristics of molecular
informational field
– all of them is providing
universality, diversity and flexibility
of description for compounds of
different structural types
; • HIT4QSAR that depending on the concrete aims of research allows to construct the
optimal strategy
of QSAR models generation, avoiding at the same time the superfluous complication that doesn't results in the adequacy increase. • on the every stage of
the molecular structure features that are important for the studied activity
HIT4QSAR , and exclude the rest. It shows unambiguously the limits of expedient QSAR models complication and allows not to waste superfluous resources for needless calculations.
usage we can
determine
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Advantages of
HIT4QSAR
Relatively Competitive Approaches and Models
(CoMFA, CoMSiA, HASL, CODESSA, EMMA, DRAGON, HQSAR)
• Unlike HIT4QSAR , a majority of descriptors in CODESSA, EMMA, DRAGON have interpretation difficulties and little suitable for molecular design. These approaches are applicable, mainly, for an activity prediction. • HIT4QSAR does not have the restrictions of such well-known and widely used approaches as CoMFA (Comparative Molecular Field Analysis), CoMSIA, and HASL, usage of the lasts is limited in the structurally homogeneous set of molecules and only one conformer.
• HIT4QSAR has not the HQSAR restrictions (only topological representation of molecular structure) and lacks (ambiguity of descriptors formation when procedure of hashing of molecular holograms is realized). Besides, on the contrary to HQSAR, in SiRMS, different physical and chemical properties of atoms (charge, lipophilicity, etc.) can be taken into account.
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Y
The comparative analysis of efficacy of HIT4QSAR
Angiotensin Converting Enzyme (ACE) inhibitors R3 R2 n R4 N H R1 O N Work set – 76 compounds Test set – 38 X
Results from
Sutherland J.J.; O’Brien L.A.; Weaver D.F. A Comparison of Methods for Modeling Quantitative Structure—Activity Relationships. J. Med. Chem. 2004, 47, 5541-5554
CoMFA Cerius2 Comparative Molecular Field Analysis CoMSiA Comparative Molecular Similarity Indices Analysis EVA Eigenvalue Analysis HQSAR Holographic QSAR QSAR software
1 0.95
0.9
R
0.85
0.8
CoMFA
2
CoMSIA 0.75
0.7
EVA
HIT4QSAR on Base Simplex Representation of Molecular Structure
HQSAR Cerius SiRMS 2D SiRMS 3D
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
Q
CoMFA
2
CoMSIA EVA HQSAR Cerius SiRMS 2D SiRMS 3D
Our models are more adequately and have more high statistical rates.
18 Criterion
Competitive Matrix
Adequcy of representation of molecular structure 1D-4D
HIT4QSAR 1D - 4D
CoMFA HASL GRID MolLa t
3D No Yes
Molecular alignment
problem
Explicit consideration of stereochemistry and chirality Consideration of physical-chemical properties of atoms Charges, lipophilicity, polarizability etc. Possibility of molecular design
Yes Yes Yes Partly Partly Partly Impair the method quality
CODESSA EMMA DRAGON
2D
H 3 C NH 2 O OH
3D No No Partly No
HQSAR H 3 C
2D
O NH 2 OH
No No No Partly Improoving the method quality
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Stage of development of HIT4QSAR
HIT4QSAR
is realized as
software
, tested, available for demonstration, field testing was carried out.
Here are presented high activity compounds, which was designed by means of developed HIT4QSAR
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Targeted Market Segment
• The developed HIT4QSAR
property”.
allows to decide
any tasks “structure –
On the basis of this technology, carry out of molecular design of various compounds is possible with the complex of useful properties.
We have experience etc.
of molecular design of
perspective drugs
(antiviral, antitumor, psychotropic, antimicrobial, anti-inflammation, etc),
pesticides, optical materials, complexones, food supplements, quenching agents,
The results of the use of our technology can be interesting and useful for all, who carry out development of new drugs, materials, reagents, etc.
•
Potential consumers
of HIT4QSAR
developers of software
for QSAR. are
pharmaceutical companies
and • Cost of project that related to the prediction and design of new drugs, substances and materials depends on the amount of concrete tasks, presence and content of information for the construction of training set, special requirements to the results.
Rough estimate – 10 000 $ for: test set 30-50 compounds, level of modeling - 2D, term of contract - 1 month
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Opportunity for joint work
• We seek a potential clients, which need the results of the use of our technology. We are ready to
vend the service –
structure optimization
,
molecular design, prediction of new compounds with complex of demand properties – by means of HIT4QSAR .
• We are ready for collaboration with colleagues – chemists- synthesist and specialists of investigation different properties of substances (biologist, virologists, pharmacologist, etc) for carry out joint projects.
Contact information
Doctor of chemical science Victor E. Kuz’min Head of laboratory on theoretical chemistry A.V. Bogatsky Phys.
– Chem. Institute NAS of Ukraine, Odessa +380-48-7225127 [email protected]