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CADD and Molecular
Modeling : Importance inPharmaceutical Development
Dr. Sanjeev Kumar SinghDepartment of BioinformaticsAlagappa Universitye-mail- skysanjeev@gmail.com
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Working at the Intersection
Structural Biology
Biochemistry
Medicinal Chemistry
Toicology
Pharmacology
Biophysical Chemistry
In!ormation Technology
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Structural Biology
"astest gro#ing
area o! $iology
Protein and nucleic
acid structure and
!unction
%o# proteins
control livingprocesses
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Medicinal Chemistry
&rganic Chemistry
Applied to disease
'ample: design
ne# en(ymeinhi$itor drugs
doxorubicin (anti-cancer)
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Pharmacology
Biochemistry o! %uman Disease
Di!!erent !rom Pharmacy: distri$utiono! pharmaceuticals) drug deliverysystems
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*e# Ideas "rom *ature
Natural ProductsChemistry
Chemical Ecology
During the next twodecades: the majoractivity in organismalbiology
Examles: enicillin!taxol (anti-cancer)
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Bio+Chem,in!ormatics
The collection) representation and organisation o!
chemical data to create chemical in!ormation) to #hich
theories can $e applied to create chemical kno#ledge-
Aim Toeamine ho# computational techni.ues can $e used
to assist in the design o! novel $ioactive compounds-
To give an idea o! ho# computational techni.ues can
similarly $e applied to other emerging areas such as Bio,
in!ormatics) Chemin!ormatics / Pharmain!ormatics-
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&vervie#
Drug discovery process
%o# do drugs #ork0
&vervie# o! Computer,Aided DrugDesign
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Pharmaceutical+AgrochemicalIndustry
Identi!ication o! novel compounds #ith use!ul andcommercially valua$le $iological properties-
vastly comple) multi,disciplinary task
many stages over etended periods o! time
1isk most novel compounds do not result in a drug-
those that do may cause unepected) long,termside,e!!ects"
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Why CADD20
Drug Discovery today are !acing a serious
challenge $ecause o! the increased cost and
enormous amount o! time taken to discover
a ne# drug) and also $ecause o! rigorouscompetition amongst di!!erent
pharmaceutical companies-
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Drug Discovery / Development
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
cale-up
F!" approval
(2-3 years)
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Drug Development Process
On average it takes 12 -15years and costs ~$500 -800million to bring a drug tomarket
develop
assay
lead
optimisation
lead
identification
clinical
trials
to market
10,000scompounds
1 drug
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Cont2
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Technology is impacting this process
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and #personali$ed% targets
creening up to 100&000 compounds a
day for activity against a target protein
'sing a computer to
predict activity
apidly producing vast numers
of compounds
*omputer grap+ics , models +elp improve activity
issue and computer models egin to replace animal testing
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utomating t!e "## rocess
Gene sequence data
X-ray or
Homology
Screening
Library synthesis
%ed "!em&"ombic!em
'ibmaker(%
Designed libraries
Ligand binding data
PharmacophoreModel
Skelgen
Designed Templates
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Target
Identification
Target
Validation
Lead
IdentificationLead
Optimization
Target discovery Lead discovery
!ases o) "##
SAVING12 15 years, Costs: 500 - 800 million
US $
VHTSVHTS
Similarity
analysis
Similarity
analysis
Database
filtering
Database
filtering
Computer ided
Drug Design!CDD"
de novo
design
de novo
design
diversity
selection
diversity
selection
#iop$ores#iop$ores
lignmentlignment
Combinatorial
libraries
Combinatorial
libraries
D%&TD%&T
'S('S(
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%o# Drugs Work
Substrate&nzyme
+
&nzyme)substrate
comple*
Lock)and)key model
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Methodologies and strategies o!CADD:
Structure $ased drug design 3SBDD4 5DI1'CTD'SI6*7
"ollo#ed #hen the spatial structure o! thetarget is kno#n-
8igand $ased drug design 38BDD4 5I*DI1'CTD'SI6*7
"ollo#ed #hen the structure o! the target isunkno#n-
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Computer,Aided Drug Design
9,D target structure unkno#n 38BDD4 1andom screening i! no actives are kno#n
Similarity searching
Pharmacophore mapping
SA1 3;D / 9D4 etc-
Com$inatorial li$rary design etc-
Structure,$ased drug design 3SBDD4 Molecular Docking
De novo design
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#n Pharmacohore$
Pharmacoporic Studies on AC'inhi$itors
Pharmacological Studies on %I
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What is Pharmacophore20
Pharmacohore model
%et o& oints in sace de&ining the binding o& ligandswith target"
'ey &actors in develoing such a model are the
determination o& &unctional grous essential &orbinding! their corresondence &rom one ligand toanother! and the common satial arrangement o& thesegrous when bound to the recetor
The pharmacophore model o! %I< protease-
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Pharmacophore2--0 a molecular &ramewor that carries (horos) the
essential &eatures resonsible &or a drug*s(harmacon) biological activity+ Paul 'rlich) early=??@
a set o& structural &eatures in a molecule that isrecogni,ed at a recetor site and is resonsible &orthat molecule*s activity+ Peter 6und) =?
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Basic "eatures
A set o! !eatures common to a series o! activemolecules
What are the !eatures20 %BD
%BA ve /,ve charged groups and %ydropho$ic regions
"unctional groups or molecules #ith similarphysical and chemical properties
Bioisosteres , su$stituents or groups thathave chemical or physical similarities and#hich produce $roadly similar $iologicalproperties
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Pharmacophore model
%et o& oints in sace de&ining the binding o& ligandswith target"
'ey &actors in develoing such a model are thedetermination o& &unctional grous essential &orbinding! their corresondence &rom one ligand toanother! and the common satial arrangement o& thesegrous when bound to the recetor"
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AC'
Angiotensionconverting en(yme
Converts
angiotensinI toangiotension II
Inhi$its $radykinin3vasodilator4
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AC',inhi$itor
&rally availa$le/ potent drug
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AC' distance map
oints de&ined
.ive distances
de&ined
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Donor Hydrophobic core
Charged negative
Acceptor
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deoy nucleoside
triphosphate !d"#$%
&'()' dideoy nucleoside)'-a*ido thymidine !A+#%
&'()'- didehydro dideoy nucleoside)'-nitro nucleoside
,S$ contours for nucleosidic drugs. ed coloured contours indicate a value of -./0 forelectrostatic potential and yello1 contours indicate a value of -/./2
Pharmacophoric Features ofNucleosidic HIV-1RT Inhibitors
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Concluding remarks on Nucleosidicinhibitors
Different su3stituents at the )
position sho1 similar sugar ring puckeringand only slight differences in nucleosidic 3ase disposition and interactionsprotein.
,S$ plots have clearly indicated that the charge environment of the
drugs is complementary to the receptor charge environment. $ositivepotential areas have 3een o3served in the active site of 456-0# 1hereD"A 3inding occurs.
P+armacop+oric Features of .ucleosidic HI/-1 In+iitors. Arpita 7adav8 and Sanjeev Kumar Singhioorg , ed *+em 11& 2003& 101
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-09.0) kcal:mol &')' dideoy thymidine-0).)) kcal:mol A+# -0;.-"itro nucleoside-&0.)/ kcal:mol
).2?
Threshold interaction energ of NRTI!s"nucleosidic inhibitors for Re#erse
transcriptase$ to undergo competiti#e
inhibition
0 2 * + 8 10 12
-22
-20
-18
-1+
-1*
-12
Inter
action&nergy
!+cal,mol"
,"50
%.
orrelation of interaction energy 1ith potency
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orrelation graph indicates the reuirement of a threshold 3indingenergy C0& kcal:mol for the drug to 3e a3le to undergo competitiveinhi3ition efficiently. ess than this 3inding energy: interaction energy 1ill
make the drug ineffective or very high concentrations 1ill 3e reuired forinhi3ition of en*yme. Ehich may lead to cytotoicity.
+res+old interaction energy of .I4s (nucleosidic in+iitors for everse
transcriptase) to undergo competitive in+iition
Arpita 7adav8 and Sanjeev Kumar Singh ioorg , ed *+em letts 1& 200&2677-260
ncluding remarks on interaction energ studi
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2.785
2.021
2.514
$yrrolyl hetro aryl sulfone 1ith lysine
2.514
4.0
2.785
2.514
Pyrrolyl hetro aryl sulfone with HEPTlys101
Pyrrolyl hetro aryl sulfone with trovirdinelys101
Common binding mode for structuralland chemicall di#erse non- nucleosidic
HIV-1RT inhibitors
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Concluding remarks of Non nucleosidi
onformational study of non-nucleosidic drugs indicated that eachdrug has a F6>- shaped conformation.
ach drug has a -"4 group in a position that it can make 4- 3ond
1ith the car3onyl group of lysine 0/0 in conformity 1ith earlier studieson pyrrolyl hetero aryl sulfone. #his indicates the importance of lysine0/0 in 3inding ""#5>s.
*ommon inding mode for structurally and c+emically diverse non- nucleosidic
HI/-1 in+iitors8
"rpita 9adav: and an;eev *H=& 723& 2005& 205-20?
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DISC&: DIStance C&mparisons
6enerate some num$er o! lo#,energy con!ormations
!or each active compound
The resulting con!ormations are represented $y the
positions o! potential pharmacophore points-
%ydrogen,$ond donors and acceptors charged
atoms ring centroids and centres o! hydropho$ic
regions-
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uantitative Structure,Activity1elationships 3SA14
A SA1 relates a numerical description o! molecular structure
or properties to kno#n $iological activity
Activity f 3molecular descriptors4
Success o! SA1: right descriptors right method 3!orm o!
f 4
A SA1 should $e
eplanatory 3!or structures #ith activity data4
predictive 3!or structures #ithout activity data4
A SA1 can $e used to eplain or optimise: localised properties o! molecules such as $inding
properties
#hole molecule properties such as uptake and distri$ution
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9D SA1
CoM"A and CoMSIA
Molecules are descri$ed $y the values o!molecular !ields calculated at points in a 9Dgrid
The molecular !ields are usually steric andelectrostatic
Partial least s.uares 3P8S4 analysis used tocorrelate the !ield values #ith $iologicalactivity
A common pharmacophore is re.uired-
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Esing the Model
The P8S results arepresented as contourplots
Steric Bulk:
6reen Steric"avoura$le
Fello# StericEn!avoura$le
'lectrostatics:
1ed 'lectronegative"avoura$le
Blue 'lectronegativeEn!avoura$le
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- reen contours stand )or points /!ere sterically bulkier groups areanticipated to increase t!e biological activity3- (!e yello/ contours are used to underscore t!e points /!ere bulkiergroups could lo/er t!e biological property3- (!e electrostatic red plots s!o/ /!ere t!e presence o) a negativec!arge is e6pected to en!ance t!e activity3- (!e blue contours indicate /!ere introducing or keeping positivec!arges are e6pected to better t!e observed activity3
"o% 7teric "ontours "o% lectrostatic "ontours
- 3D-S!" #oM$! Study on !minothia%ole Deri&ati&es as #yclin Dependent 'inase (
)nhibitors* +igus Dessale, San.ee& 'umar Singh/ and P*0* 1haratam S!" #omb* Sci*
(245 (667 89-:4*
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SA1 W&1G2
The developed model sho#ed a strong correlative and
predictive capa$ility having a cross validated correlation
co,e!!icient o! @-H !or CDGH and @- !or CDG;
inhi$itions-/ 3D-QSAR CoMFA studies on Indenopyrazole as CDK2 Inhibitors
San!ee" Ku#ar Sin$h%& 'i$us Dessale(& and ) * +harata# ,ur of Med Che#& ./& 2001& /3/0-/3/
The conventional and predictive correlation coe!!icients
#ere !ound to $e respectively @-?H9 and @-@J !or CDG=
and @-? and @-J !or CDG;-/ 3D-QSAR CoMFA Study on 4indole Deri"ati"es as Cy5lin
Dependent Kinase / 6CDK/7 and Cy5lin Dependent Kinase 26CDK27 Inhibitors San!ee" Ku#ar Sin$h%& 'i$us Dessale(& and )* +harata#& Med Che# 36/7& 2008& 89-:.
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Structure Based Drug Design
eter!ine Protein "tru#ture
$dentify $ntera#tion "ites
e %ovo esi&n ' ata(ase
Evaluate "tru#ture
"ynthesi)e *andidate
Test *andidate
ead *o!,ound
Discovery or design ofmolecules that interact1ith 3iochemical targetsof kno1n )D structure
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Structure $ased drug design
Molecular data$ase mining
Compounds #ith $est complementarity to$inding site are selected-
D&CG) Autodock) "le K etc-
De no"odrug designing
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Structural Targets
9D structure o! target receptors determined$y
K,ray crystallography
*M1 %omology modeling
Protein Data Bank
Archive o! eperimentally determined 9Dstructures o! $iological macromolecules
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K,ray crystallography
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*M1
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Molecular docking
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Docking algorithms
Molecular !lei$ility
$oth ligand and protein rigid
!lei$le ligand and rigid protein
$oth ligand and protein !lei$le
search algorithm
use to eplore optimal positions o! the ligand#ithin the active site
scoring !unction
value should correspond to pre!erred $indingmode
e!!iciency very important !or data$ase searching
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Scoring !unction
8igand,receptor $inding is driven $y
'lectrostatics 3including h,$onding4
Dispersion o! vd#Ls !orces
%ydropho$ic interaction Desolvation o! ligand and receptor
Molecular mechanics
Attempt to calculate interaction energy
directly
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igand data3ase #arget $rotein
,olecular docking
igand docked into protein>s active site
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%o# do my ligands dock into theprotein0
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Collaboration with$
Pro&" %handhar 0hamad! National #nstitute o&1iomedical #nnovation! 2aan
Dr" Nigus Desselaw 0ddis 0baba 3niversity!
Ethioia Pro&" 2" 'astner! 3niversity o& %tuttgart! 4ermany
Pro&" '" Dharmalingam! 5adurai 'amaraj 3ni"!5adurai
Dr" 0rita 6adav! C%25 3niversity 'anur
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T%A*G F&E