Lead Optimization in Drug Discovery

Lead Optimization is an important technique in new drug development. It encompasses several fields such as synthetic chemistry, phytochemistry, analysis, pharmacology and microbiology.

1. Prof. A. S. Dhake
Professor,
S.M.B.T. College of Pharmacy
Dhamangaon, Nashik (Maharashtra)
Web. : www.smbt.edu.in

5. Lead Compound –A prototype compound which has the desired biological activity, but
may have other undesirable characteristics.
Drug discovery without a lead –
Penicillin G –Discovered by Alexander Fleming in 1928. Penicilliumnotatuminhibited
the growth of Staphylococcus aureus.
Bioassay –A prerequisite for drug discovery studies.

6. I) From Known Active Compounds:
A) Compounds naturally regulating functions –
(Hormones/ neurotransmitters)
Norepinephrine(NE) –lead for sympathetic agonists and antagonists.OH
OH
NH
2
OH
NE
OH
OH
NH
OH
CH
3
CH
3
Isoproterenol ( agonist)
OH
NH
OH
CH
3
CH
3
OH
CH
3
Salbutamol ( agonist)
β
β
2

7. B) Therapeutically used drugs:
a) Active principles from medicinal plants-
I)
Cocaine Procaine
Cocaine –alkaloid from Erythroxyloncoca
.O
NCH
3
NH
2
OCH
2
CH
2
NEt
2
O
O
b) Clinical observation of side effects –
i) Sulphonamides Antidiabeticsulphonylureas

8. C) Drug Metabolism Studies –
Metabolites are isolated and screened. Active metabolite may serve as a
lead.
II)Generation Of New Leads:
A) Biomolecularprocesses (Rational Approach) –If disease process
is known, drug can be designed on the basis of structure of agonists
or of receptors or enzymes.

9. Cancer Therapy –
Substances required for the rapid growth of cancer cells serve as leads.
Lead Structure Anticancer Drug
Pyrimidinenucleus - 5 –fluorouracil
Purinenucleus - 6 –mercaptopurine
Folic acid - Methotrexate, aminopterine
B) Random Screening –
Screen of soil samples for antibiotics –
Streptomycin and tetracyclines.
Random screen for anticancer drugs –
National Cancer Institute.

10. Structural variation of the lead to obtain a drug with desired profile.
Objectives of molecular manipulation –
1.To develop substitutes for existing bioactive compounds eg. Hormones, vitamins,
neurotransmitters..
2.To change the spectrum of activity of lead –
a)To obtain antagonists from agonists.
b)To separate activities -
Androgenic steroids Anabolic steroids.
Sulphonamides Antidiabetics; diuretics.
c)To combine action of different drugs -
Action of oxytocin+ vasopressin Oxypressin.
d)To eliminate side effects –
Glucocorticoids: Cortisone Prednisone Dexamethasone
Mineralocorticoidactivity: 0.8 0.6 0
e)To obtain species/organ selectivity in action -selective blockade of DHF
reductasefrom tumourcells.

11. 3.To modulate the pharmacokinetics of lead –
a)To decrease sensitivity to degrading enzymes-
Acetylcholine Methacholine
Penicillins
b)To modify time –conc. Relationship –
Steroid hormone esters –
Depot preparation –dexycortisoneacetate
I.v. therapy –hydrocortisone sodium succinate
c)To modify drug distribution –
Methylatropine–quaternary compound, no CNS activity.
Sulphonamidesfor treatment of intestinal infections –phthalylsulphacetamide.

12. Identification of pharmacophore–
Morphine Pharmacophorefor narcotic analgesic.
A)Non computational methods
a) Functional group optimization –
Substituentsrequired for maximal activity can be predicted.
Carbutamide Tolbutamide
(Antibacterial)(Antidiabetic) N
OOH
OH
CH
3
C
CH
2
CH
2
N CH
3
SO
2
NH
NH
O
n - Bu NH
2
SO
2
NH
NH
O
n - Bu

13. N
SO
2
NH
Cl
SO
2
NH
2
N
SO
2
NH
Cl Antihypertensive-
Chlorothiazide Diazoxide
(With diuretic action) (Without diuretic action)

14. b) Structure –Activity Relationships (SAR) –
Activities of series of compounds are interpreted in terms of structural features.
Sulphonamidesshow three major activities.
General Structures –NH
R
1
R SO
2NH C
X
Antidiabetic
H
2N SO
2NHR Antibacterial
N
S
O
2
NH
R
2
S
O
2
H
2N
R
1
Diuretic

15. c) Homologation –
Homologous series –compounds differ by a constant unit, C
In –n –alcohols –max. hypnotic activity seen from 1 –hexanolto 1 –octanol.
In 4 –n –alklyresorsinols–max. antibacterial activity seen for 4 -n –
hexylresorsinol.
Balance of hydrophilic –lipophilicnature is important.

16. d) Bioisosterism–
Bioisosteres–groups which have similar physical and chemical properties and hence
give a similar pattern of biological activity.
Grimm –Hydride displacement law (1925)
Classical isosteres- -CH
3, -NH
2, -OH, -F, -Cl.
Ring equivalents – benzene, pyridine
Nonclassicalisosteres--COOH, -SO
3H, -SO
2
Antihistamines –Ar
Ar
1
X CH
2
CH
2 N
CH O
N
CH
X Class
Aminoalkylethers
Ethylenediamines
Propylamines
Examples
Diphenhydramine
Pyrilamine
Chlorpheniramine

17. Development of Procainamide–H
2N
O
O
CH
2CH
2NEt
2
H
2N
O
NH
CH
2CH
2NEt
2
Procaine (Local anaesthetic)
Procainamide (Antiarrhythmic)

18. e) Chain branching –S
N
R
Phenothiazines
R
-CH
2
CH
2
CH
2
N(CH
3
)
2
-CH
2
CH(CH
3
)N(CH
3
)
2
-CH
2
CH(CH
3
)CH
2
N(CH
3
)
2
Drug
Promazine
Promethazine
Trimeprazine
Major activity
Tranquilizer
Antihistaminic
Antipruritic

19. f) Ring –chain transformation
Trimeprazineand methdilazinehave similar antipruriticactivity.
Amphetamine Tranylcypromine
CNS Stimulant Antidepressant
g) Conversion of natural products –
Competitive antagonists may be developed from structures of agonist molecules.R
CH
2 CH
CH
2
CH
2
CH
2
N CH
3
Methdilazine Ph CH
2CH
NH
2
CH
3
Ph CH CH NH
2
CH
2

20. i - Bu
COOH
CH
3
Ibuprofen
(in acid chloride form)
NH
2 R
R = H Aniline
R = OMe p - Anisidine
R = OH p - Aminophenol
i - Bu
CH
3
O
NH
R h) Formation of twin compounds-
Two drug molecules are combined by covalent binding.
2 Quinine Diquininecarbonate
2 Salicylic acid Salicylsalicylate
Twins of ibuprofen
The twin drugs were tested for analgesic and anti-inflammatory activity.

21. i) Microbial Transformations –
Microorganisms supplied with suitable, unnatural precursors, can
synthesize new chemical analogs of the natural product. Penicillium
mould supplied with phenoxyacetic acid Phenoxymethyl
penicillin (penicillin V).
Similar synthesis using Br –ions –bromotetracycline,
bromogriseofulvin.

22. a) Quantitative Structure –Activity Relationships (QSAR)
Biological activity is a function of physicochemical properties. Parameters based
on –Lipophilicity–log P, , Rm.
Electronic effects –, I, *, F, R, pKa, 
Stericeffects –Es, rv, X, MR, MSD, P.
Rb= f(P). kx
HanschAnalysis –
log (1/C) = k
1(log P)
2
+ k
2log P + k
3b + k
4E
s+ k
5
B) Computational methods

23. QSAR modeling and data analysis facilities
2D QSAR
•Rapid calculation of 2000+ descriptors including
2D, 3D, alignment independent & interaction
descriptors.
•Applicability domain check
•AutoQSARfor multiple model building
3D QSAR
•Novel molecular field analysis based on kNN
method (kNNMFA)
•Molecular field descriptors with biological
activity
•Consideration of non-linear relationships between
activity and descriptors using kNNMFA
•Contour visualization with PLSMFA
•AutoQSARfor multiple model building

24. Data preprocessing
•Graphical representation of relative distribution of descriptor values by
distribution and pattern plot
•Univariateanalysis of descriptors
•Cross correlation matrix to investigate the relationship between different
descriptors
Data processing
•Multiple response QSARmodeling
•Training and test set selection methods: Manual, Sphere Exclusion,
Random

25. Pharmacophore identification and modeling
•Features such as H-bond donor, H-bond acceptor, positive charge,
negative charge and hydrophobe
•Application of conformer flexibility of molecules for generation of
several pharmacophorehypotheses
•Pattern search for 3 point, 4 point, 5 point and upto n-point
pharmacophoreidentification with RMSD & distance
•Generation of automated query for 3D database searches through
integration with ChemDBS

26. Virtual combinatorial library generation
•Ability to define multiple sites for substitution
•ADMEscreen based on extended Lipinski's rule
•Predicting activity of virtually generated library of
molecules through QSARPlus
•Applicability domain check on generated library model
•GRIP docking based screening
•kNNMFA model based optimization and screening

27. 3D Property Visualization & Evaluation
•Calculation and visualization of wide variety of Quantum Mechanical
properties including ED, MESP, EMD, ELF, AIE
•Calculation of molecular surface area , hydrophobicity, charge based ESP
•Moments of charge distribution, Mullikenpopulation analysis, HOMO, LUMO

28. Efficient searches for compound databases
•Comprehensive Database Creation and Management
•Comprehensive search criteria: 2D/3D substructure, similarity or descriptor based
•Advanced molecular fingerprint and Pharmacophorebased searches
•Comprehensive search criteria

29. Residue
(Trimyristin)
Crude oil
Evaporation
Crushed nutmeg
Chloroform extract
Residue
Precipitation of Trimyristin
Myristic acid
Filtrate
Myristicin
Reflux for CHCl
3for 10 hr.
Dissolved in ethanol and cooled in ice
1.Alc. KOH
2.Con. HCl
Distillation
Col. Chrom.
Ikan, R.. In Natural Products –A Laboratory Guide;
Academic Press, New York; 1969: pp. 25, 30

30. COOC
13
H
27
COOC
13
H
27
COOC
13
H
27 CH
3
(CH
2
)
12
COOH O
O
CH
3
O
CH
2
-CH=CH
2 S.No.Isolated
principle
Chemical structureM.p. (
0
C)Yield
(%)
Rf
value
a
1Trimyristin 52-54 20 0.41
2 Myristic
acid
53-55 40 0.14
3 Myristicin 152-154
b
30 0.74COOC
13
H
27
COOC
13
H
27
COOC
13
H
27 CH
3
(CH
2
)
12
COOH O
O
CH
3
O
CH
2
-CH=CH
2
a
TLCmobilephase:Benzene
b
B.p.

31. S.No.Isolated
principle
MIC (g/ml)
S.
aureus
B.
subtilis
M.
luteus
P.
aeru
E.
coli
C.
albicans
A.
niger
1 Trimyristin1.0000.6001.2500.6001.2501.2501.250
2
Myristic
acid
0.7501.2500.7500.6251.2500.7500.750
3 Myristicin0.7501.0000.6250.6001.2501.2501.250
4
Salicylic
acid
(Reference)
0.5000.5000.4000.6000.6250.3120.312
NarasimhanB.,DhakeA.S.,AntibacterialprinciplesfromMyristicafragransseeds,JournalofMedicinalFood,
9(3),395-399(2006).

32. Scheme for syntheses of myristicacid derivativesCH
3
[CH
2
]
12
COOH
ROH/H
2
SO
4
[M
1
]
[M
2
- M
9
, M
21
- M
23
]
SOCl
2
Amine
[M
11
- M
20
, M
24
- M
27
]
CH
3
[CH
2
]
12
COOR
CH
3
[CH
2
]
12
COOH
[M
1
]
CH
3
[CH
2
]
12
COCl
CH
3
[CH
2
]
12
COR
ROH
CH
3
[CH
2
]
12
COOR
[M
10
]

33. QSAR studies of Myristicacid derivatives
vTraining and prediction tests
vCalculation of molecular descriptors –CAChe
Pro 6.0 for Windows
vConstruction of correlation matrix
vMLR analysis& “LOO” approach
vPrediction of activity based on best MLR model
vComparison of observed and calculated activity

34. Physicochemical properties of myristicacid derivatives
Comp
ound
R Molecular
formula
M. Wt. mp/ bp*(
0
C) Rf value
(benzene)
Yield
(%)
Training set
M-1 H C
14
H
28
O
2
228.42 52-54 0.14 40
M-2 Me C
15
H
30
O
2
242.45 121-124* 0.62 76
M-3 i-Pr C
17
H
34
O
2
270.51 207-211* 0.58 88
M-4 i-Bu C
18
H
36
O
2
284.54 227-229* 0.65 79
M-5 n-Pen C
19
H
38
O
2
298.57 156-158* 0.79 62
M-6 i-Amyl C
19
H
38
O
2
298.57 281-283* 0.66 89
M-7 n-Hex C
20
H
40
O
2
312.60 185-187* 0.76 91
M-8 n-Hep C
21
H
42
O
2
326.63 243-245* 0.56 68
M-9 n-Oct C
22
H
44
O
2
340.66 235-237* 0.76 42
M-10 CH
2
-Ph C
21
H
34
O
2
318.55 288-290* 0.69 35
M-11 NH-NH
2
C
14
H
30
ON
2
242.46 116-119 0.10 83
M-12CH
3
CH
2
CH
2
-NH C
17
H
35
ON 269.53 135-137 0.56 47
M-13 CH
3
(CH
2
)
3
-NH C
18
H
37
ON 283.56 166-168 0.45 62
M-14 Ph-NH C
20
H
33
ON 303.54 71-74 0.38 68
M-15 (4-NO
2
)Ph-NH C
20
H
32
O
3
N
2
348.54 130-132 0.49 22
M-16 (2-Cl)Ph-NH C
20
H
32
ONCl 337.98 95-97 0.61 59

35. Compou
nd
R Molecular
formula
M. Wt. mp/ bp*(
0
C) Rf value
(benzene)
Yield
(%)
M-17 (3-Cl)Ph-NH C
20
H
32
ONCl 337.98 136-138 0.42 69
M-18 (4-Cl)Ph-NH C
20
H
32
ONCl 337.98 115-117 0.54 72
M-19 (2-CH
3
O)Ph-
NH
C
21
H
35
O
2
N 333.57 156-158 0.67 86
M-20 (4-CH
3
O)Ph-
NH
C
21
H
35
O
2
N 333.57 165-167 0.58 46
Test set
M-21 Et C
16
H
32
O
2
256.48 180-182* 0.60 82
M-22 n-Pr C
17
H
34
O
2
270.51 217-219* 0.61 66
M-23 n-Bu C
18
H
36
O
2
284.54 271-273* 0.58 74
M-24 NH
2
C
14
H
29
ON 227.44 80-82 0.10 87
M-25(2-NO
2
)Ph-NH C
20
H
32
O
3
N
2
348.54 146-148 0.45 18
M-26(3-NO
2
)Ph-NH C
20
H
32
O
3
N
2
348.54 211-213 0.22 84
M-27 NH(Et)
2
C
18
H
37
ON 283.56 68-70 0.13 24CH
3
[CH
2
]
12
COOR CH
3
[CH
2
]
12
COR
[M
1
- M
10
, M
21
- M
23
] [M
11
- M
20
, M
24
- M
27
]

36. Antimicrobial activity of myristic acid derivatives
Compound -log MIC
S. aureus M. luteus E. coli
Training set
M-1 2.48 2.48 2.26
M-2 2.38 2.29 2.38
M-3 2.65 2.65 2.43
M-4 2.76 2.76 2.50
M-5 2.70 2.70 2.52
M-6 2.78 2.70 2.52
M-7 2.80 2.89 2.59
M-8 2.91 2.82 2.61
M-9 2.93 2.93 2.63
M-10 2.80 2.73 2.60
M-11 2.38 2.29 2.38
M-12 2.59 2.59 2.43
M-13 2.67 2.61 2.45
M-14 2.71 2.71 2.50
M-15 2.86 2.76 2.56
M-16 2.83 2.75 2.57

37. M-17 2.83 2.93 2.57
M-18 2.83 2.83 2.57
M-19 2.82 2.75 2.62
M-20 2.82 2.75 2.62
Test set
M-21 2.41 2.61 2.61
M-22 2.63 2.63 2.34
M-23 2.58 2.36 2.45
M-24 2.59 2.29 2.51
M-25 2.86 2.76 2.46
M-26 2.56 2.46 2.56
M-27 2.58 2.67 2.65
S* 3.33 3.33 3.33
*Standard drug -Ciprofloxacin

38. Comp.logPMR
0

v 2

v

1

1
3

v
LUMO Te NuE SA IP
Training set
M-14.8267.8810.844.6816.0015.630.061.03 -2822.89 14014.40346.1411.11
M-24.8572.6511.804.8617.0016.630.061.15 -2978.14 15413.50368.8511.10
M-35.6181.8213.385.8219.0018.630.291.24 -3289.64 18420.30409.4311.03
M-46.0786.3914.096.3620.0019.630.471.20 -3445.45 19944.70429.8411.06
M-56.4691.1214.636.2121.0020.630.061.20 -3601.42 20933.20455.9911.06
M-66.4691.0014.796.4821.0020.630.351.20 -3601.25 21519.40458.9211.06
M-76.8595.7315.346.5722.0021.630.061.20 -3757.26 22335.40477.0311.06
M-87.25100.3316.046.9223.0022.630.061.20 -3913.09 23749.30498.9611.05
M-97.64104.9316.757.2724.0023.630.061.20 -4068.93 25178.70521.5211.05
M-106.6397.2614.906.4321.0419.900.180.22 -3800.85 22399.10450.429.69
M-113.9474.1911.474.8517.0016.590.071.02 -2942.72 15417.90367.1010.28
M-125.0183.8713.315.6419.0018.630.071.58 -3189.91 18095.40417.979.79
M-135.4188.4714.015.9920.0019.630.071.58 -3345.74 19462.80439.509.79
M-145.8894.3814.286.2020.0518.900.160.35 -3545.33 20868.50432.188.75
M-155.84101.7015.476.6423.0421.450.27-1.01 -4376.26 25273.70458.999.57

39. Comp.
log
P
MR
0

v 2

v

1

1
3

v
LUMO Te NuE SA IP
M-166.4099.1815.406.7521.0420.19
0.32-0.21
-3905.39 22987.40445.479.21
M-176.4099.1815.406.8221.0420.19
0.35-0.20
-3905.40 22933.60446.009.29
M-186.4099.1815.406.8121.0420.19
0.350.02
-3905.45 22274.50447.848.79
M-195.63100.8415.616.5322.0420.85
0.200.34
-4021.19 24501.00460.598.49
M-205.63100.8415.616.5622.0420.85
0.220.34
-4021.17 24126.30465.218.37
Test set
M-215.1977.4012.515.0918.0017.63
0.061.20
-3133.93 16792.00389.6911.08
M-225.6681.9213.225.5119.0018.63
0.061.20
-3289.76 18164.50411.7711.07
M-236.0686.5213.925.8620.0019.63
0.061.20
-3445.59 19545.00434.1011.07
M-243.9569.7010.974.7516.0015.63
0.081.55
-2722.89 14022.90354.2210.52
M-255.84101.7015.476.6123.0421.45
0.25-1.20
-4376.14 26473.70454.399.72
M-265.84101.7015.476.6423.0421.45
0.27-1.00
-4376.19 25385.70458.389.48
M-275.1388.9914.265.8520.0019.63
0.181.53
-3345.13 20257.10433.329.56

40. -logMICLog PMR
0

v 1

v 2

v

1

1
Te NuE SA IPLUMO
-logMIC 1.000
Log P 0.860 1.000
MR 0.942 0.7661.000
0

v
0.963 0.8420.9801.000
1

v
0.948 0.8810.9580.9871.000
2

v
0.979 0.8720.9580.9840.9701.000


0.941 0.8190.9550.9770.9750.9471.000


0.917 0.8590.9020.9570.9730.9330.9831.000
Te -0.919-0.705-0.963-0.937-0.894-0.912-0.940-0.8691.000
NuE 0.948 0.7580.9850.9760.9460.9490.9740.922-0.9831.000
SA 0.914 0.8700.9120.9620.9840.9350.9740.991-0.8490.9151.000
IP -0.2820.113-0.473-0.323-0.240-0.287-0.235-0.0900.440-0.407-0.1301.000
LUMO -0.412-0.126-0.513-0.376-0.282-0.385-0.345-0.1880.636-0.516-0.1550.6551.000

41. Molecualr
descriptor
-logMIC
S. aureus M. luteusE. coliB. Subtilis P.
aeruginosa
C. albicans A. niger
log P 0.860 0.864 0.773 0.788 0.410 0.940 0.111
MR 0.942 0.866 0.961 0.851 0.589 0.730 0.470
0

v
0.963 0.899 0.963 0.917 0.591 0.772 0.327
1

v
0.948 0.898 0.937 0.889 0.502 0.826 0.256
2

v
0.979 0.934 0.932 0.909 0.650 0.798 0.332
3

v
0.382 0.370 0.262 0.359 0.756 0.066 0.356

1
0.941 0.857 0.938 0.876 0.465 0.787 0.310

1
0.917 0.851 0.901 0.885 0.426 0.806 0.159
R 0.918 0.824 0.947 0.810 0.560 0.698 0.548
W 0.921 0.823 0.935 0.805 0.510 0.732 0.518
Te -0.919 -0.821 -0.922 -0.821 -0.574 -0.690 -0.563
NuE 0.948 0.854 0.958 0.870 0.570 0.727 0.454
SA 0.914 0.852 0.912 0.879 0.425 0.815 0.141
IP -0.282 -0.217 -0.382 -0.211 -0.464 0.111 -0.603
LUMO -0.412 -0.332 -0.414 -0.212 -0.438 -0.206 -0.925

42. QSARmodel for antibacterial activity against E. coli
-logMIC= 0.061
0

v
+ 1.635 (1)
n =20r = 0.963 F = 232.661s = 0.027 r
2
cv= 0.902
QSARmodel for antibacterial activity against S. aureus
-logMIC= 0.217
2

v
+ 1.375 (2)
n =20r = 0.978 F = 407.85s = 0.030 r
2
cv= 0.931
QSARmodel for antibacterial activity against M. luteus
-logMIC= 0.229
2

v
+ 1.268 (3)
n =20r = 0.934 F = 123.968s = 0.064 r
2
cv= 0.810
BalasubramanianNarasimhan,VishnukantMouryaandAvinash
Dhake,Design,Synthesis,AntibacterialandQSARStudiesof
MyristicacidDerivatives,BioorganicandMedicinalChemistry
Letters,16,2006,3023-3029

43. -logMIC observed
3.02.92.82.72.62.52.42.3
-logMIC predicted by Eq.2.
3.0
2.9
2.8
2.7
2.6
2.5
2.4
2.3 Comparison of residual and observed activity for
myristicacid derivatives against S. aureususing Eq. 2

44. Compound -log MIC (µM/ml)
B. Subtilis P.
aeruginosa
C. albicans A. niger
M-1 2.36 2.36 2.48 2.46
M-2 2.38 2.38 2.38 2.48
M-3 2.71 2.53 2.43 2.48
M-4 2.76 2.85 2.61 2.45
M-5 2.78 2.52 2.70 2.48
M-6 2.78 2.70 2.70 2.48
M-7 2.80 2.54 2.80 2.49
M-8 2.91 2.56 2.82 2.51
M-9 2.93 2.58 2.93 2.44
M-10 2.73 2.73 2.73 2.55
M-11 2.29 2.43 2.29 2.51
M-12 2.59 2.53 2.43 2.43
M-13 2.67 2.55 2.45 2.45
M-14 2.48 2.39 2.71 2.60
M-15 2.70 2.54 2.69 2.66

45. Compound -log MIC (µM/ml)
B. Subtilis P.
aeruginosa
C. albicans A. niger
M-16 2.83 2.83 2.63 2.57
M-17 2.83 2.93 2.68 2.57
M-18 2.83 2.93 2.63 2.57
M-19 2.92 2.75 2.52 2.52
M-20 2.92 2.82 2.52 2.52
M-21 2.45 2.45 2.51 2.45
M-22 2.34 2.43 2.56 2.56
M-23 2.36 2.45 2.58 2.36
M-24 2.40 2.51 2.26 2.40
M-25 2.78 2.59 2.86 2.69
M-26 2.84 2.76 2.54 2.64
M-27 2.45 2.50 2.36 2.45
S* 3.33 3.33 3.10** 3.10**

46. Antifungal activity against C. albicans(Eq.1)
-logMIC= 0.174 log P + 1.569 (1)
n =20r = 0.940 q2 = 0.863 F = 137.85 s = 0.057
Antifungal activity against A. niger(Eq. 2)
-logMIC= -0.079 LUMO+ 2.569 (2)
n =20r = 0.924 q2 = 0.828 F = 106.17 s = 0.023
Antibacterial activity against P. aeruginosa(Eq. 3)
-logMIC= 1.036
3

v
+ 2.429 (3)
n =20r = 0.756 q2 = 0.484 F = 24.03 s = 0.122
Antibacterial activity against P. aeruginosa(Eq. 4)
-logMIC= 0.007 MR+ 0.881
3

v
+ 1.815 (4)
n =20r = 0.854 q2 = 0.655 F = 22.97 s = 0.099

47. Antibacterial activity against B. subtilis(Eq. 5 & 6)
-log MIC = 0.113
0

v
+ 1.077 (5)
n =20r = 0.917 F = 95.65s = 0.079q
2
= 0.808
-log MIC = 0.244
2

v
+ 1.187 (6)
n =20r = 0.908 F = 85.10s = 0.083q
2
= 0.796
B.NarasimhanandA.S.Dhake,Theoreticalmodelingofantimicrobialactivityof
myristicacidderivativesbyHanschanalysis,Nationalsymposiumonchallengesindrug
discoveryresearch:NetworkingopportunitiesbetweenacademiaandIndustries,Birla
InstituteofTechnologyandScience,Pilani,April7-8,2006,P.93[AbstractNo.CC-902].

48. Termination of Search -
For each new drug developed –
No. of compounds synthesized ~ 10,000
Time required 15 Years
Cost ~ $ 800-850 million
When optimum profile is reached, search may be terminated.

49. Histamine 5-Methylhistamine
H
2agonist
H
1 receptor –allergic and hypersensitivity reactions.
H
2 receptor –stimulation of gastric acid secretion.
H
2 antagonist may be useful in gastric and duodenal ulcers. Smith Kline & French
Laboratories, UK, initiated search for lead compound in 1964.α
Later, this lead and its analogs were found to be partial agonists. This was attributed to
protonationof side chain.NH N
NH NH
2
NH
III
N - Guanyl histamine
(Lead)
αNH N
NH
2
I II
NH N
NH
2
CH
3

50. Thioureaanalog (First H
2antagonist)
Weak antagonist activity, no agonist activity.
Increase in chain length to 4 C gave pure competitive antagonist. N –Methyl analog –
Burimamide
First H
2antagonist tested in humans. Poor oral potency.N NH
NH NHMe
S
V NH N
NH NH
2
S
IV

51. 1, 3 –prototropictautomerismin histamine and related compounds.
N–H tautomeris favouredby –
I) e
-
-withdrawing side chain
CH
2–S –CH
2-CH
2Thiaburimamide. VI
II) e
-
-releasing group at C5 –
~ 9 times more potent than Burimamide. Side effect –granulocytopenia; associated with thioureagroup.NH N
CH
3
S
NH NHCH
3
S
VII
Metiamide N NH
R
HN N
R
N - H Tautomer
Less active
N - H Tautomer
More active

 π π
π

52. Isostericreplacement of Thioureagroup
Analogs were 20 times less potent than X=S. To remove guanidine basicity, the N was
substituted with e-withdrawing groups –CN, NO
2.
X = NCN, X = NNO
2
Both compounds were potent H
2antagonists; X = NCNbeing more potent.
Cimetidine
Marketed in U. K. in 1976.
Later, ranitidine was introduced by GlaxoLabs.NH
2NH
2
X
X = S Thiourea
X = O Urea
X = NH Guanidine NH N
CH
3
S
NH NHMe
NCN
VIII

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Selected References:

54. Thank You