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. Penicillium notatum inhibited
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.
O
H
O
H
NH2
OH
NE
O
H
O
H
NH
OH
CH3
C
H3
Isoproterenol ( agonist)
O
H
NH
OH
CH3
C
H3
O
H
CH3
Salbutamol ( agonist)
β
β2

7. B) Therapeutically used drugs:
a) Active principles from medicinal plants-
I)
Cocaine Procaine
Cocaine – alkaloid from Erythroxylon coca
.
O
N CH3
N
H2
OCH 2CH2NEt 2
O
O
b) Clinical observation of side effects –
i) Sulphonamides Antidiabetic sulphonyl ureas

8. C) Drug Metabolism Studies –
Metabolites are isolated and screened. Active metabolite may serve as a
lead.
II) Generation Of New Leads:
A) Biomolecular processes (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
Pyrimidine nucleus - 5 – fluoro uracil
Purine nucleus - 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
Mineralocorticoid activity: 0.8 0.6 0
e) To obtain species/organ selectivity in action - selective blockade of DHF
reductase from tumour cells.

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 – dexycortisone acetate
I.v. therapy – hydrocortisone sodium succinate
c) To modify drug distribution –
Methylatropine – quaternary compound, no CNS activity.
Sulphonamides for treatment of intestinal infections – phthalylsulphacetamide.

12. Identification of pharmacophore –
Morphine Pharmacophore for narcotic analgesic.
A) Non computational methods
a) Functional group optimization –
Substituents required for maximal activity can be predicted.
Carbutamide Tolbutamide
(Antibacterial) (Antidiabetic)
N
O
OH OH
CH3
C
CH2
CH2
N
C
H3 SO2
NH
NH
O
n - Bu
N
H2 SO2
NH
NH
O
n - Bu

13. N
SO2
NH
Cl
SO2
N
H2
N
SO2
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.
Sulphonamides show three major activities.
General Structures –
NH
R1
R SO2NH C
X
Antidiabetic
H2N SO2NHR Antibacterial
N
S
O2
NH
R2
S
O2
H2N
R1
Diuretic

15. c) Homologation –
Homologous series – compounds differ by a constant unit, C
In – n – alcohols – max. hypnotic activity seen from 1 – hexanol to 1 – octanol.
In 4 – n – alklyresorsinols – max. antibacterial activity seen for 4 - n –
hexylresorsinol.
Balance of hydrophilic – lipophilic nature 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 - -CH3, - NH2, -OH, -F, -Cl.
Ring equivalents – benzene, pyridine
Nonclassical isosteres - -COOH, -SO3H, -SO2
Antihistamines –
Ar
Ar1
X CH2 CH2 N
CH O
N
CH
X Class
Aminoalkylethers
Ethylenediamines
Propylamines
Examples
Diphenhydramine
Pyrilamine
Chlorpheniramine

17. Development of Procainamide –
H2N
O
O
CH2CH2NEt2
H2N
O
NH
CH2CH2NEt2
Procaine (Local anaesthetic)
Procainamide (Antiarrhythmic)

18. e) Chain branching –
S
N
R
Phenothiazines
R
-CH2CH2CH2N(CH 3)2
-CH2CH(CH 3)N(CH 3)2
-CH2CH(CH 3)CH2N(CH 3)2
Drug
Promazine
Promethazine
Trimeprazine
Major activity
Tranquilizer
Antihistaminic
Antipruritic

19. f) Ring – chain transformation
Trimeprazine and methdilazine have similar antipruritic activity.
Amphetamine Tranylcypromine
CNS Stimulant Antidepressant
g) Conversion of natural products –
Competitive antagonists may be developed from structures of agonist molecules.
R
CH2 CH
CH2
CH2
CH2
N CH3
Methdilazine
Ph CH2 CH NH2
CH3
Ph CH CH NH2
CH2

20. i - Bu
COOH
CH3
Ibuprofen
(in acid chloride form)
N
H2 R
R = H Aniline
R = OMe p - Anisidine
R = OH p - Aminophenol
i - Bu
C
H3
O
NH
R
h) Formation of twin compounds-
Two drug molecules are combined by covalent binding.
2 Quinine Diquinine carbonate
2 Salicylic acid Salicyl salicylate
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 phenoxy acetic 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, 
Steric effects – Es, rv, X, MR, MSD, P.
Rb = f(P). kx
Hansch Analysis –
log (1/C) = k1 (log P)2 + k2 log P + k3 b + k4 Es + k5
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
•AutoQSAR for multiple model building
3D QSAR
•Novel molecular field analysis based on kNN
method (kNN MFA)
•Molecular field descriptors with biological
activity
•Consideration of non-linear relationships between
activity and descriptors using kNN MFA
•Contour visualization with PLS MFA
•AutoQSAR for multiple model building

24. Data preprocessing
• Graphical representation of relative distribution of descriptor values by
distribution and pattern plot
• Univariate analysis of descriptors
• Cross correlation matrix to investigate the relationship between different
descriptors
Data processing
• Multiple response QSAR modeling
• 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 pharmacophore hypotheses
•Pattern search for 3 point, 4 point, 5 point and upto n-point
pharmacophore identification 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
•ADME screen 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
•kNN MFA 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, Mulliken population 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 Pharmacophore based searches
• Comprehensive search criteria

29. Residue
(Trimyristin)
Crude oil
Evaporation
Crushed nutmeg
Chloroform extract
Residue
Precipitation of Trimyristin
Myristic acid
Filtrate
Myristicin
Reflux for CHCl3 for 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. COOC13H27
COOC13
H27
COOC13H27
CH3(CH2)12COOH
O
O
CH3O
CH2-CH=CH2
S.No. Isolated
principle
Chemical structure M.p. (0C) Yield
(%)
Rf
valuea
1 Trimyristin 52-54 20 0.41
2 Myristic
acid
53-55 40 0.14
3 Myristicin 152-154b 30 0.74
COOC13H27
COOC13
H27
COOC13H27
CH3(CH2)12COOH
O
O
CH3O
CH2-CH=CH2
aTLC mobile phase: Benzene bB.p.

31. S.No. Isolated
principle
MIC (g/ml)
S.
aureus
B.
subtilis
M.
luteus
P.
aeru
E.
coli
C.
albicans
A.
niger
1 Trimyristin 1.000 0.600 1.250 0.600 1.250 1.250 1.250
2
Myristic
acid
0.750 1.250 0.750 0.625 1.250 0.750 0.750
3 Myristicin 0.750 1.000 0.625 0.600 1.250 1.250 1.250
4
Salicylic
acid
(Reference)
0.500 0.500 0.400 0.600 0.625 0.312 0.312
Narasimhan B., Dhake A.S., Antibacterial principles from Myristica fragrans seeds, Journal of Medicinal Food,
9(3), 395-399 (2006).

32. Scheme for syntheses of myristic acid derivatives
CH3
[CH2
]12
COOH
ROH/H2
SO4
[M1]
[M2 - M9, M21- M23]
SOCl2
Amine
[M11 - M20, M24 - M27]
CH3[CH2]12COOR
CH3[CH2]12COOH
[M1]
CH3
[CH2
]12
COCl
CH3[CH2]12COR
ROH
CH3
[CH2
]12
COOR
[M10]

33. QSAR studies of Myristic acid derivatives
v Training and prediction tests
v Calculation of molecular descriptors – CAChe
Pro 6.0 for Windows
v Construction of correlation matrix
v MLR analysis & “LOO” approach
v Prediction of activity based on best MLR model
v Comparison of observed and calculated activity

34. Physicochemical properties of myristic acid derivatives
Comp
ound
R Molecular
formula
M. Wt. mp/ bp*(0C) Rf value
(benzene)
Yield
(%)
Training set
M-1 H C14H28O2 228.42 52-54 0.14 40
M-2 Me C15H30O2 242.45 121-124* 0.62 76
M-3 i-Pr C17H34O2 270.51 207-211* 0.58 88
M-4 i-Bu C18H36O2 284.54 227-229* 0.65 79
M-5 n-Pen C19H38O2 298.57 156-158* 0.79 62
M-6 i-Amyl C19H38O2 298.57 281-283* 0.66 89
M-7 n-Hex C20H40O2 312.60 185-187* 0.76 91
M-8 n-Hep C21H42O2 326.63 243-245* 0.56 68
M-9 n-Oct C22H44O2 340.66 235-237* 0.76 42
M-10 CH2-Ph C21H34O2 318.55 288-290* 0.69 35
M-11 NH-NH2 C14H30ON2 242.46 116-119 0.10 83
M-12 CH3CH2CH2-NH C17H35ON 269.53 135-137 0.56 47
M-13 CH3(CH2)3-NH C18H37ON 283.56 166-168 0.45 62
M-14 Ph-NH C20H33ON 303.54 71-74 0.38 68
M-15 (4-NO2) Ph-NH C20H32O3N2 348.54 130-132 0.49 22
M-16 (2-Cl) Ph-NH C20H32ONCl 337.98 95-97 0.61 59

35. Compou
nd
R Molecular
formula
M. Wt. mp/ bp*(0C) Rf value
(benzene)
Yield
(%)
M-17 (3-Cl) Ph-NH C20H32ONCl 337.98 136-138 0.42 69
M-18 (4-Cl) Ph-NH C20H32ONCl 337.98 115-117 0.54 72
M-19 (2-CH3O) Ph-
NH
C21H35O2N 333.57 156-158 0.67 86
M-20 (4-CH3O) Ph-
NH
C21H35O2N 333.57 165-167 0.58 46
Test set
M-21 Et C16H32O2 256.48 180-182* 0.60 82
M-22 n-Pr C17H34O2 270.51 217-219* 0.61 66
M-23 n-Bu C18H36O2 284.54 271-273* 0.58 74
M-24 NH2 C14H29ON 227.44 80-82 0.10 87
M-25 (2-NO2) Ph-NH C20H32O3N2 348.54 146-148 0.45 18
M-26 (3-NO2) Ph-NH C20H32O3N2 348.54 211-213 0.22 84
M-27 NH(Et)2 C18H37ON 283.56 68-70 0.13 24
CH3
[CH2
]12
COOR CH3
[CH2
]12
COR
[M1
- M10
, M21
- M23
] [M11
- M20
, M24
- M27
]

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. logP MR 0v 2v 1 1
3v LUMO Te NuE SA IP
Training set
M-1 4.82 67.88 10.84 4.68 16.00 15.63 0.06 1.03 -2822.89 14014.40 346.14 11.11
M-2 4.85 72.65 11.80 4.86 17.00 16.63 0.06 1.15 -2978.14 15413.50 368.85 11.10
M-3 5.61 81.82 13.38 5.82 19.00 18.63 0.29 1.24 -3289.64 18420.30 409.43 11.03
M-4 6.07 86.39 14.09 6.36 20.00 19.63 0.47 1.20 -3445.45 19944.70 429.84 11.06
M-5 6.46 91.12 14.63 6.21 21.00 20.63 0.06 1.20 -3601.42 20933.20 455.99 11.06
M-6 6.46 91.00 14.79 6.48 21.00 20.63 0.35 1.20 -3601.25 21519.40 458.92 11.06
M-7 6.85 95.73 15.34 6.57 22.00 21.63 0.06 1.20 -3757.26 22335.40 477.03 11.06
M-8 7.25 100.33 16.04 6.92 23.00 22.63 0.06 1.20 -3913.09 23749.30 498.96 11.05
M-9 7.64 104.93 16.75 7.27 24.00 23.63 0.06 1.20 -4068.93 25178.70 521.52 11.05
M-10 6.63 97.26 14.90 6.43 21.04 19.90 0.18 0.22 -3800.85 22399.10 450.42 9.69
M-11 3.94 74.19 11.47 4.85 17.00 16.59 0.07 1.02 -2942.72 15417.90 367.10 10.28
M-12 5.01 83.87 13.31 5.64 19.00 18.63 0.07 1.58 -3189.91 18095.40 417.97 9.79
M-13 5.41 88.47 14.01 5.99 20.00 19.63 0.07 1.58 -3345.74 19462.80 439.50 9.79
M-14 5.88 94.38 14.28 6.20 20.05 18.90 0.16 0.35 -3545.33 20868.50 432.18 8.75
M-15 5.84 101.70 15.47 6.64 23.04 21.45 0.27 -1.01 -4376.26 25273.70 458.99 9.57

39. Comp.
log
P
MR 0v 2v 1 1
3v LUMO Te NuE SA IP
M-16 6.40 99.18 15.40 6.75 21.04 20.19 0.32 -0.21 -3905.39 22987.40 445.47 9.21
M-17 6.40 99.18 15.40 6.82 21.04 20.19 0.35 -0.20 -3905.40 22933.60 446.00 9.29
M-18 6.40 99.18 15.40 6.81 21.04 20.19 0.35 0.02 -3905.45 22274.50 447.84 8.79
M-19 5.63 100.84 15.61 6.53 22.04 20.85 0.20 0.34 -4021.19 24501.00 460.59 8.49
M-20 5.63 100.84 15.61 6.56 22.04 20.85 0.22 0.34 -4021.17 24126.30 465.21 8.37
Test set
M-21 5.19 77.40 12.51 5.09 18.00 17.63 0.06 1.20 -3133.93 16792.00 389.69 11.08
M-22 5.66 81.92 13.22 5.51 19.00 18.63 0.06 1.20 -3289.76 18164.50 411.77 11.07
M-23 6.06 86.52 13.92 5.86 20.00 19.63 0.06 1.20 -3445.59 19545.00 434.10 11.07
M-24 3.95 69.70 10.97 4.75 16.00 15.63 0.08 1.55 -2722.89 14022.90 354.22 10.52
M-25 5.84 101.70 15.47 6.61 23.04 21.45 0.25 -1.20 -4376.14 26473.70 454.39 9.72
M-26 5.84 101.70 15.47 6.64 23.04 21.45 0.27 -1.00 -4376.19 25385.70 458.38 9.48
M-27 5.13 88.99 14.26 5.85 20.00 19.63 0.18 1.53 -3345.13 20257.10 433.32 9.56

40. -logMIC Log P MR 0v 1v 2v 1 1 Te NuE SA IP LUMO
-logMIC 1.000
Log P 0.860 1.000
MR 0.942 0.766 1.000
0v 0.963 0.842 0.980 1.000
1v 0.948 0.881 0.958 0.987 1.000
2v 0.979 0.872 0.958 0.984 0.970 1.000
 0.941 0.819 0.955 0.977 0.975 0.947 1.000
 0.917 0.859 0.902 0.957 0.973 0.933 0.983 1.000
Te -0.919 -0.705 -0.963 -0.937 -0.894 -0.912 -0.940 -0.869 1.000
NuE 0.948 0.758 0.985 0.976 0.946 0.949 0.974 0.922 -0.983 1.000
SA 0.914 0.870 0.912 0.962 0.984 0.935 0.974 0.991 -0.849 0.915 1.000
IP -0.282 0.113 -0.473 -0.323 -0.240 -0.287 -0.235 -0.090 0.440 -0.407 -0.130 1.000
LUMO -0.412 -0.126 -0.513 -0.376 -0.282 -0.385 -0.345 -0.188 0.636 -0.516 -0.155 0.655 1.000

41. Molecualr
descriptor
-logMIC
S. aureus M. luteus E. coli B. 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. QSAR model for antibacterial activity against E. coli
-logMIC = 0.061 0v + 1.635 (1)
n =20 r = 0.963 F = 232.661 s = 0.027 r2
cv = 0.902
QSAR model for antibacterial activity against S. aureus
-logMIC = 0.217 2v + 1.375 (2)
n =20 r = 0.978 F = 407.85 s = 0.030 r2
cv = 0.931
QSAR model for antibacterial activity against M. luteus
-logMIC = 0.229 2v + 1.268 (3)
n =20 r = 0.934 F = 123.968 s = 0.064 r2
cv = 0.810
Balasubramanian Narasimhan, Vishnukant Mourya and Avinash
Dhake, Design, Synthesis, Antibacterial and QSAR Studies of
Myristic acid Derivatives, Bioorganic and Medicinal Chemistry
Letters, 16, 2006, 3023 - 3029

43. -logMIC observed
3.0
2.9
2.8
2.7
2.6
2.5
2.4
2.3
3.0
2.9
2.8
2.7
2.6
2.5
2.4
2.3
Comparison of residual and observed activity for
myristic acid derivatives against S. aureus using 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 =20 r = 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 =20 r = 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 =20 r = 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 =20 r = 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 =20 r = 0.917 F = 95.65 s = 0.079 q2 = 0.808
-log MIC = 0.244 2v + 1.187 (6)
n =20 r = 0.908 F = 85.10 s = 0.083 q2 = 0.796
B. Narasimhan and A.S.Dhake, Theoretical modeling of antimicrobial activity of
myristic acid derivatives by Hansch analysis, National symposium on challenges in drug
discovery research: Networking opportunities between academia and Industries, Birla
Institute of Technology and Science, Pilani, April 7-8, 2006, P.93 [Abstract No. 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
H2 agonist
H1 receptor – allergic and hypersensitivity reactions.
H2 receptor – stimulation of gastric acid secretion.
H2 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
protonation of side chain.
N
H N
NH NH2
NH
III
N - Guanyl histam
ine
(Lead)
α
N
H N
NH2
I II
N
H N
NH2
C
H3

50. Thiourea analog (First H2 antagonist)
Weak antagonist activity, no agonist activity.
Increase in chain length to 4 C gave pure competitive antagonist. N – Methyl analog –
Burimamide
First H2 antagonist tested in humans. Poor oral potency.
N NH
NH NHMe
S
V
N
H N
NH NH2
S
IV

51. 1, 3 – prototropic tautomerism in histamine and related compounds.
N – H tautomer is favoured by –
I) e- - withdrawing side chain
CH2 – S – CH2 - CH2 Thiaburimamide. VI
II) e- - releasing group at C5 –
~ 9 times more potent than Burimamide. Side effect – granulocytopenia; associated with thiourea group.
N
H N
C
H3
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. Isosteric replacement of Thiourea group
Analogs were 20 times less potent than X=S. To remove guanidine basicity, the N was
substituted with e- withdrawing groups – CN, NO2.
X = NCN, X = NNO2
Both compounds were potent H2 antagonists; X = NCN being more potent.
Cimetidine
Marketed in U. K. in 1976.
Later, ranitidine was introduced by Glaxo Labs.
NH2
N
H2
X
X = S Thiourea
X = O Urea
X = NH Guanidine
N
H N
C
H3
S
NH NHMe
NCN
VIII

53. 1. Silverman R. B. : The Organic Chemistry of Drug Design and Drug Action;
Academic
Press, San Diego, 7 – 105 (2004).
2. Ariens E. J. (Ed.) : Drug Design, Vol. X, Academic Press, New York, 1 – 69 (1980).
3. Kulkarni V. M. and Bothara K. G. : Drug Design, Nirali Prakashan, Pune, 1 – 22
(1995).
4. Testa B., Kyburz E., Fuhrer W. and Giger R. (Eds.) : Perspectives in Medicinal
Chemistry, VCH, Weinheim, 475 – 531 (1993).
5. Wolff M. E. (Ed.) : Burger’s Medicinal Chemistry and Drug Discovery, Vol. I :
Principles and Practice, John Wiley & Sons, New York, 1 – 8, 497 – 571, 983 –
1033 (2003).
6. Hansch C., Sammes P. G. and Taylor J. B. (Eds.) : Comprehensive Medicinal
Chemistry Vol. 1 – General Principles, 261 – 278, Vol. 4 – Quantitative Drug
Design, 1 – 31, 497 – 560, Pergamon Press, Oxford (1990).
7. Krogsgaard – Larsen P., Liljefors T. & Madsen U. (Eds) : Textbook of Drug Design
and Discovery, 117-155 (2002)
Selected References:

54. Thank You