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PPT – Quantitative Structure Activity Relationships QSAR PowerPoint presentation | free to view - id: 95eb2-N2ExZ

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Quantitative Structure- Activity Relationships

(QSAR)

Rationale for QSAR Studies

- In drug design, in vitro potency addresses only

part of the need a successful drug must also be

able to reach its target in the body while still

in its active form. - The in vivo activity of a substance is a

composite of many factors, including the

intrinsic reactivity of the drug, its solubility

in water, its ability to pass the blood-brain

barrier, its non- reactivity with non-target

molecules that it encounters on its way to the

target, and others.

Rationale for QSAR Studies...

- A quantitative structure-activity relationship

(QSAR) correlates measurable or calculable

physical or molecular properties to some specific

biological activity in terms of an equation. - Once a valid QSAR has been determined, it should

be possible to predict the biological activity of

related drug candidates before they are put

through expensive and time-consuming biological

testing. In some cases, only computed values

need to be known to make an assessment.

History of QSAR

- The first application of QSAR is attributed to

Hansch (1969), who developed an equation that

related biological activity to certain electronic

characteristics and the hydrophobicity of a set

of structures. - log (1/C) k1log P - k2(log P)2 k3s

k4 - for C minimum effective dose
- P octanol - water partition coefficient
- s Hammett substituent constant
- kx constants derived from regression analysis

Hanschs Approach

- Log P is a measure of the drugs hydrophobicity,

which was selected as a measure of its ability to

pass through cell membranes. - The log P (or log Po/w) value reflects the

relative solubility of the drug in octanol

(representing the lipid bilayer of a cell

membrane) and water (the fluid within the cell

and in blood). - Log P values may be measured experimentally or,

more commonly, calculated.

Calculating Log P

- Log P Log K (o/w) Log

(Xoctanol/Xwater) - most programs use a group additivity approach
- 1 Aromatic ring 0.780
- 7 Hs on Carbon 1.589
- 1 C-Br bond -0.120
- 1 alkyl C 0.195 Sum 2.924 calc. log P
- some use more complicated algorithms, including

factors such as the dipole moment, molecular size

and shape.

Hanschs Approach...

- The Hammett substituent constant (s) reflects the

drug molecules intrinsic reactivity, related to

electronic factors caused by aryl substituents. - In chemical reactions, aromatic ring substituents

can alter the rate of reaction by up to 6 orders

of magnitude! - For example, the rate of the reaction below is

105 times slower when X NO2 than when X CH3

Hammett Equation

- Hammett observed a linear free energy

relationship between the log of the relative rate

constants for ester hydrolysis and the log of the

relative acid ionization (equilibrium) constants

for a series of substituted benzoic esters

acids. - log (kx/kH) log (Kx/KH) rs
- He arbitrarily assigned r, the reaction constant,

of the acid ionization of benzoic acid a value of

1.

Definition of Hammett r

These sp values are obtained from the best fit

line having a slope 1

Hammett Plot

- Aryl substituent constants (s) were determined by

measuring the effect of a substituent on a

reaction rate (or Keq). These are listed in

tables, and are constant in widely different

reactions. - Reaction constants (r) for other reactions may

also be determined by comparison of the relative

rates (or Keq) of two differently substituted

reactants, using the substituent constants

described above. - Some of these values (s and r) are listed on the

following slide.

Hammett Rho Sigma Values

Reaction (Rho) Values r

- Substituent (Sigma) Values s (the electronic

effect of the substituent - negative values are electron donating)
- p-NH2 -0.66 p-Cl 0.23
- p-OCH3 -0.27 p-COCH3 0.50
- p-CH3 -0.17 p-CN 0.66
- m-CH3 -0.07 p-NO2 0.78

Molecular Properties in QSAR

- Many other molecular properties have been

incorporated into QSAR studies some of these are

measurable physical properties, such as - density ? pKa
- ionization energy ? boiling point
- Hvaporization ? refractive index
- molecular weight ? dipole moment (m)
- Hhydration ? reduction potential
- lipophilicity parameter

p

log PX - log PH

Molecular Properties in QSAR

- Other molecular properties (descriptors) that

have been incorporated into QSAR studies

include calculated properties, such as - ovality ? surface area, molec. volume
- HOMO energy ? LUMO energy
- polarizability ? charges on individual atoms
- molecular volume ? solvent accessible surface

area - vdW surface area ? maximum and - charge
- molar refractivity ? hardness
- hydration energy ? Tafts steric

parameter

QSAR Methodology

- Often it is found that several descriptors are

correlated that is, they describe observables

that are closely related, such as MW and boiling

point in a homologous series. - Statistical analysis is used to determine which

of the variables best describe (correlate with)

the observed biological activity, and which are

cross-correlated. The final QSAR involves only

the most important 3 to 5 descriptors,

eliminating those with high cross-correlation.

Limit to the of Descriptors

- The data set should contain at least 5 times as

many compounds as descriptors in the QSAR. - The reason for this is that too few compounds

relative to the number of descriptors will give

a falsely high correlation - 2 points exactly determine a line (2 compds, 2

prop) - 3 points exactly determine a plane (etc., etc.)
- A data set of drug candidates that is similar in

size to the number of descriptors will give

a high (and meaningless) correlation.

Statistical Analysis of Data

- Multiple linear regression analysis can be

accomplished using standard statistical software,

typically incorporated into sophisticated (and

expensive) drug design software packages, such as

MSIs Cerius2 (academic price, over 20K) - An inexpensive statistical analysis software

StatMost (academic price, 39) works just fine. - To discover correlated variables and determine

which descriptors correlate best, a partial least

squares or principal component analysis is done.

Example of a QSAR

Anti-adrenergic Activity and Physicochemical

Properties of 3,4- disubstituted

N,N-dimethyl-a-bromophenethylamines

p Lipophilicity parameter s

Hammett Sigma (for benzylic cations)

Es(meta) Tafts steric parameter

Example of a QSAR...

Calc.

Calc.

- m-X p-Y p s Es(meta)

log (1/C)obs log (1/C)a log (1/C)b - H H 0.00 0.00 1.24 7.46 7.82 7.88
- F H 0.13 0.35 0.78 7.52 7.45 7.43
- H F 0.15 -0.07 1.24 8.16 8.09 8.17
- Cl H 0.76 0.40 0.27 8.16 8.11 8.05
- Cl F 0.91 0.33 0.27 8.19 8.38 8.34
- Br H 0.94 0.41 0.08 8.30 8.30 8.22
- I H 1.15 0.36 -0.16 8.40 8.61 8.51
- Me H 0.51 -0.07 0.00 8.46 8.51 8.36
- Br F 1.09 0.34 0.08 8.57 8.57 8.51
- H Cl 0.70 0.11 1.24 8.68 8.46 8.60
- Me F 0.66 -0.14 0.00 8.82 8.78 8.65
- H Br 1.02 0.15 1.24 8.89 8.77 8.94
- Cl Cl 1.46 0.51 0.27 8.89 8.75 8.77
- Br Cl 1.64 0.52 0.08 8.92 8.94 8.94
- Me Cl 1.21 0.04 0.00 8.96 9.15 9.08
- Cl Br 1.78 0.55 0.27 9.00 9.06 9.11
- Me Br 1.53 0.08 0.00 9.22 9.46 9.43
- H I 1.26 0.14 1.24 9.25 9.06 9.26

Example of a QSAR...

- QSAR Equation a (using 2 variables)
- log (1/C) 1.151 p - 1.464 s 7.817
- (n 22 r 0.945)
- QSAR Equation b (using 3 variables)
- log (1/C) 1.259 p - 1.460 s 0.208 Es(meta)

7.619 (n 22 r 0.959)

Example of a QSAR...

Calc.

Calc.

- m-X p-Y p s Es(meta)

log (1/C)obs log (1/C)a log (1/C)b - H H 0.00 0.00 1.24 7.46 7.82 7.88
- F H 0.13 0.35 0.78 7.52 7.45 7.43
- H F 0.15 -0.07 1.24 8.16 8.09 8.17
- Cl H 0.76 0.40 0.27 8.16 8.11 8.05
- Cl F 0.91 0.33 0.27 8.19 8.38 8.34
- Br H 0.94 0.41 0.08 8.30 8.30 8.22
- I H 1.15 0.36 -0.16 8.40 8.61 8.51
- Me H 0.51 -0.07 0.00 8.46 8.51 8.36
- Br F 1.09 0.34 0.08 8.57 8.57 8.51
- H Cl 0.70 0.11 1.24 8.68 8.46 8.60
- Me F 0.66 -0.14 0.00 8.82 8.78 8.65
- H Br 1.02 0.15 1.24 8.89 8.77 8.94
- Cl Cl 1.46 0.51 0.27 8.89 8.75 8.77
- Br Cl 1.64 0.52 0.08 8.92 8.94 8.94
- Me Cl 1.21 0.04 0.00 8.96 9.15 9.08
- Cl Br 1.78 0.55 0.27 9.00 9.06 9.11
- Me Br 1.53 0.08 0.00 9.22 9.46 9.43
- H I 1.26 0.14 1.24 9.25 9.06 9.26

QSAR of Antifungal Neolignans

- The PM3 semi-empirical method was employed to

calculate a set of molecular properties

(descriptors) of 18 neolignan compounds with

activities against Epidermophyton floccosum, a

most susceptible species of dermophytes. The

correlation between biological activity and

structural properties was obtained by using the

multiple linear regression method. The QSAR

showed not only statistical significance but also

predictive ability. The significant molecular

descriptors related to the compounds with

antifungal activity were hydration energy (HE)

and the charge on C1' carbon atom (Q1'). The

model obtained was applied to a set of 10 new

compounds derived from neolignans five of them

presented promising biological activities against

E. floccosum.

Neolignans

Descriptors Used

- Log P the values of this property were obtained

from the hydrophobic parameters of the

substituents - superficial area (A) and molecular volume (V),

log of the partition coefficient (Log P),

hydration energy (HE) properties evaluated with

the molecular modeling package HyperChem 5.0 - partial atomic charges (Qn) and bond orders (Ln)

derived from the electrostatic potential - energy of the HOMO (H) and LUMO (L) frontier

orbitals - hardness (h) obtained from the equation h

(ELUMO-EHOMO)/2 - Mulliken electronegativity (c) calculated from

the equation c -(EHOMOELUMO)/2 - other electronic properties were calculated

total energy (ET), heat of formation (DHf)

ionization potential (IP), dipole moment (m)

and polarizability (POL), whose values were

obtained from the molecular orbital pprogram

Ampac 5.0.

Two Most Important Descriptors

Antifungal QSAR

- Log 1/C -2.85 - 0.38 HE - 1.45 Q1'
- F29.63, R20.86, Q20.80, SEP0.
- where
- F is the Fisher test for significance of the

eqn. R2 is the general correlation coefficient,

Q2 is the predictive capability, and

SEP is the

standard error of prediction.

A.A.C. Pinheiro, R.S. Borges, L.S. Santos, C.N.

Alves, Journal of Molecular Structure THEOCHEM,

Vol 672, pp 215-219 (2004).

QSAR-Calculated Antifungal Activity

New Neolignans

Example of a Pharmacophore 2D Hypothesis and

Alignment

3 Dimensional QSAR Methods

- Important regions of bioactive molecules are

mapped in 3D space, such that regions of

hydrophobicity, hydrophilicity, H-bonding

acceptor, H-bond donor, p-donor, etc. are

rendered so that they overlap, and a general 3D

pattern of the functionally significant regions

of a drug are determined. - CoMFA (Comparative

Molecular Field Analysis)

is one such

approach

testosterone

CoMFA of Testosterone

Blue means electronegative groups enhance, red

means Electng. grps reduce binding

Green means bulky groups enhance, yellow means

they reduce binding