Quantitative SAR (QSAR) Analysis

Quantitative SAR (QSAR)  QSAR studies attempt to identify/quantify physicochemical properties of a drug to establish whether these properties have an effect on the drug’s biological activity  If there are relationships between biological activity and a physicochemical property, it might be possibl...

Quantitative SAR (QSAR)  QSAR studies attempt to identify/quantify physicochemical properties of a drug to establish whether these properties have an effect on the drug’s biological activity  If there are relationships between biological activity and a physicochemical property, it might be possible to describe this relationship with an equation  Such equation can then be used to predict whether a novel molecule has biological activity  Two advantages:  Saves effort/time in synthesizing molecules that are predicted to have poor activities  If a bioactive molecule is found that does not fit the equation, it implies that some other feature or property is important (starting point for further drug development)  In practice it is best to focus on two (or three) physicochemical properties in QSAR, because relationships (equations) should be established with molecules that vary in one property at a time while the other ones should remain roughly constant (note: that is easier said than done). 1  In the simplest case, molecules are produced that vary in one parameter (e.g., log P)  The biological activity is typically expressed as log (1/C) where C = concentration of the molecule required to achieve a defined level of biological activity  In the example below, the relationship appears to be linear  The equation describing this relationship is therefore: k1 and k2 are constants Notes:  The data should be fit using various equations (linear, parabolic, exponential, …) using non‐linear least‐squares regression  The trendline denotes the best fit to the linear relationship  The error or standard deviations are important (R value) since large errors would make reasonable predictions for new/unsynthesized molecules difficult 2 Physicochemical parameters used in QSAR Hydrophobicity  The hydrophobic character of a drug‐like molecule is important when it comes to crossing membranes and to interactions with a target  One of the QSAR parameters is the partition coefficient, P  P can be determined for a variety of similar compounds with various substituents  If the log P range into which these compounds fall is small (e.g., from 1 to 3), a linear relationship is obtained  Example: Binding of 42 drugs to human serum albumin follows the following linear equation In this study, the log P values ranged from 0.8 to 3.8. 3  If the hydrophobicity is increased (beyond log P = 4), a drug might not be soluble or might remain in the lipid bilayer  This would imply that the biological activity must decrease beyond a certain log P value (which implies that the function cannot be linear until infinity)  In reality, a parabolic function is often obtained (with the maximum denoting the highest biological activity) dominated by dominated by Example: Equation for the general anaesthetic properties of ethers: ca. 2.3 for general anaesthetics 4  General anaesthetics need to pass the blood brain barrier to get into the CNS  It was found that a log P of 2.3 affords the highest biological activities  For example, the log P values for ether (least effective), chloroform and halothane (most effective) are 0.98, 1.97, and 2.3 Example: Changing log P can remove CNS side effects:  Compound (I) is a cardiotonic* agent that has the side effect of producing bright visions → related to the drug entering the CNS. The log P value is 2.6.  Replacing the methoxy group with the similarly‐sized, but more polar SOMe group decreases log P to 1.2  Compound (II) does not display CNS side effects (too polar to enter the CNS) *cardiotonic agents improve heart muscle contraction (improved blood flow) 5  Log P requires experimental determination (i.e., compounds need to be synthesized and then measured)  It is possible to estimate log P values using substituent hydrophobicity constants ()   is a measure of how hydrophobic a substituent is relative to hydrogen (H)   values are experimentally determined for a standard compound such as benzene, with and without a variety of substituents (X) using  > 0: substituent X is more hydrophobic than H  < 0: substituent X is less hydrophobic than H 6  For a lead compound, log P needs to be determined experimentally, but when the lead compound is modified with different substituents, their log P values can be calculated (ClogP) based on  values  Example: = 2.13 + 0.71 – 1.49 = 1.35 (exp: 1.5)  (CONH2) = 0.64 – 2.13 = – 1.49 7 Substituent electronic effects  Electronic effects can have profound effects on a drug’s polarity or ionization  For aromatic substituents, electronic effects are expressed through the Hammett substituent constant ()   is a measure of the electron‐donating or withdrawing ability of the substituent  Example: benzoic acid >0  0)  Final note on aliphatic substituents: Hammett parameters are determined by measuring the effect of substituents on the rate of ester hydrolysis Resonance does not play a role with aliphatic substituents! 11 Steric effects  Size and shape can influence how a drug binds to its target  Steric properties are more difficult to quantify because a bulky substituent may increase or decrease target affinity  There are a variety of approaches to treat steric effects  Taft’s steric factor (ES):  Determined by comparing rates of hydrolysis of substituted (X) aliphatic esters against a methylester (reference; k0) 12  Another measure of steric effects is the molar refractivity (MR)  n = refractive index; MW = molecular weight; d = density  MR correlates to the volume occupied by an atom or group Correction factor defining how easily a substituent can be polarized Defines the volume 13 The Hansch Equation  The biological activity of most drugs is related to a combination of physicochemical properties  The Hansch equation incorporates multiple properties (previously discussed) including log P, ,  and a steric factor  Examples: (For log P values covering a larger range; parabolic behaviour)  Example: QSAR equation for no dependence on steric effects! Inhibitors of adrenergic activity 14 The Craig Plot  The Craig Plot is a good visualization tool for  and  values (instead of looking at extensive tables)  The plot on the right shows the values for para‐ aromatic substituents Advantages of using the plot:  The plot shows that there is no relationship between  and  values (they are spread over all quadrants)  It is easy to see which substituents have similar  or  values (see red and blue lines) 15 The Craig Plot  The plot is useful for planning QSAR studies (in general analogues with  and  values from all four quadrants should be synthesized to get to the most accurate equations)  After the derivation of the Hansch equation, it is easy to see whether  and  values should be positive or negative to improve biological activity (helps in lead optimization)  Final note: Craig plots can be made to compare other parameters (e.g., hydrophobicity and ES or MR) 16 The Craig Plot for meta aromatic substituents 17 The Topliss Scheme Topliss scheme for aromatic substituents  Sometimes it is not feasible to generate a large enough number of compounds to obtain a Hansch equation (e.g., difficult syntheses)  In this case, it is possible to follow a flow diagram (Topliss scheme) to synthesize a compound, then analyze its biological activity, and then plan the next synthesis, and so on. Topliss scheme for aliphatic substituents  There are two general Topliss schemes (one for aromatic substituents, one for aliphatic ones) 18 Topliss scheme for aromatic substituents M = more activity E = equally active L = lower activity The scheme assumes that a lead compound has biological activity and contains a monosubstituted aromatic ring  The starting point is to synthesize the p‐Cl derivative (Cl is more hydrophobic and electron‐withdrawing than H; i.e.,  and  values are positive)  When the p‐Cl derivative is tested for activity, there are 3 possible outcomes: L, E or M 19 Topliss scheme for aromatic substituents M = more activity E = equally active L = lower activity The possible outcome of the testing of the p‐Cl derivative (L, E or M) determines which path to follow for the next synthesis  e.g., for M: The next step would be to add another chlorine atom in the 3‐position to see whether even more positive  and  values enhance the biological effect  If that is not the case (L or E branch), it is possible that steric effects or excessive hydrophobicity play a role  This can now be tested by making 4‐CF3 or 4‐Br (same  but different  values – see Craig Plot) 20  Example: Topliss Scheme for a series of sulfonamides  Step 3: Addition of a 3‐Cl substituent decreased activity  Indicates that the decrease could be due to steric effects or that the hydrophobicity is too high  Step 4: 4‐Br derivative (equal activity compared to 3,4‐Cl2)  This means that the 4‐Br derivative is less potent than the 4‐Cl derivative!  Br has a larger hydrophobicity (), but the same electronic effect () as Cl (see Craig Plot)  Interpretation: The lower activity of 4‐Br could be due to the larger hydrophobicity  Step 5: 4‐Nitro derivative (highest activity)  The NO2 group has a much smaller hydrophobicity (than Br) and a large electron‐withdrawing effect (large ) Overall, it seems that high activities are obtained with a large value of , and a smaller value of  21 Bioiosteres in QSAR  Tables of substituent constants are useful to decide which bioisosteres to use in drug design  Example: Scenario 1 (p is most important for activity)  COCH3 (0.5) is a good bioisostere of SOCH3 (0.49) – and vice versa  Example: Scenario 2 ( is most important for activity)  COCH3 (‐0.55) is not a good bioisostere of SOCH3 (‐1.58)  SO2CH3 (‐1.63) is a good bioisostere of SOCH3 (‐1.58) 22 Planning of QSAR studies  At the beginning it needs to be decided which parameters to study  Most often QSAR studies start with  and  (and potentially ES)  Based on the parameters, a number of compounds need to be synthesized such that there is a considerable variation in the parameters (note: Craig Plots are very useful here)  Rule‐of‐thumb: At least 5 molecules should be synthesized per parameter studied  It is best not to use substituents in initial QSAR studies that could ionize (CO2H, NH2) or be metabolized (esters, nitro group)  After the first QSAR equation is generated, more and more analogues are prepared to refine the equation (e.g., introduction of new parameters)  The refinement is an iterative process (synthesis → activity → refinement) 23 Final example of a QSAR study on antiallergic pyranenamines  The initial study included 19 compounds and gave the following equation  The negative constant (‐0.14, for ) and the dependence on 2 are quite unusual (for 2: both electron‐donating and withdrawing substituents decrease activity)  A refined QSAR expression was generated with 61 compounds, and was then further refined with 98 compounds F‐5 345‐HBD M‐V 4‐OCO HB‐intra F‐test (7 variables) inductive effect at 5 H‐bonding (at 3,4,5) volume of m‐substituents para‐acyloxy group ortho‐standing HB groups 24