1. Lecture 7: Computer aided drug design: Statistical approach. Lecture 7: Computer aided drug design: Statistical approach. Chen Yu Zong Department of Computational Science National University of Singapore w Introduction of methodology. w 2D QSAR and Pharmacophore w SVM study of drug absorption, excretion, side-effect

2. Strategies for improving drug design cycle: w Computer-aided drug design: Receptor 3D structure unknown: QSAR (Quantitative Structure-Activity Relationship). Pharm. Res. 10, 475-486 (1993).

3. Basic Idea: w Binding => Activity (Reaction): Binding: Binding free energy  G –Intermolecular forces (interactions). –Hydrophobic effect, solvation. Activity: Reaction constant K eq w Relationship:  G = -RT ln K eq

4. Basic Idea: Objective: w Derive a function that links biological activity of a group of related chemical compounds with parameters that describe a structural feature of these molecule. w This feature also reflects the property of binding cavity on protein target. w The derived function is used as a guide to select best candidate for drug design.

5. Illustration: Log(Biological activity) = f(structural, physical, chemical parameters) Example: Log(Biological activity) => Size of a side chain of a compound => Size and shape of protein cavity

6. Illustration: Log(Biological activity) => Size of a side chain of a compound => Size and shape of protein cavity

7. 2D QSAR: LogA =  c c f chem (x c ) + c h f hydr (x h ) + c e f elec (x e ) + c st f st (x s ) AbbreviationMeaning mg / L, mol / L (and similar) milligram per litre, mole per litre etc. log Plogarithm of the octanol-water partition coefficient log Ddistribution coefficient pi, PiHansch hydrophobic substituent constant MW, Mol Wtmolecular weight LUMO, Elumoenergy of the lowest unoccupied molecular orbital HOMO, Ehomoenergy of the high occupied molecular orbital HofF, Heat Formheat of formation Dipoledipole moment sigma, sigmaXHammett constant (on position X) I-Xindicator variable for presence of X RXindicates substituent at position X MRmolar refractivity

8. Construction of Pharmacophore: Superimposing common molecular interaction field contour resulted in the identification of the consensus pharmacophore The Concensus Pharmacophore: w purple = hydrophobic area w green = electron-deficient aromatic system w red = electronegative heteroatoms w pink = protonated nitrogen w blue = large planar ring system.

9. Prediction of drug transport by statistical method: w Absorption at intestine w CNS activity w BBB penetration w Drug excretion (Multidrug resistance, P-glycoprotein substrates) Drug safety prediction by statistical method: w Prediction of a minor side-effect TdP

10. Membrane transport:

13. Drug data: Substrates and non-substrates of P-glycoproteins 116 P-gp substrates and 85 P-gp nonsubstrates Human intestine absorbing and non-absorbing drugs 167 HIA positive and 20 HIA negative drugs TdP inducing and non-inducing drugs 85 TdP inducing agents and 162 non-TdP causing agents

14. SVM Classification of drugs : How to represent a drug? w Each drug represented by specific feature vector assembled from encoded representations of tabulated physicochemical properties: Global properties (Total number: 18) Connectivity (Total number: 20) Shape and flexibility (Total number: 8) Electrotopological states (Total number: 84) Quantum chemical (Total number: 13) Geometrical properties (Total number: 16)

15. SVM Classification of drugs : How to evaluate prediction results? As in the case of all discriminative methods, the performance of SVM classification can be measured by the quantity of w true positives TP w true negatives TN w false positives FP w false negatives FN w sensitivity SE=TP/(TP+FN) which is the prediction accuracy for the positive samples w specificity SP=TN/(TN+FP) which is the prediction accuracy for the negative samples w the overall prediction accuracy (Q): (8)

16. Drug descriptors: ClassDescriptors Global properties (Total number: 18) Molecular weight, Numbers of rings, rotatable bonds, H-bond donors, H-bond acceptors, Element counts Connectivity (Total number: 20) Molecular connectivity indices, Valence molecular connectivity indices Shape and fexibility (Total number: 8) Molecular shape Kappa indices, Kappa alpha indices, flexibility index Electrotopological state (Total number: 84) Electrotopological state indices and Atom type electrotopological state indices Quantum chemical (Total number: 13) Atomic charge on the most positively charged H atom, Largest negative charge on an non-H atom, Polarizability index, Hydrogen bond acceptor basicity (covalent HBAB), Hydrogen bond donor acidity (covalent HBDA), Molecular dipole moment, Absolute hardness, Softness, Ionization potential, Electron affinity, Chemical potential, Electronegativity index, Electrophilicity index Geometrical properties (Total number: 16) Length vectors (longest distance, longest third atom, 4 th atom), Molecular van der Waals volume, Solvent accessible surface area, Molecular surface area, van der Waals surface area, Polar molecular surface area, Sum of solvent accessible surface areas of positively charged atoms, Sum of solvent accessible surface areas of negatively charged atoms, Sum of charge weighted solvent accessible surface areas of positively charged atoms, Sum of charge weighted solvent accessible surface areas of negatively charged atoms, Sum of van der Waals surface areas of positively charged atoms, Sum of van der Waals surface areas of negatively charged atoms, Sum of charge weighted van der Waals surface areas of positively charged atoms, Sum of charge weighted van der Waals surface areas of negatively charged atoms

17. Prediction of drug human intestine absorption: Cross validation Feature selection Absorbing agentsNon-absorbing agentsQ (%) TPFNSE(%)TNFPSP(%) 1No24292.314930.7771.8 2No23388.469469.2382.1 3No24292.3112192.3192.3 4No22484.6210376.9282.1 5No270100.004930.7777.5 averageNo 91.54 60.0081.1 1RFE21580.7711284.6282.1 2RFE24292.3110376.9287.2 3RFE24292.3112192.3192.3 4RFE22484.6210376.9282.1 5RFE24388.8910376.9285.0 averageRFE 87.78 81.5485.7

18. Prediction of drug CNS activity: MethodNo. of agents CNS + (%) CNS – (%) referenc e Bayesian neural network 27592.071.0Ajay et al Principle component analysis 12090.065.0Crivori et al Support vector machine 30478.960.4Trotter et al Support vector machine 5297.690.0This work

19. Prediction of drug BBB penetration: Cross validation Feature selection BBB penetrating drugsBBB non-penetrating drugsQ (%) TPFNSE(%)TNFPSP(%) 1No20386.968947.0670.0 2No18578.2610758.8270.0 3No230100.0061135.2972.5 4No16769.5711664.4767.5 5No19579.1712570.5975.6 averageNo 82.79 55.2571.1 1RFE19482.6114382.3582.5 2RFE17673.9112570.5972.5 3RFE230100.0014382.3592.5 4RFE17673.9111664.7170.0 5RFE20483.3313476.4780.5 averageRFE 82.75 75.2979.6

20. Prediction of drug TdP side-effect: Cross validation Feature selection TdP-inducing drugsNon-TdP-causing agentsQ (%) TPFNSE(%)TNFPSP(%) 1No9852.9450590.9181.9 2No9852.9451492.7383.3 3No71041.1851492.7380.5 4No8947.0652394.5583.3 5No71041.1852492.8680.8 averageNo 47.06 92.7682.0 1RFE11664.7153296.3688.9 2RFE10758.8252394.5586.1 3RFE71041.1851492.7380.5 4RFE13476.4751492.7388.9 5RFE10758.8551591.0783.6 averageRFE 60.01 93.4985.6