Supplementary Material s29

Supplementary Material

Supplementary Material Molecular Docking Simulations and GRID-Independent Molecular Descriptor (GRIND) Analysis to Probe Stereoselective Interactions of CYP3A4 Inhibitors Sadia Mukhtar1 . Yusra Sajid Kiani1 . Ishrat Jabeen1*

1 Research Center for Modeling and Simulation (RCMS), National University of Science and Technology (NUST), Sector H-12, 44000, Islamabad, Pakistan

*Ishrat Jabeen E-mail: [email protected]

Table S1 Physicochemical parameters are given in the following table for all the dataset that was included in the study

LIPINSKIS RULE OF FIVE BBB hERG inhibition BBB+ (IF IC50( Molecular lip_ac model model Gold Drugs lip_dona logP(o/w)a thresho µM) Weighta ca I b II c Score ld > 0.02)b 0.8859 0.6047 Weak 0.27 237.73 2 1 3.081 0.06 inhibito 41.011 inhibito Ketamine R r r 0.8859 0.6047 Weak 0.25 237.73 2 1 3.081 0.06 inhibito 43.869 inhibito Ketamine S r r 0.9258 0.8403 Weak 0.54 531.44 8 0 3.067 -0.032 inhibito 73.164 Ketoconazol inhibito r e R r 0.9258 0.8403 Weak 0.23 531.44 8 0 3.067 -0.032 inhibito 75.67 Ketoconazol inhibito r e S r Supplementary Material

0.7687 0.8188 Weak 6.46 412.53 6 0 3.54548 -0.032 inhibito 77.681 inhibito Verapamil R r r 0.7687 0.8188 Weak 2.97 412.53 6 0 3.54548 -0.032 inhibito 78.593 inhibito Verapamil S r r 0.5782 0.6096 Weak Weak - Itraconazole 0.04 705.647 12 0 6.376 -0.032 inhibito inhibito 20.497 2R,4S.2R r r 0.5782 0.6096 Itraconazole 0.00 Weak Weak - 705.647 12 0 6.376 -0.032 2R,4S.2S 37 inhibito inhibito 58.343 r r 0.5782 0.6096 0.01 Weak Itraconazole 705.647 12 0 6.376 -0.032 inhibito -53.44 48 inhibito 2S,4R.2R r r 0.5782 0.6096 0.00 Weak Itraconazole 705.647 12 0 6.376 -0.032 inhibito 44.21 83 inhibito 2S,4R.2S r r 0.6152 0.8112 Weak 5 295.304 2 1 4.23176 0.075 inhibito 49.718 Norfluoxetin inhibito r e R r 0.6152 0.8112 Weak 11 295.304 2 1 4.23176 0.075 inhibito 50.522 Norfluoxetin inhibito r e S r 0.7754 0.717 strong 14.3 313.397 4 1 3.11374 0.032 inhibito 60.924 Reboxetine inhibito r R,R r 0.7754 0.717 strong 21.3 313.397 4 1 3.11374 0.032 inhibito 50.116 Reboxetine inhibito r S,S r 0.9090 0.6942 weak non 54.220 0.75 328.243 4 1 3.359 -0.017 Diclobutrazo inhibito inhibito 8 l R,R r r 0.9090 0.6942 weak non 1.5 328.243 4 1 3.359 -0.017 52.989 Diclobutrazo inhibito inhibito l S,S r r 0.6058 0.8467 strong 80 309.331 2 1 4.75676 0.076 inhibito 51.224 inhibito Fluoxetine R r r 47 309.331 2 1 4.75676 0.076 0.6058 0.8467 48.915 strong inhibito Fluoxetine S inhibito r Supplementary Material

r a Physicochemical properties calculated using MOE (Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2015.49.). b Blood brain barrier prediction using SVM_MACCSFP BBB (www.cbligand.org/BBB/) b Probability predicted using binary classification model to distinguish between hERG inhibitors and non-blockers ( Marchese Robinson, Glen et al . 2011; Cheng, Li et al . 2012) c Probability predicted using binary classification model based on recursive partitioning and Bayesian classification to distinguish between hERG inhibitors and non-blockers ( Cheng, Li et al . 2012; Wang, Li et al . 2012). Supplementary Material Supplementary Material

Fig S2. Important pharmacophoric features in (a) S-verapamil representing DRY-DRY probes (DRY: yellow hot spots) two hydrophobic groups at a distance of 12.0-12.40 Å from one another that are contributing positively in the activity while DRY-N1 represents a hydrophobic probe (DRY: yellow hot spots) at a distance of 13.20-13.60Å from a hydrogen bond acceptor (N1: blue hotspots). O–TIP outline a hydrogen bond donor (OH) (O: red hot spots) at a distance of 4.80-5.20Å from the edge contributing negatively in the activity. A hydrogen bond acceptor (-NH) (N: blue hotspots) at a distance of 8.8-9.20Å form hydrogen bond donor (O: red hot spots) is also contributing negatively in the activity, (b) R-verapamil is also depicting similar pharmacophoric features as S-verapamil except the distance of 8.8-9.20Å between a hydrogen bond donor (O: red hot spots) and hydrogen bond acceptor (N1: blue hotspots) is absent in R-verapamil, (c) S-norfluoxetine is showing distance of 13.20-13.60Å between hydrophobic (DRY: yellow hot spots) and hydrogen bond acceptor (N: blue hotspots) probes that is contributing positively in the activity while the distance of 8.8-9.20Å between hydrogen bond donor and hydrogen bond acceptor as well as the distance of 4.80-5.20 Å between hydrogen bond donor (O: red hot spots) and molecular edge (TIP: green probes) are contributing negatively in the activity, (d) R-norfluoxetine is also illustrating similar pharmacophoric features as S-norfluoxetine except the distance of 8.8-9.20Å between a hydrogen bond donor (O: red hot spots) and hydrogen bond acceptor (N1: blue hotspots) is absent in R-norfluoxetine, (e) S-Fluoxetine is showing two negative contributing features a hydrogen bond acceptor (-NH) (N: blue hotspots) at a distance of 8.8-9.20Å form hydrogen bond donor and a distance of 4.80-5.20 Å between molecular edge (TIP: green probes) and hydrogen bond donor (O: red hot spots) in the figure, (f) R-fluoxetine is illustrating pharmacophoric features similar to that of S-fluoxetine, (g) S-dichlobutrazol is depicting three important distances including DRY-DRY probes (DRY: yellow hot spots) two hydrophobic groups at a distance of 12.0-12.40 Å, a hydrogen bond donor (OH) (O: red hot spots) at a distance of 4.80-5.20 Å from the edge (TIP: green probes) contributing negatively in the activity. A hydrogen bond acceptor (-NH) (N: blue hotspots) at a distance of 8.8-9.20Å form hydrogen bond donor is also contributing negatively in the activity, (h) R-dichlobutrazol is Supplementary Material displaying identical distances among similar pharmacophoric groups highlighted in S- dichlobutrazol.

References

(Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2015.49.).

Cheng, F., W. Li, et al. (2012). admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties, ACS Publications.

Marchese Robinson, R. L., R. C. Glen, et al. (2011). "Development and comparison of hERG Blocker classifiers: assessment on different datasets yields markedly different results." Molecular Informatics 30(5): 443-458.

Wang, S., Y. Li, et al. (2012). "ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage." Molecular pharmaceutics 9(4): 996-1010.