Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors

• Published in: Journal of computer-aided molecular design Vol. 16; no. 7; pp. 443 - 458
• Main Authors: Afzelius, Lovisa, Masimirembwa, Collen M, Karlén, Anders, Andersson, Tommy B, Zamora, Ismael
• Format: Journal Article
• Language: English
• Published: Netherlands Springer Nature B.V 01.07.2002
• Subjects: Aryl Hydrocarbon Hydroxylases - antagonists & inhibitors; Competition; Cytochrome P-450 CYP2C9; Discriminant analysis; Enzyme Inhibitors - chemistry; Enzyme Inhibitors - pharmacology; Inhibitors; Medicin och hälsovetenskap; Models, Molecular; Software
• ISSN: 0920-654X; 1573-4951
• DOI: 10.1023/a:1021281008423

This study describes the use of alignment-independent descriptors for obtaining qualitative and quantitative predictions of the competitive inhibition of CYP2C9 on a serie of highly structurally diverse compounds. This was accomplished by calculating alignment independent descriptors in ALMOND. These GRid INdependent Descriptors (GRIND) represent the most important GRID-interactions as a function of the distance instead of the actual position of each grid-point. The experimental data was determined under uniform conditions. The inhibitor data set consists of 35 structurally diverse competitive stereospecific inhibitors of the cytochrome P450 2C9 and the non -inhibitor data set of 46 compounds. In a PLS discriminant analysis 21 inhibitors and 21 non-inhibitors (1 and 0 as activities) were analyzed using the ALMOND program obtaining a model with an r2 of 0.74 and a cross-validation value (q2) of 0.64. The model was externally validated with 39 compounds (14 inhibitors/25 non-inhibitors). 74% of the compounds were correctly predicted and an additional 13% was assigned to a borderline cluster. Thereafter, a model for quantitative predictions was generated by a PLS analysis of the GRIND descriptors using the experimental Ki-value for 21 of the competitive inhibitors (r2 = 0.77, q2 = 0.60). The model was externally validated using 12 compounds and predicted 11 out of 12 of the Ki-values within 0.5 log units. The discriminant model will be useful in screening for CYP2C9 inhibitors from large compound collections. The 3D-QSAR model will be used during lead optimization to avoid chemistry that result in inhibition of CYP2C9.