Predicting Human Serum Albumin Affinity of Interleukin-8 (CXCL8) Inhibitors by 3D-QSPR Approach

• Published in: Journal of medicinal chemistry Vol. 48; no. 7; pp. 2469 - 2479
• Main Authors: Aureli, Loretta, Cruciani, Gabriele, Cesta, Maria Candida, Anacardio, Roberto, De Simone, Lucio, Moriconi, Alessio
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 07-04-2005
• Subjects: Anti-Inflammatory Agents, Non-Steroidal - chemistry; Binding Sites; Biological and medical sciences; Bones, joints and connective tissue. Antiinflammatory agents; Chemical Phenomena; Chemistry, Physical; Humans; In Vitro Techniques; Interleukin-8 - antagonists & inhibitors; Interleukin-8 - chemistry; Medical sciences; Models, Molecular; Pharmacology. Drug treatments; Phenylpropionates - blood; Phenylpropionates - chemistry; Protein Binding; Quantitative Structure-Activity Relationship; Serum Albumin - chemistry; Serum Albumin - metabolism; Stereoisomerism; Human; Prediction; Serum albumin; Serum protein; Modeling; Three dimensional model; Structure activity relation; Molecular model; Affinity; Benzenic compound; Inhibitor; Property structure relationship; Chemical synthesis; Chemometrics; Interleukin 8; Physicochemical properties
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm049227l

A novel class of 2-(R)-phenylpropionamides has been recently reported to inhibit in vitro and in vivo interleukin-8 (CXCL8)-induced biological activities. These CXCL8 inhibitors are derivatives of phenylpropionic nonsteroidal antiinflammatory drugs (NSAIDs), high-affinity ligands for site II of human serum albumin (HSA). Up to date, only a limited number of in silico models for the prediction of albumin protein binding are available. A three-dimensional quantitative structure−property relationship (3D-QSPR) approach was used to model the experimental affinity constant (K i) to plasma proteins of 37 structurally related molecules, using physicochemical and 3D-pharmacophoric descriptors. Molecular docking studies highlighted that training set molecules preferentially bind site II of HSA. The obtained model shows satisfactory statistical parameters both in fitting and predicting validation. External validation confirmed the statistical significance of the chemometric model, which is a powerful tool for the prediction of HSA binding in virtual libraries of structurally related compounds.