QSAR modeling of beta-lactam binding to human serum proteins

The binding of beta-lactams to human serum proteins was modeled with topological descriptors of molecular structure. Experimental data was the concentration of protein-bound drug expressed as a percent of the total plasma concentration (percent fraction bound, PFB) for 87 penicillins and for 115 beta-lactams. The electrotopological state indices (E-State) and the molecular connectivity chi indices were found to be the basis of two satisfactory models. A data set of 74 penicillins from a drug design series was successfully modeled with statistics: r2 = 0.80, s = 12.1, q2 = 0.76, spress = 13.4. This model was then used to predict protein binding (PFB) for 13 commercial penicillins, resulting in a very good mean absolute error, MAE = 12.7 and correlation coefficient, q2 = 0.84. A group of 28 cephalosporins were combined with the penicillin data to create a dataset of 115 beta-lactams that was successfully modeled: r2 = 0.82, s = 12.7, q2 = 0.78, spress = 13.7. A ten-fold 10% leave-group-out (LGO) cross-validation procedure was implemented, leading to very good statistics: MAE = 10.9, spress = 14.0, q2 (or r2press) = 0.78. The models indicate a combination of general and specific structure features that are important for estimating protein binding in this class of antibiotics. For the beta-lactams, significant factors that increase binding are presence and electron accessibility of aromatic rings, halogens, methylene groups, and =N- atoms. Significant negative influence on binding comes from amine groups and carbonyl oxygen atoms.