QSAR modeling of β-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: r(2)=0.80, s=12.1, q(2)=0.76, s(press)=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, q(2)=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: r(2)=0.82, s=12.7, q(2)=0.78, s(press)=13.7. A ten-fold 10% leave-group- out (LGO) cross-validation procedure was implemented, leading to very good statistics: MAE=10.9, s(press)=14.0, q(2) (or r(press)(2))=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.