A rapid computational filter for predicting the rate of human renal clearance.

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.

Components of successful lead generation.

This review aims to give an overview of current good practice in the prosecution of Lead Generation. It will assess experiences across the field as judged from the contents of the limited number of peer-review disclosures to date. It will also rely heavily on the experiences of the authors from many campaigns within this organisation. Its focus will be on the assembly of an appropriate compound collection for application in High Throughput Screening (HTS), the prosecution of HTS, the profiling of HTS output and, lastly the Hit-to-Lead optimisation of selected HTS output. Excluded from the scope are detailed aspects of library design [1], parallel synthesis [2], virtual library applications [3], virtual screening [4] and fragment screening [5] approaches, all of which have been the subject of recent reviews.

Impact of incorporating the 2C5 crystal structure into comparative models of cytochrome P450 2D6.

Cytochrome P450 2D6 (CYP2D6) metabolizes approximately one third of the drugs in current clinical use. To gain insight into its structure and function, we have produced four different sets of comparative models of 2D6: one based on the structures of P450s from four different microorganisms (P450 terp, P450 eryF, P450 cam, and P450 BM3), another on the only mammalian P450 (2C5) structure available, and the other two based on alternative amino acid sequence alignments of 2D6 with all five of these structures. Principal component analysis suggests that inclusion of the 2C5 crystal structure has a profound effect on the modeling process, altering the general topology of the active site, and that the models produced differ significantly from all of the templates. The four models of 2D6 were also used in conjunction with molecular docking to produce complexes with the substrates codeine and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP); this identified Glu 216 [in the F-helix; substrate recognition site (SRS) 2] as a key determinant in the binding of the basic moiety of the substrate. Our studies suggest that both Asp 301 and Glu 216 are required for metabolism of basic substrates. Furthermore, they suggest that Asp 301 (I-helix, SRS-4), a residue thought from mutagenesis studies to bind directly to the basic moiety of substrates, may play a key role in positioning the B'-C loop (SRS-1) and that the loss of activity on mutating Asp 301 may therefore be the result of an indirect effect (movement of the B'-C loop) on replacing this residue.