Can History Foretell Future ?

A set of diverse bioactive molecules, relevant from a medicinal chemistry viewpoint was assembled and applied to fashion a tool. The tool allows navigation of the physico-chemical property space of new and old, or traditional, drugs against a larger set of 12,000 diverse bioactive small molecules. The majority of drugs on the market are occupying only a fraction of the property space of the bioactive molecules. New molecular entities approved since 2002 are moving away from this traditional drug space. In this new territory of property space, some semi-empirical rules derived from knowledge accumulated with historic, older molecules are not necessarily valid and different liabilities such as very low plasma unbound concentration and efflux become more prominent

Systematic Evaluation of Wajima Superposition (Css-MRT) in the Estimation of Human Intravenous Pharmacokinetic Profile

Synopsis The prediction of human pharmacokinetics became an integral part of drug development as it helps the planning stage of clinical development, such as the projection of efficacious human dose or optimal dosing paradigm. The objective of this paper is to systematically examine the application of the Wajima’s superposition on a set of 57 compounds, with a balanced representation of metabolically and renally eliminated compounds and diverse chemical space with a wide variety of lipophilicity as well as ionization characters (acid/base). The present work also aims at offering some guidance, and at exploring caveats which may be encountered during the application toward the generation of predicted human i.v. PK profiles

In silico Prediction of Total Human Plasma Clearance

The prediction of the total human plasma clearance of novel chemical entities continues to be of paramount importance in drug design and optimization, because it impacts both dose size and dose regimen. Although many in vivo and in vitro methods have been proposed, a well-constructed, well-validated, and less resource-intensive computational tool would still be very useful in an iterative compound design cycle. A new completely in silico linear PLS (partial least-squares) model to predict the human plasma clearance was built on the basis of a large data set of 754 compounds using physicochemical descriptors and structural fragments, the latter able to better represent biotransformation processes. The model has been validated using the “ELASTICO” approach (Enhanced Leave Analog-Structural, Therapeutic, Ionization Class Out) based on ten therapeutic/structural analog classes. The model yields a geometric mean fold error (GMFE) of 2.1 and a percentage of compounds predicted within 2- and 3-fold error of 59% and 80%, respectively, showing an improved performance when compared with previous published works in predicting clearance of neutral compounds, and a very good performance with ionized molecules at pH 7.5, able to compare favorably with fairly accurate in vivo methods

Interaction of DDSDEEN peptide with N-CAM protein. Possible mechanism enhancing neuronal differentiation

DDSDEEN chromatin peptide, after dansylation, was studied for its ability to bind N-CAM protein. The binding causes a quenching of the Dns-peptide fluorescence emission. Dose- and time-dependent binding of Dns-peptide with N-CAM has been shown. Fluorescence quenching is completely lost if the Dns-peptide is subjected to carboxypeptidase digestion. Moreover the undansylated peptide pEDDSDEEN competes with the DnsDDSDEEN peptide for the binding with the N-CAM protein. The Dns-peptide-N-CAM bond has been related to the peptide biological activity probably involved in the promotion of neuronal differentiation. An attempt to recognize a possible N-CAM binding site for Dns-peptide was performed by alignment of N-CAM from various sources with some sequences that have been previously reported as binding sites for the pEDDSDEEN and DDSDEEN peptides. Interestingly, the alignment of N-CAM from various sources with the peptides WHPREGWAL and WFPRWAGQA recognizes on rat and human N-CAM a unique sequence that could be the specific binding site for chromatin peptide: WHSKWYDAK. This sequence is present in fibronectin type-III domain of N-CAM. In addition molecular modeling studies indicate the N-CAM sequence WHSKWYDAK as, probably, the main active site for DnsDDSDEEN (or pEDDSDEEN) peptide ligand. Accordingly the binding experiments show a high affinity between WHSKWYDAK and DnsDDSDEEN peptides

Clearance Mechanism Assignment and Total Clearance Prediction in Human Based upon in Silico Models

We introduce a two-tier model based on an exhaustive data set, where discriminant models based on principal component analysis (PCA) and partial least squares (PLS) are used separately and in conjunction, and we show that PCA is highly discriminant approaching 95% accuracy in the assignment of the primary clearance mechanism. Furthermore, the PLS model achieved a quantitative predictive performance comparable to methods based on scaling of animal data while not requiring the use of either in vivo or in vitro data, thus sparing the use of animal. This is likely the highest performance that can be expected from a computational approach, and further improvements may be difficult to reach. We further offer the medicinal scientist a PCA model to guide in vitro and/or in vivo studies to help limit the use of resources via very rapid computations

Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: Volume of distribution at steady state

The authors present a comprehensive analysis on the estimation of volume of distribution at steady state (VDss) in human based on rat, dog, and monkey data on nearly 400 compounds for which there are also associated human data. This data set, to the authors' knowledge, is the largest publicly available, has been carefully compiled from literature reports, and was expanded with some in-house determinations such as plasma protein binding data. This work offers a good statistical basis for the evaluation of applicable prediction methods, their accuracy, and some methods-dependent diagnostic tools. The authors also grouped the compounds according to their charge classes and show the applicability of each method considered to each class, offering further insight into the probability of a successful prediction. Furthermore, they found that the use of fraction unbound in plasma, to obtain unbound volume of distribution, is generally detrimental to accuracy of several methods, and they discuss possible reasons. Overall, the approach using dog and monkey data in the Oie-Tozer equation offers the highest probability of success, with an intrinsic diagnostic tool based on aberrant values (1) for the calculated fraction unbound in tissue. Alternatively, methods based on dog data (single-species scaling) and rat and dog data (Oie-Tozer equation with 2 species or multiple regression methods) may be considered reasonable approaches while not requiring data in nonhuman primates. © The Author(s) 2012

Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 2: Clearance

A comprehensive analysis on the prediction of human clearance based on intravenous pharmacokinetic data from rat, dog, and monkey for approximately 400 compounds was undertaken. This data set has been carefully compiled from literature reports and expanded with some inhouse determinations for plasma protein binding and rat clearance. To the authors' knowledge, this is the largest publicly available data set. The present examination offers a comparison of 37 different methods for prediction of human clearance across compounds of diverse physicochemical properties. Furthermore, this work demonstrates the application of each prediction method to each charge class of the compounds, thus presenting an additional dimension to prediction of human pharmacokinetics. In general, the observations suggest that methods employing monkey clearance values and a method incorporating differences in plasma protein binding between rat and human yield the best overall predictions as suggested by approximately 60% compounds within 2-fold geometric mean-fold error. Other single-species scaling or proportionality methods incorporating the fraction unbound in the corresponding preclinical species for prediction of free clearance in human were generally unsuccessful. © The Author(s) 2012