Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Use of MIF-based VolSurf Descriptors in Physicochemical and Pharmacokinetic Studies

The abstract is not availabl

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

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

Interaction of DDSDEEN peptide with N-CAM protein. Possible mechanism enhancing neuronal differentiation Valeria Marsili a,b,*, Giulio Lupidi c, Giuliano Berellini d, Isabella Calzuola a,b, Stefano Perni a,b, Gabriele Cruciani d, Gian Luigi Gianfranceschi a,b a Dipartimento di Biologia Cellulare e Ambientale, Universita` di Perugia, 06123 Perugia, Italy b CEMIN (Centro Eccellenza Materiali Innovativi Nanostrutturati), 06123 Perugia, Italy c Dipartimento di Biologia MCA, Universita` di Camerino, 62032 Camerino, Italy d Dipartimento di Chimica, Universita` di Perugia, 06123 Perugia, Italy ab s t r a c t 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. Doseand 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. © 2014 American Chemical Society

Integrating Crystallography into Early Metabolism Studies

Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information to be produced early in the discovery phase. Once obtained, researchers can judge whether or not a potential candidate should be eliminated from the pipeline, or modified to improve chemical stability or safety. The use of in silico methods to predict the site of metabolism in Phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction. This paper presents a new method, which provides the site of metabolism for any CYP-mediated reaction acting on unknown substrates. The methodology can be applied automatically to all the cytochromes whose Xray 3D structure is known, but can be also applied to homology model 3D structures. The fully automated procedure can be used to detect positions that should be protected in order to avoid metabolic degradation, or to check the suitability of a new scaffold or pro-drug. Therefore the procedure is also a valuable new tool in early ADME-Tox, where drug-safety and metabolic profile patterns must be evaluated as soon, and as early, as possible