Comparative molecular field analysis combined with physicochemical parameters for prediction of polydimethylsiloxane membrane flux in isopropanol

Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and its application on modeling ligand functionality for 5HT-subtype GPCR families

Advanced high-throughput screening (HTS) technologies generate great amounts of bioactivity data, and this data needs to be analyzed and interpreted with attention to understand how these small molecules affect biological systems. As such, there is an increasing demand to develop and adapt cheminformatics algorithms and tools in order to predict molecular and pharmacological properties on the basis of these large data sets. In this manuscript, we report a novel machine-learning-based ligand classification algorithm, named Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), for data-mining and modeling of large chemical data sets to predict pharmacological properties in an efficient and accurate manner. The performance of LiCABEDS was evaluated through predicting GPCR ligand functionality (agonist or antagonist) using four different molecular fingerprints, including Maccs, FP2, Unity, and Molprint 2D fingerprints. Our studies showed that LiCABEDS outperformed two other popular techniques, classification tree and Naive Bayes classifier, on all four types of molecular fingerprints. Parameters in LiCABEDS, including the number of boosting iterations, initialization condition, and a "reject option" boundary, were thoroughly explored and discussed to demonstrate the capability of handling imbalanced data sets, as well as its robustness and flexibility. In addition, the detailed mathematical concepts and theory are also given to address the principle behind statistical prediction models. The LiCABEDS algorithm has been implemented into a user-friendly software package that is accessible online at http://www.cbligand.org/LiCABEDS/ .

Quantitative structure-permeation relationships for solute transport across silicone membranes

PURPOSE

The purpose of this work was to assess the molecular properties that influence solute permeation across siliconemembranes and to compare the results with transport across human skin.

METHODS

The permeability coefficients (log Kp) of a series of model solutes across silicone membranes were determined from the analysis of simple transport experiments using a pseudosteady-state mathematical model of the diffusion process. Subsequently, structure permeation relationships were constructed and examined, focusing in particular on the difference between solute octanol/water and 1,2 dichloroethane/water partition coefficients (deltalog P(oct-dce)), which re ported upon H-bond donor activity, and the computationally derived molecular hydrogen-bonding potential.

RESULTS

The hydrogen-bond donor acidity and the lipophilicity of the compounds examined greatly influenced their permeation across sil cone membranes. Furthermore, for a limited dataset, a significant correlation was identified between solute permeation across silicone membranes and that through human epidermis.

CONCLUSION

The key molecular properties that control solute perme ation across silicone membranes have been identified. For the set of substituted phenols and other unrelated compounds examined here a similar structure-permeation relationship has been derived for their transport through human epidermis, suggesting application of the results to the prediction of flux across biological barriers.

Quantitative structure-property relationships in pharmaceutical research - Part 2

Part one of this two-part review described the advantages and limitations of quantitative structure-property relationships (QSPR), and offered an overview of the components involved in the development of correlations1. Part two provides a discussion of a few notable examples of relationships with organoleptic, physicochemical and pharmaceutical properties.

Quantitative structure-property relationships in pharmaceutical research - Part 1

Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of new drugs. Although a QSAR does not completely eliminate the trial and error factor involved in the development of a new drug, it certainly decreases the number of compounds synthesized by facilitating the selection of the most promising examples. The success of QSAR has tempted scientists, particularly in the pharmaceutical arena, to investigate relationships of molecular parameters with properties other than activity. The purpose of this two-part review is to provide a broad overview of the development of quantitative structure-property relationships (QSPR) and review the applications in pharmaceutical research. Part one discusses the advantages and limitations of QSPR, and various types of structural descriptors and properties, together with techniques to establish correlations between the two.

Design of a potent combined pseudopeptide endothelin-A/endothelin-B receptor antagonist, Ac-DBhg16-Leu-Asp-Ile-[NMe]Ile-Trp21 (PD 156252): examination of its pharmacokinetic and spectral properties

The endothelins (ETs) are a family of bicyclic 21-amino acid peptides that are potent and prolonged vasoconstrictors. It has been shown that highly potent combined ETA/ETB receptor antagonists can be developed from the C-terminal hexapeptide of ET (His16-Leu17-Asp18-Ile19-Ile20-Trp21), such as Ac-(D)Dip16-Leu-Asp-Ile-Ile-Trp21 (PD 142893) and Ac-DBhg16-Leu-Asp-Ile-Ile-Trp21 (PD 145065). However, these compounds are relatively unstable to enzymatic proteolysis as determined in an in vitro rat intestinal perfusate assay. This instability is thought to be due to carboxypeptidase activity. In fact, incubation of PD 145065 with carboxypeptidase inhibitors greatly increased its half-life in rat intestinal perfusate. By performing a reduced amide bond and N-methyl amino acid scan, it was discovered that N-methylation of Ile-20 resulted in a compound (Ac-DBhg16-Leu-Asp-Ile-[NMe]Ile-Trp21, PD 156252) that retained full receptor affinity at both endothelin receptor subtypes along with enhanced proteolytic stability and cellular permeability. Interestingly, N-methylation of this bond allows the cis configuration to be readily accessible which greatly alters the preferred structure of the entire molecule and may be responsible for the observed enhanced metabolic stability.