Legacy data sharing to improve drug safety assessment: the eTOX project

The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility.

COMDECOM: predicting the lifetime of screening compounds in DMSO solution

The technological evolution of the 1990s in both combinatorial chemistry and high-throughput screening created the demand for rapid access to the compound deck to support the screening process. The common strategy within the pharmaceutical industry is to store the screening library in DMSO solution. Several studies have shown that a percentage of these compounds decompose in solution, varying from a few percent of the total to a substantial part of the library. In the COMDECOM (COMpound DECOMposition) project, the compound stability of screening compounds in DMSO solution is monitored in an accelerated thermal, hydrolytic, and oxidative decomposition program. A large database with stability data is collected, and from this database, a predictive model is being developed. The aim of this program is to build an algorithm that can flag compounds that are likely to decompose-information that is considered to be of utmost importance (e.g., in the compound acquisition process and when evaluation screening results of library compounds, as well as in the determination of optimal storage conditions).

Prediction of pKa values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors

Two quantitative pKa prediction models for aliphatic carboxylic acids and for alcohols were developed by multiple linear-regression (MLR) analysis with empirical atomic descriptors. The acid and alcohol molecules were described by a set of five and four atomic descriptors, respectively. For the pKa model of 1122 aliphatic carboxylic acids, the squared correlation coefficient is 0.813 with a standard error of prediction of 0.423; for the pKa model of 288 alcohols, the squared correlation coefficient is 0.817 with a standard error of prediction of 0.755, respectively. The good predictive abilities of the models obtained were indicated by both cross-validation and by external validation. An atomic descriptor was developed to model the inductive effect of the neighboring atoms for a central atom in a molecule. The ability of the descriptor to measure the inductive effect of substituent groups was demonstrated by a good correlation of this descriptor with Taft sigma* constants in aliphatic carboxylic acids. It provides a new approach to estimate Taft sigma* constants directly from molecular structures. An algorithm using Kohonen neural networks for splitting a data set into a training set and a test set is also presented.

Decision support systems for chemical structure representation, reaction modeling, and spectra simulation

The choice of an appropriate structure coding scheme is the secret to success in QSAR studies. Depending on the problem at hand, 2D or 3D descriptors have to be chosen; the consideration of electronic effects might be crucial, conformational flexibility has to be of special concern. Artificial neural networks, both with unsupervised and with supervised learning schemes, are powerful tools for establishing relationships between structure and physical, chemical, or biological properties. The EROS system for the simulation of chemical reactions is briefly presented and its application to the degradation of s-triazine herbicides is shown. It is further shown how the simulation of chemical reactions can be combined with the simulation of infrared spectra for the efficient identification of the structure of degradation products.