PubMed · 10885944
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W A Warr. Infotech pharma 2000.. https://pubmed.ncbi.nlm.nih.gov/10885944/
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Simple methods for determining the human pharmacokinetics of known and unknown drug-like compounds is a much sought-after goal in the pharmaceutical industry. The current study made use of artificial neural networks (ANNs) for the prediction of clearances, fraction bound to plasma proteins, and volume of distribution of a series of structurally diverse compounds. A number of theoretical descriptors were generated from the drug structures and both automated and manual pruning were used to derive optimal subsets of descriptors for quantitative structure-pharmacokinetic relationship models. Models were trained on one set of compounds and validated with another. Absolute predicted ability was evaluated using a further independent test set of compounds. Correlations for test compounds ranged from 0.855 to 0.992. Predicted values agreed closely with experimental values for total clearance, renal clearance, and volume of distribution, while predictions for protein binding were encouraging. The combination of descriptor generation, ANNs, and the speed and success of this technique compared with conventional methods shows strong potential for use in pharmaceutical product development.
A method is described for use in analysis of samples from pharmaceutical profiling of early drug discovery compounds. The method consists of a high capacity autosampler which injects samples into one of two solid phase extraction columns operated in parallel for alternating trapping, washing and elution into a tandem quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) MS/MS mode. A primary method, which is useful for 80-90% of compounds, and a secondary method, which is useful for a majority of the remaining compounds, are described. No analytical HPLC column is used and the analysis rate is approximately 50 samples/h. Specificity is obtained using MRM analysis. Application of the method for high capacity analysis of metabolic stability samples is described.
Histone deacetylases (HDAC) are promising targets for cancer chemotherapy. HDAC inhibitors are thought to act in part by disrupting normal cell cycle regulation, resulting in apoptosis and/or differentiation of transformed cells. Several HDAC inhibitors, which contain hydrophobic tails and the Zn(2+) chelator hydroxyamic acid as a head group, are potent inhibitors of HDACs both in vitro and in vivo. In this study, a related class of compounds with a N-formyl hydroxylamino head group has been synthesized and their ability to inhibit HDACs have been assayed in biochemical and cellular assays. These compounds were found to have comparable activities to suberoylanilide hydroxyamic acid (SAHA) in HDAC enzymatic assays and histone hyperacetylation cellular assays.