High-throughput screening (HTS) has been postulated in several quarters to be a contributory factor to the decline in productivity in the pharmaceutical industry. Moreover, it has been blamed for stifling the creativity that drug discovery demands. In this article, we aim to dispel these myths and present the case for the use of HTS as part of a proven scientific tool kit, the wider use of which is essential for the discovery of new chemotypes.

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Multiple Sclerosis is a demyelinating disease of the CNS and the primary cause of neurological disability in young adults. Loss of myelinating oligodendrocytes leads to neuronal dysfunction and death and is an important contributing factor to this disease. Endogenous oligodendrocyte precursor cells (OPCs), which on differentiation are responsible for replacing myelin, are present in the adult CNS. As such, therapeutic agents that can stimulate OPCs to differentiate and remyelinate demyelinated axons under pathologic conditions may improve neuronal function and clinical outcome. We describe the details of an automated, cell-based, morphometric-based, high-content screen that is used to identify small molecules eliciting the differentiation of OPCs after 3 days. Primary screening was performed using rat CG-4 cells maintained in culture conditions that normally support a progenitor cell-like state. From a library of 73,000 diverse small molecules within the Sanofi collection, 342 compounds were identified that increased OPC morphological complexity as an indicator of oligodendrocyte maturation. Subsequent to the primary high-content screen, a suite of cellular assays was established that identified 22 nontoxic compounds that selectively stimulated primary rat OPCs but not C2C12 muscle cell differentiation. This rigorous triaging yielded several chemical series for further expansion and bio-or cheminformatics studies, and their compelling biological activity merits further investigation.

Samples of air taken from a high temperature coke oven plant were assayed for the concentration of PAHs. The analyses were focused on a group of 16 PAHs as recommended by US Environmental Protection Agency. Their electronic absorption spectra and GC-MS traces were recorded. The GC-MS was used to provide information on the concentrations. Partial least squares regression was applied to form a calibration model between the EAS spectra and the corresponding concentrations of the PAHs in a complex mixture, such as coal tar pitch volatiles. The influence of different data preprocessing techniques was investigated. Emphasis was placed on the selection of optimal calibration data sets. Variable (wavelength channel) selection plays an important role in the formation of multivariate calibration models and influences its prediction ability. Variables were selected according to correlation coefficients with the corresponding concentration profiles.

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