Sentence examples for screening of pharmaceutical from inspiring English sources

This review reflects state-of-the-art technology and reports on applications in the fields of early stage melanoma detection, skin aging, nanoparticle imaging, tissue engineering, and in situ screening of pharmaceutical and cosmetic products.

High throughput screening of pharmaceutical compound libraries with protein targets, and microarray screening of protein protein interactions for proteomics are examples of applications that require the sensitivity and throughput afforded by this approach.

The LNISO method is found to be a powerful high-throughput technique for rapid preformulation stability screening of pharmaceutical compounds by binning the compounds into categories based on the predicted t90% or t98% values to discern the most stable compound at the tested condition(s).

Several reports have described automatic clustering and classification of chemical substructures using various types of descriptor and fingerprint analyses to select candidates for scaffold hopping and second-stage screening of pharmaceutical compounds from vast amounts of high-throughput screening (HTS) data [3].

It may be used for in vivo screening of pharmaceutical drugs that have the potential of stimulating the generation of new sources of insulin-producing cells in aspects previously inaccessible to investigation.

Screening of pharmaceutical excipients revealed that cellulose acetate phthalate (CAP), commonly used for enteric coating of tablets and capsules [ 1], has anti-HIV-1 activity.

Furthermore, the combined in vitro model of (conditioned medium from) steatotic hepatocytes and HSCs can be used to study the interaction of these two critical cell types in NAFLD progression and for screening of pharmaceuticals.

The present review focuses on the techniques for compatibility screening of active pharmaceutical ingredient with their potential merits and demerits.

There have been several studies in which intestinal organoids carrying specific disease-relevant mutations were generated for pathophysiological study and for screening of new pharmaceutical interventions.

Virtual screening of imbalanced pharmaceutical data has been carried out before: in one study the classifiers used did not use misclassification costs [4], and in another, the datasets were very small with only a slight imbalance [5].

Comprehensive testing of a panel of chemicals and accompanying mechanistic research will need to be performed in order to determine the potential usefulness of the Tp53 knockout rat for screening of new pharmaceutical compounds.