Biomedical subjects

Yingyao Zhou

Publications and source records attributed to Yingyao Zhou.

21 records · Page 2Linked to original sources

PubMedDate not supplied

Purifying the masses: integrating prepurification quality control, high-throughput LC/MS purification, and compound plating to feed high-throughput screening.

In this paper we report using a parallel, four-channel HPLC/MUX/MS purification system, the Purification Factory, to purify thousands of compounds destined for high-throughput screening in a single month. The maximum sample throughput during this 20-workday month was 704 samples/day. Since this purification throughput exceeded the postpurification sample and data handling capabilities provided by commercial solutions, a custom-integrated solution was designed to address these shortcomings. In this paper we detail the key improvements in automation, solvent handling, and sample handling logistics implemented to sustain a mean throughput of 528 samples/day over a multimonth time period.

Autoanalysis↗

PubMedDate not supplied

Novel statistical approach for primary high-throughput screening hit selection.

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.

Algorithms↗

PubMedDate not supplied

Learning from the data: mining of large high-throughput screening databases.

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.

Algorithms↗