Mass spectrometry (MS) has many advantages as a quantitative detection technology for applications within drug discovery. However, current methods of liquid sample introduction to a detector are slow and limit the use of mass spectrometry for kinetic and high-throughput applications. We present the development of an acoustic mist ionization (AMI) interface capable of contactless nanoliter-scale "infusion" of up to three individual samples per second into the mass detector. Installing simple plate handling automation allowed us to reach a throughput of 100 000 samples per day on a single mass spectrometer. We applied AMI-MS to identify inhibitors of a human histone deacetylase from AstraZeneca's collection of 2 million small molecules and measured their half-maximal inhibitory concentration. The speed, sensitivity, simplicity, robustness, and consumption of nanoliter volumes of sample suggest that this technology will have a major impact across many areas of basic and applied research.

A new approach to the storage, processing, and interrogation of the quality data for screening samples has improved analytical throughput and confidence and enhanced the opportunities for learning from the accumulating records. The approach has entailed the design, development, and implementation of a database-oriented system, capturing information from the liquid chromatography-mass spectrometry capabilities used for assessing the integrity of samples in AstraZeneca's screening collection. A Web application has been developed to enable the visualization and interactive annotation of the analytical data, monitor the current sample queue, and report the throughput rate. Sample purity and identity are certified automatically on the chromatographic peaks of interest if predetermined thresholds are reached on key parameters. Using information extracted in parallel from the compound registration and container inventory databases, the chromatographic and spectroscopic profiles for each vessel are linked to the sample structures and storage histories. A search engine facilitates the direct comparison of results for multiple vessels of the same or similar compounds, for single vessels analyzed at different time points, or for vessels related by their origin or process flow. Access to this network of information has provided a deeper understanding of the multiple factors contributing to sample quality assurance.

The AstraZeneca Compound Management group uses high-performance liquid chromatography-mass spectrometry for structure elucidation and purity determination of the AstraZeneca compound collection. These activities are conducted in a high-throughput environment where the rate-limiting step is the review and interpretation of analytical results, which is time-consuming and experience dependent. Despite the development of a semiautomated review system, manual interpretation of results remains a bottleneck. Data-mining techniques were applied to archived data to further automate the review process. Various classification models were evaluated using WEKA and Pipeline Pilot (Pipeline Pilot version 8.5.0.200, BIOVIA, San Diego, CA). Results were assessed using criteria including precision, recall, and receiver operating characteristic area. Each model was evaluated as a cost-insensitive classifier and again using MetaCost to apply cost sensitivity. Pruning and variable importance were also investigated. A 10-tree random forest generated with Pipeline Pilot reduced the number of analyses requiring manual review to 36.4% using a threshold of 90% confidence in predictions. This represents a 45% reduction in manual reviews compared with the previous system, delivering an annual savings of $45,000 or an increase in capacity from 25,000 analyses per month up to 45,000 with the same resource levels.