Two-Site Evaluation of the Repeatability and Precision of an Automated Dual-Column Hydrogen/Deuterium Exchange Mass Spectrometry Platform.

Hydrogen/deuterium exchange coupled with mass spectrometry (HDX-MS) is an information-rich biophysical method for the characterization of protein dynamics. Successful applications of differential HDX-MS include the characterization of protein-ligand binding. A single differential HDX-MS data set (protein ± ligand) is often comprised of more than 40 individual HDX-MS experiments. To eliminate laborious manual processing of samples, and to minimize random and gross errors, automated systems for HDX-MS analysis have become routine in many laboratories. However, an automated system, while less prone to random errors introduced by human operators, may have systematic errors that go unnoticed without proper detection. Although the application of automated (and manual) HDX-MS has become common, there are only a handful of studies reporting the systematic evaluation of the performance of HDX-MS experiments, and no reports have been published describing a cross-site comparison of HDX-MS experiments. Here, we describe an automated HDX-MS platform that operates with a parallel, two-trap, two-column configuration that has been installed in two remote laboratories. To understand the performance of the system both within and between laboratories, we have designed and completed a test-retest repeatability study for differential HDX-MS experiments implemented at each of two laboratories, one in Florida and the other in Spain. This study provided sufficient data to do both within and between laboratory variability assessments. Initial results revealed a systematic run-order effect within one of the two systems. Therefore, the study was repeated, and this time the conclusion was that the experimental conditions were successfully replicated with minimal systematic error.

Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set.

Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly's far larger internal data set to assess the PAINS alerts on four criteria: promiscuity (over six assay formats including AlphaScreen), compound stability, cytotoxicity, and presence of a high Hill slope as a surrogate for non-1:1 protein-ligand binding. It was found that only three of the alerts show pan-assay promiscuity, and the alerts appear to encode primarily AlphaScreen promiscuous molecules. Although not enriching for pan-assay promiscuity, many of the alerts do encode molecules that are unstable, show cytotoxicity, and increase the prevalence of high Hill slopes.

Integrating Everything: The Molecule Selection Toolkit, a System for Compound Prioritization in Drug Discovery.

In recent years there have been numerous papers on the topic of multiattribute optimization in pharmaceutical discovery chemistry, applied to compound prioritization. Many solutions proposed are static in nature; fixed functions are proposed for general purpose use. As needs change, these are modified and proposed as the latest enhancement. Rather than producing one more set of static functions, this work proposes a flexible approach to prioritizing compounds. Most published approaches also lack a design component. This work describes a comprehensive implementation that includes predictive modeling, multiattribute optimization, and modern statistical design. This gives a complete package for effectively prioritizing compounds for lead generation and lead optimization. The approach described has been used at our company in various stages of discovery since 2001. An adaptable system alleviates the need for different static solutions, each of which inevitably must be updated as the needs of a project change.

Multiplex proteomic approaches to sepsis research: case studies employing new technologies.

Sepsis is a multifactorial disease that provides unique challenges to the critical care physician. Diagnosis is hampered by the lack of a quantitative in vitro diagnostic test, instead, it relies on a series of clinical measures. The complex nature of the disease, with involvement of several physiologic systems, suggests a need to simultaneously monitor many clinical parameters. Novel proteomic technologies now exist that enable the multiplex measurement of multiple protein analytes from the same sample. Integration of these analytical measures with patient clinical data may provide the foundation for a better understanding of disease diagnosis, disease progression and the selection of optimal therapeutic regimen. The future challenge is the translation of these multiplex approaches from investigative research to clinical diagnostics for the greatest impact on patient treatment decisions.

Selectivity data: assessment, predictions, concordance, and implications.

Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between predicted and experimental data to be comparable to that found between experimental results from different sources. However, for molecules that are either highly selective or potent, the concordance between different experimental sources is significantly higher than the concordance between experimental and predicted values. We also show that computational models built from one data set are less predictive for other data sources and highlight the importance of bias correction for assessing selectivity data. Finally, we show that small-molecule target space relationships derived from different data sources and predictive models share overall similarity but can significantly differ in details.

Brain to plasma amyloid-beta efflux: a measure of brain amyloid burden in a mouse model of Alzheimer's disease.

The deposition of amyloid-beta (Abeta) peptides into amyloid plaques precedes the cognitive dysfunction of Alzheimer's disease (AD) by years. Biomarkers indicative of brain amyloid burden could be useful for identifying individuals at high risk for developing AD. As in AD in humans, baseline plasma Abeta levels in a transgenic mouse model of AD did not correlate with brain amyloid burden. However, after peripheral administration of a monoclonal antibody to Abeta (m266), we observed a rapid increase in plasma Abeta and the magnitude of this increase was highly correlated with amyloid burden in the hippocampus and cortex. This method may be useful for quantifying brain amyloid burden in patients at risk for or those who have been diagnosed with AD.