David J. Cummins

Peripheral anti-Aβ antibody alters CNS and plasma Aβ clearance and decreases brain Aβ burden in a mouse model of Alzheimer's disease

Active immunization with the amyloid β (Aβ) peptide has been shown to decrease brain Aβ deposition in transgenic mouse models of Alzheimers disease and certain peripherally administered anti-Aβ antibodies were shown to mimic this effect. In exploring factors that alter Aβ metabolism and clearance, we found that a monoclonal antibody (m266) directed against the central domain of Aβ was able to bind and completely sequester plasma Aβ. Peripheral administration of m266 to PDAPP transgenic mice, in which Aβ is generated specifically within the central nervous system (CNS), results in a rapid 1,000-fold increase in plasma Aβ, due, in part, to a change in Aβ equilibrium between the CNS and plasma. Although peripheral administration of m266 to PDAPP mice markedly reduces Aβ deposition, m266 did not bind to Aβ deposits in the brain. Thus, m266 appears to reduce brain Aβ burden by altering CNS and plasma Aβ clearance.

Neuroinflammation‐induced acceleration of amyloid deposition in the APPV717F transgenic mouse

It has been postulated that neuroinflammation plays a critical role in the pathogenesis of Alzheimers disease (AD). To directly test whether an inflammatory stimulus can accelerate amyloid deposition in vivo, we chronically administered the bacterial endotoxin, lipopolysaccharide (LPS), intracerebroventricularly (i.c.v.) to 2‐month‐old APPV717F+/+ transgenic (TG) mice, which overexpress a mutant human amyloid precursor protein (APP 717V‐F) with or without apolipoprotein E (apoE) for 2 weeks. Two weeks following central LPS administration a striking global reactive astrocytosis with increased GFAP immunoreactivity was found throughout the brains of all LPS‐treated wild‐type and transgenic mice including the contralateral brain hemisphere. Localized microglial activation was also evident from lectin immunostaining adjacent to the cannula track of LPS‐treated mice. Quantification of thioflavine‐S‐positive Aβ deposits revealed a marked acceleration of amyloid deposition in LPS‐treated APPV717F+/+–apoE+/+ mice compared to nontreated or vehicle‐treated APPV717F+/+–apoE+/+ mice (P = 0.005). By contrast, no amyloid deposits were detected by thioflavine‐S staining in LPS or vehicle‐treated apoE‐deficient APPV717F TG mice. Our data suggest that neuroinflammation can accelerate amyloid deposition in the APPV717F+/+ mouse model of AD and that this process requires the expression of apoE.

Statistical aspects of quantitative image analysis of β-amyloid in the APPV717F transgenic mouse model of Alzheimer's disease

Cerebral beta-amyloidosis is a central part of the neuropathology of Alzheimers disease (AD). Quantitation of beta-amyloid plaques in the human AD brain, and in animal models of AD, is an important study endpoint in AD research. Methodologic approaches to the measurement of beta-amyloid in the brain vary between investigators, and these differences affect outcome measures. Here, one quantitative approach to the measurement of beta-amyloid plaques in brain sections was analyzed for sources of variability due to sampling. Brain tissue was from homozygous APP(V717F) transgenic male mice. Sampling variables were at the mouse and microscopic slide and field levels. Results indicated that phenotypic variability in the mouse sample population was the largest contributor to the standard error of the analyses. Within each mouse, variability between slides or between fields within slides had smaller effects on the error of the analyses. Therefore, when designing studies of adequate power, in this and in other similar models of cerebral beta-amyloidosis, sufficient numbers of mice per group must be included in order for change in mean plaque burden attributable to an experimental variable to outweigh phenotypic variability.

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.

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.

Pharmaceutical Drug Discovery: Designing the Blockbuster Drug

Twenty years ago, drug discovery was a somewhat plodding and scholastic endeavor; those days are gone. The intellectual challenges are greater than ever but the pace has changed. Although there are greater opportunities for therapeutic targets than ever before, the costs and risks are great and the increasingly competitive environment makes the pace of pharmaceutical drug hunting range from exciting to overwhelming. These changes are catalyzed by major changes to drug discovery processes through application of rapid parallel synthesis of large chemical libraries and high-throughput screening. These techniques result in huge volumes of data for use in decision making. Besides the size and complex nature of biological and chemical data sets and the many sources of data “noise”, the needs of business produce many, often conflicting, decision criteria and constraints such as time, cost, and patent caveats. The drive is still to find potent and selective molecules but, in recent years, key aspects of drug discovery are being shifted to earlier in the process. Discovery scientists are now concerned with building molecules that have good stability but also reasonable properties of absorption into the bloodstream, distribution and binding to tissues, metabolism and excretion, low toxicity, and reasonable cost of production. These requirements result in a high-dimensional decision problem with conflicting criteria and limited resources. An overview of the broad range of issues and activities involved in pharmaceutical screening is given along with references for further reading.

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.