Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator.

In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry's endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.

Multivariate analysis of low-dose radiation-associated changes in cytokine gene expression profiles using microarray technology.

OBJECTIVE: The availability of microarray technology, which permits evaluation of the entire cellular transcriptome in a single experiment, has provided new insights on the function of the genome under normal and pathological conditions, as well as in response to genotoxic stimuli, including ionizing radiation. The aims of this study were to: 1) determine whether specific cytokine gene expression profiles can be delineated in individuals exposed to chronic, low-dose radiation; and 2) compare analyses from three multivariate analytic methodologies, hierarchical clustering analysis (HCA), principal component analysis (PCA), and projection pursuit (PP), in evaluating transcriptional responses in human mononuclear cells to low doses of ionizing radiation (IR), as determined by cDNA microarrays. MATERIALS AND METHODS: Total RNA isolated from mononuclear cells of 19 apparently healthy adult individuals exposed to low doses of IR ranging from 0.18 mSv to 49 mSv over a period of 11 to 13 years, as a result of the Chernobyl Nuclear Power Plant catastrophe, was reverse transcribed in the presence of radioactive dATP to generate radiolabeled complementary cDNA. Target cDNA was hybridized to human cytokine and receptor arrays and mRNA transcriptional patterns were evaluated using HCA, PCA, and PP. RESULTS: Statistical analyses of the data generated from 19 microarrays revealed distinct gene expression patterns in mononuclear cells of individuals exposed to radiation doses of greater than 10 mSv or less than 10 mSv. Genes encompassed within clusters discerned by HCA, PCA, and PP varied depending on the methodology used to analyze the microarray data. The most frequently expressed genes across all radiation doses were serine/threonine protein kinase receptor (11/19), transforming growth factor (TGF) receptor (11/19), EB13 (10/19), and CD40 ligand. CONCLUSIONS: Although our findings suggest that it may be possible to assign gene expression profiles to low-dose-irradiated individuals, we show that gene expression profiles vary, depending on the statistical method used to analyze the data. Since there is, as of yet, no consensus regarding the best method to analyze a multivariate dataset, and since discarding the raw data and repeating the experiment at a later date constitutes an unwarranted expenditure, it is important to submit microarray data to public databases where these data can be reevaluated and interpreted by investigators holding expertise in various fields within the scientific community, including radiation biology, statistics, and bioinformatics.

Application of chemoinformatics to high-throughput screening: practical considerations.

The objective of this chapter is to summarize and evaluate some of the most common chemoinformatic methods that are applied to the analysis of high-throughput-screening data. The chapter will briefly describe current high-throughput-screening practices and will stress how the major constraint on the application of chemoinformatics is often the quality of high-throughput-screening data. Discussion of the NCI dataset and how it differs from most high-throughput-screening datasets will be made to highlight this point.

Decorin content and near infrared spectroscopy analysis of dried collagenous biomaterial samples.

The efficient removal of proteoglycans, such as decorin, from the hide when processing it to leather by traditional means is generally acceptable and beneficial for leather quality, especially for softness and flexibility. A patented waterless or acetone dehydration method that can generate a product similar to leather called Dried Collagenous Biomaterial (known as BCD) was developed but has no effect on decorin removal efficiency. The Alcian Blue colorimetric technique was used to assay the sulfated glycosaminoglycan (sGAG) portion of decorin. The corresponding residual decorin content was correlated to the mechanical properties of the BCD samples and was comparable to the control leather made traditionally. The waterless dehydration and instantaneous chrome tanning process is a good eco-friendly alternative to transforming hides to leather because no additional effects were observed after examination using NIR spectroscopy and additional chemometric analysis.

Reverse fingerprinting and mutual information-based activity labeling and scoring (MIBALS).

A mutual information based activity labeling and scoring (MIBALS) approach to reverse fingerprint analysis is presented. Whole molecule scores produced by the method are shown to be capable of ranking compounds in virtual high-throughput screening (vHTS) experiments, while fragment scores produced by the method are able to identify pharmacophore moieties important for biological activity. The performance of MIBALS in vHTS experiments is assessed using reference ligands active against 40 different biological targets, and MIBALS retrieval rates are compared with those obtained using more traditional group fusion similarity search methods. The use of MIBALS to identify important pharmacophore fragments is demonstrated by comparing ligand fragment scores with known pharmacophores and known ligand/protein contacts. The ability of MIBALS to highlight beneficial and detrimental groups in a congeneric series is examined by comparing MIBALS fragment scores with features in known structure-activity relationships.

Data shaving: a focused screening approach.

The number of compounds available for evaluation as part of the drug discovery process continues to increase. These compounds may exist physically or be stored electronically allowing screening by either actual or virtual means. This growing number of compounds has generated an increasing need for effective strategies to direct screening efforts. Initial efforts toward this goal led to the development of methods to select diverse sets of compounds for screening, methods to cluster actives into related groups of compounds, and tools to select compounds similar to actives of interest for further screening. In this work we extend these earlier efforts to exploit information about inactive compounds to help make rational decisions about which sets of compounds to include as part of a continuing screening campaign, or as part of a focused follow-up effort. This method uses the information from inactive compounds to "shave" off or deprioritize compounds similar to inactives from further consideration. This methodology can be used in two ways: first, to provide a rational means of deciding when sufficient compounds containing certain structural features have been tested and second as a tool to enhance similarity searching around known actives. Similarity searching is improved by deprioritizing compounds predicted to be inactive, due to the presence of structural features associated with inactivity.