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Item In Silico Target Prediction by Training Naive Bayesian Models on Chemogenomics Databases(2006-06-29T19:50:21Z) Nidhi; Merchant, MaheshThe completion of Human Genome Project is seen as a gateway to the discovery of novel drug targets (Jacoby, Schuffenhauer, & Floersheim, 2003). How much of this information is actually translated into knowledge, e.g., the discovery of novel drug targets, is yet to be seen. The traditional route of drug discovery has been from target to compound. Conventional research techniques are focused around studying animal and cellular models which is followed by the development of a chemical concept. Modern approaches that have evolved as a result of progress in molecular biology and genomics start out with molecular targets which usually originate from the discovery of a new gene .Subsequent target validation to establish suitability as a drug target is followed by high throughput screening assays in order to identify new active chemical entities (Hofbauer, 1997). In contrast, chemogenomics takes the opposite approach to drug discovery (Jacoby, Schuffenhauer, & Floersheim, 2003). It puts to the forefront chemical entities as probes to study their effects on biological targets and then links these effects to the genetic pathways of these targets (Figure 1a). The goal of chemogenomics is to rapidly identify new drug molecules and drug targets by establishing chemical and biological connections. Just as classical genetic experiments are classified into forward and reverse, experimental chemogenomics methods can be distinguished as forward and reverse depending on the direction of investigative process i.e. from phenotype to target or from target to phenotype respectively (Jacoby, Schuffenhauer, & Floersheim, 2003). The identification and characterization of protein targets are critical bottlenecks in forward chemogenomics experiments. Currently, methods such as affinity matrix purification (Taunton, Hassig, & Schreiber, 1996) and phage display (Sche, McKenzie, White, & Austin, 1999) are used to determine targets for compounds. None of the current techniques used for target identification after the initial screening are efficient. In silico methods can provide complementary and efficient ways to predict targets by using chemogenomics databases to obtain information about chemical structures and target activities of compounds. Annotated chemogenomics databases integrate chemical and biological domains and can provide a powerful tool to predict and validate new targets for compounds with unknown effects (Figure 1b). A chemogenomics database contains both chemical properties and biological activities associated with a compound. The MDL Drug Data Report (MDDR) (Molecular Design Ltd., San Leandro, California) is one of the well known and widely used databases that contains chemical structures and corresponding biological activities of drug like compounds. The relevance and quality of information that can be derived from these databases depends on their annotation schemes as well as the methods that are used for mining this data. In recent years chemists and biologist have used such databases to carry out similarity searches and lookup biological activities for compounds that are similar to the probe molecules for a given assay. With the emergence of new chemogenomics databases that follow a well-structured and consistent annotation scheme, new automated target prediction methods are possible that can give insights to the biological world based on structural similarity between compounds. The usefulness of such databases lies not only in predicting targets, but also in establishing the genetic connections of the targets discovered, as a consequence of the prediction. The ability to perform automated target prediction relies heavily on a synergy of very recent technologies, which includes: i) Highly structured and consistently annotated chemogenomics databases. Many such databases have surfaced very recently; WOMBAT (Sunset Molecular Discovery LLC, Santa Fe, New Mexico), KinaseChemBioBase (Jubilant Biosys Ltd., Bangalore, India) and StARLITe (Inpharmatica Ltd., London, UK), to name a few. ii) Chemical descriptors (Xue & Bajorath, 2000) that capture the structure-activity relationship of the molecules as well as computational techniques (Kitchen, Stahura, & Bajorath, 2004) that are specifically tailored to extract information from these descriptors. iii) Data pipelining environments that are fast, integrate multiple computational steps, and support large datasets. A combination of all these technologies may be employed to bridge the gap between chemical and biological domains which remains a challenge in the pharmaceutical industry.Item Use of AD Informer Set compounds to explore validity of novel targets in Alzheimer's disease pathology(Wiley, 2022-04-12) Potjewyd, Frances M.; Annor-Gyamfi, Joel K.; Aubé, Jeffrey; Chu, Shaoyou; Conlon, Ivie L.; Frankowski, Kevin J.; Guduru, Shiva K.R.; Hardy, Brian P.; Hopkins, Megan D.; Kinoshita, Chizuru; Kireev, Dmitri B.; Mason, Emily R.; Moerk, Charles T.; Nwogbo, Felix; Pearce, Kenneth H., Jr.; Richardson, Timothy I.; Rogers, David A.; Soni, Disha M.; Stashko, Michael; Wang, Xiaodong; Wells, Carrow; Willson, Timothy M.; Frye, Stephen V.; Young, Jessica E.; Axtman, Alison D.; Medicine, School of MedicineIntroduction: A chemogenomic set of small molecules with annotated activities and implicated roles in Alzheimer's disease (AD) called the AD Informer Set was recently developed and made available to the AD research community: https://treatad.org/data-tools/ad-informer-set/. Methods: Small subsets of AD Informer Set compounds were selected for AD-relevant profiling. Nine compounds targeting proteins expressed by six AD-implicated genes prioritized for study by Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) teams were selected for G-protein coupled receptor (GPCR), amyloid beta (Aβ) and tau, and pharmacokinetic (PK) studies. Four non-overlapping compounds were analyzed in microglial cytotoxicity and phagocytosis assays. Results: The nine compounds targeting CAPN2, EPHX2, MDK, MerTK/FLT3, or SYK proteins were profiled in 46 to 47 primary GPCR binding assays. Human induced pluripotent stem cell (iPSC)-derived neurons were treated with the same nine compounds and secretion of Aβ peptides (Aβ40 and Aβ42) as well as levels of phosphophorylated tau (p-tau, Thr231) and total tau (t-tau) peptides measured at two concentrations and two timepoints. Finally, CD1 mice were dosed intravenously to determine preliminary PK and/or brain-specific penetrance values for these compounds. As a final cell-based study, a non-overlapping subset of four compounds was selected based on single-concentration screening for analysis of both cytotoxicity and phagocytosis in murine and human microglia cells. Discussion: We have demonstrated the utility of the AD Informer Set in the validation of novel AD hypotheses using biochemical, cellular (primary and immortalized), and in vivo studies. The selectivity for their primary targets versus essential GPCRs in the brain was established for our compounds. Statistical changes in tau, p-tau, Aβ40, and/or Aβ42 and blood-brain barrier penetrance were observed, solidifying the utility of specific compounds for AD. Single-concentration phagocytosis results were validated as predictive of dose-response findings. These studies established workflows, validated assays, and illuminated next steps for protein targets and compounds.