scholarly journals Multi-Omics Technologies Applied to Tuberculosis Drug Discovery

2020 ◽  
Vol 10 (13) ◽  
pp. 4629 ◽  
Author(s):  
Aaron Goff ◽  
Daire Cantillon ◽  
Leticia Muraro Wildner ◽  
Simon J Waddell
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Discovery ◽  
High Throughput ◽  
A Priori ◽  
Drug Action ◽  
Biological Molecules ◽  
Lead Compound ◽  
Omics Technologies ◽  
Lead Compounds ◽  
High Throughput Screens

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase

2017 ◽  
Vol 12 (9) ◽  
pp. 2448-2456 ◽  
Author(s):  
John P. Santa Maria ◽  
Yumi Park ◽  
Lihu Yang ◽  
Nicholas Murgolo ◽  
Michael D. Altman ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
High Throughput ◽  
Dihydrofolate Reductase ◽  
High Throughput Screens

scholarly journals Assay Optimization and Screening of RNA-Protein Interactions by AlphaScreen

2007 ◽  
Vol 12 (7) ◽  
pp. 946-955 ◽  
Author(s):  
Nicholas L. Mills ◽  
Anang A. Shelat ◽  
R. Kiplin Guy
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
High Throughput ◽  
Protein Interactions ◽  
Proof Of Concept ◽  
Rna Molecules ◽  
Lead Compounds ◽  
Assay Optimization ◽  
Rna Targets ◽  
Hiv 1

The lack of lead compounds that specifically recognize and manipulate the function of RNA molecules limits our ability to consider RNA targets valid for drug discovery. Herein is reported a high-throughput biochemical screen for inhibitors of RNA-protein interactions based on AlphaScreen technology that incorporates several layers of specificity measurements into the primary screen. This screen was used to analyze approximately 5500 compounds from a collection of bioactive small molecules to detect inhibitors of the HIV-1 Rev-RRE and BIV Tat-TAR interactions. This proof-of-concept screen validates the assay as one that accurately identifies hit molecules and determines the selectivity of those hits. ( Journal of Biomolecular Screening 2007: 946-955)

High-Throughput siRNA-Based Functional Target Validation

2004 ◽  
Vol 9 (4) ◽  
pp. 286-293 ◽  
Author(s):  
Hong Xin ◽  
Alejandro Bernal ◽  
Frank A. Amato ◽  
Albert Pinhasov ◽  
Jack Kauffman ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Target Genes ◽  
Discovery Process ◽  
Target Validation ◽  
Drug Discovery Process ◽  
Functional Identification ◽  
Screening Process ◽  
Lead Compounds

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Potent inhibitors of toxic alpha-synuclein oligomers identified via cellular time-resolved FRET biosensor

2020 ◽  
Author(s):  
Anthony R. Braun ◽  
Elly E. Liao ◽  
Mian Horvath ◽  
Malaney C. Young ◽  
Chih Hung Lo ◽  
...  
Keyword(s):  
Small Molecules ◽  
High Throughput ◽  
Structural Changes ◽  
Lewy Bodies ◽  
Alpha Synuclein ◽  
Time Resolved ◽  
Lead Compounds ◽  
Fret Biosensors ◽  
Self Association ◽  
High Throughput Screens

ABSTRACTPreventing or reversing the pathological misfolding and self-association of alpha-synuclein (aSyn) can rescue a broad spectrum of pathological cellular insults that manifest in Parkinson’s Disease (PD), Dementia with Lewy bodies (DLB), and other alpha-synucleinopathies. We have developed a high-throughput, FRET-based drug discovery platform that combines high-resolution protein structural detection in living cells with an array of functional and biophysical assays to identify novel lead compounds that protect SH-SY5Y cells from aSyn induced cytotoxicity as well as inhibiting seeded aSyn aggregation, even at nanomolar concentrations.Our combination of cellular and cell-free assays allow us to distinguish between direct aSyn binding or indirect mechanisms of action (MOA). We focus on targeting oligomers with the requisite sensitivity to detect subtle protein structural changes that may lead to effective therapeutic discoveries for PD, DLB, and other alpha-synucleinopathies. Pilot high-throughput screens (HTS) using our aSyn cellular FRET biosensors has led to the discovery of the first nanomolar-affinity small molecules that disrupt toxic aSyn oligomers in cells and inhibit cell death. Primary neuron assays of aSyn pathology (e.g. phosphorylation of mouse aSyn PFF) show rescue of pathology for two of our tested compounds. Subsequent seeded thioflavin-t (ThioT) aSyn aggregation assays demonstrate these compounds deter or block aSyn fibril assembly. Other hit compounds identified in our HTS are known to modulate oxidative stress, autophagy, and ER stress, providing validation that our biosensor is sensitive to indirect MOA as well.

scholarly journals High-Throughput Cellular Thermal Shift Assays in Research and Drug Discovery

2019 ◽  
Vol 25 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Mark J. Henderson ◽  
Marc A. Holbert ◽  
Anton Simeonov ◽  
Lorena A. Kallal
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Microtiter Plate ◽  
Target Engagement ◽  
Biochemical Activity ◽  
Future Directions ◽  
Protein Target ◽  
High Throughput Screens ◽  
Beta Galactosidase ◽  
Thermal Shift

Thermal shift assays (TSAs) can reveal changes in protein structure, due to a resultant change in protein thermal stability. Since proteins are often stabilized upon binding of ligand molecules, these assays can provide a readout for protein target engagement. TSA has traditionally been applied using purified proteins and more recently has been extended to study target engagement in cellular environments with the emergence of cellular thermal shift assays (CETSAs). The utility of CETSA in confirming molecular interaction with targets in a more native context, and the desire to apply this technique more broadly, has fueled the emergence of higher-throughput techniques for CETSA (HT-CETSA). Recent studies have demonstrated that HT-CETSA can be performed in standard 96-, 384-, and 1536-well microtiter plate formats using methods such as beta-galactosidase and NanoLuciferase reporters and AlphaLISA assays. HT-CETSA methods can be used to select and characterize compounds from high-throughput screens and to prioritize compounds in lead optimization by facilitating dose–response experiments. In conjunction with cellular and biochemical activity assays for targets, HT-CETSA can be a valuable addition to the suite of assays available to characterize molecules of interest. Despite the successes in implementing HT-CETSA for a diverse set of targets, caveats and challenges must also be recognized to avoid overinterpretation of results. Here, we review the current landscape of HT-CETSA and discuss the methodologies, practical considerations, challenges, and applications of this approach in research and drug discovery. Additionally, a perspective on potential future directions for the technology is presented.

Current Omics Technologies in Biomarker Discovery

2013 ◽  
pp. 465-497
Author(s):  
Wei Ding ◽  
Ping Qiu ◽  
Yan-Hui Liu ◽  
Wenqing Feng
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Molecular Profiling ◽  
Mechanism Study ◽  
Current Application ◽  
Omics Technologies ◽  
Intensive Research ◽  
Omics Technology ◽  
Biomarker Research

Biomarkers are playing an increasingly important role in drug discovery and development and can be applied for many purposes, including disease mechanism study, diagnosis, prognosis, staging, and treatment selection. Advances in high-throughput “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, significantly accelerate the pace of biomarker discovery. Comprehensive molecular profiling using these “omics” technology has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics. Although each “omics” technology plays important roles in biomarker research, different “omics” platforms have different strengths and limitations. This chapter aims to give an overview of these “omics” technologies and their current application in the biomarker discovery.

scholarly journals AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

2021 ◽  
Author(s):  
Kate Stafford ◽  
Brandon M. Anderson ◽  
Jon Sorenson ◽  
Henry van den Bedem
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Conformational Ensemble ◽  
Binding Modes ◽  
Convolutional Network ◽  
In Silico Docking ◽  
High Throughput Screens ◽  
Affinity Score

Structure-based, virtual High Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking to generate an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo, ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here we introduce AtomNet PoseRanker, a graph convolutional network trained to identify, and re-rank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and non-cognate binding modes corresponding to distinct receptor conformations. AtomNet PoseRanker significantly enriched pose quality in docking to cognate and non-cognate receptors of the PDBbind v2019 dataset. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns, and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

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