scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

Photo-cross-linked small-molecule affinity matrix as a tool for target identification of bioactive small molecules

2016 ◽  
Vol 33 (5) ◽  
pp. 709-718 ◽  
Author(s):  
Naoki Kanoh
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
Affinity Matrix ◽  
Industrial Chemical ◽  
Drug Target Identification ◽  
The Status

This review describes the status of the photo-cross-linked small-molecule affinity matrix while providing a useful tutorial for academic and industrial chemical biologists who are involved or interested in drug target identification.

scholarly journals A Bayesian machine learning approach for drug target identification using diverse data types

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Drug Binding ◽  
Learning Approach ◽  
Data Types ◽  
Public Data ◽  
Machine Learning Approach ◽  
Drug Target Identification ◽  
Bayesian Machine Learning

AbstractDrug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.

scholarly journals Small molecule photocatalysis enables drug target identification via energy transfer

2021 ◽  
Author(s):  
Aaron D Trowbridge ◽  
Ciaran P Seath ◽  
Frances P Rodriguez-Rivera ◽  
Beryl X Li ◽  
Barbara E Dul ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Drug Discovery ◽  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
High Energy ◽  
Therapeutic Benefit ◽  
Cellular Targets ◽  
Cellular Environment ◽  
Drug Target Identification

The identification of cellular targets that can be exploited for therapeutic benefit, broadly known as target ID, remains a fundamental goal in drug discovery. In recent years, the application of new chemical and biological technologies that accelerate target ID has become commonplace within drug discovery programs, as a complete understanding of how molecules react in a cellular environment can lead to increased binding selectivity, improved safety profiles, and clinical efficacy. Established approaches using photoaffinity labelling (PAL) are often costly and time-consuming due to poor signal-to-noise coupled with extensive probe optimization. Such challenges are exacerbated when dealing with low abundance membrane proteins or multiple protein target engagement, typically rendering target ID unfeasible. Herein, we describe a general platform for photocatalytic small molecule target ID, which hinges upon the generation of high-energy carbene intermediates via visible light-mediated Dexter energy transfer. By decoupling the reactive warhead from the drug, catalytic signal amplification results in multiple labelling events per drug, leading to unprecedented levels of target enrichment. Through the development of cell permeable photocatalyst conjugates, this method has enabled the quantitative target and off target identification of several drugs including (+)-JQ1, paclitaxel, and dasatinib. Moreover, this methodology has led to the target ID of two GPCRs, ADORA2A and GPR40m, a class of drug target seldom successfully uncovered in small molecule PAL campaigns.

scholarly journals Discovery of small molecule pathway regulators by image profile matching

2021 ◽  
Author(s):  
Mohammad H. Rohban ◽  
Ashley M. Fuller ◽  
Ceryl Tan ◽  
Jonathan T. Goldstein ◽  
Deepsing Syangtan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Basic Research ◽  
Phenotypic Information ◽  
Profile Matching ◽  
Public Data ◽  
Computational Strategy ◽  
Virtual Screening Approach ◽  
Cellular Organelles

Identifying chemical regulators of biological pathways is currently a time-consuming bottleneck in developing therapeutics and small-molecule research tools. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to a disease of interest. Here, we instead use a broad, virtual screening approach that matches compounds to pathways based on phenotypic information in public data. Our computational strategy efficiently uncovered small molecule regulators of three pathways, containing p38ɑ (MAPK14), YAP1, or PPARGC1A (PGC-1α). We first selected genes whose overexpression yielded distinct image-based profiles in the Cell Painting assay, a microscopy assay involving six stains that label eight cellular organelles/components. To identify small molecule regulators of pathways involving those genes, we used publicly available Cell Painting profiles of 30,616 small molecules to identify compounds that yield morphological effects either positively or negatively correlated with image-based profiles for specific genes. Subsequent assays validated compounds that impacted the predicted pathway activities. This image profile-based drug discovery approach could transform both basic research and drug discovery by identifying useful compounds that modify pathways of biological and therapeutic interest, thus using a computational query to replace certain customized labor- and resource-intensive screens.

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals Discovery and Optimization of Selective Inhibitors of Meprin α (Part II)

2020 ◽  
Author(s):  
Chao Wang ◽  
Juan Diez ◽  
Hajeung Park ◽  
Christoph Becker-Pauly ◽  
Gregg B. Fields ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Hydroxamic Acid ◽  
Therapeutic Potential ◽  
Preceding Paper ◽  
Close Relative ◽  
Specific Inhibition ◽  
Selective Inhibitors

Meprin α is a zinc metalloproteinase (metzincin) that has been implicated in multiple diseases, including fibrosis and cancers. It has proven difficult to find small molecules that are capable of selectively inhibiting meprin α, or its close relative meprin β, over numerous other metzincins which, if inhibited, would elicit unwanted effects. We recently identified possible molecular starting points for meprin α-specific inhibition through an HTS effort (see part I, preceding paper). In part II we report the optimization of a potent and selective hydroxamic acid meprin α inhibitor probe which may help define the therapeutic potential for small molecule meprin α inhibition and spur further drug discovery efforts in the area of zinc metalloproteinase inhibition.

In depth analysis of kinase cross screening data to identify chemical starting points for inhibition of the nek family of kinases

2017 ◽  
Author(s):  
Carrow I. Wells ◽  
Nirav R. Kapadia ◽  
Rafael M. Couñago ◽  
David H. Drewry
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Biological Functions ◽  
Chemical Probes ◽  
High Quality ◽  
Depth Analysis ◽  
Number Of Citations ◽  
Shed Light

AbstractPotent, selective, and cell active small molecule kinase inhibitors are useful tools to help unravel the complexities of kinase signaling. As the biological functions of individual kinases become better understood, they can become targets of drug discovery efforts. The small molecules used to shed light on function can also then serve as chemical starting points in these drug discovery efforts. The Nek family of kinases has received very little attention, as judged by number of citations in PubMed, yet they appear to play many key roles and have been implicated in disease. Here we present our work to identify high quality chemical starting points that have emerged due to the increased incidence of broad kinome screening. We anticipate that this analysis will allow the community to progress towards the generation of chemical probes and eventually drugs that target members of the Nek family.

Niche-Based Screening Reveals Leukemia Stem Cell Specific Therapeutics

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 760-760
Author(s):  
Kimberly A. Hartwell ◽  
Peter G. Miller ◽  
Alison L. Stewart ◽  
Alissa R. Kahn ◽  
David J. Logan ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Stem Cell ◽  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
High Throughput ◽  
Hematopoietic Cells ◽  
Leukemia Stem Cells

Abstract Abstract 760 Recent insights into the molecular and cellular processes that drive leukemia have called attention to the limitations intrinsic to traditional drug discovery approaches. To date, the majority of cell-based functional screens have relied on probing cell lines in vitro in isolation to identify compounds that decrease cellular viability. The development of novel therapeutics with greater efficacy and decreased toxicity will require the identification of small molecules that selectively target leukemia stem cells (LSCs) within the context of their microenvironment, while sparing normal cells. We hypothesized that it would be possible to systematically identify LSC susceptibilities by modeling key elements of bone marrow niche interactions in high throughput format. We tested this hypothesis by creating and optimizing an assay in which primary murine stem cell-enriched leukemia cells are plated on bone marrow stromal cells in 384-well format, and examined by a high content image-based readout of cobblestoning, an in vitro morphological surrogate of cell health and self-renewal. AML cells cultured in this way maintained their ability to reinitiate disease in mice with as few as 100 cells. 14,720 small molecule probes across diverse chemical space were screened at 5uM in our assay. Retest screening was performed in the presence of two different bone marrow stromal types in parallel, OP9s and primary mesenchymal stem cells (MSCs). Greater than 60% of primary screen hits positively retested (dose response with IC50 at or below 5 μM) on both types of stroma. Compounds that inhibited leukemic cobblestoning merely by killing the stroma were identified by CellTiter-Glo viability analysis and excluded. Compounds that killed normal primary hematopoietic stem and progenitor cell inputs, as assessed by a related co-culture screen, were also excluded. Selectivity for leukemia over normal hematopoietic cells was additionally examined in vitro by comingling these cells on stroma within the same wells. Primary human CD34+ AML leukemia and normal CD34+ cord blood cells were also tested, by way of the 5 week cobblestone area forming cell (CAFC) assay. Additionally, preliminary studies of human AML cells pulse-treated with small molecules ex vivo, followed by in vivo transplantation, provided further evidence of potent leukemia kill across genotypes. A biologically complex functional approach to drug discovery, such as the novel method described here, has previously been thought impossible, due to presumed incompatibility with high throughput scale. We show that it is possible, and that it bears fruit in a first pilot screen. By these means, we discover small molecule perturbants that act selectively in the context of the microenvironment to kill LSCs while sparing stroma and normal hematopoietic cells. Some hits act cell autonomously, and some do not, as evidenced by observed leukemia kill when only the stromal support cells are treated prior to the plating of leukemia. Some hits are known, such as parthenolide and celastrol, and some are previously underappreciated, such as HMG-CoA reductase inhibition. Others are entirely new, and would not have been revealed by conventional approaches to therapeutic discovery. We therefore present a powerful new approach, and identify drug candidates with the potential to selectively target leukemia stem cells in clinical patients. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Gliptin Repurposing for COVID-19

2020 ◽  
Author(s):  
Matthew Groves ◽  
Alexander Domling ◽  
Angel Jonathan Ruiz Moreno ◽  
Atilio Reyes Romero ◽  
Constantinos Neochoritis ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Drug Class ◽  
De Novo ◽  
Drug Repurposing ◽  
Therapeutic Modality ◽  
Antidiabetic Drug ◽  
Short Term ◽  
Computational Screening ◽  
Approved Drugs

<i>De novo</i> drug discovery of any therapeutic modality (e.g. antibodies, vaccines or small molecules) historically takes years from idea/preclinic to the market and it is therefore not a short-term solution for the current SARS-CoV-2 pandemic. Therefore, drug repurposing – the discovery novel indication areas for already approved drugs - is perhaps the only approach able to yield a short term relieve. Here we describe computational screening results suggesting that certain members of the drug class of gliptins are inhibitors of the two SARS-CoV-2 proteases 3CLpro and PLpro. The oral bioavailable antidiabetic drug class of gliptins are safe and have been introduced clinically since 2006 and used by millions of patients since then. Based on our repurposing hypothesis the nitrile containing gliptins deserve further investigation as potential anti-COVID19 drugs.

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