scholarly journals Translating in vitro CFTR rescue into small molecule correctors for cystic fibrosis using the Library of Integrated Network‐based Cellular Signatures drug discovery platform

Author(s):  
Matthew D. Strub ◽  
Shyam Ramachandran ◽  
Dmitri Y. Boudko ◽  
Ella A. Meleshkevitch ◽  
Alejandro A. Pezzulo ◽  
...  
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 ◽  
...  

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.


2021 ◽  
Author(s):  
Zhiting Wei ◽  
Sheng Zhu ◽  
Xiaohan Chen ◽  
Chenyu Zhu ◽  
Bin Duan ◽  
...  

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as Connectivity Map (CMap) and Library of Integrated Network-Based Cellular Signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed, and among them, a fundamental issue is to define the proper similarity between the transcriptional profiles to elucidate the drug mechanism of actions and identify new drug indications. Traditionally, this similarity has been defined in an unsupervised way, and due to the high dimensionality and the existence of high noise in those high-throughput data, it lacks robustness with limited performance. In our study, we present Dr. Sim, which is a general learning-based framework that automatically infers similarity measurement rather than being manually designed and can be used to characterize transcriptional phenotypic profiles for drug discovery with generalized good performance. We evaluated Dr. Sim on comprehensively publicly available in vitro and in vivo datasets in drug annotation and repositioning using high-throughput transcriptional perturbation data and indicated that Dr. Sim significantly outperforms the existing methods and is proved to be a conceptual improvement by learning transcriptional similarity to facilitate the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and usage of Dr. Sim is available at https://github.com/bm2-lab/DrSim/.


2010 ◽  
Vol 9 ◽  
pp. S14 ◽  
Author(s):  
F. Van Goor ◽  
S. Hadida ◽  
P.D.J. Grootenhuis ◽  
J.H. Stack ◽  
B. Burton ◽  
...  

Author(s):  
Alan S. Verkman ◽  
Luis J. V. Galietta

Chloride transport across cell membranes is broadly involved in epithelial fluid transport, cell volume and pH regulation, muscle contraction, membrane excitability, and organellar acidification. The human genome encodes at least 53 chloride transporting proteins with expression in cell plasma or intracellular membranes, which include chloride channels, exchangers and cotransporters, some having broad anion specificity. Loss of function mutations in chloride transporters cause a wide variety of human diseases, including cystic fibrosis, secretory diarrhea, kidney stones, salt wasting nephropathy, myotonia, osteopetrosis, hearing loss and goiter. While impactful advances have been made in the past decade in drug treatment of cystic fibrosis using small molecule modulators of the defective cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel, other chloride channels and solute carrier proteins (SLCs) represent relatively underexplored target classes for drug discovery. New opportunities have emerged for development of chloride transport modulators as potential therapeutics for secretory diarrheas, constipation, dry eye disorders, kidney stones, polycystic kidney disease, hypertension and osteoporosis. Approaches to chloride transport-targeted drug discovery are reviewed herein, with focus on chloride channel and exchanger classes in which recent preclinical advances have been made in the identification of small molecule modulators and in proof of concept testing in experimental animal models.


2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>


2019 ◽  
Vol 22 (8) ◽  
pp. 509-520
Author(s):  
Cauê B. Scarim ◽  
Chung M. Chin

Background: In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays. Objective: Current approaches to drug discovery for Chagas disease. Method: This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases. Results: Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease. Conclusion: There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.


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