Publications by authors named "Jeremy Jenkins"

73 Publications

CYP27A1-dependent anti-melanoma activity of limonoid natural products targets mitochondrial metabolism.

Cell Chem Biol 2021 Mar 30. Epub 2021 Mar 30.

Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA. Electronic address:

Three limonoid natural products with selective anti-proliferative activity against BRAF(V600E) and NRAS(Q61K)-mutation-dependent melanoma cell lines were identified. Differential transcriptome analysis revealed dependency of compound activity on expression of the mitochondrial cytochrome P450 oxidase CYP27A1, a transcriptional target of melanogenesis-associated transcription factor (MITF). We determined that CYP27A1 activity is necessary for the generation of a reactive metabolite that proceeds to inhibit cellular proliferation. A genome-wide small interfering RNA screen in combination with chemical proteomics experiments revealed gene-drug functional epistasis, suggesting that these compounds target mitochondrial biogenesis and inhibit tumor bioenergetics through a covalent mechanism. Our work suggests a strategy for melanoma-specific targeting by exploiting the expression of MITF target gene CYP27A1 and inhibiting mitochondrial oxidative phosphorylation in BRAF mutant melanomas.
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http://dx.doi.org/10.1016/j.chembiol.2021.03.004DOI Listing
March 2021

Acceptance and Commitment Therapy (ACT) Guided Online for Distressed Caregivers of Persons Living with Dementia.

Clin Gerontol 2021 Apr 1:1-12. Epub 2021 Apr 1.

School of Social Work, The University of Alabama, Tuscaloosa, Alabama, USA.

: This study examined the effects of a guided online acceptance and commitment therapy (ACT) intervention on distressed family caregivers of persons living with dementia and explored the experiences of these caregivers in the ACT intervention. Seven family caregivers experiencing psychological distress individually participated in 10 ACT videoconference sessions guided by a trained coach. Quantitative data, such as psychological distress, burden, and ACT processes, were collected at pretest and posttest and analyzed using the Wilcoxon signed-rank test. Individual interviews were conducted at posttest and analyzed using interpretative phenomenological analysis. Statistically significant reductions were found in depressive symptoms, anxiety, stress, and burden ( < .05) with medium effect sizes. ACT sessions helped caregivers gain renewed strength by: being equipped with resources to use under distress throughout the caregiving journey; being more self-compassionate and taking care of one's self; and being more patient with relatives with dementia. Findings contribute to the limited evidence in guided online ACT for caregivers of persons living with dementia. Further studies with a larger sample size are needed to evaluate the efficacy of guided online ACT. Guided online ACT may reduce depressive symptoms, anxiety, stress, and burden of family caregivers of persons living with dementia.
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http://dx.doi.org/10.1080/07317115.2021.1908475DOI Listing
April 2021

Phenotypic landscape of intestinal organoid regeneration.

Nature 2020 10 7;586(7828):275-280. Epub 2020 Oct 7.

Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland.

The development of intestinal organoids from single adult intestinal stem cells in vitro recapitulates the regenerative capacity of the intestinal epithelium. Here we unravel the mechanisms that orchestrate both organoid formation and the regeneration of intestinal tissue, using an image-based screen to assay an annotated library of compounds. We generate multivariate feature profiles for hundreds of thousands of organoids to quantitatively describe their phenotypic landscape. We then use these phenotypic fingerprints to infer regulatory genetic interactions, establishing a new approach to the mapping of genetic interactions in an emergent system. This allows us to identify genes that regulate cell-fate transitions and maintain the balance between regeneration and homeostasis, unravelling previously unknown roles for several pathways, among them retinoic acid signalling. We then characterize a crucial role for retinoic acid nuclear receptors in controlling exit from the regenerative state and driving enterocyte differentiation. By combining quantitative imaging with RNA sequencing, we show the role of endogenous retinoic acid metabolism in initiating transcriptional programs that guide the cell-fate transitions of intestinal epithelium, and we identify an inhibitor of the retinoid X receptor that improves intestinal regeneration in vivo.
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http://dx.doi.org/10.1038/s41586-020-2776-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116869PMC
October 2020

Systematic Chemogenetic Library Assembly.

Cell Chem Biol 2020 09 23;27(9):1124-1129. Epub 2020 Jul 23.

Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA. Electronic address:

Chemogenetic libraries, collections of well-defined chemical probes, provide tremendous value to biomedical research but require substantial effort to ensure diversity as well as quality of the contents. We have assembled a chemogenetic library by data mining and crowdsourcing institutional expertise. We are sharing our approach, lessons learned, and disclosing our current collection of 4,185 compounds with their primary annotated gene targets (https://github.com/Novartis/MoaBox). This physical collection is regularly updated and used broadly both within Novartis and in collaboration with external partners.
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http://dx.doi.org/10.1016/j.chembiol.2020.07.004DOI Listing
September 2020

Acceptance and commitment therapy for family caregivers: A systematic review and meta-analysis.

J Health Psychol 2021 Jan 10;26(1):82-102. Epub 2020 Jul 10.

Telehealth Private Practice: jeremyjenkins.icouch.me, Montana, USA.

Acceptance and commitment therapy is an emerging evidenced-based practice, but no systematic review regarding the effects of ACT on family caregivers has been conducted. This article examined the effects of ACT on family caregivers by conducting meta-analysis with a random effects model. Twenty-four articles were identified from four electronic databases searched up to 30 March 2020. Meta-analyses found moderate effects of ACT on depressive symptoms and quality of life, small effects on anxiety, and small to moderate effects on stress. Further ACT studies should be conducted to measure effects on different outcomes for various family caregiver populations.
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http://dx.doi.org/10.1177/1359105320941217DOI Listing
January 2021

Benchmarking network algorithms for contextualizing genes of interest.

PLoS Comput Biol 2019 12 20;15(12):e1007403. Epub 2019 Dec 20.

Respiratory Disease Area Department, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, United States of America.

Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.
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http://dx.doi.org/10.1371/journal.pcbi.1007403DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944391PMC
December 2019

CPSF3-dependent pre-mRNA processing as a druggable node in AML and Ewing's sarcoma.

Nat Chem Biol 2020 01 9;16(1):50-59. Epub 2019 Dec 9.

Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

The post-genomic era has seen many advances in our understanding of cancer pathways, yet resistance and tumor heterogeneity necessitate multiple approaches to target even monogenic tumors. Here, we combine phenotypic screening with chemical genetics to identify pre-messenger RNA endonuclease cleavage and polyadenylation specificity factor 3 (CPSF3) as the target of JTE-607, a small molecule with previously unknown target. We show that CPSF3 represents a synthetic lethal node in a subset of acute myeloid leukemia (AML) and Ewing's sarcoma cancer cell lines. Inhibition of CPSF3 by JTE-607 alters expression of known downstream effectors in AML and Ewing's sarcoma lines, upregulates apoptosis and causes tumor-selective stasis in mouse xenografts. Mechanistically, it prevents the release of newly synthesized pre-mRNAs, resulting in read-through transcription and the formation of DNA-RNA hybrid R-loop structures. This study implicates pre-mRNA processing, and specifically CPSF3, as a druggable target providing an avenue to therapeutic intervention in cancer.
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http://dx.doi.org/10.1038/s41589-019-0424-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116157PMC
January 2020

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

Cell Chem Biol 2019 05 4;26(5):765-777.e3. Epub 2019 Apr 4.

HMS LINCS and Druggable Genome Centers, Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Warren Alpert 444, 200 Longwood Avenue, Boston, MA 02115, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. Electronic address:

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online tool http://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1,852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections.
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http://dx.doi.org/10.1016/j.chembiol.2019.02.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526536PMC
May 2019

Discovery of a ZIP7 inhibitor from a Notch pathway screen.

Nat Chem Biol 2019 02 14;15(2):179-188. Epub 2019 Jan 14.

Novartis Institutes for Biomedical Research, Cambridge, MA, USA.

The identification of activating mutations in NOTCH1 in 50% of T cell acute lymphoblastic leukemia has generated interest in elucidating how these mutations contribute to oncogenic transformation and in targeting the pathway. A phenotypic screen identified compounds that interfere with trafficking of Notch and induce apoptosis via an endoplasmic reticulum (ER) stress mechanism. Target identification approaches revealed a role for SLC39A7 (ZIP7), a zinc transport family member, in governing Notch trafficking and signaling. Generation and sequencing of a compound-resistant cell line identified a V430E mutation in ZIP7 that confers transferable resistance to the compound NVS-ZP7-4. NVS-ZP7-4 altered zinc in the ER, and an analog of the compound photoaffinity labeled ZIP7 in cells, suggesting a direct interaction between the compound and ZIP7. NVS-ZP7-4 is the first reported chemical tool to probe the impact of modulating ER zinc levels and investigate ZIP7 as a novel druggable node in the Notch pathway.
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http://dx.doi.org/10.1038/s41589-018-0200-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251565PMC
February 2019

DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery.

Nat Commun 2018 10 17;9(1):4307. Epub 2018 Oct 17.

Neuroscience Research, Novartis Institutes for Biomedical Research, 250 Massachusetts, Cambridge, MA, 02139, USA.

Here we report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. Pharmaceutical discovery relies on high-throughput screening, yet current platforms have limited readouts. RNA-seq is a powerful tool to investigate drug effects using transcriptome changes as a proxy, yet standard library construction is costly. DRUG-seq captures transcriptional changes detected in standard RNA-seq at 1/100 the cost. In proof-of-concept experiments profiling 433 compounds across 8 doses, transcription profiles generated from DRUG-seq successfully grouped compounds into functional clusters by mechanism of actions (MoAs) based on their intended targets. Perturbation differences reflected in transcriptome changes were detected for compounds engaging the same target, demonstrating the value of using DRUG-seq for understanding on and off-target activities. We demonstrate DRUG-seq captures common mechanisms, as well as differences between compound treatment and CRISPR on the same target. DRUG-seq provides a powerful tool for comprehensive transcriptome readout in a high-throughput screening environment.
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http://dx.doi.org/10.1038/s41467-018-06500-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192987PMC
October 2018

Indolyl-Pyridinyl-Propenone-Induced Methuosis through the Inhibition of PIKFYVE.

ACS Omega 2018 Jun 5;3(6):6097-6103. Epub 2018 Jun 5.

Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

Methuosis is a form of nonapoptotic cell death characterized by the accumulation of macropinosome-derived vacuoles. Herein, we identify PIKFYVE, a class III phosphoinositide (PI) kinase, as the protein target responsible for the methuosis-inducing activity of indolyl-pyridinyl-propenones (3-(5-methoxy-2-methyl-1-indol-3-yl)-1-(4-pyridinyl)-2-propen-1-one). We further characterize the effects of chemical substitutions at the 2- and 5-indolyl positions on cytoplasmic vacuolization and PIKFYVE binding and inhibitory activity. Our study provides a better understanding of the mechanism of methuosis-inducing indolyl-pyridinyl-propenones.
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http://dx.doi.org/10.1021/acsomega.8b00202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130785PMC
June 2018

Growth-restricting effects of siRNA transfections: a largely deterministic combination of off-target binding and hybridization-independent competition.

Nucleic Acids Res 2018 10;46(18):9309-9320

Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.

Perturbation of gene expression by means of synthetic small interfering RNAs (siRNAs) is a powerful way to uncover gene function. However, siRNA technology suffers from sequence-specific off-target effects and from limitations in knock-down efficiency. In this study, we assess a further problem: unintended effects of siRNA transfections on cellular fitness/proliferation. We show that the nucleotide compositions of siRNAs at specific positions have reproducible growth-restricting effects on mammalian cells in culture. This is likely distinct from hybridization-dependent off-target effects, since each nucleotide residue is seen to be acting independently and additively. The effect is robust and reproducible across different siRNA libraries and also across various cell lines, including human and mouse cells. Analyzing the growth inhibition patterns in correlation to the nucleotide sequence of the siRNAs allowed us to build a predictor that can estimate growth-restricting effects for any arbitrary siRNA sequence. Competition experiments with co-transfected siRNAs further suggest that the growth-restricting effects might be linked to an oversaturation of the cellular miRNA machinery, thus disrupting endogenous miRNA functions at large. We caution that competition between siRNA molecules could complicate the interpretation of double-knockdown or epistasis experiments, and potential interactions with endogenous miRNAs can be a factor when assaying cell growth or viability phenotypes.
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http://dx.doi.org/10.1093/nar/gky798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182159PMC
October 2018

Quantitative Prioritization of Tool Compounds for Phenotypic Screening.

Methods Mol Biol 2018 ;1787:195-206

Novartis Institutes for BioMedical Research Inc., Cambridge, MA, USA.

Phenotypic screens are increasingly utilized in drug discovery for multiple purposes such as lead and/or tool compound finding, and target discovery. Using potent and selective chemical tool compounds against well-defined targets in phenotypic screens can help elucidate biological processes modulating assay phenotypes. Unfortunately the identification of such tools from large heterogeneous bioactivity databases is nontrivial and there is repeated use of published unselective compounds as phenotypic tools. Here we describe a computational model, the compound-target tool score (TS), which is an evidence-based quantitative confidence metric that can be used to systematically rank tool compounds for targets. The identified selective and nonselective tool compounds have applications in phenotypic assays for target hypothesis validation as well as assay development.
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http://dx.doi.org/10.1007/978-1-4939-7847-2_15DOI Listing
February 2019

Size uniformity of animal cells is actively maintained by a p38 MAPK-dependent regulation of G1-length.

Elife 2018 03 29;7. Epub 2018 Mar 29.

Cell Biology Program, The Hospital for Sick Children, Toronto, Canada.

Animal cells within a tissue typically display a striking regularity in their size. To date, the molecular mechanisms that control this uniformity are still unknown. We have previously shown that size uniformity in animal cells is promoted, in part, by size-dependent regulation of G1 length. To identify the molecular mechanisms underlying this process, we performed a large-scale small molecule screen and found that the p38 MAPK pathway is involved in coordinating cell size and cell cycle progression. Small cells display higher p38 activity and spend more time in G1 than larger cells. Inhibition of p38 MAPK leads to loss of the compensatory G1 length extension in small cells, resulting in faster proliferation, smaller cell size and increased size heterogeneity. We propose a model wherein the p38 pathway responds to changes in cell size and regulates G1 exit accordingly, to increase cell size uniformity.
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http://dx.doi.org/10.7554/eLife.26947DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876018PMC
March 2018

Recurrent ubiquitin B silencing in gynecological cancers establishes dependence on ubiquitin C.

J Clin Invest 2017 12 13;127(12):4554-4568. Epub 2017 Nov 13.

Chemical Biology and Therapeutics, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.

Transcriptional repression of ubiquitin B (UBB) is a cancer-subtype-specific alteration that occurs in a substantial population of patients with cancers of the female reproductive tract. UBB is 1 of 2 genes encoding for ubiquitin as a polyprotein consisting of multiple copies of ubiquitin monomers. Silencing of UBB reduces cellular UBB levels and results in an exquisite dependence on ubiquitin C (UBC), the second polyubiquitin gene. UBB is repressed in approximately 30% of high-grade serous ovarian cancer (HGSOC) patients and is a recurrent lesion in uterine carcinosarcoma and endometrial carcinoma. We identified ovarian tumor cell lines that retain UBB in a repressed state, used these cell lines to establish orthotopic ovarian tumors, and found that inducible expression of a UBC-targeting shRNA led to tumor regression, and substantial long-term survival benefit. Thus, we describe a recurrent cancer-specific lesion at the level of ubiquitin production. Moreover, these observations reveal the prognostic value of UBB repression and establish UBC as a promising therapeutic target for ovarian cancer patients with recurrent UBB silencing.
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http://dx.doi.org/10.1172/JCI92914DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707153PMC
December 2017

Explicit Modeling of siRNA-Dependent On- and Off-Target Repression Improves the Interpretation of Screening Results.

Cell Syst 2017 02 15;4(2):182-193.e4. Epub 2017 Feb 15.

Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland. Electronic address:

RNAi is broadly used to map gene regulatory networks, but the identification of genes that are responsible for the observed phenotypes is challenging, as small interfering RNAs (siRNAs) simultaneously downregulate the intended on targets and many partially complementary off targets. Additionally, the scarcity of publicly available control datasets hinders the development and comparative evaluation of computational methods for analyzing the data. Here, we introduce PheLiM (https://github.com/andreariba/PheLiM), a method that uses predictions of siRNA on- and off-target downregulation to infer gene-specific contributions to phenotypes. To assess the performance of PheLiM, we carried out siRNA- and CRISPR/Cas9-based genome-wide screening of two well-characterized pathways, bone morphogenetic protein (BMP) and nuclear factor κB (NF-κB), and we reanalyzed publicly available siRNA screens. We demonstrate that PheLiM has the overall highest accuracy and most reproducible results compared to other available methods. PheLiM can accommodate various methods for predicting siRNA off targets and is broadly applicable to the identification of genes underlying complex phenotypes.
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http://dx.doi.org/10.1016/j.cels.2017.01.011DOI Listing
February 2017

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform.

SLAS Discov 2017 03 13;22(3):238-249. Epub 2016 Dec 13.

3 Developmental and Molecular Pathways, NIBR, Postfach, Basel, Switzerland.

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an "off-the-shelf," open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.
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http://dx.doi.org/10.1177/1087057116679993DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322829PMC
March 2017

Drug discovery and development in the era of Big Data.

Future Med Chem 2016 Oct 21;8(15):1807-1813. Epub 2016 Sep 21.

Relay Therapeutics, 215 First St, Cambridge, MA 02142, USA.

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http://dx.doi.org/10.4155/fmc-2014-0081DOI Listing
October 2016

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.

J Chem Inf Model 2016 09 16;56(9):1622-30. Epub 2016 Aug 16.

Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus, 4056 Basel, Switzerland.

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.
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http://dx.doi.org/10.1021/acs.jcim.6b00244DOI Listing
September 2016

Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery.

Cell Chem Biol 2016 07 14;23(7):862-874. Epub 2016 Jul 14.

Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA. Electronic address:

The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
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http://dx.doi.org/10.1016/j.chembiol.2016.05.016DOI Listing
July 2016

Identifying compound efficacy targets in phenotypic drug discovery.

Drug Discov Today 2016 Jan 10;21(1):82-89. Epub 2015 Aug 10.

Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA. Electronic address:

The identification of the efficacy target(s) for hits from phenotypic compound screens remains a key step to progress compounds into drug development. In addition to efficacy targets, the characterization of epistatic proteins influencing compound activity often facilitates the elucidation of the underlying mechanism of action; and, further, early determination of off-targets that cause potentially unwanted secondary phenotypes helps in assessing potential liabilities. This short review discusses the most important technologies currently available for characterizing the direct and indirect target space of bioactive compounds following phenotypic screening. We present a comprehensive strategy employing complementary approaches to balance individual technology strengths and weaknesses.
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http://dx.doi.org/10.1016/j.drudis.2015.08.001DOI Listing
January 2016

Structure of the DDB1-CRBN E3 ubiquitin ligase in complex with thalidomide.

Nature 2014 Aug 16;512(7512):49-53. Epub 2014 Jul 16.

1] Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland [2] University of Basel, Petersplatz 10, CH-4003 Basel, Switzerland.

In the 1950s, the drug thalidomide, administered as a sedative to pregnant women, led to the birth of thousands of children with multiple defects. Despite the teratogenicity of thalidomide and its derivatives lenalidomide and pomalidomide, these immunomodulatory drugs (IMiDs) recently emerged as effective treatments for multiple myeloma and 5q-deletion-associated dysplasia. IMiDs target the E3 ubiquitin ligase CUL4-RBX1-DDB1-CRBN (known as CRL4(CRBN)) and promote the ubiquitination of the IKAROS family transcription factors IKZF1 and IKZF3 by CRL4(CRBN). Here we present crystal structures of the DDB1-CRBN complex bound to thalidomide, lenalidomide and pomalidomide. The structure establishes that CRBN is a substrate receptor within CRL4(CRBN) and enantioselectively binds IMiDs. Using an unbiased screen, we identified the homeobox transcription factor MEIS2 as an endogenous substrate of CRL4(CRBN). Our studies suggest that IMiDs block endogenous substrates (MEIS2) from binding to CRL4(CRBN) while the ligase complex is recruiting IKZF1 or IKZF3 for degradation. This dual activity implies that small molecules can modulate an E3 ubiquitin ligase and thereby upregulate or downregulate the ubiquitination of proteins.
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http://dx.doi.org/10.1038/nature13527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423819PMC
August 2014

Using information from historical high-throughput screens to predict active compounds.

J Chem Inf Model 2014 Jul 26;54(7):1880-91. Epub 2014 Jun 26.

Novartis Institutes for BioMedical Research, Novartis Pharma AG , Novartis Campus, 4056 Basel, Switzerland.

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.
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http://dx.doi.org/10.1021/ci500190pDOI Listing
July 2014

Target identification for a Hedgehog pathway inhibitor reveals the receptor GPR39.

Nat Chem Biol 2014 May 16;10(5):343-9. Epub 2014 Mar 16.

Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.

Hedgehog (Hh) signaling determines cell fate during development and can drive tumorigenesis. We performed a screen for new compounds that can impinge on Hh signaling downstream of Smoothened (Smo). A series of cyclohexyl-methyl aminopyrimidine chemotype compounds ('CMAPs') were identified that could block pathway signaling in a Smo-independent manner. In addition to inhibiting Hh signaling, the compounds generated inositol phosphates through an unknown GPCR. Correlation of GPCR mRNA expression levels with compound activity across cell lines suggested the target to be the orphan receptor GPR39. RNA interference or cDNA overexpression of GPR39 demonstrated that the receptor is necessary for compound activity. We propose a model in which CMAPs activate GPR39, which signals to the Gli transcription factors and blocks signaling. In addition to the discovery of GPR39 as a new target that impinges on Hh signaling, we report on small-molecule modulators of the receptor that will enable in vitro interrogation of GPR39 signaling in different cellular contexts.
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http://dx.doi.org/10.1038/nchembio.1481DOI Listing
May 2014

Small molecule-facilitated degradation of ANO1 protein: a new targeting approach for anticancer therapeutics.

J Biol Chem 2014 Apr 5;289(16):11029-11041. Epub 2014 Mar 5.

Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139,. Electronic address:

ANO1, a calcium-activated chloride channel, is highly expressed and amplified in human cancers and is a critical survival factor in these cancers. The ANO1 inhibitor CaCCinh-A01 decreases proliferation of ANO1-amplified cell lines; however, the mechanism of action remains elusive. We explored the mechanism behind the inhibitory effect of CaCCinh-A01 on cell proliferation using a combined experimental and in silico approach. We show that inhibition of ANO1 function is not sufficient to diminish proliferation of ANO1-dependent cancer cells. We report that CaCCinh-A01 reduces ANO1 protein levels by facilitating endoplasmic reticulum-associated, proteasomal turnover of ANO1. Washout of CaCCinh-A01 rescued ANO1 protein levels and resumed cell proliferation. Proliferation of newly derived CaCCinh-A01-resistant cell pools was not affected by CaCCinh-A01 as compared with the parental cells. Consistently, CaCCinh-A01 failed to reduce ANO1 protein levels in these cells, whereas ANO1 currents were still inhibited by CaCCinh-A01, indicating that CaCCinh-A01 inhibits cell proliferation by reducing ANO1 protein levels. Furthermore, we employed in silico methods to elucidate novel biological functions of ANO1 inhibitors. Specifically, we derived a pharmacophore model to describe inhibitors capable of promoting ANO1 degradation and report new inhibitors of ANO1-dependent cell proliferation. In summary, our data demonstrate that inhibition of the channel activity of ANO1 is not sufficient to inhibit ANO1-dependent cell proliferation, indicating that the role of ANO1 in cancer only partially depends on its function as a channel. Our results provide an impetus for gaining a deeper understanding of ANO1 modulation in cells and introduce a new targeting approach for antitumor therapy in ANO1-amplified cancers.
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http://dx.doi.org/10.1074/jbc.M114.549188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4036244PMC
April 2014

Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.

J Biomol Screen 2014 Jun 11;19(5):791-802. Epub 2014 Feb 11.

Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.

Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
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http://dx.doi.org/10.1177/1087057114522690DOI Listing
June 2014

Drug discovery: Rethinking cellular drug response.

Authors:
Jeremy L Jenkins

Nat Chem Biol 2013 Nov;9(11):669-70

Department of Developmental and Molecular Pathways at the Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA.

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http://dx.doi.org/10.1038/nchembio.1365DOI Listing
November 2013