Activation of human STING by a molecular glue-like compound.

Stimulator of interferon genes (STING) is a dimeric transmembrane adapter protein that plays a key role in the human innate immune response to infection and has been therapeutically exploited for its antitumor activity. The activation of STING requires its high-order oligomerization, which could be induced by binding of the endogenous ligand, cGAMP, to the cytosolic ligand-binding domain. Here we report the discovery through functional screens of a class of compounds, named NVS-STGs, that activate human STING. Our cryo-EM structures show that NVS-STG2 induces the high-order oligomerization of human STING by binding to a pocket between the transmembrane domains of the neighboring STING dimers, effectively acting as a molecular glue. Our functional assays showed that NVS-STG2 could elicit potent STING-mediated immune responses in cells and antitumor activities in animal models.

Phenotypic landscape of intestinal organoid regeneration.

The development of intestinal organoids from single adult intestinal stem cells in vitro recapitulates the regenerative capacity of the intestinal epithelium1,2. 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.

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

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

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.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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

OBJECTIVES: 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. METHODS: 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. RESULTS: Statistically significant reductions were found in depressive symptoms, anxiety, stress, and burden (p < .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. CONCLUSIONS: 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. CLINICAL IMPLICATIONS: Guided online ACT may reduce depressive symptoms, anxiety, stress, and burden of family caregivers of persons living with dementia.

Discovery of a ZIP7 inhibitor from a Notch pathway screen.

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.

Benchmarking network algorithms for contextualizing genes of interest.

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.

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

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.

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

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.

Large-scale prediction and testing of drug activity on side-effect targets.

Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 µM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

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

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.

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

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.

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

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.

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

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.

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery.

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

The feasibility and preliminary effects of a pilot randomized controlled trial: Videoconferencing acceptance and commitment therapy in distressed family caregivers of people with dementia.

This pilot randomized controlled trial (RCT) examined preliminary effects of an 8-week videoconferencing acceptance and commitment therapy (ACT) program supplemented with psychoeducation materials on distressed family caregivers of persons living with dementia (PLWD) compared to the control group provided with psychoeducation materials only. Nineteen family caregivers of PLWD in the USA were randomly assigned to the ACT group or the control group. Data was collected at pretest, posttest, and 1-month follow-up (F/U). Compared to the control group, the ACT group showed a significantly larger reduction in grief at posttest, with a medium effect size. Small effects of ACT were found in anxiety, psychological quality of life, and engagement in meaningful activities at posttest and grief, engagement in meaningful activities, and psychological flexibility at F/U compared to the control group. These promising findings warrant a full-scale RCT with adequate power to measure the efficacy of videoconferencing ACT for caregivers of PLWD.

Effects of a coach-guided videoconferencing acceptance and commitment therapy intervention combined with psychoeducation on distressed individuals living with spinal cord injury: a preliminary mixed-methods study.

PURPOSE: This study examined effects of coach-guided videoconferencing acceptance and commitment therapy (ACT) accompanied by psychoeducation on distressed individuals with spinal cord injury (SCI) and explored participants' experiences in the intervention. MATERIALS AND METHODS: Ten people with SCI participated in 8 individual videoconferencing sessions delivered by trained coaches. Data using self-reported questionnaires and individual interviews was collected at pretest and posttest and analyzing using Wilcoxon signed-rank tests and interpretative phenomenological analysis (ClinicalTrials.gov ID: NCT04670406). RESULTS: Statistically significant improvements were found in depression, anxiety, stress, grief, engagement in meaningful activities, and self-compassion with medium to large effect sizes. There was no significant change in quality of life, resilience, and ACT processes. Participants gained a new way of thinking by: being aware of thoughts and emotions; exploring perceptions of others; and focusing on the present. Also, the intervention equipped participants to deal with challenges by: improving coping with SCI-related conditions; practicing self-compassion, acceptance, and meditation; and acquiring skills of value-based decision making and committed action. CONCLUSIONS: Findings contribute to the limited evidence as the first study that measured effects of videoconferencing ACT on people with SCI. Future randomized controlled trials are needed to measure efficacy of internet-delivered ACT for people with SCI.IMPLICATIONS FOR REHABILITATIONGuided videoconferencing ACT may reduce depressive symptoms, anxiety, stress, and grief and increase engagement in meaningful activities and self-compassion in people with SCI.Professionals may consider ACT as a supportive or adjunct service for people with SCI who experience psychological distress.