High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Retro Drug Design: From Target Properties to Molecular Structures.

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability.

Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/).

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2.

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Application of niclosamide and analogs as small molecule inhibitors of Zika virus and SARS-CoV-2 infection.

Zika virus has emerged as a potential threat to human health globally. A previous drug repurposing screen identified the approved anthelminthic drug niclosamide as a small molecule inhibitor of Zika virus infection. However, as antihelminthic drugs are generally designed to have low absorption when dosed orally, the very limited bioavailability of niclosamide will likely hinder its potential direct repurposing as an antiviral medication. Here, we conducted SAR studies focusing on the anilide and salicylic acid regions of niclosamide to improve physicochemical properties such as microsomal metabolic stability, permeability and solubility. We found that the 5-bromo substitution in the salicylic acid region retains potency while providing better drug-like properties. Other modifications in the anilide region with 2'-OMe and 2'-H substitutions were also advantageous. We found that the 4'-NO2 substituent can be replaced with a 4'-CN or 4'-CF3 substituents. Together, these modifications provide a basis for optimizing the structure of niclosamide to improve systemic exposure for application of niclosamide analogs as drug lead candidates for treating Zika and other viral infections. Indeed, key analogs were also able to rescue cells from the cytopathic effect of SARS-CoV-2 infection, indicating relevance for therapeutic strategies targeting the COVID-19 pandemic.

High-throughput screening with nucleosome substrate identifies small-molecule inhibitors of the human histone lysine methyltransferase NSD2.

The histone lysine methyltransferase nuclear receptor-binding SET domain protein 2 (NSD2, also known as WHSC1/MMSET) is an epigenetic modifier and is thought to play a driving role in oncogenesis. Both NSD2 overexpression and point mutations that increase its catalytic activity are associated with several human cancers. Although NSD2 is an attractive therapeutic target, no potent, selective, and bioactive small molecule inhibitors of NSD2 have been reported to date, possibly due to the challenges of developing high-throughput assays for NSD2. Here, to establish a platform for the discovery and development of selective NSD2 inhibitors, we optimized and implemented multiple assays. We performed quantitative high-throughput screening with full-length WT NSD2 and a nucleosome substrate against a diverse collection of bioactive small molecules comprising 16,251 compounds. We further interrogated 174 inhibitory compounds identified in the primary screen with orthogonal and counter assays and with activity assays based on the clinically relevant NSD2 variants E1099K and T1150A. We selected five confirmed inhibitors for follow-up, which included a radiolabeled validation assay, surface plasmon resonance studies, methyltransferase profiling, and histone methylation in cells. We found that all five NSD2 inhibitors bind the catalytic SET domain and one exhibited apparent activity in cells, validating the workflow and providing a template for identifying selective NSD2 inhibitors. In summary, we have established a robust discovery pipeline for identifying potent NSD2 inhibitors from small-molecule libraries.

Identification of Combinations of Approved Drugs With Synergistic Activity Against Ebola Virus in Cell Cultures.

Background: A need to develop therapeutics to treat Ebola virus disease patients in remote and resource-challenged settings remains in the wake of the 2013-2016 epidemic in West Africa. Toward this goal, we screened drugs under consideration as treatment options and other drugs of interest, most being small molecules approved by the Food and Drug Administration. Drugs demonstrating in vitro antiviral activity were advanced for evaluation in combinations because of advantages often provided by drug cocktails. Methods: Drugs were screened for blockade of Ebola virus infection in cultured cells. Twelve drugs were tested in all (78 pair-wise) combinations, and 3 were tested in a subset of combinations. Results: Multiple synergistic drug pairs emerged, with the majority comprising 2 entry inhibitors. For the pairs of entry inhibitors studied, synergy was demonstrated at the level of virus entry into host cells. Highly synergistic pairs included aripiprazole/piperacetazine, sertraline/toremifene, sertraline/bepridil, and amodiaquine/clomiphene. Conclusions: Our study shows the feasibility of identifying pairs of approved drugs that synergistically block Ebola virus infection in cell cultures. We discuss our findings in terms of the theoretic ability of these or alternate combinations to reach therapeutic levels. Future research will assess selected combinations in small-animal models of Ebola virus disease.

The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity.

A chemical genomics "Toxmatrix" method was developed to elucidate mechanisms of cytotoxicity using neuronal models. Quantitative high-throughput screening (qHTS) was applied to systematically screen each toxicant against a panel of 70 modulators, drugs or chemicals that act on a known target, to identify interactions that either protect or sensitize cells to each toxicant. Thirty-two toxicants were tested at 10 concentrations for cytotoxicity to SH-SY5Y human neuroblastoma cells, with results fitted to the Hill equation to determine an IC50 for each toxicant. Thirty-three toxicant:modulator interactions were identified in SH-SY5Y cells for 14 toxicants, as modulators that shifted toxicant IC50 values lower or higher. The target of each modulator that sensitizes cells or protects cells from a toxicant suggests a mode of toxicant action or cellular adaptation. In secondary screening, we tested modulator-toxicant pairs identified from the SH-SY5Y primary screening for interactions in three differentiated neuronal human cell lines: dSH-SY5Y, conditionally immortalized dopaminergic neurons (LUHMES), and neural stem cells. Twenty toxicant-modulator pairs showed pronounced interactions in one or several differentiated cell models. Additional testing confirmed that several modulators acted through their primary targets. For example, several chelators protected differentiated LUHMES neurons from four toxicants by chelation of divalent cations and buthionine sulphoximine sensitized cells to 6-hydroxydopamine and 4-(methylamino)phenol hemisulfate by blocking glutathione synthesis. Such modulators that interact with multiple neurotoxicants suggest these may be vulnerable toxicity pathways in neurons. Thus, the Toxmatrix method is a systematic high-throughput approach that can identify mechanisms of toxicity and cellular adaptation.

Selective targeting of JAK/STAT signaling is potentiated by Bcl-xL blockade in IL-2-dependent adult T-cell leukemia.

Adult T-cell leukemia (ATL) develops in individuals infected with human T-cell lymphotropic virus-1 (HTLV-1). Presently there is no curative therapy for ATL. HTLV-1-encoded protein Tax (transactivator from the X-gene region) up-regulates Bcl-xL (B-cell lymphoma-extra large) expression and activates interleukin-2 (IL-2), IL-9, and IL-15 autocrine/paracrine systems, resulting in amplified JAK/STAT signaling. Inhibition of JAK signaling reduces cytokine-dependent ex vivo proliferation of peripheral blood mononuclear cells (PBMCs) from ATL patients in smoldering/chronic stages. Currently, two JAK inhibitors are approved for human use. In this study, we examined activity of multiple JAK inhibitors in ATL cell lines. The selective JAK inhibitor ruxolitinib was examined in a high-throughput matrix screen combined with >450 potential therapeutic agents, and Bcl-2/Bcl-xL inhibitor navitoclax was identified as a strong candidate for multicomponent therapy. The combination was noted to strongly activate BAX (Bcl-2-associated X protein), effect mitochondrial depolarization, and increase caspase 3/7 activities that lead to cleavage of PARP (poly ADP ribose polymerase) and Mcl-1 (myeloid cell leukemia 1). Ruxolitinib and navitoclax independently demonstrated modest antitumor efficacy, whereas the combination dramatically lowered tumor burden and prolonged survival in an ATL murine model. This combination strongly blocked ex vivo proliferation of five ATL patients' PBMCs. These studies provide support for a therapeutic trial in patients with smoldering/chronic ATL using a drug combination that inhibits JAK signaling and antiapoptotic protein Bcl-xL.

Small Molecule Inhibition of the Ubiquitin-specific Protease USP2 Accelerates cyclin D1 Degradation and Leads to Cell Cycle Arrest in Colorectal Cancer and Mantle Cell Lymphoma Models.

Deubiquitinases are important components of the protein degradation regulatory network. We report the discovery of ML364, a small molecule inhibitor of the deubiquitinase USP2 and its use to interrogate the biology of USP2 and its putative substrate cyclin D1. ML364 has an IC50 of 1.1 µm in a biochemical assay using an internally quenched fluorescent di-ubiquitin substrate. Direct binding of ML364 to USP2 was demonstrated using microscale thermophoresis. ML364 induced an increase in cellular cyclin D1 degradation and caused cell cycle arrest as shown in Western blottings and flow cytometry assays utilizing both Mino and HCT116 cancer cell lines. ML364, and not the inactive analog 2, was antiproliferative in cancer cell lines. Consistent with the role of cyclin D1 in DNA damage response, ML364 also caused a decrease in homologous recombination-mediated DNA repair. These effects by a small molecule inhibitor support a key role for USP2 as a regulator of cell cycle, DNA repair, and tumor cell growth.

Novel lead structures with both Plasmodium falciparum gametocytocidal and asexual blood stage activity identified from high throughput compound screening.

BACKGROUND: Blocking malaria transmission is an important step in eradicating malaria. In the field, transmission requires the production of sexual stage Plasmodium parasites, called gametocytes, which are not effectively killed by the commonly used anti-malarials allowing individuals to remain infectious after clearance of asexual parasites. METHODS: To identify new gametocytocidal compounds, a library of 45,056 compounds with diverse structures was screened using a high throughput gametocyte viability assay. The characteristics of active hits were further evaluated against asexual stage parasites in a growth inhibition assay. Their cytotoxicity were tested against mammalian cells in a cytotoxicity assay. The chemical scaffold similarity of active hits were studied using scaffold cluster analysis. RESULTS: A set of 23 compounds were identified and further confirmed for their activity against gametocytes. All the 23 confirmed compounds possess dual-activities against both gametocytes responsible for human to mosquito transmission and asexual parasites that cause the clinical symptoms. Three of these compounds were fourfold more active against gametocytes than asexual parasites. Further cheminformatic analysis revealed three sets of novel scaffolds, including highly selective 4-1H-pyrazol-5-yl piperidine analogs. CONCLUSIONS: This study revealed important new structural scaffolds that can be used as starting points for dual activity anti-malarial drug development.

Blockade of oncogenic IκB kinase activity in diffuse large B-cell lymphoma by bromodomain and extraterminal domain protein inhibitors.

In the activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL), NF-κB activity is essential for viability of the malignant cells and is sustained by constitutive activity of IκB kinase (IKK) in the cytoplasm. Here, we report an unexpected role for the bromodomain and extraterminal domain (BET) proteins BRD2 and BRD4 in maintaining oncogenic IKK activity in ABC DLBCL. IKK activity was reduced by small molecules targeting BET proteins as well as by genetic knockdown of BRD2 and BRD4 expression, thereby inhibiting downstream NF-κB-driven transcriptional programs and killing ABC DLBCL cells. Using a high-throughput platform to screen for drug-drug synergy, we observed that the BET inhibitor JQ1 combined favorably with multiple drugs targeting B-cell receptor signaling, one pathway that activates IKK in ABC DLBCL. The BTK kinase inhibitor ibrutinib, which is in clinical development for the treatment of ABC DLBCL, synergized strongly with BET inhibitors in killing ABC DLBCL cells in vitro and in a xenograft mouse model. These findings provide a mechanistic basis for the clinical development of BET protein inhibitors in ABC DLBCL, particularly in combination with other modulators of oncogenic IKK signaling.

High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell-like diffuse large B-cell lymphoma cells.

The clinical development of drug combinations is typically achieved through trial-and-error or via insight gained through a detailed molecular understanding of dysregulated signaling pathways in a specific cancer type. Unbiased small-molecule combination (matrix) screening represents a high-throughput means to explore hundreds and even thousands of drug-drug pairs for potential investigation and translation. Here, we describe a high-throughput screening platform capable of testing compounds in pairwise matrix blocks for the rapid and systematic identification of synergistic, additive, and antagonistic drug combinations. We use this platform to define potential therapeutic combinations for the activated B-cell-like subtype (ABC) of diffuse large B-cell lymphoma (DLBCL). We identify drugs with synergy, additivity, and antagonism with the Bruton's tyrosine kinase inhibitor ibrutinib, which targets the chronic active B-cell receptor signaling that characterizes ABC DLBCL. Ibrutinib interacted favorably with a wide range of compounds, including inhibitors of the PI3K-AKT-mammalian target of rapamycin signaling cascade, other B-cell receptor pathway inhibitors, Bcl-2 family inhibitors, and several components of chemotherapy that is the standard of care for DLBCL.

High-content screening identifies small molecules that remove nuclear foci, affect MBNL distribution and CELF1 protein levels via a PKC-independent pathway in myotonic dystrophy cell lines.

Myotonic dystrophy (DM) is a multi-system neuromuscular disorder for which there is no treatment. We have developed a medium throughput phenotypic assay, based on the identification of nuclear foci in DM patient cell lines using in situ hybridization and high-content imaging to screen for potentially useful therapeutic compounds. A series of further assays based on molecular features of DM have also been employed. Two compounds that reduce and/or remove nuclear foci have been identified, Ro 31-8220 and chromomycin A3. Ro 31-8220 is a PKC inhibitor, previously shown to affect the hyperphosphorylation of CELF1 and ameliorate the cardiac phenotype in a DM1 mouse model. We show that the same compound eliminates nuclear foci, reduces MBNL1 protein in the nucleus, affects ATP2A1 alternative splicing and reduces steady-state levels of CELF1 protein. We demonstrate that this effect is independent of PKC activity and conclude that this compound may be acting on alternative kinase targets within DM pathophysiology. Understanding the activity profile for this compound is key for the development of targeted therapeutics in the treatment of DM.

A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery.

As part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, the National Center for Advancing Translational Sciences is dedicated to the development of new pharmacological tools and investigational drugs for managing and treating pain as well as the prevention and treatment of opioid misuse and addiction. In line with these objectives, we created a comprehensive, annotated small molecule library including drugs, probes, and tool compounds that act on published pain- and addiction-relevant targets. Nearly 3000 small molecules associated with approximately 200 known and hypothesized HEAL targets have been assembled, curated, and annotated in one collection. Physical samples of the library compounds have been acquired and plated in 1536-well format, enabling a rapid and efficient high-throughput screen against a wide range of assays. The creation of the HEAL Targets and Compounds Library, coupled with an integrated computational platform for AI-driven machine learning, structural modeling, and virtual screening, provides a valuable source for strategic drug repurposing, innovative profiling, and hypothesis testing of novel targets related to pain and opioid use disorder (OUD). The library is available to investigators for screening pain and OUD-relevant phenotypes.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations.

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships. SIGNIFICANCE: CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products.

Dietary supplements and natural products are often marketed as safe and effective alternatives to conventional drugs, but their safety and efficacy are not well regulated. To address the lack of scientific data in these areas, we assembled a collection of Dietary Supplements and Natural Products (DSNP), as well as Traditional Chinese Medicinal (TCM) plant extracts. These collections were then profiled in a series of in vitro high-throughput screening assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-glycoprotein (P-gp) transporter assay activities. This pipeline facilitated the interrogation of natural product-drug interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection (the NCATS Pharmaceutical Collection or NPC). Many of the approved drugs have well-annotated mechanisms of action (MOAs), while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NPC to predict the MOAs of the DSNP/TCM substances. Our results suggest that many of these substances may have significant bioactivity and potential toxicity, and they provide a starting point for further research on their clinical relevance.

Preclinical Evaluation of the FGFR-Family Inhibitor Futibatinib for Pediatric Rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. Despite decades of clinical trials, the overall survival rate for patients with relapsed and metastatic disease remains below 30%, underscoring the need for novel treatments. FGFR4, a receptor tyrosine kinase that is overexpressed in RMS and mutationally activated in 10% of cases, is a promising target for treatment. Here, we show that futibatinib, an irreversible pan-FGFR inhibitor, inhibits the growth of RMS cell lines in vitro by inhibiting phosphorylation of FGFR4 and its downstream targets. Moreover, we provide evidence that the combination of futibatinib with currently used chemotherapies such as irinotecan and vincristine has a synergistic effect against RMS in vitro. However, in RMS xenograft models, futibatinib monotherapy and combination treatment have limited efficacy in delaying tumor growth and prolonging survival. Moreover, limited efficacy is only observed in a PAX3-FOXO1 fusion-negative (FN) RMS cell line with mutationally activated FGFR4, whereas little or no efficacy is observed in PAX3-FOXO1 fusion-positive (FP) RMS cell lines with FGFR4 overexpression. Alternative treatment modalities such as combining futibatinib with other kinase inhibitors or targeting FGFR4 with CAR T cells or antibody-drug conjugate may be more effective than the approaches tested in this study.

Drug combinations identified by high-throughput screening promote cell cycle transition and upregulate Smad pathways in myeloma.

Drug resistance and disease progression are common in multiple myeloma (MM) patients, underscoring the need for new therapeutic combinations. A high-throughput drug screen in 47 MM cell lines and in silico Huber robust regression analysis of drug responses revealed 43 potentially synergistic combinations. We hypothesized that effective combinations would reduce MYC expression and enhance p16 activity. Six combinations cooperatively reduced MYC protein, frequently over-expressed in MM and also cooperatively increased p16 expression, frequently downregulated in MM. Synergistic reductions in viability were observed with top combinations in proteasome inhibitor-resistant and sensitive MM cell lines, while sparing fibroblasts. Three combinations significantly prolonged survival in a transplantable Ras-driven allograft model of advanced MM closely recapitulating high-risk/refractory myeloma in humans and reduced viability of ex vivo treated patient cells. Common genetic pathways similarly downregulated by these combinations promoted cell cycle transition, whereas pathways most upregulated were involved in TGFß/SMAD signaling. These preclinical data identify potentially useful drug combinations for evaluation in drug-resistant MM and reveal potential mechanisms of combined drug sensitivity.