Identify Drug Combinations Research Articles

Implantable Microdevices Identify Microenvironment-Modulating Drug Combinations

Use of an implantable microdevice identified drug combinations that improve therapeutic efficacy in tumors.

Identifying drug combinations that enhance treatment responses mediated by the tumor microenvironment.

Identifying drug combinations that enhance treatment responses mediated by the tumor microenvironment.

Abstract 3892: High-throughput combination screen for identifying novel therapies against high-risk neuroblastoma

Abstract Neuroblastoma is a childhood solid tumor commonly located in the adrenal gland. High-risk neuroblastoma patients undergo multidrug and multi-interventional treatment but despite these efforts many patients develop resistance to chemotherapy and eventually relapse. Neuroblastoma patients face an urgent need of novel therapeutic strategies that could effectively combat the disease. In a recent study we identified kinesin spindle protein (KSP) as a highly effective target in high-risk neuroblastoma. Pharmacological inhibition of KSP resulted in complete regression of a subset of neuroblastoma subcutaneous PDX tumors and tumor growth delay in orthotopic PDXs (Hansson, Radke et al, Sci. Transl. Med 2020). The purpose of the present study is to identify novel synergistic drug combinations effective against neuroblastoma and investigate their translational potential. We designed, optimized, and performed a high-throughput combination screen using 3D tumor organoids that represent three MYCN-amplified neuroblastoma tumors. KSP inhibition was tested in combination with the FIMM drug set of 527 approved and investigational drugs. Screening was performed stepwise: • Step 1: High-throughput drug screening 527 drug combinations and machine learning prediction (DECREASE tool) of drug combination synergy based on minimal required input. • Step 2: Screening of 6x6 dose-response matrices of top 26 predicted hits in high-risk neuroblastoma models. • Step 3: Screening of 6x6 dose-response matrices of top 26 hits in CD34+ cord blood controls to identify compounds that have low toxicity towards proliferating fraction of hematopoietic cells. Overall, we identified seven drug candidates (P1-P7). They classify as: kinase inhibitors, differentiating modifiers, and one hormone therapy. Screened combinations were chosen based on selective efficacy (difference between anti-neuroblastoma efficacy and efficacy towards CD34+ cord blood controls). Identified drug combinations are common among three high-risk neuroblastoma models and have most synergistic area score (MSA) over five. We further plan to validate seven hits in neuroblastoma models in vitro, investigate MOA of the most promising combination and test it in PDX models in vivo. Citation Format: Katarzyna Radke, Karin Hansson, Jani Saarela, Aleksandr Ianevski, Philipp Ianevski, Aurélie Baudet, Mattias Magnusson, Daniel Bexell. High-throughput combination screen for identifying novel therapies against high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3892.

Abstract 3691: Dynamic BH3 profiling identifies novel combinations in malignant pleural mesothelioma with in vivo efficacy

Abstract Currently, malignant pleural mesothelioma (MPM) is mainly treated with conventional therapies such as surgery, chemotherapy and radiation, and patients often suffer from the lack of second line therapy options. In this study, we aimed to identify efficacious drug combinations in primary MPM patient samples and novel MPM patient-derived xenograft (PDX) models to provide better therapeutic options for MPM patients. BH3 profiling is a functional assay that measures mitochondrial priming of the cell. It uses BH3 peptides derived from the BH3 domain of pro-apoptotic BH3-only Bcl-2 family members to provoke a response from viable mitochondria. When tumor cells are treated with drugs prior to BH3 profiling, early changes in their apoptosis signaling can be captured before frank apoptosis occurs. This method is termed dynamic BH3 profiling (DBP), which has been applied in multiple cancer types to identify efficacious drugs. Here, we used high throughput dynamic BH3 profiling (HTDBP) to assess hundreds of drug treatments simultaneously to identify drug combinations that increased apoptotic priming in primary MPM cells ex vivo. Next, we created novel MPM PDX models and tested these drug combinations to determine the ones that primed MPM PDX cells. The HTDBP result from MPM PDX cells recapitulated the chemical vulnerabilities of the primary MPM cells. Moreover, one of the top combination candidates that comprised of navitoclax (BCL-xL, BCL-2 and BCL-w antagonist) and AZD8055 (mTORC1/2 inhibitor) was shown to be safe and efficacious in vivo in our CPDM_011x MPM PDX model. Finally, on the molecular level, we revealed that treatment with navitoclax drove the anti-apoptotic dependence of MPM cell lines towards MCL-1, whose level was downregulated by AZD8055 treatment. In this study, we identified a novel drug combination for MPM with HTDBP that consists of navitoclax and AZD8055. In addition, our results further corroborated that DBP, as a functional assay, can be used to rationally construct novel drug combinations to improve treatment outcomes for cancer patients. Citation Format: Danielle S. Potter, Ruochen Du, Kin-Hoe Chow, Keith L. Ligon, Raphael Bueno, Anthony Letai. Dynamic BH3 profiling identifies novel combinations in malignant pleural mesothelioma with in vivo efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3691.

Therapeutic targeting of LCK tyrosine kinase and mTOR signaling in T-cell acute lymphoblastic leukemia

AbstractRelapse and refractory T-cell acute lymphoblastic leukemia (T-ALL) has a poor prognosis, and new combination therapies are sorely needed. Here, we used an ex vivo high-throughput screening platform to identify drug combinations that kill zebrafish T-ALL and then validated top drug combinations for preclinical efficacy in human disease. This work uncovered potent drug synergies between AKT/mTORC1 (mammalian target of rapamycin complex 1) inhibitors and the general tyrosine kinase inhibitor dasatinib. Importantly, these same drug combinations effectively killed a subset of relapse and dexamethasone-resistant zebrafish T-ALL. Clinical trials are currently underway using the combination of mTORC1 inhibitor temsirolimus and dasatinib in other pediatric cancer indications, leading us to prioritize this therapy for preclinical testing. This combination effectively curbed T-ALL growth in human cell lines and primary human T-ALL and was well tolerated and effective in suppressing leukemia growth in patient-derived xenografts (PDX) grown in mice. Mechanistically, dasatinib inhibited phosphorylation and activation of the lymphocyte-specific protein tyrosine kinase (LCK) to blunt the T-cell receptor (TCR) signaling pathway, and when complexed with mTORC1 inhibition, induced potent T-ALL cell killing through reducing MCL-1 protein expression. In total, our work uncovered unexpected roles for the LCK kinase and its regulation of downstream TCR signaling in suppressing apoptosis and driving continued leukemia growth. Analysis of a wide array of primary human T-ALLs and PDXs grown in mice suggest that combination of temsirolimus and dasatinib treatment will be efficacious for a large fraction of human T-ALLs.

Ex vivo organotypic cultures for synergistic therapy prioritization identify patient-specific responses to combined MEK and Src inhibition in colorectal cancer.

Translating preclinical studies to effective treatment protocols and identifying specific therapeutic responses in individuals with cancer is challenging. This may arise due to the complex genetic makeup of tumor cells and the impact of their multifaceted tumor microenvironment on drug response. To find new clinically relevant drug combinations for colorectal cancer (CRC), we prioritized the top five synergistic combinations from a large in vitro screen for ex vivo testing on 29 freshly resected human CRC tumors and found that only the combination of mitogen-activated protein kinase kinase (MEK) and proto-oncogene tyrosine-protein kinase Src (Src) inhibition was effective when tested ex vivo. Pretreatment phosphorylated Src (pSrc) was identified as a predictive biomarker for MEK and Src inhibition only in the absence of KRASG12 mutations. Overall, we demonstrate the potential of using ex vivo platforms to identify drug combinations and discover MEK and Src dual inhibition as an effective drug combination in a predefined subset of individuals with CRC.

Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells.

In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.

SYNTHETIC BIOLOGY MEETS PRECISION MEDICINE: DRUG REPURPOSING FOR BLOOD CANCER PRECISION MEDICINE

Optimizing Drug discovery and Translation is one of the key tracks in Global Challenges Annual meeting 2019 and is the critical factor in achieving UN Sustainable Development Goals 3 Good Health and Well Being. WHO reports Cancer is the second leading cause of death globally. The aim of the proposal is to establish robust drug screening platform which can identify drugs and drug combinations that are effective in precision medicine for relapsed individual South African Leukemia patients. Here, cancer cell will be either directly analyzed using high-throughput drug screening or single cell drug screening will be performed using flow cytometry /microfluidics to provide datasets on drug sensitivity for individual patientsI. WP1: Establishing the culture setting for CLL/MM and high-throughput drug screening on CLL/MM.II. WP2: “Signaling pathway-only” limited drug screening for CLL/MMIII. WP3: Establishing setting for Precision Microfluidics-driven single cell drug screening; Expected Results are using full-library drug screening results, we will identify effective drugs and drug combinations that inhibit cancer cell proliferation either through cytostatic or cytotoxic effects. These results will provide the basis for identifying effective drug combinations using our predictive analytics, which will be packaged as preclinical information for a precision clinical trial. Thus, we would establish cutting-edge platform that can handle blood cancer and also solid tumor. Using this pipeline, we aim to identify drug combinations that can overcome relapse stage and ultimately provide tailored-specific therapy options for Cancer patients. Our intention is to develop technologies that provide clinically relevant drug combinations information to oncologists within a timeframe of 7 days. The development and validation of the screening pipeline will incorporate the first CSIR platforms for cancer translational research with respect to identifying effective cancer drugs.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

BackgroundMultiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.MethodsHere, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.ResultsAnalysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.ConclusionsOur approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Abstract 1053: A large cellular screen charting the landscape of synergistic drug combinations in lung cancer

Abstract Introduction Non-small-cell lung cancer (NSCLC) is among the most common cancers, accounting for 85% of all lung cancer cases. Drug combinations are known to more effective than single drugs and can delay drug resistance. NSCLC has a poor prognosis and there are no approved targeted cancer drug combinations. Here we have conducted the first large-scale screen of drug combinations in NSCLC cell lines to identify new candidate synergistic combinations and map the landscape of synthetic lethal and synergistic pathways underlying them. Results We conducted the largest of its kind in vitro drug combination screening of 81 NSCLC cell-lines for 21 x 242 cancer drug combinations at 5 different doses. Our collection of approved and investigational drugs included mainly targeted drugs as well as some chemotherapy drugs. For each drug combination, we computed the synergy score using the Bliss model. Our key findings are as follows: 1. We identify drug combinations (like CHEK and WEE1 inhibitors) that are highly synergistic in many NSCLC cell lines. 2. Combinations of targeted therapies are overall more synergistic than chemotherapeutic drug combinations. 3. Synergistic combinations occur more within pathways. 4. We see that most drug combinations do not make cell lines more sensitive than the most sensitive cell line to the corresponding single agents across all the cell lines tested. 5. We identify a new class of drug combinations - called super-sensitizers - whose combined effects are larger than the effects observed for the individual drugs composing across all of the cohort cell-lines. 6. Combinations of multi-target drugs tend to be more synergistic than single-target drugs. 7. We identify and characterize a set of context-specific drug combinations that are differentially synergistic or differentially effective (i.e. viability after treatment) between wild-type vs mutant cell lines of various cancer drivers (KRAS, TP53, STK11, PIK3CA, EGFR). 8. Studying potential mechanistic underpinnings of drug synergism in NSCLC, we find that: (a) synergistic combinations target genes that have more proximal physical interactions, (b) some synergistic combinations target synthetic lethal combinations, (c) a subset of synergistic combinations have further support by NSCLC patients survival analysis. Conclusions We performed the first large in vitro drug combination screen in NSCLC. We identify top drug combinations that are highly synergistic and effective in many cell lines and characterize key biological properties and mechanisms underlying them. The screen results are provided in a user-friendly public data resource that can help facilitate the development of targeted drug combinations in lung cancer therapy. Citation Format: Nishanth Ulhas Nair, Adam Friedman, Patricia Greninger, Avinash D. Sahu, Ellen Murchie, Giovanna Stein Crowther, Joe McClanaghan, Joo Sang Lee, Daniel A. Haber, Jeffrey Engelman, Eytan Ruppin, Cyril Benes. A large cellular screen charting the landscape of synergistic drug combinations in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1053.

Dynamic BH3 Profiling Reveals Novel Therapeutic Strategies for the Treatment of Double-Hit Lymphoma

Introduction: Double-hit lymphomas (DHL), a subset of diffuse large B-cell lymphoma (DLBCL) harboring both a MYC and BCL-2 or BCL-6 gene rearrangement, are associated with rapidly progressive disease that is often refractory to aggressive therapy. Given these poor outcomes, novel therapeutic strategies and drug combinations are desperately needed.BCL-2 family proteins, consisting of both pro- and anti-apoptotic members, are the key mediators of the intrinsic pathway of apoptosis. BH3 profiling is a functional assay that uses synthetic peptides that mimic the BH3 domains of pro-apoptotic BCL-2 family members to measure mitochondrial “priming”, or how close a cell is to its apoptotic threshold. This tool is also extremely powerful in that it can identify which anti-apoptotic proteins (i.e. BCL-2, BCL-XL, MCL-1) the cell relies on for survival. Dynamic BH3 profiling (DPB) further demonstrates whether ex vivo drug treatment enhances priming, which is an effective predictor of in vivo response, or anti-apoptotic dependencies, which can identify synergistic partners to use with BH3 mimetics such as venetoclax.Methods: We utilized five DHL (Dogkit, DHL-4, DHL-6, OCI-Ly8, OCI-Ly18) and three non-DHL (OCI-Ly7, Toledo, and Pfeiffer) cell lines as well as two DHL patient-derived xenograft (PDX) models (available at http://www.PRoXe.org). We performed BH3 profiling in order to functionally measure overall priming as well as dependence on specific anti-apoptotic proteins. DBP was performed by treating cells with various concentrations of drugs for short intervals (16-24 hours) and subsequently performing BH3 profiling. We screened nine drugs, including conventional chemotherapy and targeted agents with suspected activity in lymphoma. Delta priming represents the change in cyctochrome c release following exposure to BH3 peptides in treated cells as compared to untreated cells. We assessed cell viability using annexin/PI staining and measured protein expression levels with immunoblotting.Results: Baseline BH3 profiling of DHL cell lines and cell lines derived from PDX models revealed that cells rely on multiple simultaneous anti-apoptotic proteins for survival. There was a universal dependency on BCL-2, as well as in some cases, MCL-1 and BCL-XL.Conventional chemotherapy, including doxorubicin, vincristine, mafosfamide and etoposide, had a variable effect on priming and BCL-2 dependency in DHL cell lines. Doxorubicin for example, significantly enhanced priming and BCL-2 dependency in the cell line Dogkit, but not in DHL-4 or DHL-6. In vivo treatment of two DHL PDX models with chemotherapy, including doxorubicin and cyclophosphamide, enhanced priming and dependency on the anti-apoptotic proteins, BCL-2, BCL-XL and MCL-1.DBP was also performed using a variety of targeted agents, including lenalidomide, ibrutinib, azacitidine, bortezomib and EPZ-6438 (an EZH2 inhibitor). Interestingly, treatment with the hypomethylating agent, azacitidine, consistently enhanced priming and BCL-2 dependency across DHL cell lines and cell lines derived from PDX mice. Azacitidine treatment resulted in enhanced cell death when combined with venetoclax in cell viability assays and was associated with increased protein expression of Bim and Bad. Treatment with bortezomib also enhanced priming, though non-specifically enhanced all anti-apoptotic dependencies.Drug treatment in non-DHL cell lines less reliably increased dependency on BCL-2 for survival, but also enhanced BCL-XL, MCL-1 and BFL-1 dependency, depending on the cell line.Conclusions: BH3 profiling can be used to identify targetable dependencies on BCL-2, BCL-XL and MCL-1 in DHL cells lines and cells derived from DHL PDX mice. DBP additionally provides a novel approach to rationally identify drug combinations with venetoclax and other emerging inhibitors of apoptotic proteins. This strategy can identify which chemotherapy would be most efficacious for a specific cell type, especially in combination with BH3 mimetics. Furthermore, we have identified azacitidine as a promising combination therapy to use with venetoclax for the treatment of DHL. Further exploration of the role of azacitidine in combination with venetoclax for the treatment of DHL is warranted. [Display omitted] DisclosuresDavids:Celgene: Consultancy; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Research Funding; Astra-Zeneca: Consultancy; Merck: Consultancy; InCyte: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees. Letai:AbbVie, AstraZeneca, Novartis: Consultancy, Research Funding.

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case-time-control design.

Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication-induced delirium in older adults is well understood, limited population-level evidence is available, particularly on combinations of medications associated with delirium in older adults. We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case-time-control design. We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005-2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti-inflammatory medications, were considered drugs of interest. The first-time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case-time-control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l-day with a 35-day washout period) that are statistically associated with delirium, and the association was tested by computing a time-trend adjusted matched odds-ratio (MOR) and its 95% confidence interval (CI). We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case-time-control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR=6.17; 95%CI=[2.05-18.54]), haloperidol (MOR=4.81; 95%CI=[3.16-6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR=3.94; 95%CI=[3.03-5.10]), and fentanyl exposure (MOR=3.46; 95%CI [2.05-9.21]). The association rule method applied to a case-time-control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.

Selective drug combination vulnerabilities in STAT3- and TP53-mutant malignant NK cells

Mature natural killer (NK) cell neoplasms are rare but very aggressive types of cancers. With currently available treatments, they have a very poor prognosis and, as such, are an example of group of cancers in which the development of effective precision therapies is needed. Using both short- and long-term drug sensitivity testing, we explored novel ways to target NK-cell neoplasms by combining the clinically approved JAK inhibitor ruxolitinib with other targeted agents. We profiled 7 malignant NK-cell lines in drug sensitivity screens and identified that these exhibit differential drug sensitivities based on their genetic background. In short-term assays, various classes of drugs combined with ruxolitinib seemed highly potent. Strikingly, resistance to most of these combinations emerged rapidly when explored in long-term assays. However, 4 combinations were identified that selectively eradicated the cancer cells and did not allow for development of resistance: ruxolitinib combined with the mouse double-minute 2 homolog (MDM2) inhibitor idasanutlin in STAT3-mutant, TP53 wild-type cell lines; ruxolitinib combined with the farnesyltransferase inhibitor tipifarnib in TP53-mutant cell lines; and ruxolitinib combined with either the glucocorticoid dexamethasone or the myeloid cell leukemia-1 (MCL-1) inhibitor S63845 but both without a clear link to underlying genetic features. In conclusion, using a new drug sensitivity screening approach, we identified drug combinations that selectively target mature NK-cell neoplasms and do not allow for development of resistance, some of which can be applied in a genetically stratified manner.

Combination therapies induce cancer cell death through the integrated stress response and disturbed pyrimidine metabolism

By accentuating drug efficacy and impeding resistance mechanisms, combinatorial, multi‐agent therapies have emerged as key approaches in the treatment of complex diseases, most notably cancer. Using high‐throughput drug screens, we uncovered distinct metabolic vulnerabilities and thereby identified drug combinations synergistically causing a starvation‐like lethal catabolic response in tumor cells from different cancer entities. Domperidone, a dopamine receptor antagonist, as well as several tricyclic antidepressants (TCAs), including imipramine, induced cancer cell death in combination with the mitochondrial uncoupler niclosamide ethanolamine (NEN) through activation of the integrated stress response pathway and the catabolic CLEAR network. Using transcriptome and metabolome analyses, we characterized a combinatorial response, mainly driven by the transcription factors CHOP and TFE3, which resulted in cell death through enhanced pyrimidine catabolism as well as reduced pyrimidine synthesis. Remarkably, the drug combinations sensitized human organoid cultures to the standard‐of‐care chemotherapy paclitaxel. Thus, our combinatorial approach could be clinically implemented into established treatment regimen, which would be further facilitated by the advantages of drug repurposing.

Acquired FGFR and FGF Alterations Confer Resistance to Estrogen Receptor (ER) Targeted Therapy in ER+ Metastatic Breast Cancer.

To identify clinically relevant mechanisms of resistance to ER-directed therapies in ER+ breast cancer. We conducted a genome-scale functional screen spanning 10,135 genes to investigate genes whose overexpression confer resistance to selective estrogen receptor degraders. In parallel, we performed whole-exome sequencing in paired pretreatment and postresistance biopsies from 60 patients with ER+ metastatic breast cancer who had developed resistance to ER-targeted therapy. Furthermore, we performed experiments to validate resistance genes/pathways and to identify drug combinations to overcome resistance. Pathway analysis of candidate resistance genes demonstrated that the FGFR, ERBB, insulin receptor, and MAPK pathways represented key modalities of resistance. The FGFR pathway was altered via FGFR1, FGFR2, or FGF3 amplifications or FGFR2 mutations in 24 (40%) of the postresistance biopsies. In 12 of the 24 postresistance tumors exhibiting FGFR/FGF alterations, these alterations were acquired or enriched under the selective pressure of ER-directed therapy. In vitro experiments in ER+ breast cancer cells confirmed that FGFR/FGF alterations led to fulvestrant resistance as well as cross-resistance to the CDK4/6 inhibitor palbociclib. RNA sequencing of resistant cell lines demonstrated that FGFR/FGF induced resistance through ER reprogramming and activation of the MAPK pathway. The resistance phenotypes were reversed by FGFR inhibitors, a MEK inhibitor, and/or a SHP2 inhibitor. Our results suggest that FGFR pathway is a distinct mechanism of acquired resistance to ER-directed therapy that can be overcome by FGFR and/or MAPK pathway inhibitors.

Synergistic tumor inhibition of colon cancer cells by nitazoxanide and obeticholic acid, a farnesoid X receptor ligand

The tumor-suppressive role of Farnesoid X receptor (FXR) in colorectal tumorigenesis supports restoring FXR expression as a novel therapeutic strategy. However, the complicated signaling network and tumor heterogeneity hinder the effectiveness of FXR agonists in the clinical setting. These difficulties highlight the importance of identifying drug combinations with potency and specificity to enhance the antitumor effects of FXR agonists. In this study, we found that the β-catenin level affected the antitumor effects of the FXR agonist OCA on colon cancer cells. Mechanistic studies identified a novel FXR/β-catenin complex in colon cancer cells. Furthermore, the depletion of β-catenin expedited FXR nuclear localization and enhanced its occupancy of the SHP promoter and thereby sensitized colon cancer cells to OCA. Furthermore, we utilized a drug combination study and identified that the antiparasitic drug nitazoxanide (NTZ) abrogated β-catenin expression and acted synergistically with OCA in colon cancer cells. The combination of OCA plus NTZ exerts synergistic tumor inhibition in CRC both in vitro and in vivo by cooperatively upregulating SHP expression. In conclusion, our study offers useful evidence for the clinical use of FXR agonists combined with β-catenin inhibitors in combating CRC.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis.

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.