Identification Of Synergistic Drug Combinations Research Articles

Abstract 4563: Ferroptotic mechanism of drug induced toxicity of PARPi and AurKi in prostate cancer

Abstract BACKGROUND: Cancer is typically treated with a combination of drugs rather than monotherapy stratagems, but identification of synergistic drug combinations for the treatment of cancers is challenging. Here we present the combination of PARP and Aurora Kinase inhibitors synergize through induction of the ferroptosis pathway of cell death. METHODS: We identified these drugs through an unbiased screen of enriched drug class response correlations. We then validated a synergistic cell killing effect of multiple drugs of each class by in vitro drug screening. Next, we performed reversal experiments using a ferroptosis inhibitor and lipid peroxide staining to confirm ferroptosis is a relevant cell death pathway. We performed Western immunoblot analysis in cells overexpressing putative Aurora Kinase phospho-substrates in the relevant pathway of interest, and performed site directed mutagenesis experiments to show the relevant amino acid altered by kinase activity. We also performed a series of qPCR and co-IP experiments to show PARPi effect on the antioxidant signaling pathway. RESULTS: We validated and identified synergy in a prostate cancer specific context. Next, we found Talazoparib and Alisertib induced ferroptosis after synergy was reduced with a ferroptosis inhibitor and lipid peroxides increased accumulation. We found a novel mechanism action for Alisertib, wherein Aurora Kinase inhibitors can alter the phospho-status and activity of the iron reduction enzyme Steap3.We also found that PARP inhibitors can affect the Nrf2-driven antioxidant signaling pathway on a transcriptional level. CONCLUSION: Aurora Kinase inhibitors and PARP inhibitors for the treatment of prostate cancer was identified and was validated as synergistic, which revealed new functional roles of both drugs in the context of cell killing. These findings represent a new clinical combination of drugs in prostate cancer and reveal a novel role for this drug class in ferroptosis. Citation Format: August John, Kuntian Luo, Zhenkun Lou, Liewei Wang. Ferroptotic mechanism of drug induced toxicity of PARPi and AurKi in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4563.

Abstract B079: Identification of synergistic drug combinations including MEK inhibitor and agents modulating the tumor microenvironment in an electroporation-induced pancreatic cancer model

Abstract Pancreatic ductal adenocarcinoma (PDAC) is refractory to cytostatic and immuno-oncology therapeutic strategies, leaving surgical resection of the tumor as the only treatment that can significantly prolong survival. We are therefore exploring optimized treatment strategies targeting cancer cells as well as infiltrating immune cells and the tumor microenvironment (TME) in an autochthonous mouse model for PDAC. Single focal tumors are induced through in vivo electroporation of gene constructs into the pancreas which encode mutated Kras and facilitate knock out the tumor suppressor genes P53, Cdkn2a and/or Smad4 via CRISPR/Cas9. The resulting tumors recapitulate the human disease with respect to genetics, histology and TME composition. Furthermore, this model allows the generation of tumors reflecting different PDAC subtypes, as well as the resection of primary tumors in combination with the (neo-)adjuvant regimens. Like the human disease, these tumors are refractory to chemotherapy and immune checkpoint blockade, whereas targeted therapy by means of MEK inhibitors merely leads to temporary delay of tumor outgrowth. We therefore tested the combination of MEK inhibitor treatment with different, potentially complementary modalities. We found the combination with agonist anti-CD40 antibodies to be highly effective. Prominent regressions are observed, and in combination with primary tumor resection long-term complete responses can be achieved even in a metastatic variant of the model. Apart from the direct cytostatic impact of MEKi on the Kras-transformed tumor cells, the mechanism of action of this synergistic drug combination involves enhancement of the anti-tumor T-cell response, suppression of CD4+ Tregs, and reduction of MDSCs and M2-type macrophages in favor of M1-type pro-inflammatory macrophages. A second highly effective regimen as identified in our experiments involves the combination of MEK inhibitors with the multi-kinase inhibitor regorafenib. In contrast to the aforementioned regimen, the complementary action of these drugs involves modulation of the TME by regorafenib. While the clinical translation of the MEKi/CD40 Ab regimen is hampered by toxicity issues encountered with agonist anti-CD40 Ab, the synergy as observed in the MEKi/regorafenib regiment provides a tangible path towards clinical testing. Citation Format: Aline S. Konrad, Tanja Hägele, Stefan Zens, Caroline Vent, Moritz Hamberger, Sarah Baiker, Aron Maes, Carl-Stephan Leonhardt, Oliver Strobel, Oliver Politz, Dieter Zopf, Daniel Baumann, Julian Mochayedi, Rienk Offringa. Identification of synergistic drug combinations including MEK inhibitor and agents modulating the tumor microenvironment in an electroporation-induced pancreatic cancer model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B079.

Identification of Synergistic Drug Combinations to Target KRAS-Driven Chemoradioresistant Cancers Utilizing Tumoroid Models of Colorectal Adenocarcinoma and Recurrent Glioblastoma.

Treatment resistance is observed in all advanced cancers. Colorectal cancer (CRC) presenting as colorectal adenocarcinoma (COAD) is the second leading cause of cancer deaths worldwide. Multimodality treatment includes surgery, chemotherapy, and targeted therapies with selective utilization of immunotherapy and radiation therapy. Despite the early success of anti-epidermal growth factor receptor (anti-EGFR) therapy, treatment resistance is common and often driven by mutations in APC, KRAS, RAF, and PI3K/mTOR and positive feedback between activated KRAS and WNT effectors. Challenges in the direct targeting of WNT regulators and KRAS have caused alternative actionable targets to gain recent attention. Utilizing an unbiased drug screen, we identified combinatorial targeting of DDR1/BCR-ABL signaling axis with small-molecule inhibitors of EGFR-ERBB2 to be potentially cytotoxic against multicellular spheroids obtained from WNT-activated and KRAS-mutant COAD lines (HCT116, DLD1, and SW480) independent of their KRAS mutation type. Based on the data-driven approach using available patient datasets (The Cancer Genome Atlas (TCGA)), we constructed transcriptomic correlations between gene DDR1, with an expression of genes for EGFR, ERBB2-4, mitogen-activated protein kinase (MAPK) pathway intermediates, BCR, and ABL and genes for cancer stem cell reactivation, cell polarity, and adhesion; we identified a positive association of DDR1 with EGFR, ERBB2, BRAF, SOX9, and VANGL2 in Pan-Cancer. The evaluation of the pathway network using the STRING database and Pathway Commons database revealed DDR1 protein to relay its signaling via adaptor proteins (SHC1, GRB2, and SOS1) and BCR axis to contribute to the KRAS-PI3K-AKT signaling cascade, which was confirmed by Western blotting. We further confirmed the cytotoxic potential of our lead combination involving EGFR/ERBB2 inhibitor (lapatinib) with DDR1/BCR-ABL inhibitor (nilotinib) in radioresistant spheroids of HCT116 (COAD) and, in an additional devastating primary cancer model, glioblastoma (GBM). GBMs overexpress DDR1 and share some common genomic features with COAD like EGFR amplification and WNT activation. Moreover, genetic alterations in genes like NF1 make GBMs have an intrinsically high KRAS activity. We show the combination of nilotinib plus lapatinib to exhibit more potent cytotoxic efficacy than either of the drugs administered alone in tumoroids of patient-derived recurrent GBMs. Collectively, our findings suggest that combinatorial targeting of DDR1/BCR-ABL with EGFR-ERBB2 signaling may offer a therapeutic strategy against stem-like KRAS-driven chemoradioresistant tumors of COAD and GBM, widening the window for its applications in mainstream cancer therapeutics.

The Combined Treatment with the FLT3-Inhibitor AC220 and the Complex I Inhibitor Iacs-010759 Synergistically Depletes Wt- and FLT3-Mutated Acute Myeloid Leukemia Cells

Acute myeloid leukemia (AML) cells are highly dependent on mitochondrial function for survival 1. We have recently reported a novel oxidative phosphorylation (OXPHOS) inhibitor IACS-010759 that potently inhibits mitochondrial complex I, suppresses OXPHOS and selectively inhibits the growth of AML cells in vitro and in vivo2. In this study, we aimed to identify chemotherapeutic agents that synergistically deplete AML cells when administered in combination with IACS-010759. We performed a high-throughput screening of a drug library (289 anti-cancer compounds) administered either individually or in combination with IACS-010759 on two leukemia cell lines (OCI-AML3, MOLM-13) and three bone marrow stromal cell lines (HS-5, HS-27A, MSC) in both hypoxia (1% O2) and normoxia conditions. Based on the cell viability datasets, we selected top candidates for combinations based on the following criteria: either bliss index > 0.1 (synergy of the combination treatment; red in Fig. 1A&B), or high cytotoxicity to leukemia cells (relative cell viability < 0.5, blue in Fig. 1A&B), as well as low toxicity against normal cells (relative cell viability in normal cells > 0.8, yellow in Fig. 1A&B). Twenty-four compounds satisfied the selection criteria above, either in normoxia or hypoxia, or both. Out of the 24 compounds, 5 agents (Fig. 1C) are known FLT3 (FMS-like tyrosine kinase 3) inhibitors, including AC220 (quizartinib), dovitinib, nintedanib, SGI-1776, and rebastinib, pointing to a molecular target of great potential interest in the design of synergistic drug combinations with IACS-010759. Thus, we investigated more in-depth the synergism between IACS-010759 (10nM) and 13 FLT3 inhibitors, all currently in clinical trials (AC220, sorafenib, gilteritinib, sunitinib, ponatinib, midostaurin, ibrutinib, TP-0903, crenolanib, tandutinib, FF-10101, lestaurtinib, and KW-2449; 0.0128:5x:5000nM), in AML cell lines (FLT3-wt KG-1, U937, OCI-AML2, OCI-AML3; and FLT3-mutant MOLM-13 and MOLM-14). Among the 13 FLT3 inhibitors, only AC220 combined with IACS-010759 showed concentration windows with bliss index higher than 0.1 across different lines. Next, we further characterized the synergism between AC220 and IACS-010759 in AML cell lines (U937 and OCI-AML3) under hypoxic conditions using metabolic flux analysis (MFA) to trace the incorporation of 13C5,15N2-glutamine and 1,2-13C2-glucose and study the metabolic modulation associated with the synergy. Leukemia cells were incubated with unlabeled/labeled medium for 24h and concurrently treated with 5nM IACS-010759 and/or 500nM AC220. While both individual agents modulate glutamine consumption and TCA cycle dynamics, by far the most dramatic metabolic effects on TCA cycle intermediates are observed following administration of the combined treatment. Severe drops in the levels of TCA cycle metabolites, (Fig. 1D) point to a reduced mitochondrial activity following the combined treatment, which is also validated by the increased ratio of oxidized/reduced forms of nicotinamide adenine dinucleotide (NAD/NADH). Interestingly, the total pool of the oncometabolite 2-hydroxyglutarate, while increasing following the individual treatments, significantly dropped to very low levels in response to the combined treatment. The significantly reduced metabolite levels as well as the glucose-derived enrichment fractions of glucose 6-phosphate, fructose bisphosphate, phosphoenolpyruvate and ribose 5-phosphate in the AC220-containing treatment groups (significantly more pronounced in the combined treatment) point to impaired glycolysis /pentose phosphate pathway (Fig. 1E). In turn this results in lower de novo nucleotide biosynthesis (based on the decreased glutamine and glucose incorporation). Similar results were observed in OCI-AML3 cells. Overall, the combinatorial treatment with IACS-010759 and AC220 impaired AML cell metabolism tremendously and to a much greater extent than any of the individual treatments alone. Influx inhibition of both the two main carbon sources, glucose and glutamine, was observed leading to impairment of the TCA cycle and glycolysis for energy production, as well as pentose phosphate pathway and de novo nucleotide biosynthesis. In conclusion, we identified a novel drug combination AC220 and IACS-010759 which synergistically inhibits AML cell growth regardless of FLT3 mutation at least by metabolism disruption. Disclosures Konopleva: Kisoji: Consultancy; Agios: Research Funding; Amgen: Consultancy; Cellectis: Research Funding; Eli Lilly: Research Funding; Rafael Pharmaceutical: Research Funding; Ablynx: Research Funding; Sanofi: Research Funding; Reata Pharmaceutical Inc.;: Patents & Royalties: patents and royalties with patent US 7,795,305 B2 on CDDO-compounds and combination therapies, licensed to Reata Pharmaceutical; AbbVie: Consultancy, Research Funding; AstraZeneca: Research Funding; Calithera: Research Funding; Forty-Seven: Consultancy, Research Funding; Ascentage: Research Funding; Stemline Therapeutics: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; F. Hoffmann La-Roche: Consultancy, Research Funding.

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening invitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

A high-throughput drug combination screen identifies an anti-glioma synergism between TH588 and PI3K inhibitors

BackgroundGlioblastoma multiforme (GBM) is the most common and lethal type of primary brain tumor. More than half of GBMs contain mutation(s) of PTEN/PI3K/AKT, making inhibitors targeting the PI3K pathway very attractive for clinical investigation. However, so far, PI3K/AKT/mTOR inhibitors have not achieved satisfactory therapeutic effects in clinical trials of GBM. In this study, we aimed to develop a high-throughput screening method for high-throughput identification of potential targeted agents that synergize with PI3K inhibitors in GBM.MethodsA Sensitivity Index (SI)-based drug combination screening method was established to evaluate the interactions between BKM120, a pan-PI3K inhibitor, and compounds from a library of 606 target-selective inhibitors. Proliferation, colony and 3D spheroid formation assays, western blotting, comet assay, γ-H2AX staining were used to evaluate the anti-glioma effects of the top-ranked candidates. The drug combination effects were analyzed by the Chou-Talalay method.ResultsSix compounds were successfully identified from the drug screen, including three previously reported compounds that cause synergistic antitumor effects with PI3K/mTOR inhibitors. TH588, an putative MTH1 inhibitor exhibited significant synergy with BKM120 in suppressing the proliferation, colony formation and 3D spheroid formation of GBM cells. Further investigation revealed that both DNA damage and apoptosis were markedly enhanced upon combination treatment with TH588 and BKM120. Finally, activation of PI3K or overexpression of AKT compromised the anti-glioma efficacy of TH588.ConclusionsThe screening method developed in this study demonstrated its usefulness in the rapid identification of synergistic drug combinations of PI3K inhibitors and targeted agents.

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology.

Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

Single-cell genomic approaches for developing the next generation of immunotherapies.

Recent progress in single-cell genomics urges its application in drug development, particularly of cancer immunotherapies. Current immunotherapy pipelines are focused on functional outcome and simple cellular and molecular readouts. A thorough mechanistic understanding of the cells and pathways targeted by immunotherapy agents is lacking, which limits the success rate of clinical trials. A large leap forward can be made if the immunotherapy target cells and pathways are characterized at high resolution before and after treatment, in clinical cohorts and model systems. This will enable rapid development of effective immunotherapies and data-driven design of synergistic drug combinations. In this Perspective, we discuss how emerging single-cell genomic technologies can serve as an engine for target identification and drug development.

Identification of synergistic drug combinations using breast cancer patient-derived xenografts

Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) is associated with relatively poor outcomes due to its metastatic propensity, frequent failure to respond to chemotherapy, and lack of alternative, targeted treatment options, despite decades of major research efforts. Our studies sought to identify promising targeted therapeutic candidates for TNBC through in vitro screening of 1,363 drugs in patient-derived xenograft (PDX) models. Using this approach, we generated a dataset that can be used to assess and compare responses of various breast cancer PDXs to many different drugs. Through a series of further drug screening assays and two-drug combination testing, we identified that the combination of afatinib (epidermal growth factor receptor (EGFR) inhibitor) and YM155 (inhibitor of baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5; survivin) expression) is synergistically cytotoxic across multiple models of basal-like TNBC and reduces PDX mammary tumor growth in vivo. We found that YM155 reduces EGFR expression in TNBC cells, shedding light on its potential mechanism of synergism with afatinib. Both EGFR and BIRC5 are highly expressed in basal-like PDXs, cell lines, and patients, and high expression of both genes reduces metastasis-free survival, suggesting that co-targeting of these proteins holds promise for potential clinical success in TNBC.

Mechanistically detailed systems biology modeling of the HGF/Met pathway in hepatocellular carcinoma

Hepatocyte growth factor (HGF) signaling through its receptor Met has been implicated in hepatocellular carcinoma tumorigenesis and progression. Met interaction with integrins is shown to modulate the downstream signaling to Akt and ERK (extracellular-regulated kinase). In this study, we developed a mechanistically detailed systems biology model of HGF/Met signaling pathway that incorporated specific interactions with integrins to investigate the efficacy of integrin-binding peptide, AXT050, as monotherapy and in combination with other therapeutics targeting this pathway. Here we report that the modeled dynamics of the response to AXT050 revealed that receptor trafficking is sufficient to explain the effect of Met–integrin interactions on HGF signaling. Furthermore, the model predicted patient-specific synergy and antagonism of efficacy and potency for combination of AXT050 with sorafenib, cabozantinib, and rilotumumab. Overall, the model provides a valuable framework for studying the efficacy of drugs targeting receptor tyrosine kinase interaction with integrins, and identification of synergistic drug combinations for the patients.

Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells

Lung cancer is associated with high prevalence and mortality, and despite significant successes with targeted drugs in genomically defined subsets of lung cancer and immunotherapy, the majority of patients currently does not benefit from these therapies. Through a targeted drug screen, we found the recently approved multi-kinase inhibitor midostaurin to have potent activity in several lung cancer cells independent of its intended target, PKC, or a specific genomic marker. To determine the underlying mechanism of action we applied a layered functional proteomics approach and a new data integration method. Using chemical proteomics, we identified multiple midostaurin kinase targets in these cells. Network-based integration of these targets with quantitative tyrosine and global phosphoproteomics data using protein-protein interactions from the STRING database suggested multiple targets are relevant for the mode of action of midostaurin. Subsequent functional validation using RNA interference and selective small molecule probes showed that simultaneous inhibition of TBK1, PDPK1 and AURKA was required to elicit midostaurin's cellular effects. Immunoblot analysis of downstream signaling nodes showed that combined inhibition of these targets altered PI3K/AKT and cell cycle signaling pathways that in part converged on PLK1. Furthermore, rational combination of midostaurin with the potent PLK1 inhibitor BI2536 elicited strong synergy. Our results demonstrate that combination of complementary functional proteomics approaches and subsequent network-based data integration can reveal novel insight into the complex mode of action of multi-kinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. Finally, we illustrate how this knowledge can be used for the rational design of synergistic drug combinations with high potential for clinical translation.

High-throughput Identification of Synergistic Drug Combinations by the Overlap<sup>2</sup> Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap<sup>2</sup> Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method.

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

Polypharmacology-based ceritinib repurposing using integrated functional proteomics.

Targeted drugs are effective when they directly inhibit strong disease drivers, but only a small fraction of diseases feature defined actionable drivers. Alternatively, network-based approaches can uncover new therapeutic opportunities. Applying an integrated phenotypic screening, chemical and phosphoproteomics strategy, here we describe the anaplastic lymphoma kinase (ALK) inhibitor ceritinib as having activity across several ALK-negative lung cancer cell lines and identify new targets and network-wide signaling effects. Combining pharmacological inhibitors and RNA interference revealed a polypharmacology mechanism involving the noncanonical targets IGF1R, FAK1, RSK1 and RSK2. Mutating the downstream signaling hub YB1 protected cells from ceritinib. Consistent with YB1 signaling being known to cause taxol resistance, combination of ceritinib with paclitaxel displayed strong synergy, particularly in cells expressing high FAK autophosphorylation, which we show to be prevalent in lung cancer. Together, we present a systems chemical biology platform for elucidating multikinase inhibitor polypharmacology mechanisms, subsequent design of synergistic drug combinations, and identification of mechanistic biomarker candidates.

Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination.

The efficacy of pharmacological perturbation to the signaling transduction network depends on the network topology. However, whether and how signaling dynamics mediated by crosstalk contributes to the drug resistance are not fully understood and remain to be systematically explored. In this study, motivated by a realistic signaling network linked by crosstalk between EGF/EGFR/Ras/MEK/ERK pathway and HGF/HGFR/PI3K/AKT pathway, we develop kinetic models for several small networks with typical crosstalk modules to investigate the role of the architecture of crosstalk in inducing drug resistance. Our results demonstrate that crosstalk inhibition diminishes the response of signaling output to the external stimuli. Moreover, we show that signaling crosstalk affects the relative sensitivity of drugs, and some types of crosstalk modules that could yield resistance to the targeted drugs were identified. Furthermore, we quantitatively evaluate the relative efficacy and synergism of drug combinations. For the modules that are resistant to the targeted drug, we identify drug targets that can not only increase the relative drug efficacy but also act synergistically. In addition, we analyze the role of the strength of crosstalk in switching a module between drug-sensitive and drug-resistant. Our study provides mechanistic insights into the signaling crosstalk-mediated mechanisms of drug resistance and provides implications for the design of synergistic drug combinations to reduce drug resistance.

A streamlined search technology for identification of synergistic drug combinations.

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p < 0.001, CI < 0.3).

Abstract 1792: Identification of synergistic drug combinations with the oral HSP90 inhibitor Debio 0932 in non-small cell lung cancer and renal cell cancer

Abstract Background: Drug resistance is a major problem in cancer therapy, which can be addressed by simultaneously targeting multiple critical nodes of the signalling networks controlling growth and survival of cancer cells. One such approach is to target heat shock protein 90 (HSP90), a chaperone for many potent oncogenic proteins involved in proliferation, survival, invasion, metastasis and angiogenesis. Pharmacologic inhibition of HSP90 results in the proteasomal degradation of client oncoproteins thereby eliminating their oncogenic activity. The oral HSP90 inhibitor Debio 0932 displays favorable pharmacologic features. The goal of this study was to identify novel synergistic drug combinations for Debio 0932 in non-small cell lung cancer (NSCLC) and renal cell cancer (RCC) which would support the clinical development of Debio 0932 in those two indications. Material and Methods: For NSCLC, an in vitro high-throughput combination screen was performed using 6 human NSCLC cell lines bearing various genetic alterations, where Debio 0932 was combined pairwise with 128 commercially available oncology compounds in a cell viability assay. Some additional compounds not included in the panel were also tested independently. For RCC, combination of Debio 0932 with several selected standard-of-care drugs was analyzed on a panel of 8 human RCC cell lines. Synergy was assessed by using an AUC-based curve shift analysis method or according to the Chou-Talalay equation. A selection of synergistic drug combinations was further studied using tumor xenograft mouse models of human NSCLC and RCC. Results: In NSCLC, anti-proliferative synergism with Debio 0932 was observed in vitro in combination with the standard-of-care drugs docetaxel, paclitaxel or gemcitabine, as well as with mTOR inhibitors. In RCC cell lines, combinations of Debio 0932 with RCC standard-of-care drugs also displayed anti-proliferative synergy. The synergy observed in vitro was further confirmed in mouse xenografts of human NSCLC and RCC cell lines, where the drug combinations caused marked anti-tumor activity that was superior to either monotherapy. Conclusion: Several synergistic drug combinations were identified for the HSP90 inhibitor Debio 0932 in NSCLC and RCC. These findings underline the feasibility of using in vitro high-throughput screening for the discovery of novel drug combinations with increased in vivo anti-tumor efficacy . Furthermore, they provide a rationale for the combination of Debio 0932 with standard-of-care drugs in NSCLC and RCC and are the basis for ongoing clinical trials in several cancer types. Citation Format: Casey G. Langdon, Norbert Wiedemann, Hélène Maby-El Hajjami, Mathew A. Held, James T. Platt, Grégoire Vuagniaux, Marcus W. Bosenberg, David F. Stern, Frédéric Lévy. Identification of synergistic drug combinations with the oral HSP90 inhibitor Debio 0932 in non-small cell lung cancer and renal cell cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1792. doi:10.1158/1538-7445.AM2014-1792

Synergistic and Antagonistic Drug Combinations Depend on Network Topology

Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems.

Network target for screening synergistic drug combinations with application to traditional Chinese medicine.

BackgroundMulticomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.ResultsIn this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.ConclusionsNIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.