Highest Single Agent Research Articles

Novel Combination Therapy for Heart Failure: Trimebutine-Methoxsalen Identified through Synergistic Network Virtual Screening and Experimental Validation.

Combination therapy is increasingly favored by pharmaceutical companies and researchers as an effective way to quickly discover new drugs with excellent efficacy, especially in the treatment of complex diseases. Previously, we successfully developed a computational screening method to identify such combinations, although it fell short in elucidating their synergistic mechanisms. In this work, we have transitioned to a highest single agent (HSA) synergy model for network screening, which streamlines the discovery of promising combinations and facilitates the investigation of their synergistic effects. Through this refined approach, the trimebutine-methoxsalen combination emerged as a promising candidate for heart failure (HF) treatment, exhibiting significant in vitro cardioprotective effects and effectively mitigating isoproterenol (ISO)-induced structural remodeling in the mouse heart. Further mechanistic studies have demonstrated that trimebutine and methoxsalen could synergistically inhibit intracellular calcium overload in myocardial cells and reduce the production of ROS, thus exerting cardioprotective effects. Overall, this study introduces an advanced computational strategy that not only identifies a novel combination therapy against HF but also sheds light on its underlying synergistic mechanisms.

Abstract 7600: Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer related mortality in the United States, largely due to ineffective systemic therapy. While novel KRAS target strategies are in development, few agents have been investigated to target the transcriptional machinery for global inhibition of expression. Recent small molecule inhibitors targeting transcription (i.e., CDK7) have shown preliminary evidence of disease control; however, combination therapies have lagged, likely due to uncertainty in clinical toxicity. Text mining analysis suggests combination BET and CDK9 inhibition may be promising against PDAC. Here, we present preclinical evidence of synergistic activity for the combination of bromodomain and extraterminal (BET) inhibitor (ZEN3694) and CDK9 inhibitor (VIP152) using patient derived cancer organoids (PCOs). Methods: Dose response curves were generated for ZEN3694 and VIP152 to assess their single agent activity against PCOs using a low volume format. ZEN3694 was treated continuously while VIP152 was removed after 24h to mimic pharmacokinetics with assay endpoint at 144h. Synergy assays were designed using a combination dose titration grid. Viability was determined in all assays using 3D CellTiter Glo (CTG, 50% v/v). Synergy scores were produced using SynergyFinder 3.0 including Zero Interaction Potency (ZIP), Bliss Independence (Bliss), Highest Single Agent (HSA), and Loewe Additivity (Loewe). Western blot analysis was performed against global markers of active RNA polymerase II (phosphorylation at serine 2, 5, 7) and antiapoptotic protein, BCL xL, and MCL 1. Results: ZEN3694 was observed to have a single agent IC50 in the single uM range across 3 independent PDAC PCO lines (PDAC1 3.7uM, PDAC2 1.1uM, PDAC3 2.9uM). VIP152 showed significant potency in the same 3 PDAC PCO lines (PDAC1 140.2nM, PDAC2 30.5nM, PDAC3 43.4nM). The combination of ZEN3694 and VIP152 was found to have increased therapeutic activity with double digit synergy scores across all reference models: PDAC1 (Loewe 17.3, HSA 19.8, ZIP 20.2, and Bliss 17.2) and PDAC2 (Loewe 34.7, HSA 28.3, ZIP 19.7, and Bliss 19.6). Western blots of protein collected from organoids treated for 48h showed on target activity, including decrease in the phosphorylation of Rbp1 (Ser 2, Ser 5, and Ser 7) compared to both control and single agents and decreased expression in antiapoptotic proteins (MCL 1 and BCL xL). Conclusion: Here, we show the activity of dual transcription targeting with BET and CDK9 inhibition in patient derived pancreatic cancer organoid models. The combination of ZEN3694 and VIP152 has in vitro synergy in PDAC PCO models with on target activity. Ongoing work is validating this combination in animal models to assess toxicity, in vivo activity, and RNA expression profiling to understand transcriptional pathway dependency and therapeutic resistance. Citation Format: Lucas J. Koeppel, Austin Stram, Robert J. Millikin, Ellie Riedl, Md Shahadat Hossan, Ethan Samuel Lin, Ron Stewart, Jeremy D. Kratz. Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic 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 7600.

Abstract 3369: Dual inhibition of ATR and PI3K pathways promotes cell death in platinum-resistant endometrial cancer cells by increasing DNA damage

Abstract Background: Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality in the U.S. Among the four subtypes characterized by the Cancer Genome Atlas, the serous-like copy number high (CNH) subtype which occurs in 15-25% of EC is associated with poor prognosis and platinum resistance, requiring alternative treatment options. ~85% CNH EC cases have impaired G1/S checkpoint regulation, making cells rely on ATR-mediated G2/M cell cycle checkpoints for optimal DNA replication and repair. Additionally, over 80% of ECs exhibit PI3K pathway activation (e.g., PIK3CA mutations), linked to enhanced DNA repair and drug resistance. Therefore, we hypothesized that dual ATR and PI3Kα inhibition would enhance DNA damage, increasing cell death in EC. Methods: CNH EC cell lines included platinum-resistant (KLE, MFE280, HEC1A, ARK2) as well as platinum-sensitive (ARK1) lines used to evaluate the activity of ATR inhibitor (ATRi) camonsertib and PI3Kα inhibitor (PI3Kαi) inavolisib. Cell growth was assessed by 3-day XTT and 9-12-day colony-forming assays. The degree of combination synergy, additivity, or antagonism was calculated using SynergyFinder with a reference highest single agent (HSA) model. An HSA synergy score > 10 indicates synergy, -10 to 10 suggests additivity and < -10 represents antagonism. DNA damage endpoints were measured by alkaline comet assay and immunoblotting for γ-H2AX. Data were repeated in triplicate, analyzed using one-way ANOVA test, and shown as mean ± SD. P < 0.05 was statistically significant. Results: All tested EC cell lines showed sensitivity to clinically attainable doses of ATRi monotherapy (IC50 0.09 - 0.98 μM), but varying sensitivity to PI3Kαi monotherapy (IC50 0.03 - 67.01 μM) unrelated to their PIK3CA mutation status. Combination treatment using clinically attainable concentrations (camonsertib < 6 μM; inavolisib < 10 μM) yielded additivity in all cell lines independent of platinum sensitivity (HSA synergy scores -0.8-10.0). Notably, increased cytotoxic effects were observed in both PIK3CA wild-type (KLE) and PIK3CA-mutant (MFE280 and HEC1A) platinum-resistant lines by colony-forming assays (4.8- to 15.4-fold decrease in colony-forming ability relative to ATRi; 1.5- to 3.5-fold decrease relative to PI3Kαi). ATRi and PI3Kαi combination induced greater DNA damage, evidenced by elevated mean comet tail moment relative to ATRi (increased 2.5- to 6.7-fold; P < 0.01) or PI3Kαi (increased 2.1- to 5.6-fold; P < 0.01) regardless of PIK3CA mutation status. Increased γ-H2AX levels were found in HEC1A cells with combination treatment (3.2-fold increase relative to ATRi; 16-fold increase relative to PI3Kαi). Conclusion: Our results suggest that dual inhibition of ATR and PI3K pathways induces greater cell death by increasing DNA damage in platinum-resistant EC cells independent of PIK3CA mutation status. Citation Format: Kristen R. Ibanez, Tzu-Ting Huang, Sotirios Sotiriou, Yvonne G. Lin, Jung-Min Lee. Dual inhibition of ATR and PI3K pathways promotes cell death in platinum-resistant endometrial cancer cells by increasing DNA damage [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 3369.

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening.

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Abstract A164: Activity of dual BET and CDK9 inhibition in Pancreatic Ductal Adenocarcinoma informed from KinderMiner Prediction

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer mortality in the United States (US) with a 10% 5-year survival. Acquired resistance plays a critical role in progression, however targeted therapeutics have shown minimal clinical activity. There remains an unmet need to provide combination targeted therapies across the breadth of clinical development. KinderMiner is a highly efficient text mining algorithm developed to predict association with search terms across publicly available research. BMS986158 is a novel bromodomain and extraterminal (BET) inhibitor in early phase clinical trials, recently reported to have preclinical activity in combination with chemotherapy. Methods: Potential genes for targeting in combination with BMS986158 were queried using KinderMiner (KM) and Serial KinderMiner (SKiM). Search terms included synonymous terms of “drug resistance” and “BET”. BMS986158 was assayed for single agent activity at 72h and 144h. Lead agents in active clinical trials were screened for synergistic activity. All assays were performed with patient derived organoids (PCOs) in a low volume format with high content imaging, using dose titration in combination grid to assay synergy. Viability was assessed using 3D CellTiterGlo (CTG, Promega Inc) (50% v/v) with synergy scores determined by SynergyFinder 3.0 for Bliss Independence (Bliss), Highest Single Agent (HSA), and Loewe Additivity (Loewe). Results: A KM/SKiM query was performed against >30M primary literature reports resulting in 39 unique genes with significant relationship with BET. CDK9 was selected as a lead gene target based on CDK9 inhibitor, AZD4573, in active clinical trials with highly significant prediction from text mining screen (p<0.001). BMS986158 was found to have nanomolar single agent IC50 activity across multiple primary PDAC PCOs (n=5) that improved from 72h [43-411nM] in comparison to 144h [12-54nM]. AZD4573 was found to have single agent IC50 ranging from 18-246nM across 3 primary cultures. Therapeutically relevant dosing of 6.25nM BMS986158 and 12.5nM AZD4573 in combination was found to induce significant response including normalized change in diameter relative to control, single agent BMS986158, and AZD4573 (effect sizes=1.7, 1.4, 0.7) The combination of BMS986158 and AZD4573 was also found to have augmented activity with positive synergy scores across all reference models: Bliss=3.47, HSA=8.81, and Loewe=15.92. Conclusion: Here, we adapt a novel text-mining algorithm, KinderMiner, to screen agents for combination with BMS986158 to inform combination therapeutics. The combination of BMS986158 and AZD4573 was shown to have improved activity in a model of PDAC PCOs. Validation of the combination efficacy across different PDAC PCOs is ongoing to compare this activity across a diversity of mutational profiles. Future directions will assay the mechanistic modeling to characterize the interaction between transcriptional regulation and the induction of apoptosis. Citation Format: Austin Stram, Luke Koeppel, Rob Millikin, Eleanor Riedl, Md Shahadat Hossan, Ethan S Lin, Ron Stewart, Jeremy D Kratz. Activity of dual BET and CDK9 inhibition in Pancreatic Ductal Adenocarcinoma informed from KinderMiner Prediction [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A164.

The Transcription Factors NFATc1 and NFATc2 Control Glucocorticoid Resistance in Pediatric T-Cell Acute Lymphoblastic Leukemia

Background: The overall survival of pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) is significantly improved (˜75-80%) with the current therapeutic approaches. Nevertheless, for those patients that do not respond to conventional treatment and experience relapse, the prognosis is extremely poor. In this context, it is well defined that resistance to glucocorticoids (GCs), pillar drugs in the treatment protocol, can predispose pediatric T-ALL patients to a poor outcome. A pivotal role of LCK kinase has been elucidated in supporting GC resistance in T-ALL cells in our previous study and recently confirmed by other colleagues. However, to date, the biological processes modulated by LCK in this context are not yet defined. Aim: the identification of new mechanisms underlying GC resistance can lead to alternative therapeutic approaches to prevent or overcome GC resistance and ameliorate the outcome of this subgroup of patients. Methods and Results: we uncovered the involvement of NFATc1 and NFATc2 transcription factors, that act downstream LCK kinase, in guiding GC resistance in T-ALL cells. Of note, a high NFATc1 and NFATc2 transcriptional activity characterizes pediatric GC resistant T-ALL patients at diagnosis, that in turns show a low Glucocorticoid Receptor (GR) activity. In agreement, NFATc1 or NFATc2 specific gene silencing in 3 T-ALL GC resistant cell line models and primary cells increases dexamethasone response, by restoring GR canonical transcriptional activity through the increaseexpression of BIM GR targetgene (p value <0.05). Conversely, NFATc1 or NFATc2 overexpression in a murine T-ALL GC sensitive cell line confers resistance to dexamethasone treatment. Interestingly, by Gene Expression Profile (GEP) and Nuclear Magnetic Resonance (NMR) analysis, we observed that NFATc1 gene silencing in GC resistant cells significantly downregulates intracellular cholesterol abundance. Additionally, by Chromatin Immunoprecipitation (ChIP) we revealed that NFATc1 can directly control the transcription of HMGCS1, EBP and DHCR7, key enzymes of cholesterol biosynthesis process (n≥3, p value <0.05 for all the three genes). In agreement, exogenous cholesterol addition to NFATc1 knock-down cell lines rebuild dexamethasone resistance. Conversely, by GEP and flow cytometry analysis we observed that NFATc2 gene silencing in T-ALL GC resistant cells leads to a downregulation of the Wnt/β-catenin signaling pathway and to an increased T-cell differentiation. Furthermore, by ChIP analysis we revealed that in T-ALL GC resistant cells NFATc2 can directly affect the transcription of LRP6, a key Wnt/β-catenin signaling component (n=3, p value <0.05). In agreement, the Wnt/β-catenin signaling activation, by Wnt3a stimulation, restores dexamethasone resistance in NFATc2 knock-down T-ALL cells. Finally, we observed that the inhibition of cholesterol biosynthesis by simvastatin or of Wnt/β-catenin by PRI-724 increases GC sensitivity in T-ALL GC resistant cells by the High-Throughput drug synergism Screening (HTS) and the Highest Single Agent (HSA) approach. Conclusions: Overall, we revealed for the first time the involvement of NFATc1 and NFATc2 transcription factors in supporting GC resistance in T-ALL cells by the modulation of cholesterol biosynthesis and Wnt/β-catenin signaling, both processes well-described in sustaining chemotherapy resistance, paving the rationale to alternative therapeutic options for T-ALL GC resistant pediatric patients.

Additive Effects of Helix aspersa Mucus Fractions with Chemotherapy Drugs in Breast Cancer: Exploring Anti-proliferative Potential

This study investigated the anti-proliferative effects of biologically active substances (BAS) derived from the mucus of the garden snail Helix aspersa on breast cancer cell lines (MCF-7 and MDA-MB-231) and a non-cancerous cell line (MCF-10A). Two mucus fractions with different molecular weights (Mw) were examined, and their interactions with the chemotherapy drugs cisplatin and tamoxifen were evaluated.
 The results showed that the mucus fractions exhibited a slight anti-proliferative effect on breast cancer cells, with the fraction having Mw above 50 kDa demonstrating stronger activity compared to the fraction with Mw above 20 kDa. Furthermore, the combination of the mucus fractions with cisplatin or tamoxifen resulted in significant reductions in the half effective dose, indicating possible synergistic effects which were more pronounced with the fraction having Mw above 20 kDa. Dose-response curves and microscopic images supported the presence of such effects, revealing lower IC50 values and significant cell death when the cancer cells were treated with the combination of the mucus fractions and chemotherapy drugs. However, the analysis using the (highest single agent) HSA model indicated mostly additive interactions between the mucus fractions and chemotherapy drugs.
 These findings suggest the potential of biologically active substances derived from Helix aspersa mucus in enhancing the anti-proliferative effects of chemotherapy drugs in breast cancer treatment. The identification of additive interactions provides insights into the development of combination therapies with reduced drug dosage. Overall, this study contributes to the understanding of the anti-proliferative effects of Helix aspersa mucus fractions and their interactions with chemotherapy drugs, highlighting their potential as novel therapeutic approaches in breast cancer treatment.

Statistical Assessment of Drug Synergy from In Vivo Combination Studies using Mouse Tumor Models.

Drug combination therapy is a promising strategy for treating cancer, however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods.

Synergistic Activity of Remdesivir–Nirmatrelvir Combination on a SARS-CoV-2 In Vitro Model and a Case Report

Background: This study aims to investigate the activity of the remdesivir–nirmatrelvir combination against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and to report a case of Coronavirus Disease 2019 (COVID-19) cured with this combination. Methods: A Vero E6 cell-based infection assay was used to investigate the in vitro activity of the remdesivir–nirmatrelvir combination. The SARS-CoV-2 strains tested were 20A.EU1, BA.1 and BA.5. After incubation, a viability assay was performed. The supernatants were collected and used for viral titration. The Highest Single Agent (HSA) reference model was calculated. An HSA score >10 is considered synergic. Results: Remdesivir and nirmatrelvir showed synergistic activity at 48 and 72 h, with an HSA score of 52.8 and 28.6, respectively (p < 0.0001). These data were confirmed by performing supernatant titration and against the omicron variants: the combination reduced the viral titer better than the more active compound alone. An immunocompromised patient with prolonged and critical COVID-19 was successfully treated with remdesivir, nirmatrelvir/ritonavir, tixagevimab/cilgavimab and dexamethasone, with an excellent clinical–radiological response. However, she required further off-label prolonged therapy with nirmatrelvir/ritonavir until she tested negative. Conclusions: Remdesivir–nirmatrelvir combination has synergic activity in vitro. This combination may have a role in immunosuppressed patients with severe COVID-19 and prolonged viral shedding.

Abstract 5321: Large scale pan cancer drug combination screening to identify effective and actionable combinations and biomarker hypothesis

Abstract Drug combinations are highly desirable in oncology as they can improve therapeutic response and overcome drug resistance. However, responses can be context-specific, meaning there is a real need for large-scale studies to fully explore the drug combination response landscape. We present a screen of 109 drug combinations in over 750 diverse cell lines covering 41 hematological and solid tumor types. Each combination was screened in a full 7 × 7 matrix format, representing over 2.3 million individual data points, the largest such study to date. We took a novel approach to assess combination benefit. Growth inhibition and highest single agent (HSA) and Bliss excess combination heatmaps were generated for all >68,000 combination-cell line pairs, and each was scored for high activity (combination Emax>0.5) and combination benefit/synergy (HSA>0.1). Out of 4578 combination-cancer type pairs 489 met the filtering criteria. We prioritized combinations based on their efficacy and cancer type selectivity to minimize risk of potential tissue toxicity. We identified several known combination-cancer type pairs (e.g. MCL1 inhibitor AZD5991 plus BCL2 inhibitor venetoclax in AML) as well as novel indications for combinations already known to be active in a different cancer type (e.g. AZD5991 plus AKT inhibitor capivasertib in endometrial cancer or AURKB inhibitor AZD2811 plus venetoclax in DLBCL). We selected 4 combinations based on clinical need and validated them further in vitro and in vivo (selumetinib plus venetoclax or AZD5991, AZD2811 plus venetoclax, and capivasertib plus AZD5991). To understand how molecular context affects drug response, we also used GDSC tools ANOVA to perform over 5.4 million statistical tests to identify statistically significant associations between drug response metrics and multi-omics features including mutations, CNAs, gene expression and methylation. Associations were identified with five response metrics, covering both single agent and combination responses. We looked for significant associations across 21 different subgroups of cell lines and 6 molecular ‘baskets’ (TP53, KRAS, MLL2/KMT2D, PTEN, PIK3CA, and BRAF). We identified 11,611 biomarkers which met the significance criteria (p⇐0.001, FDR⇐10%, and both positive and negative Glass deltas >=1). Moreover, we used biomarkers of single-agent response to identify any combination Emax or Bliss associated biomarkers that could not be explained by the action of either drug. This resulted in identification of 1,631 ‘emergent’ biomarkers. In summary, our screen and pipeline have been designed to optimize preclinical interpretation with a focus on actionability, particularly by our unique approach, and to provide a valuable resource for exploration by the wider research community. As a result of this screen, we identified and validated novel combination-tumor types in multiple cancer types. Citation Format: Azadeh Bashi, Elizabeth A. Coker, Krishna Bulusu, Marta Milo, Claire Crafter, James Lynch, David Jenkins, Howard Lightfoot, Brandon Willis, Courtney Andersen, Omid Tavana, Kevin Mongeon, Jacob Gordon, Paul Smith, Simon Barry, Ultan McDermott, Lisa Drew, Susan Critchlow, Mathew J. Garnett, Jerome Mettetal. Large scale pan cancer drug combination screening to identify effective and actionable combinations and biomarker hypothesis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5321.

Enhanced chemotherapeutic efficacy of docetaxel in human lung cancer cell line via GLUT1 inhibitor.

The dose-dependent adverse effects of anticancer agents neednew methods with lesser toxicity. The objective of the current research was to evaluate the efficacy of GLUT1 inhibitor, as an inhibitor of glucose consumption in cancer cells, in augmenting the efficiency of docetaxel with respect to cytotoxicity and apoptosis. Cell cytotoxicity was assessed by using methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay. Annexin V/PI double staining was employed to evaluate apoptosis percentage. Quantitative real-time polymerase chain reaction(RT-PCR) analysis was accomplished to detect the expression of genes involved in the apoptosis pathway. The IC50 values for docetaxel and BAY-876 were 3.7 ± 0.81and 34.1 ± 3.4 nM, respectively. The severity of synergistic mutual effects of these agents on each other was calculated by synergy finder application. It showed that the percentage of apoptotic cells following co-administration of docetaxel and BAY-876 increased to 48.1 ± 2.8%. In comparison without GLUT1 co-administration, the combined therapy decreased significantly the transcriptome levels of the Bcl-2 and Ki-67 and a remarkable increase in the level of the Bax as proapoptotic protein(p < 0.05). Co-treatment of BAY-876 and docetaxel depicted a synergistic effect which was calculated using the synergy finder highest single agent(HSA) method (HSA synergy score: 28.055). These findings recommend that the combination of GLUT-1 inhibitor and docetaxel can be considered as a promising therapeutic approach for the treatment of patients with lung cancer.

Influence of concurrent administration of the methanol leaf extract ofLeptadenia hastata(Pers) decne (Apocynaceae) with metformin on blood glucose in diabetic rats

Objectives:Leptadenia hastata(Pers) Decne (Apocynaceae) is a common medicinal plant used in northern Nigeria either singly or together with conventional drugs to treat diabetes. This study investigated the influence of concurrent administration of the methanol leaf extract ofL. hastatawith metformin in streptozotocin/ nicotinamide-induced diabetic rats.Materials and Methods:Possible synergistic activity between the extract and metformin was assessed using 3 models of synergy analysis (Loewe additivity, Bliss independent and highest single agent [HSA] models). Eleven groups of Wistar rats (eight animals per group) consisting of ten groups of diabetic rats and one normal control group were used in this study. Six groups were administered with either the extract or metformin at three different doses each (50, 150 and 500 mg/kg for the extract and 30, 100 and 300 mg/kg for metformin), while another three groups were co-administered with the extract and metformin at three different ratios each (50 mg/kg: 30 mg/ kg, 150 mg/kg: 100 mg/kg and 500 mg/kg: 300 mg/kg). An oral glucose tolerance test (OGTT) was conducted at baseline and on day 14.Result:The results revealed that the extract-metformin combination brought about a synergistic reduction in the total area under the OGTT curve (based on Loewe and HSA models) as well as a synergistic reduction in blood glucose (based on Loewe, Bliss and HSA models).Conclusion:The methanol leaf extract ofL. hastataproduced a synergistic antidiabetic activity in streptozotocin/ nicotinamide-induced diabetic rats when combined with metformin.

N, N’‐Disubstituted Benzimidazolium Salts: Synthesis, Characterization, Micromolar Detection of Fe(III) ions in Aqueous system, Biological Evaluation and Molecular Docking Studies

AbstractA series of dicationic N, N’‐disubstituted benzimidazolium salts with Br− and PF6− as the counter anions were synthesized in 75–98 % yields. The single crystal X‐ray diffraction studies of one of the benzimidazolium salts confirmed the structure of these dicationic compounds. These salts exhibited a selective “turn‐off” fluorescence response toward Fe3+ ions in aqueous solution over the other competitive metal ions such as Ag+, Al3+, Ba2+, Ca2+, Co2+, Cr3+, Cu2+, Fe2+, K+, Mg2+, Na+, Ni2+, Pb2+, and Zn2+. Upon addition of 5 equivalents Fe3+ ions, emission intensity was quenched by 94–99 % in pure aqueous media. Detection limits for all probes with Fe3+ ions were found in micromolar range. Further, drug combination analysis was conducted by evaluating the combinatorial treatment effects of two benzimidazolium salts with doxorubicin on breast cancer cells using the Chou‐Talalay method and the Highest Single Agent (HSA) model, and it was found that these compounds showed synergism with doxorubicin. Further, molecular docking studies revealed the best docking score (−6.11) for one of the benzimidazolium salts, and explained its binding affinity with checkpoint kinase 2 (chk2) protein.

Defining Clinical and Molecular Biomarkers for Venetoclax-Based Drug Combinations to Augment AML Therapy

Background: The combination of the BCL2 inhibitor venetoclax with azacytidine (Ven+Aza) is efficacious for elderly patients with acute myeloid leukemia (AML) who are unfit for standard chemotherapy. However, the overall and progression-free survival of these patients after 1 year of therapy is approximately 30-40%, and resistance to Ven+Aza, either primary or adaptive through clonal evolution, limits its durability. Therefore, we evaluated new venetoclax combinations to elucidate mechanisms and biomarkers of response and resistance. Methods: We tested a set of 31 venetoclax (Ven) combinations using an ex vivo assay on primary AML patient specimens (n=433, though not all combinations were screened for all samples). Enhanced efficacy was determined using a combination ratio (CR), defined as the sensitivity of the combination (by AUC) divided by the sensitivity of the most potent single agent AUC. Drug combination synergy was evaluated using the highest single agent (HSA) method. Clinical, genetic, and disease status information were collated, and ex vivo combination sensitivities were compared to matched single-agent data by Friedman test, across groups by Wilcoxon rank sum or Kruskal-Wallis test, and with continuous variables by Spearman rank correlation. For patient specimens with RNAseq data collected (n=279 patients), transcriptomic gene signatures of AML tumor cell state (van Galen, Cell 2019) were correlated with combination sensitivity by Spearman rank. Uni- and multi-variate analyses were applied to the full dataset to identify biomarkers of differential sensitivity to each combination. For one combination, Ven plus the JAK1/2 inhibitor ruxolitinib (Rux), additional genome wide CRISPR screens and follow up in vivo patient xenograft and mechanistic validation experiments using AML cell line models were performed. Results: Using Ven+Aza as a benchmark, we found several venetoclax combinations that exceeded both the ex vivo activity and enhanced efficacy observed with Ven+Aza in primary AML patient samples (Fig. 1A), while showing limited to no effect on stromal cell controls. Evaluation of clinical and genetic data with combination sensitivity revealed many significant transcriptomic, genetic, and immunophenotypic associations. The strong bias in sensitivity of Ven with respect to transcriptomic signatures of primitive tumor cell states compared to more mature, monocytic tumor states was unchanged with Ven+Aza and select other combinations. Importantly, this differential sensitivity was overcome when pairing Ven with several new drug partners such as Rux or the p38 MAPK inhibitor doramapimod (Fig. 1B). Uni- and multi-variate analyses further revealed an array of different biomarkers involving clinical and genetic features that correlated with significant differential sensitivity to each combination. Many of these likewise involved markers of cell differentiation state, such as CD14. To elucidate the mechanisms of enhanced efficacy of the Ven+Rux combination, which is currently under phase 1 clinical investigation, in vivo murine patient xenograft models and genome-wide CRISPR screens in OCI-AML2 cells were performed. Ven+Rux demonstrated superior inhibition of tumor growth in an AML xenograft model compared to either single-agent. The Ven+Rux CRISPR screen identified many genes whose knockdown was enriched in combination-resistant cells, including three (MDM2, MED13L, MEIS2) that were confirmed to confer resistance in subsequent immunoblotting and drug sensitivity validation experiments. In comparison, analysis of a control CRISPR screen using Ven alone revealed numerous gene hits in the JAK/STAT signaling pathway as enhancing the sensitivity of Ven. Conclusion: To expand the utility of venetoclax-inclusive combinations, we prospectively profiled a set of drug combinations and identified several with activity that exceeds Ven+Aza in ex vivo assays. Associations of clinical features with sensitivities to these drug combinations provide insight into their effectiveness which may be used to align the most promising combinations with translation to treatment of discrete subsets of AML patients. Together, these findings support opportunities for expanding the impact of Ven-based drug combinations in AML by leveraging clinical and molecular biomarkers of response. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Caenorhabditis elegans as a valuable model for the study of anthelmintic pharmacodynamics and drug-drug interactions: The case of ivermectin and eprinomectin.

Caenorhabditis elegans is a free-living nematode that has been validated for anthelmintic drug screening. However, this model has not been used to address anthelmintic dose-response-time and drug-drug interactions through matrix array methodology. Eprinomectin (EPM) and Ivermectin (IVM) are macrocyclic lactones widely used as anthelmintics. Despite being very similar, EPM and IVM are combined in commercial formulations or mixed by farmers, under the assumption that the combination would increase their efficacy. However, there is no data reported on the pharmacological evaluation of the combination of both drugs. In this study, we assessed the pharmacodynamics and drug-drug interactions of these two anthelmintic drugs. Since the action of these drugs causes worm paralysis, we used an infrared motility assay to measure EPM and IVM effects on worm movement over time. The results showed that EPM was slightly more potent than IVM, that drug potency increased with drug time exposure, and that once paralyzed, worms did not recover. Different EPM/IVM concentration ratios were used and synergy and combination sensitivity scores were determined at different exposure times, applying Highest Single Agent (HSA), Loewe additivity, Bliss and Zero Interaction Potency (ZIP) models. The results clearly indicate that there is neither synergy nor antagonism between both macrocyclic lactones. This study shows that it is more relevant to prioritize the exposure time of each individual drug than to combine them to improve their effects. The results highlight the utility of C. elegans to address pharmacodynamics studies, particularly for drug-drug interactions. Models in vitro can be integrated to facilitate preclinical and clinical translational studies and help researchers to understand drug-drug interactions and achieve rational therapeutic regimes.

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning.

BackgroundIn cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories.ResultsHere, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers.ConclusionsThe performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Abstract 2739: Defining and evaluating drug additivity in clinical trials of combination cancer therapy

Abstract BACKGROUND The benefits of combination therapy are often attributed to synergy, that is, drug interactions resulting in an anti-cancer effect that is more than the sum of its parts. Accordingly, the rationale for designing new drug combinations is often based on synergy measured in preclinical models. However, preclinical metrics of drug interaction are not applicable to clinical trial data, and there has been no established quantitative method to assess synergy versus additivity in clinical settings. We recently showed that because of extensive patient-to-patient heterogeneity in single drug responsiveness, increasing the chance of a good response to at least one drug was a quantitatively sufficient explanation for the clinical efficacy of many approved combination therapies. Some combinations surpass this ‘highest single-agent’ model, which could be due to either drug additivity or synergy. Here we propose and test a model of drug additivity for Progression-Free Survival (PFS) data from clinical trials, to identify if any approved combinations are clinically synergistic, as compared to additive. METHODS We used PFS from trials as the clinical measure of drug efficacy. We defined ‘clinical drug additivity’ as the sum of PFS times observed from each drug in a combination. Inter-patient heterogeneity in drug responses was simulated by sampling from the joint distribution of drugs’ PFS distributions. For each combination, the clinically observed PFS distribution was compared to the additivity model, or the highest single-agent model (Palmer &amp; Sorger, 2017). Synergy is exhibited if an observed PFS distribution is significantly superior to PFS expected from additivity (by Cox Proportional Hazards). To search for drug synergy in clinical data, we analyzed approved combination therapies where synergy was most likely to explain efficacy, which are combinations where part of the regimen is not approved as monotherapy in the same disease. We analyzed 11 approved combination therapies for advanced cancers in the breast, ovary, pancreas, colon, cervix, and lymphatic system. RESULTS None of the 11 approved combinations analyzed were significantly superior to the model of clinical additivity. For five combinations, the additivity model made the most accurate predictions of clinical efficacy (mean R2=0.97 for additivity, versus R2=0.83 for highest single-agent), and the other six combinations were most accurately described by the highest single-agent model (mean R2=0.97 for highest single-agent, versus R2=0.91 for additivity). CONCLUSIONS Approved combination therapies are rarely ‘more than the sum of their parts’ in quantitative terms. A straightforward definition of clinical drug additivity accurately matched trial results for combination therapies where synergy was expected. This suggests that single-agent efficacy by each drug is usually required for the clinical success of combination therapy. Citation Format: Haeun Hwangbo, Adam C. Palmer. Defining and evaluating drug additivity in clinical trials of combination cancer therapy [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 2739.

Abstract 4024: Combining selumetinib with BH3 mimetics enhances activity in MAPK-activated acute myeloid leukemia

Abstract Mitogen-activated protein kinase (MAPK) pathway alterations comprise some of the most frequent mutations in newly diagnosed acute myeloid leukemia (AML). Moreover, MAPK pathway alterations are also emerging as potential mechanisms of resistance to targeted therapy in AML including FLT3 inhibitors and venetoclax. In an ex vivo pharmacologic analysis of primary AML samples, sensitivity to the MEK inhibitor selumetinib (ARRY-142886) was enriched in patient samples resistant to venetoclax. Clinical activity of MAPK pathway inhibitors such as selumetinib has been explored in AML but monotherapy responses were modest. Another MEK inhibitor, cobimetinib, is currently being tested in combination with venetoclax in AML. We sought to understand whether combining selumetinib with BH3 mimetics such as venetoclax would improve efficacy in AML models. We evaluated combination activity of selumetinib plus venetoclax or the MCL1 inhibitor AZD5991 in a panel of AML cell lines. Cells were exposed to a 6x6 matrix of both agents for 72hrs and then viability assessed using CellTiter-Glo. Combination benefit was assessed using highest single agent (HSA) analysis. Selumetinib+AZD5991 demonstrated strong combination benefit (HSA score &amp;gt;0.1, Emax &amp;gt;0.5) in 4/19 AML cell lines. Selumetinib+venetoclax showed strong combination activity in 6/19 lines. Three lines showed benefit with both combinations. Many of these cell lines harbor MAPK pathway mutations including OCI-AML5 (SOS1N233Y, NF1K1385R), ML-2 (KRASA146T), HL-60 (NRASQ61L), and Nomo-1 (KRASG13D). Selumetinib treatment also led to robust BIM induction in vitro. Nomo-1 xenografts were evaluated for in vivo sensitivity to venetoclax (100 mg/kg qd PO), 5-azacytidine (1mg/kg BID q8h 3days on/4days off IP), AZD5991 (30mpk bid q2h qw IV), selumetinib (10 mg/kg bid q8h PO), as well as combinations of venetoclax+5-aza, AZD5991+selumetinib, and venetoclax+selumetinib. Venetoclax and venetoclax+5-aza treatment were ineffective. Selumetinib monotherapy led to 63% tumor growth inhibition (TGI) but tumors eventually grew out through treatment. The combination of selumetinib+venetoclax slightly improved efficacy (81% TGI) but markedly delayed tumor outgrowth (selumetinib monotherapy arm reached mean tumor volume &amp;gt;1000 mm3 on day 17, selumetinib+venetoclax reached average of ~966 mm3 on day 28). Reducing venetoclax dose (30mg/kg qd) or frequency (100mg/kg 3days on/4days off) maintained most of the combination efficacy. Selumetinb+AZD5991 also strongly improved efficacy compared to either monotherapy (88% TGI, mean tumor volume did not exceed ~400mm3 by day 28). Together these data suggest potential for combining MEK inhibitors with BH3 mimetics in AML. Work is ongoing to further understand how scheduling impacts combination efficacy, evaluate the triple combination of selumetinib+BH3 mimetic+azacytidine, and assess efficacy in disseminated models. Citation Format: Courtney L. Andersen, Azadeh Cheraghchi-Bashi, Patricia Jaaks, Elizabeth A. Coker, Kathleen Burke, Justin Cidado, Paul Smith, Corinne Reimer, Raoul Tibes, Mathew Garnett, Jerome Mettetal. Combining selumetinib with BH3 mimetics enhances activity in MAPK-activated acute myeloid leukemia [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 4024.

Drug Interactions in Lenacapavir-Based Long-Acting Antiviral Combinations.

Long-acting (LA) anti-HIV regimens show promise for increasing dosing intervals and consequently, improving the patients’ quality of life. The first FDA-approved LA therapy is Cabenuva, which comprises rilpivirine (a non-nucleoside reverse transcriptase inhibitor) and cabotegravir (integrase strand transfer inhibitor). Novel promising LA anti-HIV agents such as lenacapavir (a capsid-targeting antiviral) and islatravir (EFdA, a nucleoside reverse transcriptase translocation inhibitor) need to be explored as combination therapies. Therefore, we sought to determine whether combination of lenacapavir with islatravir, rilpivirine, or cabotegravir displayed synergy, additivity, or antagonism. We performed dose-response matrices of these drug combinations in an HIV-1 reporter cell line and subsequently analyzed the data with SynergyFinder Plus, which employs four major drug interaction models: highest single agent, Bliss independence, Loewe additivity, and zero interaction potency. Most of these models predict additive inhibition by the studied drug combinations This work highlights the importance of effective drug combinations in LA-regimens.