MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

Predicting the synergistic effects of drug combinations can accelerate the identification process of novel potential combination therapies for clinical studies. Although extensive efforts have been made in the field, the problem is still challenging due to the high sparsity of drug combinations’ synergy data and the existence of false positive combinations resulted from the noise in experiments. In this paper, we develop a Knowledge Graph Embedding-based method for predicting the synergistic effects of Drug Combinations, namely KGE-DC, which fully extracts the features of drug combinations. Firstly, a largescale knowledge graph including drugs, targets, enzymes and transporters is constructed, therefore, the sparsity of the drug combinations’ data is reduced and the reliability of the data is increased. Then, knowledge graph embedding, which are capable of capturing complex semantic information of various entities in the knowledge graph, is adopted for learning low-dimensional representations for the drugs and cell lines. Finally, the synergy scores of drug combinations are predicted based on the drug and cell line embeddings of the drug combinations’ synergy data. Extensive experiments on benchmark dataset with four different synergy types demonstrate that KGE-DC outperforms state-of the-art methods on both the regression and classification tasks, namely predicting the synergy scores of drug combinations and predicting whether the drug combinations are synergistic combinations. Our results indicate that KGE-DC is a valuable tool to facilitate the discovery of novel combination therapies for cancer treatment. The implemented code and experimental dataset are available online at https://github.com/yushenshashen/KGE-DC.

Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.

BackgroundBreast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.ResultsThis study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.ConclusionsThe framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Disclosure: P. Mishra: None. B. Sobieski: None. D. Laha: None. S.A. Forsythe: None. M. Shen: None. Y. Zhang: None. B. Ramamurthy: None. S.M. Sadowski: None. J.D. Rivero: None. J.M. Hernandez: None. N. Nilubol: None. Adrenocortical cancer (ACC) is a rare, aggressive cancer with poor survival due to a lack of effective treatments; therefore, it is critically important to identify therapies to be readily studied in clinical trials. Quantitative high-throughput drug combination screening identified potential synergy between phosphatidylinositol-3-kinase (PI3K) inhibitors and heat shock protein 90 (HSP90) inhibitors. We determined the effective doses and cytotoxicity of the HSP90 inhibitors (STA9090, HSP990, NVP-AUY922) and PI3K inhibitors (PIK-75, BGT-226) and their combinations in preclinical models of ACC. The synergistic effects of drug combinations were determined on cancer phenotypes and the mechanism of action using RNA-sequencing with appropriate confirmatory protein studies. Preclinical in-vitro and in-vivo studies were performed to validate the synergistic efficacy of the inhibitor combinations in ACC cell lines, patient-derived organoids (PDOs), and human ACC xenografts. The combination of PIK75 or BGT226 and either STA9090, HSP990, or NVP-AUY922 synergistically inhibited cell proliferation (monolayer and 3-dimensional), reduced cell migration-invasion, epithelial-to-mesenchymal transition, with decreased phosphorylated proteins in PI3K/mTOR signaling pathway. To assess the clinical relevance of the associated genes, we analyzed transcriptomic data of genes in the HSP90 and PI3K/AKT signaling pathway and the overall survival (OS) and disease-free survival (DFS) of patients with ACC in The Cancer Genome Atlas cohort (N=79). ACC patients with high expression of HSP90 family members HSP90B1 (OS: P =0.007, DFS: P <0.001), HSP90B3P (OS: P =0.092, DFS: P =0.084), HSP90AB2P (DFS: P=0.019) and HSP90B2P (DFS: P=0.044) and PI3K family members PIK3R1 (OS: P <0.010, DFS: P =0.092), AKT2 (OS: P=0.002, DFS: P <0.001), had either shorter OS or shorter DFS or both. The mRNA expression of HSP90B1, HSP90B2P, and AKT2 was significantly higher in primary human ACC samples with stage III/IV (N=31) and in samples with distant metastasis (N=15). Mechanistically, PIK75-STA9090 combination induced G2/M cell cycle arrest and caspase-dependent apoptosis, while the BGT226-STA9090 combination induced autophagy-related cell death in ACC cells. These combinations were more effective than the respective monotherapies in the PDOs (n=5) with different pathogenic mutations at clinically achievable concentrations. Further validation of synergistic efficacy of BGT226 and STA9090 in NOD SCID-gamma mice showed a reduction in the mean volume of ACC xenografts in STA9090-BGT226 treatment group to 28.4% of the vehicle control group and 38.4% and 37.7 % of those in STA9090-only and BGT226-only groups, respectively. In conclusion, the synergistic combinations of the PI3K and HSP90 inhibitors were effective in preclinical studies of ACC, warranting a clinical trial in patients with advanced ACC. Presentation: Saturday, July 12, 2025

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer-related deaths in the United States. While Poly (ADP-ribose) polymerase (PARP) inhibitors hold promise in treating PDAC with Homologous Recombination Deficiency (HRD). However, HRD occurs in only 10% of cases, leaving 90% with intact Homologous Recombination Proficiency (HRP). Our research highlights the key role of wild-type isocitrate dehydrogenase 1 (IDH1) in maintaining cellular function. We found that inhibiting IDH1 increases DNA damage, suppressing the DNA repair pathway crucial for genomic integrity. This makes cancer cells susceptible to PARP inhibitors, offering a new PDAC treatment approach. Methods: The effect of IDH1 inhibition (Ivosidenib) combined with PARP inhibition (Olaparib) on in vitro growth inhibition were assessed through Trypan blue and PicoGreen in drug combination assays. Additionally, we investigated the molecular mechanism underlying the synergistic effects of drug combination. To further evaluate the therapeutic potential of IDH1 inhibition in combination with PARP inhibitors, we examined tumor growth in xenograft models of PDAC in athymic nude mice. Results: Our data report that, mechanistically, IDH1 inhibition decreased expression of HR DNA repair pathway molecules, such as BRCA1 and BRCA2, and leading to decreased HR repair efficiency in pancreatic cancer cells. Short-term cell viability data demonstrated that targeting IDH1 with ivosidenib when combined with DNA-damaging agent (Olaparib) had a synergistic effect in HRP cell lines (MiaPaCa-2 and Panc-1) with a positive synergy score and Bliss score greater than 1. Additionally, we assessed long-term cell survival using colony formation assays, which yielded a dramatic reduction in cell survival for both Panc1 and MiaPaCa-2 cells when olaparib was combined with ivosidenib, as compared to single-agent controls. Consequently, this reduction increased the levels of apoptosis induced by olaparib. Furthermore, we discovered that these two compounds complemented each other in DNA damage response, leading to an increase in the DNA damage biomarker γ-H2AX. In vivo studies using xenograft models demonstrated that the combination of ivosidenib and olaparib synergistically enhanced anti-tumor activity as compared to either single-agent used alone. Conclusion: Our data provides insights into the mechanisms underlying the susceptibility of PDAC tumor growth to a dual treatment approach involving an IDH1 inhibitor and a PARP inhibitor, particularly in the context of HRP cancer. This highlights the promise of precision medicine for pancreatic cancer treatment, though further pre-clinical studies are needed for validation and refinement. Citation Format: Mehrdad Zarei, Omid Hajihassani, Hallie J. Graor, Alexander W. Loftus, Soubhi Tahhan, Faith Nakazzi, Parnian Naji, Luke D. Rothermel, Jonothan R. Brody, Jordan M. Winter. Inhibition of IDH1 impairs homologous recombination and induces sensitivity to PARP inhibitor in 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 7125.

Background: PARP inhibitors (PARPi) benefit only a fraction of breast cancer with BRCA mutation, and are even more limited in triple negative breast cancer (TNBC), due to clinical primary and acquired resistance. Here we found that the efficacy of PARPi in TNBC can be expanded with CDK4/6 inhibitors (CDK4/6i). Methods: We screened sensitive and resistant PARPi cell lines from existing TNBC cell lines and cultured PARPi-resistant strains by gradient concentration increase method. The effects of PARP inhibitor Olaparib and CDK4/6 inhibitor Palbociclib on TNBC cells lines were examined in vitro and in vivo. Combination index (CI) was used to evaluate the synergistic effects of drug combination. Results: We screened out the cell line MDA-MB-436, which was the most sensitive to PARPi, and the cell lines HCC1937 and SUM149, which were relatively resistant to PARPi, all of which were BRCA1 mutants. We demonstrated for the first time that the combination of PARPi and CDK4/6i has synergistic effects against some TNBCs both in vitro and in vivo, and was verified by CI<0.9. Further experiments confirmed that PARPi combined with CDK4/6i inhibited cell proliferation and migration, and increased apoptosis and DNA damage. In the PARPi sensitive BRCA-/TNBC cell (MDA-MB-436), the inhibitory effect of monotherapy PARPi was obvious; In the PARPi resistant BRCA-/TNBC cells (HCC1937 and SUM149), CDK4/6i was added to achieve significant growth inhibition. In the timing of medication, PARPi followed by CDK4/6i had better inhibitory effect.Conclusions: In some BRCA-/ TNBCs, PARPi combined with CDK4/6i had a synergistic effect. Even in PARPi-resistant cells, combined treatment could enhance the efficacy and might reverse the drug resistance to some extent. The combination index index of HCC1937 at different drug concentrations (uM)The combination index index of HCC1937 at different drug concentrations (uM)OlaparibPalbociclib25.0016.6711.117.414.943.292.191.460.980.65P-25.000.459110.668320.866540.869640.993450.989890.97841.003221.073741.00809P-20.000.537090.851131.022651.149561.231561.270271.189071.145021.14811.25795P-15.000.594121.018141.134481.056960.928140.955670.889760.842040.81060.81348P-10.000.475540.860580.77190.717480.62330.615880.561390.52960.510250.49752P-5.000.509450.762230.746560.683340.56030.499410.467390.41490.390670.37502P-2.500.573130.706410.714750.661230.519270.455960.350980.29340.319310.2756 Citation Format: Xiuzhi Zhu, Li Chen, Zhonghua Wang. Efficacy of combined CDK4/6 inhibitor and PARP inhibitor in the treatment of BRCA1 mutant triple negative breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-39.

Non-steroidal anti-inflammatory drugs (NSAIDs) regimens enhance synergistic selective anticancer efficacy of chemotherapeutic agents on cultured cells.

Multi-drug delivery based on polymer nano-scaffolds is an essential protocol to be developed for better administration of anticancer drugs to enhance their therapeutic efficacies against cancer cells. Here, we report dual delivery polysaccharide nano-vesicles that are capable of loading and delivering both water soluble and water insoluble drugs together in a single polymer scaffold. The selective rupture of the nano-vesicular assembly under intracellular enzyme conditions allowed the simultaneous delivery of a hydrophobic drug camptothecin (CPT) and hydrophilic drug doxorubicin (DOX) supporting their synergistic killing of breast and colon cancer cells. The polysaccharide nano-vesicles have allowed us to address a few important questions regarding the need for multiple drug administration in cancer cells including (a) the role of simultaneous drug release, (b) antagonistic versus synergistic effects of drug combinations and (c) how these are affected by the ratio of drugs. Further, evaluation of the role of caveolae in endocytosis of these polymer scaffolds was also made. The vesicular scaffolds were found to preserve and deliver DOX resulting in 50-60% better killing of cancer cells than the free drug. Additionally, dual loaded nano-vesicles when compared to drug cocktails with individual drugs in separate nano-vesicles (at comparable molar ratios) suggest the relative drug concentration following release and mode of delivery to be both important in cancer cell killing. Results from these experiments have revealed newly developed polysaccharide nano-vesicles loaded with DOX and CPT drugs as potential candidates for improved breast cancer cell killing. Thus, these custom-designed polysaccharide nano-vesicles provide a new perspective on multi-anticancer drug delivery systems and their efficacy.

Identification of effective drug combinations inhibiting multiple target genes that are essential for cancer cell survival is seen as an effective strategy to overcome drug resistance. Cellular pathways are often redundant, and network rewiring in the face of an assault promotes the emergence of resistant cells that contribute to clinical relapse. However, the number of possible drug-target combinations to investigate by conducting experiments is exponentially large and impractical, therefore warranting the need for systematic approaches to prioritize the most effective drug combinations. Here, we aimed to integrate large-scale drug screening, drug-target affinity knowledge and public RNAi screening datasets on a huge compendium of cancer cell lines to predict synergistic drug combinations. In a case study in an individual breast cancer cell line, MDA-MB-231, we identified drugs selectively active in this cell line compared to the background distribution. We find that target genes of active drugs and other genes involved in related cellular processes were also differentially essential in RNAi screening data. Further, we observe that the drug combinations of these selectively active drugs were synergistic. We propose a systematic approach for integrating chemical screening, drug target and cellular processes knowledge, and functional genetic screens to optimize drug combination testing efforts. Citation Format: Alok Jaiswal, Prson Gautam, Jing Tang, Krister Wennerberg, Tero Aittokallio. Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A26.

Neuroblastoma is the most common tumor in infants younger than 1 year of age and one of the most common solid tumors in childhood. The disease is highly variable: while spontaneous tumor regression occurs more often than in any other cancer type, patients with more severe variant face therapy-resistant recurrence. Therefore we have set out to develop new therapeutic approaches to the disease with a particular focus on aggressive subtypes. Using a high-throughput systems level approach we have identified a novel synergistic combination of approved drugs and compounds in clinical trials. Our initial screen assessed the potency of a panel of clinically applicable drugs by proliferation assays versus a defined panel of isogenic cell lines that are representative of the different neuroblastoma variants. We identified potential drugs combinations by three-dimensional dose response matrices and adopted Bliss values as a quantitative measure of the synergy. The striking synergy was observed in SH-SY5Y cell line using combinations of EGFR/HER2 inhibitors with the apoptosis inducing agents. We were able to validate the observation using colony formation assay. In contrast to single drug treatment, the combination completely abolished the clonogenic growth of neuroblastoma cells, showing the most tangible evidence of the synergy. In order to evaluate our results in more aggressive disease subtypes, we tested our combination in SH-SY5Y cell line with transiently overexpressed N-Myc and in N-Myc-amplified IMR5-75 cell line. Notably, even in this setting we could recapitulate our findings, suggesting that this combination of drugs is highly potent in the chemo-resistant tumor subtypes as well. The same effect was observed when we transiently overexpressed TrkA in SH-SY5Y, mimicking yet another neuroblastoma variant. Pursuing the exact mechanism of the synergy, we have tested the combination in breast cancer cells. To our surprise, the synergistic effect was still present in HER2 negative cell line, but completely absent in the HER2 positive one. We also exchanged the dual EGFR/HER2 inhibitor drug with more potent EGFR inhibitors to conclude that a combination seems to be specific to a particular set of compounds and not necessarily to their main target spectra. Our synergistic combination is highly potent in a large number of neuroblastoma conditions. Strong synergistic effect can be reached at doses much lower than required for the single drug treatment. Animal models that we are currently testing will enable quick translation of our data in clinics. Citation Format: Branka Radic, Uwe Rix, Stefan Kubicek, Giulio Superti-Furga. Probing network fragilities in neuroblastoma by synergistic drug combinations. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5038. doi:10.1158/1538-7445.AM2013-5038

BackgroundCoinfections of Pseudomonas aeruginosa and Staphylococcus aureus usually fail to respond to antibiotic treatment, and combination therapy may prove to be a substantial approach to prevent the spread of antimicrobial resistance.ObjectivesTo evaluate the antimicrobial and antibiofilm effects of drugs against the sensitive as well as resistant strain co-cultures of P. aeruginosa and S. aureus.MethodsA pair of biofilm-forming, sensitive, and resistant strains was identified from 52 clinical isolates of P. aeruginosa and 43 of S. aureus using phenotypic, genotypic, antimicrobial susceptibility tests, and colorimetric microtiter plate assays. The antimicrobial susceptibility patterns of the planktonic and biofilm cultures and co-cultures (1:1) from these resistant (PA-14 and PS-107) and sensitive (PA-27 and PS-100) strains were determined for the specific set of drugs. Based on the Minimum Inhibitory Concentration (MIC), and Minimum Biofilm Inhibitory Concentration (MBIC) data, the different drug combinations were shortlisted and tested against the co-cultures.ResultsIn the drug combination studies, 33.3% (4/12) of drug combinations showed synergy against resistant and 41.7% (5/12) against sensitive strain planktonic co-cultures among the shortlisted drug combinations. Furthermore, 8.3% (2/24) of drug combinations were found to be effective against the biofilm co-cultures of resistant strains and 80% (4/5) of drug combinations showed synergy against sensitive strains.ConclusionsDrug combination studies revealed a few effective drug combinations that demonstrated synergy against the planktonic and biofilm co-cultures. Despite their synergistic effects against the bacterial co-cultures, the drugs involved in these combinations (colistin, linezolid, trimethoprim, nitrofurantoin, and vancomycin) displayed inhibitory effects against the pure cultures of P. aeruginosa and S. aureus within the susceptible ranges noted in the initial studies. Owing to this, however, mitigates the synergistic effect of these drug combinations. Conversely, it shows a notable decrease in the MICs (≥ 50%) when compared to the MICs of these drugs against the pure bacterial cultures, suggesting the therapeutic benefits of adjusted doses of the combined drugs over monotherapy in treating these coinfections.

A combination drug therapy comprising biocompatible nanoparticles as delivery systems represents a promising and challenging strategy to treat complex pathologies, such as cancer, neurodegenerative disorders and severe infectious diseases.

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

The goal of combinatorial therapy is to improve the effectiveness of treatment by targeting multiple aspects of cancer cells or overcoming resistance to single drugs. Therefore, we performed an automated combinatorial drug screening in meningioma cell lines and patient-derived tumor organoids (TOs). Three grade 3 meningioma cell lines (NCH93, IOMM-Lee, and KT21-MG1) were stably transduced with blue, green, and red fluorescence proteins. The cell lines were multiplexed into 384-well plates and treated in 5x5 dose-response (0-1000 nmol/l) matrices for 48h by the automated liquid handler Hamilton MicroLAB STAR®. The drug library consisted of 166 FDA-approved anticancer drugs. The viability of each cell line was demultiplexed by using fluorescence signals and CellTiterGlo. TOs were established from single cell suspensions of freshly resected meningioma tissue (n=20). The ZIP model was used to determine synergism. This drug screening effort generated 13,695 unique drug-drug combinations per cell line. Most synergistic combinations were observed in IOMM-Lee (1.14%), followed by KT21-MG1 (0.88%), and NCH93 (0.54%). The 110 most effective drug combinations were validated in wild-type meningioma cells. 75 drug combinations demonstrated a positive most synergistic area (MSA) score, indicating synergistic effects. Based on synergy scores and literature evidence, the 16 most promising combinations were selected for screening in TOs. The proteasome inhibitor carfilzomib in combination with the androgen receptor inhibitor enzalutamide exhibited the highest synergy in TOs. In 40% (n=8/20) of the cases, the combination showed strong synergism (MSA>10) resulting in an overall average MSA of 7.36, followed by carfilzomib and neratinib, and romidepsin and gemcitabine with average MSAs of 5.49, and 4.32, respectively. Furthermore, carfilzomib and enzalutamide induced apoptosis in TOs assessed by Caspase-3/7. This comprehensive combinatorial drug screening identified the synergistic combination of the proteasome inhibitor carfilzomib and androgen receptor inhibitor enzalutamide that can be an effective treatment option for selected meningioma patients.

Oral cancer is one of the most common forms of cancer among developing countries but despite current efforts, the survival of oral cancer has remained constant. Current research on oral cancer is centered on clinical studies focused on single drugs or very few combinations of the most common therapeutics historically used to treat oral cancer. However, there is a major gap in the literature about combinations of drug treatments for oral cancer. The goal of this study is to explore combination treatments and assess whether any synergistic effects can be produced. To accomplish this, two oral squamous cell carcinoma (OSCC) cell lines, Cal‐27 and OECM‐1 from the tongue and the oral cavity, respectively, will be used to investigate the efficacy of single drugs as well as combinations of drugs from a wide array of chemotherapy agents currently used to treat oral cancer as well as other forms of cancer. Thus far, Cell Titer Glo assays have been performed in triplicate for both Cal‐27 and OECM‐1 to analyze their viability in the presence of four drugs used to treat other forms of cancer with different mechanisms of action: PD0, a MEK inhibitor, IBET‐762, a BET inhibitor, ABT‐199, a Bcl‐2 inhibitor, and JQ1, a BRD4 inhibitor. GI50 values were gathered from each assay. Cal‐27 presented higher GI50 values than OECM‐1 for IBET‐762 and JQ1 at 1.52 mM and 1.64 mM, respectively. The GI50 values for OECM‐1 treatment with IBET‐762 and JQ1 were lower at 1.07 mM and 1.21 mM, respectively. Both cell lines were found to have similar resistance to PD0, around 1.05 mM, and ABT‐199, around 1.02 mM. High GI50 values across the cell lines indicate that both Cal‐27 and OECM‐1 are resistant to the four drugs that were tested. In the future, Cal‐27 and OECM‐1 will be treated with a panel of 10 drugs currently used clinically to treat oral cancer to gather baseline data to understand how these cell lines react to current treatment. The drugs that will be used include: Cisplatin, 5‐FU, Taxol, Docetaxel, Hydroxyurea, Methotrexate, Bleomycin, Gemcitabine, and Vincristine, all of which interfere with DNA synthesis and cell growth. Then, Cal‐27 and OECM‐1 will be treated with new combinations of drugs currently used to treat oral cancer and drugs not currently used for oral cancer but used to treat other forms of cancer. Some of these drugs include: Gefitinib, a EGFR inhibitor, Venetoclax, a BCL‐2 inhibitor, Irinotecan, a topoisomerase inhibitor, Mitoxantrone, a topoisomerase 2 and PKC inhibitor, and several other drugs. Cellular responses will then be assessed to determine if any combinations are synergistic. The data generated can aid researchers in understanding how different OSCC cells respond to drug combinations, which may lead to more successful clinical treatment options in the future.