Drug Combination Synergy Research Articles

Abstract 637: Glucocorticoids act synergistically with FLT3 inhibitors to enhance cell death in FLT3 mutant acute myeloid leukemia

Abstract FLT3 internal tandem duplication (FLT3-ITD) is a frequent mutation associated with a poor prognosis of less than 30% five-year survival rate in acute myeloid leukemia (AML). Quizartinib, a selective FLT3 inhibitor, is currently showing promising results in phase III clinical trials for AML patients with mutant FLT3. Despite an impressive initial response to quizartinib, most patients relapse with drug resistance. The mechanism by which resistance arises and the initial response to drug treatment that promotes cell survival is poorly understood. Drug-tolerant “persister” cells (DTPs) are a transiently maintained subpopulation of drug-sensitive cells that survive cytotoxic drug exposure despite lacking resistance-conferring mutations. Here, we explore the impact of DTPs in FLT3-ITD AML cells treated with cytotoxic doses of quizartinib. We demonstrate that treatment with quizartinib induces significant changes in gene expression pattern that promote survival of the DTP population of cells. These changes are transient and result in a window of therapeutically exploitable vulnerabilities. Using an unbiased genome-wide transcriptome analysis, we report that inflammatory pathways to be the most significantly upregulated pathways in DTPs relative to drug naive cells. Independently, a drug-screening using a panel of FDA approved drugs reveals that DTPs are significantly more sensitive to anti-inflammatory drugs, particularly glucocorticoids. Consistent with this observation, a combination of glucocorticoids and quizartinib as well as other FLT3 inhibitors results in a synergistic cell death of FLT3 mutant but not wildtype cells. We show that this synergistic cell death is mediated through the glucocorticoid receptor which, when activated, upregulates the expression of the pro-apoptotic protein BIM. Glucocorticoid-induced increase in BIM together with quizartinib-induced decrease in the expression of the anti-apoptotic protein MCL-1 synergizes and results in augmentation of apoptosis in combination treated cells. CRISPR/Cas9-mediated knockout of the glucocorticoid receptor as well as BIM can completely rescue the observed drug combination synergy. Furthermore, inhibition of MCL-1 using a BH3-mimetic inhibitor, MIK665, displays a significant synergy with quizartinib. The synergy between quizartinib and glucocorticoids has been further validated using AML patient cells ex-vivo as well as AML mouse models in-vivo. Although glucocorticoids are the mainstay of treatment for lymphoblastic leukemia, their use in AML has been shown to be negligible. Our data demonstrates that treatment with FLT3 inhibitors sensitizes AML cells to glucocorticoids and thereby provides a basis for combining glucocorticoids with FLT3 inhibitors in AML patients with FLT3 mutation. Critically, since glucocorticoids have decades of clinical use and experience, the proposed combination therapy could rapidly be explored in the clinic to improve patient outcome. Citation Format: Melat Tsegaye Gebru, Hong-Gang Wang, Jennifer Xavier. Glucocorticoids act synergistically with FLT3 inhibitors to enhance cell death in FLT3 mutant acute myeloid leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 637.

Rapid Processing and Drug Evaluation in Glioblastoma Patient-Derived Organoid Models with 4D Bioprinted Arrays

SummaryGlioblastoma is the most common and deadly primary brain malignancy. Despite advances in precision medicine oncology (PMO) allowing the identification of molecular vulnerabilities in glioblastoma, treatment options remain limited, and molecular assays guided by genomic and expression profiling to inform patient enrollment in life-saving trials are lacking. Here, we generate four-dimensional (4D) cell-culture arrays for rapid assessment of drug responses in glioblastoma patient-derived models. The arrays are 3D printed with thermo-responsive shape memory polymer (SMP). Upon heating, the SMP arrays self-transform in time from 3D cell-culture inserts into histological cassettes. We assess the utility of these arrays with glioblastoma cells, gliospheres, and patient derived organoid-like (PDO) models and demonstrate their use with glioblastoma PDOs for assessing drug sensitivity, on-target activity, and synergy in drug combinations. When including genomic and drug testing assays, this platform is poised to offer rapid functional drug assessments for future selection of therapies in PMO.

Abstract D073: Combination of insulin-like growth factor-1 receptor/insulin receptor (IGF1R/IR) and androgen receptor (AR) antagonists with metformins inhibits triple-negative breast cancer (TNBC) progression in vitro and in vivo

Abstract Triple-negative breast cancer (TNBC) affects only about 15% of women with breast cancer (BC) but accounts for almost 50% of all BC deaths. At this time, development of targeted treatments for TNBC is urgently needed. Insulin-like growth factors and diabetes mellitus type 2 are noted to be associated with increased incidence of estrogen receptor-negative BCs such as TNBC particularly among African American patients. Since receptors for insulin-like growth factor-1 receptor/insulin receptor (IGF1R/IR) and androgen receptor (AR) are known to be enriched and to cross-communicate in subtypes of TNBC (Hamilton N et al., Int J Mol Sci 2017;18:2305 [PMCID: PMC5713274]; Hamilton N et al., Biomed Res Int. 2015;2015:925703 [PMCID: PMC4385615]), we first assessed effects of these receptor antagonists on proliferation of human and murine TNBC cells in vitro. Combination treatment with BMS-754807 and/or NVP-AEW541 (IGF1R/IR inhibitors) plus enzalutamide (AR inhibitor) effectively reduced proliferation of human MDA-MB-231, BT549, HCC1937 and murine 4T1 TNBC cells in vitro (P<0.001). Moreover, metformin and analogues of metformin that inhibit TNBC progression in part by interactions with AMPK and with IGFR1/IR pathways elicited further suppression of TNBC cell proliferation when combined with the IGFR1/IR-AR antagonists (P<0.001). Importantly, the combination treatments also reduced the migration in vitro of human and murine TNBC cells to a greater extent than any single agent given alone. Calculations to determine the coefficient of drug interaction (CDI) revealed therapeutic synergy in drug combinations predominantly in those containing BMS-754807. Of note, combination treatments containing BMS-754807 were found to significantly stimulate excess reactive oxygen species (ROS) in vitro, a process that may promote tumor cell apoptosis. In ongoing experiments, novel metformin analogues are also found to inhibit human MDA-MB-231 xenograft progression in vivo (P<0.001). Our findings suggest that combination treatments containing IGF1R/IR and AR antagonists alone and combined with metformins suppress TNBC progression, with potential to impact patient outcome in the future. [Funding by NCI U54 CA1433930; California Breast Cancer Research Program]. Citation Format: Nalo M. Hamilton, PhD, APRN-BC, Diana C. Márquez-Garbán, MD, Michael E. Jung, PhD, Jaydutt Vadgama, Richard J. Pietras, MD, PhD. Combination of insulin-like growth factor-1 receptor/insulin receptor (IGF1R/IR) and androgen receptor (AR) antagonists with metformins inhibits triple-negative breast cancer (TNBC) progression in vitro and in vivo [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr D073.

Mass‐Action Law Based Pharmacodynamics General Theory, Equation, Algorithm, and Computer Simulation for Econo‐Green Biomedical R&D and for Quantitative/indexed Drug Evaluation Guidance

The Mass‐Action Law (MAL) based Median‐Effect Equation (MEE) is the Unified General Theory of Pharmacodynamics (PD) ‐‐Biodynamics (BD) – Bioinformatics (BI) (MAL‐PD/BD/BI) [Chou TC, Pharmacol. Rev. 58: 621–681, 2006]. The derived MEE yields the algorithms that provide efficient, quantitative, econo‐green, digital, automated computer simulations for bio‐medical research & development (R&D). Its extension yields Combination Index Equation (CIE), which quantitatively defines drug combination synergy (CI<1), additive effect (CI=1) and antagonism (CI>1) [in: Chou TC and Talalay P. Adv. Enz. Regul. 22: 27–55, 1984], which has been cited 6,275 times in over 1,249 biomedical journals worldwide, encompassing nearly all disciplines of biomedical sciences, as of Oct. 2, 2019. The derived MEE theory reveals the following new basic paradigms: 1. “Dose” and “Effect” are interchangeable. 2. All dose‐effect curves of MAL can be transformed into straight lines.3. The theoretical minimum of only 2‐data points are required to simulate a dose‐effect curve, where the 3rd point is Dose Zero, and the 4th point is the median‐effect dose (Dm), which is the universal reference‐point and the dynamic common‐link. 4. The fact that only 2‐data‐points theory are required to simulate a dose‐effect curve, is of particularly importance, since fewer number of doses are needed in animals studies or in clinical protocol trial designs. 5. It is noted that most clinical trials had used only single dose. However, a single dose (for a single drug or for a drug‐combination dose) generates a Potency “Point”, but a “point” has no “shape”, and PD requires both “Potency” and “Shape”. 6. The PD indicates that Phase I clinical trial protocol‐design shall use 2 to 3 doses for single drug, and only ten doses (3+3+3+1=10) for two‐drug combination synergy quantification, using Chou‐Talalay combination index (CI) method and computer software. Thus, the new PD paradigm is the theoretical basis for simple, efficient, quantitative “econo‐green” bio‐R&D and drug PD parameter evaluations. 7. The explicit digital definition of “Synergism” of drug combination is now established. 8. The PD software, e.g., CompuSyn, allows automated generations of five graphic transformations for diagnostic plots [i.e., the dose‐effect curves, the median‐effect plots (for curve linearization), the CI‐Plot and the Isobologram (for synergy/antagonism determination), as well as the DRI‐Plot (for the dose‐reduction calculation in synergistic combinations). These automatic illustrations by computer simulation is completed within 1 second after data entries. Thus, the MAL‐PD/BD unified general theory is proven to have broad and cost‐effective utilities in biomedical R&D, provide automated quantitative/indexed basic parameters, diagnosis and conclusions for drug evaluations, and streamline the drug‐evaluation regulatory processes.Support or Funding InformationSelf Funding (Retired Professor)The Mass‐Action Law PD/BD Theory, Equations, and New Paradigm Revelations.Figure 1Mass‐Action Law Based Pharmacodynamics, Biodynamics and Bioinformatics Theory: Applications, Citation Metrics and Trend.Figure 2

SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. In the current preclinical drug combination screening, the top combinations for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested in Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler prioritized as top hits those synergistic drug pairs that showed higher selective efficacy (difference between efficacy and toxicity), which offers an improved likelihood for clinical success.

Prediction of drug combination effects with a minimal set of experiments.

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

Targeted Polo-like Kinase Inhibition Combined With Aurora Kinase Inhibition in Pediatric Acute Leukemia Cells.

Recent studies have shown that cell cycle events are tightly controlled by complex and shared activities of a select group of kinases. Among these, polo-like kinases (Plks) are regulatory mitotic proteins that are overexpressed in several types of cancer and are associated with poor prognosis. We have evaluated, in preclinical in vitro studies, the activity of a panel of Plk inhibitors against cell lines derived from refractory pediatric leukemia, as well as primary leukemia cells, in culture. Through in vitro growth inhibition studies, Western blot analysis for the expression and activation of key regulators of cell growth and survival and gene silencing studies, we specifically examined the ability of these agents to induce cytotoxicity through the activation of apoptosis and their capacity to interact and modulate the expression and phosphorylation of Aurora kinases. Our findings show that the various Plk-1 inhibitors in development show potential utility for the treatment of pediatric leukemia and exhibit a wide range of phosphorylation and target modulatory capabilities. Finally, we provide evidence for a complex interregulatory relationship between Plk-1 and Aurora kinases enabling the identification of synergy and biologic correlates of drug combinations targeting the 2 distinct enzyme systems. This information provide the rationale for the evaluation of Plk-1 as an effective target for therapeutics in refractory pediatric leukemia and indicate compensatory activities between Plk-1 and Aurora kinases, providing insight into some of the complex mechanisms involved in the process of cell division.

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer.

High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Quantifying Drug Combination Synergy along Potency and Efficacy Axes.

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

Mechanistic Insights of Pomalidomide Activity in Combination with Bortezomib and Dexamethasone in Multiple Myeloma and Immune Cells

Introduction: Results from the Phase III, randomized, open-label, clinical study OPTIMISMM comparing the combination of pomalidomide, bortezomib and low-dose dexamethasone (PVd) to bortezomib and low-dose dexamethasone (Vd) in patients with relapsed/refractory multiple myeloma who have all received 1-3 prior lines of therapy, demonstrated significant improvement in progression-free survival (PFS) for pomalidomide versus the comparator arm. Each agent exhibits cytotoxic effects on multiple myeloma (MM) cells when given alone, but acts synergistically in combination, due to non-redundant mechanisms of action. Furthermore, pomalidomide has immuno-stimulatory functionality and enhances T cell and NK-cell-mediated immunity. Pomalidomide binds Cereblon and facilitates degradation of Aiolos and Ikaros, conferring direct cytotoxic effects, while stimulating immune activity. MG132, a proteasome inhibitor (PI), can stabilize pomalidomide-mediated degradation of Aiolos and Ikaros at relatively high concentrations in vitro. A key question is whether PIs, at therapeutic levels, inhibit pomalidomide activity. One obstacle to achieving greater understanding of the bortezomib impact on pomalidomide activity is the highly cytotoxic effect of bortezomib on cultured MM cells. Following intravenous bortezomib injection at the maximal tolerated dose, the Cmax in blood is 260-520 nM, 5 minutes after injection, followed by a rapid drop-off. In vitro, bortezomib is cytotoxic at 1-4 nM, limiting the usefulness of the cell culture model. To circumvent this issue, we implemented a pulse method developed by Shabaneh (2013) and identified sublethal concentrations of bortezomib that had synergistic anti-proliferative effects at concentrations within the clinical window.Results: Pomalidomide alone had a dose-dependent anti-proliferative effect on all MM cell lines tested and addition of dexamethasone led to further inhibition of proliferation. Bortezomib pulse at concentrations ≥ 40 nM (MM1.S), 70 nM (H929) or 2 uM (LP-1) led to complete inhibition of proliferation at all concentrations of pomalidomide or dexamethasone used. The synergy and/or additivity of drug combinations was analyzed using the Chou-Talalay Method. Synergies of pomalidomide and bortezomib were observed in the 1-4 nM concentration range for continuous treatment, and 50-560 nM for pulsed treatment, with pomalidomide most synergistic around 100nM. Degradation of Ikaros, Aiolos and ZFP91 was confirmed in MM cells treated with pomalidomide alone and in combination with bortezomib, indicating no inhibitory effect of proteasomal inhibition on substrate degradation.In peripheral blood mononuclear cells (PBMCs), IL-2 and Granzyme B production was induced by pomalidomide and inhibited by bortezomib. The combination of pomalidomide and bortezomib (PV) also enhanced IL-2 and Granzyme B secretion albeit to a lesser extent than pomalidomide alone. Degradation of Aiolos, Ikaros and ZFP91 was also observed in isolated T-cells and results closely paralleled those of MM cells. T-cell proliferation was also enhanced by pomalidomide, with maximal effects observed in the 100 nM-1 uM concentration range. While bortezomib alone decreased T-cell proliferation by ~3-fold, the proliferation effect was comparable in cells treated with pomalidomide alone and PV.Conclusion: Pomalidomide, bortezomib and dexamethasone exert synergistic anti-proliferative effects on tumor cells. Despite inhibiting proteasomal activity, bortezomib does not antagonize pomalidomide-mediated degradation of Cereblon substrates Aiolos, Ikaros and ZFP91 when used at therapeutically relevant concentrations. Pulsing experiments provide the advantage of mimicking bortezomib dosing regimens; under these conditions, pomalidomide-mediated degradation of substrates was still observed. In immune cells, the PV combination has an overall positive impact on T-cell proliferation despite inhibitory activity of single agent bortezomib. Our results suggest that in combination, pomalidomide can overcome the immunosuppressive effects of bortezomib, when bortezomib is used at therapeutic levels. DisclosuresKatz:Celgene: Consultancy. Bjorklund:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene Corporation: Employment, Equity Ownership. Serbina:Celgene: Employment.

Investigating Different Mechanisms of Action in Combination Therapy for Influenza.

Combination therapy for influenza can have several benefits, from reducing the emergence of drug resistant virus strains to decreasing the cost of antivirals. However, there are currently only two classes of antivirals approved for use against influenza, limiting the possible combinations that can be considered for treatment. However, new antivirals are being developed that target different parts of the viral replication cycle, and their potential for use in combination therapy should be considered. The role of antiviral mechanism of action in the effectiveness of combination therapy has not yet been systematically investigated to determine whether certain antiviral mechanisms of action pair well in combination. Here, we use a mathematical model of influenza to model combination treatment with antivirals having different mechanisms of action to measure peak viral load, infection duration, and synergy of different drug combinations. We find that antivirals that lower the infection rate and antivirals that increase the duration of the eclipse phase perform poorly in combination with other antivirals.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations.

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients

The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targeting multiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL.Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. Cancer Res; 78(9); 2407-18. ©2018 AACR.

Neurofibromin level directs RAS pathway signaling and mediates sensitivity to targeted agents in malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumor (MPNST) is a type of soft-tissue sarcoma strongly associated with dysfunction in neurofibromin; an inhibitor of the RAS pathway. We performed high-throughput screening of an array of FDA approved and promising agents in clinical development both alone and in combination at physiologically achievable concentrations against a panel of established MPNST cell line models. We found that drugs targeting a variety of factors in the RAS pathway can effectively lead to cell death in vitro with considerable drug combination synergy in regimens that target MEK or mTOR. We observed that the degree of relative sensitivity to chemotherapeutic agents was associated with the status of neurofibromin in these cell line models. Using a combination of agents that target MEK and mTORC1/2, we effectively silenced RAS/PI3K/MEK/mTOR signaling in vitro. Moreover, we employed RNAi against NF1 to establish that MPNST drug sensitivity is directly proportional to relative level of intracellular neurofibromin. Thus, two-drug combinations that target MEK and mTORC1/2 are most effective in halting the RAS signaling cascade, and the relative success of this and related small molecule interventions in MPNSTs may be predicated upon the molecular status of neurofibromin.

In Vitro Elucidation of Drug Combination Synergy in Treatment of Pancreatic Ductal Adenocarcinoma

Advances in therapies targeting proteins and pathways affected by genetic alterations has raised the possibility of personalized cancer treatments. The efficacy of targeting molecular aberrations was determined in the pancreatic ductal adenocarcinoma (PDAC) cell line, CAPAN2. Two mutations were targeted, KRAS (p.G12V) and ABL1 (p.G1060D), and cells were treated with regorafenib and trametinib, individually and in combination. Exposure to either drug significantly increased cell death compared to the current standard of care, gemcitabine. Treatment with combinations of the drugs led to significant increases in cell death compared to either monotherapy. Strong additive/synergistic interactions were observed across a range of dosages and ratios, reducing dose requirements with potential clinical relevance. The data obtained in this PDAC cell model: i) support the use of matched monotherapies; ii) indicate the effectiveness of matched combination therapies; and iii) provide potential proof-of-concept for precision medicine approach to cancer treatment.

Drug Combination Synergy in Worm-like Polymeric Micelles Improves Treatment Outcome for Small Cell and Non-Small Cell Lung Cancer.

Nanoparticle-based systems for concurrent delivery of multiple drugs can improve outcomes of cancer treatments, but face challenges because of differential solubility and fairly low threshold for incorporation of many drugs. Here we demonstrate that this approach can be used to greatly improve the treatment outcomes of etoposide (ETO) and platinum drug combination ("EP/PE") therapy that is the backbone for treatment of prevalent and deadly small cell lung cancer (SCLC). A polymeric micelle system based on amphiphilic block copolymer poly(2-oxazoline)s (POx) poly(2-methyl-2-oxazoline- block-2-butyl-2-oxazoline- block-2-methyl-2-oxazoline) (P(MeOx- b-BuOx- b-MeOx) is used along with an alkylated cisplatin prodrug to enable co-formulation of EP/PE in a single high-capacity vehicle. A broad range of drug mixing ratios and exceptionally high two-drug loading of over 50% wt. drug in dispersed phase is demonstrated. The highly loaded POx micelles have worm-like morphology, unprecedented for drug loaded polymeric micelles reported so far, which usually form spheres upon drug loading. The drugs co-loading in the micelles result in a slowed-down release, improved pharmacokinetics, and increased tumor distribution of both drugs. A superior antitumor activity of co-loaded EP/PE drug micelles compared to single drug micelles or their combination as well as free drug combination was demonstrated using several animal models of SCLC and non-small cell lung cancer.

In silico drug combination discovery for personalized cancer therapy

BackgroundDrug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.ResultsWe propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.ConclusionUsing various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

Combination of selective polo-like kinase 1 (PLK1) inhibitor PCM-075 with abiraterone in prostate cancer and non-androgen-driven cancer models.

369 Background: The majority of metastatic castration-resistant prostate cancer (mCRPC) patients will become resistant to abiraterone. Drug combinations with abiraterone offer the possibility to extend patient benefit to anti-androgen therapy. PLK1, an essential mitotic kinase for onset of G2/M transition and cytokinesis, is over-expressed in prostate cancer, correlated positively with Gleason grade and PLK1 RNAi induces G2/M arrest and apoptosis in prostate cancer cells. PCM-075 (formerly NMS-1286937) is a potent, highly selective PLK1 inhibitor currently in clinical trials for the treatment of acute myeloid leukemia. Combination of PCM-075 with abiraterone was examined in mCRPC models and other cancer types for synergistic interaction. Methods: We used a variety of tumor cell lines (C4-2, LNCaP, BT-20, AU565, SK-OV-3), animal and computational modeling techniques to elucidate the mechanism and assess the potential clinical utility of synergistic tumor cell killing by PLK1 inhibitors (PLK1i), including PCM-075, with abiraterone. Results: Synergy was observed between abiraterone and PLK1i, including PCM-075, in models of CRPC (C4-2). In addition, PLK1i and abiraterone were synergistic in murine C4-2 xenografts. Unexpectedly, PLK1i sensitized a subset of non-prostate cancer cells to abiraterone, including AR-negative breast and ovarian cancer cells. In contrast to PLK1i, other mitotic arrest agents do not synergize with abiraterone. To elucidate the molecular basis this synergy, we screened a panel of cell lines and implemented a new computational method that links RNA expression patterns to drug combination synergy. This analysis implicated signaling through the retinoic acid (RA) nuclear receptors as a component of this synergy, which was validated experimentally. Increased basal expression of genes involved in RA metabolism in cancer cells is associated with increased synergy. Conclusions: Abiraterone has AR-independent effects that provide a mechanism and biomarkers predictive for synergistic killing when combined with the PLK1i PCM-075. This combination has potential to extend mCRPC patient benefit to abiraterone regardless of AR status.