In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index

Investigation of the cytotoxicity induced by cannabinoids on human ovarian carcinoma cells

Cannabinoids have been shown to induce anti-tumor activity in a variety of carcinoma cells such as breast, prostate, and brain. The aim of the present study is to investigate the anti-tumor activity of cannabinoids, CBD (cannbidiol), and CBG (cannabigerol) in ovarian carcinoma cells sensitive and resistant to chemotherapeutic drugs. Sensitive A2780 cells and resistant A2780/CP70 carcinoma cells and non-carcinoma cells were exposed to varying concentrations of CBD, CBG, carboplatin or CB1 and CB2 receptor antagonists, AM251 and AM630, respectively, alone or in combination, at different exposure times and cytotoxicity was measured by MTT assay. The mechanism of action of CBD and CB in inducing cytotoxicity was investigated involving a variety of apoptotic and cell cycle assays. Treatment with CBD and CBG selectively, dose and time dependently reduced cell viability and induced apoptosis. The effect of CBD was stronger than CBG in all cell lines tested. Both CBD and CBG induced stronger cytotoxicity than afforded by carboplatin in resistant cells. The cytotoxicity induced by CBD was not CB1 or CB2 receptor dependent in both carcinoma cells, however, CBG-induced cytotoxicity may involve CB1 receptor activity in cisplatin-resistant carcinoma cells. A synergistic effect was observed when cannabinoids at sublethal doses were combined with carboplatin in both carcinoma cells. The apoptotic event may involve loss of mitochondrial membrane potential, Annexin V, caspase 3/7, ROS activities, and cell cycle arrest. Further studies are required to investigate whether these results are translatable in the clinic. Combination therapies with conventional cancer treatments using cannabinoids are suggested.

RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.

Dual Vinorelbine bitartrate and Resveratrol Loaded Polymeric Aqueous core Nanocapsules for Synergistic Efficacy in Breast Cancer

AIM

The current study focused on the development and evaluation of aqueous core nanocapsules (ACNs) as an effective carrier to deliver an optimal synergistic combination of a highly water soluble Vinorelbine bitartrate (VRL) and a poorly water-soluble Resveratrol (RES) for treatment of breast cancer.

METHODS

Various molar ratios of VRL to RES were screened against MCF-7 cell lines to determine the synergistic effects using Chou-Talalay method. Synergistic ratio of therapeutic agents was then incorporated into aqueous core nanocapsules utilizing a double emulsion solvent evaporation technique to yield dual drug loaded nanocapsules (dd-ACNs). The dd-ACNs were optimized using Box-Behnken design and characterized for physicochemical parameters such as particle size, zeta potential, polydispersity index, total drug content and encapsulation efficiency, surface morphology, drug excipient compatibility by FTIR and DSC, release kinetics, toxicity studies and anticancer efficacy (in-vitro and in-vivo).

RESULTS

Results demonstrated that the combination exhibited maximum synergy when higher doses of VRL were combined with smaller doses of RES (1:1, 5:1, and 10:1). The dual drug loaded ACNs were found to be stable and depicted a core-shell structure, narrow size range (150.2 ± 3.2 nm) with enhanced encapsulation (80% for VRL and 99% for RES). Moreover, the dd-ACNs were 5 times more efficacious in-vitro than a combination of free drugs, while reducing systemic toxicity. Also, pre-clinical evaluation of dd-ACNs also depicted drastic reduction of tumor volume as compared tp pristine VRL and physical combination of drugs.

CONCLUSION

The developed dd-ACNs can be applied as potential carrier for delivery of combination of chemotherapeutics at a synergistic ratio at tumor site.

Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization

Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Differential effects of cisplatin combined with the flavonoid apigenin on HepG2, Hep3B, and Huh7 liver cancer cell lines

The potential of apigenin (APG) to enhance cisplatin's (CDDP) chemotherapeutic efficacy was investigated in HepG2, Hep3B, and Huh7 liver cancer cell lines. The presence of 20 μM APG sensitized all cell lines to CDDP treatment (degree of sensitization based on the MTT assay: HepG2>Huh7>Hep3B). As reflected by sister chromatid exchange levels, the degree of genetic instability as well as DNA repair by homologous recombination differed among cell lines. CDDP and 20 μM APG cotreatment exhibited a synergistic genotoxic effect on Hep3B cells and a less than additive effect on HepG2 and Huh7 cells. Cell cycle delays were noticed during the first mitotic division in Hep3B and Huh7 cells and the second mitotic division in HepG2 cells. CDDP and CDDP + APG treatments reduced the clonogenic capacity of all cell lines; however, there was a discordance in drug sensitivity compared with the MMT assay. Furthermore, a senescence-like phenotype was induced, especially in Hep3B and Huh7 cells. Unlike CDDP monotherapy, the combined treatment exhibited a significant anti-invasive and anti-migratory action in all cancer cell lines. The fact that the three liver cancer cell lines responded differently, yet positively, to CDDP + APG cotreatment could be attributed to variations they present in gene expression. Complex mechanisms seem to influence cellular responses and cell fate.

Synergistic Effect of 1,3,6-Trihydroxy-4,5,7-Trichloroxanthone in Combination with Doxorubicin on B-Cell Lymphoma Cells and Its Mechanism of Action Through Molecular Docking

Background

The increasing rate of cancer chemoresistance and adverse side effects of therapy have led to the wide use of various chemotherapeutic combinations in cancer management, including lymphoid malignancy.

Objective

We investigated the effects of a combination of 1,3,6-trihydroxy-4,5,7-trichloroxanthone (TTX) and doxorubicin on the Raji lymphoma cell line.

Methods

Raji cells were treated with different concentrations of TTX, doxorubicin, or combinations thereof. Cancer cell growth inhibition was evaluated using 3-(4,5-dimethyltiazol-2-yl)-2,5- diphenyltetrazolium bromide/MTT assay to determine the half-maximal inhibitory concentration. Combination index values were calculated using CompuSyn (ComboSyn, Inc, Paramus, NJ). Molecular docking was conducted using a Protein-Ligand ANT System.

Results

The mean (SD) half-maximal inhibitory concentration values of TTX and doxorubicin were 15.948 (3.101) µM and 25.432 (1.417) µM, respectively. The combination index values of the different combinations ranged from 0.057 to 0.285, indicating strong to very strong synergistic effects. The docking study results reveal that TTX docks at the active site of Raf-1 and c-Jun N-kinase receptors with predicted free energies of binding of -79.37 and -75.42 kcal/mol, respectively.

Conclusions

The xanthone-doxorubicin combination showed promising in vitro activity against lymphoma cells. The results also indicate that the TTX and doxorubicin combination's effect was due to the interaction between TTX with Raf-1 and c-Jun N-kinase receptors, 2 determinants of doxorubicin resistance progression.

Identification of a Synergistic Multi-Drug Combination Active in Cancer Cells via the Prevention of Spindle Pole Clustering

A major limitation of clinically used cancer drugs is the lack of specificity resulting in toxicity. To address this, we performed a phenotypically-driven screen to identify optimal multidrug combinations acting with high efficacy and selectivity in clear cell renal cell carcinoma (ccRCC). The search was performed using the Therapeutically Guided Multidrug Optimization (TGMO) method in ccRCC cells (786-O) and nonmalignant renal cells and identified a synergistic low-dose four-drug combination (C2) with high efficacy and negligible toxicity. We discovered that C2 inhibits multipolar spindle pole clustering, a survival mechanism employed by cancer cells with spindle abnormalities. This phenotype was also observed in 786-O cells resistant to sunitinib, the first line ccRCC treatment, as well as in melanoma cells with distinct percentages of supernumerary centrosomes. We conclude that C2-treatment shows a high efficacy in cells prone to form multipolar spindles. Our data suggest a highly effective and selective C2 treatment strategy for malignant and drug-resistant cancers.

Synthesis, In Vitro Evaluation, Molecular Docking and DFT Studies of Some Phenyl Isothiocyanates as Anticancer Agents

BACKGROUND

Isothiocyanates (ITCs) are small molecules that are important in synthetic organic chemistry, but their actual importance lies in their potential as anti-carcinogens. Through this piece of work, an effort was made to assess the anti-cancer activity of some simple ITCs which can be synthesized through easy greener pathways.

METHODS

Cell proliferation assay was performed on ovarian cancer cells (PA-1) and non-tumorigenic ovarian epithelial cells (IOSE-364). Furthermore, qRT-PCR for transcript expression levels of Spindlin1 and caspases in ovarian cancer cells and cell cycle analysis was performed. In silico studies were incorporated to understand the mode of ligand-protein interaction, ADME/Toxicity and drug-likeliness parameters. Density functional theory studies have been also been employed on the ITCs to assess their efficiency in anticancer activity.

RESULTS

An inexpensive, environmentally benign pathway has been developed for synthesizing a series of ITCs. Among the synthesized ITCs, NC6 showed better cytotoxic effects as compared to its counterparts. Novel findings revealed that NC6 had 5-folds lower transcript expression levels of Spindlin1 and induced caspases 3 and 7 expressions assessed by qRT-PCR in ovarian cancer cells. Furthermore, flow cytometry assay showed the cell cycle arrest at G1/S phase of cell cycle. The molecular docking studies revealed favorable binding affinities and the physiochemical parameters were predicted to be compatible with drug-likeliness.

CONCLUSION

The results demonstrated the possibility that small isothiocyanate molecules which can be synthesized by a simple green methodology, can pose as promising candidates for their application as anticancer agents.

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.

COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining

BACKGROUND

Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated.

RESULTS

Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects. COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells.

CONCLUSIONS

COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases.

Effect of Spheroidal Age on Sorafenib Diffusivity and Toxicity in a 3D HepG2 Spheroid Model

The enhanced predictive power of 3D multi-cellular spheroids in comparison to conventional monolayer cultures makes them a promising drug screening tool. However, clinical translation for pharmacology and toxicology is lagging its technological progression. Even though spheroids show a biological complexity resembling native tissue, standardization and validation of drug screening protocols are influenced by continuously changing physiological parameters during spheroid formation. Such cellular heterogeneities impede the comparability of drug efficacy studies and toxicological screenings. In this paper, we demonstrated that aside from already well-established physiological parameters, spheroidal age is an additional critical parameter that impacts drug diffusivity and toxicity in 3D cell culture models. HepG2 spheroids were generated and maintained on a self-assembled ultra-low attachment nanobiointerface and characterized regarding time-dependent changes in morphology, functionality as well as anti-cancer drug resistance. We demonstrated that spheroidal aging directly influences drug response due to the evolution of spheroid micro-structure and organo-typic functions, that alter inward diffusion, thus drug uptake.

COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies

BACKGROUND

Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements.

RESULTS

COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer. This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use.

CONCLUSIONS

COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.

Mutant KRAS promotes liver metastasis of colorectal cancer, in part, by upregulating the MEK-Sp1-DNMT1-miR-137-YB-1-IGF-IR signaling pathway

Although the role of insulin-like growth factor-I receptor (IGF-IR) in promoting colorectal liver metastasis is known, the mechanism by which IGF-IR is upregulated in colorectal cancer (CRC) is not defined. In this study, we obtained evidence that mutant KRAS transcriptionally activates IGF-IR gene expression through Y-box-binding protein (YB)-1 upregulation via a novel MEK-Sp1-DNMT1-miR-137 pathway in CRC cells. The mechanistic link between the tumor suppressive miR-137 and the translational regulation of YB-1 is intriguing because epigenetic silencing of miR-137 represents an early event in colorectal carcinogenesis due to promoter hypermethylation. This proposed signaling axis was further verified by the immunohistochemical evaluations of liver metastases from a cohort of 46 KRAS mutant CRC patients, which showed a significant correlation in the expression levels among Sp1, miR-137, YB-1, and IGF-1R. Moreover, suppression of the expression of YB-1 and IGF-IR via genetic knockdown or the pharmacological inhibition of MEK hampers KRAS-driven colorectal liver metastasis in our animal model studies. From a translational perspective, the identification of this KRAS-driven pathway might provide a mechanistic rationale for the use of a MEK inhibitor as an adjuvant, in combination with standard of care, to prevent the recurrence of colorectal liver metastasis in KRAS mutant CRC patients after receiving liver resection, which warrants further investigation.

Rosiglitazone diminishes the high-glucose-induced modulation of 5-fluorouracil cytotoxicity in colorectal cancer cells

Colorectal cancer (CRC) is the third most leading cause of morbidity and mortality throughout the world. 5-fluorouracil (5-FU), which is often administrated to disrupt carcinogenesis, was found to elevate blood glucose level among CRC patients. Thus, this study was conducted to evaluate the influence of rosiglitazone on antiproliferative effect of 5-FU using cellular model. Two human colonic carcinoma cell lines (HCT 116 and HT 29) were cultured in the presence of 5-FU, rosiglitazone or in combination under normal and high glucose concentration. The drug cytotoxicity was evaluated using the MTT assay whereas the assessment of cell cycle was carried out using the flow cytometry technique. Combination index (CI) method was used to determine the drug interaction between rosiglitazone and 5-FU. High glucose diminished the cytotoxic effect of 5-FU but at a high drug dosage, this effect could be overcome. Cell cycle analysis demonstrated that 5-FU and rosiglitazone caused G1-phase arrest and S-phase arrest, respectively. CI values indicated that rosiglitazone exerted synergistic effect on 5-FU regardless of glucose levels. This study is the first to demonstrate the influence of rosiglitazone on cytotoxicity of 5-FU under normal or high glucose level. Rosiglitazone may be a promising drug for enhancing the efficacy of 5-FU in the treatment of CRC associated with hyperglycemia.

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

Background

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations.

Methods

The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications.

Results

Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990.

Conclusions

Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s13048-017-0365-9) contains supplementary material, which is available to authorized users.

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

Background

To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test^®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods.

Methods

We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements.

Results

The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor.

Conclusion

Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

Combination screening in vitro identifies synergistically acting KP372-1 and cytarabine against acute myeloid leukemia

Cytogenetic lesions often alter kinase signaling in acute myeloid leukemia (AML) and the addition of kinase inhibitors to the treatment arsenal is of interest. We have screened a kinase inhibitor library and performed combination testing to find promising drug-combinations for synergistic killing of AML cells. Cytotoxicity of 160 compounds in the library InhibitorSelect™ 384-Well Protein Kinase Inhibitor I was measured using the fluorometric microculture cytotoxicity assay (FMCA) in three AML cell lines. The 15 most potent substances were evaluated for dose-response. The 6 most cytotoxic compounds underwent combination synergy analysis based on the FMCA readouts after either simultaneous or sequential drug addition in AML cell lines. The 4 combinations showing the highest level of synergy were evaluated in 5 primary AML samples. Synergistic calculations were performed using the combination interaction analysis package COMBIA, written in R, using the Bliss independence model. Based on obtained results, an iterative combination search was performed using the therapeutic algorithmic combinatorial screen (TACS) algorithm. Of 160 substances, cell survival was ⩽50% at <0.5μM for Cdk/Crk inhibitor, KP372-1, synthetic fascaplysin, herbimycin A, PDGF receptor tyrosine kinase inhibitor IV and reference-drug cytarabine. KP372-1, synthetic fascaplysin or herbimycin A obtained synergy when combined with cytarabine in AML cell lines MV4-11 and HL-60. KP372-1 added 24h before cytarabine gave similar results in patient cells. The iterative search gave further improved synergy between cytarabine and KP372-1. In conclusion, our in vitro studies suggest that combining KP372-1 and cytarabine is a potent and synergistic drug combination in AML.