Drug Combination Screening Research Articles

Optimization of on-chip bacterial culture conditions using the Box-Behnken design response surface methodology for faster drug susceptibility screening

Optimized culture conditions are essential for the investigation of biological processes. In this work, on-chip optimization of bacterial culture conditions by combining microfluidics with the Box-Behnken design response surface methodology is presented. With this methodology, the effects of several cultivation variables and their interactions were investigated enabling very fast drug susceptibility screening. The proposed measurement protocol for the determination of minimum inhibitory concentration (MIC) consist of three steps: i) single factor experiments to determine the effect of pH, nutrient concentration, and temperature on the bacterial culture; ii) analyses of the relationship between variables and the effect of the individual variables by means of the Box-Behnken design and response surface methodology (BBD-RSM) optimization; and iii) bacterial susceptibility screening of drugs and drug combinations. BBD-RSM is efficient to determine the optimal growth conditions of bacteria species with a strongly reduced amount of required experiments. On top of that, these experiments can in principle all be performed at the same time, yielding significant time-savings. The found optimized culture conditions of E. coli were applied to determine the MIC values of the drugs penicillin-streptomycin and baicalein, and combinations of those. MIC values were obtained within 8–14 h, including the 6–8 h required to determine the optimal growth parameters. The microfluidic BBD-RSM method results in a significant time reduction compared to the standard 2–4 days required to determine MIC values and is, therefore, a potential alternative in the management of bacterial infections.

A Plug-and-Play, Drug-on-Pillar Platform for Combination Drug Screening Implemented by Microfluidic Adaptive Printing.

Traditional high-throughput drug combination screening requires automatic pipetting of drugs into high-density microtiter plates. Here, a drug-on-pillar platform is proposed for efficient combination drug screening. Using the proposed approach, combination drug screening can be carried out in a plug-and-play manner, allowing for high-throughput screening of large permutations of drug combinations at various concentrations, such that drug dispensing and cell-based screening can be temporally separated and therefore can potentially be performed at distant laboratories. The dispensing is implemented using our recently developed microfluidic pneumatic printing platform, which features a low-cost disposable cartridge that minimizes cross contamination. Moreover, our previously developed drug nanoformulation method with amphiphilic telodendrimers has been utilized to maintain drug stability in a dry form, allowing for convenient drug storage, shipping, and subsequent rehydration. Combining the features described above, we have implemented a 1260-spot drug combination array to study the effect of paired drugs against MDA-MB-231 triple negative human breast cancer cells. This study supports the feasibility of the drug-on-pillar platform for combination drug screening and has provided valuable insight into drug combination efficacy against breast cancer.

Combinatorial Therapeutic Discovery Using Perturbational Gene-Expression Signatures

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, thereafter coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a geneexpression- based approach, which uses the recurrent perturbation–transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound–protein target counterparts, reproducible in external datasets, and correlated with small-molecule sensitivity. Using this new approach, we predicted synergistic drug pairs for cancer, including most frequently the combinations of a topoisomerase inhibitor and an mTOR or PI3K inhibitor. We also experimentally confirmed the synergistic effects of one combination of CD-437 (a retinoid) and sirolimus (an mTOR inhibitor) and the other combination of narciclasine (a protein synthesis inhibitor) and purvalanol A (a CDK inhibitor) in two breast and two lung cancer cell lines. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

Microfluidics platform lowers barrier to drug combination screening.

Nanodroplets promise to reduce the equipment, cost and time constraints of high-throughput screening, if researchers can overcome long-standing hurdles. Nanodroplets promise to reduce the equipment, cost and time constraints of high-throughput screening, if researchers can overcome long-standing hurdles.

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.

Scalable Multiplexed Drug-Combination Screening Platforms Using 3D Microtumor Model for Precision Medicine.

Cancer heterogeneity is a notorious hallmark of this disease, and it is desirable to tailor effective treatments for each individual patient. Drug combinations have been widely accepted in cancer treatment for better therapeutic efficacy as compared to a single compound. However, experimental complexity and cost grow exponentially with more target compounds under investigation. The primary challenge remains to efficiently perform a large-scale drug combination screening using a small number of patient primary samples for testing. Here, a scalable, easy-to-use, high-throughput drug combination screening scheme is reported, which has the potential of screening all possible pairwise drug combinations for arbitrary number of drugs with multiple logarithmic mixing ratios. A "Christmas tree mixer" structure is introduced to generate a logarithmic concentration mixing ratio between drug pairs, providing a large drug concentration range for screening. A three-layer structure design and special inlets arrangement facilitate simple drug loading process. As a proof of concept, an 8-drug combination chip is implemented, which is capable of screening 172 different treatment conditions over 1032 3D cancer spheroids on a single chip. Using both cancer cell lines and patient-derived cancer cells, effective drug combination screening is demonstrated for precision medicine.

Network Propagation Predicts Drug Synergy in Cancers.

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

BET Inhibition-Induced GSK3β Feedback Enhances Lymphoma Vulnerability to PI3K Inhibitors

The phosphatidylinositol 3 kinase (PI3K)-glycogen synthase kinase β (GSK3β) axis plays a central role in MYC-driven lymphomagenesis, and MYC targeting with bromodomain and extraterminal protein family inhibitors (BETi) is a promising treatment strategy in lymphoma. In a high-throughput combinatorial drug screening experiment, BETi enhance the antiproliferative effects of PI3K inhibitors in a panel of diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma cell lines. BETi or MYC silencing upregulates several PI3K pathway genes and induces GSK3β S9 inhibitory phosphorylation, resulting in increased β-catenin protein abundance. Furthermore, BETi or MYC silencing increases GSK3β S9 phosphorylation levels and β-catenin protein abundance through downregulating the E2 ubiquitin conjugating enzymes UBE2C and UBE2T. In a mouse xenograft DLBCL model, BETi decrease MYC, UBE2C, and UBE2T and increase phospho-GSK3β S9 levels, enhancing the anti-proliferative effect of PI3K inhibitors. Our study reveals prosurvival feedbacks induced by BETi involving GSK3β regulation, providing a mechanistic rationale for combination strategies.

Abstract B04: Palbociclib-based combinatorial drug screening in ER-positive cell lines identifies efficacious single agents and novel combinations with synergistic interactions

Abstract Estrogen receptor (ER)-positive breast cancer is associated with a good prognosis, but when ER+ breast tumors progress, treatment options are limited. The combination of the CDK inhibitor palbociclib plus the ER degrader fulvestrant was recently approved for patients who have recurrences of ER+ disease. However, durable responses are not typically seen with this combination, and patients eventually progress on therapy. We sought to test other drug combinations using palbociclib as the backbone of therapy. We tested 13 different FDA-approved drugs in combination with palbocilib in a panel of 7 ER+ breast cancer cell lines (600MPE, MDAMB134vii, MDAMB415, ZR75-1, ZR75B, and MCF7). Molecularly, all lines were characterized by amplification of the CCND1 region, high levels of expression of ER, and low RNA expression of EGFR. CAMA1 showed general resistance to the drugs used, never reaching a GR50 (growth rate inhibition of 50%) even at the highest dose tested with palbociclib. CAMA1 was also resistant to the majority of the other 13 drugs, with the exception of panobinostat, vorinostat, and everolimus. Across the entire cell line set, these three compounds were the most effective as single agents. For example, the HDAC inhibitors panobinostat and vorinostat had low nanomolar GR50 values in 7/7 and 4/7 cell lines, respectively. However, significant antagonism was observed between both HDAC inhibitors and palbociclib in MCF7, ZR75B, and ZR75-1, indicating that combining these agents may not be clinically effective. Everolimus plus palbociclib and trametinib plus palbociclib combinations were significantly synergistic in 3/7 cell lines (600MPE, ZR75B, and MCF7 in both cases). Interestingly, CAMA1 and MDAMB134 never showed any evidence of synergistic interactions with any of the combinations tested, suggesting that they may be a distinct subset of ER+ breast cancers. This study indicates that HDAC inhibitors may be effective for treating ER+ tumors that have progressed on antiestrogen therapy, and that combinations of palbociclib plus everolimus or trametinib may also have efficacy. Citation Format: Rebecca Smith, Tiera Liby, Joe Gray, James Korkola. Palbociclib-based combinatorial drug screening in ER-positive cell lines identifies efficacious single agents and novel combinations with synergistic interactions [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr B04.

High-Throughput Flow Cytometry Drug Combination Discovery with Novel Synergy Analysis Software, SynScreen

Classical therapeutic regimens are subject to toxicity, low efficacy, and/or the development of drug resistance. Thus, the discovery of synergistic drug combinations would permit treatment with lower, tolerable dosages of each agent and restored sensitivity. We describe the development and use of the SynScreen software application, which allows for visual and mathematical determinations of compound concentrations that produce super-additive effects. This software uses nonlinear regression fits of dose responses to determine synergism by the Bliss independence and Loewe additivity analysis models. We demonstrate the utility of SynScreen with data analysis from in vitro high-throughput flow cytometry (HTFC) combination screens with repurposed drugs and multiplexed synergy analysis of multiple biologic parameters in parallel. The applicability of SynScreen was confirmed by testing open-source data sets used in published drug combination literature. A key benefit of SynScreen for high-throughput drug combination screening is that observed measurements are graphically depicted in comparison with a three-dimensional surface that represents the theoretical responses at which Bliss additivity would occur. These images and summary tables for the calculated drug interactions are automatically exported. This allows for substantial data sets to be visually assessed, expediting the quick identification of efficacious drug combinations and thereby facilitating the design of confirmatory studies and clinical trials.

Abstract 4100: Personalized medicine in pancreatic cancer using cellular-resolution optical metabolic imaging

Abstract Pancreatic cancer has the worst prognosis of all cancers (5-year survival rate of 7%). There is a critical need for a tool to match individual patients with optimal drugs for their cancer. The goal of this work is to validate Optical Metabolic Imaging (OMI) of patient-derived pancreatic tumor organoids as a high-throughput predictive drug screen for patients. Drug induced changes in cell metabolism precede changes in tumor volume and thus present an earlier marker of treatment response. OMI is sensitive to cellular metabolism because it measures the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), and the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox balance, and the fluorescence lifetimes report on the binding activity of these coenzymes. OMI, performed using high-resolution multiphoton microscopy, has the unique ability to non-invasively monitor metabolism in living, intact 3D samples on the single-cell level, and can thus quantify inter-cellular heterogeneity in drug response. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These patient-specific organoids are grown in a 3D matrix that mimics the natural tumor environment. Drug response can be quantified separately in tumor cells and tumor-associated fibroblasts. Organoids were successfully generated from 9 of 11 surgically resected pancreatic tumors and 2 of 2 pancreatic intraepithelial neoplasias. These organoids were treated with a panel of standard and experimental therapies, and early metabolic changes were measured using OMI. Changes at the single-cell level were quantified using the OMI Index, a linear combination of the optical redox ratio and the mean NAD(P)H and FAD fluorescence lifetimes. Patient survival data after surgery and drug treatment was compared to drug response in organoids to that same treatment. Early results have focused on the standard gemcitabine + 5-fluorouracil (G+F) adjuvant treatment. Organoids generated from a patient whose CA19-9 biomarker levels increased while on G+F adjuvant therapy contained a subpopulation of 68% of the cells which were resistant to G+F therapy. Moreover, no G+F resistant cell subpopulations were found in organoids generated from a patient with no signs of disease recurrence over 14 months of G+F adjuvant therapy. Follow-up with the remaining patients is on-going. The ability of OMI to capture heterogeneous cell-level drug responses shows promise for predicting treatment response of individual pancreatic cancer patients. Citation Format: Joe T. Sharick, Tongcheng Qian, Jillian K. Johnson, Lingjun Li, Cheri A. Pasch, Dustin A. Deming, Alexander A. Parikh, Melissa C. Skala. Personalized medicine in pancreatic cancer using cellular-resolution optical metabolic imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4100.

Abstract 576: A cancer drug atlas enables prediction of parallel drug vulnerabilities

Abstract Personalized cancer treatments using synergistic combinations of drugs is attractive but proves to be highly challenging. The combinatorial nature of such problems results in an enormous parameter space that cannot be resolved by empirical research, i.e. testing all combinations for all molecularly defined tumors. In addition, effective drug synergy is hard to predict. Here we present an approach to map data of drug-response encyclopedias and represent these as a drug atlas. This atlas consists of a framework of chemotherapeutic responses that represents a drug vulnerability landscape of cancer. Based on data from the literature we found that many synergistic drug combinations show distinct inter therapy responses and drug sensitivities. We confirmed this by performing a drug combination screen against glioblastoma where we used 270 combination experiments. From the identified dual therapies we were able to predict and validate a triple drug synergy which was validated in vivo. This new and generalizable strategy opens the door to unforeseen personalized multidrug combination approaches. Citation Format: Ravi Narayan, Piet Molenaar, Jian Teng, Bakhos Tannous, Tom Wurdinger, Jan Koster, Bart Westerman. A cancer drug atlas enables prediction of parallel drug vulnerabilities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 576.

Abstract 2939: ETC159, a porcupine inhibitor, exhibits synergism with PI3K inhibitors in 3-dimensional cell culture

Abstract Aberrations in WNT signaling, a pathway regulated by a family of secreted glycoproteins known as WNTs, is implicated as a cause for cancer. Post-translational modification of WNTs by porcupine is critical for their activity and eventual activation of the WNT pathway. ETC-159 is an orally available, potent porcupine inhibitor. Addition of ETC-159 suppresses cancer proliferation in several cell lines by blocking the secretion and activity of all WNTs. Nevertheless, tumor growth is driven by complex molecular interactions that dysregulate several pathways at any one time. Thus, early drug discovery studies have increasingly evolved to target multiple molecules or pathways. As therapeutic approaches using combinations of drugs directed at multiple targets can improve treatment response, in this study we explored the possibility of identifying compounds that are synergistic with ETC-159. Unlike conventional two-dimensional combination drug screening, we used the three-dimensional soft agar colony formation assays for the study. Dose-response curves were first independently established to obtain the EC50 ranges for each drug, before proceeding to combination treatments. Using the Chou-Talalay Drug Combination Software, CompuSyn, we identified several inhibitors, which target the PI3K pathway, to act synergistically with ETC-159 in pancreatic cell lines HPAF-II, ASPC-1 and CFPAC-I. Similar treatments in nonpancreatic cell lines indicated that the observed synergism was not cell-line specific, hence highlighting the applicability of such combination treatments for different cancers. Citation Format: Sugunavathi Sepramaniam, Xin Hui Chew, Kao Chin Ngeow, May Ann Lee. ETC159, a porcupine inhibitor, exhibits synergism with PI3K inhibitors in 3-dimensional cell culture [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2939.

A microfluidics platform for combinatorial drug screening on cancer biopsies

Screening drugs on patient biopsies from solid tumours has immense potential, but is challenging due to the small amount of available material. To address this, we present here a plug-based microfluidics platform for functional screening of drug combinations. Integrated Braille valves allow changing the plug composition on demand and enable collecting >1200 data points (56 different conditions with at least 20 replicates each) per biopsy. After deriving and validating efficient and specific drug combinations for two genetically different pancreatic cancer cell lines and xenograft mouse models, we additionally screen live cells from human solid tumours with no need for ex vivo culturing steps, and obtain highly specific sensitivity profiles. The entire workflow can be completed within 48 h at assay costs of less than US$ 150 per patient. We believe this can pave the way for rapid determination of optimal personalized cancer therapies.

PO-023 A multi-step framework to analyse high-throughput drug combination screens

Introduction Tailored, genotype-driven therapies are the future for treatment of heterogeneous cancers like breast cancer. However, despite their high therapeutic index, targeted agents often have limited success in single agent treatment setups due to variable clinical response rates and resistance. Thus, a framework to develop drug combinations for treatment of cancer is needed. Material and methods Our group has an ongoing effort to systematically screen two-drug combinations in cancer type-specific and pan-cancer high-throughput drug combination screens. We have screened over 1000 combinations, including both broad-acting chemotherapeutics and targeted compounds. Cancer type-specific drug combination screens were conducted in breast and colorectal cancer cell lines, using a panel of 50 cell lines in each screen. Acquired data has been used to determine a strategy to extract the most promising drug combinations in each tissue. Results and discussions We propose a multi-step analytical framework for the identification and prioritisation of synergistic combinations. Initial filtering is based on several parameters, including synergy (z-score), XMID and Emax. Robustness of drug response effects is assessed through rescreening of selected drug combinations. Further prioritisation of promising combinations in a tissue-specific setting is achieved through stratification of the combination drug response in cell line subgroups, including segregation based on commonly mutated genes and molecular subtypes (e.g. PAM50 subgrouping in breast cancer). Moreover, systematic exploration of genotype-drug synergy associations will be used to identify biomarkers of response for patient stratification. Conclusion In conclusion, we have developed a multi-step framework to identify clinically relevant synergistic drug combinations complementing current cancer therapy options. This pipeline may facilitate future combination drug synergy predictions and validation.

Abstract P5-01-01: Personalized neoadjuvant treatment planning using optical metabolic imaging

Abstract Currently, there are no reliable methods to optimize treatment regimens for individual breast cancer patients. Oncologists choose drug treatments based on expression levels of tumor cell signaling receptors (i.e. HER2, ER, PR) and other factors, and assess whether the treatment is effective after significant time has passed. Unfortunately, over one third of patients exhibit resistance to their initial treatment, increasing their risk of future metastasis and death. Morbidities from sub-optimal drug regimens could be reduced with a personalized drug screen for breast cancer at the time of diagnosis. With the vast number of therapeutic options available to patients (&amp;gt;50 drugs approved with more on the way), a high-throughput screening technology is needed to accurately evaluate how a patient will respond to these options. Here we present Optical Metabolic Imaging (OMI) of tumor-derived organoids as a predictive drug screening platform for individual breast cancer patients. Changes in cell metabolism precede changes in tumor volume and thus present an earlier marker of treatment response. OMI is sensitive to these early changes by exploiting the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), as well as the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox balance, and the fluorescence lifetimes report on the binding activity of these coenzymes. OMI has the unique ability to non-invasively monitor metabolism in living, intact samples on the single-cell level, and can thus quantify heterogeneity in drug response. Changes were quantified at the single-cell level using the OMI Index, a linear combination of the optical redox ratio and the mean NAD(P)H and FAD fluorescence lifetimes. This index was derived using a multivariate analysis of variance and has been shown previously to correlate with treatment response in human cancer cells. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These samples are grown as organoids in a 3D matrix that mimics the natural tumor environment. Organoids were successfully generated from core needle biopsies of untreated breast tumors. These organoids were treated with the patient's prescribed neoadjuvant therapy, and early metabolic changes were quantified using OMI. Organoids grew from a variety of untreated breast tumor subtypes, and early metabolic changes could be resolved at the single-cell level after only 24 hours of treatment in vitro. In parallel, each patient's Residual Cancer Burden (RCB) score was quantified by a surgical pathologist after neoadjuvant treatment and served as gold standard validation of tumor drug response. Results from an early cohort of patients suggest that OMI could be used to predict patient clinical response to therapy. A linear combination of OMI variables measured in vitro in only 48 hours correlated strongly with patient RCB score (Pearson correlation coefficient=0.97, n=5). This methodology could allow oncologists to determine the ideal treatment regimen for their patients at the time of diagnosis. Citation Format: Sharick JT, Walsh AJ, Sanders ME, Kelley MC, Meszoely IM, Hooks MA, Burkard ME, Esbona K, Choudhary A, Skala MC. Personalized neoadjuvant treatment planning using optical metabolic imaging [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-01-01.

Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition.

High-throughput techniques allow for massive screening of drug combinations. To find combinations that exhibit an interaction effect, one filters for promising compound combinations by comparing to a response without interaction. A common principle for no interaction is Loewe Additivity which is based on the assumption that no compound interacts with itself and that two doses from different compounds having the same effect are equivalent. It then should not matter whether a component is replaced by the other or vice versa. We call this assumption the Loewe Additivity Consistency Condition (LACC). We derive explicit and implicit null reference models from the Loewe Additivity principle that are equivalent when the LACC holds. Of these two formulations, the implicit formulation is the known General Isobole Equation (Loewe, 1928), whereas the explicit one is the novel contribution. The LACC is violated in a significant number of cases. In this scenario the models make different predictions. We analyze two data sets of drug screening that are non-interactive (Cokol et al., 2011; Yadav et al., 2015) and show that the LACC is mostly violated and Loewe Additivity not defined. Further, we compare the measurements of the non-interactive cases of both data sets to the theoretical null reference models in terms of bias and mean squared error. We demonstrate that the explicit formulation of the null reference model leads to smaller mean squared errors than the implicit one and is much faster to compute.

A Microfluidic Droplet Array System for Cell-Based Drug Combination Screening.

In the last few decades, drug combination therapy has been widely applied in oncology and in other complex diseases. Due to its potential advantage of lower drug toxicity and higher therapeutic efficacy, drug combination treatment has been more and more studied in fundamental labs and pharmacy companies. In this chapter, we report cell-based drug combination screening using a microfluidic droplet system based on a sequential operation droplet array (SODA) technique. In this system, an oil-covered two-dimensional droplet array chip was used as the platform for cell culture and analysis. This chip was fixed in an x-y-z translation stage under control of a computer program. A tapered capillary connected with a syringe pump was coupled with the droplet array chip to achieve multiple droplet manipulations including liquid metering, aspirating, depositing, mixing, and transferring. Complex multistep operations for drug combination screening involving long-term cell culture, medium changing, schedule-dependent drug dosage and stimulation, and cell viability testing were achieved in parallel using the present system. The drug consumption for each screening test was substantially decreased to 5ng-5μg, corresponding to 10- to 1000-fold reductions compared with traditional drug screening systems with 96- or 384-well plates.

Methods for High-throughput Drug Combination Screening and Synergy Scoring.

Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

Orthogonal Screening of Anticancer Drugs Using an Open-Access Microfluidic Tissue Array System.

Screening for potential drug combinations presents significant challenges to the current microfluidic cell culture systems, due to the requirement of flexibility in liquid handling. To overcome this limitation, we present here an open-access microfluidic tissue array system specifically designed for drug combination screening. The microfluidic chip features a key structure in which a nanoporous membrane is sandwiched by a cell culture chamber array layer and a corresponding media reservoir array layer. The microfluidic approach takes advantage of the characteristics of the nanoporous membrane: on one side, this membrane permits the flow of air but not liquid, thus acting as a flow-stop valve to enable automatic cell distribution; on the other side, it allows diffusion-based media exchange and thus mimics the endothelial layer. In synergy with a liquid-transferring platform, the open-access microfluidic system enables complex multistep operations involving long-term cell culture, medium exchange, multistep drug treatment, and cell-viability testing. By using the microfluidic protocol, a 10 × 10 tissue array was constructed in 90 s, followed by schedule-dependent drug testing. Morphological and immunohistochemical assays indicated that the resultant tumor tissue was faithful to that in vivo. Drug-testing assays showed that the incorporation of the nanoporous membrane further decreased killing efficacy of the anticancer agents, indicating its function as an endothelial layer. Robustness of the microfluidic system was demonstrated by implementing a three-factor, three-level orthogonal screening of anticancer drug combinations, with which 67% of the testing (9 vs. 27) was saved. Experimental results demonstrated that the microfluidic tissue system presented herein is flexible and easy-to-use, thus providing an ideal tool for performing complex multistep cell assays with high efficiencies.