Discovery Of Drug Combinations Research Articles

Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Abstract The battle against many cancers and infectious diseases has long been hindered due to the complexity of finding potent and effective drug combinations. With each new drug considered, the number of combinations to potentially test increases exponentially, posing substantial challenges in screening throughput. These challenges further intensify when accounting for the number of doses of each drug that need to be tested in a drug combination matrix (known as matrix density). There is a pressing need to screen large amounts of combinations at sufficient density to discover new therapies for diseases like cancer, but this has traditionally been out of reach. However, the recent widespread adoption of acoustic liquid handling robots has shown promise to overcome these obstacles by allowing for intricate drug screening template designs which were previously not possible to make. Despite these advances, the throughput achieved by these technologies has been limited due to lack of broadly accessible protocols and analytical tools for drug combination screening. We present Combocat, an end-to-end platform that allows for substantial increases in throughput of drug combination screens by combining experimental protocols that can be deployed for acoustic liquid handlers, machine learning algorithms for data imputation, and software that allows for in-depth analysis of results. We first generated a reference dataset of over 250,000 unique drug combination measurements in multiple cancer cell lines. The combination data were collected in a dense format (10×10 combination matrices) using a novel drug-drug template and achieved a dramatic increase in throughput compared to conventional methods. We then used this dataset to build a computational model which allowed us to accurately estimate drug combination effects using sparse measurements and imputing non-measured values with machine learning. The sparse measurements are collected in 1536-well microplates and substantially boost the throughput capabilities of drug-drug screens. As proof-of-concept, we used our method to screen a preclinical model of neuroblastoma with 9,045 drug combinations. This represents 10% the scale of the largest drug combination studies ever reported, achieved using a fraction of the resources, and in dense formats. We validated our findings by re-screening top hits using the fully-measured, non-imputed method and demonstrate the accuracy of our platform. The Combocat platform’s documentation and codebase is open-source, and we also make a GUI available for interactive exploration of screening results. By integrating advanced experimental and computational methods, we provide a generalizable pipeline that will expedite synergy screens and the drug combination discovery process for many diseases. Citation Format: William C. Wright, Paul Geeleher. An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4936.

Natural product based anticancer drug combination discovery assisted by deep learning and network analysis

Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products compared with the textbook approach of utilizing existing chemotherapy drug combinations. Many natural products achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, a drug protein interaction (DPI) prediction DNN, DeepDPI, was developed to employ DPI drug representations and achieved state-of-the-art performance using the DrugBank database. Two DNNs were also developed to predict novel drug combinations: DeepNPD, which predicts combinations in natural products, and DeepCombo, which predicts synergy in chemotherapy drugs, using the HERB and DrugCombDB databases respectively. An ensemble architecture enhanced with a novel similarity based weight adjustment (SBWA) approach was used and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepNPD predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepNPD notably identified Thioguanine and Hydroxyurea and DeepCombo discovered Vinblastine and Dasatinib as hits for potential new anticancer drug combinations. DeepNPD illustrates how natural products are a novel path where new drug combinations can be discovered.

ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.

Systematic high-throughput combination drug screen to enhance poly (ADP-ribose) polymerase (PARP) inhibitor efficacy in ovarian cancer.

5546 Background: PARP inhibitors currently offer the greatest benefit to patients with loss-of-function BRCA1 or 2 mutations or other causes of homologous recombination deficiency (HRD). We hypothesized that combination therapy, possibly by augmenting or inducing an HRD phenotype, would enhance the efficacy of PARP inhibition in epithelial ovarian cancer. Methods: We performed a high-throughput screen of an informer set containing FDA-approved drugs, clinical candidates, and small-molecule probes to identify synergistic partners with olaparib in representative HRD and non-HRD human epithelial ovarian cancer cells. Single agent activity was described by area under the curve of the Hafner growth rate index (AUC_GRI) and synergy was ranked by cumulative difference from Bliss (ΔBliss). Analysis of the drug screen was performed using algorithms developed by the Gulf Coast Consortia’s Combinatorial Drug Discovery Program at the Texas A&M Institute of Bioscience and Technology. MTT assays were then independently performed to verify and characterize synergy between CT7439, an agent selected following the drug screen, and olaparib. MTT data were analyzed using the SynergyFinder Plus web application. Results: In an initial single agent screen, we identified 62 compounds with a mean AUC_GRI <0.75, indicating inhibitory activity. Prioritizing more potent agents and those of mechanistic interest, we selected 40 hits to validate and advance to a combination screen. Diverse mechanisms of action were represented among the hits, including many small molecule inhibitors of various cell cycle pathways. Following a combination screen with olaparib, the compounds selected for final validation were: AZD 7762 (CHK1/2 inhibitor), dinaciclib (pan-CDK inhibitor), onalespib (HSP90 inhibitor), and SN-38 (topoisomerase 1 inhibitor). The ΔBliss for these agents are presented in the table. Due to mechanistic rationale and concern for overlapping toxicities among the other candidates, we focused further study on CDK inhibitors. In MTT assays, we identified robust synergy between olaparib and CT7439, a next-generation oral CDK12/13 inhibitor, with a mean Bliss synergy score of 1.31 and scores as high as 22.03 at certain concentrations in the non-HRD model OVCAR-5. These findings were recapitulated in the HRD cell lines OVCAR-4 (mean Bliss synergy score 1.56) and OVCAR-8 (mean Bliss synergy score 5.69). Results from an in vivo model will be presented as well. Conclusions: Pairing a PARP inhibitor and CDK inhibitor, a strategy identified by a high-throughput drug screen and known to impair homologous recombination, resulted in robust synergy in both HRD and non-HRD ovarian cancer models. Clinical translation of this combination may enhance the efficacy of PARP inhibitors in patients with ovarian cancer. [Table: see text]

Harmonizing across datasets to improve the transferability of drug combination prediction

Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose–response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.

CDCDB: A large and continuously updated drug combination database

In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book®, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/.

Discovery of Synergistic Drug Combinations for Colorectal Cancer Driven by Tumor Barcode Derived from Metabolomics "Big Data".

The accumulation of cancer metabolomics data in the past decade provides exceptional opportunities for deeper investigations into cancer metabolism. However, integrating a large amount of heterogeneous metabolomics data to draw a full picture of the metabolic reprogramming and to discover oncometabolites of certain cancers remains challenging. In this study, a tumor barcode constructed based upon existing metabolomics “big data” using the Bayesian vote-counting method is proposed to identify oncometabolites in colorectal cancer (CRC). Specifically, a panel of oncometabolites of CRC was generated from 39 clinical studies with 3202 blood samples (1332 CRC vs. 1870 controls) and 990 tissue samples (495 CRC vs. 495 controls). Next, an oncometabolite-protein network was constructed by combining the tumor barcode and its involved proteins/enzymes. The effect of anti-cancer drugs or drug combinations was then mapped into this network by the random walk with restart process. Utilizing this network, potential Irinotecan (CPT-11)-sensitizing agents for CRC treatment were discovered by random forest and Xgboost. Finally, a compound named MK-2206 was highlighted and its synergy with CPT-11 was validated on two CRC cell lines. To summarize, we demonstrate in the present study that the metabolomics “big data”-based tumor barcodes and the subsequent network analyses are potentially useful for drug combination discovery or drug repositioning.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry

SummaryDrugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Enzyme Activity of Natural Products on Cytochrome P450.

Drug-metabolizing enzymes, particularly the cytochrome P450 (CYP450) monooxygenases, play a pivotal role in pharmacokinetics. CYP450 enzymes can be affected by various xenobiotic substrates, which will eventually be responsible for most metabolism-based herb–herb or herb–drug interactions, usually involving competition with another drug for the same enzyme binding site. Compounds from herbal or natural products are involved in many scenarios in the context of such interactions. These interactions are decisive both in drug discovery regarding the synergistic effects, and drug application regarding unwanted side effects. Herein, this review was conducted as a comprehensive compilation of the effects of herbal ingredients on CYP450 enzymes. Nearly 500 publications reporting botanicals’ effects on CYP450s were collected and analyzed. The countries focusing on this topic were summarized, the identified herbal ingredients affecting enzyme activity of CYP450s, as well as methods identifying the inhibitory/inducing effects were reviewed. Inhibitory effects of botanicals on CYP450 enzymes may contribute to synergistic effects, such as herbal formulae/prescriptions, or lead to therapeutic failure, or even increase concentrations of conventional medicines causing serious adverse events. Conducting this review may help in metabolism-based drug combination discovery, and in the evaluation of the safety profile of natural products used therapeutically.

Multi-Organs-on-Chips for Testing Small-Molecule Drugs: Challenges and Perspectives.

Organ-on-a-chip technology has been used in testing small-molecule drugs for screening potential therapeutics and regulatory protocols. The technology is expected to boost the development of novel therapies and accelerate the discovery of drug combinations in the coming years. This has led to the development of multi-organ-on-a-chip (MOC) for recapitulating various organs involved in the drug–body interactions. In this review, we discuss the current MOCs used in screening small-molecule drugs and then focus on the dynamic process of drug absorption, distribution, metabolism, and excretion. We also address appropriate materials used for MOCs at low cost and scale-up capacity suitable for high-performance analysis of drugs and commercial high-throughput screening platforms.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

Systems Pharmacology-Based Precision Therapy and Drug Combination Discovery for Breast Cancer.

Simple SummaryBreast cancer (BC) is a typical global cancer and the second leading cause of cancer-related deaths among women worldwide. BC is a heterogeneous disease with several subtypes, and it is a challenge to use multi-omic data effectively to find suitable drugs for patients. In this paper, we used the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drug combinations that could be used to treat different BC subtypes. Our results showed that this precision drug discovery strategy identified important disease-related genes in individuals and specific groups, supporting its efficiency, high reliability, and practical application value in drug discovery.Breast cancer (BC) is a common disease and one of the main causes of death in females worldwide. In the omics era, researchers have used various high-throughput sequencing technologies to accumulate massive amounts of biomedical data and reveal an increasing number of disease-related mutations/genes. It is a major challenge to use these data effectively to find drugs that may protect human health. In this study, we combined the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drugs that could be used to treat different BC subtypes. We used this strategy to screen four BC subtype-specific drug combinations and verified the potential activity of combining gefitinib and irinotecan in triple-negative breast cancer (TNBC) through in vivo and in vitro experiments. The results of cell and animal experiments demonstrated that the combination of gefitinib and irinotecan can significantly inhibit the growth of TNBC tumour cells. The results also demonstrated that this systems pharmacology-based precision drug discovery strategy effectively identified important disease-related genes in individuals and special groups, which supports its efficiency, high reliability, and practical application value in drug discovery.

Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-Like Acute Lymphoblastic Leukemia.

Systems biology approaches can identify critical targets in complex cancer signaling networks to inform new therapy combinations that may overcome conventional treatment resistance. We performed integrated analysis of 1,046 childhood B-ALL cases and developed a data-driven network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome-like B-acute lymphoblastic leukemia (Ph-like B-ALL), a common high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. We identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Genetic cotargeting of the synergistic key regulator pair STAT5B and BCL2-associated athanogene 1 (BAG1) significantly reduced leukemia cell viability in vitro. Pharmacologic inhibition with dual small molecule inhibitor therapy targeting this pair of key nodes further demonstrated enhanced antileukemia efficacy of combining the BCL-2 inhibitor venetoclax with the tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple childhood Ph-like ALL patient-derived xenograft models. Consistent with network controllability theory, co-inhibitor treatment also shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1-rearranged (Ph+) B-ALL and more similar to prognostically favorable childhood B-ALL subtypes. Our study represents a powerful conceptual framework for combinatorial drug discovery based on systematic interrogation of synergistic vulnerability pathways with pharmacologic inhibitor validation in preclinical human leukemia models.

Abstract 168: Computational model for prediction of actionable drug combinations in cancer

Abstract Synthetic essentiality presents an effective approach towards finding connected genetic targets in cancer by identifying genes that produce an inviable phenotype upon joint inactivation. However, it is less studied how synthetic lethal gene pairs may be contextualized with respect to deregulation of different cell functions and how these pairs act in different cell types and tissues. To study cancer-specific synthetic essentiality, we devised an integrated network-based algorithm to analyze relationships between tumor gene expression and patient survival in The Cancer Genome Atlas (TCGA) and expression data from the Genotype-Tissue Expression (GTEx) project. In particular, we identify gene pairs in each cancer cohort that exhibit upregulation in tumors but are associated with improved survival for patients whose tumors feature reduced expression of the gene pair. Our findings reveal variability in synthetic lethal pair activity across different cancer types, and the identified pairs connect distinct signaling and metabolic cellular pathways to different cancer types. Altogether, our findings present a pan-cancer reference for gene expression-based synthetic lethal gene pairs as well as a nuanced perspective on changes in synthetic essentiality across different cellular and tissue contexts, pointing to a new avenue for drug combination discovery oriented on targeting key pathways based on the cancer type. Citation Format: Sairahul Pentaparthi, Brandon Burgman, S. Stephen Yi. Computational model for prediction of actionable drug combinations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 168.

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy. Conventional strategies to overcome radioresistance involve understanding the underpinning molecular mechanisms, and subsequently using combinatorial treatment strategies involving radiation and targeted drug combinations against these radioresistant tumors. These strategies exploit and target the molecular fingerprint and vulnerability of the radioresistant clones to achieve improved efficacy in tumor eradication. However, conventional drug-screening approaches for the discovery of new drug combinations have been proven to be inefficient, limited and laborious. With the increasing availability of computational resources in recent years, novel approaches such as Quadratic Phenotypic Optimization Platform (QPOP), CURATE.AI and Drug Combination and Prediction and Testing (DCPT) platform have emerged to aid in drug combination discovery and the longitudinally optimized modulation of combination therapy dosing. These platforms could overcome the limitations of conventional screening approaches, thereby facilitating the discovery of more optimal drug combinations to improve the therapeutic ratio of combinatorial treatment. The use of better and more accurate models and methods with rapid turnover can thus facilitate a rapid translation in the clinic, hence, resulting in a better patient outcome. Here, we reviewed the clinical observations, molecular mechanisms and proposed treatment strategies for tumor radioresistance and discussed how novel approaches may be applied to enhance drug combination discovery, with the aim to further improve the therapeutic ratio and treatment efficacy of radiotherapy against radioresistant cancers.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

Abstract SY45-03: Capturing the therapeutic response heterogeneity at the functional level

Abstract SY45-03: Capturing the therapeutic response heterogeneity at the functional level

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.

DrugComb: an integrative cancer drug combination data portal.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.