DrugComb Documentation


This documentation is a user manual for DrugComb data portal. If you have any questions after going through it, feel free to contact the group leader Dr. Jing Tang on jing.tang@helsinki.fi or reach us using the ”Feedback” button directly on the website. Note: the second option might require disabling your adblock extension in the browser.


It is suggested to use any modern web browser with Javascript enabled. Functionality is guaranteed under Google Chrome version 59 and above, as well as Mozilla Firefox version 50 and above. The website is not thoroughly tested under Safari.

It is recommended to use Google Chrome and Mozilla Firefox. The website is not tested under Safari

Getting Started #back to top

DrugComb is an integrated data portal aimed at collecting, analysing and distributing the results of drug combination screens on cancer cell lines. Its main purpose is harmonisation of drug combination research results. Due to various experimental methodologies used in drug combination studies under the umbrella of personalised anticancer research, it is often problematic to integrate compound screening results from different studies. Various definitions of drug synergy used by researchers exacerbate the issue. As a result, in addition to scientifically challenging task of explaining and predicting effective drug combinations, researchers in the field end up having to invest considerable effort into data preparation and cleaning.

DrugComb data portal provides free access to experimental data for researchers and layperson alike with the purpose of being a unified source of information about drug combination screens. In addition to being a repository containing data from the existing drug combination studies, it allows raw data upload, semi-automated data quality control, calculation and visualisation of drug combination synergy values, dose-response heatmaps, single drug dose-response curves, box plots for CSS, and S score. Third party APIs provide access to STITCH database in order to access chemical-protein association networks between compounds; to PubChem in order to generate molecular structural formulae of compounds; and to ChEMBL for ligand-based target predictions using compounds as input. Future plans include prediction of novel, previously untested drug combinations using machine learning and network-centric tools using drug names and cancer types as input parameters.

User information#back to top

The General Data Protection Regulation 2016/679 of the European Parliament and the Council of the European Union mandates appropriate technical and organizational measures for the purpose of user data protection. Currently, DrugComb portal does store user-submitted data for three days, including cookies and submitted drug combinations data. All the access to data portal’s data is performed over Hypertext Transfer Protocol Secure (https). Bidirectional data encryption protects sensitive user data from man-in-the-middle attacks. In the future, there will be an option to create user accounts for the purpose of session storage, customized analysis pipelines and data view settings.

Drug synergy #back to top

Drug synergy is a dimensionless measure of drug-drug interaction. It assesses the degree of deviation of an observed response from the null hypothesis, that is an expected effect of non-interaction. Different mathematical models have different definitions of what synergy is, which results in varying synergy values for the same drug and cell line combination. In DrugComb data portal synergy is calculated using four different reference models:

  • ● Bliss model [Bliss, 1939] assumes a stochastic process in which two drugs elicit their effects independently, and the expected combination effect can be calculated based on the probability of independent events as:

    \begin{equation} \label{eq:1} y_{BLISS}=y_1+y_2-y_1*y_2 \\ \end{equation} where $$y_{1,2} \in [0,1]$$ are single drug effects measured as fractional cell death or cell growth

  • ● Highest Single Agent (HSA) [Berenbaum, 1989] states that the expected combination effect equals to the higher effect of individual drugs: $$yHSA = max(y1, y2).$$
  • ● Loewe additivity model [Loewe, 1953] defines the expected effect yLOEW E as if a drug was combined with itself. Unlike the HSA and the Bliss independence models giving a point estimate using different assumptions, the Loewe additivity model considers the dose-response curves of individual drugs. The expected effect yLOEWE must satisfy:

    \begin{equation} \label{eq:3} \frac {x_1}{\chi_{LOEWE}^1} + \frac{x_2}{\chi_{LOEWE}^2} = 1 \end{equation} where $$x_{1,2}$$ are drug doses and $$\chi_{LOEWE}^1, \chi_{LOEWE}^2$$ are the doses of drug 1 and 2 alone that produce y_LOEWE. Using 4-parameter log-logistic (4PL) curves to describe dose-response curves the following parametric form of equation 3 is derived: where $$E_{min},E_{max} \in [0,1]$$ are minimal and maximal effects of the drug, $$m_{1,2}$$ are the doses of drugs that produce the midpoint effect of $$E_{min} + E_{max}$$, also known as relative $$EC_{50}$$ or $$IC_{50}$$, and $$\lambda_{1,2}(\lambda > 0)$$are the shape parameters indicating the sigmoidicity or slope of dose-response curves. A numerical nonlin- ear solver (such as index.htmlfor R or optimize.nonlin.html for Python) can be then used to determine yLOEWE for (x1, x2).

  • ● Zero Interaction Potency (ZIP) [Yadav et al., 2015] calculates the expected effect of two drugs under the assumption that they do not potentiate each other:

Drug combination sensitivity #back to top

CSS - drug combination sensitivity score is derived using relative IC50 values of compounds and the area under their dose-response curves. AAC [Yang et al., 2013] and DSS [Yadav et al., 2014] metrics have been used as an inspiration for the CSS derivation. CSS of a drug combination is calculated such that each of the compounds is used at a fixed concentration (background drug) and another is at varying concentrations (foreground drug) resulting in two CSS values, which are then averaged. Each drug’s dose-response is modelled using 4-parameter log-logistic curve, such that: \begin{equation} y = y_{min} + \frac{y_{max} - y_{min}}{1+10^{\lambda{(log_{10} IC_{50}- x')}}} \end{equation} where $$y_{min},y_{max}$$ are minimal and maximal inhibition and $$x'=\log_{10} x$$ The area under the log-scaled dose-response curve (AUC) is then determined according to where $$[c_1,c_2]$$ is the concentration of the foreground drug tested [Malyutina et al., 2019].

Data portal Interface #back to top

DrugComb data portal provides access to an ever-growing number of drug combinations. At the moment of writing there is 2276 unique drugs, 93 cell lines representing 10 tissues and over 430k unique drug combinations obtained from four different studies. Please see "Studies as data sources" section for more information on the studies used.

Upon initial access user is informed about the data statistics of DrugComb, including the number of drugs, the number of cell lines, the number of tissue types and the number of drug combinations. We considered a drug combination as an experiment where a drug combination has been tested with multiple doses on a certain cell line, resulting in a dose-response matrix. Therefore, if the same drug combination has been tested in multiple cell lines, each of dose-response matrices will be considered as one drug combination. There is a search bar that allows searching for a specific data type (Cell Line, Tissue, Drug name and Study source).

Search is initiated by clicking on the search bar and choosing the initial search category. The following categories are available: Cell line, Tissue, Drug, Study. Category value to include (“=”) or exclude from search (“!=”) should be given sequentially. Different categories could be combined using AND or OR operators. Manual entry of the search string or full text search at the moment of writing is not available.

It is possible to select user-refined categories using the panel on the left side. A histogram gives a visual overview of cell lines and the number of drug combinations per tissue, upon selection this overview gets updated.

Table view

By default 10 drug combinations are shown on the landing page of the Table tab (see below), however, this number can be increased up to 100 drug combinations per page.

It is possible to sort drug combinations in ascending or descending order using values in any of the columns.

Graph view

When a drug combination is selected, it will be directed to the Graph view which contains the graphical results of sensitivity, synergy and annotation, each of which consists of one sub tabs.

Sensitivity view

It gives access to the graphical results of drug combination sensitivity, including the histogram of the selected drug combinations across all the cell lines, the histogram of the selected cell line across all the drug combinations, the drug combination sensitivity (CSS) - synergy (S) bar plot with the selected drug combination highlighted, the full dose-response matrix and the monotherapy dose-response curves.

Synergy view

It gives access to the graphical results of drug combination synergy, including the synergy landscapes over the dose matrix that are determined using four mathematical models (ZIP, BLISS, LOEWE and HSA). The synergy scores are in the unit of % inhibition and color-coded in the range of -30 (green) to +30 (red). Higher synergy scores indicate more synergistic interaction that leads to higher % inhibition of cancer cells. See below

Annotation view

It gives access to annotation of the selected drug combinations in terms of their chemical structures, protein targets and affected biological pathways according to third-party databases including STITCH (http://stitch.embl.de/) , PubChem (https://pubchem.ncbi.nlm.nih.gov/) , and ChEMBL (https://www.ebi.ac.uk/chembl/) STITICH databse is accessed using the following parameters to refine the drug combination network: (edited)

  • ● Threshold of significance for the interaction to be displayed (0 to 1000) = 550;
  • ● Limit, as max number of nodes to return = 20;
  • ● Network flavour = “evidence”, where various colours represent various nature of entities’ interactions;
  • ● Species = 9606 for Human, please refer to http://www.uniprot.org/taxonomy for taxa information;

Network generated with the aforementioned parameters for Gefitinib and Memantine is shown below:

Pubchem view provides the compounds' skeletal formula. More information can be retrieved by the highlighted CID hyperlinks:

ChEMBL view provides the predicted drug targets with the probability > 0.3. The value column shows the binding affinity in nM. The target_accession column shows the UniProt ID (https://www.uniprot.org/) for the protein target. More information can be retried from ChEMBL via the highlighted ChEMBL IDs.

Example data input #back to top

DrugComb data portal provides example data in csv format with the obligatory column naming scheme (case insensitive). The following columns must be present:

  • ● Block_id – numerical drug combination ID. Positive integer used to refer to a particular dose-response matrix experiment for a given drug combination-cell pair.
  • ● Conc_r – non-negative numerical value. Row compound (DrugA) concentration;
  • ● Conc_c – non-negative numerical value. Column compound (DrugB) concentration;
  • ● Inhibition – % inhibition values which can be either positive or negative. Negative % inhibition values indicate that the cancer cells grow more than the DMSO control.
  • ● Drug_row – standardized name of row compound. Please refer to e.g. https://www.drugbank.ca or https://www.ebi.ac.uk/chembl/ for more information on standardized naming scheme;
  • ● Drug_col – standardized name of column compound. Please refer to e.g. https://www.drugbank.ca or https://www.ebi.ac.uk/chembl/ for more information on standardized naming scheme;
  • ● Conc_r_unit – micro molar concentration unit of the row compound (DrugA);
  • ● Conc_c_unit – micro molar concentration unit of the column compound (DrugB);
  • ● Cell_line_name – cell line in which the drug combination is tested. Please refer to https://web.expasy.org/cellosaurus/ for more information on standardized cell line naming scheme;

See example input file below:

One single upload file of less than 40 MB is allowed, where the number of drug combinations indicated by block_id is maximally 120. For analyzing a bigger input file please contact Dr. Jing Tang (givenname.surname@helsinki.fi).

There is a sample file available as a template under the “Download template data” in the “Analysis” page.

Upon data submission and after pressing “Analyze” button, given that the input data format is correct, four types of synergy values are calculated, namely Bliss, HSA (Highest Single Agent), Loewe and ZIP (Zero Interaction Potency). Additionally, drug combination sensitivity score (CSS) and CSS-based S synergy score are calculated. The results will be shown in the Table tab. See below:

Graph tab provides Sensitivity and Synergy view options. For more information please refer to the Drug synergy and Drug combination sensitivity sections.

Contributing user data #back to top

User account creation is a prerequisite for contributing combination screening results to DrugComb. There is an option available to create a user account in the DrugComb under ”Contribute” section. The signup requires an email address. After creating a user account it is possible to submit combination drug screening results to be inserted in the DrugComb main database.

Database structure #back to top

DataBase Schema_V6 [Last Update: 10 Sep. 2019]


Previous Schemas:


Data flow diagram #back to top


Studies as data sources: #back to top

  • ● Forcina et.al. (2017) Systematic Quantification of Population Cell Death Kinetics in Mammalian Cells. Cell Syst. 2017 Jun 28; 4(6): 600–610.e6. 10.1016/j.cels.2017.05.002
  • 1819 drugs were screened in 2 cancer cell lines
  • ● Holbeck et.al. (2017) The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity. , Cancer Res. 2017 Jul 1;77(13):3564-3576. doi: 10.1158/0008-5472.CAN-17-0489
  • Over 5000 pairs of FDA-approved cancer drugs are screened against a panel of 60 well-characterized human tumor cell lines (NCI-60) in 3x3 or 5x3 dosing regimen
  • ● Licciardello et.al. (2018) A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor. Nat Chem Biol, 10.1038/nchembio.2382
  • 308 drugs were screened in 40160 combinations in KBM7 cell line.
  • ● O’Neil et.al (2017) An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies. Mol Cancer Ther.;15(6):1155-62. 10.1158/1535-7163.MCT-15-0843. Epub 2016 Mar 16.
  • 22737 experiments of 583 doublet combinations in 39 diverse cancer cell lines using a 4x4 dosing regimen.

Bibliography #back to top

  • [Berenbaum, 1989] Berenbaum, M. C. (1989). What is synergy? Pharmacol. Rev., 41(2):93–141.
  • [Bliss, 1939] Bliss, C. I. (1939). The toxicity of poisons applied jointly1. Annals of Applied Biology, 26(3):585–615.
  • [Gaulton et al., 2017] Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrian-Uhalte, E., Davies, M., Dedman, N., Karlsson, A., Magarinos, M. P., Overington, J. P., Papadatos, G., Smit, I., and Leach, A. R. (2017). The ChEMBL database in 2017. Nucleic Acids Res., 45(D1):D945–D954.
  • [Kim et al., 2016] Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., and Bryant, S. H. (2016). PubChem Substance and Compound databases. Nucleic Acids Res., 44(D1):D1202–1213.
  • [Loewe, 1953] Loewe, S. (1953). The problem of synergism and antagonism of combined drugs. Arzneimit- telforschung, 3(6):285–290.
  • [Malyutina, 2019] Malyutina, A. et al. Malyutina, A., Majumder, M.M., Wang, W., Pessia, A., Heckman, C.A. and Tang, J. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. (2019). Preprint: https://www.biorxiv.org/content/10.1101/512244v1
  • [W3C Working Group, 2016] W3C Working Group (2016). CSV on the Web: Use Cases and Requirements. https://www.w3.org/TR/csvw-ucr/. Accessed: 2019-02-14.
  • [Yadav et al., 2014] Yadav, B., Pemovska, T., Szwajda, A., Kulesskiy, E., Kontro, M., Karjalainen, R., Majumder, M. M., Malani, D., Murumagi, A., Knowles, J., Porkka, K., Heckman, C., Kallioniemi, O., Wennerberg, K., and Aittokallio, T. (2014). Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci Rep, 4:5193.
  • [Yadav et al., 2015] Yadav, B., Wennerberg, K., Aittokallio, T., and Tang, J. (2015). Searching for Drug Synergy in Complex Dose-Response Landscapes Using an Interaction Potency Model. Comput Struct Biotechnol J, 13:504– 513.
  • [Yang et al., 2013] Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., Ramaswamy, S., Futreal, P. A., Haber, D. A., Stratton, M. R., Benes, C., McDermott, U., and Garnett, M. J. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res., 41(Database issue):D955–961.