Author: US EPA’s Center for Computational Toxicology and Exposure ccte@epa.gov

# ctxR: Exposure API

#### US EPA’s Center for Computational Toxicology and Exposure ccte@epa.gov

## Introduction

In this vignette, the CTX Exposure API will be explored.

Data provided by the Exposure API are broadly organized in four different areas, Functional Use Information, Product Data, List Presence Data, and Exposure estimates. Data from the Functional Use, Product Data, and List Presence resources (aside from the Functional Use Probability endpoint) are developed from publicly available documents and are also accessible using the Chemical Exposure Knowledgebase (ChempExpo) interactive web application developed by the United States Environmental Protection Agency. The underlying database for the Functional Use, Product Data, and List Presence endpoints of the Exposure API and ChemExpo is the Chemicals and Products Database (CPDat). CPDat provides reported information on how chemicals are used in commerce and (where possible) at what quantities they occur in consumer and industrial products; see (Dionisio et al. 2018) for more information on CPDat. The data provided by the Functional Use Probability endpoint are predictions from EPA’s Quantitative Structure Use Relationship (QSUR) models (Phillips et al. 2017). Exposure data is represented by predictions from the `httk` R package, introduced in (Pearce, R. et al. 2017) and several exposure models including the SEEM models. Information on the SEEM2 model can be found at (Wambaugh, J. et al. 2014) and on the SEEM3 model can be found at (Ring, C. et al. 2018)

Product Data are organized by harmonized Product Use Categories (PUCs). The PUCs are assigned to products (which are associated with Composition Documents) and indicate the type of product associated to each data record. They are organized hierarchicially, with General Category containing Product Family, which in turn contains Product Type. The Exposure API also provide information on how the PUC was assigned. Do note that a natural language processing model is used to assign PUCs with the “classificationmethod” equal to “Automatic”. As such, these assignments are less certain and may contain inaccuracies. More information on PUC categories can be found in (Isaacs et al. 2020).

List Presence Data reflect the occurrence of chemicals on lists present in publicly available documents (sourced from a variety of federal and state agencies and trade associations). These lists are tagged with List Presence Keywords (LPKs) that together describe information contained in the document relevant to how the chemical was used. LPKs are an updated version of the cassettes provided in the Chemical and Product Categories (CPCat) database; see (Dionisio et al. 2015). For the most up to date information on the current LPKs and to see how the CPCat cassettes were updated, see (Koval et al. 2022).

Both reported and predicted Function Use Information is available. Reported functional use information is organized by harmonized Function Categories (FCs) that describe the role a chemical serves in a product or industrial process. The harmonized technical function categories and definitions were developed by the Organisation for Economic Co-operation and Development (OECD) (with the exception of a few categories unique to consumer products which are noted as being developed by EPA). These categories have been augmented with additional categories needed to describe chemicals in personal care, pharmaceutical, or other commercial sectors. The reported function data form the basis for ORD’s QSUR models (Phillips et al. 2016). These models provide the structure-based predictions of chemical function available in the Functional Use Probability endpoint. Note that these models were developed prior to the OECD function categories, so their function categories are not yet aligned with the harmonized categories used in the reported data. Updated models for the harmonized categories are under development.

The R package `httk` provides users with a variety of tools to incorporate toxickinetics and in vitro-in vivo extrapolation into bioinformatics and comes with pre-made models that can be used with specific chemical data. The SEEM models were developed to provide predictions for potential human exposure to chemicals with little or no exposure data. For SEEM2, Bayesian methods were used to infer ranges of exposure consistent with data from the National Health and Nutrition Examination Survey. Predictions for different demographic groups were made. For SEEM3, chemical exposures through four different pathways were predicted and in turn weighting of different models through these exposure pathways was conducted to produce consensus predictions.

Information for ChemExpo is sourced from: Sakshi Handa, Katherine A. Phillips, Kenta Baron-Furuyama, and Kristin K. Isaacs. 2023. “ChemExpo Knowledgebase User Guide”. https://comptox.epa.gov/chemexpo/static/user_guide/index.html.

NOTE: Please see the introductory vignette for an overview of the ctxR package and initial set up instruction with API key storage.

Several ctxR functions can be used to access the CTX Exposure API data, as described in the following sections. Tables output in each example have been filtered to only display the first few rows of data.

# Functional Use Resource

Functional uses for chemicals may be searched.

## Functional Use

`get_exposure_functional_use()` retrieves FCs and associated metadata for a specific chemical (by DTXSID).

    exp_fun_use <- get_exposure_functional_use(DTXSID = 'DTXSID7020182')

id | dtxsid | datatype | docid | doctitle | docdate | reportedfunction | functioncategory
--- | --- | --- | --- | --- | --- | --- | ---
22724 | DTXSID7020182 | Chemical presence list | 1371471 | The 25 Chemicals Found in All Nine of the Biosolids Studied |  | fire retardant | Flame retardant
22722 | DTXSID7020182 | Chemical presence list | 1497376 | A regional assessment of chemicals of concern in surface waters of four Midwestern United States national parks- Table 1 | 6 December 2016 | plastic component | NA
22728 | DTXSID7020182 | Composition | 1389481 | halcyon radiant barrier shades | 2015-08-25 | monomer, polycarbonate | Monomers
22726 | DTXSID7020182 | Composition | 1550827 | Thin-Set_Epoxy_Terrazzo_Flooring-Master_Terrazzo_Technologies-2017-02-02 | february 2, 2017 | curing agent | Hardener
22727 | DTXSID7020182 | Composition | 1389695 | kerapoxy 410- part b | 2015-01-19 | resin amide | NA
22732 | DTXSID7020182 | Composition | 1390773 | universal litter and recycling receptacle - 30 gallon with | 2016-03-15 | monomer, polycarbonate | Monomers

## Functional Use Probability

`get_exposure_functional_use_probability()` retrieves the probability of functional use within different FCs for a given chemical (by DTXSID). Each value represents the probability of the chemical being classified as having this function, as predicted by the QSUR models.

    exp_fun_use_prob <- get_exposure_functional_use_probability(DTXSID = 'DTXSID7020182')

harmonizedFunctionalUse | probability
--- | ---
antimicrobial | 0.3722
antioxidant | 0.8941
catalyst | 0.2031
colorant | 0.1560
crosslinker | 0.7743
flame_retardant | 0.2208

## Functional Use Categories

`get_exposure_functional_use_categories()` retrieves definitions of all the available FCs. This is not specific to a chemical, but rather a list of all FCs.

    exp_fun_use_cat <- get_exposure_functional_use_category()

id | title | description
--- | --- | ---
28 | Coalescing agent | Chemical substance used in polymer emulsions that lower the glass-transition temperature (Tg) which results in the decrease in the minimum film-forming temperature (MFT) and upon evaporation, yields a hard film. Used in polishes; e.g., glycol; ether; pyrrolidines; and benzoates. Also referred to as a minimum film-forming temperature (MFT) modifier.
29 | Conductive agent | Chemical substance used to conduct electrical current. Also referred to as an electrolyte; or electrode material.
30 | Corrosion inhibitor | Chemical substance used to prevent or retard corrosion on metallic materials. Used in many products packaged in metal containers (such as aerosol products). Used in lubricants and other metal treatment products to provide protection to the substrates or surfaces on which the lubricants are used. Also referred to as a corrosion‑inhibiting additive; rust preventative; anticorrosion agent; or antirust agent.
16 | Anti-static agent | Chemical substance that prevents or reduces the tendency of a material to accumulate a static charge or alters the electrical properties of materials by reducing their tendency to acquire an electrical charge. Used in diesel fuel to prevent the build-up of static electricity. Also referred to as a charge stabilizer.
17 | Anti-streaking agent | Chemical substance which serves to enhance evaporation or reduce film formation in order to prevent the formation of streaks on a surface during cleaning. Also referred to as a film reducer.
18 | Binder | Chemical substances that are either synthetic/polymeric resins that further polymerize, provide structure and cohesiveness or are substances added to compounded dry powders to provide adhesive qualities during and after compression to make tablets or cakes. Also referred to as a binding agent or resin.

# Product Data Resource

There are a few resources for retrieving product use data associated with chemical identifiers (DTXSID) or general use.

## Product Data

`get_exposure_product_data()` retrieves the product data (PUCs and related data) for products that use the specified chemical (by DTXSID).

    exp_prod_dat <- get_exposure_product_data(DTXSID = 'DTXSID7020182')

id | dtxsid | docid | doctitle | docdate | productname | gencat | prodfam | prodtype | classificationmethod | rawmincomp | rawmaxcomp | rawcentralcomp | unittype | lowerweightfraction | upperweightfraction | centralweightfraction | weightfractiontype | component
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
589086 | DTXSID7020182 | 1297201 | EPOCAST 87005 B-60, FPC2172 | 08/07/1991 | epocast 87005 b-60_ fpc2172 | Raw materials | adhesives |  | Manual |  |  | 5 | percent | NA | NA | 0.05 | reported |
657348 | DTXSID7020182 | 1314861 | EPOCAST 87005 B-80, FPC2248 | 08/04/1992 | epocast 87005 b-80_ fpc2248 | Raw materials | adhesives |  | Manual |  |  | 2 | percent | NA | NA | 0.02 | reported |
192133 | DTXSID7020182 | 1178630 | EPOCAST HARDENER 946, FPC 5000 | 01/11/1990 | epocast hardener 946_ fpc 5000 | NA | NA | NA | NA |  |  | 45 | percent | NA | NA | 0.45 | reported |
655734 | DTXSID7020182 | 1314342 | EPOCAST HARDNER 946 | 10/04/1994 | epocast hardner 946 | NA | NA | NA | NA |  |  |  | NA | NA | NA | NA | reported |
85935 | DTXSID7020182 | 1135022 | EPOLITE 1301 HARDENER(FORM. 916 AD HARDENER | 01/01/1985 | epolite 1301 hardener(form. 916 ad hardener | NA | NA | NA | NA |  |  |  | NA | NA | NA | NA | reported |
25284 | DTXSID7020182 | 1106842 | EPOLITE 1350 HANDENER | 08/09/1993 | epolite 1350 handener | NA | NA | NA | NA | 35 | 50 |  | percent | 0.35 | 0.5 | NA | reported |

## Product Use Category Data

`get_exposure_product_data_puc()` retrieves the definitions of all the PUCs. This is not specific to a chemical, but rather a list of all PUCs.

    exp_prod_data_puc <- get_exposure_product_data_puc()

id | kindName | genCat | prodfam | prodtype | definition
--- | --- | --- | --- | --- | ---
45 | Formulation | Cleaning products and household care | oven |  | cleaning or other products used in or on ovens that do not fit in a more refined category
44 | Formulation | Cleaning products and household care | metal specific |  | cleaning or care products specific to metals, which do not fit into a more refined category
43 | Formulation | Cleaning products and household care | laundry and fabric treatment |  | Cleaning or care products for laundry and fabric treatment which do not fit into a more refined category
42 | Formulation | Cleaning products and household care | laundry and fabric treatment | anti-static spray | anti-static sprays for fabrics (spray formulation assumed)
291 | Formulation | Cleaning products and household care | bathroom | urinal cakes and deodorizers | Includes urinal cakes and screens for use in urinals, also includes deodorizers used in portable toilets
292 | Formulation | Cleaning products and household care | jewelry | jewelry cleaner | Cleaning solutions specifically for jewelry products

# `httk` data

There is a single resource that returns `httk` model data when available

    bpa_httk <- get_httk_data(DTXSID = 'DTXSID7020182')
    head(bpa_httk)
    #>       id        dtxsid    parameter measuredText measured predictedText
    #> 1 101171 DTXSID7020182          Css       0.0083   0.0083         1.114
    #> 2 101172 DTXSID7020182          Css       0.0083   0.0083        0.5297
    #> 3 101173 DTXSID7020182          Css       0.0083   0.0083         1.076
    #> 4 101174 DTXSID7020182          Css       0.0083   0.0083        0.5116
    #> 5 101175 DTXSID7020182 TK.Half.Life         0.19   0.1900         139.5
    #> 6 101176 DTXSID7020182     Days.Css           NA       NA           112
    #>   predicted units          model              reference percentile species
    #> 1    1.1140  mg/L           PBTK Wambaugh et al. (2018)        95%     Rat
    #> 2    0.5297  mg/L           PBTK Wambaugh et al. (2018)        50%     Rat
    #> 3    1.0760  mg/L 3compartmentss Wambaugh et al. (2018)        95%     Rat
    #> 4    0.5116  mg/L 3compartmentss Wambaugh et al. (2018)        50%     Rat
    #> 5  139.5000 hours   1compartment Wambaugh et al. (2018)         NA     Rat
    #> 6  112.0000  Days           PBTK                     NA         NA     Rat
    #>   dataSourceSpecies dataVersion                  importDate
    #> 1               Rat          NA 2024-06-13T16:53:14.622350Z
    #> 2               Rat          NA 2024-06-13T16:53:14.622350Z
    #> 3               Rat          NA 2024-06-13T16:53:14.622350Z
    #> 4               Rat          NA 2024-06-13T16:53:14.622350Z
    #> 5               Rat          NA 2024-06-13T16:53:14.622350Z
    #> 6               Rat          NA 2024-06-13T16:53:14.622350Z

# List Presence Resource

There are a few resources for retrieving list data for specific chemicals (by DTXSID) or general list presence information.

## List Presence Tags

`get_exposure_list_presence_tags()` retrieves all the list presence keywords. This is not specific to a chemical, but rather a list of the the list presence keywords. Note that some List Presence Keywords align with PUCs, but the keywords are assigned to documents that refer to product category as a whole, while PUCs are assigned to documents referring to specific products (e.g., ingredient list).

    exp_list_tags <- get_exposure_list_presence_tags()

id | tagName | tagDefinition | kindName
--- | --- | --- | ---
52 | detected | chemicals measured or identified in environmental media or products | Modifiers
53 | drinking_water | water intended for drinking, or related to drinking water; includes bottled water, finished water from drinking water treatment plants, and untreated water that has been denoted as a drinking source | Media
54 | Electronics/small appliances - computers and accessories/supplies - printer ink | ink for inkjet printers | PUC - formulation
25 | Canada | Sources specific to Canada | Location
26 | CEDI | The U.S. FDA Cumulative Estimated Daily Intake database of publicly available cumulative estimated daily intakes (CEDIs) for a large number of food contact substances | Specialty list
27 | children | pertaining to, or intended for use specifically by children | Subpopulation

## List Presence Tag Data

`get_exposure_list_presence_tags_by_dtxsid()` retrieves LPKs and associated data for a specific chemical (by DTXSID).

    exp_list_tags_dat <- get_exposure_list_presence_tags_by_dtxsid(DTXSID = 'DTXSID7020182')

id | dtxsid | docid | doctitle | docsubtitle | docdate | organization | reportedfunction | functioncategory | component | keywordset
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
127967 | DTXSID7020182 | 1557970 | Experimental Small Molecule Drugs |  |  | DrugBank | NA | NA |  | Canada; pharmaceutical
9997 | DTXSID7020182 | 1359540 | Actively Registered AI’s by Common Name |  |  | California Department of Pesticide Regulation | NA | NA |  | active_ingredient; Pesticides
40538 | DTXSID7020182 | 1372213 | Indirect Additives used in Food Contact Substances | FDA authorizes Indirect Food Additives by identity, intended use, and conditions of use; the presence of a substance in this list indicates that only certain intended uses and use conditions are authorized by FDA regulations | 10/4/2018 | FDA | NA | NA |  | Indirect additives food contact (10/2018)
135524 | DTXSID7020182 | 1558005 | Chemicals of High Concern to Children |  | 9/1/2020 | Vermont Department of Health | NA | NA |  | children
113048 | DTXSID7020182 | 1551584 | Chemicals of high concern to children reporting list |  |  | State of Washington Department of Ecology | NA | NA |  | children; WA Children’s Safe Product Act (4/2020)
76175 | DTXSID7020182 | 1373540 | Exposure of children and unborn children to selected chemical substances - Table 4.2.3 | Table 4.2.3 Regulation in the food area | Apr-17 | Danish Environmental Protection Agency | NA | NA |  | Europe; Food contact items

### Exposure Predictions

There are two functions that provide access to exposure prediction data. The first provides general information on exposure pathways while the second provides exposure predictions from a variety of exposure models. The general information corresponds to SEEM3 predictions of exposure pathways, while the exposure predictions feature SEEM2 predictions broken down by demographic groups, general consensus predictions from SEEM3, and in some cases additional exposure predictions from other models

#### General Exposure Predictions

`get_general_exposure_prediction()` returns general exposure information for a given chemical.

    bpa_general_exposure <- get_general_exposure_prediction(DTXSID = 'DTXSID7020182')
    head(bpa_general_exposure)
    #>           dtxsid productionVolume  units stockholmConvention probabilityDietary
    #>           <char>            <int> <char>               <int>              <num>
    #> 1: DTXSID7020182          2780000 kg/day                   0                  1
    #> 5 variable(s) not shown: [probabilityResidential <num>, probabilityPesticde <num>, probabilityIndustrial <num>, dataVersion <lgcl>, importDate <char>]

#### Demographic Exposure Predictions

`get_demographic_exposure_prediction()` returns exposure prediction information split across different demographics for a given chemical.

    bpa_demographic_exposure <- get_demographic_exposure_prediction(DTXSID = 'DTXSID7020182')
    bpa_demographic_exposure
    #>        id        dtxsid        demographic       predictor       median
    #> 1  768361 DTXSID7020182              Total    Food.Contact 1.766000e-02
    #> 2  769393 DTXSID7020182              Total            FINE 9.460000e-06
    #> 3  772655 DTXSID7020182              Total          RAIDAR 3.770000e+00
    #> 4  784083 DTXSID7020182              Total     USETox.Pest 5.624000e-02
    #> 5  785935 DTXSID7020182              Total   USETox.Indust 1.372000e-04
    #> 6  749502 DTXSID7020182            Age 66+ SEEM2 Heuristic 6.608350e-05
    #> 7  751534 DTXSID7020182           BMI > 30 SEEM2 Heuristic 7.073042e-05
    #> 8  760855 DTXSID7020182              Total  SHEDS.Indirect 7.150000e-05
    #> 9  761591 DTXSID7020182              Total    SHEDS.Direct 0.000000e+00
    #> 10 763267 DTXSID7020182          BMI <= 30 SEEM2 Heuristic 6.245051e-05
    #> 11 488214 DTXSID7020182              Total SEEM3 Consensus 5.497000e-05
    #> 12 797784 DTXSID7020182              Total      USETox.Res 4.395000e-02
    #> 13 807431 DTXSID7020182              Total     USETox.Diet 1.498000e-04
    #> 14 709226 DTXSID7020182              Males SEEM2 Heuristic 3.867956e-05
    #> 15 735410 DTXSID7020182          Age 12-19 SEEM2 Heuristic 5.871957e-05
    #> 16 697139 DTXSID7020182 Repro. Age Females SEEM2 Heuristic 1.364275e-05
    #> 17 711258 DTXSID7020182            Females SEEM2 Heuristic 1.244431e-05
    #> 18 737451 DTXSID7020182          Age 20-65 SEEM2 Heuristic 5.675943e-05
    #> 19 723306 DTXSID7020182           Age 6-11 SEEM2 Heuristic 6.296203e-05
    #>              medianText          l95              l95Text         u95
    #> 1               0.01766           NA                   NA          NA
    #> 2              9.46e-06           NA                   NA          NA
    #> 3                  3.77           NA                   NA          NA
    #> 4               0.05624           NA                   NA          NA
    #> 5             0.0001372           NA                   NA          NA
    #> 6  6.60834995383669e-05 2.798634e-07  2.7986341540408e-07 0.019477870
    #> 7  7.07304192271297e-05 3.136219e-07 3.13621919723853e-07 0.018576052
    #> 8              7.15e-05           NA                   NA          NA
    #> 9                     0           NA                   NA          NA
    #> 10  6.2450508333388e-05 2.591822e-07 2.59182177179327e-07 0.013621125
    #> 11            5.497e-05 1.923000e-07            1.923e-07 0.020440000
    #> 12              0.04395           NA                   NA          NA
    #> 13            0.0001498           NA                   NA          NA
    #> 14 3.86795578537834e-05 2.846711e-07 2.84671057884619e-07 0.006306170
    #> 15 5.87195691748974e-05 2.809632e-07 2.80963221822448e-07 0.017185596
    #> 16 1.36427543462443e-05 5.637240e-08 5.63723993835891e-08 0.004176617
    #> 17 1.24443070751952e-05 4.901108e-08 4.90110833197268e-08 0.002897798
    #> 18 5.67594250809775e-05 2.080289e-07 2.08028872989558e-07 0.011509267
    #> 19 6.29620332442998e-05 3.049913e-07 3.04991342892185e-07 0.010537090
    #>                u95Text           units ad      reference dataVersion
    #> 1                   NA       mg/kg/day  1    Biryol 2017          NA
    #> 2                   NA          mg/day  1      Shin 2012          NA
    #> 3                   NA       mg/kg/day  1     Arnot 2008          NA
    #> 4                   NA intake fraction  1    Fantke 2013          NA
    #> 5                   NA intake fraction  1 Rosenbaum 2008          NA
    #> 6   0.0194778699251516       mg/kg/day  1  Wambaugh 2014          NA
    #> 7   0.0185760522525412       mg/kg/day  1  Wambaugh 2014          NA
    #> 8                   NA       mg/kg/day  1    Isaacs 2017          NA
    #> 9                   NA       mg/kg/day  1    Isaacs 2017          NA
    #> 10  0.0136211249503816       mg/kg/day  1  Wambaugh 2014          NA
    #> 11             0.02044       mg/kg/day  1      Ring 2018          NA
    #> 12                  NA intake fraction  1     Huang 2016          NA
    #> 13                  NA intake fraction  1  Ernstoff 2016          NA
    #> 14 0.00630617035849566       mg/kg/day  1  Wambaugh 2014          NA
    #> 15  0.0171855959252902       mg/kg/day  1  Wambaugh 2014          NA
    #> 16 0.00417661734132225       mg/kg/day  1  Wambaugh 2014          NA
    #> 17 0.00289779809405841       mg/kg/day  1  Wambaugh 2014          NA
    #> 18  0.0115092672875229       mg/kg/day  1  Wambaugh 2014          NA
    #> 19  0.0105370896882791       mg/kg/day  1  Wambaugh 2014          NA
    #>                     importDate
    #> 1  2024-06-13T19:25:16.277317Z
    #> 2  2024-06-13T19:25:16.277317Z
    #> 3  2024-06-13T19:25:16.277317Z
    #> 4  2024-06-13T19:25:16.277317Z
    #> 5  2024-06-13T19:25:16.277317Z
    #> 6  2024-06-13T19:25:16.277317Z
    #> 7  2024-06-13T19:25:16.277317Z
    #> 8  2024-06-13T19:25:16.277317Z
    #> 9  2024-06-13T19:25:16.277317Z
    #> 10 2024-06-13T19:25:16.277317Z
    #> 11 2024-06-13T19:25:16.277317Z
    #> 12 2024-06-13T19:25:16.277317Z
    #> 13 2024-06-13T19:25:16.277317Z
    #> 14 2024-06-13T19:25:16.277317Z
    #> 15 2024-06-13T19:25:16.277317Z
    #> 16 2024-06-13T19:25:16.277317Z
    #> 17 2024-06-13T19:25:16.277317Z
    #> 18 2024-06-13T19:25:16.277317Z
    #> 19 2024-06-13T19:25:16.277317Z

## Batch Search

There are batch search versions for several endpoints that gather data specific to a chemical. Namely, `get_exposure_functional_use_batch()`, `get_exposure_functional_use_probability()`, `get_exposure_product_data_batch()`, `get_exposure_list_presence_tags_by_dtxsid_batch()`, `get_general_exposure_prediction_batch()`, and `get_demographic_exposure_prediction_batch()`. The function `get_exposure_functional_use_probability()` returns a data.table with each row corresponding to a unique chemical and each column representing a functional use category associated to at least one input chemical. The other batch functions return a named list of data.frames or data.tables, the names corresponding to the unique chemicals input and the data.frames or data.tables corresponding to the information to each individual chemical.

## Functional Use Probability Batch

We demonstrate how the individual results differ from the batch results when retrieving functional use probabilities.

    bpa_prob <- get_exposure_functional_use_probability(DTXSID = 'DTXSID7020182')
    caf_prob <- get_exposure_functional_use_probability(DTXSID = 'DTXSID0020232')

    bpa_caf_prob <- get_exposure_functional_use_probability_batch(DTXSID = c('DTXSID7020182', 'DTXSID0020232'))

    #>    harmonizedFunctionalUse probability
    #> 1            antimicrobial      0.3722
    #> 2              antioxidant      0.8941
    #> 3                 catalyst      0.2031
    #> 4                 colorant      0.1560
    #> 5              crosslinker      0.7743
    #> 6          flame_retardant      0.2208
    #> 7                flavorant      0.0314
    #> 8                fragrance      0.2071
    #> 9          heat_stabilizer      0.5119
    #> 10        skin_conditioner      0.1168
    #> 11         skin_protectant      0.3306
    #> 12             uv_absorber      0.8046

    #>   harmonizedFunctionalUse probability
    #> 1           antimicrobial      0.4808
    #> 2                  buffer      0.6370
    #> 3                colorant      0.3962
    #> 4        skin_conditioner      0.9821

    #>           DTXSID antimicrobial antioxidant catalyst colorant crosslinker
    #>           <char>         <num>       <num>    <num>    <num>       <num>
    #> 1: DTXSID7020182        0.3722      0.8941   0.2031   0.1560      0.7743
    #> 2: DTXSID0020232        0.4808          NA       NA   0.3962          NA
    #> 8 variable(s) not shown: [flame_retardant <num>, flavorant <num>, fragrance <num>, heat_stabilizer <num>, skin_conditioner <num>, skin_protectant <num>, uv_absorber <num>, buffer <num>]

Observe that Caffeine only has probabilities assigned to four functional use categories while Bisphenol A has probabilities assigned to twelve categories. For single chemical search, functional use categories denote the row. However, when using the batch search function, all reported categories are included as columns, with rows corresponding to each chemical. If a chemical does not have a probability associated to a functional use, the corresponding entry is given by an NA.

# Conclusion

There are several CTX `Exposure` API endpoints and ctxR contains functions for each, and batch versions for some of these as well. These allow users to access various types of exposure data associated to a given chemical. In this vignette, we explored all of the non-batch versions and discussed the batch versions. We encourage the user to experiment with the different endpoints to understand better what sorts of data are available.