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 five different areas: Functional Use, Product Data, List Presence, High Throughput Toxicokinetic (HTTK) parameters, and Exposure estimates.

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 hierarchically, 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). The associated endpoints are organized within the Product Data Resource.

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). The associated endpoints are organized within the List Presence Resource.

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 associated endpoints are organized within the Functional Use Resource.

The R package httk provides users with a variety of tools to incorporate toxicokinetics and IVIVE into bioinformatics and comes with pre-made models that can be used with specific chemical data. The httk endpoint is found within the httk Data Resource.

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. The exposure prediction endpoints are organized within Exposure 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
221654 DTXSID7020182 Function 1631119 4 phenolics NA
221655 DTXSID7020182 Function 1630343 Octolite 485 antioxidants>phenolics Antioxidant
221656 DTXSID7020182 Chemical presence list 1371471 The 25 Chemicals Found in All Nine of the Biosolids Studied fire retardant Flame retardant
221657 DTXSID7020182 Function 1514560 4,4’-isopropylidenediphenol not reported No specific technical function
221658 DTXSID7020182 Composition 1550827 Thin-Set_Epoxy_Terrazzo_Flooring-Master_Terrazzo_Technologies-2017-02-02 february 2, 2017 epoxy hardener NA
221659 DTXSID7020182 Composition 1550827 Thin-Set_Epoxy_Terrazzo_Flooring-Master_Terrazzo_Technologies-2017-02-02 february 2, 2017 curing agent Hardener

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')
dtxsid harmonizedFunctionalUse probability
DTXSID7020182 antimicrobial 0.3722
DTXSID7020182 antioxidant 0.8941
DTXSID7020182 catalyst 0.2031
DTXSID7020182 colorant 0.1560
DTXSID7020182 crosslinker 0.7743
DTXSID7020182 flame_retardant 0.2208

Functional Use Probability Batch

We demonstrate how the individual results differ from the batch results when retrieving functional use probabilities via get_exposure_functional_use_probability_batch().

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'))
#>           dtxsid harmonizedFunctionalUse probability
#> 1  DTXSID7020182           antimicrobial      0.3722
#> 2  DTXSID7020182             antioxidant      0.8941
#> 3  DTXSID7020182                catalyst      0.2031
#> 4  DTXSID7020182                colorant      0.1560
#> 5  DTXSID7020182             crosslinker      0.7743
#> 6  DTXSID7020182         flame_retardant      0.2208
#> 7  DTXSID7020182               flavorant      0.0314
#> 8  DTXSID7020182               fragrance      0.2071
#> 9  DTXSID7020182         heat_stabilizer      0.5119
#> 10 DTXSID7020182        skin_conditioner      0.1168
#> 11 DTXSID7020182         skin_protectant      0.3306
#> 12 DTXSID7020182             uv_absorber      0.8046
#>          dtxsid harmonizedFunctionalUse probability
#> 1 DTXSID0020232           antimicrobial      0.4808
#> 2 DTXSID0020232                  buffer      0.6370
#> 3 DTXSID0020232                colorant      0.3962
#> 4 DTXSID0020232        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 variables 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.

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 category definition
36 Deflocculant Chemical substance used to fluidize concentrated slurries to reduce their bulk viscosity or stickiness in processing or handling. See closely related: anti-caking agent.
37 Defoamer Chemical substance that controls foam; prevents foam from forming; breaks down any foam that does form; and reduces foaming from proteins, gases, or nitrogenous materials. Reduces the tendency of finished products to generate foam upon shaking or agitation. The ability of a material to act as antifoam depends on its tendency to concentrate on the surface of existing or forming bubbles and to disrupt the continuous films of liquid surrounding them. Used as a process aid to improve filtration, dewatering, washing, and drainage of many types of suspensions, mixtures, and slurries. Also referred to as an antifoaming agent.
38 Degradant/impurity (EPA) Chemical substance reported as a degradant, impurity, contaminant, etc.
39 Dehydrating agent (desiccant) Chemical substance used to absorb and remove water from gases or liquids to induce or maintain a state of dryness. Substances are usually hygroscopic materials. See closely related: humectant; adsorbent.
40 Demulsifier Chemical substance used to destroy an emulsion or prevent its formation.
41 Density modifier Chemical substance that modifies the density of a material. Also referred to as density modifying agents. See closely related: viscosity modifier; thickening agent.

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
10934 DTXSID7020182 1098930 EPOLITE 2152 HARDENER 03/09/1992 epolite 2152 hardener Raw materials adhesives Manual Batch 30 45 percent 0.30 0.45 NA reported
12328 DTXSID7020182 1099279 HYSOL KS4008 PART B KB4008, KS4008 02/12/1992 hysol ks4008 part b kb4008_ ks4008 Specialty occupational products aviation Manual Batch 1 15 percent 0.01 0.15 NA reported
169768 DTXSID7020182 1153212 PRIMER COATING, EPOXY PART B 09 ID724116 08/16/1988 primer coating_ epoxy part b 09 id724116 Home maintenance paint/stain and related products Manual Batch NA NA NA NA reported
18357 DTXSID7020182 1100923 SCOTCH-WELD EC-3578 B/A PART A STRUCTURAL (SUPDAT) 08/12/1993 scotch-weld ec-3578 b/a part a structural (supdat) Home maintenance adhesives and adhesive removers multipurpose adhesive Manual Batch 1 10 percent 0.01 0.10 NA reported
174406 DTXSID7020182 1154642 RP 1250 HARDENER 12/07/1989 rp 1250 hardener Raw materials coatings Manual Batch 1.665 percent NA NA 0.01665 reported
22439 DTXSID7020182 1102189 CHEMLOK 805 ELASTOMER ADHESIVE, 98050W 01/28/1993 chemlok 805 elastomer adhesive_ 98050w Laboratory supplies Manual Batch NA NA NA 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
254 Formulation Personal care sexual wellness sexual wellness products, including personal lubricants
255 Formulation Personal care shaving and hair removal shaving or hair removal products which do not fit into a more refined category
256 Formulation Personal care shaving and hair removal aftershave products applied to the skin following shaving to provide scent, or improve skin characteristics
257 Formulation Personal care shaving and hair removal clipper lubricant/cleaner cleaning and lubricating products for hair clippers
258 Formulation Personal care shaving and hair removal depilatory products for removal of body or facial hair
259 Formulation Personal care shaving and hair removal shaving cream shaving creams, foams, balms and soaps

httk Data Resource

Predictions from the httk R package are available.

httk Data

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

bpa_httk <- get_httk_data(DTXSID = 'DTXSID7020182')
head(data.table(bpa_httk))
#>        id        dtxsid    parameter measuredText measured predictedText
#>     <int>        <char>       <char>       <char>    <num>        <char>
#> 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
#> 7 variables not shown: [predicted <num>, units <char>, model <char>, reference <char>, percentile <char>, species <char>, dataSourceSpecies <char>]

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 tagKind
46 Cleaning products and household care - shoes Products related to the care of footwear which do not fit into a more refined category PUC - formulation
47 Cleaning products and household care - shoes - shoe polish or protectant products applied to footwear to color, polish, clean, or add a protective surface PUC - formulation
48 Cons electronics mech appliances and machinery Including small and large consumer electronics appliances (e.g. refrigerator, washing machine, vacuum cleaner, computer, phone, smoke detector, electric tools, lamps); includes small electronics with direct personal contact such as massagers; excludes electronics specifically intended for use by children; excludes electronics with direct food contact (e.g. electric blender) PUC - article
49 Construction and building materials Materials used for construction (e.g. flooring, tile, sinks, bathtubs, mirrors, wall materials/drywall, wall-to-wall carpets, insulation, playground surfaces); includes semi-permanent fixtures such as faucets and light fixtures PUC - article
50 consumer_product Indicated as related to, or in, consumer products, but with no more specific information designating product category General use
51 cotton of or related to the growing of cotton, or processed cotton used as a textile for consumer goods Foods & Agriculture

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
24597 DTXSID7020182 1371471 The 25 Chemicals Found in All Nine of the Biosolids Studied median concentrations are normalized to organic carbon and are given in micorgrams per kilogram of organic carbon (µg/kg OC) USGS fire retardant Flame retardant detected; wastewater
31967 DTXSID7020182 1372153 Emission and evaluation of chemical substances from selected electrical and electronic products- Table 2.10 Table 2.10 Unwanted substances in electric and electronic equipment according to the UMP system. 2003 Danish Environmental Protection Agency NA NA Cons electronics mech appliances and machinery; Europe
21358 DTXSID7020182 1370059 FDA Cumulative Estimated Daily Intake The database lists publicly available cumulative estimated daily intakes (CEDIs) for a large number of food contact substances FDA NA NA CEDI
28659 DTXSID7020182 1372108 Annex to the HYDROCHECK procedure for acceptance of Materials in contact with Drinking Water 1.2 Compounds that may be used in the manufacture of plastics, elastomers and natural and synthetic rubber products 23 March 2010 Belgaqua NA NA drinking_water; Europe; manufacturing; plastic_additive
43751 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)
9996 DTXSID7020182 1359540 Actively Registered AI’s by Common Name California Department of Pesticide Regulation NA NA active_ingredient; Pesticides

Exposure Predictions

There are two endpoints 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 from the first endpoint corresponds to SEEM3 consensus predictions of exposure pathways. The exposure predictions from the second endpoint feature SEEM2 predictions broken down by demographic groups, general consensus exposure rate 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
#> 3 variables not shown: [probabilityResidential <num>, probabilityPesticde <num>, probabilityIndustrial <num>]

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')
head(data.table(bpa_demographic_exposure))
#>        id        dtxsid        demographic       predictor       median
#>     <int>        <char>             <char>          <char>        <num>
#> 1: 488214 DTXSID7020182              Total SEEM3 Consensus 5.497000e-05
#> 2: 772655 DTXSID7020182              Total          RAIDAR 3.770000e+00
#> 3: 768361 DTXSID7020182              Total    Food.Contact 1.766000e-02
#> 4: 697139 DTXSID7020182 Repro. Age Females SEEM2 Heuristic 1.364275e-05
#> 5: 709226 DTXSID7020182              Males SEEM2 Heuristic 3.867956e-05
#> 6: 711258 DTXSID7020182            Females SEEM2 Heuristic 1.244431e-05
#> 8 variables not shown: [medianText <char>, l95 <num>, l95Text <char>, u95 <num>, u95Text <char>, units <char>, ad <int>, reference <char>]

Comptox Chemicals Dashboard (CCD)

There are a variety of endpoints that provide access to data available from the CCD.

Product Data

Retrieve the product use categories via get_product_use_category().

# Caffeine product use categories
caffeine_product_use <- get_product_use_category('DTXSID0020232')
head(data.table(caffeine_product_use))
#>       id        dtxsid                displayPuc     pucKind prodCount
#>    <int>        <char>                    <char>      <char>     <int>
#> 1: 54945 DTXSID0020232 Personal care:acne treatm Formulation         2
#> 2: 55118 DTXSID0020232 Personal care:after sun p Formulation        13
#> 3: 55433 DTXSID0020232 Personal care:eye care an Formulation       130
#> 4: 55438 DTXSID0020232 Personal care:facial clea Formulation       175
#> 5: 56864 DTXSID0020232 Food and drug:pharmaceuti Formulation         2
#> 6: 57096 DTXSID0020232 Personal care:self-tanner Formulation        19
#> 4 variables not shown: [genCat <char>, prodfam <char>, prodtype <char>, definition <char>]

Retrieve production volume data via get_production_volume().

# Caffeine production volume
caffeine_prod_vol <- get_production_volume('DTXSID0020232')
data.table(caffeine_prod_vol)
#>       id        dtxsid                      name  amount
#>    <int>        <char>                    <char>  <char>
#> 1:   425 DTXSID0020232 2019 NATIONALLY AGGREGATE 219,582
#> 2:   426 DTXSID0020232 2018 NATIONALLY AGGREGATE 292,600
#> 3:   427 DTXSID0020232 2017 NATIONALLY AGGREGATE 543,400
#> 4:   428 DTXSID0020232 2016 NATIONALLY AGGREGATE 627,000

Biomonitoring data

Retrieve biomonitoring data via get_biomonitoring_data().

# BPA biomonitoring data
bpa_biom <- get_biomonitoring_data('DTXSID7020182')
head(data.table(bpa_biom))
#>       id        dtxsid   demographic     median upperBound lowerBound
#>    <int>        <char>        <char>      <num>      <num>      <num>
#> 1:   606 DTXSID7020182         Total 1.7293e-05 1.7903e-05 1.6691e-05
#> 2:   607 DTXSID7020182          Male 1.8757e-05 2.1005e-05 1.6888e-05
#> 3:   608 DTXSID7020182        Female 1.6025e-05 1.7202e-05 1.4924e-05
#> 4:   609 DTXSID7020182   3 - 5 years 6.3964e-05 7.9916e-05 5.1602e-05
#> 5:   610 DTXSID7020182  6 - 11 years 2.7678e-05 3.1685e-05 2.4398e-05
#> 6:   611 DTXSID7020182 12 - 19 years 1.6023e-05 1.7608e-05 1.4722e-05
#> 1 variable not shown: [nhanesCohort <char>]

Chemical use and weight fractions

Retrieve general use keywords via get_general_use_keywords().

# BPA general use keywords
bpa_gen_use <- get_general_use_keywords('DTXSID7020182')
head(data.table(bpa_gen_use))
#>       id                keywordset sourceCount        dtxsid
#>    <int>                    <char>       <int>        <char>
#> 1: 40511 OEHHA Proposition 65 (3/2           1 DTXSID7020182
#> 2: 40512 detected, Europe, Toys an           2 DTXSID7020182
#> 3: 40513 Europe, nondetect, Other            3 DTXSID7020182
#> 4: 40514 detected, Europe, Other d           6 DTXSID7020182
#> 5: 40515 artificial_sweat, detecte           2 DTXSID7020182
#> 6: 40516 detected, MN Chemical Scr           8 DTXSID7020182

Retrieve functional use via get_reported_functional_use().

# BPA reported functional use
bpa_reported_use <- get_reported_functional_use('DTXSID7020182')
head(data.table(bpa_reported_use))
#>       id        dtxsid        category             definition
#>    <int>        <char>          <char>                 <char>
#> 1: 28665 DTXSID7020182        Hardener               hardener
#> 2:  3071 DTXSID7020182     Antioxidant antioxidants>phenolics
#> 3:  4351 DTXSID7020182          Binder                 binder
#> 4:  5717 DTXSID7020182        Catalyst               catalyst
#> 5:  9261 DTXSID7020182        Hardener           curing agent
#> 6: 14100 DTXSID7020182 Flame retardant         fire retardant

Retrieve chemical weight fractions via get_chemical_weight_fraction().

# BPA chemical weight fractions
bpa_weight_fractions <- get_chemical_weight_fraction('DTXSID7020182')
head(data.table(bpa_weight_fractions))
#>         id        dtxsid                  prodName          displayPuc pucKind
#>      <int>        <char>                    <char>              <char>  <char>
#> 1: 1322890 DTXSID7020182     cr-180 hardener_ pt b Not yet Categorized    <NA>
#> 2: 1322891 DTXSID7020182       curing agent ta30-b Not yet Categorized    <NA>
#> 3: 1322892 DTXSID7020182 cw 1661 cronasphere harde Not yet Categorized    <NA>
#> 4: 1322889 DTXSID7020182 concresive 1001 lpl part  Not yet Categorized    <NA>
#> 5: 1322811 DTXSID7020182  cat-l-ink 50-407r medium Not yet Categorized    <NA>
#> 6: 1322808 DTXSID7020182          3197 steel works Not yet Categorized    <NA>
#> 12 variables not shown: [lowerweightfraction <num>, upperweightfraction <num>, weightfractiontype <char>, gencat <char>, prodfam <char>, prod_type <char>, pucDefinition <char>, sourceName <char>, sourceDescription <char>, sourceUrl <char>, ...]

Multimedia Database (MMDB)

There are several endpoints that provide access to data from the MMDB.

Medium Categories

First, one can retrieve the MMDB medium categories using get_medium_categories().

medium_categories <- get_medium_categories()
head(medium_categories)
#>                                                                                                                                       harmonizedMediumDesc
#> 1                                                                                                                                      Outdoor ambient air
#> 2                                                                                                                                        Human breast milk
#> 3                                                           Treated or untreated drinking water supplies, tap water, bottled drinking water, cooking water
#> 4 Processed food products, including dairy products, breads, cooked meats, processed (e.g., canned or frozen) fruit and vegetable products, infant formula
#> 5                                                                                                         Water from groundwater sources (wells, aquifers)
#> 6                                                Human whole blood, blood cells, serum, plasma, or other extractants, including fetal or umbilical samples
#>                   harmonizedMedium
#> 1                      ambient air
#> 2                      breast milk
#> 3                   drinking water
#> 4                     food product
#> 5                      groundwater
#> 6 human blood (whole/serum/plasma)

Single Sample records

Single sample records from MMDB can be retrieved either by DTXSID or by medium.

# Data on methylphenanthrene
methylphenanthrene <- get_single_sample_records_by_dtxsid(DTXSID = 'DTXSID001025673')
head(data.table(methylphenanthrene))
#>          id            fullSourceName       chemicalName          dtxsid
#>       <int>                    <char>             <char>          <char>
#> 1: 18925051 Chem Theatre              methylphenanthrene DTXSID001025673
#> 2: 18926743 Chem Theatre              methylphenanthrene DTXSID001025673
#> 3: 18924367 Chem Theatre              methylphenanthrene DTXSID001025673
#> 4: 18925501 Chem Theatre              methylphenanthrene DTXSID001025673
#> 5: 18925267 Chem Theatre              methylphenanthrene DTXSID001025673
#> 6: 18926365 Chem Theatre              methylphenanthrene DTXSID001025673
#> 33 variables not shown: [preferredName <char>, casrn <char>, result <char>, units <char>, cleanedUnits <char>, lod <lgcl>, loq <lgcl>, detectionFlag <lgcl>, resultFlag <lgcl>, detected <int>, ...]

# Data from soil
indoor_air <- get_single_sample_records_by_medium(Medium = 'indoor air')
head(data.table(indoor_air$data))
#>           id            fullSourceName              chemicalName        dtxsid
#>        <int>                    <char>                    <char>        <char>
#> 1: 118822214 USGS Monitoring Data Nati trans-1,2-Dichloroethylen DTXSID7024031
#> 2: 118822215 USGS Monitoring Data Nati            Vinyl chloride DTXSID8021434
#> 3: 119838929 USGS Monitoring Data Nati       Tetrachloroethylene DTXSID2021319
#> 4: 123900830 USGS Monitoring Data Nati      1,1-Dichloroethylene DTXSID8021438
#> 5: 123900831 USGS Monitoring Data Nati  cis-1,2-Dichloroethylene DTXSID2024030
#> 6: 123900832 USGS Monitoring Data Nati         Trichloroethylene DTXSID0021383
#> 33 variables not shown: [preferredName <char>, casrn <char>, result <char>, units <char>, cleanedUnits <char>, lod <lgcl>, loq <char>, detectionFlag <char>, resultFlag <char>, detected <int>, ...]

Aggregate Records

Aggregate records from MMDB can also be retrieved either by DTXSID or by medium.

# Data on caffeine
caffeine_agg <- get_aggregate_records_by_dtxsid(DTXSID = 'DTXSID0020232')
head(data.table(caffeine_agg))
#>          id            fullSourceName chemicalName        dtxsid preferredName
#>       <int>                    <char>       <char>        <char>        <char>
#> 1: 61423179 EPA Discharge Monitoring      Caffeine DTXSID0020232      Caffeine
#> 2: 34309226 Information Platform for      Caffeine DTXSID0020232      Caffeine
#> 3: 61425076 EPA Discharge Monitoring      Caffeine DTXSID0020232      Caffeine
#> 4: 63662830 Comparative Toxicogenomic     Caffeine DTXSID0020232      Caffeine
#> 5: 48499404 EPA Discharge Monitoring      Caffeine DTXSID0020232      Caffeine
#> 6: 61426047 EPA Discharge Monitoring      Caffeine DTXSID0020232      Caffeine
#> 32 variables not shown: [casrn <char>, result <char>, units <char>, cleanedUnits <char>, statistic <char>, sampleSize <char>, lod <lgcl>, loq <char>, numDetects <lgcl>, numNonDetects <char>, ...]

# Data from soil
indoor_air_agg <- get_aggregate_records_by_medium(Medium = 'indoor air')
head(data.table(indoor_air_agg$data))
#>          id            fullSourceName chemicalName        dtxsid preferredName
#>       <int>                    <char>       <char>        <char>        <char>
#> 1: 63216485 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 2: 63216491 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 3: 63216492 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 4: 63216493 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 5: 63216498 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 6: 63216499 California Air Resources  Chlorpyrifos DTXSID4020458  Chlorpyrifos
#> 32 variables not shown: [casrn <char>, result <char>, units <char>, cleanedUnits <char>, statistic <char>, sampleSize <lgcl>, lod <char>, loq <lgcl>, numDetects <lgcl>, numNonDetects <lgcl>, ...]

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.