Advancing systemic toxicity risk assessment: Evaluation of a NAM-based toolbox approach
Data files
Apr 29, 2025 version files 184.88 MB
-
Data_Repository.zip
184.87 MB
-
README.md
10.63 KB
Abstract
For many years, a method that allowed systemic toxicity safety assessments to be conducted without generating new animal test data, seemed out of reach. However, several different research groups and regulatory authorities are beginning to use a variety of in silico, in chemico, and in vitro techniques to inform safety decisions. To manage this transition to animal-free safety assessments responsibly, it is important to ensure that the level of protection offered by a safety assessment based on new approach methodologies (NAMs), is at least as high as that provided by a safety assessment based on traditional animal studies. To this end, we have developed an evaluation strategy to assess both the level of protection and the utility offered by a NAM-based systemic safety “toolbox.” The toolbox comprises physiologically based kinetic models to predict internal exposures, and bioactivity NAMs designed to give broad coverage across many different toxicity modes of action. The output of the toolbox is the calculation of a bioactivity:exposure ratio (analogous to a margin of internal exposure), which can be used to inform decision-making. In this work, we have expanded upon an initial pilot study of 10 chemicals with an additional 38 chemicals and 70 consumer exposure scenarios. We found that, for the majority of these (>90%), the NAM-based workflow is protective of human health, enabling us to make animal-free safety decisions for systemic toxicity and preventing unnecessary animal use. We have also identified critical areas for improvement to further increase our confidence in the robustness of the approach.
Contents
The NGRA toolbox described in the manuscript is composed of four new approach methodology (NAM) platforms. These are:
1. PBK modelling for estimation of internal exposures
2. In vitro pharmacological profiling (IPP) to assess activity of chemicals against receptors of pharmacological interest
3. A cellular stress panel (CSP) to assess potential of chemical to induce chemical stress
4. A high-throughput transcriptomics (HTTr) platform to assess biological activity towards other endpoints
Data for each NAM are shared within this package. The contents of the package are as follows:
Cell stress panel data/Data – all data generated within the CSP platform. The data are all comma separated value (.csv) file types. File <CYP_ID><ROUND_ID><assay_name>-httr-support-file*. contains the raw cell stress panel data for assay <assay_name>. The data are stored in a flat format, with each row corresponding to unique measurement sample, with the following columns:
| Column name | Description |
|---|---|
| SAMPLE_ID | Sample ID number |
| FILE_NAME | File name |
| ASSAY_TYPE | Assay type |
| MEASUREMENT_TYPE | Type of measurement |
| CELL_TYPE | Cell type |
| CELL_BATCH_ID | Cell Batch ID |
| SUPPLIER_ID | Supplier ID |
| MEASUREMENT_DATE | Measurement date |
| TEST_SUBSTANCE | Test substance |
| CONTROL_FLAG | Control Flag (1 indicates positive control, -1 negative control) |
| CONCENTRATION | Concentration of test substance |
| CONCENTRATION_UNITS | Concentration units |
| VALUE | Value of measurement in MEASUREMENT_TYPE |
| VALUE_UNITS | Units of measurement |
| EXPOSURE_TYPE | Exposure type |
| EXPOSURE_TIME | Exposure time |
| TIMEPOINT_UNITS | Timepoint units |
| VESSEL_ID | Vessel ID (i.e., of plate) |
| VESSEL_TYPE | Vessel type (e.g., 384 well place) |
| WELL_ID | Well ID |
| DROP_TYPE | Drop type |
| DROP_CODE | Drop code |
| CYPR_ID | Cyprotex (CRO which generated the data) internal ID |
| CYP_ID | Cyprotex study ID (appears in filename) |
| ROUND_ID | Study round ID (appears in filename) |
** **
HTTr data/Data – all raw data generated using high throughput transcriptomics. Contains the counts table (Phase3_round1_publication_counts.csv, Phase3_round2_publication_counts.csv), meta data (Phase3_round1_publication_meta.csv, Phase2_round1_publication_meta.csv), manifest (190620 Human Whole Transcriptome 2.0 Manifest.xlsx, 20SEPT2021+Human+Whole+Transcriptome+2.1+Manifest.xlsx) and attenuation manifest (190620 Human Whole Transcriptome 2.0 Attenuation Manifest.xlsx, 100739 Human Whole Transcriptome 2.1 Attenuation Factors.xlsx) generated within the HTTr platform. The metadata tables indicate which manifest version was used to generate the counts (see PROBE_MANIFEST_VERSION column).
The counts table has following format: each row corresponds to a probe (e.g., CHAC1_1279, as indicated by the 1st column) and each column to a sample ID (e.g., S_50-50ratio_HG2_P1). Numerical entries are probe counts per condition.
The meta data files gives details of each condition and contains the following columns:
| Column name | Description |
|---|---|
| SAMPLE_ID | Sample ID number |
| CELL_TYPE | Cell type |
| CELL_BATCH_ID | Cell batch ID |
| TEST_SUBSTANCE | Test substance |
| CONTROL_FLAG | Control flag (1 indicates positive control, -1 negative control) |
| CONCENTRATION | Concentration |
| CONCENTRATION_UNITS | Concentration units |
| EXPOSURE_TYPE | Exposure type |
| EXPOSURE_TIME | Exposure time |
| TIMEPOINT_UNITS | Timepoint units |
| VESSEL_ID | Vessel ID |
| VESSEL_TYPE | Vessel type |
| WELL_ID | Well ID |
| FASTQ_FILENAME | FastQ filename |
| SUPPLIER | Supplier |
| MEASUREMENT_DATE | Measurement date |
| NUM_UNMAPPED_READS | Number unmapped reads |
| NUM_MAPPED_READS | Number mapped reads |
| PERCENT_MAPPED_READS | Percent mapped reads |
| SEQUENCING_PLATE_ID | Sequencing plate ID |
| SEQUENCING_WELL_ID | Sequencing well ID |
| ATTENUATION_MANIFEST_VERSION | Attenuation manifest version |
| PROBE_MANIFEST_VERSION | Probe manifest version |
| LYSIS_BUFFER | Lysis buffer |
| FLOW_CELL_CHANNEL | Flow cell channel |
The attenuation manifest is a table that contains two columns: the probe ID and the corresponding attenuation factor. The manifests provided contains the following columns:
| Column name | Description |
|---|---|
| Probe Name | Probe ID |
| Gene Symbol | Gene symbol |
| Entrez ID | Entrez ID |
| ENSEMBL Gene ID | ENSEMBL Gene ID |
| Probe Sequence | Sequence of the probe |
| Probe Genomic Coordinates | Genomic coordinate of the probe |
| Transcripts Covered | Transcripts that are covered by the probe |
IPP data – data and results (screening and concentration-response) for the IPP platform.
The file ‘IPP results Supp Table Paper.xlsx’ contains two sheets, ‘%response screening’ and ‘IC50 dose response Bayesian mod’.
The first sheet contains a table with the following columns:
| Column name | Description |
|---|---|
| Assay name | Name of the assay |
| Ligand/control | Ligand used in the experiment |
| Response/Readout | Type of readout |
These are followed by additional columns for each test substance and screening concentration (as indicated by the column header). Values are the percentage of the response, with those exceeding 50% highlighted in bold.
The second sheet (‘IC50 dose response Bayesian mod’) contains the IC50 values that were calculated from the follow dose response experiments. The table follows the same format as in the first sheet, except where the entries are the 95% confidence interval for the IC50 value.
The file ‘IPP Supplementary Data dose response Paper’ contains the dose response data, which each sheet corresponding to a single chemical (as indicated by the sheet name). The data in each sheet is organised as a table, with the following columns:
| Column name | Description |
|---|---|
| Compound | Compound that was tested |
| Target | Pharmacological target |
| Replicate | Replicate number |
| Dose (uM) | Dose in micromolar |
| Response % | Response of target to compound in percentage. |
If no dose response experiment was conducted for a particular chemical, then the sheet will appear blank.
PBK data – contains text files which were generated automatically using the software Gastroplus. Each text file contains all the input and output details for each PBK run (see main text for details), which the name of text indicating the chemical, exposure scenario and PBK parameterisation level.
Contact
For additional queries pertaining to the data/results package, please contact the primary author of the main manuscript (S Cable).
