Are non-animal systemic safety assessments protective? A toolbox and workflow
Data files
Mar 30, 2025 version files 73.91 MB
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Middleton_et_al_Toolbox_pilotstudy.zip
73.90 MB
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README.md
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Abstract
An important question in toxicological risk assessment is whether non-animal New Approach Methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative. In this work, we propose a core NAM toolbox and workflow for conducting systemic safety assessments for adult consumers. We also present an approach for evaluating how protective and useful the toolbox and workflow are by benchmarking against historical safety decisions. The toolbox includes physiologically-based kinetic (PBK) models to estimate systemic Cmax levels in humans, and three bioactivity platforms, comprising high-throughput transcriptomics, a cell stress panel and in vitro pharmacological profiling, from which points of departure are estimated. A Bayesian model was developed to quantify the uncertainty in the Cmax estimates depending on how the PBK models were parameterised. The feasibility of the evaluation approach was tested using 24 exposure scenarios from 10 chemicals, some of which would be considered high risk from a consumer goods perspective (e.g., drugs that are systemically bioactive) and some low risk (e.g., existing food or cosmetic ingredients). Using novel protectiveness and utility metrics, it was shown that up to 69% (9/13) of the low-risk scenarios could be identified as such using the toolbox, whilst being protective against all (5/5) the high-risk ones. The results demonstrated how robust safety decisions could be made without using animal data. This work will enable a full evaluation to assess how protective and useful the toolbox and workflow are across a broader range of chemical exposure scenarios.
Readme for the supplementary data/results package for Middleton et el. 2022.
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 and analysis for these platforms are shared within this package. The contents of the package are as follows:
CSP/Data – all data generated within the CSP platform. The data are stored using either as comma separated value (.csv) or as Microsoft excel spreadsheet (.xslx) file types. File <assay_name>httr-support-file*.
| 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) ID |
HTTr/Data – all raw data generated using high throughput transcriptomics. Contains the counts table (Counts_per_gene_per_sample_raw_BCL-SP0122_April2021.csv), meta data (BIOCLAVIS_HTTR_PHASEII_META_DATA_FINAL.csv), manifest (190620 Human Whole Transcriptome 2.0 Manifest.csv) and attenuation manifest (190620 Human Whole Transcriptome 2.0 Attenuation Manifest (2).csv) generated within the HTTr platform.
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 file 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 manifest 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 and results (screening and concentration-response) for the IPP platform.
The file ‘Supplementary Data IPP Screening.xlsx’ 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 file ‘Supplementary Data IPP dose response’ 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. |
PBK – detailed output of PBK modelling for each chemical-exposure scenario discussed in the manuscript, stored as a Microsoft excel spreadsheet (.xlsx) files. The first sheet contains various input parameters to the PBK model, with columns:
| Column name | Description |
|---|---|
| Tissue | Tissue in the PBK model |
| Type of tissue model | Type of tissue model being used |
| Volume [mL] | Volume of tissue compartment |
| Blood flow [mL/s] | Blood flow rate |
| Tissue to plasma partition coefficient | Partition coefficient |
The remaining sheets correspond to simulation outputs for the various exposure scenarios considered in the manuscript for a given chemical and PBK parameterization level (L1, L2 or L3). Note that for these sheets, columns “A” to “P” are the PBK outputs in units of g/mol (i.e., which are standard units used by Gastroplus), whereas columns “T” to “AH” are the same PBK output values, converted to µM (row S demarks the separation between the two sets of values in the spreadsheet). Note that row 2 contains multiple zero values as it corresponds to timepoint 0 (see columns B and T) when all compartments in the model have an initial value of zero. Columns Q, R, S are intentionally left blank for readability. The names of columns C to P vary from sheet to sheet, but each follows the following convention: <Free/Total>
| Column name | Description |
|---|---|
| CpU-Venous Return-<Compound/exposure scenario> (ug/mL) | Free plasma concentration |
| Cp-Venous Return-<Compound/exposure scenario>(ug/mL) | Total plasma concentration |
| Ct-Adipose-<Compound/exposure scenario>(ug/mL) | Total adipose tissue concentration |
| CtU-Adipose-<Compound/exposure scenario>(ug/mL) | Free adipose tissue concentration |
| Ct-Liver-<Compound/exposure scenario>(ug/mL) | Total liver tissue concentration |
| CtU-Liver-<Compound/exposure scenario>(ug/mL) | Free liver tissue concentration |
| Ct-Brain-<Compound/exposure scenario>(ug/mL) | Total brain tissue concentration |
| CtU-Brain-<Compound/exposure scenario>(ug/mL) | Free brain tissue concentration |
| Ct-Kidney-<Compound/exposure scenario>(ug/mL) | Total kidney tissue concentration |
| CtU-Kidney-<Compound/exposure scenario>(ug/mL) | Free kidney tissue concentration |
| Ct-ReproOrg-<Compound/exposure scenario>(ug/mL) | Total reproductive organ tissue concentration |
| CtU-ReproOrg-<Compound/exposure scenario>(ug/mL) | Free reproductive organ tissue concentration |
| Ct-Heart-<Compound/exposure scenario>(ug/mL) | Total heart tissue concentration |
| CtU-Heart-<Compound/exposure scenario>(ug/mL) | Free heart tissue concentration |
Columns AJ to AP contain the Cmax information in micromolar for each compartment, calculated using the MAX function in excel from columns T to AH.
Contact
For additional queries pertaining to the data/results package, please contact the primary author of the main manuscript (A Middleton).