DOI: 10.5061/dryad.fbg79cnx1
title: Are non-animal systemic safety assessments protective? A toolbox and workflow

description: 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.
description: **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
 &lt;*assay_name*&gt;httr-support-file*. contains the
 raw cell stress panel data for assay &lt;*assay_name*&gt;. 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) 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:
 : | **Column name** | **Description** | |
 :------------------------------------------------------ |
 :-------------------------------------------- | | CpU-Venous
 Return-\ (ug/mL) | Free plasma
 concentration | | Cp-Venous Return-\(ug/mL) | Total plasma concentration | |
 Ct-Adipose-\(ug/mL) | Total adipose
 tissue concentration | | CtU-Adipose-\(ug/mL) | Free adipose tissue concentration | |
 Ct-Liver-\(ug/mL) | Total liver tissue
 concentration | | CtU-Liver-\(ug/mL) |
 Free liver tissue concentration | | Ct-Brain-\(ug/mL) | Total brain tissue concentration | |
 CtU-Brain-\(ug/mL) | Free brain tissue
 concentration | | Ct-Kidney-\(ug/mL) |
 Total kidney tissue concentration | | CtU-Kidney-\(ug/mL) | Free kidney tissue concentration | |
 Ct-ReproOrg-\(ug/mL) | Total
 reproductive organ tissue concentration | |
 CtU-ReproOrg-\(ug/mL) | Free
 reproductive organ tissue concentration | |
 Ct-Heart-\(ug/mL) | Total heart tissue
 concentration | | CtU-Heart-\(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).