IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making
Abstract
:1. Introduction
1.1. Multiple Definitions of IVIVE in Literature
1.2. Overview of Regulatory Applications of IVIVE
1.3. Introduction to the IVIVE Workgroup
2. Methods
3. Regulatory Application of IVIVE
4. Applications of IVIVE Approaches
4.1. Review of IVIVE Literature
4.2. IVIVE of Dosimetry
4.2.1. Summary of Common Applications
4.2.2. Challenges and Additional Considerations for IVIVE of Dosimetry
4.3. IVIVE of ADME Parameters
4.3.1. Summary of Common Applications
4.3.2. Evaluations and Additional Considerations for IVIVE of ADME Parameters
4.4. Employing IVIVE to Predict In Vivo Toxicity
5. Case Examples from the Literature
5.1. For Prioritization
5.2. Developmental Toxicity
5.3. Endocrine Effects
5.4. Case Examples of IVIVE of ADME Parameters
5.5. IVIVE Application to Engineered Nanomaterials (ENMs)
6. IVIVE Resources and Tools
6.1. Information Obtained from Literature and Agency’s Responses
6.2. Resources for Chemical Properties and In Vitro Data
6.2.1. Resources for Chemical Properties Data
6.2.2. Resources for In Vitro ADME Data (Reviews or Multiple Topics)
6.3. Models and Tools for PBPK Modeling and IVIVE
6.3.1. Resources Explicitly Designed to Support IVIVE of Dosimetry and Related Activities
6.3.2. Other Models and Tools for PBPK Modeling and IVIVE
7. Agency Needs, Areas of Research Needed, and Future Opportunities
7.1. Agency Needs, Gaps, and Uncertainty in IVIVE
7.2. Efforts to Address Needs and Future Opportunity
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Disclaimer
FDA Disclaimer
NIST Disclaimer
Abbreviations
3Rs | to replace, reduce, and refine (or replacement, reduction, and refinement of) the use of animal models |
ADME | absorption, distribution, metabolism and excretion |
ATSDR | Agency for Toxic Substances and Disease Registry |
AUC | area under curve |
BMD | benchmark dose, the dose of a chemical that is required to achieve a predetermined response of a toxicological effect |
BMD10 | derived benchmark dose that is associated with a 10% extra risk of adverse effect in the exposed test animals |
BMDL10 | the lower bound of 95% confidence interval on BMD10 |
CFSAN | FDA Center for Food Safety and Applied Nutrition |
Cmax | the highest concentration of a chemical in the blood or a tissue after a dose is given |
CPSC | U.S. Consumer Product Safety Commission |
Css | steady-state concentration |
DoD | U.S. Department of Defense |
EAD | equivalent administered dose |
ENM | engineered nanomaterial |
EPA | U.S. Environmental Protection Agency |
ER | estrogen receptor |
EURL-ECVAM | European Union Reference Laboratory for Alternatives to Animal Testing |
FDA | U.S. Food and Drug Administration |
HTS | high throughput screening |
HT-IVIVE | high throughput in vitro to in vivo extrapolation |
ICCVAM | Interagency Coordinating Committee on the Validation of Alternative Methods |
ICE | Integrated Chemical Environment |
IVIVE | in vitro to in vivo extrapolation |
IVIVE-WG | ICCVAM in vitro to in vivo Extrapolation Workgroup |
LOAEL | low observed adverse effects level |
log Kow | the n-octanol / water partition ratio or coefficient |
NAM | new approach methodology |
NIEHS | National Institute of Environmental Health Sciences |
NIST | National Institute of Standards and Technology |
NLM | U.S. National Library of Medicine |
NOAEL | no observed adverse effect level |
NOEL | no observed effect level |
NTP | National Toxicology Program |
OECD | Organisation for Economic Co-operation and Development |
OED | oral equivalent dose |
OPP | EPA Office of Pesticide Programs |
QSAR | quantitative structure activity relationship |
PD | pharmacodynamics |
PK | pharmacokinetics |
PBK | physiologically based kinetics |
PBPK | physiologically based pharmacokinetics |
PBTK | physiologically based toxicokinetics |
PFOA | perfluorooctanoic acid |
POD | point of departure |
ToxCastTM | Toxicity forecaster |
Tox21 | Toxicology in the 21st century |
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Agency/Organization | Use of In Vitro to In Vivo Extrapolation (IVIVE) in Risk Characterization | Use of IVIVE or In Vitro Data Outside of Quantitative Risk Characterization |
---|---|---|
Agency for Toxic Substances and Disease Registry (ATSDR) | Application of IVIVE approaches would require the ability to derive health guidance values using high-throughput in vitro data. Several uncertainties and assumptions remain; hence, IVIVE is not used in health assessments. | In vitro data are used or potentially used as weight of evidence. |
U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition (FDA/CFSAN) | Use IVIVE to develop physiologically based pharmacokinetic (PBPK) models, specifically to account for metabolism in the liver and transport in the kidney. | Not applicable (N/A) |
FDA Center for Drug Evaluation and Research (FDA/CDER) | The role of IVIVE in risk assessment has generally been limited to relating in vitro human ether-à-go-go-related gene (hERG) channel assay results to the risk of QT prolongation and PBPK modeling. Following established decision trees in dedicated guidance [59], in vitro data can be used to predict drug–drug interactions and therefore dismiss the need for clinical trials. It is anticipated that appropriately constructed IVIVE algorithms will play a critical role in assessing the utility of new approach methodologies (NAMs) proposed to be used in risk assessment, which may include the support of starting dose selection in first-in-human trials of products using the Minimum Anticipated Biological Effect Level [60]. | In vitro data can predict efficacy of drugs and estimate doses to use with high potential in the field of rare diseases [61]. |
Consumer Product Safety Commission (CPSC) | Has not used the approach but could use the information during any applicable risk evaluation; the approach could be used in a weight of evidence approach for risk assessments. | N/A |
U.S. Environmental Protection Agency, Office of Pesticide Programs (EPA/OPP) | Use IVIVE to perform a rapid risk screening for chemicals without in vivo toxicity data [62] or to support a weight of evidence approach to identify data needs or to derive extrapolation factors [63]. | Identify chemicals that act on a common mechanism. |
U.S. Department of Defense (DoD) | Various applications use IVIVE to derive human-relevant numbers to address operational human toxicity issues providing informed assessment of risk. This approach has also been used in a corroborative weight of evidence evaluation of hazard (comparisons across various data streams). | N/A |
National Institute of Environmental Health Sciences, National Toxicology Program (NIEHS/NTP) | N/A | Perform hazard characterization. Use IVIVE to estimate external doses needed to achieve blood levels that equate to the identified in vitro potencies. The approach is applied to multiple species including human. |
European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) | N/A—does not conduct regulatory risk assessments. | Development of case studies to explore and illustrate applicability of in vitro data and IVIVE. |
Agency/Organization | Publications or Guidance Documents |
---|---|
ATSDR | ATSDR does not have guidance on IVIVE. |
CPSC | CPSC has no guidance document related to IVIVE. There is a proposed Guidance on Alternative Test Methods and Integrated Testing Approaches, 86 FR 16704, 31 March 2021. |
DoD | The DoD has no specific guidance on IVIVE implementation; however, other guidance frameworks are currently being developed. |
EPA | Guidance Documents: [49,64,65] Publications grouped into the following categories: |
NIEHS/NTP | Publications: [29,41,73,76] |
FDA/CDER | Publications: [57,93,94] Guidance Documents:
|
European Commission/EURL ECVAM | There is no specific guidance on IVIVE so far, but various approaches have been reviewed or explored [39,43,99,100].
|
Health Canada | Science approach document on bioactivity exposure ratio: application in priority setting and risk assessment [58]. |
Agency/Organization | Models or Software Tools |
---|---|
ATSDR | Models or software tools such as PBPK modeling have been used for dosimetric adjustments in the minimal risk level (MRL) determination process. |
CPSC | There are no current plans to use models or software for facilitating IVIVE analysis and decision-making. |
DoD | Current software use runs the spectrum of options. Current legacy software is used for PBPK (e.g., acslX for PBPK modeling); widely available software (e.g., R, also for PBPK modeling); high-throughput toxicokinetics (httk) R package; molecular docking and deep learning (TensorFlow); AOP wiki; STRING, REACTOME, OECD QSAR Toolbox, and BIOVIA software packages; and tools developed within image analysis tools for cell cultures. |
EPA/ORD | Developed httk R package [87]; SimcypTM for PBPK modeling; PBPK model knowledgebase [150]; Database of PK time-series data and parameters [120]. |
NIEHS/NTP | No decision-making. Use httk R package, GastroPlus & ADMET Predictor (Simulations Plus), as well as the Integrated Chemical Environment (ICE) tool. |
European Commission/EURL ECVAM | No decision-making. Use httk R package (for the Accelerating the Pace of Chemical Risk Assessment [APCRA] project); Berkeley Madonna PBK model; explored application of the Wetmore IVIVE approach [31] and the BMD approach in a reverse dosimetry way; ongoing work from EFSA on the toxicokinetic plate and EPAA project on IVIVE. An IVIVE EPAA project [211,212] conducted by Health and Safety Executive, UK, is ongoing, which is based on work from McNally et al. [43] (led by G. Loizou). It will provide a tool to translate in vitro concentration–response relationships to in vivo dose–responses, determine in vivo benchmark dose (BMD) values from the translated data, and compare the predicted in vivo BMD to existing experimental BMD values used in chemical safety assessments by a regulatory agency. |
In Vitro Assay Data Type | Data Summary | References |
---|---|---|
Overview or summary of in vitro and in silico data | Comparison of metabolic clearance assay systems; discussion of computational systems with built-in in vitro biochemical scaling | [29] |
In vitro ADME methods overview | [135] | |
Kidney enzymes, transporters, scaling factors | [143,225,226] | |
This review has an emphasis on test systems and dosimetry in the respiratory tract. | [227] | |
As part of an assessment of QSAR quality and reproducibility, 80 models of 31 ADME-related endpoints were identified. | [228] | |
A summary table in the Supplementary Materials of Patel et al. [228] notes published sources for in vitro data and QSARs pertaining to oral absorption (7 data sets), distribution across the blood–brain barrier (1 data set), and metabolism data (8 data sets: in vitro metabolic clearance, Vmax, and Km). | [228] | |
Summaries of resources of ADME data sets, models, and predictive software (designated as freely available or commercial products); while these tables do not emphasize in vitro data, these resources are well represented. | [223] | |
Review of “high-throughput toxicokinetics”—the combination of in vitro chemical-specific methods with generic toxicokinetic models for IVIVE | [85] | |
In vitro data: metabolism in hepatocytes, microsomes, and purified enzymes | Hepatocyte, microsomal, and purified (non-recombinant) hepatic enzyme data assembled by Pirovano et al. for QSAR development | [229,230] |
Literature curated intrinsic clearance data from pooled hepatocyte suspensions for 1015 chemicals measured using human hepatocytes and 225 chemicals using rat hepatocytes. Included in R package “httk” | [87] | |
In vitro scaling data for scaling liver metabolism | Age-specific data (5-year bins, for adult humans aged 20–95 years old) for microsomal protein content of liver and liver weight used in Simcyp | [231] |
“Age-dependent protein abundance of cytosolic alcohol and aldehyde dehydrogenases in human liver.” (neonates to adults) | [232] | |
Human hepatic microsomal protein yields and hepatocellularity collated from multiple sources. Weakly statistically significant inverse relationship to age; no relationship with gender, smoking, or alcohol consumption | [233] | |
Human hepatic CYP content (total, and per isoform, for 7 isoforms; n = 60 subjects); rat and human hepatocyte numbers and microsomal protein yield | [234] | |
Human hepatic CYP content central tendencies and variation (total and per isoform, 10 isoforms, 42–350 white subjects); reviews of data on impact of disease, age, sex, environment, and genetics on hepatic clearance | [235] | |
Distribution of hepatic microsomal protein yields for 128 adult (Chinese) humans | [236] | |
Human hepatic microsomal protein yields (20 adults from the United Kingdom) | [237] | |
Hepatic metabolism scaling factors for rainbow trout (microsomal protein yield, hepatocellularity, liver S9 yield, and CYP content (CYP2M1, CYP2K1, and CYP3A27) | [83] | |
Population variability in hepatocellularity, liver blood flow, liver volume and liver density for estimating in vivo hepatic clearance from in vitro data. Implemented in R package “httk” | [74] | |
Partition coefficients (PCs) | A decision tree was described to choose the best predicted tissue partition coefficients for a certain physicochemical space, selecting among 6 algorithms, based on a 122-drug training set. | [238] |
Reports Quantitative Property Relationship (QPPR) models for human and rat blood:air PCs for diverse volatile organic chemicals | [239] | |
Examines and compares the relative accuracy, strengths, and limitations of 7 published models for human tissue–air and 10 models for tissue–blood PCs. The most accurate models for each category were identified. | [240] | |
Reports a QSAR model for predicting physicochemical and biochemical properties of industrial chemicals of various groups | [241] | |
Evaluation of QSAR predictions for 964 experimentally derived chemical–tissue PC combinations (143 chemicals, 12 tissues) with calibration and uncertainty quantification; Data and results are implemented in R package “httk”. | [242] |
Agency/Organization | Agency Needs | Concerns on Gaps or Uncertainty |
---|---|---|
ATSDR |
|
|
FDA/CFSAN | To establish a consistent approach for IVIVE. |
|
FDA/CDER | IVIVE needed to support the qualification of NAM(s) associated with specific regulatory context(s) of use. | Concerns will depend on the context of use being addressed by a NAM being qualified and include:
|
CPSC | The method needs to be effective for mixture risk assessment. | Demonstration of effectiveness for mixture risk assessment. |
EPA/OPP |
|
|
EPA Office of Pollution Prevention and Toxics (OPPT) | Determine plausible route(s) of exposure: dermal, inhalation, oral. | Many chemicals are considered rapidly with only structure and physicochemical properties available. No time for even in vitro measurements of TK. Must rely on QSAR. |
EPA/ORD |
|
|
DoD |
|
|
NIEHS/NTP | Agency does not develop regulatory risk assessments. | The standard issues with IVIVE might be explored further, e.g., domain of applicability, parameter estimation, uncertainty, inter-individual variability, accuracy, sensitivity, and specificity. |
European Commission/EURL ECVAM | Agency does not develop regulatory risk assessments. |
|
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Chang, X.; Tan, Y.-M.; Allen, D.G.; Bell, S.; Brown, P.C.; Browning, L.; Ceger, P.; Gearhart, J.; Hakkinen, P.J.; Kabadi, S.V.; et al. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. Toxics 2022, 10, 232. https://doi.org/10.3390/toxics10050232
Chang X, Tan Y-M, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, et al. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. Toxics. 2022; 10(5):232. https://doi.org/10.3390/toxics10050232
Chicago/Turabian StyleChang, Xiaoqing, Yu-Mei Tan, David G. Allen, Shannon Bell, Paul C. Brown, Lauren Browning, Patricia Ceger, Jeffery Gearhart, Pertti J. Hakkinen, Shruti V. Kabadi, and et al. 2022. "IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making" Toxics 10, no. 5: 232. https://doi.org/10.3390/toxics10050232