Transformation of Thresholds of Toxicological Concern (TTCs) to Internal TTC: Why Internal Exposure Matters and How We Will Get There

Corie Ellison, PhDThe Procter & Gamble Company, USAellison.ca@pg.com

Conflict of Interest Statement

No conflicts of interest

Outline

The role of pharmacokinetics (PK) in the safety assessment Internal thresholds of toxicological concern (iTTC) PK analog concept

Traditional Exposure Based Risk Assessment

Animal Toxicity Data

Point of Departure

Human Health Reference Value

PK, PDUncertainties

Dose-Response

Animal Based Approach

Common Uncertainty Factors

Food Additives and Contaminants, 1998, Vol. 15, Supplement, 17-35

Including Pharmacokinetics in the Risk Assessment

PK is an integration of ADME– Absorption

Oral, dermal, inhalation– Distribution

Protein binding, tissue partitioning– Metabolism

Hepatic, dermal, renal, plasma– Excretion

Renal Understanding PK enables a better understanding

of overall toxicological profile and risk assessment PK is an opportunity to refine a risk assessment

Toxicology Letters 120 (2001) 97–110

Tiered Approach for PK Data in a Risk Assessment

PK data can be utilized in a risk assessment in a tiered approach– Tier I: No chemical specific PK data

Default risk assessment approach

– Tier II: Some chemical specific PK data e.g., dermal penetration refinement

– Tier III: Significant amount of chemical specific PK data e.g., PBPK modeling

Using PK for Chemicals Lacking Toxicity Data

Can PK data be integrated into a risk assessment for a chemical that lacks toxicity data?

• Why would this be desired?• What approach would be utilized?• When would such an approach be available?

Threshold of Toxicological Concern (TTC) TTC is a risk assessment tool that establishes acceptable low level exposure values to be

applied to chemicals with limited toxicological data Non-cancer TTC databases consist of distributions of oral No Observed Effect Levels (NOELs) TTC threshold limits established by identifying a low percentile NOEL value from the database

and applying appropriate uncertainty factors TTC threshold limits are based on oral (administered) doses

Internal TTC–What is It and Why It is Relevant

Internal TTC (iTTC; a TTC based on blood concentration) has been suggested as a possible evolution for TTC and could be helpful to avoid future animal testing

– Metabolism based read-across assessments– Support de minimis exposures without toxicity data– Low level chemical exposure from more than one exposure route – Incorporate dermal penetration into dermal TTC assessments– Threshold for in vitro biological assays for systemic exposure

iTTC will be a “second tier” to the existing TTC

iTTC will be an exposure-based risk assessment tool

iTTC will expand the domain of chemicals which can be supported with minimal toxicity data (analogous to the existing TTC based on external dose)

An iTTC will have broad applicability across industrial chemicals

iTTC as Part of a Tiered Safety Assessment

Computational Toxicology 4 (2017) 31–44

Internal TTC Approach

Regulatory Toxicology and Pharmacology 103 (2019) 63–72

Comparing Human Exposure to iTTC

iTTC Chemicals

iTTCN=1251

COSMOSTTC database

enriched with cosmetic chemicals

Munro (1996)Landmark paper for

TTC

RIFM DatabaseHas a significant

number of Cramer Class II chemicals Number of chemicals: 1251

Species: rat, mouse, rabbit, hamster, dog, primateRoute: oral (gavage, diet, drinking water)Durations: studies ≥ 28 daysChronic NOAELs preferred (mg/kg/day)Broad chemical representation: industrial, pharmaceuticals, food substances, environmental, agricultural, consumer

http://www.cosmostox.eu/home/welcome/Food and Chemical Toxicology 34 (1996) 829-867https://www.rifm.org/rifm-science-database.php

Mapping Chemical Space for iTTC Chemicals

Chemical space mapping will be used to help identify representative chemicals for inclusion in final iTTC chemical dataset

• Utilized Principal component analysis (PCA)• PCA includes structural and ADME

descriptors• Will make PBPK modeling portion of iTTC

project more manageable

Final iTTC chemical dataset will contain a distribution of chemicals covering a broad chemical and PK space

PCA for iTTC Chemicals

Literature Search for 1,251 Chemicals

Literature search conducted in PubMed for hepatic metabolism and in vivo PK dataValue of the literature search:

• Identify existing PK and ADME data• Help prioritize chemicals needing more data for modeling• Help identify existing in vivo data to support verification of PBPK models

80% 10% 5%

No data In vivo PK In vitro metabolism In vivo & vitro

5%

Existing PK and ADME Data for iTTC Chemicals The existing in vivo PK data is spread across the iTTC chemical space

o PBPK modeling simulations for iTTC chemicals will be compared to in vivo data (when available)

o The PBPK modeling approach can be evaluated for the iTTC chemical space to determine how well it works

New in vitro ADME data will be generated for representative chemicals in areas of the chemical space map that lack data

Goal is to have final iTTC chemical dataset contain a distribution of chemicals covering a broad chemical and PK space

in vitro metabolism no datain vivo PK

PBPK Modeling Strategy for iTTC

Exposure ConditionsMatch tox study:

• Route (gavage, diet, water)• Dose (NOAEL, LOAEL when no NOAEL)• Species (rat, mouse, dog)

Chemical ParametersIn silico

o Density; logP; Molecular weight; Vapor pressure; Water solubility; Fraction unbound to plasma protein

In vitro or in vivo*o Hepatic metabolismo Oral absorption rate *in vivo using existing historical animal data

Software• PBPK model platform: PLETHEM• QSAR property predictions: ADMET Predictor, ACD, OPERA

Factors that can Impact PK–TransportersTransporters are located throughout the body and can impact the ADME of a chemicalTransporters will not be included in the PBPK model structure for iTTCCan we flag chemicals where transporters may have an impact on their PKWhat are the relevant transporters?

– Transporters that lower systemic exposure in vivo; if omitted from animal PBPK model, the model would over estimate systemic exposure in the animal (not protective for risk assessment)

Toxicol Sci. 2008 Feb;101(2):186-96.

A Strategy for Predicting Substrates for TransportersQSAR Models

ChembenchADMET Predictor

Vienna LiverTox Workspace

Prediction OutputProbability (0 to 1)0 = no substrate

1 = substrate

Final DeterminationAgreement needed between 2 models,

otherwise considered ambiguous

MDR1

Transporter effects possible; flag chemicals

Chemical predicted to be efflux substrate

Consider passive permeability

Low passive permeabilityHigh passive permeability

Transporter effects not expected

Accounting for permeability and transporter predictions

Non substrate 1169Substrate 48Ambiguous 69

Case Study Example: Use of iTTC to Refine TTC-Based Assessment for Dermal Exposure

Hypothetical Risk Assessment for Chemical ABC at 0.5% in a Facial Moisturizer

Exposure to ABC

0.5% infacial moisturizer

0.13 mg/kg/day (SCCS 2018 H&Ps)

Safety Data

Negative in vitro Ames and micronucleus

No direct data for repeat dose or

developmental safety

SAR Analogs

No analogs identified via an analog search

TTC as an Option

Existing TTC not an option since external exposure to ABC is greater than TTC limits

Read across not an option due to lack of analogs

Read Across as an Option

Tier 0

Tier Exposure Limit

Cramer Class III 1.5 ug/kg/day

Cramer Class II 9 ug/kg/day

Cramer Class I 30 ug/kg/day

Hypothetical Risk Assessment for Chemical ABC at 0.5% in a Facial Moisturizer (cont.)

Internal exposure to ABC < internal TTC safety assessment supported

Internal exposure to ABC > internal TTC further evaluation needed; possible need for new safety data

Physiological constants from Davies and Morris1993

Tier 1 Potential in vitro ADM Data to GenerateDermal penetration: 1E-03 ug/cm2/hr

Plasma protein binding: 80% unboundHepatic metabolism: in vitro metabolism CLint is converted to

an in vivo CLint = 80 L/h

Internal Exposure Estimate with Dermal Penetration & Metabolism Refinement

Internal exposure: 1.3E-05 mg/L

Jmax x SA X D Css =

(GFR x Fup) (Ql x Fup x Clint) (Ql + Fup x Clint)

X 1 24 hrs

Internal Exposure Estimate with Dermal Penetration Refinement

Internal exposure: 2.6E-03 mg/L

Future Outlook for PBPK Modeling in Animal Alternative Safety Assessment Model evaluation has historically depended on PK data for the chemical of interest Can PBPK models survive in an animal free safety assessment

Step Change in ApproachPK analogs for PBPK model evaluation

Adipose

SlowlyPerfused

RapidlyPerfused

Veno

us B

lood

Arte

rial B

lood

metabolism urine

Gut PO

Liver

Human risk assessment PK studies

In vitro, in silico, literature data

Adipose

SlowlyPerfused

RapidlyPerfused

Veno

us B

lood

Arte

rial B

lood

metabolism urine

Gut PO

Liver

Human risk assessment PK analogs

In vitro, in silico, literature data

PK Analog Concept

Hypothesis: chemicals that have similar structure, physiochemical, and/or ADME properties will have similar pharmacokinetics

PK Analog Schematic

PBPK Model

Target Chemical Source Chemical

PK data availableTime

Con

cent

ratio

n

PK data not availableTime

Con

cent

ratio

n

PK Analog Search

Similarity…StructuralADME

Time

Con

cent

ratio

n

Simulation with target chemical PBPK model and exposure conditions of source chemical

Structural PK Analog Examples

AUC ratio: 1.26 Cmax ratio: 0.70

AUC ratio: 1.90 Cmax ratio: 0.99

Name Oxycodone HydromorphoneCAS # 76-42-6 466-99-9LogP 0.9 1.2MW 315.4 285.3MolVol 301 273HBD 1 1HBA 5 4pKa acid None 9.66pKa base 8.12 8.55Tanimoto score 0.88; 0.91; 0.41

Name Theophylline CaffeineCAS # 58-55-9 58-08-2LogP -0.1 -0.2MW 180.2 194.2MolVol 161 182HBD 1 0HBA 3 3pKa acid 9.36 NonepKa base 2.11 2.24Tanimoto score 0.96; 0.95; 0.43

Cmax & AUC Ratios for 15 Structural PK Analog Pairs

Cmax AUC

Functional PK Analog Examples

Metronidazole LinezolidCAS # 443-48-1 165800-03-3F; CL sys; Vd 90; 3.8; 41 97; 3.9; 41difference score 3.12

AUC ratio: 1.2Cmax ratio: 0.76

AUC ratio: 0.91Cmax ratio: 0.81

Ifosfamide CaffeineCAS # 3778-73-2 58-08-2F; CL sys; Vd 95; 4.6; 44 94; 4.7; 45difference score 3.06

Cmax & AUC Ratios for 93 Functional PK analog Pairs

Cmax AUC

In Silico Approaches for Safety – Beyond PK

Allergy

Developmental or Reproductive toxicity

Exposure

SAR read-across

Summary

iTTC is an extension of the existing TTC and can be considered a “second tier” to the existing TTC

A PBPK modeling approach will be used to convert animal NOAELs in the existing TTC databases to internal exposures

iTTC will expand the domain of chemicals which can be supported with minimal toxicity data (analogous to the existing TTC based on external dose)

The experience gained through the iTTC work will be applicable to broader issues as well:– Increase acceptance of PBPK modeling– Creation of a PK and ADME databases that can be leverage for broader applications– Route to route extrapolation

For PBPK modeling to remain relevant in an animal free safety assessment, it will be necessary to utilize new approaches for evaluating the models – one such approach is the use of PK analogs

References Renwick 1998. Food Additives and Contaminants, 1998, Vol. 15, Supplement, 17-35.

https://www.tandfonline.com/doi/pdf/10.1080/02652039809374612 Renwick and Walton 2001. Toxicology Letters 120 (2001) 97–110.

https://www.sciencedirect.com/science/article/pii/S0378427401002880?via%3Dihub Berggren et al., 2017. Computational Toxicology 4 (2017) 31–44. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695905/ Ellison et al., 2019. Regulatory Toxicology and Pharmacology 103 (2019) 63–72.

https://www.sciencedirect.com/science/article/pii/S0273230019300169?via%3Dihub RIFM database https://www.rifm.org/rifm-science-database.php Munro et al., 1996. Food and Chemical Toxicology 34 (1996) 829-867.

https://www.sciencedirect.com/science/article/pii/S027869159600049X?via%3Dihub COSMOS database http://www.cosmostox.eu/home/welcome/ Klaassen and Lu 2008. Toxicol Sci. 2008 Feb;101(2):186-96. https://academic.oup.com/toxsci/article/101/2/186/1639031 SCCS 2018. https://ec.europa.eu/health/sites/health/files/scientific_committees/consumer_safety/docs/sccs_o_224.pdf Davies and Morris 1993. Pharm Res. 1993 Jul;10(7):1093-5. https://link.springer.com/article/10.1023/A:1018943613122 Ellison 2018. Regulatory Toxicology and Pharmacology 99 (2018) 61–77.

https://www.sciencedirect.com/science/article/pii/S0273230018302307?via%3Dihub

AcknowledgementsKaren Blackburn Cathy LesterGeorge Daston Chris RuarkShengde Wu John Troutman