transformation of thresholds of toxicological concern (ttcs ......transformation of thresholds of...
TRANSCRIPT
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, [email protected]
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