NEXT GENERATION RISK ASSESSMENT (NGRA) FOR

COSMETIC SAFETY ASSESSMENT AND CASE STUDIES

CARL WESTMORELANDSAFETY & ENVIRONMENTAL ASSURANCE CENTRE, UNILEVER, UK

用于化妆品安全评估的新一代风险评估（NGRA）和案例研究

联合利华安全与环境保障合作中心（SEAC），英国

CAN WE USE A NEW INGREDIENT SAFELY?

Can we safely use x% of ingredient y in product z?

我们可以安全地使用新成分吗？

我们可以安全地在产品z中使用x％的成分y吗？

EXPOSURE SCIENCE OVERVIEW 暴露科学概述

暴露评估：引导风险评估过程。

量化在消费者使用产品时，某一化学物成分实际外部暴露剂量，然后将其与可导致毒理作用的相关剂量进行比较，以确定安全风险.

MAXIMISING USE OF EXISTING INFORMATION AND NON-ANIMAL APPROACHES

• All available safety data

• in silico predictions

• Exposure-based waiving approaches

• History of safe use

• Read across

• Use of existing OECD in vitro approaches

• Next Generation Risk Assessment (NGRA)

最大限度地利用现有信息和非动物方法

• 所有可用的安全数据

• 计算机预测

• 基于暴露的豁免方法

• 安全使用史

• 交叉参照

• 使用现有的OECD体外方法

• 新一代风险评估（NGRA）

ICCR PRINCIPLES OF RISK ASSESSMENT WITHOUT ANIMAL TESTING

PART 2: https://www.iccr-cosmetics.org/files/8315/4322/3079/ICCR_Integrated_Strategies_for_Safety_Assessment_of_Cosmetic_Ingredients_Part_2.pdf

国际化妆品监管合作组织（ICCR）关于使用无动物试验的风险评估原则

ICCR NINE PRINCIPLES OF NGRA

Main overriding principles:

• The overall goal is a human safety risk assessment

• The assessment is exposure led

• The assessment is hypothesis driven

• The assessment is designed to prevent harm

国际化妆品监管合作组织（ICCR）:新一代风险评估（NGRA)的九大原则

总体目标是人体安全风险评估

评估应以暴露为引导

评估是以假设为驱动的

评估旨在防止危害

4个主要原则：

Principles describe how a NGRA should be conducted:

• Following an appropriate appraisal of existing information

• Using a tiered and iterative approach

• Using robust and relevant methods and strategies

Principles for documenting NGRA:

• Sources of uncertainty should be characterized and documented

• The logic of the approach should be transparently and documented

ICCR NINE PRINCIPLES OF NGRA

3个关于如何进行NGRA的原则

• 对现有信息进行适当评估

• 使用分层和迭代方法

• 使用健全而相关的方法和策略

• 对不确定性来源进行表征和记录

• 该方法的逻辑应该透明并记录在案

2个关于记录NGRA的原则：

国际化妆品监管合作（ICCR）关于新一代风险评估(NGRA)的九大原则

A CASE STUDY APPROACH – IMAGINE WE HAD NO DATA ...

案例研究方法 -想象我们没有数据......

产品：身体乳物质：caffeine

产品：漱口水物质：curcumin

产品：面霜物质：sulforophane

产品：洗衣液物质：coumarin

EXPOSURE-LED

Parameters Model 4 output for Pre-treatment

Model 4 output for Main wash

Cmax (ug/mL) 0.1 (0.68 µM) 0.46 (3.15 µM)

Tmax (h) 48.85 48.85

AUC 0-inf (ug-h/ml) 1.73

8.64

AUC 0-t (ug-h/ml) 1.74

8.57

Cmax Liver 0.01 (0.068 µM) 0.061 (0.4 µM)

Laundry scenarios Systemic exposure (mg/kg bw per day)

Local dermal exposure (ug/cm2)

Pre-Treatment 0.19 50

Main wash 0.3 8.4

Residues on clothes 0.06 0.04

Total (Main wash+ residues on clothes)

0.36 8.44

Total (pre-treatment+ main wash+ residues on clothes)

0.55 58.44

Plasma 血液 Heart 心脏 Liver肝脏 Brain脑 Adipose脂肪组织

3.1 - 41.7 0.6 - 7.2 0.2 - 3.0 0.01 - 0.15 0.002 - 0.03

PBPK model predicted free concentrations (µM)

Exposure scenario: • Worst case in US– 32.97 µg/cm2 • Used one time per day• Skin surface area: 4712.5 cm2 (95 percentile)• Amount of product used per day: 5.18 g/day• Amount of ingredient in contact with skin: 155 mg/day

Skin penetration Caffeine Free Concentration

以暴露为主导

暴露情景：最坏的情况：在美国-32.97微克/平方厘米每天使用一次皮肤表面积：4712.5平方厘米（95百分位数）每天使用的产品量：5.18克/天与皮肤接触的成分量：155毫克/天

皮肤穿透 Caffeine的体内游离浓度

PBPK模型预测的Caffeine的体内浓度

A TIERED AND ITERATIVE APPROACH 使用分层和迭代方法

计算机预测优先 毒性通路识别 毒性通路表征

计算机预测用于识别毒性通路，建立证据权重和制

定测试假设

通过实验数据生成，信息学数据挖掘和计算建模来识别/表征主要的分子起始事件和通路

表征生物相关体外测试的反应及用于决策的复

杂计算模型

PREDICTING SKIN SENSITISATION

The SARA Weight of Evidence (WoE) human potency model* is a high-dimensional probability distribution describing data from the following sources:

• DPRA OECD TG442D

• KeratinoSensTM OECD TG442C

• H-Clat OECD TG442E

• U-SENSTM OECD TG 442E*Reynolds, J, MacKay C, Gilmour N, Miguel-Vilumbrales D and Maxwell G (2019) Computational Toxicology, 9, 36-49

AOP for skin sensitisation

https://aopwiki.org/aops/40

SARA证据权重（WoE）人群致敏强度模型*是描述来自以下多维数据来源的概率分布：

预测皮肤致敏

皮肤致敏的有害结局路径

化学结构/性质 分子起始事件 细胞水平 器官水平

亲电子化学品 ->共价结合皮肤蛋白->角质细胞激活->树突状细胞活化->细胞活化和增殖->皮肤过敏

PREDICTION OF PROBABILITY OF SENSITISATION OCCURRING IN HUMAN REPEAT INSULT PATCH TEST

DNCBMethyl heptine

carbonate Coumarin Lactic acid

= No Expected Sensitization Induction Level(www.ifraorg.org)

预测基于人类重复损伤斑贴试验(HRIPT)发生致敏的概率

PROBABILITY OF CONSUMER BECOMING SENSITISED

Face cream面霜

Shampoo洗发水

消费者致敏的概率

至少在一个个体中导致致敏的几率 至少在一个个体中导致致敏的几率

剂量 剂量

RISK ASSESSMENT FOR SYSTEMIC TOXICITYTHE TRADITIONAL APPROACH

R&D - SEAC

传统方法对于全身毒性的风险评估

TOXCAST: COMBINING IN VITRO ACTIVITY AND DOSIMETRY

Slide from Dr Rusty Thomas, EPA, with thanksRotroff, et al. (2010) Toxicol.Sci 117, 348-58

ToxCast：结合体外活性和剂量测定

由体外AC50范围值推导的人体日暴露剂量

安全边际

人体实际暴露剂量

ASSAYS FOR IN VITRO ACTIVITY

• Specific receptor-mediated activity

特异性受体介导的活性▪ follow-up to specific literature or in silico ‘flags’

跟进特定文献或电子计算机预测“潜在的相互作用”

▪ e.g. AR-CALUX® assay to measure androgen receptor activity

例如 AR-CALUX®测定法测定雄激素受体活性

• Cell Stress Responses 细胞应激反应

• Transcriptomic Responses 转录组学反应

Dent et al (2019), Toxicological Science, 167, 375-384

体外活性的测定

CELL STRESS PANEL

‘Low-risk’ compounds:

Phenoxyethanol

Niacinamide

Caffeine

Known ‘high-risk’ compounds:

Doxorubicin

Diclofenac

Troglitazone

14 chemicals, including14个化学物，包括

Mitochondrial Toxicity

Oxidative Stress

DNA damage

Inflammation

ER Stress

Metal Stress

Osmotic Stress

Heat Shock

Hypoxia

Cell Health

Stress pathways

Platform

Technology: High content imaging

Cell line: HepG2

Timepoints: 1, 6 & 24 hours

Calculate ‘free concentration’

Use in vitro exposure models:

Groothuis et al (2015) Toxicology, 332, 30-40

细胞应激反应组

使用体外暴露模型计算“游离浓度”

'低风险'化合物

'高风险'化合物

CELL STRESS PANEL

6 hours24 hours

Doxorubicin

Coumarin6 hours

24 hours

细胞应激反应组

NOTEL* is the derived concentration of a compound that does not elicit a meaningful change in gene expression (i.e. the threshold of the concentration that elicits minimal mechanistic activity).

NOTEL *为化合物不引起有意义的基因表达变化的推导出的浓度（即引起最小机理变动的浓度阈值）。

HIGH THROUGHPUT TRANSCRIPTOMICS

Farmahin et al (2017) Arch Tox 91, 2045-65

Thomas et al (2013). Toxicol Sci, 134, 180-94*NOTEL = No observed transcriptional effect level

高通量转录组学

20SEAC Unilever Information: Internal Use

HIGH THROUGHPUT TRANSCRIPTOMICS

Fold change >1.5Genes in pathway >3Fishers exact test >0.1(3 independent experiments)

HepG2BMD minimum – 295BMD medium– 450BMD maximum 962

MCF7BMD minimum – 117 BMD medium – 167BMD maximum 290

HepaRGBMD minimum –237BMD medium – 344BMD maximum 913

POD - NOTEL value

Conc Phenoxyethanol (µM)

Acc

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tio

n高通量转录组学

NGRA – EXPOSURE-DRIVEN CASE STUDIES

Log (1

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NGRA-以暴露为主导的案例研究

Log (1

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NGRA – EXPOSURE-DRIVEN CASE STUDIES NGRA-以暴露为主导的案例研究

ICCR PRINCIPLES OF RISK ASSESSMENT

• Importance of understanding consumer exposure including the relevance of metabolism了解消费者暴露的重要性，包括新陈代谢的相关性

• Non-standard, bespoke data generation driven by the risk assessment question由风险评估问题驱动的非标准，定制数据生成

• Ensuring quality, robustness of non-standard (non-TG, non-GLP?) work. In silico modelling approaches and bespoke in vitro solutions

确保非标准化（非技术指南，非GLP？）数据的质量及健全性。计算机模拟方法和定制的体外解决方案

• Importance of defining points-of-departure and understanding adverse vs. adaptive responses

定义毒理拐点（POD）和分辨不良反应vs.适应性反应的重要性

• Understanding uncertainty in risk assessments to allow informed decision-making了解风险评估中的不确定因素，以便做出明智的决策

国际化妆品监管合作组织（ICCR）关于风险评估的原则

24SEAC Unilever Information: Internal Use

ACKNOWLEDGEMENTS 感谢

UnileverThe SEAC NGRA Team with special thanks to the following for their help with this talk:

• Maria Baltazar• Sarah Cooper• Matt Dent• Nicky Gilmour• Steve Gutsell• Julia Head• Cameron MacKay• Sophie Malcomber• Gavin Maxwell• Alistair Middleton• Victor Oreffo• Gurjit Phgura• Paul Russell• Hequn Li• Jin Li• Paul Carmichael• Joe Reynolds• Andy White

AMMS

• Prof Peng

• Jiabin Guo

• Haitao Yuan

US EPA

• Josh Harrell

• Richard Judson

• Imran Shah

• Rusty Thomas

Cyprotex

• Caroline Bauch

• Laura Purdie

• Paul Walker