考慮異環胺化學動力學以評估國人食用高溫烹飪肉品的機率風險

Abstract

異環胺(HCAs)可自發性的在高溫烹調的肉品形成，科學家已鑑定出約有30種HCAs，其中10種被國際癌症研究機構(IARC)歸為致癌物。HCAs在體內會被CYP 4501A2、轉硫酵素(sulfotransferase)和 N-乙醯轉移酶（N-acetyltransferase）代謝進而與DNA鹼基反應，導致基因傷害而可能形成癌組織。食用高溫烹調的肉品而暴露HCAs，進而潛在對健康的造成影響，但肉品中HCAs的含量因受烹飪溫度和時間影響。但是一般家庭製備高溫烹飪肉品時或是食用即時高溫烹飪肉品並不會量烹飪時間與溫度因此評估食用高溫烹飪肉品的HCA暴露與風險時，需要利用HCAs化學動力學，以估算在可能烹飪的溫度與時間下的肉品中可能產生的各種HCA的含量。因此，本研究目的是建立在高溫烹飪肉品中HCAs的化學動力學模式，以評估國人食用高溫烹調肉品暴露多種HCA的致癌風險。 為建立HCAs的化學動力學以估算在特定烹飪溫度(T)和時間(t)下肉品中HCAs含量。首先假設HCAs降解率可以忽略不計時（k2≈0），可以導出HCA濃度Ct=C_0+(k_1×M×t)/V。第二假設降解率不可忽略時，可以導出動力學方程式為Ct=C_0 e^(-(k_2×t)/V)+M×k_1/k_2 (1-e^((-k_2×t)/V))，其中Ct為在特定溫度下烹飪時間為t的HCA濃度(ng/g)，C_0為在特定溫度下的HCA起始濃度(ng/g)，k_1為HCA生成的速率常數，k_2為HCA降解的速率常數(g/min)，M為反應物的濃度，V為肉品的體積(g)。而k_1會先用Eyring方程式以RStudio 4.1.0寫程式碼。接著，在Excel中作線性回歸和RStudio 4.1.0軟體中作非線性回歸模擬文獻數據以估算數學模型的參數。接著寫Python程式碼作蒙地卡羅模擬，進行溫度及時間為一統計分佈進行隨時抽樣，自動計算HCAs每日終生平均劑量和致癌風險。 以國人每日高溫烹飪肉品攝取量結合撰寫的Python指令進行風險評估，國人每日高溫烹飪肉品攝取量（kg/天）×特定肉類中的Ct（mg/kg）x特定HCA的致癌斜率因子/體重（kg）來評估風險。並利用蒙特卡羅(Monte Carlo simulation)進行10,000次試算，得到致癌風險的統計分佈。 本篇研究藉由數學模型的建構，結合R與Excel軟體獲得參數，再以Python自寫軟體進行風險評估，也計算濃度、終生平均每天暴露劑量與風險值在不同的時間或溫度下，隨溫度或時間的變化，得到牛肉、鯖魚與豬肉最適合的烹調條件，在以Python自寫軟體進行風險評估的情況下，使得風險評估容易執行，以此可提供政府相關機構有效的管理與監測高溫肉品的致癌風險模式。

Heterocyclic amines (HCAs) are spontaneously formed in meats processed at high-temperature. Approximate 30 HCAs have been identified, and 10 of them are classified as carcinogens by International Agency for Research on Cancer (IARC). HCAs could be metabolized by CYP 4501A2, sulfotransferase, and N-acetyltransferas in vivo, and their active metabolites can react with DNA and are probably responsible for genotoxicity and carcinogenicity. The potential risks from intakes of HCAs due to daily consumption of high-temperature processed meats have been of great concerns. However, the formation of HCAs in the high-temperature processed meats were reported mainly affected by the cooking temperature and time, which are usually not measured when they were cooked at home or restaurant. To assess HCAs exposures and cancer risk, the chemical kinetics are needed to formulate to estimate HCAs residues in high-temperature processed meats. Therefore, the objective of this study was to assess exposures and cancer risk to multiple HCAs through daily consumption of high-temperature processed meats by incorporating the chemical kinetics of HCAs. The chemical kinetics of an individual HCA was formulated first as a function of cooking temperature (T) and time (t) in a particular meat product. Therefore, an HCA at t when the degradation rate is negligible (k2≈0) can be described by Ct and Ct=C_0+(k_1×M×t)/V. And the generalized model when the degradation rate is not negligible can be Ct=C_0 e^(-(k_2×t)/V)+M×k_1/k_2 (1-e^((-k_2×t)/V)). The Ct is the concentration of HCA at time t (ng/g), C_0 is the concentration of HCA before cooking at a particular temperature(ng/g), k_1 is the reaction rate constant of HCA formation , k_2 is the reaction rate constant of HCA degradation (g/min), M is the concentration of reactant include creatine、reducing sugar and amino acid, V is the volume of the model system(g or ml). Since k_1 is a function of T and can be described with the Eyring equation, and the kinetic models can be fit with experimental data by simple linear regression or using self-programmed nonlinear regression code under RStudio 4.1.0. Probabilistic cancer risk assessment for HCAs was performed with self-programmed code using the Python 3.10 to automatically calculate the lifetime average daily dose and cancer risk while HCAs concentrations were estimated with the kinetic models by assuming normally-distributed processed time and temperature for a given meat and HCA. The distribution of life-time cancer risk can be estimated with meat intake rate (kg/day) × Ct in a particular meat (mg/kg) x cancer slope factor of a particular HCA / body weight (kg). The Monte Carlo simulation was run for 10,000 trials with Python program to estimate the distribution of cancer risk. In this study, mathematical model is successfully built, and R and Excel software calculate the parameters. Then, use Python self-written software to implement risk assessment and calculate the concentration, lifetime averagedaily dose and cancer risk at different times or temperatures. With changing in temperature or time, this study could obtain the most suitable cooking conditions for beef, mackerel and pork. This Python software makes the risk assessment easy to conduct, and provides relevant government agencies to effectively manage and monitor the cancer risk of high-temperature meat products.