药物杂质遗传毒性评价策略与监管研究

伴随大量创新及仿制药面世而来的是对遗传毒性杂质加强监管的迫切需求。一系列与国际接轨的指南性文件的出台，弥补了我国杂质监管的空白，但难点尚存。传统评价方法具有局限性，新方法与传统方法间缺乏一致性比较，药物特定合成路线实际产生的大量含警示结构杂质缺乏毒理学评价数据支持，毒性预测软件的预测效力不足等。本文就国内外杂质遗传毒性杂质监管科学现状、遗传毒性杂质控制策略、杂质遗传毒性评价方法进行论述，并提出充分结合计算机毒理学、毒性评价方法和符合我国国情的监管限度制定三个维度开展杂质监管工作。     

药物杂质的毒理学评价要求及进展

 药物原料或制剂中的杂质可能引起临床不良反应。杂质毒理学评价是药物研究的重要内容。ICH关于药物及制剂杂质方面指导原则规定了杂质的报告、鉴定和质控限度,含量超过质控限度的杂质应进行毒理学评价。但指导原则对于研发阶段的药物杂质和遗传毒性杂质的限度未作明确要求。EMEA对于遗传毒性杂质制定了专门的指导原则,引入了毒理学担忧阈值（TTC）的概念对遗传毒性杂质限度进行控制,遗传毒性杂质每日接触量应小于1.5 μg。FDA也推荐采用TTC原则控制遗传毒性和致癌性杂质。本文结合ICH,EMEA及美国FDA等指导原则,对药物杂质毒理学评价的要求及其进展进行了综述。     

天然药物成分致突变性风险预测与评价方法研究进展

天然药物成分复杂，无法逐一完成系统的致癌性风险评价及体内遗传毒性试验。鉴于计算机毒理学结合体外致突变风险评价方法在遗传毒性杂质致突变性评价方面的快速发展，也可考虑将此评价模式应用于天然药物成分的致突变性筛选与机制研究。从计算机毒理学和体外致突变性评价两方面出发，综述天然药物致突变性的研究方法，为其遗传毒性试验评价及监管提供借鉴。     

亚硝胺类遗传毒性杂质的监管策略及思考

如何对药品中存在致癌风险的亚硝胺类杂质进行控制,已成为企业和监管部门关注的热点。本文对亚硝胺类杂质的常见类型、来源、致癌性作用特点进行梳理,并结合EMA、FDA、ICH及我国相关遗传毒性杂质控制指导原则,对制定符合我国国情的亚硝胺类杂质的监管限度和监管工作提出建议。尽管国外已陆续出台了一系列针对药品中亚硝胺类杂质的含量限定的指南性文件,但众多亚硝胺类杂质的毒性剂量、人体暴露量尚不明确,且药物合成工艺存在差异,我国无法完全照搬欧美等国的监管方法。当前应深入研究亚硝胺类杂质的遗传毒性和致癌性,从而制定符合我国国情的监管限度值和药品中亚硝胺杂质监管策略;此外,本文从解决实际问题的角度出发,讨论如何根据已有指导原则,确定在已知毒理学数据、毒理学数据不足和短期使用药物不同情况下亚硝胺类杂质的监管限度。本文将为药物生产和杂质评价与研究和监管领域相关人员提供借鉴。     

药物毒性数据库与监管科学

药物毒性数据库的开发和计算毒理学新模型研究已逐渐成为21世纪药物毒性评价的新范式.通过数据挖掘技术和计算机预测技术,可以快速了解、分析和评估药物毒性、作用机制或暴露特征等相关信息.药物毒性数据的分析与预测结果也成为支持药品研发或监管决策的重要新手段.世界发达国家许多毒理研究机构都开发和建立了各自特点的毒理学数据库.本文...     

亚硝胺类遗传毒性杂质的监管策略及思考

如何对药品中存在致癌风险的亚硝胺类杂质进行控制,已成为企业和监管部门关注的热点。本文对亚硝胺类杂质的常见类型、来源、致癌性作用特点进行梳理,并结合EMA、FDA、ICH及我国相关遗传毒性杂质控制指导原则,对制定符合我国国情的亚硝胺类杂质的监管限度和监管工作提出建议。尽管国外已陆续出台了一系列针对药品中亚硝胺类杂质的含量限定的指南性文件,但众多亚硝胺类杂质的毒性剂量、人体暴露量尚不明确,且药物合成工艺存在差异,我国无法完全照搬欧美等国的监管方法。当前应深入研究亚硝胺类杂质的遗传毒性和致癌性,从而制定符合我国国情的监管限度值和药品中亚硝胺杂质监管策略;此外,本文从解决实际问题的角度出发,讨论如何根据已有指导原则,确定在已知毒理学数据、毒理学数据不足和短期使用药物不同情况下亚硝胺类杂质的监管限度。本文将为药物生产和杂质评价与研究和监管领域相关人员提供借鉴。     

遗传毒性早期快速评价方法

遗传毒性评价是药物安全性评价的重要组成部分。如何早期、快速地获得药物可能的毒性反应数据,是当前遗传毒理学领域的研究热点之一。介绍几种目前应用比较广泛或有较好应用前景的早期、快速的遗传毒性评价方法,包括AmesII试验、Gadd45 Green Screen试验、高通量体外彗星试验和流式细胞术检测微核试验,主要围绕这些方法的基本原理、简要操作流程、预测毒性的可信度以及与传统方法相比的优缺点等展开。同时简要介绍计算机辅助毒性预测模型以及基因芯片技术在遗传毒性评价中的应用。     

超微结构病理学在药物非临床安全性评价毒理学研究中的应用现状和关注点

超微结构病理学是药物非临床毒理学研究中毒性病理学评估的重要辅助工具。简要介绍了超微结构病理学在药物非临床安全性评价毒理学研究中的应用现状和关注点,并举例说明了透射电子显微镜等超微结构病理学技术在药物非临床安全性评价中的应用。目前,超微结构病理学技术在药物非临床毒理研究中虽然使用率不高,却是非临床毒理学研究中毒性病理学评估的重要辅助工具,可用于对光学显微镜检查结果的进一步研究,以作为早期药物发现和非临床安全性评价毒理学研究的有益补充。在使用超微结构病理学技术进行药物非临床毒理学研究时,应关注其使用的必要性、良好实验室规范（GLP）依从性、专业性、科学性和使用局限性等方面。     

基于计算模型结合数据库技术快速评价注射用左奥硝唑及其主要杂质的潜在神经毒性

目的 快速评价注射用左奥硝唑及其主要杂质的潜在神经毒性,为加强临床用药安全性提供参考。方法 利用在线数据库准备建模数据,分别采用人工神经网络和支持向量机算法构建2种不同原理的分类预测模型进行交互快速评价,并采用特征结构域初步探索毒性成因。结果 新建模型中,编号为ANN-3和SVM-2模型预测的假阳性、假阴性结果均小于4.0%,其灵敏度、准确性和稳健性良好;采用新建模型预测评估注射用左奥硝唑及3个主要杂质具有潜在神经毒性,可信度高于90%;特征结构域分析显示结构中羟基取代增加分子极性,是表现出潜在神经毒性的可能原因。结论 左奥硝唑及其主要杂质均表现出潜在神经毒性,需要密切关注注射剂在临床使用中的安全性;也为其他抗生素药物及其杂质的毒性快速识别与评价提供借鉴。     

专家评估在杂质遗传毒性（定量）构效关系评价中的应用

目的 通过专家评估获得或变更（定量）构效关系[（Q）SAR]评价分类结果实例，提供一种基于人用药品技术要求国际协调理事会（ICH）M7(R1)指导原则进一步利用专家进行评估的杂质遗传毒性评价程序。方法 采用Lhasa公司的Nexus 2.5.0版软件平台（整合了Derek Nexus和Sarah Nexus）对杂质进行遗传毒性评价，任意选择3个预测得到阳性、阴性、相互矛盾或未分类结果的实例，进行专家回顾评估，获得最终分类结果。结果 实例1,Derek预测结果为阴性，Sarah预测结果为阳性，专家评估后分类结果由3类变更为5类；实例2,Derek预测结果为阴性，Sarah预测结果为模棱两可，专家评估后界定为5类；实例3,Derek和Sarah预测结果均为阴性，专家评估后分类结果由5类变更为1类。结论 通过对实例化合物遗传毒性分类结果论证过程的描述，为专家评估在杂质遗传毒性（Q）SAR评价中的应用提供了基本的参考程序。     

Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products

Active ingredients in pharmaceutical products undergo extensive testing to ensure their safety before being made available to the American public. A consideration during the regulatory review process is the safety of pharmaceutical contaminants and degradents which may be present in the drug product at low levels. Several published guidances are available that outline the criteria for further testing of these impurities to assess their toxic potential, where further testing is in the form of a battery of toxicology assays and the identification of known structural alerts. However, recent advances in the development of computational methods have made available additional resources for safety assessment such as structure similarity searching and quantitative structure-activity relationship (QSAR) models. These methods offer a rapid and cost-effective first-pass screening capability to assess toxicity when conventional toxicology data are limited or lacking, with the potential to identify compounds that would be appropriate for further testing. This article discusses some of the considerations when using computational toxicology methods for regulatory decision support and gives examples of how the technology is currently being applied at the US Food and Drug Administration.     

An EU perspective on the use of in vitro methods in regulatory pharmaceutical toxicology

Some in vitro methods such as those used in the assessment of genotoxicity, receptor-binding and QT-prolongation are well established in regulatory pharmaceutical toxicology. In vitro systems to study metabolic profiles, P450 isoforms, drug interactions, etc. or to provide metabolic activation in genotoxicity assays are extremely useful, but are subject to a number of important limitations. In vitro models are also employed on an ad-hoc basis for other purposes, for example, to help investigate mechanisms underlying in vivo findings. At the current stage of technical development of alternative methods, rapid replacement of the pivotal animal studies used in drug safety assessment seems unlikely. The existing in vivo models have good predictive ability regarding toxic effects in humans, are underpinned by an extensive literature and form the basis of most regulatory toxicology guidelines. Integrated in vitro testing strategies, meant to replace conventional repeated-dose studies, are still relatively undeveloped. Emerging technologies such as transgenics, toxicogenomics and toxicoproteomics, although they rely on the continued use of animals, have considerable potential in terms of reduction and refinement of in vivo methods.     

Application of advanced in silico methods for predictive modeling and information integration

INTRODUCTION: In silico predictive methods are well-known tools to the drug discovery process. In recent years, these tools have become of strategic interest to regulatory authorities to support risk-based approaches and to complement, and potentially strengthen evidence when considering product quality and safety of human pharmaceuticals. AREAS COVERED: This editorial reviews how chemically intelligent systems and computational models using structure-based assessments are important for providing predictive data on drug toxicity and safety liabilities considered at the FDA. The example of regulatory interest in application of in silico systems for mutagenicity predictions of drug impurities is discussed. EXPERT OPINION: The importance of information integration is emphasized toward the application of in silico predictive methods and enhancing data mining capabilities for safety signal detection. Modeling for cardiovascular drug safety based on human clinical trial data is one area of active testing of predictive technologies at the FDA. The FDA has taken appropriate steps in its strategies and initiatives aimed to enhance and support innovation for regulatory science and medical product development by developing and implementing the use of in silico predictive models and medical toxicity databases. This science priority area will ultimately help improve and protect public health.     

符合中药特点的致癌风险评价方法

如何对中药的致癌性风险作出科学而合理的评价是毒理学研究的难点之一。因中药成分和配伍的复杂性,现有的常规遗传毒性评价方法在评价中药方面有所局限。在原有试验原理基础上开发高通量筛选方法,以及利用新型生物标志物和基于新原理的试验技术已在中药毒性预测和评价领域显露一定的应用价值。本文聚焦适合大量成分毒性筛选及靶器官毒性评价的试验方法,包括改良的传统试验方法、自动化检测手段、生物标志物、三维组织培养技术和计算机毒理学的引入等,为中药致癌性评价提供借鉴思路。     

Toxicokinetics and physiologically based toxicokinetics in toxicology and risk assessment

Toxicokinetics is the study of kinetics of absorption, distribution, metabolism, and excretion of a xenobiotic under the conditions of toxicity evaluation. Conventional toxicokinetics uses the hypothetical compartments, and the model is composed of rate equations that describe the time course of drug and chemical disposition. The utility of toxicokinetics in toxicity evaluation and interpretation of animal toxicology data is emerging as an important tool in product discovery and development. With implementation of the International Conference on Harmonization (ICH) guidelines on systemic exposure and dose selection, toxicokinetics have been integrated in routine toxicity evaluations. Although traditional compartmental/noncompartmental models are generally adequate for assessing systemic exposure, they are unable to the predict time course of drug disposition in target tissues and often fail to relate systemic drug levels to a biological response. Physiologically based toxicokinetic (PB-TK) models address this deficiency of traditional compartmental models. PB-TK models are the kinetic models of the uptake and disposition of chemicals based on rates of biochemical reactions, physiological and anatomical characteristics. These models, when developed appropriately, can predict target organ drug distribution in different species under variety of conditions. This minireview discusses the basic principles, and applications of traditional compartmental toxicokinetic and physiologically based toxicokinetics (PB-TK) models in drug development and risk assessment. Special emphasis will be placed on discussion related to interpretation of the ICH guidelines related to toxicokinetics and the utility of toxicokinetics data in dose selection for toxicity and carcinogenicity studies. The utility of PB-TK models in risk assessment of methylene chloride, vinyl chloride, retinoic acid, dioxin, and inhaled organic esters is discussed.     

The challenges involved in modeling toxicity data in silico: a review

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.     

计算毒理学与中药毒性预测的研究进展

计算毒理学近几年受到美国及欧盟相关立法及研究机构的重视,被越来越多地应用于新药毒性预测及环境化合物的安全评价。化合物毒性预测方法可分为两大类:一类是以化合物本身为基础的计算方法,主要是研究结构与毒性的定量关系;另一类是以毒性靶分子结构为基础的方法,又被称为分子机理法。我国在环境化合物的毒性预测、算法及建立构效模型上取得进展,将计算毒理学应用于新药研发已经起步,但尚未见到计算毒理学用于中药及其化合物毒性的研究。而国外在天然化学成分的毒性预测、结构毒性关系的研究上取得了一些进展。由于中药化学成分与人工合成化合物在化学空间的差别,同时中药是多组分的混合物,毒性预测有较大的挑战性。随着组学技术被更多地应用于化合物毒性的研究,计算毒理学在中药毒性研究中的应用也有新的机遇。