FDA:药物基因毒杂质如何进行科学的(Q)SAR评估

转至 药有源

（Q)SAR Evaluation of Drug Impurities from the US FDA Scientific Perspective
从美国FDA科学角度对药物杂质进行(Q)SAR评估

Naomi L. Kruhlak, Ph.D.

Scientific Lead, Computational Toxicology Consultation Service
计算毒理学咨询服务科学负责人
FDA’s Center for Drug Evaluation and Research
FDA药物评价与研究中心
2019 Pharmaceutical Industry and Regulators Symposium, Brasilia, Brazil
2019年制药行业和监管机构专题讨论会，巴西利亚，巴西


FDA/CDER使用的(Q)SAR软件

基于统计模型
CASE Ultra                                        MultiCASE, Inc.
Model Applier--Statistical  Models        Leadscope, Inc.
Sarah Nexus                                      Lhasa Limited

基于专家规则
Derek Nexus                                     Lhasa Limited
Model Applier - Expert Alerts              Leadscope, Inc.
CASE Ultra-Expert Alerts                    MultiCASE, Inc.


(Q)SAR软件选择标准

Different methodologies yield different predictions
不同的方法学得出不同的预测结果
&#8226redictions are complementary
预测是互补的
•Yields higher sensitivity and negative predictivity
获得更高的灵敏度和阴性预测
•Second statistical system improves coverage
第二统计系统提高了覆盖率
Predictions are chemically meaningful and transparent
预测具有化学意义且透明
•Structural alerts and associated training set structures can be identified to explain why a prediction was made
可以识别结构警示和相关的培训集结构，以解释为什么做出该预测
•Application of expert knowledge is facilitated
促进专家知识的应用
Software and models are publicly available
软件和模型是公开可用的
•Our results are reproducible by pharmaceutical applicants and others
我们的结果药品申请人和其他人可以复制


Common ICH M7 Review Questions
ICH M7审评常见问题

Have we seen this compound before?
我们以前见过这种化合物吗？
Are experimental data available?
是否有实验数据？
Havewe previously performed a (Q)SAR analysis for thiscompound?
我们是否对该化合物进行过(Q)SAR分析？
Are there data for related compounds?
是否有相关化合物的数据？
图片
Chemical registration enables us to answer these questions
化学注册使我们能够回答这些问题


CTCS Chemical Registration Process
CTCS化学注册流程



Evaluation of Applicant (Q)SAR Data
评估申请人的(Q)SAR数据

(Q)SAR Software Acceptability
(Q)SAR软件可接受度

Under the ICH M7 guideline, Applicants may submit (Q)SAR analyses performed using models that are fit-for-purpose
根据ICH M7指南，申请人可以提交使用适合目的的模型所进行的(Q)SAR分析
•Commercially available
市售
•Freely available
免费提供
•Constructed in-house
内部建立
CDER has prior knowledge of several commercial and freely available (Q)SAR software
CDER对一些商业和免费的(Q)SAR软件具有先验知识
For software that CDER has no prior knowledge of, supporting documentation demonstrating that a model is fit-for-purpose is recommended (e.g., QMRF)
对于CDER不了解的软件，建议使用证明模型适合目的的支持文档（例如QMRF）
•Predict bacterial (Ames) mutagenicity
预测细菌（Ames）的致突变性
•2 models: expert rule-based and statistical-based
2个模型：基于专家的规则和基于统计的模型
•Consistent with OECD Validation Principles
符合经合组织验证原则


Applicant (Q)SAR submissions
(Q)SAR的申请提交

Typically, only problematic (Q)SAR submissions are sent to us for evaluation
通常，仅将有问题的(Q)SAR提交给我们进行评估
•Well-documented submissions are handled by review divisions
备有证明文件的提交文件由审核部门处理
•If a reviewer is concerned about the quality of a submission, or it uses an unfamiliar software, it is sent to us.
如果审评人员担心提交的质量，或使用的软件不熟悉，则会将其发送给我们。
•Quality Issues: single methodology, read-across only, overall conclusions conflict with predictions with no explanation
质量问题：单一方法论、只能通读、总体结论与预测相冲突，没有任何解释。
General rule is: Trust, but verify. Predictions are re-run only if there is a concern.
一般规则是：信任，但要验证。仅在存在问题时才重新运行预测。
Predictions with the most recent software version are preferred. Old predictions are acceptable unless there are known model changes that could impact conclusions.
最好使用最新版本的软件预测。除非存在可能影响结论的已知模型更改，否则旧的预测是可以接受的。
Expert analysis serves as a buffer to prediction changes with different software versions
专家分析可作为不同软件版本变化预测的缓冲。


Out-of-Domain Results
域外结果

Common problem for new drug impurities
新药杂质的常见问题
18% of impurities in new drugs approved in2016 and 2017 had an out-of-domain (Q)SAR result, based on an internalstudy
根据内部研究，2016年和2017年批准的新药中18％的杂质出现域外的(Q)SAR结果
Anout-of-domain result is not a predictionand doesnot contribute to a Class 5assignment
域外的结果不是预测，并且不能将其归为第5类
Applicationof expert knowledge can be used to addressthese gapsbut higher bar toacceptance
应用专家知识可以弥补这些差距，但接受的门槛更高
FDA/CDERuses a 2nd statistical system toresolve most out-of-domains in internalanalyses
FDA/ CDER使用第二版统计系统来解决内部分析中大多数域外的问题
Theseare areas with the greatest need for improved databasesandmodels
这些领域最需要改进的数据库和模型


Relevant Information for Reporting
相关信息的报告

Materials and methods
材料和方法
Name and version of software and (Q)SAR modelsused
使用的软件和(Q)SAR模型的名称和版本
Predictionclassification criteria, such as the cutoff or threshold values to define apositive/negative/equivocalresult
预测分类标准，例如定义阳性/阴性/两可结果的临界值或阈值
Results and Conclusions
结果和结论
Individualpredictions, as well as the overall conclusion(impurityclass)
个人预测以及整体结论（杂质分类）
Confirmation that theimpurity is within the model’s domainofapplicability
确认杂质在模型的域内
Descriptionof any confirmatory application of expert knowledge, including analogs (whereappropriate)
说明对专家知识进行任何确认性应用的信息，包括类似物（如果适用）
Rationale for superseding any prediction
取代任何预测的理由
Appendix
附录
Raw (Q)SARoutputs
原始(Q)SAR输出
Ames data for structurally related compounds used to confirm or refutea prediction
用于确认或反驳预测结果的与结构相关的化合物的Ames数据

Concluding Remarks
结束语

Application of expert knowledge is an importantcomponentof (Q)SAR assessmentunder ICHM7
应用专家知识是ICH M7下(Q)SAR评估的重要组成部分
Model transparency and interpretability facilitate application of expert knowledge
模型的透明度和可解释性有助于专家知识的应用
Effective structural analog searching iscritical
有效的结构相似性检索至关重要
Expert review ofpredictions is standard practice atFDA/CDER
对预测结果的专家审查是FDA/ CDER的标准做法
Regulatory (Q)SAR submissions still varysignificantly inquality. Areas forimprovement:
提交的监管(Q)SAR质量仍然存在很大差异。需要改进的方面：
Use of appropriatemodels (expert rule-based andstatistical-based)that areconsistent with OECD validation principles
使用符合经合组织验证原则的适当模型（基于专家规则和基于统计的模型）
— Mayneed to provide supporting documentation
可能需要提供支持文件
Appropriate handlingof out-of-domainresults
恰当处理域外结果
Adequate documentation of assessments, particularly ifmodel predictions are overruled based on expert knowledge
充分的评估文件，特别是如果基于专家知识否决了模型预测的情况下

Internal process improvements enable CTCS to handle alarge volume of(Q)SAR consultationrequests 内部流程的改进使CTCS能够处理大量(Q)SAR咨询请求
Dedicated team of(Q)SARexperts
专门的(Q)SAR专家团队
Closecommunication and collaboration with review stafftoensure needs are met
与审评人员密切沟通和协作，以确保满足需求
Robust chemical registration system
稳健的化学品注册系统
Integration of (Q)SAR consults into review management platform
将(Q)SAR咨询整合到审评管理平台
External collaboration and outreach ensureaccessto state-of-the- art models anddatabases
外部合作和外延确保了对最新模型和数据库的访问
Conduitfor interacting with pharmaceutical stakeholders toshare knowledge andexperiences
与制药业利益相关者互动以共享知识和经验的渠道
Participation inindustry consortia advancing the science of(Q)SAR Modelling
参与行业协会推进(Q)SAR建模科学
Identifies opportunities for future projects
确定未来项目的机会

好好学习一下

awesome

good, thanks.

+1