～in Silico QSAR手法による遺伝毒性リスク評価

～in Silico QSAR手法による遺伝毒性リスク評価、
ICHM7ガイドラインへの対応～
Computational approaches to managing mutagenicity
risk for ICHM7 and beyond
JSOT June 2015
Chris Barber
Director of Science
[email protected]
Agenda
• What is ICH M7?
• Using two in silico systems under M7..
• Derek
• Sarah
• How to apply expert review
What is ICH M7?
• “Assessment and Control of DNA Reactive (Mutagenic)
Impurities in Pharmaceuticals to Limit Potential Carcinogenic
Risk”
• ‘Global’ guidelines – America, Europe, Japan
• Finalised - June 2014
• Expected to be in force Jan 2016
identification
categorisation
qualification
Control of mutagenic
impurities to limit potential
carcinogenic risk
変異原性不純物をコントロールすることで、潜在的な発がん性リスクを低減します。
ICH M7 – Permits the use of in silico predictions
• You may use the Ames (in vitro) assay
• Or use in silico predictions in its place...
• If you submit in silico predictions, you will need...
• Two predictions – one expert rule-based and one statistical
• 2種類の相補的なin Silicoによる予測が推奨される。
• Expert review
• “If warranted”
• “to provide additional evidence for any prediction”
• “to support the final conclusion”
• 専門的知識に基づいたレビューが必要である。
•
http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html
In silico workflow under M7
Evaluate drug substance, impurities,
degradants, intermediates…
Databases, in-house,
literature..
2 in silico predictions
expert + statistical
Known
mutagen
既知の変異原性
Both predict
positive
両方の正の予測
Disagree / fail
to predict
予測結果の相違
Both predict
negative
両方の負の予測
Known
non-mutagen
既知の非変異原性
Expert Review
Limit according to TTC or
present purge argument for loss
Ames test
Treat as nonmutagenic
Using in silico predictions…
インシリコ予測で
2 in silico predictions
expert + statistical
• “The absence of structural alerts from both is sufficient to
conclude that the impurity is of no mutagenic concern”
「相補的な二つのQSAR法において警告構造のないことが示さ
れた場合には、その不純物には懸念がないと十分に結論され、
さらなる試験は必要とされない。」
• Expert review can provide
• additional supportive evidence
• reason to dismiss an in silico prediction
• rationale to support the final conclusion
付加的証拠
予測を取り下げる根拠
結論に至る合理性
In silico systems should give you..
• A prediction
予測
• ‘out-of-domain’ or ‘indeterminate’ is not a prediction
• is there enough information to make an expert call in such cases?
• is the scope of the alert / applicability domain clearly defined?
• how good is coverage of your chemical space?
ケミカルスペースの適用範囲内かどうか？
• Accuracy
予測精度
• You should assess against your chemical space (not public data)
• A measure of the model’s confidence in a prediction
• Is it meaningful? – has it been shown to correlate with accuracy?
• …it should tell you how much to worry & why
予測は不確実である場合もある。
In silico systems should give you..
• Regularly updated with new data or knowledge
最新のデータ、知見がアップデートされていること。
• Chemical space is changing – models need to keep up
• Known to regulatory authorities 規制当局による認知
• Not essential but expect lots of questions if
•
•
•
•
they don’t understand the approach
have not seen the training data
haven’t evaluated the performance
don’t get enough supporting data
… they may need more information
In silico systems should give you..
• A transparent prediction
透明性をもった予測
• Supporting information (data, explanation)
サポート情報（データ、説明）
• THE most important criteria….. 最も重要な基準
• A model must help you to defend or challenge every prediction
予測モデルは全ての予測をサポートしている必要がある。
• This may be hard if the model automates the conclusion or does
not say why...
• A regulator may not accept an automated decision
....and ask you to explain
Derek– an expert knowledge base
専門知識ベース
• Built using public + confidential data in collaboration with
regulators and industrial members
• 25% of alerts are based upon proprietary data
• Comprises of 115 alerts for mutagenicity
• Can be further customised with private knowledge
• Rules written by experts
専門家によって定義されたルール
• Detailed expert analysis
• Incorporates chemical reactivity, metabolism, toxicology expertise
• Transparent
透明性
• Highlights areas for expert review
アラート構造のハイライト表示。
負の予測を見直し
Reviewing a negative prediction from Derek
• Expert systems should highlight any uncertainty…
エキスパートシステムは、ある種の不確実性を示すべき。
• What would an expert say?
• “I’m predicting negative as I see no alerts..
• … Nothing worries me!”
• … I have seen that feature in a positive compound which I couldn’t
explain – you may want to review that part..”
• 私はその特徴構造を変異原性不純物中に見たことがある。追加のレビュー
が必要かもしれません。
Misclassified feature
• … that bit is new to me – you may want to review it..”
• 私は過去に類似の特徴構造を見たことがありません。追加のレビューが必要
かもしれません。
Unclassified feature
負の予測を見直し
Reviewing negative predictions from Derek
• When no alerts fire, features in the query molecule are checked
• Have the alert writers seen all the features been before?
• Have any features been present in false negative predictions?
Inactive
Inactive with
misclassified
features
Inactive with
unclassified
features
Inactive with
unclassified &
misclassified
features
• Performance: how often does each category occur?
それぞれの分類の発生頻度。
Private data 1
(n=325)
86%
9%
5%
-
Private data 2
(n= 416)
89%
7%
3%
-
Private data 3
(n= 1669)
90%
7%
3%
-
負の予測を見直し
Reviewing negative predictions from Derek
• When no alerts fire, features in the query molecule are checked
• Have the alert writers seen all the features been before?
• Have any features been present in false negative predictions?
Inactive
Inactive with
misclassified
features
Inactive with
unclassified
features
Inactive with
unclassified &
misclassified
features
• Performance: how accurate are the predictions?
予測の正確性
Private data 1
(n=325)
94%
86%
86%
-
Private data 2
(n= 416)
87%
86%
94%
-
Private data 3
(n= 1669)
91%
93%
95%
-
負の予測を見直し
Reviewing a negative prediction from Derek
• Misclassified and unclassified features
• These are negative predictions
予測結果としてはNegative
• Highlights areas to consider reviewing to increase confidence
ハイライト構造について、より信頼性を高めるために検討する。
• We will see some examples later
• These can be rapidly assessed by experts
これらは専門家によって迅速に評価することが出来る。
• Reviewing the data provided is often sufficient
• Is there additional data from compounds containing the feature?
正の予測を見直し
Reviewing a positive prediction
• Use the supporting
information
支援情報を活用。
• Challenge an expert
system by focussing on
the scientific arguments
and available data
科学的な考察と利用可能
なデータに焦点を絞り、エ
キスパートシステムに挑戦
Derek Nexus – an expert knowledge base
参考文献
• Derek is the preferred system for our members
• In silico methods combined with expert knowledge rule out mutagenic
potential of pharmaceutical impurities: An industry survey
•
Regulatory Toxicology and Pharmacology, 2012, 62, 449
– Pfizer, Novartis, GSK, AZ, Lilly, Hoffmann-La Roche, Covance, Merck,
J&J
• Use of in silico systems and expert knowledge for structure-based
assessment of potentially mutagenic impurities
•
Regulatory Toxicology and Pharmacology, 2013, 67, 39
– Bayer, Sanofi, AZ, Hoffmann-La Roche, Computational Toxicology
Services LLC, BMS, Pfizer, Servier, Novartis, J&J, Abbott, Merck,
Boehringer, NCSP
• A practical application of two in silico systems for identification of
potentially mutagenic impurities.
• Regulatory Toxicology and Pharmacology, 2015, 72, 335
- Bayer, Pfizer
Agenda
• What is ICH M7?
• Using two in silico systems under M7..
• Derek
• Sarah
• How to apply expert review
透明統計システム
Sarah – a transparent statistical system
• DNA-reactivity is driven by chemical reactivity and is best
modelled at the fragment-level
• Molecules are fragmented according to a series of rules
分子は、一連のルールに応じて断片化されています
• A recursive decision tree identifies (de)activating fragments
• Each fragment forms a potential hypothesis
• Each hypothesis is assembled into a network
• Self Organising Hypotheses Network
自己組織化仮説ネットワーク
“Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge”
Hanser... Journal of Cheminformatics, 2014, 6(1), 21.
Hypotheses for each fragment are identified
各フラグメントについての仮説が定義される。
“Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge”
Hanser... Journal of Cheminformatics, 2014, 6(1), 21.
Hypotheses are combined to give a prediction
Hypothesesを組み合わせて予測を実行します。
Sarah’s confidence score correlates with accuracy
Condidence Scoreは、予測精度と相関します
Confidence
Score
0-20%
20-40%
40-60%
60-80%
80-100%
PPV
58%
74%
85%
93%
92%
NPV
62%
80%
95%
96%
97%
• Less confident predictions deserve greater scrutiny
• Default settings optimum for regulatory submissions
Sarah vs. external dataset of confidential data from Lhasa members
Possible challenges to a statistical system
統計ベースシステムにおける課題
• Has the model mis-learnt something?
• No good if the model uses obscure descriptors or doesn’t say!
• Does the model correctly predict known analogues?
• No good if the model doesn’t tell you!
• Is there data for a unclassified fragment?
• No good if the model doesn’t tell you where to look!
• Models should help you challenge a prediction
モデルは専門家のレビューを助けなければなりません
• Transparency, confidence scores, structural explanation &
supporting examples are essential
Sarah was built with members and regulators
Sarahはメンバー企業と規制当局によって構築された。
>40 companies now sponsor the development of Sarah
M7 Decision Matrix
M7結論マトリクス
O.O.D.
out of domain
ドメインのうち
O.O.D
.
+
confidence
Equiv
-
Equiv
+
Plausible
Negative
Negative
+
Misclassified
Negative
+
Unclassified
Probable
もっと確から
もらししいで
いです
す
Certain
既知
の
Performance of Sarah and Derek...
Frequency of Outcomes (%)
アウトカムの頻度
Probability of being positive (%)
陽性である頻度
O.O.D
.
2
0
3
O.O.D
.
29
-
62
+
10
0
17
+
30
-
70
Equiv
8
0
6
Equiv
23
-
51
-
38
2
12
-
8
15
34
-
Equiv
+
-
Equiv
+
• Lhasa data sharing consortium group (n=777; 32% positive)
Agenda
• What is ICH M7?
• Using two in silico systems under M7..
• Derek
• Sarah
• How to apply expert review
どのように専門家のレビューを実施するか。
Likely to conclude positive
Very strong evidence would
be needed to overturn both
predictions
Likely to conclude positive
Lack of a second prediction
suggests insufficient
evidence to draw any other
conclusion
Uncertain
Likely to conclude positive
without strong evidence to
overturn a positive
prediction
System 1
Positive
Positive
Positive
Negative
Negative
System 2
Positive
O.O.D. or
equivocal
Negative
O.O.D. or
equivocal
Negative
O.O.D. = out of domain
Uncertain
Conservatively could assign as positive.
May conclude negative with strong evidence
showing feature driving a ‘no prediction’ is
present in the same context in known negative
examples (without deactivating features)
Likely to conclude negative
Expert review should support this
conclusion – e.g. by assessing any
concerning features (misclassified,
unclassified, potentially reactive..)
- From a draft paper prepared with 11 companies and regulators
“Establishing best practise in the application of expert review of mutagenicity under ICH M7”
Expert analysis step-by-step
専門家の分析をステップ·バイ·ステップ
?
• Enter query compound(s)
クエリ化合物を入力する。
• Generate statistical and expert predictions
知識/統計ベースの予測結果を生成。
Review
• Expert review
エキスパートレビューの実施。
(
)
• (Optionally) source further supporting data
サポート情報の調査（必要に応じて）
Conclusion
• Report
レポート作成
N2C(=O)c1cccc(c1C2=O)Cl
Expert analysis step-by-step
• 1 - Run predictions through expert and statistical systems
?
Review
(
)
Conclusion
• Both predict inactive 2つのシステムでNegative
• ...but Derek identifies a misclassified feature
Expert analysis step-by-step
• 2 - Review Expert Prediction
?
Review
(
)
Conclusion
• Derek highlights the misclassified feature
Missclassified構造のハイライト
N2C(=O)c1cccc(c1C2=O)Cl
Expert analysis step-by-step
• 2 - Review Expert Prediction
?
Review
(
)
Conclusion
• Derek shows other compounds with that feature
• The positives are correctly predicted by Derek
• 1 positive compound is not identified – a Cu complex
Cu化合物：1つの陽性化合物が特定されていません
N2C(=O)c1cccc(c1C2=O)Cl
Expert analysis step-by-step
• 3 – Review Statistical Prediction review hypotheses
?
Review
(
)
Conclusion
• Sarah has 1 positive hypothesis
• Weak positive hypothesis but deserves investigating...
Expert analysis step-by-step
• 3 – Review Statistical Prediction review supporting examples
?
Review
(
• The parent ring system is reported inactive
) • Close analogues have activity
Conclusion
• ..but these have other causes of activity
• ....can process these through Derek to confirm
Derekで予測することで、裏付けすることが出来る。
Expert analysis step-by-step
• 4 – Final review
?
• Look for additional supporting information...
Review
http://www.ncbi.nlm.nih.gov/pubmed/3415689
Biochem Biophys Res Commun. 1988 Aug 30;155(1):338-43.
Abstract: The nuclease activity of 1,10-phenanthroline-copper
functions intracellularly. This was shown by its mutagenicity in
the Ames Test using the tester strain TA 102 and the in vivo
nicking of plasmids derived from this strain.
• ...Copper is not genotoxic itself
(
)
Conclusion
...but some macrocyclic chelates are mutagenic
• Conclude – the example in Derek is not relevant
– accept negative prediction
負の予測を受け入れます
Expert analysis step-by-step
• 5 - Conclusion
• Statistical system predicts inactive
?
陰性と予測
• One positive hypothesis driven by examples that
have other reasons for activity
Review
(
)
• Expert system
• Inactive
• Misclassified feature dismissed as not relevant
Misclassified featureがハイライトされている。
• Conclusion
Conclusion
• Inactive
O(S(O)(=O)=O)C
Expert analysis step-by-step
• Run predictions through expert and statistical systems
?
Review
(
)
Conclusion
• The two predictions disagree
Expert analysis step-by-step
• Review Expert Prediction
?
Review
(
)
Conclusion
Expert analysis step-by-step
• Review Statistical Prediction
?
Review
(
)
Conclusion
• Dialkyl sulphate examples are positive
• Mono-alkyl sulphates examples are negative
• Sulphuric acid is positive
Expert analysis step-by-step
• Review Statistical Prediction training examples
?
Review
(
)
Conclusion
• Running the dialkyl sulphates through Derek
confirms that these are expected to be active
• Comments explain why mono-alkyls are not
active
Expert analysis step-by-step
• Look at database records – here we are using Vitic...
?
Review
(
)
Conclusion
Sulphuric acid has been tested as negative
..but 1 example where was positive – review literature
Expert analysis step-by-step
• Conclusion
?
• Expert system predicts negative
• A related alert is limited to dialkyl sulphates
• Statistical system predicts positive
Review
• Positive examples were explained by expert system
• Sulphuric acid has been tested negative and positive
• Dismissed the positive experimental data
(
)
Conclusion
• Mechanistic explanation
• Mono alkyl sulphates are not good electrophiles
• Conclusion
• Expert assessment is that the compound is negative
CCNC(N)=NC(=N)N
Expert analysis step-by-step
• Run predictions through expert and statistical systems
?
Review
(
)
Conclusion
• Derek predicts inactive with an unclassified feature
• Sarah makes no prediction
CCNC(N)=NC(=N)N
Expert analysis step-by-step
• Review Expert Prediction
• Derek identifies an unusual feature
?
Review
(
...but knows about guanidines
)
Conclusion
...tells us that bis-guanidines are not in the Lhasa
public mutagenicity reference set (Vitic)
CCNC(N)=NC(=N)N
Expert analysis step-by-step
• Review Statistical Prediction
?
Review
(
)
Sarah identifies an unknown fragment
Conclusion
...but also shows that the rest of the compound
does not contribute to activity
Expert analysis step-by-step
• Review information
?
Review
(
)
Conclusion
• Both systems highlight uncertainty in the prediction
...there is insufficient data to support a decision
Expert analysis step-by-step
• Look for additional database information
?
Review
(
)
Conclusion
Marketed drug contains feature
and is proven non-mutagenic
Expert analysis step-by-step
• Conclusion
• Expert system predicts negative with unclassified
?
• Insufficient for expert to accept a negative prediction
• Statistical system out of domain
• Simple guanidines are inactive; bis-guanidines not known
• Both systems show no other reasons for activity
Review
...and clearly indicate the gap in knowledge
• Database search identifies the missing fragment does
(
)
not contribute to activity
• Conclusion = negative
Conclusion
• This is only possible because the systems explicitly show
missing knowledge
• This assessment was agreed with a regulator
Skills of an expert or an expert team
専門家や専門家チームのスキル
Process
chemistry
Analytical
chemistry
Chemical
structure
Mechanisms
of activity
Protocol and
limitations of
Ames assay
Impurity
profile
Chemist
化学者
Functional
groups
Toxicologist
毒物学者
Chemical
reactivity
Interpretation
of strain data
Supporting data
Similarity
(Q)SAR
Drug
Metabolist
Reactive
metabolites
Metabolic
profile
Metabolic
activation
How in silico
systems work
strengths/limitations
Where to focus
Lhasa Developing solutions for members and regulators
メンバーおよび規制当局のためのソリューションを開発
• Our unique trusted position...
•
•
•
•
Gives us access to confidential data
Collaborations with leading industrial scientists
Research collaborations with regulatory bodies
Members own Lhasa; our research is directed to meet their needs
• Our tools provide
•
•
•
•
Predictions
Supporting data
The ability to interrogate each prediction
Clear indications on where to focus expert analysis
専門家の分析を集中する場所の明確な指標
Lhasa solutions for members
• In silico predictions
• Expert knowledge-base predictions
• Statistical predictions (mutagenicity)
• Purge of impurities during synthesis
• Metabolite formation
• Forced degradation
• Database
• Toxicity database and consortia datasets
Thank you
Questions?
ありがとうございました。
質問はありますか？
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