Small Molecules and siRNA:Methods to Explore Bioactivity Data

Rajarshi GuhaNIH Chemical for

Translational Therapeutics

August 17, 2011Pfizer, Groton

Background

• Cheminformatics methods– QSAR, diversity analysis, virtual screening,

fragments, polypharmacology, networks• More recently

– siRNA screening, high content imaging,combination screening

• Extensive use of machine learning• All tied together with software

development• Integrate small molecule information &

biosystems – systems chemical biology

Outline

• Exploring the SAR landscape– The landscape view of SAR data– Quantifying SAR landscapes– Extending an SAR landscape

• Linking small molecule & RNAi HTS– Overview of the Trans NIH RNAi Screening Initiative– Infrastructure components– Linking small molecule & siRNA screens

The Landscape View of Structure Activity Datasets

Structure Activity Relationships

• Similar molecules will have similar activities• Small changes in structure will lead to small

changes in activity• One implication is that SAR’s are additive• This is the basis for QSAR modeling

Martin, Y.C. et al., J. Med. Chem., 2002, 45, 4350–4358

Structure Activity Landscapes

• Rugged gorges or rolling hills?– Small structural changes associated with large

activity changes represent steep slopes in the landscape

– But traditionally, QSAR assumes gentle slopes – Machine learning is not very good for special cases

Maggiora, G.M., J. Chem. Inf. Model., 2006, 46, 1535–1535

Characterizing the Landscape

• A cliff can be numerically characterized• Structure Activity Landscape Index (SALI)

• Cliffs are characterized by elements of the matrix with very large values

Guha, R.; Van Drie, J.H., J. Chem. Inf. Model., 2008, 48, 646–658

Visualizing SALI Values

• The SALI graph– Compounds are nodes– Nodes i,j are connected if SALI(i,j) > X– Only display connected nodes

What Can We Do With SALI’s?

• SALI characterizes cliffs & non-cliffs• For a given molecular representation, SALI’s

gives us an idea of thesmoothness of the SAR landscape

• Models try and encodethis landscape

• Use the landscape to guidedescriptor or model selection

Descriptor Space Smoothness

• Edge count of the SALI graph for varying cutoffs• Measures smoothness of the descriptor space• Can reduce this to a single number (AUC)

Other Examples

• Instead of fingerprints, we use molecular descriptors

• SALI denominator now uses Euclidean distance

• 2D & 3D random descriptor sets– None are really good– Too rough, or– Too flat

2D

3D

Feature Selection Using SALI

• Surprisingly, exhaustive search of 66,000 4-descriptor combinations did not yield semi-smoothly decreasing curves

• Not entirely clear what type of curve is desirable

Measuring Model Quality

• A QSAR model should easily encode the “rolling hills”• A good model captures the most significant cliffs• Can be formalized as

How many of the edge orderings of a SALI graph does the model predict correctly?

• Define S (X ), representing the number of edges correctly predicted for a SALI network at a threshold X

• Repeat for varying X and obtain the SALI curve

SALI Curves

Model Search Using the SCI

• We’ve used the SALI to retrospectively analyze models

• Can we use SALI to develop models?– Identify a model that captures the cliffs

• Tricky– Cliffs are fundamentally outliers– Optimizing for good SALI values implies overfitting– Need to trade-off between SALI & generalizability

Predicting the Landscape

• Rather than predicting activity directly, we can try to predict the SAR landscape

• Implies that we attempt to directly predict cliffs– Observations are now pairs of molecules

• A more complex problem– Choice of features is trickier– Still face the problem of cliffs as outliers– Somewhat similar to predicting activity differences

Scheiber et al, Statistical Analysis and Data Mining, 2009, 2, 115-122

Motivation

• Predicting activity cliffs corresponds to extending the SAR landscape

• Identify whether a new molecule will perform better or worse compared to the specific molecules in the dataset

• Can be useful for guiding lead optimization, but not necessarily useful for lead hopping

Predicting Cliffs

• Dependent variable are pairwise SALI values, calculated using fingerprints

• Independent variables are molecular descriptors – but considered pairwise– Absolute difference of descriptor pairs, or– Geometric mean of descriptor pairs– …

• Develop a model to correlate pairwise descriptors to pairwise SALI values

A Test Case

• We first consider the Cavalli CoMFA dataset of 30 molecules with pIC50’s

• Evaluate topological and physicochemical descriptors

• Developed random forest models– On the original observed

values (30 obs)– On the SALI values

(435 observations)

Cavalli, A. et al, J Med Chem, 2002, 45, 3844-3853

Double Counting Structures?

• The dependent and independent variables both encode structure.

• But pretty low correlations between individual pairwise descriptors and the SALI values

Model Summaries

• All models explain similar % of variance of their respective datasets

• Using geometric mean as the descriptor aggregation function seems to perform best

• SALI models are more robust due to larger size of the dataset

Original pIC50RMSE = 0.97

SALI, AbsDiffRMSE = 1.10

SALI, GeoMeanRMSE = 1.04

Test Case 2

• Considered the Holloway docking dataset, 32 molecules with pIC50’s and Einter

• Similar strategy as before• Need to transform SALI values • Descriptors show minimal

correlation

Holloway, M.K. et al, J Med Chem, 1995, 38, 305-317

Model Summaries

• The SALI models perform much poorer in terms of % of variance explained

• Descriptor aggregation method does not seem to have much effect

• The SALI models appear to perform decently on the cliffs – but misses the most significant

Original pIC50RMSE = 1.05

SALI, AbsDiffRMSE = 0.48

SALI, GeoMeanRMSE = 0.48

Model Summaries

• With untransformed SALI values, models perform similarly in terms of % of variance explained

• The most significant cliffs correspond to stereoisomers

Original pIC50RMSE = 1.05

SALI, AbsDiffRMSE = 9.76

SALI, GeoMeanRMSE = 10.01

Test Case 3

• 38 adenosine receptor antagonists with reported Ki values; use 35 for training and 3 for testing

• Random forest model on the SALI values performed reasonable well (RMSE = 7.51, R2=0.62)

• Upper end ofSALI rangeis better predicted

Kalla, R.V. et al, J. Med. Chem., 2006, 48, 1984-2008

Test Case 3

• For any given hold out molecule, range of error in SALI prediction is large

• Suggests that some form of domain applicability metric would be useful

• The dataset does not containing really big cliffs

• Generally, performance is poorer for smaller cliffs

Model Caveats

• Models based on SALI values are dependent on their being an SAR in the original activity data

• Scrambling results for these models are poorer than the original models but aren’t as random as expected

Conclusions

• SALI is the first step in characterizing the SAR landscape

• Allows us to directly analyze the landscape, as opposed to individual molecules

• Being able to predict the landscape could serve as a useful way to extend an SAR landscape

Joining the Dots: Integrating High Throughput Small Molecule and RNAi Screens

• Perform collaborative genome-wide RNAi screening-based projects with intramural investigators

• Advance the science of RNAi and miRNA screening and informatics via technology development to improve efficiency, reliability, and costs.

RNAi Facility Mission

Range of Assays

Pathway (Reporter assays, e.g. luciferase,

b-lactamase)

Complex Phenotypes (High-content imaging, cell cycle, translocation, etc)

Simple Phenotypes (Viability, cytotoxicity, oxidative stress, etc)

RNAi Informatics Infrastructure

• Summary statistics

• Corrections

QC

• Median• Quartile• Background

Normalization• Thresholding• Hypothesis

testing• Sum of ranks

Hit Selection

• GO semantic similarity

• Pathways• Interactions

Hit Triage

RNAi Analysis Workflow

Raw and Processed

Data

GO annotations

PathwaysInteractions

Hit ListFollow-up

RNAi Informatics Toolset

•Local databases (screen data, pathways, interactions, etc).

•Commercial pathway tools. •Custom software for loading, analysis and visualization.

Back End Services

• Currently all computational analysis performed on the backend

• R & Bioconductor code• Custom R package (ncgcrnai) to support NCGC

infrastructure– Partly derived from cellHTS2– Supports QC metrics, normalization, adjustments,

selections, triage, (static) visualization, reports• Some Java tools for

– Data loading– Library and plate registration

User Accessible Tools

User Accessible Tools

RNAi & Small Molecule Screens

Goal: Develop systems level view of small molecule activity

• Reuse pre-existing MLI data• Develop new annotated libraries

TACGGGAACTACCATAATTTACAGCATGAGTACTACAGGCCA

• Run parallel RNAi screen

What targets mediate activity of siRNA and compound

Pathway elucidation, identification of interactions

Target ID and validation

Link RNAi generated pathway peturbations to small molecule activities. Could provide insight into polypharmacology

HTS for NF-κB Antagonists

• NF-κB controls DNA transcription

• Involved in cellular responses to stimuli– Immune response,

memory formation– Inflammation,

cancer, auto-immune diseases

http://www.genego.com

HTS for NF-κB Antagonists

• ME-180 cell line• Stimulate cells using TNF, leading to NF-κB

activation, readout via a β-lactamase reporter• Identify small molecules and siRNA’s that

block the resultant activation

Small Molecule HTS Summary

• 2,899 FDA-approved compounds screened

• 55 compounds retested active• Which components of the NF-κB

pathway do they hit?– 17 molecules have target/pathway

information in GeneGO– Literature searches list a few more

Most Potent ActivesProscillaridin A

Trabectidin

Digoxin

Miller, S.C. et al, Biochem. Pharmacol., 2010, ASAP

RNAi HTS Summary

• Qiagen HDG library – 6886 genes, 4 siRNA’s per gene

• A total of 567 genes were knockeddown by 1 or more siRNA’s– We consider >= 2 as a “reliable” hit– 16 reliable hits– Added in 66 genes for

follow up via triage procedure

The Obvious Conclusion

• The active compounds target the 16 hits (at least) from the RNAi screen– Useful if the RNAi screen was small & focused

• But what if we’re investigating a larger system?– Is there a way to get more specific?– Can compound data suggest RNAi non-hits?

Small Molecule Targets

• Some small molecules interact with core components

Bortezomib (proteosome inhibitor)

Daunorubicin (IκBα inhibitor)

Small Molecule Targets

• Others are active against upstream targets

• We also get an idea of off -target effects

Montelukast (LDT4 antagonist)

Compound Networks - Similarity

• Evaluate fingerprint-based similarity matrix for the 55 actives

• Connect pairs that exhibit Tc> 0.7

• Edges are weightedby the Tc value

• Most groupings areobvious

A “Dictionary” Based Approach

• Create a small-ish annotated library– “Seed” compounds

• Use it in parallel small molecule/RNAi screens• Use a similarity based approach to prioritize

larger collections, in terms of anticipated targets– Currently, we’d use structural similarity– Diversity of prioritized structures is dependent on

the diversity of the annotated library

Compound Networks - Targets

• Predict targets for the actives using SEA• Target based compound network maps nearly

identically to the similarity based network

• But depending on the predicted target qualitywe get poor (or no) mappings to the RNAi targeted genes

Keiser, M.J. et al, Nat. Biotech., 2007, 25, 197-206

Gene Networks - Pathways

• Nodes are 1374 HDG genes contained in the NCI PID

• Edge indicates two genes/proteins are involved in the same pathway

• “Good” hits tend to be very highly connected

Wang, L. et al, BMC Genomics, 2009, 10, 220

(Reduced) Gene Networks – Pathways

• Nodes are 526 genes with >= 1 siRNA showing knockdown

• Edge indicates two genes/proteins are involved in the same pathway

Pathway Based Integration

• Direct matching of targets is not very useful• Try and map compounds to siRNA targets if the

compounds’ predicted target(s) and siRNA targets are in the same pathway– Considering 16 reliable hits, we cover 26 pathways– Predicted compound targets cover 131 pathways

• For 18 out of 41 compounds

– 3 RNAi-derived pathways not covered by compound-derived pathways

• Rhodopsin, alternative NFkB, FAS

Pathway Based Integration

• Still not completely useful, as it only handled 18 compounds

• Depending on target predictions is probably not a great idea

Integration Caveats

• Biggest bottleneck is lack of resolution• Currently, both small molecule and RNAi data

are 1-D– Active or inactive, high/low signal– CRC’s for small molecules alleviate this a bit

• High content screens can provide significantly more information and so better resolution– Data size & feature selection are of concern

Integration Caveats

• Compound annotations are key– Currently working on using ChEMBL data to

provide target ‘suggestions’• More comprehensive pathway data will be

required• RNAi and small molecule inhibition do not

always lead to the same phenotype– Could be indicative of promiscuity– Could indicate true biological differences

Weiss, W.A. et al, Nat. Chem. Biol., 2007, 12, 739-744

Conclusions

• Building up a wealth of small molecule and RNAi data

• “Standard” analysis of RNAi screens relatively straightforward

• Challenges involve integrating RNAi data with other sources

• Primary bottleneck is dimensionality of the data– Simple flourescence-based approaches do not provide

sufficient resolution– High-content is required

Acknowledgements

• John Van Drie• Gerry Maggiora• Mic Lajiness• Jurgen Bajorath

• Scott Martin• Pinar Tuzmen• Carleen Klump• Dac Trung Nguyen• Ruili Huang• Yuhong Wang

CPT Sensitization & “Central” Genes

TOP1 poisons prevent DNA religation resulting in replication-dependent double strand breaks. Cell activates DNA damage response (e.g. ATR).

Yves Pommier, Nat. Rev. Cancer, 2006.

Screening Protocol

Screen conducted in the human breast cancer cell line MDA-MB-231. Many variables to optimize including transfection conditions, cell seeding density, assay conditions, and the selection of positive and negative controls.

Hit SelectionFollow-Up Dose Response Analysis

CPT (Log M)

ATR

MAP3K7IP2

CPT (Log M)

Via

bilit

y (%

)V

iabi

lity

(%)

siNegsiATR-AsiATR-BsiATR-C

siNegsiMAP3K7IP2-AsiMAP3K7IP2-BsiMAP3K7IP2-CsiMAP3K7IP2-D

Multiple active siRNAs for ATR, MAP3K7IP2, and BCL2L1.

Screen #2

Screen #1

Sensitization Ranked by Log2 Fold Change

Sensitization Ranked by Log2 Fold Change

Are These Genes Relevant?

• Some are well known to be CPT-sensitizers• Consider a HPRD PPI sub-network

corresponding to the Qiagen HDG gene set• How “central” are these selected genes?

– Larger values of betweennessindicate that the node lies onmany shortest paths

– Makes sense - a number of them are stress-related

– But some of them have very lowbetweenness values

Are These Genes Relevant?

• Most selected genesare densely connected

• A few are not– Generally did not

reconfirm• Network metrics

could be used to provide confidencein selections