BioVariance Research Services

Off-target profile prediction for Drug Candidates Josef Scheiber, PhD www.biovariance.com

BioVariance - Overview Two distinct offerings rooted in the same data: • “Medical value content”-as-a-Service for Healthcare • making sense of genomic & other data in context as

service offering

Bio-Variance BaVarians ;)

• We aim for testable hypotheses that are well-supported by data from scientific databases and the literature

Background literature Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases Nidhi, Glick M, Davies JW, Jenkins JL. J Chem Inf Model. 2006 May-Jun;46(3):1124-33. Predicting new molecular targets for known drugs Michael J Keiser, Vincent Setola, John J Irwin, Christian Laggner, Atheir I Abbas, Sandra J Hufeisen, Niels H Jensen, Michael B Kuijer, Roberto C Matos, Thuy B Tran, Ryan Whaley, Richard A Glennon, Jérôme Hert, Kelan LH Thomas, Douglas D Edwards, Brian K Shoichet, Bryan L Roth 2009/11/1 Nature Volume 462 Issue 7270 Pages 175-181 Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis Scheiber J, Chen B, Milik M, Sukuru SC, Bender A, Mikhailov D, Whitebread S, Hamon J, Azzaoui K, Urban L, Glick M, Davies JW, Jenkins JL. J Chem Inf Model. 2009 Feb;49(2):308-17.

At a glance Target profile prediction

• Starting from early Drug Discovery it is very important to understand compound activity profiles and underlying mechanisms

• Cost restrictions render it impossible to perform a comprehensive in-vitro testing of all compounds against all targets

• Computational approaches help to identify the targets having the highest probability of becoming problems and to exclude those that will likely not become an issue

Simplified workflow

(1) Predict activity profile/targets

(2) Investigation of target- and phenotype related information

Data input

• Your compounds • Chemogenomics datasets • Your internal data incorporated where applicable

• Specifically curated scientific papers around

particular targets (especially if some interesting facts turn up in first run)

Computational description of molecules

• Descriptor selection heavily impacts outcome of analyses

• Depending on your main objectives different technologies are the best fit, we will discuss this in detail with you

0 1 0 0 2 0 0 0 1 0

Statistical modeling

• Activity is either modeled as yes/no or in categories (depending on your needs)

• Plenty of positive results with naïve Bayes, therefore method of choice

• Other technologies depending on data/on request

• Strict model validation

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Internal measure for model quality R2

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Model Validation - Example

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Internal measure for model quality R2

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External measure for model quality

R2Test,Avg

repeat at leaste 100 x

Model Validation - Example

Prediction results

• Based on model sets for each target, i.e. there are 100 prediction results for each target

• These are further analyzed, usually median predictive value taken for prediction and ranking

• Result: A ranked list with associated probabilities for each compound

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What does the result mean?

• Targets need to be annotated with phenotypic outcome – i.e. what does it mean that the compound is hitting this target?

• Do we have opportunities ( repurposing) or liabilities ( side effects) or both?

• How do different compounds compare? • What predictions should be confirmed by testing?

How are targets linked to diseases? – Data Source examples

Manolio TA. N Engl J Med 2010;363:166-176.

As of 2011, 1200 human GWASs have been published on over 400 traits

Phenocopy effect: If one can link a predicted target to one of these, you have a repurposing opportunity or symptoms as possible side effects

Possible extensions Diving into chemical biology

• Map into pathways

• Retrieve marketed drugs and clinical candidates that act in these pathways

Outlook

The right drug for the right patient at the right time & right dose is only possible

if you have the right knowledge within the right context right in place

We will further work on this!

Thank you for your attention!

josef.scheiber@biovariance.com Phone: +49 – 89 – 189 6582 – 80 Garmischer Str. 4/V 80339 Munich / Germany