Machine Learning in Pharmaceutical Research

Transcription

1 Machine Learning in Pharmaceutical Research Dr James Weatherall Global Director of Biomedical & Clinical Informatics AstraZeneca Research & Development October 2011

2 Overview Background: Pharmaceutical research at AZ Use case 1: Improving document search using feature selection Use case 2: Scientific hypothesis generation using probabilistic inference Use case 3: Clinical Trial data mining using RandomForest Use case 4: Understanding adverse events using unsupervised clustering

3 AZ Research & Development

4 AstraZeneca today We employ over 61,000 people 47% in Europe, 30% in the Americas, 23% in Asia, Africa and Australasia We invest over $4 billion in R&D each year and have over 15,500 people in our R&D organisation In 2010, our worldwide sales totalled $33.3 billion).

5 Global R&D Sites Södertälje St Petersburg Alderley Park / Macclesfield Cambridge London Montreal Mölndal Reims Mountain View Boston Wilmington Gaithersburg Shanghai Osaka Bangalore

6 Alderley Park, Cheshire

7 Our focus is on six areas of medicine Cancer Cardiovascular disease Infection Gastrointestinal disease Neurological disorders Respiratory & inflammatory conditions

8 AZ Products

9 The R&D process Discovery Development PHASE I PHASE II PHASE III PHASE IV Chemistry Biology Efficacy studies on healthy volunteers persons Clinical studies on a limited scale patients Comparativ e studies on a large number of patients! 500 5,000 patients Continued comparative studies! Registration, market introduction Clinical Studies/Trials Approximately 8-12 years from idea to marketable drug

10 Computational or In-silico Science We make extensive use of computer-based techniques like virtual experimentation, modelling and machine learning

11 Machine Learning in Pharmaceutical Research Use case 1: Improving document search using feature selection Acknowledgement: Martin Johansson, AZ

12 Overview Interested in links between genes and diseases We are often overwhelmed by the biomedical literature - e.g. search for all articles on a particular gene and receive far too many irrelevant hits X! X! X! X! X! X!

13 Example of a problematic search Interested in literature on ESR1 (EStrogen Receptor 1) - Plays a role in the development of several types of cancer - Need to know what the sum total of world research on ESR1 is A major problem in information retrieval: disambiguation

14 Feature Selection to improve the search One way to solve the signal-to-noise problem is to use more contextual information within the documents Scientific literature articles often have keywords added to describe the content more precisely However, keywords not always added accurately Nevertheless, these document metadata are interesting as features

15 How it works in practice Obtain informative training set about Gene A!! Process keyword content! Feature Relevance! Tests!

16 Feature Relevance Test (univariate) Features = keywords Feature relevance test: R = X N N X ALL Rx = Feature relevance score for keyword x Nx = Number of instances of keyword x Nall = Number of instance of all keywords

17 How it works in practice Apply threshold for each gene! i.e. only pick the most relevant keywords! These become your (ML-derived) search criteria for future searches!

18 Machine Learning in Pharmaceutical Research Use case 2: Scientific hypothesis generation using probabilistic inference Acknowledgement: Cassie Gregson, AZ

19 Overview We are scientists, but also need to be competitive How can we make new scientific findings? There are lots of chemical, biological, medical findings - Published in the literature - Buried in databases of experimental results Are we really using it all in an optimal fashion?

20 What is a scientific finding? A relationship between two relevant entities - Relevant entity is a gene, protein, drug, disease, patient - Relationship here means a directional, specific, and optionally quantified association Relieves! Relieves 80% of the time!

21 Knowledge mapping put all findings in one place

22 Knowledge mapping - prettified

23 New medical discoveries Renal Failure" 1. Treats Can cause Sirolimus" 2. Inhibits 3. May drive mtor" Pancreatic Cancer" 1. Sirolimus is a drug for renal anti-transplant rejection 2. It does this via inhibition of a protein called mtor 3. mtor activity is associated with development of pancreatic cancer 4. Perhaps Sirolimus can also be used to combat cancer?

24 Sometimes it s a hairball...

25

26 Quantifying the evidence To make the process of inferencing more numerical, more probabilistic: - Allows for selection between multiple possible options Drug 1" 0.67 Protein 1" 0.83? Disease" Drug 2" 0.78 Protein 2" 0.65

27 Machine Learning in Pharmaceutical Research Use case 3: Clinical trial data mining using random forest Acknowledgement: John Cai, AZ

28 Overview A typical clinical trial on a new drug makes many different measurements on each patient taking part, e.g. - Demographic - Medical - Genetic These days, we need to develop Personalised Medicines But how do we know which patients will benefit from our treatment?

29 Random Forest Based on Decision Trees Why Forest? - One Decision Tree could get it wrong - Use modal class of many trees instead = Ensemble learning Why Random? - At each node of each tree, only a small fraction of the input variables are used to make the decision It is possible to compute an importance for each variable: - Based on how the forest performs if a variable is scrambled

30 Prediction of patient survival 72 hrs 120 hrs 24 hrs Baseline

31 Find the most important variables Top predictors in placebo group (prognosis markers) Top predictors in in treatment group (efficacy markers) Variables Variables Importance Score

32 Use variable cut-off to predict survival Patients on placebo Patients on treatment Placebo Treatment Red = Died Blue = Survived

33 Use variable cut-off to predict survival Placebo Placebo Treatment Treatment Variable < X Marker A xxx Variable > X Marker A > xxx Red = Died Blue = Survived

34 Machine Learning in Pharmaceutical Research Use case 4: understanding adverse events using unsupervised clustering Acknowledgement: Harry Southworth, AZ

35 Aside: Supervised vs Unsupervised ML methods Supervised machine learning: - Inputs are provided and desired outputs are known - The machine learns and produces a generalised classifier - Class prediction Unsupervised machine learning - Only inputs are provided - Determination of how data are organised - Class discovery

36 Overview An Adverse Event (AE) is any undesirable medical condition experienced by a patient taking an experimental drug during a clinical trial

37 Kohonen s Self Organising Maps (SOMs) Unsupervised, online ML approach Aims to produce a low dimensional (2D), discrete view of the (multidimensional) inputs Preserves topology: i.e. similar items in the input space are near each other in the output space Thus, SOMs are a type of visual clustering tool Unlike some other clustering approaches, the number of clusters is not defined a priori

38 SOMs in Practice for Clinical Trial AEs

39 SOMs in Practice for Clinical Trial AEs

40 Inside the nosebleed cluster 40!

41 Overview Background: Pharmaceutical research at AZ Use case 1: Improving document search using feature selection Use case 2: Scientific hypothesis generation using probabilistic inference Use case 3: Clinical Trial data mining using RandomForest Use case 4: Understanding adverse events using unsupervised clustering

42

43

44 Confidentiality Notice This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 2 Kingdom Street, London, W2 6BD, UK, T: +44(0) , F: +44 (0) , 44