Data mining in the pharmaceutical industry
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Data Mining in the Pharmaceutical Industry. By Jerry Swartz. Introduction. Since I am a remote student, if there are questions, feel free to e-mail jswartz@ligand.com. Pharmaceutical Development. Four Stages of Drug Development Research finds new drugs

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Introduction l.jpg
Introduction

  • Since I am a remote student, if there are questions, feel free to e-mail jswartz@ligand.com


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Pharmaceutical Development

  • Four Stages of Drug Development

    • Research finds new drugs

    • Development tests and predicts drug behavior

    • Clinical trials test the drug in humans

    • Commercialization takes drug and sells it to likely consumers (doctors and patients)

  • I’ll show an example for the Research, Development, and Clinical Trials stages


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Research Stage

  • Huge user of data mining tools and techniques

  • Scientists run experiments to determine activity of potential drugs

  • Uses high speed screening to test tens, hundreds, or thousands of drugs very quickly – this generates microarray data


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Research Stage

  • “Bioinformatics” is a general term for the information processing activities on data generated in Research Stage, especially microarray data

  • General goal is to find activity on relevant genes or to find drug compounds that have desirable characteristics (whatever those may be)


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Research Stage

  • Data mining techniques used

    • Clustering

    • Classification

    • Neural networks


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Research Stage Example 1

  • Goal: Determine compounds with similar activity

  • Why: Compounds with similar activity may behave similarly

  • When:

    • Have known compound and are looking for something better

    • Don’t have known compound but have desired activity and want to find compound that exhibits this activity



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Research Stage Example 1

  • Cluster compounds that have similar activity

  • We like behavior of H2O and want to see what compounds have similar activity

  • Example derived from Application of Nearest-Neighbor and Cluster Analyses in Pharmaceutical Lead Discovery

  • Clustering takes place based on similar activity using Euclidean “distance.”


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Research Stage Example 1

  • For simplicity, distance in example is simply difference between Beta and Delta values, not Euclidean

  • Distances:


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Research Stage Example 1

  • Dendrogram

0.49

0.16

0.00

H2O2

CO2

H2O


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Research Stage Example 1

  • Conclusion:

    • H2O2 and CO2 are most alike but,

    • H2O2 behaves more like H2O than CO2 behaves like H2O


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Research Stage Example 1

  • Variations

    • Example clustering performed on activity

    • Clustering could have been performed on structure (i.e. find chemically similar compounds)

    • Clustering could have been performed on both structure and activity (called SAR – Structure Activity Relationship, see next slide)


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Structure

Activity

Research Stage Example 1


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Development Stage

  • Company thinks drug might have some benefit

  • Undergoes testing in animals, human tissue to observe effect; maybe limited human tests

  • Determine how much drug to consume for desired effect

  • How dangerous is drug?


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Development Stage

  • Data mining techniques used

    • Classification

    • Neural networks


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Development Stage Example 2

  • Goal: Predict if treatment will aid patients

  • Why: If drug will not aid patients, what purpose does drug serve?

  • When:

    • Have data supporting use of drug

    • Have training data that shows effects of drug (positive or negative)

    • Want to be able to predict which patients will benefit


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Development Stage Example 2

  • Will treatment help sickle cell anemia patients?

  • We have information like gender, body weight, disease state, etc.

  • Feed these into neural network and predict whether patient will benefit from drug.

  • Example derived from Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network


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Development Stage Example 2

  • Uses ARTMAP network which is similar to neural network

  • Instead of activation function, uses choice function which compares two values

  • Basically matches input to “template” and generates output

  • If input is similar enough to “template” it generates the corresponding output


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Development Stage Example 2

  • Imagine training data has one of two classifications (Yes and No)

  • Network is trained for the Yes classifications and a snapshot is taken of the neural network.

  • Network then trained for the No classifications and another snapshot is taken.

  • Output is Yes or No, depending on whether the inputs are more similar to the “Yes” or the “No” training data.


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Development Stage Example 2

  • ARTMAP

Imagine array of weights, one for each “template”

Template closest to input chosen.

Weight

Height

Patient Benefits?

Gender

Blood Pressure

Path of “least resistance” chosen for output.


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Clinical Trials Stage

  • Company tests drugs in actual patients on larger scale

  • Must keep track of data about patient progress

  • Government wants to protect health of citizens, many rules govern clinical trials

  • In USA, Food and Drug Administration oversees trials.


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Clinical Trials Stage

  • Data mining techniques used

    • Neural networks


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Clinical Trials Stage

  • Data is collected by pharmaceutical company but undergoes statistical analysis to determine success of trial

  • Data reported to FDA inspected closely. Too many negative reactions might indicate drug is too dangerous – these are “adverse events”

  • Adverse event might be medicine causing drowsiness

  • Data mining performed by FDA, not as much by pharmaceutical companies


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Clinical Trials Stage Example 3

  • Goal: Detect when too many adverse events occur or detect link between drug and adverse event

  • Why: Too many adverse events linked to a drug might indicate drug is too dangerous or health of patient is at risk

  • When:

    • As adverse events are reported to FDA

    • Or when link is suspected


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Clinical Trials Stage Example 3

  • Is a drug causing “too many” adverse events?

  • We have number of reports of adverse events pertaining to drugs.

  • Feed these into neural network and let network lead us to what is “too many.”

  • Example derived from Data mining in the US Vaccine Adverse Event Reporting System (VAERS): early detection of intussusception and other events after rotavirus vaccination


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Clinical Trials Stage Example 3

  • Sample data – cells contain number of reports linking drug and adverse event


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Clinical Trials Stage Example 3

  • Uses Bayesian neural network

  • Prior probability is probability that any report contains reference to adverse event

  • Posterior probability is probability that report has link between drug and adverse event

  • Determines “strength” of link between adverse event and drug (called Information Component or IC)

  • More complicated than appears: patient may consume multiple drugs – which one caused adverse event?


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Clinical Trials Stage Example 3

  • Bayesian Neural Network

Adverse Event

Strength of link between adverse event and drug

Drug


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Clinical Trials Stage Example 3

  • Could be solved using Bayes Theorem and correlation techniques

  • Number of possible drug/adverse event combinations is very, very, large

  • Training data is from FDA, WHO databases

  • Neural network hides statistical complexity

  • Unfortunately details of NN like activation function and hidden nodes are unknown


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Data Mining Benefits

  • Research Stage – instead of trial and error, data mining can help find drugs that have desirable activity

  • Development Stage – data mining can help predict who will benefit from drug

  • Clinical Trials Stage – data mining protects patients and helps regulate drug testing

  • Commercialization Stage – data mining can optimize use of sales resources like manpower, advertising