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Drug Safety Assessment and Data Mining. F. Gavini (IRIS, Servier), G. Le Teuff (Keyrus Biopharma). Journées Maths-Industrie ENSAI 4 mai 2010. Plan. Drug Safety Assessment Knowledge Discovery in Database & Data Mining Use of Data Mining in Drug Safety Assessment An Example in Oncology.

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drug safety assessment and data mining

Drug Safety Assessment and Data Mining

F. Gavini (IRIS, Servier), G. Le Teuff (Keyrus Biopharma)

Journées Maths-Industrie ENSAI 4 mai 2010

slide2
Plan
  • Drug Safety Assessment
  • Knowledge Discovery in Database & Data Mining
  • Use of Data Mining in Drug Safety Assessment
  • An Example in Oncology
drug safety assessment
Drug Safety Assessment

Drug safety assessment is an important goal in the drug development and Post Marketing Surveillance (PMS)

It contributes to the balance of benefits and risks of the product

It consists generally in anticipating, assessing and minimizing the cases of adverse drug reactions

drug safety assessment4
Drug Safety Assessment

At each step of the drug development

Pre clinical

Phase 1, 2-3

PMS (Post Marketing Surveillance)

Mainly based on Spontaneous Reporting Systems

Specific studies

drug safety assessment pre clinical studies
Drug Safety AssessmentPre clinical studies

Objective : to assess toxicity in animal

Acute toxicity (LD50, …)

Subchronic toxicity (13 / 26 weeks)

Chronic toxicity and cancerogenesis

Reproductive testing

Statistical analysis

Limits

drug safety assessment phase 1
Drug Safety AssessmentPhase 1

Objective

Assess safety or toxicity (depending on compound)

Define therapeutic window / Maximum Tolerated Dose

Available data

Few volunteers (or patients) / dose escalating process

Thorough clinical assessment

Statistical analysis

Limits

drug safety assessment phase 2 3
Drug Safety AssessmentPhase 2-3

Objective : to assess safety of study drug in larger studies in patients vs. placebo or reference drug

General safety parameters

Adverse events (AE) / serious adverse events (SAE) , …

Biology / Biochemistry / ECG / Vital signs, …

Disease / class specific safety parameters

e.g. CV safety in diabetes, ECG in QT prolonging drugs, …

The AE are coded using dictionary MedDRA®

drug safety assessment phase 2 38
Drug Safety AssessmentPhase 2-3

Use of MedDRA® ( Medical Dictionary for Regulatory Activities)

drug safety assessment phase 2 39
Drug Safety AssessmentPhase 2-3

Statistical analysis (adverse events)

ICH descriptive tables (counts, crude incidence, incidence rate, …)

Number of AE (NAE) in a given primary system organ class or preferred term

Number of patients (n) with at least one AE in a given preferred term or a given primary system organ class

drug safety assessment phase 2 310
Drug Safety AssessmentPhase 2-3

PRIMARY SOC /PREFERRED TERM

Treat A(N=851)

Treat B(N=846)

NAE(1)

n(2)

%(3)

NAE(1)

n(2)

%(3)

ALL

82

76

9.0

74

62

7.3

Cardiac disorders

9

9

1.1

13

13

1.5

Myocardial infarction

4

4

0.5

6

6

0.7

Angina unstable

0

0

0.0

2

2

0.2

Cardiac failure

0

0

0.0

2

2

0.1

Cardiac failure acute

…/…

…/…

slide11

Drug Safety AssessmentPhase 2-3

  • Statistical analysis (adverse events)
      • Inferential statistic
        • Adjusted odds ratios (Logistic regression)
        • Time to first event (Log-rank / Cox model)
      • Number Needed To Harm (NNTH) …
  • Limits
      • Repeated statistical testing or confidence intervals
      • Trial size, event incidence, trial population
drug safety assessment integrated analysis safety ias
Drug Safety Assessment Integrated Analysis Safety (IAS)

Objective : to integrate all phase 2 and 3 trials

Analysis of safety : estimate safety across clinical trials

Adverse events / serious adverse events, …

Biology / Biochemistry / ECG / Vital signs, …

Statistical analysis

ICH descriptive tables / confidence intervals

Meta analysis vs naïve pooling

Limits

Updates

drug safety assessment pms
Drug Safety AssessmentPMS

Key questions

Which drugs (or combinations) induces which event ?

Which patients are likely to experience the event (and which replacing therapy then) ?

Definitions

Adverse drug reaction (ADR)

Spontaneous Report / Pharmacovigilance (PV)

Detailed Case Reports

PV Spontaneous Reporting Databases (SRD),

PV Spontaneous Reporting Systems

drug safety assessment pms14
Drug Safety AssessmentPMS

Databases

FDA Spontaneous Report System : PMS of all drugs since 1969

Data in public domain

FDA Adverse Event Reporting System (AERS)

Replaced SRS New AE coding system – MedDRA ® 97

Others : VAERS / Medical Devices databases

WHO (World Health Organization) database

Including drugs marketed outside US 67 countries

National databases, …

drug safety assessment pms15
Drug Safety AssessmentPMS

Adverse events database limitations

No protocol research

No denominator

Under-reporting in general / linked with drug and event

Errors / Missing data / Duplicates

Report rates change over time - Multiple drugs, multiple events, …

Causal links ?

drug safety assessment risk management plan rmp
Drug Safety AssessmentRisk Management Plan (RMP)

Objective

Recently, Health Authorities suggest the laboratories to conduct a Risk Management Plan throughout the lifetime of a medicinal product

Guideline on risk management systems for medicinal products for human use. EMEA/CHMP/96268/2005

This plan includes the pre-authorisation phase

knowledge discovery from database and data mining
Knowledge Discovery from Database and Data Mining

With the growing of database, « classical » analysis of data become more and more difficult

Problematics are more and more complex

« The curse of dimensionality », [Bellman]

Emergence of a new concept : KDD and Data Mining

International Conferences on KDD and DM (since 1995)

Data Mining and Knowledge Discovery Journal (1997)

knowledge discovery from database and data mining18
Knowledge Discovery from Database and Data Mining

KDD was initiated in the early 90’s [Piatteski-Shapiro]

Concept

«The notion of finding useful patterns (or nuggets of knowledge) in raw data has been given various names, including knowledge discovery in data bases, data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing» [Fayyad et al., 1996]

Objective

In practise, making decisions and discovery new knowlegde

knowledge discovery from database and data mining19
Knowledge Discovery from Database and Data Mining

Definition

The term « Data Mining » has been used by the statisticians, data analysts

While the term « KDD » has been mainly used by the searchers in artificial intelligence and automatic learning

Convergence from multiple domains

Database, Data Analysis, Statistic, Neural Networks, Visualization, Automatic learning

knowledge discovery from database and data mining20
Knowledge Discovery from Database and Data Mining

Steps of KDD [Fayyad et al.1996] from data to knowledge

Acquisition of data

Creation of target data set

Data cleaning and preprocessing

Data reduction and projection

Definition of tasks

Choose of appropriate algorithms

Data mining

Mined patterns (interpretation)

Test and validation of knowledge discovery

knowledge discovery from database and data mining21
Knowledge Discovery from Database and Data Mining

Data Mining versus statistical analysis

  • Data Mining
    • At the origin, type of approach: expert system
    • Several techniques
    • Decisional use
    • Few hypotheses on the data
    • Large database
  • Statistical analysis
    • Protocol and pre-specifications
    • Sampling representativity
    • Hypotheses testing
    • Model assumptions
    • Goodness of fit assessment
    • Not adapted to deep exploration of highly multi-dimensional database
knowledge discovery from database and data mining22
Knowledge Discovery from Database and Data Mining

What types of problems ?

Classification

Prediction

Association

With what method ?

Statistic or not statistic

With or without a priori hypotheses

knowledge discovery from database and data mining23
Knowledge Discovery from Database and Data Mining

Overview of the techniques

Kmeans

Neural network

Association rules

Decision tree

use of data mining in drug safety assessment introduction
Use of Data Mining in Drug Safety Assessment: Introduction

Data Mining is now recognized as a complementary approach by regulatory agenciesin pharmacovigilance

Eudravigilance expert working group (EV-EWG). EMEA/106464/2006 rev.1. 2008

More recently, Health Authorities invite laboratories to use data mining and require a risk management plan (RMP)

This plan should be conducted as a continuing process throughout the lifetime of a medicinal product, including the pre-authorisation phase

data mining pms
Data Mining & PMS
  • Signal detection / WHO

‘Reported information on a possible relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously’

  • Role
    • Change in specific drug-event reporting pattern
    • Comparison of drug-event reporting vs other drugs (same class)
slide26
Frequentist methods

Measure of Signal disproportionate reporting :

Reporting Ratios and Proportional Reporting Ratios

Measures of discrepancy :

CHI-2

Kullback-Leibler

Data Mining & PMS

slide27
Two-way drug-AE table

Observed

Expected

True

Drug 1

Drug 2

Drug C

All Drugs

Event 1

n11

n12

n1c

n1.

Event 2

n21

n22

n2c

n2.

nij

Event r

nr1

nr2

nrc

nr.

All events

n.1

n.2

n.c

n..

Data Mining & PMS

= marginal probabilities

slide28
FREQUENTIST

RR and PRR

Estimates

Data Mining & PMS

Evans 2000

slide29

Data Mining & PMS

FREQUENTIST

  • Definition of Signal / (unexpected large cell)
  • Discrepancy

Vs.

slide30
BAYESIAN SHRINKAGE METHODS

Bate and al. 1998 Bayesian confidence propagation by neural network (BCPNN)

DuMouchel 1999 Empirical Bayes Gamma-Poisson Model (EBGM)

DuMouchel and Preibon 2001 Multi-item Gamma-Poisson Shrinkage (MGPS)

Data Mining & PMS

slide31
BCPNN METHOD

Information component

Model assumptions

Parameters / prior distributions

Data Mining & PMS

slide32
BCPNN METHOD

Posterior distr. of all p follow a Beta distribution

Etimate

Variance (CI) based on posterior distribution

Data Mining & PMS

  • Thresholds
    • - Lower 95%CI bound >0
    • - Sudden increase of 1 over 3 months
slide33
EXAMPLE

Bates & Evans 2009

Cerivastatin-rhabdomyolysis

WHO T4 1998 IC: 1.90 - 95% : [0.44-3.36]

WHO T1-T2 99 IC: 1.88 to 3.30

Data Mining & PMS

slide34

Data Mining & PMS

Empirical Bayes Gamma-Poisson Model (EBGM)

  • Parameter of interest
  • Prior
  • Posterior
  • EBGM05 5% lower bound > 2 Signal
  • Stratification
data mining clinical safety
Data Mining & Clinical Safety
  • Just at its starting point
  • Literature
  • Even if databases are not very large
  • Need in Clinical trials / Safety assessment
  • alternative to descriptive / repeated tests on hundreds of PT
  • Visual needs
data mining clinical safety36
Data Mining & Clinical Safety
  • Visual Data Mining

- identify patterns / rules for patients with AE

Algorithms / rules

Overlearning assessment

- Use of several dimensions of safety

(AE, biology, biochemistry)

data mining clinical safety37
Data Mining & Clinical Safety

Use medDRA hierarchy / hierarchical models

Use of multiple decision trees

Appropriate use of Neural networks

 Southworth & O’Connell 2009

Use of bayesian networks e.g. oncology phase I

an example in oncology
An Example in Oncology
  • Objective
    • Find the maximum tolerated dose (MTD),
    • and establish the recommended phase II dose of chemotherapy
an example in oncology39
An Example in Oncology
  • Terminated trials, +new protocols
    • 4 Dose-escalation Phase 1 trial of an IV administration of X in liquid / solid tumors
    • Prospective, non-randomised, non-comparative, open-label studies
    • A traditional Carter algorithm-based design : ‘3+3’ design as shown in the figure below
    • Dose Limiting Toxicity (Thrombocytopenia)
an example in oncology40

Dose Level i + 1

An Example in Oncology
  • Design

Dose is the MTD

Dose Level i

 2/6

 2/3

Total DLTs ?

1/3

Dose 3 more patients

Dose

3 patients

DLTs ?

0/3

=1/6

Dose is safe

Dose is safe

an example in oncology41
An Example in Oncology
  • Population set
    • N= 105 patients from 4 Phase 1 studies (N1=18, N2=30, N3=15 and N4=42)
    • With advanced solid / liquid tumours
    • Dose ranging : from 20 to 80 mg/m²by 10
    • Outcome (target variable): toxicity (0/1)
    • Covariates: dose, age, type of tumor (solid, liquid), nb of days off in cycle, platelets at baseline, race
an example in oncology42
An Example in Oncology
  • Covariates

dose,

age,

type of tumor (solid, liquid)*,

nb of days off in cycle (1 vs. 2)*,

platelets at baseline,

Race (3 races)

*depends on to study

an example in oncology43
An Example in Oncology
  • Statistical analysis
    • Conventional analysis by trial
    • Bayesian network: use conditionnal probability

Continuous covariates will be categorized

    • New protocols : help for decision making
an example in oncology44
An Example in Oncology
  • Bayesian networks
  • Graph theory and probability theory
  • Bayes theorem:
  • Use of discrete variables
an example in oncology45
An Example in Oncology
  • Oriented graph
    • Nodes : variables
    • Oriented graphs : condition dependances
  • Conditional independance:

Chemotherapy is independant of ‘Smoking’ conditional to Cancer=‘Yes’

Smoking

Lung Cancer

Chemo

an example in oncology46
An Example in Oncology
  • Probabilities associated to nodes conditional to parents
  • Find all joint and conditional probabilities

Lung Cancer

Chemo

Smoking

P(Smoking)

P(Cancer|Smoking)

P(Chemo|Cancer)

an example in oncology47
An Example in Oncology
  • Bayesian networks
    • Bayesian network: use conditional probability

Supervised Networks Learned from data

      • Naive approach
      • Markov approach

Supervised by Expertise

      • Expert approach

Network Propagation

discussion conclusion
Discussion / Conclusion

Data Mining as an alternative to conventional statistical analysis

Large use in Pharmacovigilance

First steps in clinical development

 A need for sharing experience

references
References
  • Eudravigilance expert working group (EV-EWG). EMEA/106464/2006 rev.1. 2008
  • Guideline on risk management systems for medicinal products for human use. EMEA/CHMP/96268/2005
  • Data mining for signals in spontaneous reporting databases: proceed with caution. P.S. Wendy, Hauben M. Pharmacoepidemiology and drug safety. 2007; 16: 359-365
  • Data mining and statistically guided clinical review of adverse event data in clinical trials. Southworth H, O’Connell M. Journal of Biopharmaceutical Statistics. 2009; 19: 803-817
  • Knowledge Discovery Nuggets: http://www.kdnuggets.com
references60
References
  • The Data Mine: http://cs.bham.ac.uk/~anp/TheDataMine.html
  • Mailing list: http://www.kdnuggets.com/
  • Knowledge Discovery Nuggets Directory: Data Mining and Knowledge Discovery Ressources
    • http://www.kdnuggets.com/index_kdm.htm
  • ACM Special Interest Group (SIGKDD) home page
    • http://www.acm.org/sigkdd
  • Data Mining and Knowledge Discovery Journal
    • http://www.wkap.nl/jrnltoc.htm