Improving Adverse Drug Reaction Information in Product Labels - PowerPoint Presentation

Slide1

Improving Adverse Drug Reaction Information in Product Labels

PSI Conference

May 2017

Sally Lettis GlaxoSmithKlineSlide2

The

views and opinions expressed in the following

slides are those of the individual presenter and should not be attributed to any organization with which the presenter is employed or affiliated.

DisclaimerSlide3
Slide4

Outline

Current Labelling Practice

Current Labelling LimitationsThe problem to solveThe proposed solution

Presentation title

4Slide5

Current Labelling Practice

Product labels are intended to provide health care professionals with clear and concise prescribing information that will enhance the safe and effective use of drug products.

Presentation title

5Slide6

Current Labelling Practice: US Product Label

Quantitative approach used in the Adverse Reactions section

Incidence of ‘‘common’’ ADRs presentedCommon defined as an ADR that occurs at or above a specified incidence e.g. >=3%A comparator must be provided, except in exceptional circumstances.

Typically, a single ADR table is included; however, more than 1 can be included when the ADR profile differs substantially from one setting or population to another

Presentation title

6Slide7

Current Labelling Practice: EU SPc

ADRs (from RCTs) placed into frequency categories

Convention for classification: very common (1/10), common (1/100 to <1/10), uncommon (1/1,000 to <1/100), rare (1/10,000 to <1/1,000), and very rare (<1/10,000); (CIOMS III and V)

Comparator typically not included

Category based on crude incidence on drug. Studies included don’t need common comparator

For drugs for long-term use, there is no representative duration; in practice, studies of differing durations are combined together

Only in exceptional cases is more than 1 table included for different populations

Presentation title

7Slide8

Current Labelling Limitations: The issue with no comparator data in label

In label it is an ADR and is common

Heterogeneity? Appropriate to combine? Helpful for patient?If two similar drugs tested in different populations very different incidences with no comparator data to contextualise

Presentation title

8

Disease

Severity

Duration

Incidence on drug

Category

Incidence

on Comparator

X

Moderate

3

months

0.6%

Uncommon

-

X

Moderate

6 months

1.5%

Common

1.7%

X

Severe

12 months

6.3%

Common

3.3%

X

Pooled

3.0%

Common

2.6%

Y

Pooled

0.5%

Uncommon

-

X+Y

Pooled

1.0%

Common

-Slide9

Data presented is based on crude pooling of data across multiple studies: pooling data as if from a single study

Can lead to “Simpson’s Paradox”:

When studies combined, trend seen in the individual studies either disappears or is reversed

Why? Can result in an overall baseline risk that is different among treatment groups due to differing randomization allocations within a study and different study populations across studies

Differences that could affect the incidence include age, sex, race, medical practice, differing time on study

Common for reporting proportions with an ADR in labels

Not a new issue (

Chuang-Stein and

Beltangady

(2010))

E.g. Cochran-Mantel-

Haenszel

to produce a common odds ratio across strata

If used, applied in Integrated Summaries of Safety

Too complicated for Product Labels which revert to crude incidences

Current Labelling Limitations:

The issue with crude poolingSlide10

New treatment,

n/N (%)

Placebo,

n/N

(%)

Total patients in study

Phase 2 study

30/300

(10%)

10/100

(10%)

400

Phase 3 study

133/700

(19%)

67/350

(19%)

1050

Phase 3 study in refractory patients

200/500

(40%)

200/500

(40%)

1000

Incidence

proportion:

crude pooling

363/1500

(24%)

277/950

(29%)

2450

Current

Labelling Limitations: Example showing misleading incidence proportion from crude pooling

N = total number of patients in the group; n = the number in the group that experienced the event

The last row gives the impression the new drug has a beneficial effect, though the AE incidences are equal in each study.

If you were to do a statistical test -> p=0.007Slide11

New treatment,

n/N (%)

Placebo,

n/N

(%)

Total patients in study

Allocation

Ratio

Phase 2 study

30/300

(10%)

10/100

(10%)

400

3:1

Phase 3 study

133/700

(19%)

67/350

(19%)

1050

2:1

Phase 3 study in refractory patients

200/500

(40%)

200/500

(40%)

1000

1:1

Incidence

proportion: crude pooling

363/1500

(24%)

277/950

(29%)

2450

Current

Labelling

Limitations:Why did crude pooling go wrong?

N = total number of patients in the group; n = the number in the group that experienced the event

Studies with uniformly lower ADR proportions (e.g. Phase 2) have more subjects on new treatment than placeboSlide12

The problem to solve: So what do we do?

16Aug2016

CBI’s Pharmacovigilance Final Rule Summit on IND 2016

12

We can see what the issue is

How should we present adjusted proportions?

Conclusions from crude pooling misleading

Meta-analytic techniques:

e.g. Cochran-Mantel-

Haenszel

weights

Not easy for non-statistician to understandSlide13

New treatment,

n/N (%)

Placebo,

n/N

(%)

Total patients in study

Proportion

of total patients in study

Phase 2 study

30/300

(10%)

10/100

(10%)

400

w

1

=

400/2450 (16%)

Phase 3 study

133/700

(19%)

67/350

(19%)

1050

w

2

=

1050/2450

(43%)

Phase 3 study in refractory patients

200/500

(40%)

200/500

(40%)

1000w3 = 1000/2450

(41%)Incidence proportion from crude pooling

363/1500 (24%)

277/950

(29%)2450

The Proposed Solution: A better way to go

Study-size Adjusted PercentagesSlide14

New treatment,

n/N (%)

Placebo,

n/N

(%)

Total patients in study

Proportion

of total patients in study

Phase 2 study

30/300

(10%)

10/100

(10%)

400

w

1

=

400/2450 (16%)

Phase 3 study

133/700

(19%)

67/350

(19%)

1050

w

2

=

1050/2450

(43%)

Phase 3 study in refractory patients

200/500

(40%)

200/500

(40%)

1000w3 =

1000/2450(41%)Incidence proportion from crude pooling

363/1500 (24%)

277/950

(29%)2450

Study-size-adjusted

incidence proportions

w

1

x

(30/300)

+

w

2

x (133/700) + w3 x (200/500) = 26%w1 x (10/100) +w2 x (67/350) + w3 x (200/500) = 26%

The Proposed Solution: A better way to go

Study-size Adjusted PercentagesSlide15

Comparison of Weights for New Treatment

New treatment

n/N (%)

Study Size

Weights

in Crude Pooling =

Proportion of total patients on

new drug

Weights in Study sized pooling = Proportion of patients in study

Weights

using CMH

a

j

= (n

1j

n

2j

)/ (n

1j

+n

2j

)

Study 1

30/300 (10%)

400

w

1

=

300/1500

20%

w

1

=

400/2450

16%

w1 = a1

/aj13%

Study 2133/700 (19%)1050

w

2

= 700/1500 47%

w2 =

1050/2450

43%

w

2

=

a

2

/

aj42%Study 3200/500 (40%)1000w3 = 500/1500 33%w3 = 1000/2450 41%w3 = a3/a

j

45%

N15002450w1 x (30/300) + w2 x (133/700) + w3 x (200/500) = 24%w1 x (30/300) + w2 x (133/700) + w3 x (200/500) = 26%w1 x (30/300) + w2 x (133/700) + w3 x (200/500) = 27%

15

Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are notSlide16

Comparison of Weights for Placebo

Placebo

n/N (%)

Study Size

Weights

in Crude Pooling =

Proportion of total patients on

placebo

Weights in Study sized pooling = Proportion of patients in study

Weights

using CMH

a

j

= (n

1j

n

2j

)/ (n

1j

+n

2j

)

Study 1

10/100 (10%)

400

w

1

=

100/950

11%

w

1

=

400/2450

16%

w1 = a1/a

j13%Study 267/350 (19%)

1050w2

=

350/950

37%w2

= 1050/2450 43%

w

2

=

a

2

/

a

j

42%Study 3200/500 (40%)1000w3 = 500/950 53%w3 = 1000/2450 41%w3 = a3/aj45%

N

950

2450w1 x (10/300) + w2 x (67/350) + w3 x (200/500) = 29%w1 x (10/100) + w2 x (67/350) + w3 x (200/500) = 26%w1 x (10/100) + w2 x (67/350) + w3 x

(200/500) =

27%

16

Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are notSlide17

Current labelling limitations: The Issue with “Rule of 3”

If ADR not observed in clinical trials but determined to be causally related post authorization based on spontaneous reports, ‘‘Rule of 3’’ used to estimate category using sample sizes from clinical trials

If X is number of patients exposed to drug in all relevant clinical trials, then frequency category would be 3/X, the upper limit of a 95% CI for the true incidence proportion of the event in question

This method estimates the incidence proportion by the upper end of a range of plausible values for the incidence proportion.

By doing so, ADRs that were never reported in clinical trials can be assigned to a frequency category that is higher than the category for ADRs that were reported in clinical trials.

Anomalies arise when AE was observed in clinical trials at same or lower incidence than placebo and so not considered ADR. Subsequently included as ADR based on spontaneous reports. Frequency?

Presentation title

17Slide18

That product labels that include comparator data be changed to include adjusted incidence proportions (or rates) when needed for adverse drug reactions (ADR) that are somewhat common.

Product labels better reflect the risk of a drug relative to a comparator

Not needed if:

The ratio of patients receiving the new drug to that receiving the comparator is approximately the same across all the studies included or

Incidences of AEs in the comparator group are nearly the same across the studies

If crude pooling be sure to look at the individual study results to check that pooled results are consistent with the individual studies

Including comparator incidence in product labels gives health care providers and patients appropriate information to put the absolute risks in perspective

Recommendations Slide19

References

Crowe, B., Chuang-Stein, C.,

Lettis, S., & Brueckner, A. (2016). Reporting Adverse Drug Reactions in Product Labels.

Therapeutic Innovation & Regulatory Science

, 50(4), 455-463

.

Chuang-Stein, C., and

Beltangady

, M. (2010). Reporting Cumulative Proportion of Subjects With an Adverse Event Based on Data From Multiple Studies.

Pharmaceutical Statistics, 10, 3–7.

16Aug2016

CBI’s Pharmacovigilance Final Rule Summit on IND 2016

19