How PTI-TOST, Control Strategy Principles and Acceptance Sampling Consensus

Standards can Work Together to Achieve an Appropriate Quality Assessment Strategy of Delivered Dose Uniformity of OIPs

INTRODUCTION

In 2005, the FDA proposed a parametric test procedure for

assessing the adequacy of the delivered dose uniformity for

orally inhaled products (OIPs). Despite being a more

desirable parametric test it has had little uptake amongst

pharmaceutical companies. There are at least two possible

reasons for this: first, more fit-for-purpose product will be

falsely rejected and hence, will not be available to the patients; second, the new test has an explicitly defined

minimum quality standard that is more stringent than the

minimum quality standard implied by the registered, and

deemed suitable, pharmacopoeial specifications. Both of

these reasons have interesting corollaries: either the fixed

quality standard should be redefined on a product specific

basis to minimize the risk of falsely rejecting fit-for-purpose

product and falsely accepting not-fit-for-purpose, or the

underlying explicit quality standard is actually acceptable and

complete understanding of acceptance sampling theory and

practices needs to be fully utilized and integrated in the

construction and implementation phases of the new test.

The purpose of this poster is to show that a DDU acceptance

sampling strategy could be developed that would incorporate

the desirable parametric properties of the PTI-TOST, control

strategy principles, standard acceptance sampling theory

and practices outlined in already existing consensus

standards to ensure appropriate quality assessment of DDU

for OIPs.

Helen Strickland1, Lee Clewley2 and DDU Working Group 1GSK, Zebulon, USA, GSK, London, UK

FRAMEWORK

Isolated Lot Inspection – A transactional Assessment and

constructed to have at least a 95% probability of rejecting any batch

whose true quality (i.e., batch characteristics) is at or above a pre-defined limiting quality limit (LQL).

Implies that only the information obtained from the sample is

used to infer whether or not a batch meets its critical

requirements (i.e. does the batch meet the DQL at which the DDU characteristic is fit-for-patient purpose).

Transactional testing or isolated lot testing does not allow for the

use of prior process knowledge or the appropriate use of relevant historical process data.

Lot-by-Lot Inspection - Constructed to have at least a 95%

probability of accepting batches from a process whose true quality

(i.e., average process characteristics) is at or less than a pre-defined acceptance quality limit (AQL).

Essential to aggregate Assessment

Lot-by-lot testing is performed with strict adherence to additional rules for switching to other sampling plans.

Tightened acceptance sampling criteria or criteria for

discontinuation of production until corrective action is in place) if deterioration in quality occurs

Switching to a sampling plan with less producer risk is allowed if

the demonstrated process quality is exceptionally better than the AQL

Simulations of Consensus Standard Acceptance Sampling Systems Integrated

with PTI-TOST to form part of DDU Control Strategy for OIPs

Normal Density Curves Representing Stated DQL Delivered Dose Distributions



DD Distribution BPTI-TOST False Reject Rate=0.1%

DD Distribution A

PTI-TOST False Reject Rate =99.6%

CURRENT SITUATION

BACKGROUND

The PTI-TOST was constructed to ensure meeting a specified Desired Quality Level (DQL).

DQL explicitly set such that the delivered dose distribution of

fit-for-purpose product contains no more than 6.25% of

doses outside the 80-120% label claim interval in either tail

of the distribution and that the average LC dose is between 85-115%

The PTI-TOST construction automatically sets the limiting

quality level to the DQL, and implies at least a 95% false reject rate for product defined as fit-for-purpose. Two PTI-

TOST constructions below illustrate the challenge: (left) a

coverage of 87.5%, 6.25% in each tail and a ‘typical’

population std. dev. of 13.04% which gives a false reject

rate of 99.6%; (right) coverage 99.1%, std. dev. 6% and

0.04% in the tails with a 0.1% false reject rate.

Delivered Dose Distribution

Represents Typical OIP

98 %LC Process Mean

4% Between Batch Standard Deviation

6% Within Batch Standard Deviation

ISOLATED LOT Application

Batch

Sample

Size

Batch

Mean

%LC

Within Batch

Standard

Deviation

%LC

Individual

Minimum

%LC

Individual

Maximum

%LC

Lower PTI-

TOST Bound

%LC

Upper PTI-

TOST Bound

%LC

PTI-TOST

Bounds

Within 80

to 120

1 30 96.2 4.5 85.3 104.5 86.2 106.2 Yes

2 90 92.1 5.2 80.7 104.7 82.5 101.8 Yes

3 30 92.6 5.7 81.5 104.8 80.0 105.3 Yes

4 30 101.8 5.4 87.9 111.1 89.8 113.8 Yes

5 30 98.5 6.2 83.9 109.5 84.8 112.3 Yes

Simulated Development Data Representing 5 Batches—PTI-TOST

LOT-by-LOT Application

Batch

Sample

Size

Batch

Mean

%LC

Within

Batch

Standard

Deviation

%LC

Individual

Minimum

%LC

Individual

Maximum

%LC

Lower

Traditional

Tolerance

Bound

%LC

Upper

Traditional

Tolerance

Bound

%LC

TI

Bounds

Within 80

to 120

%LC

# of

Doses

Outside

80 to 120

%LC

1 30 96.2 4.5 85.3 104.5 87.7 104.6 Yes 0

2 30 91.6 5.4 80.7 104.7 81.5 101.8 Yes 0

3 30 92.6 5.7 81.5 104.8 81.9 103.4 Yes 0

4 30 101.8 5.4 87.9 111.1 91.6 112.0 Yes 0

5 30 98.5 6.2 83.9 109.5 86.9 110.2 Yes 0

Simulated Development Data Representing 5 BatchesTI 30 Sampling Plan

Simulation notes:- Process Demonstrated Homogeneous Within Batch

Variation. Data combined to compute PTI-TOST (n=150 K value=1.800) on

Process—(84.3, 107.9) %LC. In addition, out of 150 units no results were

observed outside 80 to 120 %LC; therefore, 95% confident no more than

1.98% of the doses are below 80 %LC and no more than 1.98% of the

doses exceed 120 %LC (Binomial UCB on % Non-Conforming )

Routine Release using Continuous Verification

• Assess each batch individually using criteria that minimizes the

false reject rate of each individual batch that meets the DQL

requirements.

• Utilize Acceptance Control Charts (below) by defining Acceptable

and Rejectable Process Zones based on products estimated

within batch standard deviation for sequential sampling 10 + 30.

• If batch falls within Indifference Process zone, may use PTI-

TOST criteria to release batch.

Monitoring charts for the mean (left) and standard deviation (right)

n=10

Ba

tch

Mea

n, %

LC

85

88

91

94

97

100

103

106

109

112

115

Batch Number

1 6 11 16 21 26 31 36 41 46

n=10

Ba

tch

SD

, %

LC

0

3

6

9

12

Batch Number

1 6 11 16 21 26 31 36 41 46