risk assessment models for plasma products and blood components

Plasma Products division IPFA/PEI 17th Workshop, 26-27 May 2010

09-01-2012

Risk assessment models for plasma products and blood components

Jan Over


Outline of presentation

• Viral risk ‘models’ for blood components (single donor)

• Viral risk assessment models for plasma products (large pools): - deterministic model - probabilistic model

• Aspects of the probabilistic model for discussion

•  Future of the probabilistic model


Virus transmission risk of a blood product …

…… is the probability that an infectious virus particle (HIV, HCV, HBV, HAV or parvo B19) is present in a final container of blood product


Infection risks of the Dutch blood supply

Single-donor (or small-pool) products….


Infection numbers per 100,000 donors


Infection numbers per 100,000 NL population / donors


Residual HIV, HCV, HBV and HTLV risk of Dutch repeat blood donors (year 2008)

---------------------------------------------------------------------------------------- HIV 1/2 HCV HBV * HTLV I/II (HAV)

----------------------------------------------------------------------------------------

mean incidence 1) 2.7 0.0 32.8 2.7 (0.6)

probability 2) 0.1 0.0 5.2 0.4 (0.1) ---------------------------------------------------------------------------------------- * corrected by a factor of 3 for not detecting HBsAg peak 1) mean incidence number per million donor-years 2) probability of collecting an infectious donation per million donations


Risk modelling for single-donor (or small-pool) products ?

In the absence of the pooling of a large number of donations and of many processing steps there is not much to model: a product produced from 5 donations has about a 5 times higher risk….


European regulation on viral risk of plasma products (1) Since 2004: • Guideline on Assessing the Risk for Virus Transmission

(CPMP/BWP/5180/03*) (new chapter 6 to NfG on Plasma-derived Medicinal Products (CPMP/BWP/269/95) (effective April 2005):

“The risk assessment should, wherever possible, include a quantitative estimation of the probability of a virus contaminant being present in a defined dose of final product.”

(Given the uncertainties in much of the required information) “.. realistic worst case scenarios should be considered…”

* Rev. 4, draft of February 2009


European regulation on viral risk of plasma products (2)

• Main elements of the risk assessment: - quantitative estimation of probability of the risk - additional parameters to take into account (comprehensive approach; not just log reduction factors by process steps)

•  ‘New’ factors to take into account: - virus epidemiology in the donor population - virus titer in undetected donations - donation frequency - inventory hold - batch size - product yield - etc.


European regulation on viral risk of plasma products (3) Some issues:

•  what is a realistic worst case scenario? pool contaminated?, smallest pool?, highest undetected virus load possible?, lowest product yield?, largest possible donation volume?, others?, a combination of all those?

•  no specification of acceptable contamination risk of finished product < 1 per 106 final containers is contaminated? , < 1 per 104 or 103?

•  number of virus particles per infectious dose is it 1 in all cases?

•  probabilistic analysis instead of the conventional deterministic analysis? probabilities to be used instead of point estimates?


Present-day practice of viral risk assessment

Deterministic analysis, using point estimates for:

-  prevalence of undetected (rounded) average number, infectious donation: but at least one *

-  viral load: highest undetected titer *

-  vol. of infectious donation: largest volume possible *

-  plasma pool size: smallest size *

-  process reduction factor: average (of all) individual factors

-  process yield: average

-  product filling size: largest filling *

* worst case


Calculation of deterministic risk C1-esterase inhibitor (Cetor®) and HAV

- HAV incidence: 1 : 20,000

- Plasma batch size: 6,400 L (20,000 don.)

- Undetected load of HAV (1 x 320 ml x 27.0 IU/ml x 576): 5.0 x 106 IU

- Number of vials per batch: 3,900

- HAV-RNA per vial prior to virus reduction: 103.1 IU (1.3 x103 IU)

- Total virus reducing capacity of the process: > 10.5 log10

- Residual HAV-RNA in 500 U vial < 10-7.4 IU


Deterministic or probabilistic analysis ?

Disadvantages of deterministic analysis:

- worst case for a number of parameters - (too) many simplifying assumptions - some parameters / interactions not taken into account - no insight in spread in risk estimate - no insight in sensitivity and interaction of parameters

A probabilistic analysis provides a more comprehensive answer …..


IPFA Expert Group on Risk Assessment Methodologies

Aims:

•  comparing existing risk assessment models between IPFA members (completed)

•  evaluation of all models (including worst-case approach) by calculating the risk of two hypothetical products for two viruses (completed)

•  identifying the best elements of all models (completed) and (preferably) combining those into more harmonised model(s)

•  discussion of outcomes and questions with other parties


Development of probabilistic viral risk analysis (1) * Assumptions:

- viral incidence rate is constant (HIV, HCV, HBV, HAV) - viral replication modeled by doubling time - probability of detection is dependent on contamination level - trailing window has no impact on viral risk - virus particles distribute evenly over final product - first-in, first-out principle followed - no GMP failures and testing errors

Use of real production data: - viral incidence, donation intervals, donation volumes (recovered/source plasma), pool sizes, inventory hold, product yield

* M.P. Janssen et al., Transfusion 48: 153-162, 2008


Development of probabilistic viral risk analysis (2)

Analysis in two stages:

1)  viral risk of production pool (calculation of probability that the pool is contaminated and calculation of the load of viral contamination)

2)  contamination of finished product (starting from a contaminated pool)


Model simulation

Manufacturing pool (size and composition)

Derive plasma collection time

Simulate contamination of the production pool (titre and probability)

Manufacturing information

Donation frequency, size, type

Inventory hold

Virus reduction Calculate probability of

contamination of final container

Product yield

Pool fraction used

Virus characteristics

Manufacturing time Test characteristics

Monte Carlo simulation

Number of donors

capacity of process




Risk of a hypothetical product (1): calculation of contamination risk


Risk of a hypothetical product (2): sensitivity analysis


Probabilistic (median) risks are lower than deterministic risks

• HIV 2.1 log lower risk

• HCV 2.0 log lower risk

• HBV 0.5 log lower risk

• HAV 1.6 – 2.0 log lower risk

•  parvo 1.6 log lower risk


Virus titre modeling: HIV

* Viral doubling time: 0.85 days * NAT pool size: 48 donations * Test characteristics: X95 = 25 gEq/ml

X99.9 = 75 gEq/ml


Probability of parvovirus B19 contamination of final container of Cohn fractionated albumin


Pros and cons of the probabilistic model (1)

Advantages:

- more comprehensive, fewer assumptions, scientifically more sound - avoids unrealistic outcomes caused by addition of worst-case assumptions - provides insight in level of uncertainty - provides a sensible estimate for the worst-case risk - reveals which parameters and interactions are the most influential - predicts the effect of potential additional measures (cost-effectiveness)


Pros and cons of the probabilistic model (2)

Disadvantages:

- some assumptions are still necessary

- outcome is a probability density function of the product vial risk

- complex model: not easy to calculate, to understand and to communicate !


The full-blown probabilistic calculation procedure

Model parameters:

- Incidence rate - Donor type

- Donation frequency distribution

- Virus doubling time - Test sensitivity - Test pool size

- Inventory hold period - Production pool size

and composition - Product yield

- Process viral reduction capacity

Monte-Carlo simulation

Model outcome:

Sampling of variability in model parameters

to obtain model outcome


Approximation of the probabilistic calculation procedure

Model parameters:

Incidence rate (A) Donor type (B)

Donation frequency distribution (C)

Virus doubling time (D) Test sensitivity (E) Test pool size (F)

Inventory hold period (G) Production pool size and composition (H)

Product yield (I) Process viral reduction

capacity (J)

Meta Model

Model outcome and

approximation:

ƒ(A,F,G,I,J)

Mathematical equation providing model outcome

as function of input parameters


Remaining questions

•  Should we calculate (and present) the absolute risk or the conditional risk?

•  Criteria for product safety: what is “an adequate safety margin”? What level of inactivation/removal capacity is “clearly exceeding” the potential virus input?

•  How many genome equivalents/virus particles constitute an infectious dose?

•  Should the clinical dose be taken into account? •  Which parameters of the risk analysis are identical for every

manufacturer? (virus characteristics, clinical dose, calculation of risk….) •  Do we need a probabilistic model in all cases?


Conclusions

•  The probabilistic approach of assessing viral risk of plasma-derived products provides major advantages

• Process log reduction factor has the biggest impact on outcome

• Not all criteria are defined yet (e.g. acceptable safety margin)

• Harmonisation is highly desirable

• A (generic) simplification of the probabilistic model is feasible


Acknowledgements

- Julius Center for Health Science and Primary Care, University Medical Centre Utrecht, NL:

Mart Janssen

Cees van der Poel

- Sanquin:

Hans Zaaijer

Theo Cuijpers

- IPFA members

risk assessment models for plasma products and blood components

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