1. Predicting treatment outcome: An examination of the mechanism of action of two pre-quit smoking cessation pharmacotherapies
Associate Professor Stuart Ferguson
School of Medicine
University of Tasmania

2. Declarations
SGF:
Has worked as a consultant for GSK & Chrono Therapeutic
Serves on an advisory board for Johnson & Johnson
Has received travel funding from Pfizer
Is involved with studies funded by Rusan Pharma
Chappell has nothing to declare

3. Helping Smokers to Quit
Most smokers want to quit
Most quit attempts fail
Outcomes of existing mono-therapies is disappointing
>60% of supported quit attempts fail
Advances can come from improving current methods
Understanding how treatments work may suggest improvements to use

4. Improving NRT: Preloading
NRT traditionally used for ‘abrupt cessation’
Stop smoking then start treatment
Patch preloading directly tested in 6 RCTs
1-2 wks pre-quit patch (active vs PCB / no patch); followed by UC
Tests the incremental effect of pre-loading
Meta-analysis: OR=2.17 vs standard patch use (Shiffman & Ferguson, 2008)
Mechanism of action unclear
Further improvements possible?

5. Rational for patch preloading
Dissociate nicotine levels from smoking
Reduce reinforcement
Satisfaction Reduced Smoking Cessation

6. Rational for patch preloading
Dissociate nicotine levels from smoking
Reduce reinforcement
Satisfaction Reduced Smoking Cessation

7. Satisfaction with a lapse predicts relapse
Shiffman, Ferguson & Gwaltney, 2006

8. Satisfaction with smoking predicts daily smoking rate: Single-group pilot study
During pre-quit patch treatment, satisfaction with smoking predicts daily smoking rate
Schüz & Ferguson 2014

9. Satisfaction with smoking predicts daily smoking rate … in this study
Figure. Mediation model in path diagram form. All coefficients with subscripts are the associations of the relevant treatment with the outcome (in direction of arrow). Standard errors are in parentheses, 95% confidence intervals in square brackets. The standard patch group experienced no significant daily reduction in satisfaction in smoking, and no significant reduction in cigarettes per day across the pre-quit period. Both the active treatment groups significantly reduced cigarettes smoked per day, independently of changes in satisfaction with smoking, as shown by the c’1 and c’2 pathways. In the pre-quit patch group there was a mean daily reduction of 0.38 cigarettes per day (95% confidence interval= ). In the varenicline group there was a mean daily reduction of 0.46 cigarettes per day (95% confidence interval= ). Both the active treatment groups also experienced a daily reduction in satisfaction with smoking, which had an additional small but significant effect on cigarettes smoked per day, calculated as a1*b and a2*b (an additional reduction in cigarettes per day of 0.01 (95% confidence interval= ) and 0.03 (95% confidence interval= ) in the pre-quit patch and varenicline groups, respectively). Therefore the total daily reduction in cigarettes per day associated with treatment is ci =ci’+ ai*b for each group i=0 to 2.
Only the c’ pathway has ‘apostrophe’.  The total effect of treatment on cpd is c=c’+a*b, for each group (groups marked with subscript i’s).  That is, c is the direct effect c’ plus the indirect effect a*b (via satisfaction).  The total effect, c, is not shown on the diagram.
I’ve updated the diagram, see attached, and added a sentence at the bottom to explain this.  I’ve also removed the e-boxes.  These are the errors associated with each mean effect (not quantified).  I copied the original diagram from a paper on mediation analysis using a sem framework, and they included these error boxes, so I put some in- but really don’t think it adds anything here… so we’ll remove them.
BTW, if you do the maths, the total effects you can calculate from the figures in the diagram are slightly different to the overall effects of treatment on cpd I gave you this morning.  This is because in the mediation analysis we used a (slightly) reduced set of subject-days, because we’re missing satisfaction records for those days.  In total we are missing 119/1855 satisfaction records, or 6% (of the subject-days for which we have cpd data).  I used the same set of subject-days for all paths in the mediation analysis for consistency.
The overall results are the same, though (as in magnitude of differences, differences between groups, significance).
Lu et al, 2017

10. Rational for patch preloading
Dissociate nicotine levels from smoking
Reduce reinforcement
Satisfaction Reduced Smoking Cessation

11. Reduction promotes abstinence … when trying to reduce
28 day abstinence; Groups based on >50% reduction in CPD & >25% reduction in CO
Shiffman, Ferguson & Strahs, 2009

12. Reduction promotes abstinence … when trying to reduce
RR = ~6.7
28 day abstinence; Groups based on >50% reduction in CPD & >25% reduction in CO
Shiffman, Ferguson & Strahs, 2009

13. Rational for patch preloading
Dissociate nicotine levels from smoking
Reduce reinforcement
Satisfaction Reduced Smoking Cessation
Similar mechanism proposed for varenicline
Opportunity for tailoring?
Target of new treatments?

14. Current study
Test effect of pre-quit treatment on satisfaction with smoking and daily smoking
H1: Effect of pre-quit treatment on reduction will be mediated via satisfaction with smoking (see: Lu et al, 2017)
H2: Pre-quit reductions in smoking will predict quit outcomes (TTFL)

15. PQT Study Three group, open-label RTC Pre-quit Patch (PQP; n=72)
Varenicline (VAR; n=72)
Standard Patch (SP; n=69)
Intensive, real-time monitoring (EMA)
2wks before & 2wks after TQD
CPD, satisfaction, withdrawal & craving

16. EMA Monitoring Protocol

17. EMA Monitoring Protocol
Symptom severity & satisfaction w smoking
Daily cigarette counts (w TLFB); Lapses

18. PQT Study 5 lab visits: TLFB, NNAL, CO, COT
Focus: Pre-quit treatment period …
But measured outcomes
Full Protocol:
BMC Public Health, doi: /s
ACTRN

19. Pre-quit treatment promoted smoking reduction
PQP group reduced by ~6 CPD on average
VAR group by ~7 CPD
SP: no change
Title "Modelled mean change in cigarettes per day over the entire 14-day pre-quit period, by treatment group".
~30% reduction from baseline levels
***
***
CO & NNAL results showed similar patterns
Lu et al, 2017

20. Effect of Reduction on Time to First Lapse
Reduction did not significantly improve TTFL:
RR=1.2 ( )
RR=~6.7 (Shiffman et al, 2009)
Similar results for slopes
Current study: Groups based on median split: Low < 32%, High >= 32%
Shiffman et al 2009: Groups based on >50% reduction in CPD & >25% reduction in CO

21. Effect of Treatment on Time to First Lapse
VAR vs SP: RR=1.5 ( )
SP vs PQP: RR=1.2 ( )

22. Summary Pre-quit treatment reduces CPD & satisfaction, but …
Reductions in CPD not related to TTFL
Inconsistent w literature (e.g., Hughes & Carpenter, 2006)
Sample size / power issue?
TTFL vs abstinence outcomes
Need to explore other mechanisms of action
Changes in stimulus control
Understand of mechanism of action may improve outcomes with pre-quit medication use

23. Acknowledgements Collaborators
Dr Monica Lu, Katherine Chappell, Dr Natalie Schüz, Gudrun Wells
Dr Kelly Clemens (UNSW; animal studies)
BSRG staff & students
Funding bodies
Pfizer (GRAND)
RHHRF