1) The document proposes using an inter-comparison equivalence method to demonstrate substantial equivalence of new cigarettes to existing products by comparing variables and components through statistical analyses. 2) It describes using positive matrix factorization to target efficiency factors for ingredients and emissions in new cigarette design in order to reduce toxicity based on toxicological information. 3) The goal is to discover the most efficient ingredient and emission profiles through an iterative process of validation and feedback to design cigarettes with the same smoking pleasure but reduced health risks.

1. Demonstrating substantial EQUIVALENCE
of a new cigarette to the existing portfolio
Federico Karagulian

2. Proposing the INTER-COMPARISON
equivalence method (comparative analysis)
 Equivalence to cigarettes already marked
 Compliance with legislative requirements for new tobacco products
(USA, EU, Canada, etc.)
portfolio
new cigarettes
chemicals
components
effects
emissions
smoke chemistry studies
in vitro studies
toxicological studies
biological studies
R. Dempsey et al..Regulatory Toxicology and Pharmacology 61 (2011) 119–128

3. PORTFOLIO VARIABLES and COMPONENTS
Portfolio
products
portfolio variables (Pj) with components (pji)
and uncertainties (vji)
Pj =
pj1 pj2
pj3
pj4 pj5 ...
...
...
...
pjn
Uncertainty:
- standard deviation of the variable
- analytical uncertainty of the components
± Vj =
vj1 vj2
vj3
vj4 vj5 ...
...
...
...
vjn

4. NEW CIGARETTE VARIABLES and COMPONENTS
New
cigarette
new variables (Yj) with new components (yji)
and uncertainty (uji)
Yj =
yj1
yj2 yj3
yj4 yj5 ...
...
...
...
Uj =
uj1 uj2 uj3 uj4 uj5 ...
...
...
... ujn
yjn
Y1 =
y11
+
y12
+ ...................+ y1n
Y2 =
y21
+
y22
+ ...................+ y2n
Different cigarettes with the same variables (Yj) must be
compared with the portfolio variable (Pj)

5. Example for smoke chemistry studies
Example:
VARIABLE smoke (P1), present in all portfolio products, with
common chemical COMPONENTS (p1i)
Smoke = organic(a) + CO +...+ tar + nicotine + particle(a) +...+ gas(a)+...metals+..
P1 =
p11
+
p12
+
p13 + ...................+ p1n
How these COMPONENTS change in new cigarettes?
How to evaluate this change?
toxicological impact?

6. INTER-COMPARISON methodology I:
Pearson correlation (R2), p<0.0X
New
variables (Yj)
Portfolio
variables (Pj)
Correlation
correlation is made at components level (yji , pji )
The criterion of R2 = 0.6 is used to establish
if a variable is comparable to all the other
variables in the same portfolio category
New variables components
R2 = 0.85
R2
max
Orthogonal regression
Statistical significance p<0.01
portfolio components
1.0
1.0
0.6
0.6
0.0
0.0
NOT OK
OK

7. INTER-COMPARISON methodology II: Weighted difference
weighted comparison
Portfolio
variables (Pj) and
uncertainties (vji)
New variables (Yj)
and uncertainties (uji)
WDY j Pj
Weighted
difference
n
y ji − p ji
= 1/n∑
i =1
u 2 + v2
ji
ji
 uncertainties of the components are
considered in comparative analysis
 more robust assessment
WD
compared to Pearson correlation
Acceptability: from 0 to1
4.0
4.0
3.0
3.0
2.0
2.0
1.0
0.0
1.0
0.0
OK
NOT OK

8. INTER-COMPARISON methodology III: Z-score method
for new cigarettes’ perfomance
Defining the standard deviation for proficiency assessment ( σ p)
as criterion to evaluate new cigarettes’ performance (ISO 13528)
reference
(σ p = 50%,25%...)
z-score
z=
Y j − Pj
σp
 new cigarettes are considered coherent and satisfactory if:
z ≤2
“OK”
new cigarettes are considered questionable if:
2≤ z ≤3
“Warning”
 new cigarettes are unsatisfactory if:
z >3
“Action”

9. Z-score method: new cigarettes’ perfomances
Define a new assigned reference value (R) among new variables (Yj)
and portfolio variables (Pj)
R is generated by robust analysis iterative algorithm:
d = 1.5 s*
yi* = MED{ y ji }
{
s*j = 1.483 MED y ji − y*j
}
p ji ∈ Y j
if
y j,i > y*j + d
y ji = y + d
if
y j,i < y*j + d
y j,i = y j,i
otherwise
y ji = y − d
*
j
*
j
n
R = y*j = 1 n ∑ y j
j =1
(Analytical Methods Committee 1989a, 1989)

10. Targeting a new cigarette
New cigarette
Road map for product innovation
Same smoking pleasure
Reduced risk for health
or
reduced burning zone
v
ste
ta
fla
nd
a
co
new ingredients
ac
tob
l
(toxicological relevance)
ra
tu
na
he
pt
ee
k
reduced emissions
ting ?
e
t a r g t i ve s ?
How objec
e
hes
t
time

11. Typical cigarette combustion
(ingredients and emissions)
Cigarette + Air
Smoke
TS
D I EN
E
INGR
Combustion in
ideal conditions
Combustion in
real conditions
aCO2 + bH2O + cN2 + dO2
ONS
MISSI
E
onship
a
o lo g i c
Toxic
i
l relat
e(CO) + f(organics) + h(ash) + i(inorganic gases) + j(tar) + k…
t1
t2
t4
t5
t6
cigarette lifetime
Toxicological relationship

12. EFFICIENCY of INGREDIENTS and EMISSIONS
for targeting INNOVATIVE cigarettes
COMPONENTS EFFICIENCY
Efficiency
(ingredients and emissions)
tobacco
efficiency
tobacco
y12 / t1
......
y1i / ti
aerosols
y21 / t2
y22 / t2
......
...
additives
y31 / t3
y32 / t3
......
...
tar
y41 / t4
y42 / t4
......
...
y51 / t5
y52 / t5
......
...
.....
cigarette lifetime (ti)
=
y11 / t1
CO
componentsi
aerosols
efficiency
.....
.....
efficiency
yj1 / tj
yj2 / tj
......
yji / tj
How targeting INGREDIENT EFFICIENCY (IE) and
EMISSION EFFICIENCY (EE)
in order to reduce TOXICITY?

13. Targeting EFFICIENCY factors with
Positive Matrix Factorization (PMF)
lifetime
componentsi
efficiency
t1
t1
g11
g12
t2
g21
g22
t3
g31
g32
...
...
...
y33 / t3
...
EE
y23 / t2
t3
IE
y13 / t1
t2
lifetime
factors
...
2
source
(experimental
input data)
y ji /ti = ∑ f ki g jk + ε ji
k =1
receptor
(data analysis)
profiles
IE
EE
tar
f11
f12
aerosols
f21
f22
CO
f31
f32
nicotine
f41
f42
tobacco
f51
f52
additives
f61
f61
...
...
Minimization
of residuals for
targeting IE and EE
...
 ε ji 
min Q = min ∑∑  
 
f
f
j = 1 i = 1  u ji 
n
m
2
(Jiaying Wu et al. JEM, 2012)

14. Pulling components to simulate
best EFFICIENCY factor with reduced TOXICITY
target oriented data
profile
aux
2
QIE = (aEEi − f IEi )2 /s IEi
aux
2
QEE = (aEEi − f EEi )2 /s EEi
profile
target ingredients
and emissions for
orienting efficiency
toward desidered
outputs
target oriented data

15. Constraining factors for targeting single components
With Positive Matrix Factorization is possible to “pull”
Factors towards a desidered target
IE + EE
emission efficiency (EE)
mixed solutions
with pulling
ingredient efficiency (IE)
ingredient efficiency (IE)
no pulling
IE
EE
emission efficiency (EE)
separeted solutions

16. Overall EFFICIENCY for new cigarettes
total
efficiency
Discovery
= IE 2 + EE 2
Validation
EFFICIENCY
(toxicological info)
feedback loop:
take action on fundamental components

17. Conclusions
 Inter-comparison (comparative analysis)
has been proposed as method to prove substantial
equivalence with portfolio product
 Positive Matrix Factorization can be used as statistical
method for targeting EFFICIENCY in new cigarettes
for toxicological studies