SUPPLEMENTARY TEXT: RESULTS AND DATA FILE DESCRIPTIONS
Supplemental to: Discovering anti-platelet drug combinations with an
integrated model of activator-inhibitor relationships, activator-activator
synergies and inhibitor-inhibitor synergies
Federica Lombardi , Kalyan Golla, Darren J. Fitzpatrick, Fergal P. Casey, Niamh
Moran, Denis C. Shields.
SUPPLEMENTARY RESULTS
Model Fit and donor variability. Visualising residuals from the full model fit is
helpful in determining if there are any particular conditions that are markedly
unexplained by the model. Fig S5A indicates the mean residual across the ten
donors for each experimental condition. The spread of data points does not clearly
identify a particular subclass of reagent combinations as being particularly poorly
explained by the model, although the inhibition by epinephrine by all five inhibitors,
and its combined effects with the four other agonists (EaXa, EaTa, EaCa, EaAa)
showed the most marked departures from expectation. Overall, epinephrine (Ea) had
weak activatory effects and its Ei inhibitor yohimbine[39] had weak inhibitory effects,
which may explain why the model did not detect synergies involving this activatorinhibitor pair. It is possible that the doses of epinephrine defined in advance were
inappropriate for the particular donors in this study.
Platelet response to different activators or inhibitors is characterized by a high
degree of inter-individual variability. The factors that affect platelet response can be
environmental, such as cigarette smoke (Hung, et al 1995), caffeine (Varani, et al
2000), and alcohol (Mehta, et al 1987), or genetic, such as all the various
polymorphisms in the membrane receptors (Schafer 2003) (Lasne, et al 1997)
(Quinn and Topol 2001). In the case of genetic determinants the response across
single donors is consistent over time. Fig. S5B displays the residuals for each
experiment for each donor. Firstly, there is a more pronounced correlation of the
residuals with the raw data, which is to be expected, since the remaining variability in
individual assays consists of a combination of measurement error, and natural unmodelled variability among donors in platelet responses. Donor 6 had the largest
1
overall residuals, indicating responses that are least well explained by the model.
Thrombin stimulation is notably sensitive to a number of inhibitors in donors 6 and 7,
which was also highlighted by their pattern of joint clustering in Fig. 2. A second
feature seen in Fig. 2 is that thromboxane (Xa) activation of donors 3, 5 and 7 was
not inhibited by three inhibitors (Xi Pi and Ei), in contrast to other donors. Collagenrelated peptide (Ca) activation of donors 1,4 and 9 is much stronger than for others.
In contrast, donors 6 and 8 showed remarkably little activation in response to the
activatory combination of Ca and Xa (thromboxane), even compared to the two
agonists alone. it would be of interest to fit models allowing for such population
heterogeneity in responses, since there is an overall sense that there may well be
population variability not only in responses to agonists and antagonists, but also
variability in the nature of synergistic responses. This could have implications for
patient-specific responses to particular anti-platelet combination therapies. However,
much larger samples are likely to be needed to provide adequate statistical power for
such analyses.
2
Supplemental data files description.
Analysis code is given in two alternative statistical analysis environments, R and STATA. The same
results are obtained using either.
Analysis Program files:
R_code.r
STATA_code.do
Output files:
R_output.txt
STATA_output.log
Visualisation code (provided the initial heat-maps edited manually in Fig. 1):
Figure1.R
3
Input files (comma delimited):
Dataset_R_format.csv
Dataset_STATA_format.csv
Explanation of variables in input dataset:
first three variables are experimental data; additional variables are derived variables used in the analysis.
test
Experimental well descriptors for unactivated platelets (resting),
five activators ( A C U T E, equivalent to Aa Ca Xa Ta Ea in main paper),
their cocktail (K), and five inhibitors
(M W S B Y, equivalent to Ai Pi Xi Ti and Ei in main paper).
Name alone: single dose.
Name followed by "2": double dose (not used in linear modelling).
Name followed by 90: dose for ~90% activation (not used in modelling).
Donor identification.
ADP release measured in Arbitrary Absorbance Units (AAU).
name
var
A C U T
M W S B
K
rdadp
iMA iMC
iME iWA
iWT iWE
iSU iST
iBC iBU
iYA iYC
iYE
E
Y
Status for individual activators (0: absent from well, 1: present in well).
Status for individual inhibitors (0: absent from well, 1: present in well).
Status for activator cocktail (0: absent from well, 1: present in well) .
Rank of ADP level within each donor (ranges from 1 to 95).
iMU
iWC
iSA
iSE
iBT
iYU
aaAC aaAU
aaAE aaCU
aaCE aaUT
aaTE
iiMW iiMS
iiMY iiWS
iiWY iiSB
iiBY
modC modU
modM modS
enant
iMT
iWU
iSC
iBA
iBE
iYT
25 (5x5) inhibition of activator terms.
Coded 1 if the well contains both the inhibitor (first letter) and the activator (second letter).
aaAT
aaCT
aaUE
10 (n*(n-1)/2) activator-activator interaction synergy terms (coded 1 if both activators are present).
iiMB
iiWB
iiSY
10 (n*(n-1)/2) inhibitor-inhibitor interaction synergy terms (coded 1 if both inhibitors are present).
modT
modB
enantsyn
fake
predictedinhibitors
6 modified main effects, modified under the particular Boolean model fitted in supplemental table 3.
Indicator variable highlighting data to be used in the modelling of activator-inhibitor combinations.
(55 wells per donor including replicates: 10 main effects including 10 resting replicates,
10 cocktail(K) replicates, 25 inhibition of activator wells).
Indicator variable highlighting data to be used in the combined modelling of synergy and enantergy
(85 wells per donor, using wells in enant, plus 10 double dose wells, plus the 10 activator-activator
and 10 inhibitor-inhibitor synergy wells).
Indicates artificial data for predicting effects of all 32 possible inhibitor combinations.
Name of the 32 combinations of inhibitors to be used in predictions.
4