PainQx is a medtech company that uses EEG data and proprietary algorithms to objectively measure a patient's pain level. The PainQx system assesses neural activity in the brain's pain matrix and provides scaled pain and side effect biomarkers. These biomarkers directly correlate to a patient's pain state and can be used by pharmaceutical companies to more accurately measure drug efficacy in clinical trials by reducing subjectivity. PainQx provides quantitative reports on a patient's brain activity that identify regions of over/under activation compared to norms, allowing pain levels to be tracked over time and treatment responses to be evaluated objectively.

1. Alejandro Zamorano
PainQx
VP of Business Development
E: AZamorano@PainQx.com
M: 508.397.8440
O: 617.981.7753
www.painqx.com
Objective Quantification of Pain

2. Overview
PainQx is a med-tech diagnostic/ software company that has developed a next
generation approach to objectively measure pain in humans. The PainQx platform
achieves this by assessing neural activity from a patient’s brain and processing and
decoding the data through proprietary analysis and algorithms. The outputs are scaled
pain and neurological side effects biomarkers that directly correlate to a patient’s pain
state.
The PainQx system is currently being used as a clinical assessment tool by companies
using pain as a primary or secondary clinical end point to measure their drug’s and/or
device’s efficacy.
• Proven results in human clinical setting
• Experienced, world renowned scientific team
• Confirms the actual level of pain in chronic pain patients
• Solution to a vast unmet clinical research need
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How it Works
Pain
Score
6.0
START
***PainQx can use
the majority of EEG
devices on the
market as input to
our algorithms
PQX Technology
is the Advanced QEEG
Analysis of the Neural Data
in the Cloud

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Problem/Solution in Clinical Trials
PainQx solves the logic chain below by providing:
1. Objective Pain Measurement: Reduces clinical trial costs and risks
2. Scaled Pain and Neurological Side-effect Biomarkers: Improves data, research
outcomes; validates research products
Increased Risk/
Failure Rates of
Pain Drugs and
Treatments
Patient Reporting
Variability &
Subjectivity in
Trial Endpoints
Subjective Pain
Measurement
Large Patient
Sample Size to
smooth out
Variability
Uncertainty in
Reliability of
Clinical Data
Difficulty in
Screening
Placebo
Responders
Difficult to interpret
and determine
pain drug efficacy
Increased Trial
Costs where pain
is measured or an
End point
Issue Creates
Leads to Result

5. Partner Testimonial
“PainQx reduces clinical and regulatory risk. It is based on substantive,
human data and may transform the landscape for pain researchers as
well as patients with pain, by enabling novel and more effective pain
therapeutics to be delivered”. –Yuri Maricich MD MBA, VP of Business
Development and Neurology, Cavion Pharma
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6. PainQx assesses well-known, established brain neural network
correlated to pain also known as the “Pain Matrix*”:
Underlying Science
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Spinal
Cord
Inputs
Main regions involved in
experiencing pain
Pain Matrix
* Melzack R (Dec 2001). "Pain and the Neuromatrix in the Brain".J Dent Educ 65 (12): 1378–82. PMID 11780656
 Brain Regions and Brain Neural
Activity involved with Pain
 Captured by EEG & Neuro Imaging
 Analysis by PainQx technology
PainQx Proprietary Algorithms decode
the Pain Matrix providing an objective,
replicable method of quantifying pain.

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Core Technologies/ Competencies
NYU Brain Research Laboratory Normative Database (BRLND)
 PainQx has an exclusive license to the NYU Brain Research Laboratory
Normative Database for use in pain. The BRLND is a 20,000 patient
database of EEG data built over the past 30 years. The BRLND is the
largest of its kind in the world. EEG data has been shown to be culture,
ethnic, and gender free; but not age free.
Electroencephalogram Localization (EEGLx)
 EEGLx allows PainQx to process and decode electrical neural activity to
localize activated brain regions
 Creates a 3D representation of the brain representing over/under activated
regions relative to norms in the BRLND
Pain Matrix Correlation (PMCx)
 PMCx allows PainQx to filter out areas not correlated to the sensation and
perception of pain
 Areas of interest are isolated, identified, correlated, and weighted to produce
an objective measurement of a patient’s pain state

8. A normal brain looks different than a brain in
pain as seen using the PainQx technology.
Before
Surgery
After
Surgery
Patient Case Study:
Patient with chronic neuropathic
pain caused by nerve
compression in the spine.
Low Pain High Pain
Data: High & Low Pain State for an
Individual Patient
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Top Panel: Patient Before
Surgical Decompression
Over-activated brain regions
relative to norms correlated w/
Pain Matrix. Patient reported pain
score: [4 out of 10]
Bottom Panel: Patient After
Surgical Decompression
Low-activated brain regions
relative to norms correlated w/
Pain Matrix. After surgery, patient
reported pain score: [0 out of 10]
Prichep LS, John ER, Howard B, Merkin H, Hiesiger EM. Evaluation of the pain matrix
using EEG source localization: a feasibility study. Pain Med. 2011;12(8):1241-8.

9. 65 Patient study to compare
PainQx generated score
compared with VAS pain score
Inclusion Criteria:
 Male and Female chronic pain
patients ages 19 – 82
 All patients have a history of at
least 3 months of pain
 Patients were excluded with
neurological or psychiatric
disorders (including drug and
alcohol dependence), head
injuries or skull abnormalities.
 Demonstrates ability to clearly
differentiate the VAS scale by as
few as one point gradation
Regression (n=65) QEEG evaluation of Pain States w/ VAS
scores between 0-10: R=0.907, p<0.00001
Data: Generated PainQx Scores vs
Patient Derived VAS Pain Score
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VAS Pain Score
Publications: Prichep et al., 2015
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PainQxScore

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PainQx Value Proposition to Partners
Data Generated
PainQx provides quantitative measures of activity in different regions of the brain (Regions of
Interest, ROIs) which are involved in the sensation and perception of pain (i.e. the “Pain Matrix”)
 ROIs include: cingulate, insula, dorsolateral prefrontal and thalamic regions
Interpretation of Data
PainQx decodes the activation of the brain structures involved in the sensation and perception
of pain to provide a marker of this pain activation
 Changes in these markers can be tracked over time
 Normalization of activation of ROIs can provide a quantitative measure of positive response to a drug
 These markers can be used to identify optimal dosage levels of a drug
 Specific ROIs can be selected to facilitate testing of hypotheses related to drug targets
Extrapolation
Since ROIs include areas related to both sensation and perception, distinctive patterns can be
identified which are more closely related to different aspects of the painful response, which may
include:
 Comorbidities (e.g., depression)
 Placebo response

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Process for Generating a Pain Report
Patient EEG is
collected (10 mins
worth of data)
Collection Cloud Server
EEG data is sent to
PQX via secure
cloud
Clean Signal
PQX receives the
file and removes
artifacts
EEGLx
3D image of brain
and its activity is
created
Normative Database (BRLND)
Pain Matrix
Brain regions not
correlated to Pain
Matrix are filtered
out
PainQx Analysis
Activated brain regions are compared to
patients within their age group in the BRLND
to identify abnormal activity compared to the
“Ground State” of the brain
Regions of interest related to the Pain
Matrix are isolated, identified, correlated,
and weighted to produce an objective
measurement of a patient’s pain state
Patient
Pain
Report
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Deliverable
PainQx will issue a report which will contain quantitative measures and images
of the significance of over/under activation in brain Regions of Interest (ROIs):
 Provides an initial biomarker for patient status prior to therapeutic intervention
 Provides a quantitative method to track the significance of the change in the
biomarker (within specific ROIs) following intervention
 Individual Patient and Aggregate Report:
o EEG source localization images superimposed on an MRI atlas showing sequential
slices for the axial view of the brain, and 3 summary images (axial, sagittal and
coronal views of maximum abnormality) (Exhibit 1)
o Table of the mean z-scores for each ROI (where z=1.96, p<0.05), also shown are
standard deviation, minimal and maximal z value (Exhibit 2)
 Aggregate Report:
o Group average data can be similarly displayed for sets of patients at baseline and
after intervention (Exhibit 1)
o Significance of the changes for the group can be displayed as t-scores for the
significance of the change and displayed as above, along with tabular values for
the significance of the changes by ROI (Exhibit 3)

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Exhibit 1: Customer Deliverable
28 QEEG Images showing activation of pain matrix relative to normative
population. Colors demonstrate neural activity’s standard deviation from
the norm.
Under- Over-
Activation Activation

14. ROIs Brodmann Area Mean z Max z Min z
Posterior Cingulate 23, 29, 30, 31 2.57 3.04 2.01
L Inferior Parietal Lobule 39, 40 2.52 2.94 1.82
Post L Insula 13 2.50 2.77 2.06
L Superior Temporal Gyrus 22 2.49 2.90 1.95
L Parahippocampal Gyrus Hippocampus, Amygdala 2.45 2.70 2.27
L Precuneus 7 2.35 2.90 1.81
Post R Insula 13 2.30 2.80 1.56
Thalamus Pulvinar 2.29 2.56 2.05
R Inferior Parietal Lobule 39, 40 2.29 2.95 1.40
R Parahippocampal Gyrus Hippocampus, Amygdala 2.28 2.50 2.05
Table of quantitative values of mean z-score for each of the listed ROIs from
the previous image. A z-value ≥1.96 (red font) is significantly different from
normal at the p<0.05 level.
Exhibit 2: Customer Deliverable
Most
significant
ROIs
Brodmann
area of the
ROIs
Mean
Z-Score
• ROIs can include all regions, only significant regions, or specific regions of focus and can be
configured for particular use
• Intervention can be evaluated quantitatively as magnitude of changes in Z-scores – where
decreases indicate movement toward normal or positive change from baseline in the patient
Maximum
and
Minimum
Z-Score
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Exhibit 3: Customer Deliverable
Group analyses can also be made, statistically comparing Baseline (Pre-Drug)
and Post-Drug measures in each ROI
8.6 Hz
Region BA t-value p-vlaue
R Precuneus 7 -147.068 <0.0001
L Precuneus 7 -136.318 <0.0001
Mid Cingulate 31 -128.441 <0.0001
L DLPFC 46 -49.3934 <0.0001
Posterior Cingulate 30 -45.2018 <0.0001
Anterior Cingulate 25 -42.4683 <0.0001
R DLPFC 46 -4.74674 <0.0001
L Insula 13 64.6416 <0.0001
R Insula 13 38.9874 <0.0001
L Parahippocampus Hippocampus, Amygdala 29.4841 <0.0001
R Parahippocampus Hippocampus, Amygdala 27.0849 <0.0001
R Orbital-frontal cortex 11, 47 23.3455 <0.0001
Thalamus Anterior nucleus 17.1622 <0.0001
• Significant negative
t-values show
decreases in regions
over-activation at
baseline
• Significant positive
t-values show
increases in
activation in regions
under-activated at
baseline