Download Evaluation and Treatment of Chronic Pain: Emphasis on

Clinical research obstacles and
opportunities in developing precision
pain medicine: An overview
Michael C. Rowbotham, MD
Scientific Director
CPMC Research Institute, San Francisco, Sutter Health
Adjunct Professor of Anesthesia, Emeritus Professor of Neurology,
UCSF
Attending Neurologist, UCSF Pain Management Center
IMMPACT June, 2016
[email protected]
Outline
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Biomarkers and Precision Medicine defined
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Pain biomarkers vs cancer biomarkers
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Are placebos reliable?
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Can placebo response impact be minimized?
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Lessons from the epilepsy field about subject
selection
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Pragmatic trials
Biomarkers Defined
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National Cancer Institute:
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“A biological molecule found in blood, other body fluids, or tissues that is a sign of a
normal or abnormal process, or of a condition or disease. A biomarker may be used
to see how well the body responds to a treatment for a disease or condition. Also
called molecular marker and signature molecule.”
Wikipedia:

Anything that can be used as an indicator of a particular disease state or some
other physiological state of an organism.”
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Can be a substance introduced into an organism to examine organ function or other
aspects of health. Can be a parameter (chemical, physical or biological) to measure
disease progress or treatment effects

Disease-related biomarkers indicate probable effect of treatment on patient (risk
indicator or predictive biomarkers), if a disease already exists (diagnostic
biomarker), or how such a disease may develop in an individual case regardless of
the type of treatment (prognostic biomarker).
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Drug-related biomarkers indicate whether a drug will be effective in a specific
patient and how the patient’s body will process it
The need for a PrM approach
Pain Biomarkers
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Therapy for chronic pain will never move forward if the 0-10 NRS
continues to be the primary outcome measure
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A PrM strategy for subject selection is desperately needed
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To maximize value of enriched enrollment studies
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To enable pragmatic trials where expert examiners not available to screen
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Surrogate measures of response that are objective are also
desperately needed
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A PrM approach will make it easier to compare trials within a category
or specific disease entity, for example by eliminating false equivalence
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Minor sports injury clinical trial - pain on entry 6/10

Chronic severe PHN clinical trial - pain on entry 6/10
Valuing biomarkers
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Best = low cost, easy to obtain serially, minimally invasive

Worst = expensive, equipment intensive, invasive and entail risk,
require experts to implement, rely on patient reports
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High value:
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Predicts individual response; low false positive and false negative
rates
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Reflects current state of patient with reasonably short lag time
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Can act as a surrogate outcome measure

Measuring propensity to develop chronic pain is of less value
compared to predicting response to treating ongoing chronic pain
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Positive correlation between a biomarker and drug response within a
group of patients is a much lower bar than predicting individual
response
Pain Biomarker Candidates
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Must be objective, not based on patient response
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Skin biopsy qualifies, but is only weakly predictive

fMRI and other types of brain imaging qualify but are costly and
logistically complex
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Phenotyping via QST /sensory exam, including provocative tests,
depend on patient response

Composite phenotyping approach will likely still include patient
response measures

Genomics and other omics, generating iPSCs, etc, are objective
but still in their infancy

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i.v. infusions help enrich populations but aren’t fully objective, add
risk and expense
Are we sure current trial cohorts are homogenous enough to use for
‘omics’ and other biomarker discovery/validation research?
Cancer Biomarkers

Almost all new treatments are targeted toward tumor-specific
abnormalities – mutations, ability to evade the immune system, etc
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Drugs are developed as biomarker - therapy pairs

Imaging, death, and progression-free survival are robust measures
for comparing therapies

Trials are often specific about response to prior therapy as an
inclusion/exclusion
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NCI-Match (Molecular Analysis for Therapy Choice) study shows the
strength and weakness of the approach
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Archived biopsy tissue sample analysis to determine eligibility
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Despite 24 treatment arms, only 23% of patients expected to
qualify
Precision Medicine Issues in Pain Trials:
Can we trust placebo controls when the
outcome measure is 0-10?

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Placebo increases in efficacy
Placebo loses efficacy
Response to Placebo - Long-Term Trials
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Placebo response does not stabilize - increases over duration of
study
Placebo response differs by condition
 DPN: 26% [11-35 ]
PHN: 15-16% [4-44]
Quessy and Rowbotham 2008
Placebo response in a multiple exposure design
Fedele et al, PAIN 1989
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Five period enriched enrollment design
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Initial cycle of dysmenorrhea treated with placebo (n=152, singleblind)
Responders to placebo (n=55) randomized to receive NSAID or
placebo for 4 subsequent treatment cycles
Cycle 1 placebo 84% NSAID 96%
Cycle 2 placebo 29% NSAID 83%
Cycle 3 placebo 16% NSAID 87%
Cycle 4 placebo 11% NSAID 83%
Efforts to minimize placebo response have
relied on patient-reported outcome measures
and can’t substitute for PrM
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Increase training of both subjects and investigators
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Excluding subjects with very high baseline pain
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Dropping ‘placebo responders’
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Usually determined during a relatively brief, singleblind, placebo run-in period
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What is a ‘placebo responder’? Standard definition?
Who is appropriate for a Phase 2a pain trial?
Lessons from the epilepsy field
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Too much prior treatment = not likely to respond?
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Academic sites - too many refractory ‘hopeless’ cases?
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Can ‘untreated’ pain patients still be found?
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PHN studies conducted before year 2000; including Rowbotham et al, 2005
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‘Ideal’ subject is healthy, without obvious drug
contraindications, and relatively treatment-naïve, BUT….
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Experimental treatment before trying proven options
(especially FDA approved options) is below standard of
care in medical practice

Would a validated, objective biomarker be able to resolve
this conundrum?
Sequential treatment trials and duotherapy in epilepsy
Kwan and Brodie, NEJM 2000; 342:314-319
Likelihood of success no different if first drug ‘old’ vs ‘new’
2011 update: N > 1,000 subjects and many new drugs, failure rate
reduced from 36% to 32%
Brodie and Sills, Seizure 2011; 20:369-375
Thank you to Ken Laxer
Enriched enrollment randomized withdrawal design is
compatible with a PrM approach
Evaluate;
Randomize
responders
screening
Study compound
treatment
Study compound
placebo
“Rescue” analgesics
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How would they affect objective outcome measures?
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A highly effective rescue drug reduces the treatment effect size on
0-10 and may confound results
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Would this extend to surrogate outcome measures that are
objective?
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Brain imaging?
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Skin biopsy?
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Blood-based marker?
Pragmatic Trials
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Pragmatic (effectiveness) trials are different from explanatory (efficacy) trials
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They take place within medical practices, not in specialized study centers
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The research patient never leaves their ongoing care situation
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The primary data platform is the EHR
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With cluster randomization, RCTs can be performed
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Cost per participant is much lower and large sample sizes are feasible
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Both recruitment and tracking during the study can take place entirely
using the internet and the HER
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Research visits can utilize telemedicine
Validated objective biomarkers are even more important when there aren’t
‘experts’ assessing each subject individually for trial entry and during
collection of outcome measures
Rowbotham, et al. PAIN 154 (2013) 643–646