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EXCELLENCE EXPERTISE INNOVATION
TB ReFLECTMeta-Analysis of Fluoroquinolone-Containing Regimens
for the Treatment of Drug-Susceptible TB
Rada Savic, PhDMedical Consultant Meeting
San Antonio, TXNovember 29-30, 2018
Disclosures
Rada Savic, PhD has the following disclosures to make:
• No conflicts of interest
• No relevant financial relationships
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TB ReFLECTMeta-Analysis of Fluoroquinolone-Containing Regimens for the Treatment of Drug-Susceptible TB
12/6/2018
Rada Savic PhD
Associate Professor
Dept. of Bioengineering and Therapeutic Sciences
Div. of Pulmonary and Critical Care
University of California San Francisco
USA
EXPERIENCE
Radojka Savic, PhD
Associate Professor| UCSF
Researcher| School of Pharmacy, Uppsala University (Uppsala, Sweden)
Postdoc, Clinical Pharmacology| School of Medicine, Stanford University (CA)
Postdoc, Biostatistics | INSERM research institute (Paris, France)
Principal Investigator| Savic Lab, Bioengineering & Therapeutic Sciences, School of Pharmacy
Dr. Terrence Blaschke|
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| Pharmacometrics
Dr. France Mentre|
PhD, Pharmacometrics| School of Pharmacy, Uppsala University (Uppsala Sweden)Dr. Mats Karlsson|
MSc, Biomedical Sciences| Graduate School in Biomedical Research (Uppsala, Sweden)
BSc, Pharmacy| School of Pharmacy, Belgrade University (Belgrade, Serbia)
INTERESTS
• Using data science for impact in GH
• TB drug development
• Malnutrition, disease and ability to thrive
• Applied methodology
• Digital Health Tools
• Knowledge Integration
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12/6/20185
Tuberculosis is now the leading cause of death due to infectious diseases. ▪ Airborne infectious disease
▪ In 2017, ~10 million new cases and 1.3 million deaths due to TB
12/6/20186
WHO EndTB Strategy aims to end the TB epidemic
WHO EndTB Brochure
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Current treatment strategy is long and is prone to treatment failure/relapse due to poor adherence.
12/6/20187
4 Drug combination: • Isoniazid (H) • Rifampin (R)• Pyrazinamide (Z) • Ethambutol (E)Daily for 2 months DOT + HR 4 months
Controlled Settings 90-95%
Limitations: Long duration
Adverse events
Target Regimen Profile- Drug-Sensitive TB
Priority attributes
• 2-4 month duration
• >95% cure rate
• No requirement for lab testing for safety
• No drug interactions with first-line HIV drugs
• High barrier to emergence of resistance
http://apps.who.int/iris/bitstream/10665/250044/1/9789241511339-eng.pdf?ua=1
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Treatment Shortening Trials
Phase 2A
• EBA
• 2 weeks
Phase 2B
• Culture conversion
• 2 months
Phase 3 randomized controlled trial
• Relapse
• 18 months
• Gold standard for efficacy
Learning Confirming
Context: Study Design and Endpoints
TB ReFLECT
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One approach to improving tuberculosis therapy is to shorten the duration from 6
months to 4 months. In this trial in over 1900 patients with smear-positive
tuberculosis, two 4-month moxifloxacin-based regimens did not perform as well
as the standard 6-month regimen.
Shortening treatment regimens for tuberculosis may help control the disease. In this
trial, patients with tuberculosis in sub-Saharan Africa received either a 4-month
gatifloxacin-based regimen or the standard 6-month regimen. The gatifloxacin regimen
was less effective.
In this report from sub-Saharan Africa, a 4-month regimen of moxifloxacin and rifapentine
for pulmonary tuberculosis was not as beneficial as two 6-month regimens, and the
benefits of a 6-month regimen based on rifapentine were similar to those of the standard 6-
month regimen.
Clinical Trials not delivering
> 10 years
> $ 100M
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Context
• 4 Phase 3 Contemporary Randomized Clinical Trials attempted to shorten treatment duration (RZ+H/Fq/E)
• Phase 3 trials were preceded with Phase 2B trials with improved 2-month culture results in experimental groups
• 2-month culture results in Phase 3 were largely comparable to culture results from earlier Phase 2B trials
• Limited PK data available, therefore ”optimal dose” discussion carries multiple assumptions
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Background
TB ReFLECT
4 month Fluoroquinolone 6 month Standard
78% 93%
One Regimen Does NOT Fit AllTowards Patient Stratification
12/6/2018Presentation Title and/or Sub Brand Name Here12
• 4 month regimen worked well in 80% patients
• Hard/Easy to treat and all in between
Stratification based on
• Clinical characteristics (X-ray, Gene Xpert, Baseline Smear, HIV))
• Demographics (Nutrition, Age, Weight, etc)
• More refined biomarker (Scans + Immunological)
Goal: Identify the right regimen for the right patient at the right time
Deliverable: Smart and Easy to Use/Implement Dosing Algorithms
One Regimen Does not Fit All
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Towards Patient Stratification
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Goal:
Identify the right regimen for the right patient at the right
time:
All patients should be diagnosed and CURED
Deliverable:
Smart and Easy to Use/Implement Treatment Algorithms
One Approach & Regimen Does not Fit All
TB ReFLECT
▪ Individual Level Patient Meta Analysis
▪ Aimed to:
• Identify patient groups eligible for 4 month treatment
▪ Profile “hard-to-treat” patient populations
▪ Identify drug-specific factors predicted of unfavorable response
▪ To provide data-driven evidence for immediate impact on TB treatment implementation
▪ Findings validated in an independent dataset (Johnson, et al., TBRU trial)
12/6/2018TB ReFLECT14
TB-ReFLECT: TB Re-Analysis of FluoroquinoLone Clinical Trials
TB ReFLECT
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15 TB ReFLECT
Data Base
Approach
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Survival AnalysisTime to unfavorable outcomesMethodsKaplan Meier Cox RegressionParametric Survival Analysis
Data3612 patients with individual-level data
Non-inferiority AnalysisCompare percentage of unfavorable outcomes in subgroups of patients in 4 month vs 6 month treatment arms
Clinical ToolsRisk stratification algorithm Clinical simulation tools
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Primary efficacy endpoint
12/6/201817
• Relapse
• Deaths
• Treatment
failure
• Dropouts/
Lost to
follow-up
• Withdrawal
• Adverse
events
Model selection
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Baseline factors:
age, weight, sex, race, smear
grade, presence of cavities, HIV,
BMI
Treatment factors: composition,
adherence, number of pills,
duration, cumulative dose
Primary Outcome:
Unfavorable outcome
On treatment factor:
Positive culture at month 2,4
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Standard-of-Care, Predictors
TB ReFLECT
HRZE Outcomes
HRZE – TB related outcomes:Implications for Phase 3 Design with current definition of endpoint
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• With enhanced adherence as in RCT and
modern ART, we should expect high success
rates for TB related events with HRZE
• Non-inferiority design is challenging when
cure close to 100% - sample size
• If cure is high, events will be non-TB related
• Non-inferiority margin
TB ReFLECT
HRZE trend towards improved cure
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4-Month Regimens, Hard-to-Treat Phenotypes
21 TB ReFLECT
Risk Factors for short course
Easy- and Hard-to-Treat Phenotypes
22 TB ReFLECT
Non-inferiority Test for Subgroups
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Independent study defined low risk patients differently
23 TB ReFLECT
4-month of HRZE for Easy-to-Treat
TBRU Phase 3 (n=394); 4 and 6 months HRZE in Low RISK patients
LOW RISK Definition
TBRU M2 culture negative and cavity absent
TB ReFLECT Smear 1+ (all) or Smear 2+ and cavity
absent
Validation of “Easy-to-Treat” Phenotype TBRU: 4-month vs 6-month of HRZE
24 TB ReFLECT
4-month of HRZE for Easy-to-Treat
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Adherence and 6/7 vs 7/7 Pill Counts
25 TB ReFLECT
Unforgivness
Adherence in Continuation Phase, SOC
26 TB ReFLECT
Unforgiveness
REMOX OFLOTUB
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Stratifying Patient Population based on a Simple Algorithm
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47% Easy
Short Duration
(4 months)
Smear 1+ Smear 2+ Smear 3+
Cavity absent Easy Easy Easy
Cavity present Easy Moderate Hard
19% Moderate
Intermediate
Duration
(6 months)
34% Hard
Long Duration
(6+ months)
Building Tools: Parametric Survival Surge Model
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𝒉 𝒙, 𝒕 = 𝝀𝒕𝜷 𝐞𝐱𝐩 −𝜶𝒕𝝀 𝒙 ,𝜶(𝒙)
90
95
100
0 2 4 6 8 10 12 14 16 18
Time (Months)
Surv
ival P
roba
bility
by
ALL
Months after start of treatment
Parameter Estimate (RSE)
𝑙𝑜𝑔10(𝜆) -3.5 (13)
𝛼 0.52 (24)
𝛽 3.9 (27)
Treatment duration
TB ReFLECT
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Baseline, treatment and on treatment factors predict relapse Parameter description Estimate
(RSE)
Baseline hazard, 𝜆 3.3 x 10-5 (11)
Surge shape parameter, 𝛼 0.52 (24)
Surge shape parameter, 𝛽 3.9 (26)
Covariate effects: Percent increase in 𝜆 …
For each 7 day decrease in cumulative number of
treatment days
7.1 (10)
For male sex 70 (30)
For HIV co-infection 80 (30)
For exclusion of isoniazid 55 (35)
For exclusion of rifapentine 162 (34)
For smear 3+ relative to smear negative or 1+ at
baseline
58 (38)
For smear 2+ relative to smear negative or 1+ at
baseline
13 (40)
For cavitary disease at baseline 26 (53)
For month 2 culture positivity 128 (20)
12/6/201830
Baseline, treatment and on treatment factors predict relapse
Smear Negative or 1+ Smear 2+ Smear 3+
0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18
0.75
0.80
0.85
0.90
0.95
1.00
Months since start of treatment
Pro
port
ion o
f fa
vora
ble
or
non−
tube
rculo
sis
rela
ted o
utc
om
es
Non−cavitary disease Cavitary disease
0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18
0.75
0.80
0.85
0.90
0.95
1.00
Months since start of treatment
Pro
port
ion o
f fa
vora
ble
or
non−
tube
rculo
sis
rela
ted o
utc
om
es
0.75
0.80
0.85
0.90
0.95
1.00
0 2 4 6 8 10 12 14 16 18
Months since start of treatment
Pro
po
rtio
n o
f fa
vo
rable
or
no
n−
tub
erc
ulo
sis
re
late
d o
utc
om
es
Cumulative treatment days 119−144 Cumulative treatment days < 119
Cumulative treatment days >= 182 Cumulative treatment days 144−182
0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18
0.75
0.80
0.85
0.90
0.95
1.00
0.75
0.80
0.85
0.90
0.95
1.00
Months since start of treatment
Pro
po
rtio
n o
f fa
vo
rable
or
non−
tube
rcu
losis
re
late
d o
utc
om
es
Smear Negative or 1+ Smear 2+ Smear 3+
0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18
0.75
0.80
0.85
0.90
0.95
1.00
Months since start of treatment
Pro
port
ion o
f fa
vora
ble
or
non−
tube
rculo
sis
rela
ted o
utc
om
es
Non−cavitary disease Cavitary disease
0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18
0.75
0.80
0.85
0.90
0.95
1.00
Months since start of treatment
Pro
port
ion o
f fa
vora
ble
or
non−
tube
rculo
sis
rela
ted o
utc
om
es
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12/6/201831
Baseline, treatment and on treatment factors predict relapse
Predicting Treatment Duration, Risk Score:Smear, Cavity, Adherence, HIV, BMI, CD4+, Culture
12/6/201832
With 7/7 fully taken, up to
8-24 weeks treatment for
everyone
One Duration Does not Fit All
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Supporting Data: Risk Factors based on database of >3800 patients, externally validated
12/6/2018Presentation Title and/or Sub Brand Name Here33
Baseline Factors On Treatment
Smear Adherence
Cavity Month 4 culture
HIV/CD4 counts Month 2 culture
BMI
2-7 months (with 7/7) 2-10 months (5/7)
DURATION with
HRZE or HRZM
Risk Factors
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Risk strata require optimal treatment durations
0.75
0.80
0.85
0.90
0.95
1.00
0.93
2 4 6 8
Treatment duration (months)
Pro
po
rtio
n favo
rable
or
non−
tube
rculo
sis
rela
ted o
utc
om
es
12 m
onth
s p
ost R
x
Easy−to−treat Moderate−to−treat Hard−to−treat
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App for optimal treatment interventions
12/6/201835
Natasha Strydom
Stratified medicine to cure allThe CURE-TB Trial
12/6/2018
Rada Savic, Patrick Phillips, Payam Nahid
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12/6/201837
Cure for All
DS
INH Res
12/6/2018CURE-TB, TBTC May 201738
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Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. Kendall, et al., PLoS Medicine, 2017
12/6/2018Presentation Title and/or Sub Brand Name Here39
Emily A. Kendall Sourya Shrestha Ted Cohen Eric Nuermberger Kelly E. Dooley Lice Gonzalez-Angulo Gavin J. Churchyard Payam Nahid Michael L. Rich Cathy
Bansbach Thomas Forissier Christian Lienhardt David W. Dowdy (2017) Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.
PLOS Medicine 14(1): 2017
Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.
▪ Improving efficacy from 76% to 94% in DR TB and 94% to 99% in DS TB had the greatest impact of all variables on:
• reducing mortality (half the impact of a fully optimized regimen)
• reducing transmission
• reducing burden of disease.
Key Finding:
12/6/2018Presentation Title and/or Sub Brand Name Here40
Emily A. Kendall Sourya Shrestha Ted Cohen Eric Nuermberger Kelly E. Dooley Lice Gonzalez-Angulo Gavin J. Churchyard
Payam Nahid Michael L. Rich Cathy Bansbach Thomas Forissier Christian Lienhardt David W. Dowdy, (2017) Priority-Setting for
Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. PLOS Medicine 14(1): e1002202.
https://doi.org/10.1371/journal.pmed.1002202
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Bringing stratified medicine to TB – a paradigm shiftin trial design and overall objectives
12/6/201841
1. Cure all patients with TB
• Not 90-95% of patients, but target cure >98%
• Identify a pragmatic treatment strategy that is superior to standard of care
• Stratification must achieve therapeutic benefit that exceeds the costs of identifying the appropriate patients. Pursue cure for all and keep markers simple.
2. Abandon “One Size Fits All” approach
• Use baseline and/or on treatment markers to stratify patients into risk groups
• Different risk groups receive different durations or compositions of regimens
3. Reduce duration (and toxicity)
• Whereas treatment is extended for severe disease, a larger proportion of TB patient population can be treated with shorter than 4 months. All regimens carry significant toxicity concerns
Stratified Medicine for TB
CURE-TB Strategy Trial(Phase 3, Superiority, Pragmatic Trial to Cure All)
12/6/201842
Strategy 1: Baseline Risk Markers
Strategy 2: Baseline/On Treatment
Markers
TB CURE Trial
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Cure TB Strategy:, RPT based experimental regimens vs. HRZE
Clinical Trial Simulations, Pragmatic Trial
12/6/201843
Strategy 2: Baseline stratification
and on treatment markers
Strategy 1: Baseline stratification
“one-size-fits-all“one-size-fits-all
Stratified Cure TB
Stratified Cure TB
Status Update
▪ TB REFLECT manuscript published in Nature Medicine
▪ CURE-TB Strategy proposal accepted by CDC
▪ Sister proposal - stratified medicine for drug-resistant TB submitted to ACTG TB TSG and reviewed, pending final approval
12/6/201844
Status Update
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Specific aims: Result
To identify patient groups eligible for 4 month treatment Up to 47% patient population is
profiled
To profile “hard-to-treat” patient populations High disease burden, low BMI,
HIV+ and CD4 counts, cavitation
To identify drug-specific factors predicted of unfavorable
response
Total pill count, adherence, and
regimen composition
To provide data-driven evidence for immediate impact on TB
treatment implementation
Short course eligibility and
simulation tool
Summary
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Data Contributors:
• TB Alliance
• St. George's,
University of
London
• WHO
• Case Western
TB ReFLECT steering committee:
• Christian LIENHARDT
• Debra HANNA
• David HERMAN
• Katherine FIELDING
• Patrick PHILLIPS
• Payam NAHID
• Carl MENDEL
• Gerry DAVIS
• Bob WALLIS
• John JOHNSON
TB ReFLECT
UCSF team:
• Marjorie IMPERIAL
• William FOX
• Natasha Strydom
• Rada SAVIC
TB ReFLECT Team
TB ReFLECT
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