EXCELLENCE EXPERTISE INNOVATION TB ReFLECT Meta-Analysis of Fluoroquinolone-Containing Regimens for the Treatment of Drug-Susceptible TB Rada Savic, PhD Medical Consultant Meeting San Antonio, TX November 29-30, 2018 Disclosures Rada Savic, PhD has the following disclosures to make: • No conflicts of interest • No relevant financial relationships 1 2
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UCSF Presentation Template · 2018-12-07 · HRZE Outcomes HRZE –TB related outcomes: Implications for Phase 3 Design with current definition of endpoint 20 • With enhanced adherence
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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.
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)
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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
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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