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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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

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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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