TB ReFLECT Meta-Analysis of Fluoroquinolone-Containing Regimens for the Treatment of Drug-Susceptible TB

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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

1 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, EXCELLENCE EXPERTISE INNOVATION Disclosures Rada Savic, PhD has the following disclosures to make: No conflicts of interest No relevant financial relationships 2

TB ReFLECT Meta-Analysis of Fluoroquinolone-Containing Regimens for the Treatment of Drug-Susceptible TB Rada Savic PhD Associate Professor Dept. of Bioengineering and Therapeutic Sciences Div.

ability to thrive Applied methodology Digital Health Tools Knowledge Integration Associate Professor UCSF Principal Investigator Savic Lab, Bioengineering & Therapeutic Sciences, School of Pharmacy

2 TB ReFLECT Meta-Analysis of Fluoroquinolone-Containing Regimens for the Treatment of Drug-Susceptible TB Rada Savic PhD Associate Professor Dept. of Bioengineering and Therapeutic Sciences Div. of Pulmonary and Critical Care University of California San Francisco USA 3 EXPERIENCE Radojka Savic, PhD INTERESTS Using data science for impact in GH TB drug development Malnutrition, disease and ability to thrive Applied methodology Digital Health Tools Knowledge Integration Associate Professor UCSF Principal Investigator Savic Lab, Bioengineering & Therapeutic Sciences, School of Pharmacy Researcher School of Pharmacy, Uppsala University (Uppsala, Sweden) Pharmacometrics Postdoc, Clinical Pharmacology School of Medicine, Stanford University (CA) Dr. Terrence Blaschke Postdoc, Biostatistics INSERM research institute (Paris, France) 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) 4 4

3 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 5 5 WHO EndTB Strategy aims to end the TB epidemic WHO EndTB Brochure 6 6

Current treatment strategy is long and is prone to treatment failure/relapse due to poor adherence.

DOT + HR 4 months Controlled Settings 90-95% Limitations: Long duration Adverse events 7 7 Target

requirement for lab testing for safety No drug interactions with first-line HIV drugs High barrier

4 Current treatment strategy is long and is prone to treatment failure/relapse due to poor adherence. 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 7 7 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 8

Treatment Shortening Trials Context: Study Design and Endpoints Learning Confirming Phase 2A EBA 2 weeks Phase 2B Culture conversion 2

years > $ 100M One approach to improving tuberculosis therapy is to shorten the duration from 6 months to 4 months.

the standard 6-month regimen. Shortening treatment regimens for tuberculosis may help control the disease.

6-month regimen. The gatifloxacin regimen was less effective.

5 Treatment Shortening Trials Context: Study Design and Endpoints Learning Confirming Phase 2A EBA 2 weeks Phase 2B Culture conversion 2 months Phase 3 randomized controlled trial Relapse 18 months Gold standard for efficacy TB ReFLECT 9 Clinical Trials not delivering > 10 years > $ 100M 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

Context Background 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

6 Context Background 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 11 TB ReFLECT 4 month Fluoroquinolone 6 month Standard 78% 93% 11 One Regimen Does NOT Fit All Towards Patient Stratification One Regimen Does not Fit All 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) 12 Goal: Identify the right regimen for the right patient at the right time Presentation Title and/or Sub Brand Deliverable: Name Here Smart and Easy to Use/Implement Dosing Algorithms 12

Towards Patient Stratification One Approach & Regimen Does not Fit All Goal: Identify the right regimen for the right patient at the right time: All patients should be diagnosed and CURED

7 Towards Patient Stratification One Approach & Regimen Does not Fit All 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 13 TB ReFLECT 13 TB ReFLECT TB-ReFLECT: TB Re-Analysis of FluoroquinoLone Clinical Trials 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) 14 TB ReFLECT 14

8 Data Base 15 TB ReFLECT 15 Approach Data 3612 patients with individual-level data Survival Analysis Time to unfavorable outcomes Methods Kaplan Meier Cox Regression Parametric Survival Analysis Clinical Tools Risk stratification algorithm Clinical simulation tools Non-inferiority Analysis Compare percentage of unfavorable outcomes in subgroups of patients in 4 month vs 6 month treatment arms 16 16

9 Primary efficacy endpoint Relapse Deaths Treatment failure Dropouts/ Lost to follow-up Withdrawal Adverse events Model selection Treatment factors: composition, adherence, number of pills, duration, cumulative dose Baseline factors: age, weight, sex, race, smear grade, presence of cavities, HIV, BMI Primary Outcome: Unfavorable outcome On treatment factor: Positive culture at month 2,

Standard-of-Care, Predictors HRZE Outcomes TB ReFLECT 19 HRZE TB related outcomes: Implications for Phase 3 Design with current definition of endpoint HRZE trend towards improved cure With enhanced

10 Standard-of-Care, Predictors HRZE Outcomes TB ReFLECT 19 HRZE TB related outcomes: Implications for Phase 3 Design with current definition of endpoint HRZE trend towards improved cure 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 20 TB ReFLECT 20

11 4-Month Regimens, Hard-to-Treat Phenotypes Risk Factors for short course 21 TB ReFLECT 21 Easy- and Hard-to-Treat Phenotypes Non-inferiority Test for Subgroups 22 TB ReFLECT 22

4-month of HRZE for Easy-to-Treat Independent study defined low risk patients differently TBRU Phase 3 (n=394); 4 and 6 months HRZE in Low RISK patients LOW RISK Definition TBRU TB ReFLECT M2 culture

12 4-month of HRZE for Easy-to-Treat Independent study defined low risk patients differently TBRU Phase 3 (n=394); 4 and 6 months HRZE in Low RISK patients LOW RISK Definition TBRU TB ReFLECT M2 culture negative and cavity absent Smear 1+ (all) or Smear 2+ and cavity absent 23 TB ReFLECT 23 Validation of Easy-to-Treat Phenotype TBRU: 4-month vs 6-month of HRZE 4-month of HRZE for Easy-to-Treat 24 TB ReFLECT 24

13 Adherence and 6/7 vs 7/7 Pill Counts Unforgivness 25 TB ReFLECT 25 Adherence in Continuation Phase, SOC Unforgiveness REMOX OFLOTUB 26 TB ReFLECT 26

Survival Probability Stratifying Patient Population based on a Simple Algorithm Smear 1+ Smear 2+ Smear 3+ Cavity absent Easy Easy Easy Cavity present Easy Moderate Hard 47% Easy Short Duration (4

14 Survival Probability Stratifying Patient Population based on a Simple Algorithm Smear 1+ Smear 2+ Smear 3+ Cavity absent Easy Easy Easy Cavity present Easy Moderate Hard 47% Easy Short Duration (4 months) 19% Moderate Intermediate Duration (6 months) 34% Hard Long Duration (6+ months) Building Tools: Parametric Survival Surge Model h x, t = λt β exp αt λ x, α(x) Parameter Estimate (RSE) log 10 (λ) -3.5 (13) α 0.52 (24) β 3.9 (27) Treatment duration by Time (Months) Months after start of treatment 28 TB ReFLECT 28

15 Proportion of favorable or non tuberculosis related outcomes Proportion of favorable or non tuberculosis related outcomes Proportion of favorable or non tuberculosis related outcomes Proportion of favorable or non tuberculosis related outcomes Proportion of favorable or non tuberculosis related outcomes Proportion of favorable or non tuberculosis related outcomes 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 7.1 (10) treatment days 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 58 (38) baseline For smear 2+ relative to smear negative or 1+ at 13 (40) baseline For cavitary disease at baseline 26 (53) For month 2 culture positivity 128 (20) Baseline, treatment and on treatment factors predict relapse Smear Negative or Smear Smear 3+ Smear Negative or Smear Smear Cumulative treatment days >= 182 Cumulative treatment days Months since start of treatment Months since start of treatment Cumulative treatment days Cumulative treatment days < Non cavitary disease 1.00 Cavitary Non cavitary disease disease Cavitary disease Months since start of treatment Months since start of treatment of treatment Months since start of treatment 30 30

Baseline, treatment and on treatment factors predict relapse 31

Risk Score: Smear, Cavity, Adherence, HIV, BMI, CD4+, Culture

16 Baseline, treatment and on treatment factors predict relapse One Duration Does not Fit All Predicting Treatment Duration, Risk Score: Smear, Cavity, Adherence, HIV, BMI, CD4+, Culture With 7/7 fully taken, up to 8-24 weeks treatment for everyone 32 32

Supporting Data: Risk Factors based on database of >3800 patients, externally validated Baseline Factors On Treatment Risk Factors Smear Cavity HIV/CD4 counts Adherence Month 4 culture Month 2

17 Supporting Data: Risk Factors based on database of >3800 patients, externally validated Baseline Factors On Treatment Risk Factors Smear Cavity HIV/CD4 counts Adherence Month 4 culture Month 2 culture DURATION with HRZE or HRZM BMI 2-7 months (with 7/7) 2-10 months (5/7) 33 Presentation Title and/or Sub Brand Name Here 33 Risk strata require optimal treatment durations Proportion favorable or non tuberculosis related outcomes 12 months post Rx Easy to treat Moderate to treat Hard to treat Treatment duration (months) 34 34

18 App for optimal treatment interventions Natasha Strydom Stratified medicine to cure all The CURE-TB Trial Rada Savic, Patrick Phillips, Payam Nahid 36

19 Cure for All DS INH Res CURE-TB, TBTC May

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. Kendall, et al., PLoS Medicine, 2017 Emily A. Kendall Sourya Shrestha Ted Cohen Eric Nuermberger Kelly E.

Dowdy (2017) Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

20 Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. Kendall, et al., PLoS Medicine, 2017 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): Presentation Title and/or Sub Brand Name Here 39 Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. Key Finding: 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. 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): e Presentation Title and/or Sub Brand Name Here 40

21 Bringing stratified medicine to TB a paradigm shift in trial design and overall objectives 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) Stratified Medicine for TB 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 CURE-TB Strategy Trial (Phase 3, Superiority, Pragmatic Trial to Cure All) TB CURE Trial Strategy 1: Baseline Risk Markers Strategy 2: Baseline/On Treatment Markers 42 42

22 Cure TB Strategy:, Clinical Trial Simulations, Pragmatic Trial RPT based experimental regimens vs. HRZE Stratified Cure TB Stratified Cure TB one-size-fits-all Strategy 1: Baseline stratification one-size-fits-all Strategy 2: Baseline stratification and on treatment markers Status Update 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 44 44

23 Summary Specific aims: To identify patient groups eligible for 4 month treatment To profile hard-to-treat patient populations To identify drug-specific factors predicted of unfavorable response To provide data-driven evidence for immediate impact on TB treatment implementation Result Up to 47% patient population is profiled High disease burden, low BMI, HIV+ and CD4 counts, cavitation Total pill count, adherence, and regimen composition Short course eligibility and simulation tool 45 TB ReFLECT Team Data Contributors: TB Alliance St. George's, University of London WHO Case Western 46 TB TB ReFLECT TB ReFLECT steering committee: Christian LIENHARDT Debra HANNA David HERMAN Katherine FIELDING Patrick PHILLIPS Payam NAHID Carl MENDEL Gerry DAVIS Bob WALLIS John JOHNSON UCSF team: Marjorie IMPERIAL William FOX Natasha Strydom Rada SAVIC 46

Improving Translation in TB Drug Development Through Quantitative Modeling. CPTR Workshop 2016, Washington DC

Improving Translation in TB Drug Development Through Quantitative Modeling Lessons from Recent Phase III TB Trials CPTR Workshop 2016, Washington DC Christian Lienhardt Global TB Programme WHO, Geneva,