Academia (Consortia) Perspective Topic I: Selection of Agents, Doses and Regimens for Clinical Study Debra Hanna, Executive Director, Critical Path to TB Drug Regimens 25 November 2016
Outline Consortium Driven Methods Perspective • Integrate Academic / Industry / Regulatory Perspective on Methods • Required for Evidence-based approach Current Methodologies Landscape: TB Drug Development Pathway • Academic approach to method development versus • Methodologies designed as drug development tools • Evidenced-based methodology evaluation In vitro HFS-TB Model • Evidence-based approach • EMA qualification for use In vivo Methods focus on Sterilizing Mouse Model • Next models for evaluation
CPTR Initiative Members and Partners Government/Regulatory Nonprofit research Industry members participants members 3
CPTR Academic Partners • • Baylor Institute for Immunology Research Stanford University • • Case Western Reserve University TB Research Unit Stellenbosch University • • Colorado State University University of Florida • • Duke University University of California, San Diego • • Forschungszentrum Borstel University of California, San Francisco • • Harvard University College of London • • Johns Hopkins University University of Arkansas for Medical Sciences • • London School of Hygiene and Tropical Medicine University of Cape Town • • Munich University University of Liverpool • • NYU University of St. Andrews • • O‘Neill Institute at Georgetown Law Center University of Virginia • • Partners In Health [Harvard University] University of Texas Health Science Center at San Antonio • Radboud University • University of Toronto • RESIST-TB [Boston University] • Uppsala University, Dept. of Pharmaceutical • Rutgers [University Of Medicine & Dentistry] Biosciences • St. George's, University of London • Vanderbilt University School of Medicine 4
Outline Consortium Driven Methods Perspective • Integrate Academic / Industry / Regulatory Perspective on Methods • Required for Evidence-based approach Current Methodologies Landscape: TB Drug Development Pathway • Academic approach to method development versus • Methodologies designed as drug development tools • Evidenced-based methodology evaluation In vitro HFS-TB Model • Evidence-based approach • EMA qualification for use In vivo Methods focus on Sterilizing Mouse Model
Current TB Regimen Development Risk of Late-Stage Attrition 6
CPTR Evidence-Based Roadmap Degree of Evidence Required Pre-CPTR Stage CPTR 1. DDT 2. Exploration 3. Demonstration 4. Characterization Identification • Probable or emerging • Identify candidate in vivo • Proof of concept model/DDT • Find best candidate and models as possible DDT • Scientifically validated • Determine data needs assay Type of DDT • Define model • Determine data needs performance, sensitivity and reproducibility; predictivity DDT CoU Qualification Strategy Drug Development Pipeline Lead Target Translational Phase I & II Phase III Commercial Validation Optimization Medicine 8
Outline Consortium Driven Methods Perspective • Integrate Academic / Industry / Regulatory Perspective on Methods • Required for Evidence-based approach Current Methodologies Landscape: TB Drug Development Pathway • Academic approach to method development versus • Methodologies designed as drug development tools • Evidenced-based methodology evaluation In vitro HFS-TB Model • HFS-TB model • Evidence-based approach • EMA qualification for use In vivo Methods focus on Sterilizing Mouse Model
HFS-TB Goal Mission Evidence • Significantly more • Evidence-based • Follow EMA and FDA evaluation of innovative Guidance on novel quantitative HFS-TB drug development tools methodology and DDT PKPD data available than for any in vivo to address preclinical to qualification clinical translation methodology for TB • Gather all relevant • Focus on in vitro published and • Supported thorough methodologies unpublished data assessment of predictive accuracy for clinical supporting efficacy and sources or aggregation safety toxicology outcomes assessment • Assess clinical translation of innovative preclinical • Submission for novel regulatory endorsement methodologies/DDTs to test new TB drug candidates and regimens
Quantitative Outputs of HFS-TB Outputs from Quantitative analysis and HFS-TB experiments simulation yields • Quantitative PK/PD relationships • Drug concentration useful for target selection • Total and drug-resistant Mtb • Prediction of dose-response CFU counts curves and target attainment • RNA expression expected in patients useful for • Whole genome sequencing of optimal dose selection sampled material • Expected rates of clinical • Macrophage count and no. response and resistance emergence bacteria/macrophage 12
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Use best dose first time Optimize doses of drugs in regimens to reduce the need for dose response clinical study Identify best combinations Optimize selection of drugs for regimen design by evaluating synergy and antagonism Rank regimens by speed of sterilizing effect
• Analysis Objective to determine predictive accuracy of HFS-TB outputs for clinical trial results • Literature Search to identify relevant HFS- TB and clinical data from published literature • Systematic Review to summarize HFS-TB- generated hypotheses and outcomes of clinical trials • Quality of Evidence Scoring to provide basis for weighting in the predictive accuracy analysis • Statistical Analysis comparing HFS-TB predictions with clinical findings to examine: • descriptive correlations where HFS-TB studies post-dated clinical studies • predictive accuracy where HFS-TB studies pre-dated clinical studies
• HFS-TB qualified for use in drug development programs as additional and complementary tool • HFS-TB can be used in regulatory submissions, esp. for informed design and interpretation of clinical studies • HFS-TB is recommended to be useful as follows: To provide preliminary proof of concept for developing a specific drug or combination to treat tuberculosis To select the pharmacodynamic target (e.g. T >MIC , AUC/MIC) To provide data to support PK/PD analyses leading to initial dose selection for non-clinical and clinical studies To assist in confirming dose regimens for later clinical trials taking into account human PK data and exposure-response relationships
New Regimen Design: “FLAME” 10 8 Mtb log 10 CFU/mL FLM HIGH 6 FLM HIGH + EMB FLM Standard therapy 4 Not treated 2 0 0 7 14 21 28 Time in days Deshpande et al. A faropenem, linezolid, and moxifloxacin regimen for both drug susceptible and multidrug-resistant tuberculosis in children. Clin Infect Dis. 2016;63:S95 17
Outline Consortium Driven Methods Perspective • Integrate Academic / Industry / Regulatory Perspective on Methods • Required for Evidence-based approach Current Methodologies Landscape: TB Drug Development Pathway • Academic approach to method development versus • Methodologies designed as drug development tools • Evidenced-based methodology evaluation In vitro HFS-TB Model • Evidence-based approach • EMA qualification for use In vivo Methods focus on Sterilizing Mouse Model
Evaluation of In Vivo Models Correlations between drug concentration and pathogen survival that are based on in vitro models cannot be expected to reiterate all aspects of in vivo antimycobacterial treatment . Chilukuri et al, CID 2015; 61(S1):S32 HFS-TB qualified for use in drug development programs as additional and complementary tool – EMA Qualification Decision Advantages of in vivo models • Better reflect the phenotypic heterogeneity in bacterial populations as determined by host-pathogen interactions, including tissue pathology • Present complexities of drug distribution to, and action within, various sites of infection 19
Mouse Model of Sterilizing Activity PK/Chemical Interaction Clinical Combination Combination Combination Confirmation of Single Drug PK in Studies Efficacy (Mouse Efficacy (Mouse Efficacy Safety Mouse Acute Model) Relapse Model) (if needed) Secondary Appropriate Bactericidal Sterilizing Combination Species Dose Activity: Activity: Specific Safety Infection Model Selection in Mice Initial Screening Duration of Therapy d1 Day -14 Day 0 M2 M3 M4 M5 3 mice 15 mice held for 3 months after treatment completion to determine the proportion with microbiological evidence of relapse 20
Intended Rationale General Aim Application • • • The data from Quantify the Past and present role predictive accuracy in TB regimen experiments in mice of mouse TB efficacy development infected with M. • models to estimate Relapse endpoint tuberculosis , using considered closest the treatment- relapse as the main correlate of current shortening potential endpoint phase 3 endpoint • Will be used to of a test regimen, by • Track record in evaluating calculate forecasting differences in the treatment effect treatment- treatment duration sizes, to then shortening necessary to rank-order potential of RIF, prevent relapse regimens, and PZA • Estimate clinical compared to control • Amount of available (standard TB treatment data on regimens regimen). duration evaluated in clinical trials
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