Diagnosing and Treating Drug Resistant TB in the 21st Century Using NGS and Intelligent Decision Support Tools

Transcription

1 Diagnosing and Treating Drug Resistant TB in the 21st Century Using NGS and Intelligent Decision Support Tools Timothy C Rodwell MD, PhD, MPH 18 April 2017 APHL: 10 th National Conference on Laboratory Aspects of TB

2 Overview 1. Why we care US & Global TB Trends 2. A vision for the near future Cx free NGS solution & prediction of treatment efficacy 3. Building the solution 1: The technology NGS for TB targeted NGS and WGS A standardized knowledge base for TB AMR 4. Building the solution 2: The science Predicting MICs with mutations Integrating MICs & PK/PD to predict treatment efficacy Predicting DR TB before it happens 5. Dx needs for new drugs and regimens in 2020 BDQ, DLM, LZD, Pa

3 Why We Care

4 By the end of this lecture >150 new TB patients will have died

5 TB incidence per 100,000 population Source: Salinas et al. MMWR Morb Mortal Wkly Rep 2016;65: Year

6 ~20% of 580,000 MDR patients were diagnosed 2015 Source: tb report infographic.pdf <10% of MDR pts got 2 nd line DST

7 Reference Standard Diagnosis Conventional TB ID and DST uses culture based methods which are slow (weeks months), expensive and require BSL3 facilities

8 Is This The Future We ve Been Waiting For?

9 A Different Vision for the Near Future

10 MIC Prediction Mtb/host DNA extraction & 1 step NGS prep Strain Mapping Drug Knowledge base Mtb/Human Genomic Knowledge base AUIC Estimation ADR Prediction <24 hrs later Mtb Mutation Predicted MOX MIC Outbreak Strain G6PD Deficiency Recommendation 45% gyra 91 CCG N N MOX 800 mg (83% prob. of efficacy)

11

12 OPTIMIZATION Simple, standardized NGS workflows COLLATION Mtb genomics knowledge base IT Global AMR networks BASIC SCIENCE ADR Genomics knowledge base HARDWARE Automated Sample Prep EMPIRICAL DATA MIC and AUIC Prediction

13 OPTIMIZATION Simple, standardized NGS workflows

14 OPTIMIZATION Simple, standardized NGS workflows HARDWARE Automated Sample Prep

15 OPTIMIZATION Simple, standardized NGS workflows COLLATION Mtb genomics knowledge base HARDWARE Automated Sample Prep

16 OPTIMIZATION Simple, standardized NGS workflows COLLATION Mtb genomics knowledge base BASIC SCIENCE ADR Genomics knowledge base HARDWARE Automated Sample Prep

17 OPTIMIZATION Simple, standardized NGS workflows COLLATION Mtb genomics knowledge base BASIC SCIENCE ADR Genomics knowledge base HARDWARE Automated Sample Prep EMPIRICAL DATA MIC and AUIC Prediction

18 OPTIMIZATION Simple, standardized NGS workflows COLLATION Mtb genomics knowledge base IT Global AMR networks BASIC SCIENCE ADR Genomics knowledge base HARDWARE Automated Sample Prep EMPIRICAL DATA MIC and AUIC Prediction

19 Building the Solution: The Technology

20 Whole Genome Sequencing o Strengths o Full genome sequenced o Comprehensive solution o Weaknesses o Slow o Can t yet get Mtb WGS direct from sputum consistently or cost effectively o expensive o complicated bioinformatics Targeted Next Gen Sequencing o Strengths o Sequence DNA direct from sputum o Up to 200 gene targets o Faster, simpler o Less expensive than WGS o Weaknesses o Need some pre knowledge of targets o Less information than WGS

21

22

23 End Goal for a Simplified NGS Solution A validated, end to end, targeted amplicon NGS RDST solution for global use by end 2018 Simplified, mostly automated benchtop workflow Suitable for Cx free surveillance and RDST Instrumentation costs <$150,000 Reagents and consumable costs <$ 40/sample Time to result in 1 2 days

24 Expected Sensitivity and Specificity Sensitivity and Specificity of Mutations for Predicting M/XDR TB Phenotypes in India, Moldova, Philippines and South Africa Phenotype Primary Gene/s Associated with Resistance No. of unique SNPs Sensitivity Specificity INH R katg, inha prom. 6 ±95% (n=894) % RIF R rpob 15 ±97% (n=777) % MOX/OFX R gyra 8 ±92% (n=562) % AMK R rrs 1 ±86% (n=293) % KAN R rrs, eis promoter 6 ±88% (n=404) % CAP R rrs 1 ±87% (n=279) % Rodwell et. al J. Clin Micro. 2013, GCDD unpublished

25 Predicting XDR Phenotypes with NGS: A Pilot Study Source: Colman et al J. Clin Micro. 2016

26 A Standardized Mtb Knowledge Base

27 ReSeqTB Knowledge Base

28 Components of the ReSeqTB Knowledge Base Amplicon NGS WGS 1. A standardized and validated NGS variant detection pipeline SNPs and INDELS 2. A curated database of genotypic, phenotypic and clinical data Clinical Data SNPs Phenotypic Data 3. A cloud based data analysis and sharing platform designed for global networking

29 p value LR < < < 10 < < 5 <0.05 < Interpretation High (Hi) confidence for association with resistance strong association of the mutation with phenotypic drug resistance; sufficient evidence that the mutation confers or is strongly associated with drug resistance Moderate (Mo) confidence for association with resistance moderate association of the mutation with phenotypic drug resistance; additional data desirable for improved evidence that the mutation confers or is strongly associated with drug resistance Minimal (Mi) confidence for association with resistance weak association of the mutation with phenotypic drug resistance; inconclusive evidence that the mutation confers or is strongly associated with drug resistance. Substantial additional data required No association with resistance No evidence of association between the mutation and drug resistance Indeterminate no statistically significant threshold reached; additional data required Symbol Indeter Source: Miotto et. al. in prep

30 Drug (phenotypic testing) First line Rx Secondline Rx (group A) Gene High confidence mutations RIF rpob D516A, D516F, D516G, D516G+L533P, D516ins, D516N, D516V, D626E, Del N518, F505V+D516Y, F514dupl, H526C, H526D, H526F, H526G, H526L, H526R, H526Y, M515I+D516Y, Q513 F514ins, Q513H+L533P, Q513K, Q513L, Q513P, S512T, S522Q, S531F, S531L, S531Q, S531W Moderate confidence mutations D516Y, H526P, L533P, S522L Minimal confidence mutations H526N, I572F, L511P No association with resistance INH inha maba g 102a G NC c 15t g 47c, t 80g, T4I katg S315I, S315N, S315T, Pooled frameshifts and premature Stop codons A110V, L499M, R463L fura L68F msha A187V G NC N111S MFX gyra A90V, D94A, D94G, D94N, D94Y, G88C, S91P OFX/ LFX gyra A90V, D94A, D94G, D94H, D94N, D94Y, G88A, G88C, S91P D89N E21Q, G247S, G668D, S95T, V712L E21Q, G247S, G668D, S95T, T80A, V712L Secondline Rx (group B) gyrb A504V, E459K AM rrs a1401g, g1484t KM eis c 14t, g 10a c 12t, g 37t a1338c rrs a1401g, a514c NC, c1402t, g1484t rrs+eis rrs c517t NC + eis g 37t CM rrs a1401g, c1402t, g1484t c517t Source: Miotto et. al. in prep

31 Building the Solution: The Science

32 Going Beyond Resistant/Sensitive = treatable by WHO Standards = treatable by Dosing Optimization WHO Critical Concentration Clinical Breakpoint Number of Isolates Wild type ( susceptible ) Mutant ( resistant ) MICs above safely achievable serum concentration in patients Low MIC High MIC Lost opportunity for optimizing dosing if use critical concentrations only

33 All Resistance SNPs are not Equal: INH 40 WT Number if Isolates WHO CC (0.1 mg/l) Cmax (~3 mg/l) inha pr katg315 katg315+inha pr >10 Kambli et. al DMID 2015 INH MIC Estimate (mg/l)

34 All Resistance SNPs are not Equal: RIF Number of Isolates WHO CC (MGIT) (1.0 mg/l) Isolate RIF MIC (mg/l) rpob SNP 516 GTC 516 TAC 526 AAC 526 TAC 531 TTG Berrada et al. Am. Soc Micro. 2012

35 All Resistance SNPs are not Equal: FQs GAT LEV MOX OFX Rodwell et al, in prep

36 Predicting Rx Effectiveness

37 Serum Rx concentration (mg/l) Initial Dose Time C max AUIC (area under the inhibitory curve) WT MIC Rodwell et al, in prep

38 ECOFF WT 90 GTG 94 GCC 91 CCG 94 CAC 94 TAC 94 GGC 94 AAC 88 TGC MIC mg/l Rodwell et al, in prep

39 WT ECOFF Low Level Resistance 90 GTG 94 GCC 91 CCG 94 CAC 94 TAC Hi Level Resistance 94 GGC 94 AAC 88 TGC MIC mg/l Rodwell et al, in prep

40 Serum MOX concentration (mg/l) Time 400mg/day dosing High level resistance SNPs (mean MIC 1.35 mg/l) Low level resistance SNPs (mean MIC 0.49 mg/l) WT (mean MIC mg/l) Rodwell et al, in prep

41 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations: Results of a Pharmacological Model Percent population Rodwell et al, in prep Target AUIC (~100) WT (treated with 400mg) Area under the inhibitory curve (AUIC)

42 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations Percent population Rodwell et al, in prep Target AUIC (108) WT (treated with 400mg) Low R (treated with 400mg) Area under the inhibitory curve (AUIC)

43 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations Percent population Rodwell et al, in prep Target AUIC (108) WT (treated with 400mg) Low R (Treated with 800mg) Low R (treated with 400mg) Area under the inhibitory curve (AUIC)

44 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations Percent population Target AUIC (108) WT (treated with 400mg) High R (treated with 400mg) Area under the inhibitory curve (AUIC)

45 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations Percent population Rodwell et al, in prep Target AUIC (108) WT (treated with 400mg) High R (treated with 800mg) High R (treated with 400mg) Area under the inhibitory curve (AUIC)

46 Proportion of Patient Population Reaching Therapeutic Targets for MOX based on MICs predicted by Mtb gyra mutations Percent population Rodwell et al, in prep Target AUIC (108) WT (treated with 400mg) High R (treated with 800mg) High R (treated with 400mg) Area under the inhibitory curve (AUIC)

47 The Takeaway SNPs gyra 90GTG MIC Distributions Low R MIC for MOX Rx Efficacy >80% efficacy (800 mg/day)

48 SNPs Drug Susceptibility SNPs MIC AUIC NGS Resistance TIME

49 Predicting Resistance Before it Happens S 0.1% 1% 10% 30% 60% R 100%

50 Sample Date Resistance AMK Pheno DST AMK Geno DST rrs SNP (% R Allele) 10/1/2008 MDR S S none 11/19/2008 MDR S S none 10/15/2009 Unclassified S S none 1/19/2010 MDR S S none 7/20/2011 MDR S S 1401G (0.94%) 9/27/2011 Pre XDR S S none 4/30/2012 Pre XDR S S 1401G (3.38%) 12/21/2012 XDR R R 1401G (29.64%) 7/20/ % 4/30/ % 12/21/ % Engelthaler, Metcalfe, Warren, unpublished data

51 10/27/ /25/ /02/2010 4/28/2011 7/28/ % % 94a 47.4% % 94a 64.0% % 94a 31.4% 94b 25.6% 94b 95.0% Engelthaler, Metcalfe, Warren, unpublished data

52 ECOFF WT 90 GTG 94 GCC 91 CCG 94 CAC 94 TAC 94 GGC 94 AAC 88 TGC MIC mg/l Rodwell et al, in prep

53 The Takeaway Deep sequencing NGS can detect minor resistant variants <1% of total population There are a lot of dynamic changes in the R/S variants over time This information could potentially be exploited for Tx monitoring and preventing clinical drug resistance

54 Dx Needs for New TB Drugs and Regimens

55 MIC Prediction Mtb/host DNA extraction & 1 step NGS prep Strain Mapping Drug Knowledge base Mtb/Human Genomic Knowledge base AUIC Estimation ADR Prediction <24 hrs later Mtb Mutation Predicted MOX MIC Outbreak Strain G6PD Deficiency Recommendation 15% gyra 91 CCG N N MOX 800 mg (83% prob. of efficacy)

56 Acknowledgments MIC Work (UCSD) Marva Seifert Edmund Capparelli Mark Pettigrove ReSeqTB (Global) Marco Schito Debra Hanna ReSeqTB Consortium NGS Technology (FIND) Becky Colman Claudia Denkinger David Dolinger Paolo Miotti Sophia Georghiou Heteroresistance Work (TGen) Dave Engelthaler John Metcalf Rob Warren Funding received from National Institute of Allergy and Infectious Diseases (NIAID) Grants: U01 AI082229, P30AI036214, R01AI111435

57 All Presumptive TB cases GeneXpert GXP+ Rif R GXP+ Rif S Rec Hain LPA/ Phenotypic DST Treat with 1 st line drugs (HRZE) If FQ/Inj S Short Course MDR Tx (9 mos) If FQ/Inj R Individualize d Tx (24 mos)

58 Rapid TB + RIF detection for ALL presumptive TB cases MTB+ Rapid FQ+INH Detection MTB+ RIF R or MDR MTB+ Mono INH R MTB+ R/H/FQ S FQ S BpaMZ (3 mos) FQ R BPaL (4 mos) FQ S HRZE+FQ (6 mos) FQ R BPaL (4 mos) HRZE or BPaMZ