translating tb treatment response
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Translating TB Treatment Response
Rada Savic, Professor
Dept .of Bioengineering and Therapeutic SciencesDiv. of Pulmonary and Critical CareUCSF TB Center
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Exciting Times for TB Drug Development
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First ever 4 month Regimen for DS-TB
First ever 6 month Regimen for XDR/MDR -TB
Class Compound, Developer Development
Oxazolidinone
ribosome inhibitors
Sutezolid; Sequella, TB Alliance IIC
TBI-223; TB Alliance I
Delpazolid; LegoChem IIA
DprE1 inhibitors OPC-167832; Otsuka IIA
BTZ-043; University of Munich, DZIF IIA
TBA-7371; TB Alliance, Gates MRI IIA
QcrB inhibitor Telacebec, Q203; Qurient, Infectex IIA
Cholesterol-dependent
M.tb inhibitor
GSK286; Glaxo-Smith-Kline I
Diarylquinoline , ATP
synthase inhibitors
TBAJ587; TBAJ876; TB Alliance I
Gyrase B inhibitor SPR-720; Spero, Gates MRI I
New drugs/new MoA in the pipeline
New NGO/Pharma/Academia Partnerships
• BMGF PanTB Collaboration• UNITE4TB- EU• ACTG - US
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Optimization Combinatorics a.k.a. Too many options
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Drugs Doses FrequenciesDuration
2. Optimizing Multidrug TB Regimen
Going forward (Joint Regimen Development Program)
• 8-12 novel compounds (first in human completed) • (4 major Pharma, BMGF, Academia)• >1 million (2-5 drug combinations) possible
Immune deficient
nude mice
Rabbit lesion model
Kramniklesion model
• Immune impact
• Lesion specific site of disease
Variety of Models and Tools,however they are not quantitative and predictive yet
Hollow fiberPKPD
Balb/C
In vitro combinations
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Quantitative Translation Toolbox
Dose-rangingPK
First in human safe dosing
Rabbit lesionPK
Clinical lesionPK
New New
Existing
Existing
BALB/c & Nude mice baseline
ClinicalPhase 2a
Preclinical:
Clinical:
BALB/c PKPD(mono)
New
Existing
Control
True drug effectin combination
with immune kill
Existing
M,Z RIF, INH, PZA, MFX,LZD, RPT, BDQ,
PMD, DLM
New
BALB/c PKPD(multi)
New
Existing
Clinical Phase 2& beyond
Existing NewHRZ, JPM, JPMZ,
LJP …
BALB/c resistance
Existing
Clinical MDR
H & R
Existing
BALB/c CFU
ClinicalTTP, TTCC,
CFU
Describe the interactions
between drugs
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(I) Empirical ML/AI TOOL for regimen ranking in a mouse
Probability of Relapse
Ranking compounds based on 2 /partial 3 drug combo dataPredicting long term relapse from early (1 month) responseFor the known:
• Incubation time• Baseline• Regimen composition• CFU change at Month 1
Strydom et al, TB Science 2020
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(II) Lung Resection Studies and Lesion coverage Tool
Ernest et al, 2020, Strydom et al, PLOS Medicine 2019, Prideaux et al, Nat Medicine 2016, Rifat et al, Sci Transl Med 2018
Time after dose (h)
Method
Result
Quantitative prediction ofPhase 2B/Phase 3 results
Synergy/Additivity/Antagonism assessment
Strydom et al, PLOS Comp Biol 2020, Bartelink et al, Clin Transl Sci 2017
(III) Integrated immunology/pharmacology models: PREDICTIVE tools
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(IV) Novel Early Trial Designs linked to Translational Tools
Prioritized regimens (1) Prioritized regimens (2)
(V) Data Sharing and Modeling Tools – TB ReFLECT
Only “hard-to-treat” phenotypes require 6 month duration.; 5 item risk score can help stratify patients (Cavitation, Baseline severity, HIV, malnutrition, sex)
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Integration of Computational Tools and Data Science Approaches
Acknowledgements
UCSFTB Translational Team• Jackie Ernest • Natasha Strydom • Nan Zhang • John Fors • Amelia Deitchamn • Imke Bartelink• Chores Wang
TB Clinical Team• Marjorie Imperial• Leah Jarlseberg• Craig Shafer• Payam Nahid• Patrick Phillips
• Veronique Dartois
• Eric Nuermberger• Kelly Dooley