Transcript Dynamics Modeling as a Weapon to Defend Ourselves
Dynamics Modeling as a Weapon to Defend Ourselves Against Threats from Infectious Diseases and Bioterrorist Attacks Hulin Wu, Ph.D., Professor Director, Center for Biodefense Immune Modeling Chief, Division of Biomedical Modeling and Informatics Department of Biostatistics & Computational Biology University of Rochester Medical Center SAMSI, February 25, 2011
Outline
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Introduction: Impact of Infectious Diseases to Public Health Dynamic Modeling for HIV Dynamic Modeling for Influenza Conclusions and Discussions Acknowledgement
SARS Pandemic November 1, 2002-July 31, 2003
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Total Cases: 8096 Death: 774 Death rate: 9.6% 29 countries/regions USA: 27 cases (no death)
Bird Flu (H5N1) Epidemics in Human
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Total Cases: 285 Death: 170 Death Rate: 59.6% 12 countries/regions
Flu Pandemics: History
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1918 Spanish flu (H1N1) pandemic: kill 20-100 million people worldwide 1957 Asian Flu (H2N2): 1-4 million infections worldwide, 69,800 deaths in the US 1968 Hong Kong Flu (H3N2): 500,000 infections worldwide, 33,000 deaths in the US
An Emergency Hospital for Influenza Patients
Annual Influenza Epidemics around the World
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5-15% of the population affected 3-5 million cases of severe illness 250,000-500,000 deaths around the world
Current Estimates of the Yearly Disease Burden of Influenza in the US
Deaths Hospitalizations Illnesses Direct costs ($) Indirect costs ($) 40,000 100,000 40,000,000 4,000,000,000 8,000,000,000
Global HIV/AIDS Epidemics: 2006 Update
Global HIV/AIDS Epidemics: 2006 Update
Global HIV/AIDS Epidemics: 2006 Update
New HIV Infection Rate in 2006
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8 infections per minute 458 infections per hour
Defend Ourselves: Why and How to Use Mathematics/Statistics as a Weapon?
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Understand pathogenesis of infection by infectious agents Identify therapeutic targets for intervention Design and evaluate the effects of treatments and other intervention/prevention strategies
Example: HIV/AIDS Modeling
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1 st AIDS case: reported in late 1970s AIDS virus: discovered in 1983, named HTLV AIDS virus renamed as HIV in 1986 HIV dynamics models in late 1980s: Merrill 1987; Mclean 1988; Anderson and May 1989; Perelson 1989 HIV dynamics models for clinical studies: David Ho and Alan Perelson (Nature 1995; Science 1996; Nature 1997) My research in HIV dynamics modeling: 1997-
Ho et al, Nature 1995
Ho et al., Nature 1995
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20 HIV-1 infected patients A new antiviral drug: a protease inhibitor, ABT-538 (Ritonavir)
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Observations: Viral load declined exponentially in 2 weeks
Ho et al., Nature 1995
Ho et al., Nature 1995
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Tap-Tank Model
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cV
Solution with perfect treatment P=0
V e
0
ct or
V
0
ct
Fit a linear regression model
log
V
0
c: viral clearance rate 1/c: Mean life-span of HIV virus ln(2/c): Half-life of HIV virus
Ho et al., Nature 1995
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Estimate of c: 0.34 (range 0.21 to 0.54) Half-life of HIV virus: 2.1 (range1.3 to 3.3) days Daily production and clearance rate of HIV virus: 0.68x10^9 (range 0.05 to 2.07x10^9) virions
Perelson et al. and Ho, Science 1996
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A more complicated model
dT dV I dV
* /
dt
/ /
dt dt
cV I
kV I NI
T
* *
cV NI
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Solution
Y
V I
V NI
V e
0
ct
c cV
0 [
c c
(
e
t
e
ct
)
te
ct
] •
Clinical data: 5 HIV patients
Perelson et al. and Ho, Science 1996
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Estimate of c: 3.07
Estimate of δ: 0.49
Half-life of virus: 0.24 (about 6 hours).
Half-life of infected cells: 1.55 days
Perelson et al. and Ho, Nature 1997
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Short-lived infected cells: t 1/2 =1.1 days Long-lived inected cells: t 1/2 =14.1 days Latently infected cells: t 1/2 =8.5 days
My Research: HIV and Influenza
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HIV/AIDS: Use differential equation models to study antiretroviral treatment effects and treatment strategies in HIV/AIDS research Influenza: Use differential equation models to study immune response to influenza infections and vaccinations
Dynamic Models for AIDS Treatment
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HIV Viral Dynamic Model in Vivo
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Viral fitness is related to antiviral drug efficacy Correlate the lab data to clinical data via the proposed model
Influenza Project
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Center for Biodefense Immune Modeling: funded by NIH from 2005-2015 with $21.9 million in total To develop mathematical models and computer simulation tools to simulate immune response to influenza virus To design and conduct experiments to validate the mathematical models and simulation tools To expect that our modeling and simulation tools can help to rapidly design drugs or vaccines to fight against new and possibly engineered viruses
A Complex Dynamic System for Influenza Infection: Lee et al 2009 (J. of Virology) 6/26/09 Annual Meeing 6/2/10 Annual Meeting
Lung Compartment Sub-Model
d dt d dt d dt E E V p
*
p
E
E p E V P
E P
*
E V P
* ( )
E P E
c V V
VG G
E
*
E
*
p
k VA VM M
Lung Compartment Sub-Model Collected data
10 8 6 4 2 0 0 5
Days
10
Fig 1. HKX31 EID 50 /ml titers per murine lung
15 Fig 2. Cytokine secreting CD8+ T cells per murine lung
Lung Compartment Sub-Model Collected data Fig 3. Smoothed data for IgG and IgM pg/ml murine serum 6/26/09 Annual Meeting
Model Fitting Results
Estimation Result Summary
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The CTL effect: 6.4x10
-5 /day. Shorten the half-life of infected cells from 1.16 days to 0.59 days in average.
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The death rate of infected cells due to effects other than CTL is 0.16/day which is 26% of the death rate during the first 5 days
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Antibody effect: IgM dominates the clerance of viral particles with a rate about 4.4/day . Shorten the half life from 4 hours to 1.8 minutes in average
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Antibody IgG: not significant
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The clearance rate of viral particles due to factors other than antibody effect: very small.
Immune Response Kinetics: Useful
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Identify antiviral drug and vaccine targets
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Understand virulent viruses and their properties
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Prepareness
DEDiscover Software tool for developing, exploring, and applying differential equation models.
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Key Features: ODE & DDE Models “Real-time” interactive simulation Data fitting (Estimation) Clean, Cross-platform GUI High Quality Plots Ver 2.5b: freely available https://cbim.urmc.rochester.edu/software/dediscover
2010-06-02 CBIM DEDiscover Software 42
Conclusions and Discussions
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Efficiently fight against infectious diseases and bioterrorism:
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Need global effort with efficient collaborations and communications
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Need efficient collaborations and communications among inter-disciplinary scientists
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Need long-term effort and huge resources Use any weapons available to defend ourselves including mathematics, computer and statistics Dynamics modeling: an important weapon Can we defend ourselves?
Acknowledgments
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NIAID/NIH grant R01 AI 055290: AIDS Clinical Trial Modeling and Simulations NIAID/NIH grant N01 AI50020: Center for Biodefense Immune Modeling NIAID/NIH grant P30 AI078498: Developmental Center for AIDS Research NIAID/NIH grant R21 AI078842: Analysis of Differential Resistance Emergence Risk for Differential Treatment Applications NIAID/NIH grant RO1 AI087135: Estimation Methods for Nonlinear ODE Models in AIDS Research