A multiscale systems pharmacology framework to predict drug-class specific prophylactic efficacy of antivirals against HIV

1. A multiscale systems pharmacology framework
to predict drug-class specific prophylactic
efficacy of antivirals against HIV
Sulav Duwal
Systems Pharmacology & Disease Control
Free University Berlin
01/06/2018,PAGE, Montreux

2. The motivation
Currently, there are more than 36 million people living with HIV.
No cure. No vaccine.
Prevention strategies - for e.g. pre-exposure prophylaxis (PrEP).
2 / 28

3. The motivation
target cell
compartment
Infection
Extinction
ExposedTransmitter
3 / 28

4. The motivation
target cell
compartment
Infection
Extinction
ExposedTransmitter
Stochastic process
Low transmission probability per exposure.
Low number of transmitted/founder viruses.
Keele et al. 2008 PNAS - The range of founder
viruses is 1 to 5.
A single founder virus (80 % transmission cases).
3 / 28

5. The motivation
target cell
compartment
Infection
Extinction
ExposedTransmitter
Prophylaxis
Stochastic process
Low transmission probability per exposure.
Low number of transmitted/founder viruses.
Keele et al. 2008 PNAS - The range of founder
viruses is 1 to 5.
A single founder virus (80 % transmission cases).
Pre-Exposure Prophylaxis - PrEP
An uninfected person with high risk of HIV-1
receives antiviral orally.
Currently, Truvada is only medication approved
for PrEP.
More than 25 antivirals approved for HIV-1
treatment. Novel formulations and long acting
agents.
3 / 28

6. Aims
target cell
compartment
Infection
Extinction
ExposedTransmitter
Prophylaxis
To build a modeling framework :
to select antivirals for PrEP
to assess PrEP strategy
4 / 28

7. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
5 / 28

8. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
5 / 28

9. Pharmacodynamics (Viral replication cycle)
k(t)
(early infected
T-cell)
(productively
infected T-cell)
T2Tu⋅ β(t)
Tu ⋅CLT(t)
CL
NT(t)
(free infectious
virus)
V
δPIC
δT1
δT2
T1
latency
pL|a5
longlivedcells
pM|a4
Drug-class :
CRA - Co-receptor antagonist
RTI - Reverse transcription inhibitor
InI - Integrase inhibitor
PI - Protease inhibitor
Pharmacodynamics (Viral replication cycle)
Free infectious viruses (V) infect CD4+
T-cells.
Early-infected T-cells (T1) - the virus has
successfully completed the reverse transcription.
Late-infected T-cells (T2) - the viral genome
integration in the host genome is complete.
A T2 cell produces viruses.
6 / 28

10. Pharmacodynamics (Viral replication cycle)
k(t)
(early infected
T-cell)
(productively
infected T-cell)
T2Tu⋅ β(t)
Tu ⋅CLT(t)
CL
NT(t)
(free infectious
virus)
V
δPIC
δT1
δT2
InIs
CRA
PI
RTIs
+
T1
latency
pL|a5
longlivedcells
pM|a4
Drug-classes :
CRA - Co-receptor antagonist
RTI - Reverse transcriptase inhibitor
InI - Integrase inhibitor
PI - Protease inhibitor
Drug-class specific mechanistic integration
7 / 28

11. Pharmacodynamics (Viral replication cycle)
k(t)
(early infected
T-cell)
(productively
infected T-cell)
T2Tu⋅ β(t)
Tu ⋅CLT(t)
CL
NT(t)
(free infectious
virus)
V
δPIC
δT1
δT2
InIs
CRA
PI
RTIs
+
T1
latency
pL|a5
longlivedcells
pM|a4
Drug-classes :
CRA - Co-receptor antagonist
RTI - Reverse transcriptase inhibitor
InI - Integrase inhibitor
PI - Protease inhibitor
Drug-class specific mechanistic integration
For e.g., an integrase inhibitor inhibits the viral
genome integration in the target cell genome :
k(t) = k∅ · (1 − η(t))
where k(t) and k(∅) are the rates in the presence and
absence of an InI.
7 / 28

12. Pharmacodynamics (Viral replication cycle)
k(t)
(early infected
T-cell)
(productively
infected T-cell)
T2Tu⋅ β(t)
Tu ⋅CLT(t)
CL
NT(t)
(free infectious
virus)
V
δPIC
δT1
δT2
InIs
CRA
PI
RTIs
+
T1
latency
pL|a5
longlivedcells
pM|a4
Drug-classes :
CRA - Co-receptor antagonist
RTI - Reverse transcriptase inhibitor
InI - Integrase inhibitor
PI - Protease inhibitor
Drug-class specific mechanistic integration
For e.g., an integrase inhibitor inhibits the viral
genome integration in the target cell genome :
k(t) = k∅ · (1 − η(t))
where k(t) and k(∅) are the rates in the presence and
absence of an InI.
PK-PD linked by EMAX model (direct response) :
1 − η(t) =
IC50
m
ICm
50 + C(t)m
IC50, Hill coefficient (m) from ex vivo single-round
infectivity assay (Shen et al. 2008 Nat Med). IC50
corrected for in vivo use.
7 / 28

13. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
8 / 28

14. Viral exposure model
target cell
compartment
Infection
Extinction
ExposedTransmitter
Prophylaxis
How many viruses reach the target-cell
compartment (transmitted viruses) per exposure?
What are factors that influence the number of
transmitted viruses ?
9 / 28

15. Viral exposure model
Viruses
ExposedTransmitter
Viral load is the most dominant factor determining the transmission risk. (Quinn et al. 2000 N Engl J Med)
Each 10 fold increment in viral load increases the transmission probability per exposure by 2.45 fold.
Transmission mode : Homosexual > Heterosexual.
10 / 28

16. Viral exposure model
Viral load distribution in transmitter
Viruses
ExposedTransmitter
Viral load is the most dominant factor determining the transmission risk. (Quinn et al. 2000 N Engl J Med)
Each 10 fold increment in viral load increases the transmission probability per exposure by 2.45 fold.
Transmission mode : Homosexual > Heterosexual.
10 / 28

17. Viral exposure model
Viral load distribution in transmitter
Viruses
ExposedTransmitter
Transmitted virus distribution in exposed
Viral load is the most dominant factor determining the transmission risk. (Quinn et al. 2000 N Engl J Med)
Each 10 fold increment in viral load increases the transmission probability per exposure by 2.45 fold.
Transmission mode : Homosexual > Heterosexual.
10 / 28

18. Viral exposure model
Viral load distribution in transmitter
Viruses
ExposedTransmitter
Transmitted virus distribution in exposed
Mean transmission probability per exposure :
¯P(trans) =
∞
k=0
P(Viralload = k) ·
∞
n=0
P(Y0 = n · V|Viralload = k) · I(Y0 = n · V, ∅)
Linking the viral load of donor with the number of transmitted viruses (binomial distribution) :
P(Y0 = n · V|Viralload = k) =
kh
n
· si
· (1 − s)( kh
−i)
.
where h = log10(2.45) from Quinn 2000 et al. N Engl J Med.
11 / 28

19. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
12 / 28

20. Branching process
target cell
compartment
Infection
Extinction
ExposedTransmitter
Prophylaxis
Given a virus reaches a target-cell compartment,
what is the probability
that it establishes an infection or goes extinct?
13 / 28

21. Branching process
Sir Francis Galton and Henry William Watson in 1873. ‘What is the probability that the royal family
would eventually go extinct?’
Watson, H.W, Galton, F (1875) ‘On the probability of the Extinction of families’ Journal of the
Royal Anthropological Institute.
Branching process theory.
14 / 28

22. Branching process
Extinction / Infection probability
Probability that a virus eventually goes extinct (E), given a single virus (V)reaches the target-cell
compartment:
E(Y0 = V) = min 1, 1 − P(V → T1) · P(T1 → T2) · 1 −
1
R0
where P(V → T1), P(T1 → T2) and R0 basic reproductive number can be computed from the viral
replication cycle model.
15 / 28

23. Branching process
Extinction / Infection probability
Probability that a virus eventually goes extinct (E), given a single virus (V)reaches the target-cell
compartment:
E(Y0 = V) = min 1, 1 − P(V → T1) · P(T1 → T2) · 1 −
1
R0
where P(V → T1), P(T1 → T2) and R0 basic reproductive number can be computed from the viral
replication cycle model.
Extinction probability given n viruses reaches the target compartment :
E(Y0 = n · V) = E(Y0 = V)n
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24. Branching process
Extinction / Infection probability
Probability that a virus eventually goes extinct (E), given a single virus (V)reaches the target-cell
compartment:
E(Y0 = V) = min 1, 1 − P(V → T1) · P(T1 → T2) · 1 −
1
R0
where P(V → T1), P(T1 → T2) and R0 basic reproductive number can be computed from the viral
replication cycle model.
Extinction probability given n viruses reaches the target compartment :
E(Y0 = n · V) = E(Y0 = V)n
Infection probability I is the complement of the extinction probability:
I(Y0 = n · V) = 1 − E(Y0 = n · V)
15 / 28

25. Branching process
Extinction / Infection probability
Probability that a virus eventually goes extinct (E), given a single virus (V)reaches the target-cell
compartment:
E(Y0 = V) = min 1, 1 − P(V → T1) · P(T1 → T2) · 1 −
1
R0
where P(V → T1), P(T1 → T2) and R0 basic reproductive number can be computed from the viral
replication cycle model.
Extinction probability given n viruses reaches the target compartment :
E(Y0 = n · V) = E(Y0 = V)n
Infection probability I is the complement of the extinction probability:
I(Y0 = n · V) = 1 − E(Y0 = n · V)
These equations are valid only for the constant target-site antiviral concentrations.
15 / 28

26. An application : concentration-response curves
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
(Non-nucleoside) RT inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10
-1
10
0
10
1
10
2
0
10
20
30
40
50
60
70
80
90
100
Integrase inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10-1
100
101
102
0
10
20
30
40
50
60
70
80
90
100
Protease inhibitor
Drug/IC50
ProphylacticEfficacyϕ[%]
C D
B
IC50
IC50
IC50
Co-receptor antagonistA
Drug/IC50
ProphylacticEfficacyϕ[%]
IC50
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
Response - Prophylactic efficacy (ϕ)
Reduction in the infection probability due to the
drug w.r.t to no drug
ϕ = 1 −
I(Y0 = V, Drug)
I(Y0 = V, ∅)
IC50 reduces the target process by 50 %.
16 / 28

27. An application : concentration-response curves
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
(Non-nucleoside) RT inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10
-1
10
0
10
1
10
2
0
10
20
30
40
50
60
70
80
90
100
Integrase inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10-1
100
101
102
0
10
20
30
40
50
60
70
80
90
100
Protease inhibitor
Drug/IC50
ProphylacticEfficacyϕ[%]
C D
B
IC50
IC50
IC50
Co-receptor antagonistA
Drug/IC50
ProphylacticEfficacyϕ[%]
EC50
IC50
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
EC50
EC50
EC50
Response - Prophylactic efficacy (ϕ)
Reduction in the infection probability due to the
drug w.r.t to no drug
ϕ = 1 −
I(Y0 = V, Drug)
I(Y0 = V, ∅)
IC50 reduces the target process by 50 %.
EC50 provides 50% prophylactic efficacy.
Except for RTI, IC50 < EC50.
PrEP clinical trials guided by IC50 might be
misleading.
16 / 28

28. An application : concentration-response curves
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
(Non-nucleoside) RT inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10
-1
10
0
10
1
10
2
0
10
20
30
40
50
60
70
80
90
100
Integrase inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10-1
100
101
102
0
10
20
30
40
50
60
70
80
90
100
Protease inhibitor
Drug/IC50
ProphylacticEfficacyϕ[%]
C D
B
IC50
IC50
IC50
Co-receptor antagonistA
Drug/IC50
ProphylacticEfficacyϕ[%]
EC50
IC50
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
EC50
EC50
EC50
Translation of IC50 (target process inhibition) to
EC50 (prophylactic efficacy):
CRA, RTI, InI - Classical EMAX model :
ϕ(V) ≈
Dm
(IC50 · α
EC50
)m · +Dm
The scaling factor α ≥ 1 is dependent on the
inhibited (viral) target process. When
ϕ(V) < 1.
17 / 28

29. An application : concentration-response curves
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
(Non-nucleoside) RT inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10
-1
10
0
10
1
10
2
0
10
20
30
40
50
60
70
80
90
100
Integrase inhibitor
ProphylacticEfficacyϕ[%]
Drug/IC50
10-1
100
101
102
0
10
20
30
40
50
60
70
80
90
100
Protease inhibitor
Drug/IC50
ProphylacticEfficacyϕ[%]
C D
B
IC50
IC50
IC50
Co-receptor antagonistA
Drug/IC50
ProphylacticEfficacyϕ[%]
EC50
IC50
10
-1
10
0
10
1
10
20
10
20
30
40
50
60
70
80
90
100
EC50
EC50
EC50
Translation of IC50 (target process inhibition) to
EC50 (prophylactic efficacy):
CRA, RTI, InI - Classical EMAX model :
ϕ(V) ≈
Dm
(IC50 · α
EC50
)m · +Dm
The scaling factor α ≥ 1 is dependent on the
inhibited (viral) target process. When
ϕ(V) < 1.
PI - Power function (Switch-like behavior):
ϕ(V) =
1
R0(∅) − 1
·
Dm
ICm
50
when ϕ(V) < 1.
17 / 28

30. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
18 / 28

31. Hybrid stochastic-deterministic approach
*
*
*
*
**
*
Astochastictrajectoryof
viralreplicationprocess
Target-siteDrug
Pharmacokinetics
Time
Conc
Hybrid stochastic-deterministic approach
Time-varying target-site antiviral drug
concentration.
Pharmacokinetics - deterministic process.
Pharmacodynamics (viral replication
cycle) - stochastic process.
19 / 28

32. Hybrid stochastic-deterministic approach
*
*
*
*
**
*
Astochastictrajectoryof
viralreplicationprocess
Target-siteDrug
Pharmacokinetics
Time
Conc
Hybrid stochastic-deterministic approach
Time-varying target-site antiviral drug
concentration.
Pharmacokinetics - deterministic process.
Pharmacodynamics (viral replication
cycle) - stochastic process.
Deterministic ODE approach is
unsuitable.
Hybrid stochastic-deterministic approach
is required.
19 / 28

33. Hybrid stochastic-deterministic approach
Time [hr]
Target-processinhibitionη[%]
Time [hr]
A C
B D
NumberoffreevirusorT1
cells
0 500 1000 1500 2000 2500
10-3
10-2
10-1
100
101
102
T
2
T
1
NumberofT2
cells
V
10-2
10-1
100
101
102
Time [hr]
0 500 1000 2000 2500
1654 1656 1658 1660 1662 1664 1666 1668 1670
0
1
2
3
0
1
Time [hr]
NumberoffreevirusorT1
cells
NumberofT2
cells
V
T
1
T
2
1500
1600 1620 1640 1660 1680 1700
10
2
10
1
virus exposure virus
extinction
virus exposure
establishment of
infection
0
10
20
30
40
50
60
70
80
90
100
0
1
2
950 960 970 980 990 1000 1010 1020
EXTRANDE
Extra Reaction Algorithm for Networks in
Dynamic Environments. (Voliotis et al. 2016
PLoS Comput Biol).
Monte Carlo algorithm with thinning
techniques. (Numerically exact)
We adapted the algorithm by designing
stopping criteria.
20 / 28

34. Modeling framework
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
21 / 28

35. Results : Screening candidates
Prophylactic efficacy range
Detailed PK not available.
Cmin, Cmax and t1/2
Clow - trough conc. after 3 days
without drug administration.
Transmitted viruses distribution.
IC50 and m values from ex vivo.
(Shen et al. 2008 Nat Med)
Branching process theory
22 / 28

36. Results : Screening candidates
Prophylactic efficacy range
Detailed PK not available.
Cmin, Cmax and t1/2
Clow - trough conc. after 3 days
without drug administration.
Transmitted viruses distribution.
IC50 and m values from ex vivo.
(Shen et al. 2008 Nat Med)
Branching process theory
100
101
102
103
104
105
0
50
100
MVC*
EFV
NVP
ETR RPV*
0
50
100
ATV APV DRV*
0
50
100
LPV NFV SQV TPV
100
101
102
103
104
105
100
101
102
103
104
105100
101
102
103
104
105
Drug Concentration [nM] Drug Concentration [nM]Drug Concentration [nM]
max
min
max
min
max
min
DLV
max
min
max
min
max
min
RAL*
max
min
EVG
max
min
max
min
max
min
max
min
IDV
max
min
max
min
max
min
max
min
max
min
ProphylacticEfficacyϕ[%]
0
50
100
Drug Concentration [nM]
ProphylacticEfficacyϕ[%]
22 / 28

37. Results : Screening candidates
Prophylactic efficacy range
Detailed PK not available.
Cmin, Cmax and t1/2
Clow - trough conc. after 3 days
without drug administration.
Transmitted viruses distribution.
IC50 and m values from ex vivo.
(Shen et al. 2008 Nat Med)
Branching process theory
Efavirenz (EFV)
Nevirapine (NVP)
Etravirine (ETR)
Rilpivirine (RPV)
Darunavir (DRV) 100
101
102
103
104
105
0
50
100
MVC*
EFV
NVP
ETR RPV*
0
50
100
ATV APV DRV*
0
50
100
LPV NFV SQV TPV
100
101
102
103
104
105
100
101
102
103
104
105100
101
102
103
104
105
Drug Concentration [nM] Drug Concentration [nM]Drug Concentration [nM]
max
min
max
min
max
min
DLV
max
min
max
min
max
min
RAL*
max
min
EVG
max
min
max
min
max
min
max
min
IDV
max
min
max
min
max
min
max
min
max
min
ProphylacticEfficacyϕ[%]
0
50
100
Drug Concentration [nM]
ProphylacticEfficacyϕ[%]
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38. Results : PrEP strategies with dolutegravir
B
C
Time after dose (plateau) [hr] Time after last dose [hr]
10-3
10-2
10-1
100
101
0 24 96 120 21648 72 144 164 192
10-1
100
101
0 6 12 18 24
washout
A
DTGplasmaconc.[μM]
0
5
10
15
0 24 48 72 96
Time after initial dose [hr]
timetoplateau
withindosinginterval
central
peri-
pheral
Q/F
Vp/FVc/F
CL/F
dose
ka
D
PK-model
DTGplasmaconc.[μM]
Duwal et al. 2018 PLoS Comput Biol. accepted
Dolutegravir (DTG) PK model
Dolutegravir is an integrase inhibitor. Excellent
safety profile.
Institute of Translational Medicine, University of
Liverpool.
A two compartment model for plasma PK.
23 / 28

39. Results : PrEP strategies with dolutegravir
B
C
Time after dose (plateau) [hr] Time after last dose [hr]
10-3
10-2
10-1
100
101
0 24 96 120 21648 72 144 164 192
10-1
100
101
0 6 12 18 24
washout
A
DTGplasmaconc.[μM]
0
5
10
15
0 24 48 72 96
Time after initial dose [hr]
timetoplateau
withindosinginterval
central
peri-
pheral
Q/F
Vp/FVc/F
CL/F
dose
ka
D
PK-model
DTGplasmaconc.[μM]
Duwal et al. 2018 PLoS Comput Biol. accepted
Dolutegravir (DTG) PK model
Dolutegravir is an integrase inhibitor. Excellent
safety profile.
Institute of Translational Medicine, University of
Liverpool.
A two compartment model for plasma PK.
IC50 and m from Laskey et al. 2016 JCI insight.
IC50 corrected for in vivo use.
Hybrid stochastic-deterministic approach
(EXTRANDE)
23 / 28

40. Results : PrEP strategies with dolutegravir
Continuous PrEP
24 / 28

41. Results : PrEP strategies with dolutegravir
Continuous PrEP
% pills taken
ProphylacticEfficacyϕ[%]
25 50 75 95
0
10
20
30
40
50
60
70
80
90
100
100
50mg
10mg
2mg
50 mg DTG - Pharmacologically
forgiving.
Truvada - 96 %.
24 / 28

42. Results : PrEP strategies with dolutegravir
Continuous PrEP
% pills taken
ProphylacticEfficacyϕ[%]
25 50 75 95
0
10
20
30
40
50
60
70
80
90
100
100
50mg
10mg
2mg
50 mg DTG - Pharmacologically
forgiving.
Truvada - 96 %.
PrEP on Demand
24 / 28

43. Results : PrEP strategies with dolutegravir
Continuous PrEP
% pills taken
ProphylacticEfficacyϕ[%]
25 50 75 95
0
10
20
30
40
50
60
70
80
90
100
100
50mg
10mg
2mg
50 mg DTG - Pharmacologically
forgiving.
Truvada - 96 %.
PrEP on Demand
Timing of viral exposure after first DTG dose [hr]
50mg
12 18 23
40
50
60
70
80
90
100
0 1 3 6
ProphylacticEfficacyϕ[%]
10mg
2mg
30
Achieves high efficacy within a short
period of administration.
Truvada - 75 %-92 %.
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44. Results : PrEP strategies with dolutegravir
Continuous PrEP
% pills taken
ProphylacticEfficacyϕ[%]
25 50 75 95
0
10
20
30
40
50
60
70
80
90
100
100
50mg
10mg
2mg
50 mg DTG - Pharmacologically
forgiving.
Truvada - 96 %.
PrEP on Demand
Timing of viral exposure after first DTG dose [hr]
50mg
12 18 23
40
50
60
70
80
90
100
0 1 3 6
ProphylacticEfficacyϕ[%]
10mg
2mg
30
Achieves high efficacy within a short
period of administration.
Truvada - 75 %-92 %.
Post exposure prophylaxis
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45. Results : PrEP strategies with dolutegravir
Continuous PrEP
% pills taken
ProphylacticEfficacyϕ[%]
25 50 75 95
0
10
20
30
40
50
60
70
80
90
100
100
50mg
10mg
2mg
50 mg DTG - Pharmacologically
forgiving.
Truvada - 96 %.
PrEP on Demand
Timing of viral exposure after first DTG dose [hr]
50mg
12 18 23
40
50
60
70
80
90
100
0 1 3 6
ProphylacticEfficacyϕ[%]
10mg
2mg
30
Achieves high efficacy within a short
period of administration.
Truvada - 75 %-92 %.
Post exposure prophylaxis
ProphylacticEfficacyϕ[%]
PEP initiation after
virus challenge [hrs] Duration of PEP [days]
50
60
2
70
94 6
80
90
712
100
5
24 3
Starting earlier more important than
the duration.
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46. Summary
Viruses
ExposedTransmitter
Pharmacokinetics
Pharmacodynamics
(Viral replication cycle)
Direct response
model
Viral exposure model
Methods/Algorithms to quantity
infection probability
1) Branching process
theory
2)Hybrid stochastic
-deterministic approach
to select antivirals for PrEP
Branching process - list of candidates - EFV, NVP, ETR, RPV, DRV
to assess PrEP strategy
EXTRANDE
Continuous PrEP, PrEP on demand, Post exposure prophylaxis
Dolutegravir comparable to truvada.
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47. Related publications and links
Publications:
Duwal, S., Sunkara, V. and von Kleist, M., 2016. Multiscale Systems-Pharmacology Pipeline to
Assess the Prophylactic Efficacy of NRTIs Against HIV-1. CPT: pharmacometrics & systems
pharmacology, 5(7), pp.377-387.
Duwal, S. and von Kleist, M., 2016. Top-down and bottom-up modeling in system pharmacology
to understand clinical efficacy: An example with NRTIs of HIV-1. European Journal of
Pharmaceutical Sciences, 94, pp.72-83.
Duwal, S., Dickinson, L., Khoo, S. and von Kleist, M. 2018. Hybrid stochastic framework predicts
efficacy of prophylaxis against HIV: An example with different dolutegravir regimen. PLoS
Comput Biol, in Press
Duwal, S., Dickinson, L., Khoo, S. and von Kleist, M. Submitted 2017. Mechanistic framework
predicts drug-class specific utility of antiretrovirals for HIV prophylaxis.
Duwal, S., Schütte, C. and von Kleist, M., 2012. Pharmacokinetics and pharmacodynamics of
the reverse transcriptase inhibitor tenofovir and prophylactic efficacy against HIV-1 infection.
PloS one, 7(7), p.e40382.
Links:
Software : www.systems-pharmacology.org/prep-predictor
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48. Acknowledgement
Max von Kleist
Systems pharmacology and disease control
Free University Berlin, Germany
Vikram Sunkara
Zuse Institute Berlin, Germany
Laura Dickinson
Institute of Translational Medicine
University of Liverpool, United Kingdom
Saye Khoo
Institute of Translational Medicine
University of Liverpool, United Kingdom
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49. Thank you for your attention
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