Evaluation of Polymyxin B
Toxicodynamics & Development
of an Adaptive Feedback Control
Algorithm
ELIZABETH A. LAKOTA, PHARM.D., M.S. CANDIDATE
ADVISOR: ALAN FORREST, PHARM.D.
Background
Polymyxins
-Group of antibiotics discovered in 1947
-Spectrum of coverage: Gram negative aerobes (except proteus)
-Parenteral polymyxin B and polymyxin E (colistin) used clinically
-Associated with significant nephrotoxicity
-Used until late 1960s when aminoglycosides came to market
Polymyxins
-Significant increase in multidrug resistant organisms over past decade
-Polymyxins reemerged in clinical practice
-Few to zero other therapeutic alternatives in many situations
-Polymyxin B may be less nephrotoxic than colistin
-Often used in critically ill patients
◦ Narrow therapeutic window
Objectives
Objectives
1. Determine an target AUC window for polymyxin B
◦ Lower edge pre-determined, focus on upper edge (toxicodynamics)
2. Develop an adaptive feedback control (AFC) algorithm to increase target
attainment
Methods Part 1
DEVELOPING POLYMYXIN B TARGET WINDOW
Therapeutic Window for Polymyxin B
- AUC/MIC drives polymyxin B efficacy
- AUC, duration of treatment, and baseline CLcr drive colistin nephrotoxicity
- Polymyxin B nephrotoxicity related to dose and duration of treatment
- Lower edge pre-determined to be 50 mg·h/L (ssAUC0-24h)
◦ Based on preclinical animal data
◦ Free drug ssAUC0-24h/MIC values of 24.7 mg·h/L and 22.5 mg·h/L needed murine lung and thigh
studies
◦ Plasma protein binding in humans ~50%
◦ Css ~ 2 mg/L needed for efficacy (unpublished data)
Dudhani et al., J. Antimicrob. Chemother., 65(9):1984-90, 2010
Upper Edge of Therapeutic Window
Toxicodynamic Meta-Analysis
1. Data from literature gathered using search terms ‘polmyxin’, ‘polymyxin B’,
‘nephrotoxicity’, ‘adverse event’, and ‘toxicity’
2. Data collected from each study: number of subjects, definition of nephrotoxicity,
number of nephrotoxicity events, dosing guidelines used, statistics on the doses
patients received, and statistics on weight
3. Excluded if dosing or rates of nephrotoxicity unclear or not provided
Toxicodynamic Meta-Analysis
- Monte Carlo Simulation of 1000 subjects for each study
- Steady state AUC0-24h simulated: ssAUC0-24h=Dose24h/CL
◦ CL(L/h)=0.0276*WT, BSV=32.4%
- Nephrotoxicity rates unified into 3 categories: > 25%, > 50%, and > 75% decrease in
CLcr
- Regression analyses used to evaluate data
Sandri et al., Clin. Infect. Dis., 57(4):524-531, 2013
Results Part 1
DEVELOPING POLYMYXIN B TARGET WINDOW
Toxicodynamic Meta-Analysis
- Total of 16 articles collected, 2 excluded
- Publication dates ranged from 2003-2013
- Number of study subjects ranged from 11-235
◦ Total subjects 971
- Mean/median daily polymyxin b dose ranged from 62.9-200 mg/day
Results of ssAUC0-24h Simulations
Author (year)
Ouderkirk et al. (2003)
Holloway et al. (2006)
Teng et al. (2007)
Pastweski et al. (2008)
Ramasubban et al. (2008)
Mendes et al. (2009)
Oliveira et al. (2009)
Elias et al. (2010)
Kvitko et al. (2011)
Esaian et al. (2012)
Kubin et al. (2012)
Toun et al. (2013)
Mandal et al. (2013)
Akajagbor et al. (2013)
Median (Min-Max)
25th Percentile AUC
(mg·h/L)
43.1
47.7
28.5
48.1
46.6
37.5
35.8
49.4
51.1
46.8
45.3
52.5
39.3
47.6
46.7 (28.5-52.5)
50th Percentile AUC
(mg·h/L)
57.4
67.4
42.4
48.1
59.7
50.8
51.0
77.1
75.7
54.2
62.2
76.8
57.0
62.3
58.6 (42.5-77.1)
75th Percentile AUC
(mg·h/L)
73.9
90.6
58.9
60.1
81.0
70.2
69.2
116.6
108.0
67.6
88.0
105.3
79.8
76.4
78.1 (60.1 – 117)
Results of ssAUC0-24h Simulations
Author (year)
Ouderkirk et al. (2003)
Holloway et al. (2006)
Teng et al. (2007)
Pastweski et al. (2008)
Ramasubban et al. (2008)
Mendes et al. (2009)
Oliveira et al. (2009)
Elias et al. (2010)
Kvitko et al. (2011)
Esaian et al. (2012)
Kubin et al. (2012)
Toun et al. (2013)
Mandal et al. (2013)
Akajagbor et al. (2013)
Median (Min-Max)
25th Percentile
AUC (mg·h/L)
43.1
47.7
28.5
48.1
46.6
37.5
35.8
49.4
51.1
46.8
45.3
52.5
39.3
47.6
46.7 (28.5-52.5)
50th Percentile
AUC (mg·h/L)
57.4
67.4
42.4
48.1
59.7
50.8
51.0
77.1
75.7
54.2
62.2
76.8
57.0
62.3
58.6 (42.5-77.1)
75th Percentile
AUC (mg·h/L)
73.9
90.6
58.9
60.1
81.0
70.2
69.2
116.6
108.0
67.6
88.0
105.3
79.8
76.4
78.1 (60.1 – 117)
Portion of Subjects with >50%
Decrease in CLCr
0.140
0.261
0.266
0.370
0.244
0.310
0.20
0.0938
0.0313
0.284
0.264 (0.0313 – 0.370)
Toxicodynamic Results
Methods Part 2
ADAPTIVE FEEDBACK CONTROL (AFC) ALGORITHM DEVELOPMENT
Inf
CLD
C1
V2
V1
Population PK Model
- Linear 2 compartment model
C2
CL
◦ Total body weight as covariate
◦ Tested covariates: TBW, LBW, sex, age, CLcr, albumin, APACHE II score
Parameter
CL (L/h/kg)
V1 (L/kg)
V2 (L/kg)
CLD (L/h/kg)
SDintercept (mg/L)
SDslope
Population Estimate Between Subject Variability (%CV) Standard Error (%SE)
0.0276
0.0939
0.330
0.146
0.0392
9.59%
32.4
73.3
70.1
50.4
7.49
23.6
19.5
22.2
Sandri et al., Clin. Infect. Dis., 57(4):524-531, 2013
Sampling Strategies
-Optimal sparse sampling strategies on day 1 determined
-Andi et al population PK model used with 2.5 mg/kg loading dose following by
1.25 mg/kg q12h
-D-optimality criterion in ADAPT-5 evaluated
-Generalized linear regression approach also
evaluated
Simulations with AFC
-Monte Carlo Simulations of 5000 subjects with PK samples at proposed times
- Concentrations simulated with sampling error (10%)
-Sandri et al population PK model was used as a MAP-Bayesian PK estimator in
ADAPT-5
-New doses calculated for each simulated subject
- New Dose = estimated CL · 75 mg·h/L (middle of target ssAUC0-24h)
-ssAUC0-24h computed for each subject based on new dose and true clearance
-Probability of target attainment determined for each sampling scheme
Results 2
ADAPTIVE FEEDBACK CONTROL (AFC) ALGORITHM DEVELOPMENT
Sparse Sampling Strategies
D-optimality:
2h, 4h, 12h when constrained between 2 -12h
2h, 4h, 12h, 24h when constrained between 2-24h
Generalized Linear Model Approach:
Number of Samples
Sample Time
Variability in ssAUC0-24h
Explained
1
24h
82.5%
2
12h,24h
88.5%
3
4h,12h,24h
90.5%
Adaptive Feedback Control Simulations
Adaptive Feedback Control Simulations
Number of
Samples
0
1
1
2
2
2
2
3
3
3
4
Time of
Samples
12 h
24 h
2, 12 h
2, 24 h
4, 24 h
12, 24 h
2, 4, 12 h
4, 12, 24 h
2, 12, 24 h
2, 4, 12, 24 h
Probability of
Target
Attainment
71.0%
93.6%
95.3%
92.2%
96.5%
97.7%
98.5%
93.7%
99.5%
99.2%
99.3%
% Above Target % Below Target
19.8%
5.0%
2.5%
4.6%
1.6%
1.7%
0.9%
4.1%
0.04%
0.5%
0.6%
9.2%
1.4%
2.2%
3.1%
1.9%
0.6%
0.6%
2.2%
0%
0.3%
0%
Variability in
ssAUC0-24h
(CV%)
Range of
Adjusted Doses
(mg/kg)
32.0%
17.8%
17.4%
19.1%
14.8%
12.5%
12.0%
17.5%
10.6%
11.1%
10.6%
2
0.77-4.6
0.83-4.4
0.52-4.8
0.66-4.9
0.65-5.5
0.67-5.3
0.50-5.2
0.64-6.8
0.68-5.0
0.71-5.8
Summary
- Proposed target ssAUC0-24h window is 50-100 mg·L/h
- Better studies/data needed
- Window will need to be updated
- Target attainment for polymyxin B without AFC is 71%
- Developed AFC algorithm can improve target AUC attainment in silico >93%
with just 1 sample
- AFC algorithm needs to be tested in humans (future plans)
Thank You
Dr. Alan Forrest – Project Mentor and Advisor
Dr. Donald Mager – Committee Member
Dr. Gauri Rao
Peter Bloomingdale, Veena Thomas, Vidya Ramakrishnan
Department of Pharmaceutical Sciences Faculty
The Institute for Clinical Pharmacodynamics
Family & Fiancé
Polymyxin B vs Colistin Nephrotoxicity
106 colistin, 47 polymyxin B patients
Akajagbor et al., Clin. Infect. Dis., 2013;57(9):1300-1303, 2013
36 colistin, 96 polymyxin B patients
Tuon et al. Int J Antimicrob Agents., 43(4):349-352, 2014
RIFLE Criteria
Studies Used in Meta-Analysis
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Ouderkirk et al. Antimicrob Agents Chemother. 2003;47(8):2659-2662.
Holloway et al. Ann Pharmacother. 2006;40(11):1939-1945.
Teng et al. Int J Antimicrob Agents. 2008;31(1):80-82.
Pastewski et al. Ann Pharmacother. 2008;42(9):1177-1187.
Ramasubban et al. Indian J Crit Care Med.2008;12(4):153-157.
Mendes et al. Ann Pharmacother. 2009;43(12):1948-1955.
Oliveira et al. Diagn Microbiol Infect Dis.2009;65(4):431-434.
Elias et al. Antimicrob Agents Chemother. 2010;65(10):2231-2237.
Kvitko et al. J Antimicrob Chemother. 2011;66(1):175-179.
Esaian et al. Ann Pharmacother. 2012;46(3):455-456.
Kubin et al. J Infect.2012;65(1):80-87.
Tuon et al. Int J Antimicrob Agents. 2014;43(4):349-352.
Nandha et al. Indian J Crit Care Med. 2013;17(5):283-287.
Akajagbor et al. Clin Infect Dis. 2013;57(9):1300-1303.
Author
(year)
Comorbidities
Mechanical Ave Age Sex
Ventilation (years) (%M)
Site of
Infection
Infecting Organism
Other Nephrotoxic
Agents
Mortality
50% cardiac,
Ouderkirk et
23% cancer, 18%
al. (2003)
DM, 18% AIDs
95%
61
65%
65% lung
65% A. Baumannii
47% aminoglycoside
70% vancomycin
20%
Holloway et
al. (2006)
APACHE II Score
17 (7-38)
N/A
41
78%
50% lung,
43% BSI
100% A. Baumannii
14% aminoglycoside
27%
Teng et al.
(2007)
48% cardiac,
26% DM
N/A
63
59%
41% lung
78% A. Baumannii
N/A
30%
Pastweski et
al. (2008)
73% cardiac,
55% DM
64% vancomycin
9% amphotericin
36%
62% aminoglycosides
16% vancomycin
52%
24% aminoglyc., 48% vanc.,
21% amphotericin
62%
75% vancomycin
7% amphotericin
61%
Ramasubban
29% cancer, 13%
et al.
DM
(2008)
Mendes et
al. (2009)
Oliveira et
al. (2009)
32% cardiac,
25% DM
APACHE II Score
16 (6-31)
N/A
84%
72
53
54%
N/A
56%
49% lung,
22% surgical
wound
92%
69
67%
42% urinary,
27% lung
90%
64
45%
44% BSI,
42% lung
36% A. Baumannii
27% P. Aeruginosa
27% K. Pneumoniae
42% A. Baumannii
44% P. Aeruginosa
11% A. Baumannii
83% P. Aeruginosa
100% A. Baumannii
Mechanical Ave Age Sex
Ventilation (years) (%M)
Site of
Infection
Infecting
Organism
Other Nephrotoxic
Agents
Mortality
59%
76% lung, 22%
urinary
31% A. Baumannii
46% P. Aeruginosa
N/A
61%
62
60%
38% lung, 20%
intra-abd
100% P. Aeruginosa
N/A
67%
69
53%
34% lung, 16%
urinary
23% A. Baumannii
55% K. Pneumoniae
15% aminoglycosides
62% lung
26% A. Baumannii
33% P. Aeruginosa
45% aminoglyc., 71%
vanc., 5% amphotericin
11% aminoglycoside,
55% vancomycin, 7%
amphotericin
47%
Author
(year)
Comorbidities
Elias et al.
(2010)
48% cardiac, 32%
pulm, 30.4% neuro
67%
59
Kvitko et
al. (2011)
cardiac 33%, neuro
31%, DM 26%
62%
Esaian et
al. (2012)
DM 36%, COPD
24%
41%
Kubin et al.
(2012)
Lung 38%, DM
33%, cardiac 30%
56%
58
55%
34%
N/A
Toun et al.
(2013)
Charlson Score 0
N/A
47
61%
40% lung
32% A. Baumannii
16% P. Aeruginosa
Nandha et
al. (2013)
APACHE II 13
N/A
49
53%
38% BSI, 28%
intra-abd
82% A. Baumannii
None given
28%
Akajagbor
et al.
(2013)
Cardiac 34%, DM
33%
N/A
53
44%
50% lung, 13%
mixed sites
77% A. Baumannii
23% P. Aeruginosa
22% aminoglycoside
57% vancomycin
N/A
Author (year)
Number of
Subjects
Institution PMB Dosing
Recommendations
Daily Poly B
Doses (mg/day)
Weight
(kg)
Nephrotoxicity Definition
Nephrotoxicity
Incidence
Ouderkirk et al.
(2003)
50
1.5-2.5 mg/kg/day
Mean 110
N/A
2 fold ↑ in SCr to > 2 mg/dL
14%
N/A
1.5 fold ↑ in SCr, ↑ in SCr of >
0.5 mg/dL , or 50% reduction
in CLCr
22.5%
18.5%
Holloway et al.
(2006)
31
N/A
Median 130
Teng et al.
(2007)
27
N/A
Mean 62.9
N/A
1.5 fold ↑ in SCr, increase in
SCr of > 0.5 mg/dL , or 50%
reduction in CLCr
Pastweski et al.
(2008)
11
1.5-2, 1.25 if CLCr 30-80, 0.5
if CLCr <30
Mean 84
N/A
↑ in SCr of > 0.5 mg/dL , or
50% reduction in CLCr
54.5%
Ramasubban et al.
(2008)
45
1.5-2 mg/kg/day
Mean 120
N/A
↑ in SCr by 0.5 mg/dL
8.89%
21.9%
Mendes et al.
(2009)
114
N/A
Mean 96.7
N/A
If baseline SCr <1.5, when SCr
↑ to > 1.8. If baseline SCr
>1.5, 1.5 fold ↑in SCr
Oliveira et al.
(2009)
30
N/A
Median 100
N/A
2 fold ↑ in SCr or ↑ in SCr of
>1 if initial SCr > 1.4 mg/dL
27%
N/A
Mild: 50-100% ↑ SCr, Mod:
>/=100% ↑ SCr but no dialysis,
Severe: dialysis
Mild: 13.6%,
Mod: 26.4%,
Severe: 21.9%
Elias et al.
(2010)
235
N/A
Median 150
Author (year)
Number of
Subjects
Institution PMB Dosing
Recommendations
Daily Poly B
Weight
Doses (mg/day)
(kg)
Nephrotoxicity
Definition
Nephrotoxicity
Incidence
Kvitko et al. (2011)
45
N/A
Mean 141
N/A
Stage 1: 1.5-2 fold ↑ in SCr
Stage 2: > 2 fold ↑ in SCr
Stage1: 11% Stage 2:
24%
Esaian et al. (2012)
115
1.5-2.5 mg/kg/day, adjust for
renal dysfunction
Median 100
Median
69
Meeting any of the RIFLE
Criteria
Risk 48%, Injury 31%,
Failure 17%
Kubin et al.
(2012)
73
2.5-3,
1-1.5 if CLCr < 80mg/mL
Median 180
Median
76.4
Meeting any of the RIFLE
Criteria
Risk 27.4%, Injury or
Failure 20%
Stage 1 11.5%, Stage
2 8.33%, Stage 3
1.04%
Toun et al.
(2013)
96
N/A
Median 200
N/A
Stage 1: 1.5 -2 fold ↑ SCr or
SCr ↑ of 0.3. Stage 2: 2-3x ↑
in SCr. Stage 3: >3x ↑ in SCr
or SCr > 4 w/acute rise > 0.5
Nandha et al.
(2013)
32
1.5-2.5
Mean 111
N/A
Meeting any of the RIFLE
Criteria
Risk 18.8%, Injury
15.6%, Failure 3.13%
Akajagbor et al.
(2013)
67
1.5-2
Median 123
Median
74
Meeting any of the RIFLE
Criteria
Risk 13.4%, Injury
19.4%, Failure 8.96%
Kidney Risk Plots
Kidney Injury Plots
Kidney Failure Plots
Population PK Model Details
-24 subjects total
-Doses ranged from 0.45-3.38 mg/kg/day administered as short term
infusions (60-240 minutes) every 12 hours
-8 blood samples per subjects
- Pre-dose, 5 min, and 0.5, 1, 2, 4, 8, & 12 h after end of the infusion
-Urine collected for 17 subjects
-S-ADAPT platform, MCPEM algorithm used
-AUC0-24h = 66.9 +/- 21.6 mg·h/L (range 16.4-117)
-Fraction bound = 0.42 (range 0.26-0.64)
-Median % excreted unchanged in urine: 4.04% (range 0.98-17.4%)
Sandri et al., Clin. Infect. Dis., 57(4):524-531, 2013
Population PK Model Details
Adaptive Feedback Control