YOUNG INNOVATORS 2011
Improving Patient Pharmacotherapy via
Informative Study Design and Model-based,
Decision Support
Jeffrey S. Barrett, PhD, FCP
The Children’s Hospital of Philadelphia
University of Pennsylvania School of Medicine
ABSTRACT
• Post approval clinical experience is often essential for evolving
optimal pharmacotherapeutic strategies particularly for patient
subpopulations including pediatrics and critically ill patients.
• Clinical pharmacology studies in these "at risk" populations
provide targeted investigation focused on evaluating the
therapeutic window.
• Much of my research has focused on designing such trials and
the evaluation of PK and PK/PD in order to propose dosing
recommendations from such studies.
• These studies can improve our understanding of disease biology
and many cases these efforts culminate in changes to the
standard of care.
Young Innovators 2011
ABSTRACT
• An important element of this research is the dissemination of
the knowledge that these investigations provide to the caregiver
community that ultimately prescribe and manage these patients.
• Decision support systems integrated to a hospital’s electronic
medical records system can provide this knowledge real-time
in an manner that evolves with the science and the data.
• An emerging consortium of clinical pharmacology, IT and
pharmacometric expertise has taken up the task to build such
systems to pave the way for expert pharmacotherapy systems
in the future.
Young Innovators 2011
INTRODUCTION
• Our knowledge regarding the optimal
management of drug therapy evolves with time
Drug Development
Pre-IND
IND
Phase I
Phase II
Post Marketing Evaluation
Phase III
Disease biology
Mechanism of action
Basic ADME
PK/PD in healthy volunteers and
patients
• Therapeutic window
• Safety and efficacy in target
populations
•
•
•
•
Clinical Practice / Utilization
Special populations
Patient “extremes”
ADR reporting
Long term safety and efficacy in
target populations
• Health economics
•
•
•
•
• DDI potential
• Safety “signals” in patient
subpopulations
• Compliance factors
• Lifestyle factors
• Patient factors
. . . Sometimes, we don’t know what we should know at a particular phase
Young Innovators 2011
INTRODUCTION
Well-designed trials . . .
• Fulfill study objectives
• Are well-powered and
designed
• Collect meaningful data at
the clinically-relevant
occasions
• Evaluate clinically-relevant
dose(s) / regimen(s)
• Minimize or eliminate
sources of confounding
• Study the appropriate
populations / characteristics
Modeling and simulation
techniques can facilitate welldesigned trials . . .
Young Innovators 2011
INTRODUCTION
OUTPUTS FROM MODELING & SIMULATION RESEARCH
• Models to evaluate dose-exposure (PK),
exposure-response (PD), clinical outcomes
(CTS)
• Model diagnostics and other means of
evaluating model appropriateness and
generalizability
• Simulations that describe model precision and
evaluate parameter sensitivity
• Simulations that test scenarios under which a
clinical trial can be conducted (design, dose,
sampling scheme, population, etc)
• Forecasting of future events based on
progression of model inputs or alteration of
experimental conditions
• Feedback loops that update models based on
predefined requirements (decision logic)
• Graphical representations of model outputs or
performance
Young Innovators 2011
INTRODUCTION
Application of M&S spans many settings that
facilitate pharmacotherapy guidance
• Systems biology modeling (target
identification and mechanism of action)
• Animal disease model to clinic bridge
• Formulation development (IVIVC)
• Special population modeling (bridging)
• Disease progression modeling
Young Innovators 2011
3 CASE STUDIES FROM BARRETT LAB
• Actinomycin / Vincristine in children with
Cancer
• Fluconazole dosing in Neonates
• Pediatric Knowledgebase (PKB)
Young Innovators 2011
AMD /VCR IN CHILDREN WITH CANCER
• Old chemotherapeutic agents used in a variety of
pediatric cancers without informative dosing
guidance
• Drugs often given in combination; difficult to do
PK in children with cancer – additionally,
venapuncture dissuades parents / children
• BPCA Contract proposed by NIH/NCI
– In August of 2002, the Children’s Oncology Group
suspended 3 active protocols for paediatric
rhabdomyosarcoma after 4 AMD-associated deaths
from VOD
Young Innovators 2011
AMD /VCR IN CHILDREN WITH CANCER
Project 1
Retrospective Study
Project 2
Catheter Study
Pooled historical data from Wilms tumour
and RMS studies to define dose-toxicity
relationships
Dosing and PK sampling procedure utilizing a
single central venous catheter
Project 3
M & S Study
Project 4
Prospective Study
PK/PD models based on exposure-response
relationships that incorporate physiologicbased and mechanistic expression; CTS
PK/PD/Out come trial in children with
cancer
Young Innovators 2011
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 1
< 1 y group at greater risk for hepatotoxicity with AMD
Older children at greater risk for neurotoxicity with VCR
Langholz B, Skolnik J, Barrett JS, Renbarger J, Seibel N, Zajicek A,
Arndt C. Dactinomycin and vincristine toxicity in the treatment
of childhood cancer: A retrospective study from the Children’s
Oncology Group. Pediatric Blood & Cancer 57(2):252-7, 2011.
Young Innovators 2011
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 2
Mimic of in vivo setting
– Common catheter configurations
– Procedures, agents and conditions for
clearing
1. Cook® 5 french 27 cm catheter
fragment
2. 200 µL pipette tip
3. Cook® catheter syringe connector
4. Medex 3-way stopcock
5. 5 mL syringe for waste collection
6. 3 mL syringe for sample collection Young Innovators 2011
Skolnik JM, Zhang AY, Barrett
JS, and Adamson PC.
Approaches to clear residual
chemotherapeutics from
indwelling catheters in
children with cancer J. Ther.
Drug Monitoring 32(6): 741-8,
2010.
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 2
Parameter
Assumptions/Initial Esitmates
F2: F unbound to central
0.76
F5: F bound in catheter
0.24
Fbound: F dissociated from bound
1.00
Kno: dissociation rate from bound
0.781 hr-1
Krinse: dissociation rate with “pull-push”
1.67 hr-1
K52 = Kno + Krinse*CYCL
Young Innovators 2011
Zhang AY, Skolnik JM, Dombrowsky E, Patel D, Barrett JS.
Modeling and Simulation Approaches to Evaluate
Chemotherapeutics Contamination From Central Venous
Catheters in Pediatric Pharmacokinetic Studies (Submitted,
Cancer Chemother Pharmacol)
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 3
Pediatric Exposure Profiles following 1.5 mg/m2 AMD
2.0
LIVER
10
Predicted Concentration ( g/mL)
20
1.5
1.0
0.5
0
0.0
0
4
8
12
16
20
4
8
0.005
0
25
50
75
Time (h)
100
125
SPLEEN
HEART
MARROW
CARCASS
MUSCLE
KIDNEY
LIVER
PLASMA
0.01
0
25
50
75
100
20
2.5
24
0
4
8
12
16
20
24
20
24
Time (h)
1.0
0.015
0.010
0.005
4
8
12
16
20
24
0
4
8
12
125
Time (h)
Young Innovators 2011
16
20
24
BONE MARROW
0.8
0.6
0.4
0.2
0
Time (h)
25
4
8
12
16
Time (h)
SPLEEN
HEART
Predicted Concentration ( g/mL)
0.1
0.0001
0.0001
5.0
0.0
0.001
0.001
16
CARCASS
Time (h)
Predicted Concentration ( g/mL)
Predicted Concentration
(g/mL)
Predicted Concentration
(g/mL)
0.01
7.5
0.000
0
1
0.1
12
Predicted Concentration ( g/mL)
Predicted Concentration ( g/mL)
Predicted Concentration ( g/mL)
0.010
3.5
1
0.020
0.000
80 kg Human: 15 g/kg (1200 g)
AMD
10.0
Time (h)
MUSCLE
0.015
12.5
0.0
0
24
Time (h)
12 kg Dog: 0.03 mg/kg (360  g)
AMD
KIDNEY
15.0
Predicted Concentration ( g/mL)
Predicted Concentration (ng/mL)
PLASMA
30
3.0
2.5
2.0
1.5
1.0
0.5
Simulated Weight Ranges
20
(10th and 90th Percentiles)
15
80 KG
40 KG
20 KG
10 KG
10
5
0
0.0
0
4
8
12
Time (h)
16
20
24
0
4
8
12
16
20
24
Time (h)
Barrett JS, Gupta M, Mondick JT. Model-based Drug
Development for Oncology Agents. Expert Opinion on Drug
Discovery 2(2): 185-209, 2007.
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 3
Pop-PK model developed in 34 children with cancer
p = 0.22
200
p = 0.4
140
p = 0.12
120
Count
Count
100
50
50
40
60
40
Count
100
80
60
BIAS
100
150
80
150
10
15
20
25
First Quartile Cmax (ng/mL)
-50
0
0
0
20
20
0
15
20
25
30
35
Median Cmax (ng/mL)
40
45
20
30
40
50
60
70
Third Quartile Cmax (ng/mL)
V1
70% Power
80% Power
90% Power
n per group
300
200
CL
Q
OMV1
OMCL
100
50
Bias
Model used to
verify sample size,
sampling scheme
and dosing rules
400
V2
0
-50
100
-100
V1
0
1
2
3
4
CL (L/h)
5
6
Young Innovators 2011
V2
V3
CL
Q2
Q3
OMV1
OMCL
Mondick JT, Gibiansky L, Gastonguay MR, Skolnik J,
Veal GJ, Boddy A, Adamson PC, Barrett JS.
Population Pharmacokinetics of Actinomycin-D in
Children and Young Adults. J Clin Pharmacol: 48(1):
35-42, 2008
AMD /VCR IN CHILDREN WITH CANCER
RESULTS – PROJECT 4
• ADVL06B1, A PharmacokineticPharmacodynamic-Pharmacogenetic
Study of Actinomycin-D and
Vincristine in Children with Cancer
Study officially closed to
enrollment on October 5, 2011
• Follow-up ongoing
• PGx complete
• Data assembly ongoing
• Preliminary data analysis ongoing
Young Innovators 2011
FLUCONAZOLE DOSING IN NEONATES
• We know . . .
–
–
–
–
–
–
–
Triazole class, inhibitor of fungal P450
Excellent CSF, lung, kidney & tissue penetration
Active drug eliminated by kidney with minimal metabolism
Low incidence of adverse events in children/adults
Effective in adults and children
C. albicans & parapsilosis sensitive to Fluconazole
C. galbrata & krusei are uniformly resistant
• We need to know . . .
– Pharmacokinetics in infants
– Optimal Doses for effective treatment and prevention of emergence of
resistance
– For systemic treatment: FL (AUC)/ Candida MIC>50
– For prevention: no known target
– Safety and efficacy
Young Innovators 2011
FLUCONAZOLE DOSING IN NEONATES
HISTORICAL DATA
PK
6mg/kg
Infants
Wiest
1991
H Saxen, K Hoppu
1993
28wk
26-29 wk
PNA 40d PNA d1
N=1
N=7
Wenzl
1998
26-29 wk 26-29 wk 25-29 wk
PNA d6
PNA
PNA
N=7
d13 N=4 >30d N=3
Cl (L/hr/kg)
0.0198
0.0108
0.0198
0.03128
0.029
Vd (L/kg)
1.2
1.18
1.84
2.25
1.43
T ½ (hr)
37.4
88.6
67.5
55.2
35
Delayed CL improves with postnatal age
Long t½
Large variability in individual PK parameters
No Pharmacokinetic data < 750 g
Inadequate to support dosing
Young Innovators 2011
FLUCONAZOLE DOSING IN NEONATES
OBJECTIVES
• To conduct a prospective PK trial to establish a population
PK model of fluconazole disposition in infants 23-40 weeks
gestation and < 120 days old
• To facilitate PK trial by leveraging clinical practice
– Fluconazole exposure as routine clinical care
– Sparse microvolume blood sampling timed with clinical care
– Scavenge left over plasma from discarded hematology samples to
increase samples in PK dataset
• To determine dosage guidelines that provide adequate
exposure for treatment and prevention of invasive
candidiasis
Young Innovators 2011
FLUCONAZOLE DOSING IN NEONATES
RESULTS
• Prospective, open label PK trial
• Inclusion Criteria
• Infants receiving Fluconazole as routine care
• GA 23-40 weeks, PNA<120 days
• Informed consent
• Dose and length of therapy determined by clinician
• Enrollment stratified by GA & PNA (8 groups)
• Clinical information collected from medical record
• Sparse sampling scheme
• Up to 6 samples around single dose
• Up to 3 samples at steady state (day 7, 14, 21)
• Supplement with scavenged samples
• New, highly sensitive LC/MSMS assay (10ng/ml)
• Population PK model: Non-linear mixed effect modeling
Young Innovators 2011
FLUCONAZOLE DOSING IN NEONATES
RESULTS
Characteristics
(N=55 infants)
Median (Range)
GA at birth (wk)
26 (23-40)
Post-natal Age (days)
16 (1-88)
Weight (g)
1020 (451-7125)
Gender (% male)
56% male
Indication
# Infants (%)
23 (42 %)
Prophylaxis for broad
antibiotic exposure
11 (20 %)
Prophylaxis for NEC
8 (15 %)
Treatment Fungal Sepsis
7 (13 %)
Empiric Treatment Fungal
4 (7 %)
Treatment of + fungal urine
2 (3 %)
# samples
Prophylaxis from birth
PK dataset
•55 infants
•357 PK samples
•217 (61%) timed samples
•140 (39%) scavenged
Young Innovators 2011
45
40
35
30
25
20
15
10
5
0
23-25 w k G
26-29 w k G
30-33 w k G
34-40 w k G
1st
2nd 3rd 4th 5th
PNA in weeks of life
6th
>7th
FLUCONAZOLE DOSING IN NEONATES
RESULTS
601
605
605
601
604
604
30
605
601
604
601
20
909
909
102
501
102
603
106606
1101
904
303908601
906 912
904606
605
110
912
303
908
904
906 912
503
902 110
201
912
303902
910
105
401
908
1103
906
912
907
904
902
101
503
902
907
503
502
907102
103
503
503
111
502
902
907
105
902
904
101
503
111
1410
503
906
111
1410
1410
908
1408
1411
111
1410
1410
902
602
501
901
101
902
105
901
111
503
103
101
1407
1401
1408
601
1102
1410
105
1416
111
1408
107
104
902
111
302
901
1410
202
201
201
104
101
903
103
107
1416
1412
1417
1409
1406
903
908
901
1409
103
203
1401
1405
1410
1415
901
908
1407
1401
104
107
1906
03 503
1410
1416
1411
107
103
101
104
901
101
1409
201
906
1412
907
1411
1418
1408
1411
1406
903
1409
1402
1401
201
1403
1407
903
202
903
1410
1403
1402
1418
1407
104
1416
1405
1402
302
1412
107
103
1405
103
1406
1414
903
103110
1409
1408
1414
901
1410
1404
1416
1401
202
1403
1414
1417
1418
107
201
302
104
1412
1414
202
1418
1407
1403
1412
103
1415
203
1409
1404
1403
1403
1405
901
201
1416
107
201
904
202
1409
301
203
302
1402
201
103
1418
1403
1404
202
107
202
1411
1411
1417
1401
101
1409
1409
203
202
907
201
1415
903
903
201
1410
1416
1404
107
1416
1405
1414
1408
1418
1401
1404
1417
202
1403
107
1416
1414
907
202
1415
1414
1418
107
1416
1417
201
1402
202
1408
1409
1416
1415
1404
1418
107
202
1406
1408
1418
107
1402
1402
1414
1416
1417
1418
909
912
601 912
912
909
909
605
40
5
605
1414
1407
503
201
111
605
Weighted Residuals
Observed Drug Concentration (ug/mL)
Observed Drug Concentration (ug/mL)
601
605 605
40
0
904
50
601
10
7
601
50
605
601
604
604
30
605
604
601
601
20
501
10
0
909
909
102
102
909
912
603
606 601
106
912
912
1101
909
909
904
912
303
606
601
908
906
904
605
110
912
303908
904
906
503
902
201
110
902
303
910
912
105
401
908
1103
906
912
907
902
904
101
503
907
902
503
102
502
907
103
503
503
111
502
902
907
105
902
904
101
111
1410
503
906
111
1410
1410
908503
1408
1411
1410
1410
111
902
602
901
501
101
902
105
901
111
503
103
101
1407
1401
1408
601
111
1102
1410
1
05
1416
1408
107
104
902
111
111
111
302
901
1410
202
201
201
104
101
906
903
103
107
1417
1412
1416
1409
1406
903
908
901
1409
103
203
1401
1405
1410
1415
901
908
1407
1401
107
104
103
1410
107
1416
1411
103
104
101
901
101
1409
201
906
503
1412
907
1411
1418
1408
1411
107
1406
903
1409
1402
1401
201
1403
1407
903
903
202
1403
1410
1402
1418
104
1416
1405
1402
302
1412
103
1405
103
1406
1414
903
107
103
1409
1408
1414
901
1410
1404
1401
1416
202
1418
1414
1403
1417
107
201
302
104
1412
1414
202
1418
110
1407
1403
1412
103
1415
203
1404
1409
201
1403
1403
1405
901
201
1416
1403
107
904
202
1409
301
203
302
1402
1418
103
1403
1404
202
107
202
1411
1411
1409
1417
1401
101
1409
1409
203
202
907
201
1415
903
903
1410
201
1416
1404
1416
107
1405
1416
1414
1408
1418
1401
1404
1417
202
1403
107
1416
1414
907
1415
1418
1414
107
1416
1417
201
1402
202
1408
1418
1415
1404
1416
1409
1414
107
202
1406
1408
1418
107
1402
1414
1416
1417
1418
3
1
-1
503
904
111
201
908
1410
101
501
1410
103
303
201
111
1411
303
503
1416
904
1403
603
1403
605
1409
202904 106
1416
107
101
1411
1405
902
302
203
1417
1409
111904
902
1408
303
104
101
503
1407
1409
1409
1417
1409
902 9081101
1416
605
1410
202
1406
202
605
1407
1417
1409
1406
1408 606 606912
1416
103
1409
104
903
907
107
1412
1410
501
1412
502
101
1401
903
604605
1403
1401
203
1403
1407
107
503
907
604
908601
107
1409
102
1414
1103
201
104
1410
101
1416
902
906
1414
902
901
1412
111
105110
1401
1403
1403
901
1415
1416
1418
104
1416
906
1418
1401
903
111
902
1409
1410
902
503
1412
1418
202
103
1404
1408
107
1411
901
907110
1408
901
1407
1402
1410
104
105
1402
502
1414
1401
107
1416
101
201
104
1412
1417
202
1408
902
1402
902
1401
901
102
903
903
201
1405
1418
1418
103
111
903
203
301
1412
912604
1416
201
503
107
1415
103
1408
1410
1404
912
103
1418
1414
1418
1408
601
601
901
1411
103
909
202
1404
1401
107
1415
104
1415
111
105
906
907
1405
1402
101
602
903
202
908
1415
1405
1402
601 909
202
1418
1416
1414
907
201
1409
907
909
1416
1403
103
901
1404
107
302
202
107
103
1418
1403
1412
107
912
909 605
107
1410401
1416
906
1402
103
104
202
1414
906 912
910
907
1402
1411
1403
1402
503
912
1403
1405
1416
909
201
105
1401
202
1406
1414
1410
901
1406
1418
908
302
203
1412
912
1102906
1414
1408
1402
1401
1418
903
912
202
1417
107
107
1404
1414
1406
201908 102
1409
111
1409
1418
1404
1410
605
302
103
1418
107
107
1408
201
201
1411
1407
1409
111 110
903
1417
1417
1414201
201
101
-3
601
601
601
601
503
904
1410
-5
0
20
40
Predicted Drug Concentration (ug/mL)
60
0
1pvdv.wmf
10
20
30
40
0
50
Individual Predicted Drug Concentration (ug/mL)
1ipvdv.wmf
V (L) = 1.024 (wt/1)
CL (L/hr) = 0.015 x (wt/1) 0.75 x (BGA/26)1.739 x (PNA/2)0.237 x SCRT(-4.896)(CR)
Residual Standard Error around estimates: 3-24%
Wade KC, Wu D, Kaufman DA, Ward RM, Benjamin DK, Ramey N,
Jayaraman B, Kalle H, Adamson PC, Gastonguay M, Barrett JS. Population
Pharmacokinetics of Fluconazole in Young Infants. Antimicrob Agents
Chemother 52(11):4043-9, 2008.
Young Innovators 2011
20
40
Predicted Drug Concentration (ug/mL)
60
1wrvp.wmf
FLUCONAZOLE DOSING IN NEONATES
RESULTS
3 mg/kg twice weekly (Kaufman)
1-13 days
5
0
5
10
15
20
25
>28 days
7.5
plasma [fluconazole] mcg/ml
14-27 days
10
Equivalent to 50 mg/kg/day
adult
<10% infants maintain
[Fluc] > MIC 4
PNA groups
30
Strategies for Prevention
2.5
35
Strategies for Treatment
Dose to achieve AUC 800
7
14
0
0
28
35
42
30-40 wk GA
30-40 wk GA
6 mg/kg Saxen: Q72 h (pna<14d),
17.5
15
12.5
10
7.5
25 mg/kg load
12 mg/kg/day
*Q48 hr dosing if GA 23-25 wks
& <8 days old
3
5
0
1
1
3
5
7
9
day of therapy
7
9
Day of Therapy
11
11
13
13
0
0
0
5
10
15
20
Dose mg/kg/day
25
>28 days
5
30
14-27 days
plasma [fluconazole] mcg/ml
PNA groups
1-13 days
20
Q48 hr (pna6 mg/kg
14-28d),
Q24 Saxen
(pna interval
>28d)
Q72/48/24
Equivalent to 200 mg/kg/day
adult
80% infants maintain
[Fluc] > MIC 4
35
1000
1250
500
250
750
1000 1250 1500 1750 2000
250 500 750
AUC after 30 mg/kg load & 12 mg/kg/day dosing
Predicted
AUC by Day of Therapy
2.5
23-29 week GA
23-29 wk GA
21
Day of Therapy
Young Innovators 2011
23-29 week GA
0
30-40 wk GA
7
14
21
Day of Therapy
28
35
42
PEDIATRIC KNOWLEDGEBASE
• Global appreciation and demand for personalized
medicine
• More quantitative data on benefit:risk of drug
therapy exists today with greater appreciation for
complexities of dosing requirements
• Medication errors and adverse drug reactions
affect at least 1.5 million people every year at a
cost to the healthcare system between $77 and
$177 billion annually
• 75% of drugs on market have no information on
how to manage drug therapy in children
Young Innovators 2009
PEDIATRIC KNOWLEDGEBASE
• Data provided in compendial sources is often
based on small studies – many pediatric
subpopulations are left behind
• Children are dosed (experimented on) every day
with the caregiver using only their “best medical
judgment” to guide them
• The knowledge is static  not specific to the
patient and does not evolve
Young Innovators 2009
PEDIATRIC KNOWLEDGEBASE
E Direct Indicators of Health Status
L (vital signs, BP, Temp, HR…)
E
C
T
R
O
N
I
C
Clinical Observations &
Patient Response to Therapy R
Disease/Condition specific
assessments (Scans, Tests…)
E
C
O
R
Procedures or Interventions D
S
TDM Data (Drug/Biomarker levels)
M E D I C A L
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PEDIATRIC KNOWLEDGEBASE
Opportunities for:
- Disease progression
- Population analysis
- Meta analyses . . . correlation
Longitudinal: within patient
Data Mining: across patients
PEDIATRIC KNOWLEDGEBASE
Historical Views of
Compendial guidance and
“Like” Patients
other relevant views of static
data
Views to formulary
guidance
Dashboard
Forecasting Tools for
Concept
Guidance on:
Views to past
 Existing dosing practices
patient hospital visits
 Caregiver requested
guidance
Views to clinically-relevant
 Projection of outcomes
indicators of pharmacotherapy
associated with current
status and guidance
or modified care
PEDIATRIC KNOWLEDGEBASE
• Serviceoriented
architecture
• Compliant
with HL7
CDA
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
•Anti-folate chemotherapeutic agent
•Renal excretion
•Enterohepatic recirculation
•Toxicity at high or prolonged low exposure
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
Disease
Dose
Route
Leucovorin
ALL
8-15 mg
IT
No
ALL
20 mg/m2
PO
No
ALL
100-300 mg/m2
IV
No
NHL
1 g/m2
IV
Yes
OS
12 g/m2
IV
Yes
D os e
Infus ion (h)
N
1 - 15 g/m 2
bolus or 20
42
50 - 250 m g/k g
4
46
50 - 250 m g/k g
6
78
8 g/m 2
4
96
50 - 350 m g/k g
100 - 300 m g/k g
0.725 - 15 g/m 2
6 - 8.5 g/m 2
6
6
6
4 to 6
40
33
30
22
7.5 g/m 2
6
12
Tim e (h)
M TX (uM )
R eferenc e
48
72
24
48
48
24
48
0.5
0.5
50
0.5
0.9
10
1
Tatters all 1975
72
48
48
24
48
72
96
0.1
1
1
5
1
0.2
0.075
Is ac off 1976
S toller 1977
N irenberg 1977
P erez 1978
E tc ubanas 1978
E vans 1979
Junk a 1979
A bels on 1983
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
BLACK BOX WARNING
METHOTREXATE SHOULD BE USED ONLY BY PHYSICIANS
WHOSE KNOWLEDGE AND EXPERIENCE INCLUDE THE USE
OF ANTIMETABOLITE THERAPY.
BECAUSE OF THE POSSIBILITY OF SERIOUS TOXIC REACTIONS
(WHICH CAN BE FATAL):
• METHOTREXATE SHOULD BE USED ONLY IN LIFE THREATENING NEOPLASTIC
DISEASES, OR IN PATIENTS WITH PSORIASIS OR RHEUMATOID ARTHRITIS WITH
SEVERE, RECALCITRANT, DISABLING DISEASE WHICH IS NOT ADEQUATELY
RESPONSIVE TO OTHER FORMS OF THERAPY.
• DEATHS HAVE BEEN REPORTED WITH THE USE OF METHOTREXATE IN THE
TREATMENT OF MALIGNANCY, PSORIASIS, AND RHEUMATOID ARTHRITIS.
• PATIENTS SHOULD BE CLOSELY MONITORED FOR BONE MARROW, LIVER,
LUNG AND KIDNEY TOXICITIES. (See PRECAUTIONS.)
• PATIENTS SHOULD BE INFORMED BY THEIR PHYSICIAN OF THE RISKS
INVOLVED AND BE UNDER A PHYSICIAN'S CARE THROUGHOUT THERAPY.
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
Current procedure is to photocopy “master” nomogram for specific
protocols and hand plot individual data
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
MTX
Administration
• Urine pH must be ≥ 7
• 25 mg/ml solution in
Dextrose 5% in water (D5W)
• Maximum absolute dose: 20g
Before
Administration
MTX TDM
MTX Cleared
• Begins 24 hours after the
start of MTX infusion
• Results plotted on protocolspecific nomogram
• Continues daily until MTX
level ≤ 0.1 µM
• MTX level ≤ 0.1 µM
• Patient can be discharged
0 – 24 Hours
24 Hours Discharge
Prehydration
Continuing Hydration
LVR Administration
• 750 ml/m2 of D5 0.22% NaCl
with 40 mEq/L NaHCO3 is
given over 1 hour
• If urine pH < 7, 0.5 mEq/L
NaHCO3 is given over 30
minutes. Repeated if urine
pH is < 7 after 1 hour
• D5 0.22% NaCl with 40 mEq/L
NaHCO3 at 100 ml/m2/hr
• Urine pH measured every 8h. If
pH < 7, 10 ml/kg hydration fluid is
given over 30 min and pH
measured
• Lasts until MTX level ≤ 0.1 µM
• LVR starts 24 - 42 h after start of
MTX infusion as 15 mg/m2 IVSS
over 15 minutes, every 6 hours
• Dose can be modified based on
protocol-specific nomogram
because of excretion delay
• Lasts until MTX level ≤ 0.1 µM
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
PEDIATRIC KNOWLEDGEBASE
THE METHOTREXATE DASHBOARD
PEDIATRIC KNOWLEDGEBASE
VISION
An international consortium of pediatric centers of
excellence that support and drive the development
of the PKB
PKB-lite development for clinics, institutions
without EMRs and small physician offices
Global connectivity that accommodates regional
and global best practices with guidance options
Guidance for developing countries / institutions
DISCUSSION
• Modeling and simulation activities allow the
investigator to:
• Select the right dose or dose range
• Use the minimal, but most informative,
sampling scheme to produce meaningful
results that satisfy regulatory requirements
• Propose a design / population that has the
greatest likelihood of fulfilling study
objectives.
Young Innovators 2009
DISCUSSION
• The link between clinical pharmacology and
medical informatics will provide an excellent
form for “real” personalized medicine:
• Decision support systems which integrate
patient records with drug and diseasespecific indices.
• Disease progression with forecasting of
individual patient disease trajectories
based on treatment modality options.
Young Innovators 2009
ONGOING RESEARCH IN BARRETT LAB
•
•
•
•
•
•
•
•
•
Disease progression modeling in pancreatic cancer
Model-based approaches to study nanomedicine strategies in oncology
Disease progression modeling in Spinal Muscular Atrophy (SMA)
Translational research in Neuro AIDS
Clinical evaluation of NK1r antagonism in NeuroAIDS
PK/PD relationships for next generation COX-2 inhibitors
PK/PD for natural products (frankinsense, silymarin, etc)
Model-based strategies for Traditional Chinese Medicine (TCM)
Clinical trial design optimization for early phase drug development in oncology
(NCI/CTEP)
• PBPK strategies in children to guide hospital-based dosing in critically-ill
children
• PK/PD relationships in obese children
• Correlation of DDI potential and observed toxicity in children with cancer
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ACKNOWLEDGMENTS
LAPK/PD Staff (past and present)
•
Di Wu, PhD
•
Dimple Patel, MS
•
Erin Dombrowsky, MS
•
Sarapee Hirankarn, PhD
•
Chee Ng, PhD
•
Yin Zhang, PharmD, PhD
•
Manish Gupta, PhD
•
Divya Menon, PhD
•
Doug Marsteller, PhD
•
Jason Williams, PhD
•
James Lee, PhD
•
Ganesh Moorthy, PhD
•
Gaurav Bajaj, PhD
•
Vu Nguyen, BS
•
Mahesh Narayan, MS
•
John Mondick, PhD
•
Craig Comisar, PhD
•
Sarah Kurliand, MBA
•
Linda Pederson, MBA
•
Heng Shi, PhD
•
Bhuvana Jayaraman, MS
•
Sundarajaran Vijakumar, PhD
•
Kalpana Vijakumar, MS
Collaborators
•
Stephen Douglas, MD
•
Peter C Adamson, MD
•
Carolyn Felix, MD
•
Athena Zuppa, MD
•
Jeffrey Skolnik, MD
•
Kelly Wade, MD
•
Walter Kraft, MD
•
John van den Anker, MD
•
Mike Fossler, PharmD, PhD
•
Marc Gastonguay, PhD
•
Sander Vinks, PhD
•
Andrea Edginton, PhD
•
Ram Agharkar, PhD
•
Shashank Rohatagi, PhD
•
Jun Shi, MD
•
Bernd Meibohm, PhD
•
Stephanie Laer, PhD
•
Hong Yuan, MD
•
Olivera Marsenic, MD
•
Hartmut Derendorf, PhD
•
Gunther Hochhaus, PhD
•
Toshimi Kimura, PhD
•
Jamie Renbarger, MD
•
Pat Thompson, MD
Young Innovators 2011
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Carsten Skarke, MD
Nick Holford, MD
Brian Anderson, MD
Saskia DeWildt, MD
Leslie Mitchell, PhD
Guy Young, MD
Leslie Ruffino, MD
Garret Fitzgerald, MD
Dwight Evans, MD
Dave Flockhart, MD
Robert Gross, MD
Brian Strom, MD
Dave Cadieu, BS
Diva Deleon, MD
Richard Aplenc, MD
Scott Shulman, MD
Greg Hammer, MD
David Drover, MD
Anne Zajicek, PharmD, MD
Jane Bai, PhD
Sandeep Dutta, PhD
REFERENCES
Zuppa AF, Adamson PC, Barrett JS. Letter to the Editor, Pediatric drug labeling: improving the safety and efficacy of pediatric therapies, J Pediatr. Pharmacol Ther 9(1): 70-71, 2004.
Barrett JS, Collison KR. Dosing LMWH in special populations: safety, PK/PD and monitoring considerations. International J of Cardiovascular Med and Science 4(2): 41-54, 2004.
Barrett JS, Labbe L, Pfister M. Application and impact of population pharmacokinetics in the assessment of antiretroviral pharmacotherapy. Clinical Pharmacokinetics 44(6): 591-625, 2005.
Zuppa AF, Mondick J, Davis LA, Maka D, Tsang B, Narayan M, Nicholson C, Patel D, Collison KR, Adamson PC, Barrett JS. Drug Utilization in the Pediatric Intensive Care Unit: Monitoring
Prescribing Trends and Establishing Prioritization of Pharmacotherapeutic Evaluation of Critically-ill Children. J. Clin. Pharmacol. 45: 1305-1312, 2005.
Meibohm B, Panetta C, Barrett JS. Population pharmacokinetic studies in pediatrics: Issues in design and analysis. AAPS Journal. 7(2): Article 48: E475-E487, 2005.
Kenna LA, Labbe L, Barrett JS, Pfister M. Modeling and simulation of adherence: Approaches and applications in Therapeutics. AAPS Journal. 7(2): E390-E407, 2005.
Zuppa AF, Nicolson SC, Adamson PC, Wernovsky G, Mondick JT, Burnham N, Hoffman TM, Gaynor WJ, Davis LA, Greeley WJ, Spray TL, Barrett JS. Population Pharmacokinetics of Milrinone in
Neonates with Hypoplastic Left Heart Syndrome Undergoing Stage 1 Reconstruction, Anesthesia & Analgesia 102(4):1062-9, 2006.
Barrett JS, Gupta M, Mondick JT. Model-based Drug Development for Oncology Agents. Expert Opinion on Drug Discovery 2(2): 185-209, 2007.
Barrett JS. Facilitating Compound Progression of Antiretroviral Agents via Modeling and Simulation. J Neuroimmune Pharmacol 2:58-71, 2007.
Zuppa AF, Vijayakumar S, Mondick JT, Pavlo P, Jayaraman B, Patel D, Narayan M, Boneva T, Vijayakumar K, Adamson PC, Barrett JS. Design and implementation of a web-based hospital drug
utilization system. J Clin Pharmacol: 47(9): 1172-1180, 2007.
Barrett JS. Quantitative Pharmacology in a Translational Research Environment. Chinese J Clin Pharmacol Therapeut: 12(10): 1081-88, 2007.
Skolnik JT, Barrett JS, Jayaraman B, Patel D, Adamson PC. Shortening the Timeline of Pediatric Phase 1 Trials: The Rolling Six Design. J. Clin Oncol 26(2): 190-5, 2008
Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S. Integration of Modeling and Simulation into Hospital-based Decision Support Systems Guiding Pediatric Pharmacotherapy. BMC
Medical Informatics and Decision Making 8:6, 2008.
Barrett JS. Applying Quantitative Pharmacology in an Academic Translational Research Environment. AAPS Journal 10(1):9-14, 2008.
Barrett JS, Jayaraman B, Patel D, Skolnik JM. A SAS-based solution to evaluate study design efficiency of phase I pediatric oncology trials via discrete event simulation. Computer Methods and Programs
in Biomedicine 90: 240-250, 2008.
Barrett JS, Fossler MJ, Cadieu, KD and Gastonguay MR. Pharmacometrics, A Multidisciplinary Field to Facilitate Critical Thinking in Drug Development and Translational Research Settings. J Clin.
Pharmacol 48(5): 632-49, 2008. Published in Chinese Journal as well Chinese J Clin Pharmacol Ther. 13(5): 481-493, 2008.
Zuppa AF, Barrett JS. Pharmacokinetics and pharmacodynamics in the critically ill child. Pediatr Clin North Am. 55(3):735-55, 2008.
Skolnik JM and Barrett JS. Refining the Phase 1 Pediatric Trial. Pediatric Health 2(2): 105-106, 2008. ponse. J Clin Oncology 29(23):3109-11, 2011.
Young Innovators 2009
REFERENCES
Barrett JS, Shi J, Xie H, Huang X, Fossler MJ and Sun R. Globalization of Quantitative Pharmacology: First International Symposium of Quantitative Pharmacology in Drug Development and Regulation. J
Clin Pharmacol 48(7): 787-792, 2008.
Barrett JS, Patel D, Jayaraman B, Narayan M, Zuppa A. Key Performance Indicators for the Assessment of Pediatric Pharmacotherapeutic Guidance. J Pediatr Pharmacol Ther 13: 141-155, 2008.
Wade KC, Wu D, Kaufman DA, Ward RM, Benjamin DK, Ramey N, Jayaraman B, Kalle H, Adamson PC, Gastonguay M, Barrett JS. Population Pharmacokinetics of Fluconazole in Young Infants.
Antimicrob Agents Chemother 52(11):4043-9, 2008.
Barrett JS, Skolnik JM, Jayaraman B, Patel D, Adamson PC. Improving Study Design and Conduct Efficiency of Event-Driven Clinical Trials via Discrete Event Simulation: Application to Pediatric
Oncology. Clinical Pharmacol Ther 84(6): 729-733, 2008.
Menon-Andersen D, Mondick JT, Jayaraman B, Thompson PA, Blaney SM, Adamson PC, Barrett JS. Population Pharmacokinetics of Imatinb Mesylate and its Metabolite in Children and Young Adults.
Cancer Chemother and Pharmacol 63(2):229-38, 2009.
Wade KC, Benjamin Jr. DK, Kaufman DA, Ward RM, Smith PB, Jayaraman B, Adamson PC, Gastonguay M, Barrett JS. Fluconazole dosing for the prevention or treatment of invasive candidiasis in young
infants. Ped Infectious Disease J 28(8): 717-23, 2009.
Läer S, Barrett JS, and Meibohm B. The In Silico Child: Using Simulation to Guide Pediatric Drug Development and Manage Pediatric Pharmacotherapy. J Clin Pharmacol 49(8): 889-904, 2009.
Su F, Nicolson SC, Gastonguay MR, Barrett JS, Adamson PC, Kang DS, Godinez RI, Zuppa AF. Population Pharmacokinetics of Dexmedetomidine in Infants Following Open Heart Surgery. Anesth Analg.
110(5):1383-92, 2010.
Marsenic O, Zhang L, Zuppa A, Barrett JS, Pfister M. Application of Individualized Bayesian Urea Kinetic Modeling to pediatric hemodialysis. ASAOI J 56(3):246-53, 2010.
Kimura T, Kashiwase S, Makimoto A, Kumagai M, Taga T, Ishida Y, Ida K, Nagatoshi Y, Mugishima H, Kaneko M, Barrett JS. Pharmacokinetic and pharmacodynaminc Investigation of Irinotecan
hydrochloride in Pediatric Patients with Recurrent or Progressive Solid Tumors. Int J Clin Pharmacol Ther. 48(5):327-334, 2010.
Skolnik JM, Zhang AY, Barrett JS, and Adamson PC. Approaches to clear residual chemotherapeutics from indwelling catheters in children with cancer J. Ther. Drug Monitoring 32(6): 741-8, 2010.
Langholz B, Skolnik J, Barrett JS, Renbarger J, Seibel N, Zajicek A, Arndt C. Dactinomycin and vincristine toxicity in the treatment of childhood cancer: A retrospective study from the Children’s Oncology
Group. Pediatric Blood & Cancer 57(2):252-7, 2011.
Dombrowsky E, Jayaraman B, Narayan M, Barrett JS. Evaluating Performance of a Decision Support System to Improve Methotrexate Pharmacotherapy in Children with Cancer. J. Ther. Drug Monitoring
33(1): 99-107, 2011.
Piper L, Smith B, Hornik CP, Cheifetz IM, Barrett JS, Moorthy G, Wade KC, Cohen-Wolkowiesz, Benjamin DK. Fluconazole Loading Dose Pharmacokinetics and Safety in Infants. Pediatric Infectious
Disease J 30(5): 375-8, 2011.
Barrett JS, Zuppa AF, Adamson PC, Patel D and Narayan M. Prescribing Habits and Caregiver Satisfaction with Resources for Dosing Children: Rationale for More Informative Dosing Guidance. BMC
Pediatrics 11: 25, 2011.
Maitland ML, Bies RR, Barrett JS. A Time to Keep and a Time to Cast Away Categories of Tumor Response. J Clin Oncology 29(23):3109-11, 2011.
Young Innovators 2009
BIOS/CONTACT INFO
Biography
Dr. Jeffrey S. Barrett, is Research Professor of Pediatrics, University of Pennsylvania School of Medicine, the Director of the Laboratory
for Applied PK/PD in the Division of Clinical Pharmacology and Therapeutics at the Children's Hospital of Philadelphia (CHOP) and an
Associate Scholar in the Center for Clinical Epidemiology and Biostatistics at The University of Pennsylvania. Dr. Barrett served as the
Principal Investigator for CHOP's Pediatric Pharmacology Research Unit and heads the Kinetic Modeling and Simulation core of the
Penn/CHOP CTSA. He also manages the pharmacology and biostatistics cores for several multidisciplinary projects both within CHOP,
UPenn and various multi-center cooperative groups. He received his BS from Drexel University in Chemical Engineering and his Ph.D.
in Pharmaceutics from the University of Michigan. Dr. Barrett spent 13 years in the pharmaceutical industry involved with PK/PD
aspects of clinical drug development and was an early proponent of industrial pharmacometrics prior to joining CHOP. He is a Fellow of
the American College of Clinical Pharmacology (ACCP) and the American Association of Pharmaceutical Scientists (AAPS) and
received the Young Investigator and Clinical Pharmacology Mentorship Awards from ACCP in 2002 and 2007 respectively. He is a
member of the FDA Clinical Pharmacology Advisory Committee, the Board of Directors of the Metrum Research Institute and the
Scientific Advisory Board of Pharsight Corporation. Dr. Barrett has co-authored over 100 manuscripts, 135 abstracts and has given over
100 invited lectures on PK/PD, clinical pharmacology and pharmacometrics. He joined the Editorial Boards of the Journal of Clinical
Pharmacology in 2007 and the Journal of Pharmacokinetics and Pharmacodynamics in 2009. Dr. Barrett has mentored numerous
physician fellows and post doctoral candidates in clinical pharmacology and pharmacometrics and continues to evolve his training
program to accommodate the demand for training in this area. Dr. Barrett’s research interest is focused on investigating sources of
variation in pharmacokinetics and pharmacodynamics applying clinical pharmacologic investigation coupled with modeling and
simulation strategies to pursue rational dosing guidance. He develops pharmacometric approaches to advance PK/PD, medical
informatics and disease progression modeling. Dr. Barrett has also integrated model-based decision support systems with hospital
electronic medical records and pioneered the pediatric knowledgebase development program for the past 6 years. He is actively involved
with creating disease progression models for spinal muscular atrophy and pancreatic cancer. His team is developing model-based
development approaches for Traditional Chinese Medicine, nanomedicine PK/PD-guided delivery and gene therapy.
Contact Details:
Jeffrey S. Barrett, PhD, FCP
Colket Translational Research Building, Rm 4012
3501 Civic Center Blvd
Philadelphia, PA 19104
Ph: 267-426-5479
Fax: 267-425-0114
Email: [email protected]
Young Innovators 2011