The document discusses pharmacometrics and provides examples of its applications in drug development and clinical trials. It begins with definitions of pharmacometrics and related terms. It then provides examples of how pharmacometric modeling and simulation have been used to: - Support pediatric drug approvals without the need for separate clinical trials by linking exposure to response between adult and pediatric patients. - Optimize drug doses in clinical trials and make dosing recommendations for specific patient populations. - Simulate clinical trials to determine trial designs and evaluate outcomes.

1. PHARMACOMETRICS
DR PRAFULL
DR ANIMESH
•Please put your smartphones and mobiles on silent mode – They may wake somebody
up
•This session will be videographed for training purposes

2. “...The quick inference, the subtle trap, the clever forecast of
coming events, the triumphant indication of bold theories —
are these not the pride and the justification of our life’s work?”
Sherlock Holmes in `The Valley of Fear’ (Sir Arthur Conan Doyle)
2

3. INFERENCES AND FORECASTS
• Pharmacometrics
Google search – 1,19,000 results
Pubmed search - 162 items
• Google search for:
Prafull – 5,47,000 results
Animesh – 7,66,000 results
Are the presenters more popular than the topic ?
• Pubmed search
Prafull – 2 items
Animesh – 39 items
NOW ?
3

4. •Four limbs
•One tail
•Coarse skin
•One trunk
•Two ivory tusks
May or may not be an Elephant
Previous data:
Every elephant has a trunk and ivory tusks
Conclusion:
•The animal is definitely an elephant
•Chances of getting killed by poachers are high
4

5. The CAR paradigm
•Mechanical engineer needs to know driving too
•Driver also should know basic troubleshooting
5

6. DRUG DEVELOPMENT PROCESS
Approx $1.8 Billion
(How to improve R&D productivity: The pharmaceutical industry’s grand challenge . Nature Reviews:Drug
Discovery 2010;9:203-14)
The Food and Drug Administration (FDA) has expressed its concern in Challenge and
Opportunity on the Critical Path to New Products published in March of 2004
6

7. DRUG APPROVALS PER YEAR
7
Malorye Allison
Nature Biotechnology 30,
41–49 (2012)
doi:10.1038/nbt.2083

8. Diabetes
Increased HbA1C
Increased blood
sugar
complications
Disease treatment outcome
8

9. Quality of
life,
longevity,
affordability
safety,
efficacy
efficacy,
safety,
guidelines
business
potential,
profit
9

10. MANAGE AND LEVERAGE KNOWLEDGE
Knowledge
Information
PHARMACOMETRICS
10

11. PHARMACOMETRICS
DEFINITION:
Pharmacometrics is the science of developing and applying mathematical and
statistical methods to characterize, understand, and predict a drug’s pharmacokinetic,
pharmacodynamic, and biomarker–outcomes behavior1
FDA DEFINITION
Pharmacometrics is an emerging science defined as the science that quantifies drug,
disease and trial information to aid efficient drug development and/or regulatory
decisions
1. P. J. Williams, A. Desai, and E. I. Ette, in The Role of Pharmacometrics in Cardiovas- cular Drug Development, M. K. Pugsley
(Ed.). Humana Press, Totowa, NJ, 2003, pp. 365–387.
11

12. PK modelPD-
biomarker-
outcomes
link model
statistics
Stochastic
simulation
PD model
Data
visualization
Computer
programming
Pharmacometrics
12

13. HISTORY AND DEVELOPMENT
• Peck- Ludden 87-95
Drug concentration development paradigm
Individual PK forecasting & individualized Rx
Population PK/PD applications
Pharmacometrics derived evidence of efficacy/safety (e.g., Phase 2b-3)
Randomized concentration-controlled trial
13

14. 1997
• Population PK guidance
2001
• End of Phase 2a Meeting idea emerged CDDS meeting 5
2002
• Clinical pharmacology subcommittee emphasizing
pharmacometrics solutions
• Drug approval decision based on PM analysis
2003
• Exposure-Response guidance
2007
• Disease model & trial design started (Parkinson’s disease)
• QT trial design & concentration-response analysis
2011
• Strategic plan emphasizing PM
• Centralized PM Data warehouse-Software environment
Lesko -Woodcock-
Galson –Murphy
95-present
14

15. CORNERSTONE OF
PHARMACOMETRICS
Bayesian statistics and Probability
Modeling
Simulation
15

16. BAYESIAN STATISTICS
16

17. CALCULATION OF POSTERIOR
PROBABILITY FROM PRIOR
PROBABILITY
• Suppose someone told you he had a nice conversation with someone on the
train
• Assuming women constitute half of the population the probability of the event
that the conversation was held with a woman P(W)= 0.5
• Then we come to know that the person had long hair
• Suppose it is also known that 75% of women have long hair and 25% of men
have long hair
18

18. CALCULATION OF POSTERIOR
PROBABILITY FROM PRIOR
PROBABILITY
Now our posterior probability of the person being a woman given the fact that the
person had long hair is
or
19

19. 20
A SIMPLE EXAMPLE
• Unknown parameter (): long-term systolic blood
pressure (SBP) of one particular 60-year-old female
• 4 measurements with a mean and a standard
deviation =5
• Survey of the same population (60-year-old female):
mean SBP =120 and standard deviation =10
130Y

20. 11/13/2008NONMEM Estimation Methods
21
ESTIMATION OF 
• Frequentist
• Point estimate:
• Interval estimate (95%CI)
• Bayesian
• Prior probability
• likelihood
• Posterior probability

21. 11/13/2008NONMEM Estimation Methods
22
ESTIMATION OF 
• Frequentist
• Point estimate:
• Interval estimate (95%CI)
• Bayesian
• Posterior distribution P(|Y)
• Posterior mean
• 95% credible interval
130ˆ  Y
5.2*96.113096.1 
n
Y

4.129|ˆ Y
4.2*96.14.129 

22. 23
PRIOR, LIKELIHOOD AND POSTERIOR
xp
pdens
80 100 120 140 160
0.00.050.100.15
Population Distribution
(prior)
Individual data
(likelihood)
Individual parameter
(posterior)
Long-term systolic blood pressure (SBP) of 60-year-old woman

23. BAYESIAN STATISTICS AND
PHARMACOMETRICS
• An important fact about a pharmacometrics based experiment is that
the design of any experiment is conditioned upon the results and
understanding of previous experiments.
• The process is inherently Bayesian.
24

24. 25
DEFINITION OF MODELING
Mathematical (conceptual) modeling is describing a physical phenomenon
by logical principles characterized with quantitative relationships, e.g.,
formulas, whose parameters may be measured (or experimentally
determined)
http://www.hawcc.hawaii.edu/math/Courses/Math100/Chapter0/Glossary/Glossary.htm

25. 26
USES OF MODELS
Yates FE (1975) On the mathematical modeling of biological systems: a qualified “pro”, in Physiological Adaptation to
the Environment (Vernberg FJ ed), Intext Educational Publishers, New York.
1. Conceptualize the system
2. Codify current facts
3. Test competing hypotheses
4. Identify controlling factors
5. Estimate inaccessible system variables
6. Predict system response under new conditions

26. DISEASE MODEL
• Mathematical models to
i. describe
ii. explain
iii. investigate
iv. Predict
the changes in disease status as a function of time
• . It incorporates
• functions of natural disease progression
• Placebo effect
• Drug action which reflects the effect of a drug on disease status
27

27. POPULATION PHARMACOKINETIC
AND PHARMACODYNAMIC
MODELING
Population modeling involves the analysis of data from a group
(population) of individuals, with all their data analyzed
simultaneously to provide information about the variability of the
model's parameters.
28

28. OXCARBAZEPINE
• Adjunct and monotherapy in adult patients and as adjunct therapy in
pediatric patients with partial seizures
• Exposure-seizure frequency data collected from adult and pediatric patients
submitted originally was subjected to qualitative analysis and to build an
exposure-response model to test:
whether placebo responses in adult and pediatric patients were similar
whether the exposure-response relationships in the 2 populations were similar
Derive reasonable dosing recommendations for monotherapy in pediatric patients
• Mixed-effects modeling indicated no important differences in the placebo and
drug effects between adults and pediatric pts.
Oxcarbazepine monotherapy in pediatric patients was approved without the need
for specific controlled clinical trials
(Bhattaram VA, Booth BP, Ramchandani RP. The AAPS Journal 2005; 7 (3))
29

29. CLINICAL TRIAL SIMULATION
• Simulation of a clinical trial can provide a data set that will resemble the
results of an actual trial.
• Multiple replications of a clinical trial simulation can be used to make
statistical inferences
• Estimate the power of the trial
• Predicting p-value
• Estimate the expected % of the population that should fall within a
predefined therapeutic range
30

30. NESIRITIDE
• Drug nesiritide for treatment of acute decompensated congestive heart failure
• PD marker: Pulmonary capillary wedge pressure (PCWP)
• Nesiritide reduced PCWP but also reduced SBP.
• Desired effects cannot be achieved without undesired effects, such as hypotension
April 1999, FDA issued a nonapprovable letter to the sponsor
Exposure and response data from the original submission were modeled and model was used to
explore various alternative dosing scenarios
31

31. 2 mg/kg followed by 0.01
mg/min/kg infusion offered a
reasonable benefit-risk profile.
This dosing regimen was selected
for additional investigation in the
Vasodilation in the Management of
Acute CHF (VMAC) trial.
The results obtained from the
VMAC trial and the simulations are
in close agreement with those
observed
NESIRITIDE
May 2001, FDA approved nesiritide for CHF
Publication Committee for the VMAC Investigators.
JAMA. 2002;287:1531-1540.
32

32. 33
TOOLS FOR MODELING AND SIMULATION
• NONMEM (UCSF, Globomax)
• SAS (SAS Institute Inc)
• Splus (Insightful Corporation) or R (Free)
• WinBUGS (MRC Biostatistics, Free)
• ADAPT II (USC, Free)
• WinNonLin/WinNonMix (Pharsight)
• Trial Simulator (Pharsight)

33. 34

34. APPLICATIONS OF PHARMACOKINETICS IN
CLINICAL TRIALS
• Dose determination in pediatric trials
• Optimizing dose in clinical trials (CT)
• Faster drug development
• Drug approvals without CT
• Better trial designs
• Risk reduction in CT
• Dose finding in adults
• Strengthening pharmacogenomics
• Evolving new strategies
35

35. TRIALS IN CHILDREN
Trials in adults
• Based on mortality benefits
• Large sample size
• Homogenous population
• Ethical issues
Trials in children
• Mainly to support dosing
recommendations
• Heterogeneous population
• Smaller samples and sample
sizes
Do more with less
36

36. TIPRANAVIR
• Tipranavir capsule, was approved as an HIV-1 protease inhibitor in adult patients
• seeking an approval of APTIVUS oral solution (OS) and capsule for HIVinfected
pediatrics 2 to 18 years of age
• 48-week, open-label, parallel, randomized clinical trial with 2 doses of TPV OS with
ritonavir (RTV).
290 + 115 mg/m2 (BSA adjusted adult dose) 375 + 150 (30% higher than lower dose
Matched exposure with
adult dose
Increased virological success
But:
•Better efficacy only in pts with higher no of mutations
•And Age of the patient directly correlates with no of mutations
•Higher dose for children > 6ys
•Lower dose for children < 6 yrs
•(Salazar JC, Cahn P, Yogev R, et al. AIDS. 2008;22:1789-1798
•US Food and Drug Administration. Pediatric drug development
2009. http://www.fda.gov/cder/pediatric/index.htm)
37

37. TIPRANAVIR Contd……
•Higher dose for children > 6ys
•Lower dose for children < 6 yrs
But:
In adult patients TPV is indicated for treatment experienced pts who are
likely to have more mutations.
In paed population too, target pt group is likely to have more mutations
Higher dose range recommended for all children
38

38. TIPRANAVIR Contd……
Higher dose range recommended for all children
But:
Pharmacokinetics of TPV are a function of body wt and not BSA or age
•Simulations were done for weight based dosing in place of BSA adjusted dosing
•12/5 mg/kg and 14/6 mg/kg were new decided as lower and higher doses.
TPV/RTV in the dose of 14/6 mg/kg approved for children
For adults too, recommended dose was increased to 375/150 mg/m2
39

39. FENOLDAPAM
• Systemic and renal vasodilator, approved in adults for in-hospital, short-term
management of severe hypertension
• Adult dose: 0.01 to 1.6 μg/kg/min
• Seeking an approval of fenoldopam for the pediatric population (from 1 month to
12 years of age) for the same indication.
• The sponsor studied doses of 0.05, 0.2, 0.8, and 3.2 μg/kg/min in pediatrics
Doses upto 0.8 mcg/kg/min
were approved
(Hammer GB, Verghese ST, Drover DR, Yaster M, Tobin JR. . BMC
Anesthesiol. 2008;8:6).
40

40. CANDESARTAN• Candesartan cilexetil, an ARB,
approved for the treatment of
hypertension and heart failure in
adults.
• Two 4-week dose-ranging safety
and efficacy studies were conducted
in hypertensive pediatric
participants
• 2 age groups (6 to <17 years and 1
to <6 years) were studied
separately
1 < 6 yrs 6 yrs – 17 yrs
Beneficial Not Beneficial
Placebo corrected versus placebo anchored analysis
US Food and Drug Administration. Review.
http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/UCM189129.pdf
41

41. FLUCONAZOLE• Triazole antifungal drug that is used to treat
invasive candidiasis in neonates in intensive
care
• Pharmacokinetic study to assess the dose-
exposure relationship in neonatal infants who
were 23 to 40 weeks gestational age
• 55 infants who contributed 357 plasma samples;
60% were from specified study sample times,
40% were scavenged samples
• Investigators developed a detailed population
model that showed the influence of a number of
clinical covariates on differences in
pharmacokinetics
(Wade KC, Wu D, Kaufman DA, et al. Population pharmacokinetics of fluconazole in young infants. Antimicrob
Agents Chemother. 2008;52:4043-4049)
Dose needed for a 24-week-old infant is approximately 67% lower on an mg/kg/d
basis than that necessary to treat a 32-week-old or a 40-week-old infant
42

42. SOTALOL
• Approved for ventricular and supraventricular tachycardia in adults
• sponsor conducted 2 clinical trials to investigate the antiarrhythmic potential
in pediatrics ages 1 month to 12 years
• Dosing recommendation: 30 mg/m2 three times daily as a starting dose with
subsequent titration to a maximum of 60 mg/m2.
• The PD effects of sotalol in pediatrics were similar to those in adults for a
given exposure. Hence, the exposure in the adults was a reasonable target
in pediatrics
• Clearance of sotalol increases until the patient reaches 2 years of age
independent of body-size; after 2 yrs it depends only on body size.
43

43. • FDA proposed a dose in
patients <2 years of age that
included an age factor
• The dosing recommendations
for sotalol in pediatrics aged 1
month to 12 years old were
incorporated in the labeling
• modeling efforts led to the
specific dosing instructions,
which were not directly
studied in trials, in patients <2
years of age.
SOTALOL
(Shi J, Ludden TM, Melikian AP, Gastonguay MR, Hinderling
PH. J Pharmacokinet Pharmacodyn. 2001;28:555-575).
44

44. LEVOFLOXACIN
• Approved by FDA in 2008 as treatment for children following inhalational exposure
to anthrax.
• Pharmacokinetic (PK) data from 90 pediatric patients receiving 7 mg/kg and two
studies of 47 healthy adults receiving 500 and 750 mg/kg levofloxacin were used
for the pharmacometric analyses.
• Body weight:covariate for levofloxacin clearance and the volume of distribution
• Clearance: reduced in pediatric patients under 2 years of age due to immature
renal function
• Different dosing regimens were simulated to match adult exposure
• Dose of 8 mg/kg twice a day was found to match the exposure of the dose
approved for adults
Indication added without actually conducting clinical trial
(Li F, Nandy P, Chien S, Noel GJ, Tornoe CW. Antimicrob Agents Chemother. 2010 Jan;54(1):375-9)
45

45. IDENTIFICATION OF BIOMARKERS
(Bhattaram VA, Booth BP, Ramchandani RP. The AAPS Journal 2005; 7 (3))
46

46. PK OF DABIGATRAN
• Dabigatran etexilate is the orally bioavailable pro-drug of dabigatran, a direct
thrombin inhibitor
• Data from eight clinical studies in healthy volunteers and patients population
pharmacokinetic (PK) and pharmacodynamic (PD) models were developed
to investigate whether the PK and PD of dabigatran differ across different
populations
• Renal function was the only covariate shown to have a clinically relevant
impact on dabigatran exposure
• PK of dabigatran is sufficiently consistent to allow extrapolation of data
generated in healthy volunteers to patients with AF or undergoing OS.
(Dansirikul C, Lehr T, Liesenfeld KH, Haertter S, Staab A. Thromb Haemost. 2012 Apr;107(4):775-85)
47

47. DABIGATRAN HEMODIALYSIS
• Hemodialysis a useful method of decreasing dabigatran plasma levels.
• Seven patients with ESRD were investigated in an open-label, fixed-
sequence, two-period comparison trial
• A population pharmacokinetic model was developed to fit the data and then
used for various simulations
• Dialysis duration had the strongest impact on dabigatran plasma
concentration
• Dialysis settings such as filter properties or flow rates had only minor effects
• The final model was successfully evaluated through the prediction of plasma
concentrations from a case report undergoing dialysis.
(Liesenfeld KH, Staab A, Härtter S, Formella S, Clemens A, Lehr T. Clin Pharmacokinet. 2013 Mar 26. [Epub])
48

48. PHARMACO –
GENOMICS AND METRICS
• Patients from a pharmacokinetic sub study, were reconsented and reenrolled
into a clinical trial for genotyping analysis
• 198 single nucleotide polymorphisms were genotyped
• 1260 nevirapine plasma concentrations obtained from 271 genotyped
patients
• Nevirapine clearance was 19.4% reduced in Asian/Black patients, compared
with Caucasian/Hispanic patients
• By integration of high-throughput genotyping data into a pharmacometric
analysis of nevirapine, the impact of the CYP2B6 516G>T polymorphism on
nevirapine's exposure was confirmed and quantified
(Lehr T, Yuan J, Hall D, Zimdahl-Gelling H. Pharmacogenet Genomics. 2011 Nov;21(11):721-30)
49

49. SWOT ANALYSIS
Strength:
quantitatively explore relationships among
different disease targets, quantify risk and
benefits
• Platform for communication for decision
makers
• Portability across modelers and
regulatory agencies
Threats: Protection of intellectual
property
Lack of regulatory guidelines
Lack of data warehouse and IT
infrastucture
Opportunities:
Engage project team at discovery stage,
Integrate knowledge across phases
• Create a shared vision
• Develop standard definitions
Weakness:
Shortage of trained experienced
leaders
• No shared strategic vision
• No standard process and definition
• No defined roles, responsibilities
50
Model-Based Drug Development : Strengths, Weaknesses, Opportunities, and Threats for Broad Application of Pharmacometrics in Drug Development Pharmacol 2010 50: 31S
Jeffrey D. Wetherington, Marc Pfister, Christopher Banfield, Julie A. Stone, Rajesh Krishna, Sandy Allerheiligen

50. KNOWLEDGE SHARING IN
PHARMACOMETRICS
Pharmacometrics now entering Industrialization phase
Similar analyses to be performed over and over
Access to prior knowledge are needed ---- Knowledge Sharing
51

51. The demand for scientists with pharmacometrics skills has risen
substantially. Likewise, the salary garnered by those with these
skills appears to be surpassing their counterparts without such
backgrounds
(Barrett JS, Fossler MJ, Cadieu JS. J Clin Pharm 2008;Volume 48 (5): 632–649)
52

52. 53
Frequentist conclusion
Bayesian conclusion

53. SUGGESTED READING
• Pharmacometrics: The Science of Quantitative Pharmacology Edited by Ene
I. Ette and Paul J. Williams John Wiley & Sons, Inc
• Pharmacometrics 2020DOI: 10.1177/0091270010376977 J Clin Pharmacol
2010 50: 151S Jogarao V. S. Gobburu
• Exposure-Response Modeling of Darbepoetin Alfa in Anemic Patients With
Chronic Kidney Disease not Receiving DialysisDOI: 0.1177/0091270010377201
J Clin Pharmacol 2010 50: 75S Sameer Doshi, Andrew Chow and Juan José
Pérez Ruixo
54

54. SUGGESTED READING
• Pharmacometrics as a Discipline Is Entering the ''Industrialization'' Phase:
Standards, Automation, Knowledge Sharing, and Training Are Critical for
Future Success J Clin Pharmacol 2010 50: 9S R. Gastonguay, Bernd Meibohm
and Hartmut Derendorf
• Model-Based Drug Development : Strengths, Weaknesses, Opportunities, and
Threats for Broad Application of Pharmacometrics in Drug DevelopmentJ Clin
Pharmacol 2010 50: 31S Dennis M. GraselaJ Clin Pharmacol 2010 50: 31S
Jeffrey D. Wetherington, Marc Pfister, Christopher Banfield, Julie A. Stone,
Rajesh Krishna, Sandy Allerheiligen
55

55. Thank You
56