Pharmacometrics is the science of using mathematical and statistical methods to characterize and predict the pharmacokinetic and pharmacodynamic behavior of drugs. It aims to improve decision making in drug development and pharmacotherapy. Pharmacometric models integrate pharmacokinetic and pharmacodynamic models to describe the relationship between drug concentration, effect, and patient characteristics. Population pharmacometric modeling is useful for characterizing variability in these parameters between individuals.

1. PHARMACOMETRICS

2. INTRODUCTION
 Pharmacometrics- “measuring pharmacology”
 Defined as science of quantitative pharmacology
 Relationship between
 Exposure – Pharmacokinetics
 Response – Pharmacodynamics
 Both desired and undesired effects
 Individual patient characteristics

3. HISTORY
 Pharmacometrics first appeared in the literature in
1982 in the Journal of Pharmacokinetics and
Biopharmaceutics
 Pharmacokinetics : F. H. Dost in 1953
 Pharmacodynamics : Dungilson in 1848
Derendorf et al.

4. DEFINITION
 Science of developing and applying mathematical
and statistical methods to:
a. Characterize, understand and predict a drug’s
pharmacokinetic and pharmacodynamic behavior
b. Quantify uncertainty of information about that
behavior
c. Rationalize data-driven decision making in the
drug development process & pharmacotherapy

5. 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

6. PHARMACOMETRICS STAFF
 Multidisciplinary team consisting of :
 Quantitative clinical pharmacologists
 Statisticians
 Engineers
 Data management experts
 Clinicians

7. OBJECTIVES OF PHARMACOMETRIC WORK:
1. Primary focus :decision to approve & label the drug
product
2. Provides advice on trial design decisions
3. Research is conducted to create new knowledge basis o
n the unique data & literature -
to inform better regulatory and drug development
decisions

8. CORNERSTONE OF PHARMACOMETRICS
 MODELING refers to the development of a
mathematical representation of an entity, system
or process.
 PM model will improve both drug development and
support rational pharmacotherapy.
 SIMULATION refers to the procedure of solving
the mathematical equations on a computer that
resulted from model development.
 To provide a convincing objective evidence of a
proposed study design.

9. TYPES OF MODELS :
1. Drug models
 Typical focus of PM
 Referred as PK/PD relationship, concentration-effect,
dose-response
2. Disease models
 describe the relationship between biomarkers and
clinical outcomes, time course & placebo
3. Trial models
 describe the inclusion/exclusion criteria, patient dis-
continuation and adherence.

10. WHAT IS PK/PD MODELING
 PK/PD modeling is a scientific mathematical tool
which integrates PK model to that of PD model.
 PK model - describes the time course of drug
concentration in the plasma or blood.
 PD model - describes the relationship between drug
concentration at site of action & effect.
 Result is summation of Pharmacodynamics and
pharmacokinetics effect.

11. POPULATION PK/PD MODELLING
This includes the search for covariates such as patient weight,
age, renal function & disease status which contribute to
interindividual variability, affecting PK/PD relationship.
It is a useful tool during drug development.
OBJECTIVE : Characterisation of interindividual variability
in PK/PD parameters.
The detection and quantification of covariate effects may
influence the dosage regimen design.

12. BIOMARKERS
 NIH (National Institute of Health) defines biomarkers as
an indicator of a biological state.
 It is a characteristic that is measured and evaluated as an
indicator of normal biological processes, pathogenic
processes or pharmacologic responses to a therapeutic
intervention.
Detection of biomarker
Quantitative
a link between quantity of the marker and disease .
Qualitative
a link between existence of a marker and disease.
An Ideal Marker should have great sensitivity, specificity
and accuracy in reflecting total disease burden. A tumor
marker should be prognostic of outcome and treatment.

13. CLASSIFICATION OF BIOMARKERS
ANTECEDENT BIOMARKERS : Identifying the risk of developing
an illness. e.g. amyloidal plaques start forming before the
symptoms
SCREENING BIOMARKERS: Screening for subclinical disease.
E.g. abnormal lipid profile is a screening marker of heart disease.
DIAGNOSTIC BIOMARKERS: Recognizing overt disease. E.g.
Diagnostic kits for various diseases.
STAGING BIOMARKERS : Categorizing disease severity.
PROGNOSTIC BIOMARKERS: Predicting future disease course,
including recurrence and response to therapy and monitoring
efficacy of therapy.

14. APPLICATIONS OF BIOMARKERS
• Use in early-phase clinical trials to establish “proof of
concept”.
• Diagnostic tools for identifying patients with a specific
disease.
• As tools for characterizing or staging disease processes.
• As an indicator of disease progress.
• For predicting and monitoring the clinical response to
therapeutic intervention.

15. 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

16. CLINICAL DRUG DEVELOPMENT:
In clinical drug development, PK/PD modeling and
simulation can potentially impact both internal and
regulatory decisions.
 Drug Development process
Discovery (3years)
Preclinical (3.5 years)
Phase 1 (1 year)
Phase 2 (2 years)
Phase 3 (3 years)
 Thus it takes a molecule around 12-13 years to come
into market where it further faces the challenge of Phase 4
trials.

18. PHASE 1:
 Phase 1 starts with dose escalating studies in normal
volunteers with rigorous sampling. In addition, one may
establish an initial dose–concentration–effect relationship
that offers the opportunity to predict and assess drug
tolerance and safety in early clinical development.
 Quantitative dose–concentration–effect relationships
generated from PK/PD modeling in Phase1 can be utilized in
Phase 2 study design.
 PK/PD modeling is an important tool in assessing drug-
drug interaction potential.
 Dosage form improvements often occur based on the PK
properties of the drug candidate.

19. Phase 2 trials are typically divided into two stages, each with
some specific objectives.
Phase 2A : is to test the efficacy hypothesis of a drug
candidate, demonstrating the proof of concept.
Phase 2B : is to develop the concentration–response
relationship in efficacy and safety by exploring a large
range of doses in the target patient population.
The PK/PD relationship that has evolved from the
preclinical phase up to Phase 2B is used to assist in
designing the Phase 3 trial.
Phase 2:

20. PHASE 3:
OBJECTIVE:
 To provide confirmatory evidence that demonstrates
an acceptable benefit/risk in a large target patient
population.
 This period provides the ideal condition for final
characterization of the PK/PD in patients as well as for
explaining the sources of interindividual variability in
response, using population PK/PD approaches.

21. NDA REVIEW:
 PK/PD modeling plays an important role during the NDA
submission and review phase by integrating information
from the preclinical and development phases.
 Existence of a well defined PK/PD model furthermore
enables the reviewer to perform PK/PD simulations for
various scenarios.
 This ability helps the reviewer gain a deeper
understanding of the compound and provides a
quantitative basis for dose selection.
 Thus, PK/PD modeling can facilitate the NDA review
process and help resolve regulatory issues.

22. POST MARKETING PHASE:
Post-marketing strategy, population modeling
approaches can provide the clinician with relevant
information regarding dose individualization by:
Characterizing the variability associated with PK
and PD.
Identifying subpopulations with special needs.
PHASE 4:

23. TRAIL MODEL
 Optimize design of Phase 2 to phase 4 human trials
(set inclusion and exclusion criteria, give statistically
significant results by accounting for variation in
compliance and inter-patient variability.
 Help in making in-licensing decisions based on
predictions of effectiveness.
 Optimize target selection for a therapeutic indication.
 Formulating strategies for competitive differentiation of
novel drugs based on predicted effectiveness in clinical
and post market populations.

24. SOFTWARES USED IN PK/PD MODELING
•WinNonlin
•NONMEM
•XLMEM
•Boomer
• JGuiB (Java Graphic User Interface for Boomer)
•TOPFIT
•ADAPT II
•BIOPAK
•MULTI

25. PHARMACOMETRICS AND
REGULATORY AGENCIES
 FDA has promoted the role of pharmacometrics in
the drug approval process
 through its approach to review of applications and
by publishing its “guidances.”
 FDA has gained expertise in pharmacometrics from
self-training within and by recruitment of new highly
skilled personnel
 value of pharmacometrics continues to be
evaluated at the FDA.

26. FDA PHARMACOMETRICS 2020 STRATEGIC G
OALS
 Train 20 Pharmacometricians
 Technical track
 Disease track
 Drug development track
 Implement 15 Standard Templates
 Develop disease specific data & analysis standards
 Expect industry to follow
 Develop 5 Disease models
 Create public disease model library

27. CONTD..
 International Harmonization
 Share expertise between global regulatory bodies
 Integrated Quantitative Clinical Pharmacology
Summary
 All NDAs should have exposureresponse analysis
 Design by Simulation: 100% Pediatric Written
Requests
 Leverage prior knowledge to design Pediatrics
Written Request trials

28. CONCLUSION
 Pharmacometrics has improved the effectiveness of the drug
development process.
 It is relatively fast and inexpensive as compared to cost of
actual clinical trials.
 It can provide insight into both efficacy and cost effectiveness,
even with limited data.
 Project team members from various disciplines utilize the CTS
to explore a series of scenarios, from different trial designs, to
alternative ways of analyzing the generated data.
 It has great need of improved dosing strategy selection for the
avoidance of adverse events.