Pharmacometrics 2.2.17

Pharmacometrics, clinical trials phases, medical, healthcare, pharmacology,

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 P harmacometrics 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: Characterize, understand and predict a drug’s pharmacokinetic and pharmacodynamic behavior Quantify uncertainty of information about that behavior R ationalize 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: Primary focus :decision to approve & label the drug product Provides advice on trial design decisions  Research is conducted to create new knowledge basis on 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 : Drug models  Typical focus of PM Referred as PK/PD relationship, concentration-effect, dose-response  Disease models  describe the relationship between biomarkers and clinical outcomes, time course & placebo Trial models  describe the inclusion/exclusion criteria, patient dis­continuation 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

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.

17. 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.

18. 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:

19. 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.

20. 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.

21. 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:

22. 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.

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

24. 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.

25. FDA Pharmacometrics 2020 Strategic Goals 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

26. 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

27. 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.