Pharmacometrics

 

What is Pharmacometrics?

Pharmacometrics uses models to describe the beneficial and adverse effects of a drug therapy on a patient.

Data extracted from pre-clinical studies, clinical studies, scientific literature and competitor regulatory filings are integrated, building a complete data package describing the characteristics of a given lead compound.

From this integrated data set, mathematical models of exposure and response are built and qualified. These models become a representation of what is known about the compound’s behavior.

 

Why Pharmacometrics?

By combining different types of models with a simulation step, a pharmacometrician brings quantitative insight to the strategic decisions arising in commercial drug development.

“The single-most important strength of pharmacometric analysis is its ability to integrate knowledge across the development program and compounds, and biology.”

– U.S. FDA

 

qPharmetra's Pharmacometric Services are used across
the drug-development lifecycle

 
  • Modeling & Analysis
  • Simulations
  • Strategic Decision Making

  • PK & PopPK Modeling
  • PK/PD Modeling
  • NCA Analysis
  • Drug-Disease Modeling

Characterize PK within and across studies; examine different sampling and treatment regimens; characterize differences among patients


Problem
  • Need to characterize kinetics of new therapies, understand if drugs are getting to the action site, in the right amount
  • What are the effects of within- and between-patient variability
  • What are relevant patient covariates? Absorption or effect delays?
Solution
  • Pharmacokinetic analysis, by compartmental methods. Non-linear mixed effects analysis to characterize drug concentration vs. time, impact of covariates, alternative formulations, etc.
  • Regulatory-ready formal reports to codify the search for and findings of the best model
Benefits
  • Understand the sources of variability, including patient demographics, which are informative for trial design
  • Useful for dialog with regulatory agencies to support dosing, labeling, etc.​
  • Ability to leverage this for additional down-stream analysis

What is the relationship between exposure to the drug and its effects?


Problem
  • Can we robustly predict our drug’s effects before we invest millions of $ in further development?
  • What will our drug’s efficacy and tolerability be at doses we haven’t studied yet?
  • How do exposure, patient variability and demographics affect them?
  • Can we learn from other trials’ data?
Solution
  • Simultaneous or stepwise analysis to characterize the dose-exposure-response relationship, incorporating necessary mechanistic considerations
  • Technical competence to use the right tool for the job (NONMEM, S-PLUS/R, WinBUGS)
Benefits
  • Pool data across trials to learn about covariate effects and characterize variability across trials
  • Predict dose-response, with probability of success in user-friendly graphical forms that communicate to broad audiences
  • Ability to leverage this for more strategic analysis

Characterize your drug exposure with minimal prior assumptions


Problem
  • Need to characterize drug exposure, dose-linearity, bioequivalence ​ of alternative formulations​
  • Time-sensitive summaries in clinical or preclinical setting
Solution
  • Non-compartmental analysis of pharmacokinetic data​
  • Regulatory-ready formal reports summarizing PK data​
  • Provide results output in PP domain CDISC SDTM format ​ suitable for inclusion in clinical database
Benefits
  • Get “first-cut” information on drug exposure, clearance​
  • Useful summary of raw data, comparison among doses, etc.​
  • Ability to leverage this for additional down-stream analysis

Drug-disease modeling combines two sets of modeling approaches: population-based models, which are typically classified as pharmacometric (PMX) models and systems dynamics models, which encompass a range of models of physiology, signaling pathways in biology, and substance distribution in the body which together are often called quantitative systems pharmacology models (QSP). Drug-disease modeling integrates PMX and QSP to include selected mechanistic aspects in a fit-for-purpose configuration to address variability and the testing of covariates. These models can be used from preclinical through Phase 3.


Problem
  • What is the expected outcome of the next trial (phase II or III) given the prior information on the drug that was captured in a predictive model?
  • Which trial provides the greatest value to the program?
  • Which of several alternative trial designs are the best?
Solution
  • Leverage existing modeling work: Predictive PK/PD modeling, meta-analysis, etc.
  • Simulate model-based trial outcomes across various trial designs (dosing schemes, patient population, sample size, etc.) and keep track of how they fare
Benefits
  • Robustly predict the next trial’s outcome
  • Optimize probability of trial success, decrease time to filing, enhance information gained per trial, and maximize trial benefit/cost ratio
  • Inform investment decision with key metrics of probability, time, cost

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  • Clinical Trial Simulation

How do we optimize trial design to get the best probability of success at the least cost? “War game” the alternatives before investing.


Problem
  • What is the expected outcome of the next trial (phase II or III) given the prior information on the drug that was captured in a predictive model?
  • Which trial provides the greatest value to the program?
  • Which of several alternative trial designs are the best?
Solution
  • Leverage existing modeling work: Predictive PK/PD modeling, meta-analysis, etc.
  • Simulate model-based trial outcomes across various trial designs (dosing schemes, patient population, sample size, etc.) and keep track of how they fare
Benefits
  • Robustly predict the next trial’s outcome
  • Optimize probability of trial success, decrease time to filing, enhance information gained per trial, and maximize trial benefit/cost ratio
  • Inform investment decision with key metrics of probability, time, cost

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  • Model Based Meta-Analysis
  • Decision Analysis
  • Clinical Analysis

Where does our drug stand vis-à-vis competitors in a crowded market? At what dose does ours fare the best?


Problem
  • Does our drug stand a chance against the Standard of Care?
  • What dose of my drug is superior/non-inferior to SoC?
  • What are expected outcomes in various patient populations?
Solution
  • Integrate public domain competitor/placebo summary statistical data into a model
  • Combine public summary-level and internal patient-level data to learn from both
Benefits
  • Quantitative imprint of the entire competitive market, ready for multi-faceted trade-off assessments​
  • Efficacy / safety ratio comparisons across marketed and development drugs​
  • Head-to-head comparisons in patient populations that cannot be studied (because of time and cost) by a single sponsor

What is the best clinical plan given the probability of success, time to market, and commercial potential?


Problem
  • What is the value of information for alternative Phase II trial designs? Some delay launch for a valuable indication, but also lower the risk of expensive Phase III failure, while others do the opposite!
  • Which option provides the best shareholder value, and has the best business justification?
Solution
  • Employ Decision Analysis techniques to quantify the costs and risks, and characterize the net value to the company of each trial alternative
  • Integrate robust probabilities generated by M&S with Decision Analysis techniques such as decision trees and other valuation methods
Benefits
  • Science-driven business decisions, linking data to the recommendation via pharmacometrics
  • M&S-based analysis results that “speak the language” of executives​

Given the ambiguous trade-offs inherent between efficacy and tolerability, what dose provides the best net patient benefit?


Problem
  • Where do we focus to improve the drug?
  • What is more important to patients and providers: more efficacy or better tolerability/safety?
Solution
  • Employ the Decision Analysis technique of multi-attribute utility analysis (a.k.a. Clinical Utility) to quantify trade-offs
  • Integrate with the results from PK/PD modeling to generate predictions of Clinical Utility, its uncertainty, and key sensitivities
  • Includes the flexibility of utilizing market research data where available
Benefits
  • Illustrate how patient net benefit is optimized and at which doses
  • Dig into the results to understand what drives value, uncertainty, and thus where to focus market research, new formulations, or new therapies

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Benefits of Pharmacometrics

 
 
 

Pharmacometrics FAQs

  • When do you use Pharmacometrics?
  • Which decisions does Pharmacometrics guide in drug development?
  • How to evaluate fit with Pharmacometric consultants?
  • How to collaborate with qPharmetra?
  • Pharmacometric illustrations
 

Pharmacometrics hands-on simulation engine

Demo Coming Soon!