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Precision Dosing Using PBPK Modeling

With the discovery of newer drugs, the “one-size-fits-all” approach towards therapy is becoming a thing of the past. The new paradigm of precision medicine aims at delivering the right treatment at the right time to the right patients. An integral part of precision medicine is administration of a precise dose, which is a critical step in the larger mission to deliver personalized healthcare. In this blog post, I will discuss the latest trends in personalized medicine and explain how physiologically-based pharmacokinetic (PBPK) modeling can help identify the right dose for the right patient.

Transforming health care: from one-size-fits-all to a targeted approach

Modeling and simulation techniques for drug development include both top-down (pharmacometrics) and bottom-up (PBPK models) approaches. While these approaches have had success in drug research and development, they have yet to become a regular tool for ‘point-of-care’ clinical decisions. The transformation of health care from one-size-fits-all to a targeted approach utilizing an individual patient’s genetic information continues to accelerate as the U.S. FDA more regularly and rapidly approves new personalized medicines. In 2015, the FDA’s Center for Drug Evaluation and Research (CDER) approved 45 novel new drugs (NNDs), 13 of them — more than 25 percent — were personalized medicines as classified by the Personalized Medicine Coalition (PMC). This continues a trend that began in 2014 when nine of 41 NNDs were classified as personalized medicines.

What is a whole body PBPK model?

Whole body PBPK models account for the behavior of drugs in most tissues in the body. Depending on the route of administration, the course of the drug is tracked through each tissue as it travels in the blood. Each tissue is represented as a compartment. Unlike top-down models which are developed using clinical data, PBPK models do not require clinical plasma drug concentration-time data. PBPK models can produce a plasma concentration-time profile for a drug by combining the species’ (system) physiology/anatomy, drug characteristics, and trial design specifics.  The unique approach of independently combining the system parameters, drug parameters and trial design specifics to predict drug concentration-time profiles renders an increasingly flexible predictive platform that can be extrapolated to study ‘what-if’ scenarios such as complex drug-drug interactions (DDIs) or changes in dosing regimens.

Assessing inter-individual pharmacokinetic variability using virtual populations

Virtual human individuals can be generated to simulate a healthy population or various diseased populations which can then be used to study the absorption, distribution, metabolism, and elimination (ADME) of a drug. Generation of the virtual population takes into consideration multiple covariates such as, age, sex, ethnicity and the genetic makeup of enzymes and transporter proteins in the target population.  Generating virtual individuals considering covariate relationships gives rise to correlated Monte Carlo sampling of individuals instead of random Monte Carlo sampling which can lead to generation of unrealistic virtual subjects.

The sensitivity of each PK parameter to a potential covariate depends on the drug and the balance of elements within the network. As drugs differ in their sensitivity to these elements, effects of the covariates on the drug PK can vary and a “one-size-fits-all” solution cannot be assumed. Simplistic assumptions for covariate analysis based on purely statistical models has been a major shortfall of current “top-down” data analyses. Prior assessment of covariates ensures that the most relevant factors and the most suitable covariate models are considered during clinical studies.

Present and future applications of PBPK

The case of Aristada (injectable, extended-release aripiprazole) for treating schizophrenia illustrates the utility of PBPK for quantifying the risk of DDIs. This drug is primarily eliminated by the drug metabolizing enzymes CYP2D6 and CYP3A4. Dose adjustments were recommended on the drug label based on simulations that examined the effects of other drugs on aripiprazole pharmacokinetics. The effect of the CYP2D6 genotype was also incorporated into PBPK models and informed label claims. Aristada received FDA approval in late 2015.

The applications for PBPK will expand over time. For example, in the not-too-distant future, we will be able to create models that match the characteristics of a real patient to his or her virtual twin. The characteristics would include both intrinsic factors (age, weight, height, sex, race, and genetic information on metabolic enzymes and transporters) and extrinsic factors (environmental factors and co-medications). In brief, this is the series of steps that will be required to generate a patient’s Virtual Twin.

  1. A 3rd party profiling tool will be used to collect the relevant patient characteristics.
  2. The clinician will enter this data into a tablet device.
  3. Data will be sent to a physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) predictor in the cloud.
  4. The PBPK/PD predictor will calculate the predicted plasma drug concentration-time profile with confidence limits, relative to the known therapeutic range of the drug. It will also recommend dosing adjustments as appropriate.
  5. This information will be displayed on the clinician’s tablet via a mobile application or web-based interface.

Virtual Twin technology will enable exploring the impact of a patient’s co-medications and changes in organ function on PK/PD and help manage drug dosing. This will also be an important step on the way to truly personalized medicine.

All information presented derive from public source materials.

Taking drug development into the virtual world

In her series of profiles of bioinformatics companies, Scientific Computing World’s Sophia Ktori looked at the work of biosimulation specialists. Learn how end-to-end modeling and simulation can inform decisions on safety and efficacy of potential drugs using virtual populations.

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By: Devendra Pade

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