Physiological based pharmacokinetic (PBPK) modeling and simulation uses computer modeling that incorporates physiologic and drug-specific parameters to characterize the pharmacokinetics (PK) of drugs. Recent improvements in computational capabilities and scientific advancements have led to increased use of PBPK models in drug development. Some examples include using PBPK to optimize the dose or to improve the understanding of ADME in the population under investigation.
What is PBPK Modeling & Simulation?
PBPK modeling is a tool that uses physiologically relevant mathematical descriptions of biological processes to make quantitative PK predictions. PBPK modeling incorporates the following into one singular modeling framework:
- physiological data for populations under investigation
- predictions of tissue: blood partitioning of the drug
- elimination of the drug through metabolism and excretion processes
- the physicochemical properties of the drug itself
The goal of any drug is to be effective without causing harm. The goal of translational approaches (e.g., PBPK, clinical pharmacology, pharmacometrics) is to optimize the dose in patients and to maximize the probability of achieving efficacy with a minimal probability of having adverse events.
Sometimes quantitative approaches can be more empiric (e.g., clinical pharmacology, pharmacometrics), while other times, a more physiologic approach (e.g., PBPK) is needed. The utilization of informed PBPK modeling and simulations presents countless opportunities for improvements in drug development decisions.
Parameters Needed to Perform PBPK Modeling
PBPK models incorporate drug-specific data and physiological data into a single PBPK model. The modeling parameters can be broken down into distinct buckets including:
- Physiological parameters (e.g., cardiac output, glomerular filtration rate, tissue volumes, blood flows, body weight, body surface area, age, etc.)
- Distribution (e.g., tissue partitioning, cellular permeabilities, plasma and tissue binding, transporter driven processes)
- Metabolism (e.g., expression of enzymes in the liver, intestine, kidney, lungs)
- Route of administration (e.g., oral, inhaled, intravenous, dermal)
- Physicochemical properties of the drug (e.g., lipophilicity, solubility)
Some well-established changes in physiologic processes (due to maturation from birth through adulthood) can be incorporated into a PBPK model. These may also include changes expected in the elderly. Some parameters, such as tissue partitioning and cellular permeabilities, are estimated using Quantitative Structure Activity Relationship (QSAR) models which use the physicochemical properties of the drug(s) under investigation to predict these distribution parameters.
Early metabolism studies are generally done in vitro using isolated tissues or enzymes from both animal models and humans to identify the contribution of drug metabolizing enzymes (DMEs) responsible for metabolism of the drug of interest. These in vitro data can be included in the PBPK model and scaled based on physiologic considerations, such as expression of enzymes in the liver, to predict in vivo metabolism in the population of interest. Additionally, ontogeny or maturation models are also available for many DMEs to predict metabolism in neonates, infants and children. PBPK can also improve our understanding of complex ADME processes like multiple sites of absorption, enterohepatic recirculation, or complex drug interactions.
Most parameters needed for PBPK modeling can be found in scientific literature, an investigator’s brochure, or from pre-clinical or clinical reports. Solubility and lipophilicity data are typically investigated as part of analytical and formulation development early in drug development. Other parameters may need to be estimated using QSAR models. The key to a successful PBPK model is the availability of useful in vitro, nonclinical or clinical datasets, including datasets for model validation. Additional data requirements for PBPK models vary greatly depending on the intended use of the model.
- Determining First-in-Human (FIH) dose based on data from nonclinical studies and predicting clinical PK/PD relationships for efficacy
- Predicting PK in pediatric populations (particularly neonates and infants) based on adult data, drug metabolism, and age-based changes in physiology
- Predicting drug-drug interactions and supporting the design of clinical DDI studies or using simulations to replace clinical studies
- Predicting fetal exposure during pregnancy or exposure to nursing infants via breast milk
- Understanding the effect of multiple sites of drug absorption and any changes in absorption of orally administered drugs due to food
- Understanding unique ADME characteristics such as multiple sites of administration or enterohepatic recirculation
- Investigating the local impact of drug at the site of administration
Benefits of Using PBPK Models
PBPK modeling can predict and evaluate the effect of various intrinsic and extrinsic patient factors on the exposure of investigational drugs to support decisions on how to conduct clinical pharmacology studies, making it safer for the study participants.
PBPK models can be parameterized to reflect different sensitive populations (pediatrics or elderly populations) which would normally pose a challenge in conducting effective clinical trials. PBPK models simulate important aspects of the individual’s physiology that is necessary to understand drug kinetics. This allows us to predict tissue distribution and model physiological processes that cannot be directly observed or predicted via traditional PK approaches. Additionally, PBPK modeling allows us to extrapolate nonclinical PK data to human PK predictions by letting the difference in physiology between the two species drive the PK rather than simple allometric scaling.
All of these benefits highlight a critical need for using PBPK modeling in studies where:
- The therapeutic window of drugs is narrow
- The systemic PK of the drug does not fully capture the complexity of the physiological response, or
- The drug is expected to be administered to certain sensitive populations where toxicity concern is higher than that observed in an average individual
PBPK modeling can be utilized along multiple stages of drug development. New guidance from regulatory agencies has highlighted key areas for the use of PBPK models, showing their ever-growing acceptance. Allucent can help you untangle the complex data requirements of PBPK models. and bring these powerful modeling tools to bear on complex problems facing drug development.