Understanding PBPK Modeling in OINDPs: Insights from Will Ganley

PBPK modeling uses detailed computational models to simulate the pharmacokinetic processes of a drug, generating predictions about its behavior in the body. By starting with a ‘bottom-up’ strategy, models are built using data from various sources, including in vitro studies and clinical trials. This process involves creating a system of equations that simulate how drugs move through different compartments in the body, such as blood vessels and tissues, providing insights into their behavior over time.

When applied to inhaled drug products, PBPK models serve as a flowchart mapping the journey of a drug through the body after inhalation. This is particularly useful for OINDPs, where understanding the drug’s behavior in the lungs is crucial. Ganley explains that the lungs are divided into two primary regions: the central region, encompassing the upper airways, and the lower peripheral airways. Within these compartments, the model can simulate drug dissolution, absorption, and transportation into the bloodstream.

The development of these models for nasal drug delivery involves gathering input data such as deposition and dissolution rates. Deposition data is often obtained using computational fluid dynamics models, which leverage imaging data like CT scans to generate realistic predictions of drug deposition in the lungs. Dissolution data is derived from in vitro studies, providing critical input parameters to PBPK models, ensuring accurate simulations.

Ganley also introduces Simhalation™, Nanopharm’s in-house PBPK model tailored for OINDP development. This model integrates physiological and molecular specifications to simulate various drug formulations across different patient demographics. By running simulations, Simhalation™ can assess how factors like deposition angle, dissolution rates, and mucociliary clearance influence drug exposure at the target site. Simhalation™ enables researchers to understand inter- and intra-subject variability in drug response, providing a more efficient pathway to evaluate nasal drug delivery systems. It plays a role throughout the product life cycle, from early development through to clinical validation, helping to optimize formulations and reduce the need for extensive clinical trials.

Ensuring the credibility of PBPK models is critical for their use in clinical decision-making and regulatory submissions. Ganley emphasizes the importance of verifying model predictions against clinical data to ensure they follow expected time courses and behavior. While PBPK models are often complex, they must be used responsibly to avoid overfitting parameters to achieve desired outcomes.

Before employing PBPK models in development, it is essential to determine whether they are being used to answer preliminary research questions or to replace clinical trials. The level of credibility required varies based on the model’s intended use. This consideration allows researchers to design validation strategies that ensure robust and reliable predictions, particularly in the context of OINDPs and nasal drug delivery.

Ganley then shares examples where PBPK modeling has been effectively used at Nanopharm for OINDP development. One study explored the influence of dry powder inhaler dissolution rates on the systemic exposure of fluticasone propionate. By adjusting the PBPK model to different dissolution rates, researchers could predict how varying formulations impacted drug absorption, providing valuable data for optimizing safety and efficacy.

The acceptance of PBPK modeling by regulatory bodies like the FDA is growing, especially in the context of nasal drug delivery and OINDPs. According to Ganley, simulation approaches like PBPK models can play a key role in obtaining biowaivers or supporting bioequivalence studies. The ability to simulate clinical outcomes reduces the burden of clinical testing, accelerating the pathway to market for new drug products.

As the regulatory landscape evolves, PBPK models are expected to become integral to the approval process for nasal drug delivery systems. By providing validated predictions, these models can streamline product development and optimize patient outcomes.