There’s no one “right” modeling or simulation approach—but some are better suited to answer or explore specific types of questions. Physiologically based pharmacokinetic (PBPK) modeling is one of the most widely used approaches in drug development. In this blog post, I’ll cover the areas where PBPK offers the greatest value, common misconceptions, practical examples, and trends we’re seeing as PBPK evolves.
Where PBPK Creates the Most Value
PBPK modeling has moved well beyond its historical role as a niche drug-drug interaction (DDI) risk assessment tool. Today, many R&D organizations are using PBPK earlier in development to inform candidate selection, formulation strategy, and clinical trial design. Put simply, PBPK is the strongest choice when development decisions depend on understanding how physiology, dose route, formulation, co-administration or patient variability influence drug exposure.
The science is evolving, and so are the ways in which teams operationalize it.
Common Misconceptions
One of the biggest mistakes is treating PBPK as a “plug-and-play” prediction engine, as critical thinking should always be used when building and applying PBPK models. Mechanistic models should be used to explore biological understanding, and the impact of the availability and quality of input data should be probed through parameter sensitivity analysis and a model verification strategy.
Another common misconception is that PBPK can answer every translational question. PBPK is exceptionally strong for exposure-related questions—absorption, distribution, DDI risk, organ impairment, pediatrics—but on its own, it is not designed to capture complex disease biology or systems-level pharmacology. That’s often where quantitative systems pharmacology (QSP) or PBPK combined with QSP becomes the better fit.
PBPK focuses on what is happening mechanistically, rather than on confirmatory clinical interpretation. If the question is what do the observed clinical data tell us, then clinical pharmacokinetics is a better option to quantify variability, efficacy, safety, or dose-response relationships observed in subjects.
Practical Examples
There are many common applications for PBPK modeling. It is now routinely used during drug discovery for in vitro to in vivo translation and pharmacokinetic modeling in preclinical species, which leads to the application of verified models for first-in-human (FIH) PK predictions. A recent publication provided a strategy for FIH PBPK modeling with real-world examples for challenging compounds.
PBPK is also used to predict the PK in special populations, as these subjects often experience major physiological changes that can significantly alter drug exposure, potentially affecting both efficacy and safety. For example, predicting differences in PK in pregnant subjects and fetal exposure for metabolically cleared compounds.
Another new publication detailed how one large pharma company is now employing high-throughput PBPK modeling for rodent pharmacokinetic (PK) predictions based on only machine learning predictions for input parameters. This approach facilitates lead series prioritization and streamlines in vivo PK and PK/PD testing during lead optimization, thus accelerating drug discovery timelines.
PBPK modeling can also be used, in some cases, to avoid DDI clinical studies or justify wider dissolution specifications, accelerating the program timeline and reducing costs.
Trends We’re Seeing
- 1. PBPK is shifting earlier in the pipeline
Organizations are increasingly applying PBPK during lead optimization and preclinical translation, rather than waiting until the clinic. Early mechanistic insight can reduce downstream uncertainty around exposure, variability, and formulation performance.
2. Cross-functional decision-making is becoming more model-informed
PBPK is increasingly being used as a cross-functional decision-support tool across clinical pharmacology, formulation development, translational sciences, and regulatory strategy as part of broader model-informed drug development (MIDD) initiatives. For example, at candidate selection a company is likely to have a PBPK model for FIH predictions with the systemic disposition being preclinically verified by DMPK scientists, the absorption modeling being characterized by biopharmaceutical scientists, and the target patient population being assessed by clinical pharmacologists.
3. Greater emphasis on platform confidence, not just model fit
The quote “with great power comes great responsibility” is attributed to Spider-Man, but it now applies to PBPK as it becomes employed more widely and for higher levels of regulatory impact. This means regulators and internal governance teams are asking tougher questions around verification, assumptions, and reproducibility. The conversation is evolving from “Does the model fit observed data?” to “Can we trust this methodology prospectively to make key decisions?”
The Bigger Picture
PBPK delivers the most value when it is used as a decision-support framework — not simply a regulatory deliverable. The organizations getting the strongest return are the ones embedding mechanistic modeling into broader R&D strategy and governance, rather than treating it as an isolated modeling exercise.
If you are considering PBPK modeling for a program or want to understand where it fits within your overall modeling strategy, our team would be glad to discuss your specific questions.