Adrien Olama 1, Jean-Michel Dogné 1,2, Happy Phanio Djokoto 1, Grace Shalom Govere 1,2, Hélène Haguet 1, Lisa Hanquet 1, Camille Massaux 1, Lisa Wellin 1, Flora Musuamba Tshinanu 1,2
1 University of Namur (Namur, Belgium), 2 Belgian Federal Agency for Medicines and Health Products (Brussels, Belgium)
Introduction/Objectives: Intranasal delivery is widely used for rapidly acting analgesics in emergency and perioperative settings. Lipophilic weak bases such as ketamine and fentanyl derivatives exhibit physicochemical and pharmacokinetic properties compatible with rapid nasal absorption, including high membrane permeability and fast central nervous system penetration [1]. However, explicit nasal physiologically based pharmacokinetic (PBPK) models typically require detailed information on regional deposition, mucociliary clearance, formulation attributes, and device performance, which is often unavailable in early development.
Given these data limitations, clearly defining the context of use and establishing model credibility are essential. Model-informed drug development is increasingly integrated into regulatory decision-making, and the recently adopted ICH M15 guideline provides a harmonized framework for defining a context of use and assessing model credibility [2]. The EMA PBPK guideline further emphasizes transparent justification of assumptions, evaluation of predictive performance, and explicit discussion of regulatory applications [3].
The objectives of this work were (i) to develop a parsimonious, fit-for-purpose PBPK framework to predict adult systemic exposure following intranasal administration in data-limited settings and (ii) to evaluate its credibility according to current regulatory expectations.
Methods: Systemic pharmacokinetics following intranasal dosing of ketamine and fentanyl derivatives were described using a PBPK framework implemented in Simcyp® (version 23, Certara, Sheffield, UK). Model development followed a fit-for-purpose strategy aligned with the intended context of use (systemic exposure prediction).
The intranasal dose was mechanistically decomposed into two concurrent systemic inputs: a rapidly absorbed nasal fraction represented by a short zero-order input, and a swallowed fraction described using the mechanistic ADAM oral absorption model [4].
To preserve parameter identifiability, the relative nasal and swallowed fractions were fixed a priori using literature absolute intranasal and oral bioavailability values. Tissue distribution was predicted using the Rodgers and Rowland method [5], and systemic elimination was implemented using literature total clearance, assuming linear kinetics.
Model evaluation included cross-route consistency checks, targeted sensitivity analyses of uncertain parameters, and verification against intravenous (IV) and oral clinical data. Performance was primarily assessed using AUC across four compounds and three administration routes (IV, oral, intranasal), resulting in 12 independent scenarios (4 × 3). This cross-route design was selected to stress structural consistency and minimize route-specific overfitting in line with the fit-for-purpose objective. Cmax was used as a secondary metric, and intranasal Tmax was examined to assess the temporal adequacy of the absorption representation. Predicted-to-observed ratios between 0.5 and 2.0 were used as the primary acceptance bounds. Model credibility was evaluated qualitatively in accordance with ICH M15.
Results: Systemic exposures after intranasal administration were reliably described by the dual-input PBPK framework across products. Predicted-to-observed ratios for AUC were within the predefined 0.5–2.0 acceptance bounds for 100% (12/12) of evaluated scenarios.
Cmax predictions were more variable than AUC and were within the two-fold range for 50% (2/4) of IV datasets, 75% (3/4) of oral datasets, and 50% (2/4) of intranasal datasets. For the intranasal route, Tmax was within the predefined two-fold range for 100% (4/4) of evaluated datasets.
Cross-route verification showed that drug models developed using oral and IV data maintained predictive performance when extrapolated to the intranasal route without re-estimating systemic parameters. Physiologically realistic and stable absorption profiles were obtained for all compounds using the ADAM-based representation of the swallowed fraction.
The model’s robustness for the intended decision context was supported by sensitivity analyses of key uncertain parameters, which revealed a modest influence on peak exposure and limited impact on AUC. Model influence, consequences of a wrong decision, model risk, and model impact were all categorized as Medium on the ICH M15 three-level qualitative scale (Low, Medium, High).
Conclusions: A dual-input PBPK approach provided a consistent characterization of systemic exposure following intranasal administration of ketamine and fentanyl derivatives. Accurate prediction of intranasal AUC and Tmax across compounds supports the framework’s relevance for estimating overall systemic exposure after intranasal dosing. Cmax showed greater variability, consistent with the known sensitivity of peak concentrations to formulation properties, sampling design, and characterization of early absorption processes.
This approach may provide a basis for PBPK-based extrapolation to pediatric populations within a model-informed development context; however, further mechanistic refinement will likely be necessary to adequately capture age-related physiological and absorption differences (e.g., nasal cavity surface area or mucociliary clearance).
References:
[1] Erdő, F., Bors, L. A., Farkas, D., Bajza, Á., & Gizurarson, S. (2018). Evaluation of intranasal delivery route of drug administration for brain targeting. Brain Research Bulletin, 143, 155–170. https://doi.org/10.1016/j.brainresbull.2018.10.009
[2] International Council for Harmonisation. (2024). ICH M15 guideline: General principles for model-informed drug development. ICH.
[3] EMA. Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. EMA/CHMP/458101/2016, 2018.
[4] Jamei, M., Marciniak, S., Feng, K., Barnett, A., Tucker, G., & Rostami-Hodjegan, A. (2009). The Simcyp population-based ADME simulator. Expert Opinion on Drug Metabolism & Toxicology, 5(2), 211–223. https://doi.org/10.1517/17425250802691074
[5] Rodgers, T., & Rowland, M. (2007). Mechanistic approaches to volume of distribution predictions: Understanding the processes. Pharmaceutical Research, 24(5), 918–933. https://doi.org/10.1007/s11095-007-9258-3
Reference: PAGE 34 (2026) Abstr 12297 [www.page-meeting.org/?abstract=12297]
Poster: Drug/Disease Modelling - Absorption & PBPK