Iranian Journal of Medical Physics

Iranian Journal of Medical Physics

Pharmacokinetic and Pharmacodynamic Analysis of Protoporphyrin IX for Enhancing its Efficacy in Photodynamic Therapy

Document Type : Original Paper

Authors
Mona Alikhanzadeh
Ameneh Sazgarnia
Armin Imanparast
Medical Physics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
10.22038/ijmp.2025.83559.2473
Abstract
Introduction: Protoporphyrin IX (PpIX) is a critical photosensitizer in photodynamic therapy (PDT) with applications in oncology and dermatology. Despite its clinical importance, comprehensive understanding of its pharmacokinetic profile remains limited. This study aimed to characterize the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of PpIX using computational approaches.
Material and Methods: The molecular structure of PpIX was analyzed using two complementary computational platforms, Deep-pk and pkCSM, which utilize machine learning and deep learning algorithms trained on experimental pharmacokinetic data to predict ADMET parameters. Physicochemical properties, absorption, distribution, metabolism, excretion, and toxicity profiles were evaluated and compared between the platforms.
Results: PpIX exhibited high lipophilicity (LogP>7) with moderate hydrogen bonding capacity. Both platforms predicted good intestinal absorption (63.5-98.2%) but poor oral bioavailability, explaining the preference for topical administration in clinical settings. PpIX showed moderate tissue distribution (VDss 0.63-0.77 log L/kg) and was not predicted to be a substrate for major CYP450 enzymes, suggesting metabolic stability. However, strong inhibition of CYP1A2 (probability 0.97) and transporters (OATP1B1, BCRP) indicated potential drug interactions. The predicted short half-life (<3 hours) aligned with clinical observations. Toxicity analysis revealed non-mutagenicity and cardiac safety, but conflicting hepatotoxicity predictions and potential respiratory toxicity warrant clinical monitoring.
Conclusion: Computational analysis of PpIX confirmed pharmacokinetic properties supporting its clinical use but raised concerns about drug interactions and organ toxicity. These results provide a basis for optimizing PDT protocols and improving formulations. Differences between prediction methods highlight the need for experimental validation of key parameters to ensure clinical safety and effectiveness.
Keywords
Protoporphyrin IX
Photodynamic Therapy
Pharmacokinetics
Pharmacodynamic
Machine Learning
Deep Learning
Subjects
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Iranian Journal of Medical Physics
Volume 22, Issue 3 - Serial Number 3
May and June 2025
Pages 149-155

  • Receive Date 23 October 2024
  • Revise Date 14 April 2025
  • Accept Date 20 July 2025