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

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>۷) with moderate hydrogen bonding capacity. Both platforms predicted good intestinal absorption (۶۳.۵-۹۸.۲%) but poor oral bioavailability, explaining the preference for topical administration in clinical settings. PpIX showed moderate tissue distribution (VDss ۰.۶۳-۰.۷۷ log L/kg) and was not predicted to be a substrate for major CYP۴۵۰ enzymes, suggesting metabolic stability. However, strong inhibition of CYP۱A۲ (probability ۰.۹۷) and transporters (OATP۱B۱, BCRP) indicated potential drug interactions. The predicted short half-life (<۳ 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