The prediction of oral drug absorption and bioavailability remains a central challenge in pharmaceutical sciences, particularly in the context of complex drug molecules and advanced delivery systems. This study presents a comprehensive theoretical analysis of the integration of physiologically based pharmacokinetic (PBPK) modeling, machine learning approaches, and formulation strategies to enhance predictive accuracy in drug development. Drawing exclusively on the provided references, the research explores the evolution of compartmental absorption models, the influence of physicochemical properties on drug permeability, and the emerging role of artificial intelligence in pharmacokinetics. It further examines innovative delivery strategies, including ion-pairing, prodrug design, and microenvironment modulation, which aim to improve solubility and permeability of poorly absorbed compounds. The study employs a qualitative synthesis methodology to integrate findings across pharmacokinetics, computational modeling, and pharmaceutical formulation domains. Results indicate that while traditional models provide mechanistic insights, their predictive performance can be significantly enhanced through hybrid approaches incorporating machine learning and real-world data. Additionally, the interplay between gastrointestinal physiology, formulation design, and transporter-mediated processes emerges as a critical determinant of drug bioavailability. The discussion highlights both opportunities and challenges, including data limitations, model validation issues, and regulatory considerations. The study concludes that the convergence of computational modeling and formulation science represents a transformative pathway toward precision drug delivery and optimized therapeutic outcomes.

PBPK Modeling, Drug Absorption, Machine Learning, Bioavailability, Drug Delivery, Pharmacokinetics, Artificial Intelligence

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