Abstract

“WHEN AI MEETS PHARMACEUTICS: PREDICTING THE PATHWAYS OF DRUG DELIVERY”

Dr. D. Nirmala*, Gopagoni Akshitha, Gunnam Laxmi Rushita, Gonepally Rakshitha, Gummidi Jahnavi Sai Sreya and Jatavath Swarnalatha

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Abstract

The integration of artificial intelligence (AI) into pharmaceutical sciences is redefining the landscape of drug discovery and delivery. This review explores the convergence of AI and pharmaceutics, emphasizing how intelligent systems are revolutionizing the prediction, design, and optimization of drug delivery pathways. Leveraging advanced machine learning (ML) and deep learning (DL) techniques, AI enables the precise analysis of vast biological datasets, aiding in target identification, virtual screening, pharmacokinetics (PK), and pharmacodynamics (PD) modeling. From predicting drug absorption and distribution to optimizing dosage form design, AI provides novel insights that streamline formulation strategies and reduce experimental trial-and-error. The paper delves into AI-assisted approaches in nanocarrier development, transdermal systems, and personalized medicine, showcasing how computational models can tailor therapies to individual patient profiles. Additionally, the role of AI in drug repurposing, combination therapy prediction, and real-time Monitoring of therapeutic efficacy is highlighted. By examining the current platforms, algorithms, and tools used in pharmaceutical AI applications, this review presents a comprehensive overview of technological advancements shaping the future of drug delivery. Challenges such as data quality, regulatory concerns, and model interpretability are also addressed, along with future perspectives for integrating AI more deeply into pharmaceutics. Ultimately, this article underscores the transformative potential of AI in predicting and enhancing drug delivery pathways, paving the way for faster, safer, and more effective therapeutic interventions.

Keywords: Artificial intelligence, Drug Delivery, Pharmacokinetics, Pharmacodynamics, Machine learning.