A Review on Current Progress, Bottlenecks, and Future Prospects of Using Algorithmic and Computational Optimization of Drug Delivery Systems

Review Article

Abstract

Modern drug delivery paradigms increasingly depend on algorithmic computation to resolve longstanding challenges in bioavailability, target site specificity, and controlled release dynamics. Advanced artificial intelligence models, including machine learning ensembles, deep neural networks, and reinforcement learning, enable predictive modeling of critical formulation variables, nanoparticle assemblies, and pharmacokinetics. Integrations of computational models with physiologically based pharmacokinetic models facilitate precise mapping of absorption, distribution, metabolism, excretion, and toxicity profiles prior to experimental execution. The convergence of computational intelligence with bio-fabrication techniques, such as three-dimensional bioprinting, allows the precise spatial arrangement of therapeutics within bio-compatible matrices, while closed-loop systems linked to the Internet of Medical Things permit real-time dosage adjustments driven by continuous physiological inputs. Despite these advancements, significant bottlenecks impede widespread clinical translation, notably data heterogeneity, algorithmic opacity, material degradation complexities, and evolving regulatory standards for adaptive software architectures. Addressing these limitations requires robust physics-informed neural networks, standardized data repositories, and transparent explainability models to validate computational predictions against empirical biological outcomes. Algorithmic drug delivery represents a fundamental shift from empirical formulation design toward precise, patient-specific therapeutic interventions