A Systematic Review of Recent Advances in Drug Delivery to the CentralNervous System Through Prodrug Approach

Introduction: Central nervous system disease is one of the significant threats to human health worldwide, affecting both the physical and mental health of the patient. However, designing drugs for the central nervous system has never been easy due to the presence of the blood-brain barrier, which limits the access of drugs to the brain, preventing therapeutic levels from being reached at the intended site of action. This comprehensive systematic review aims to thoroughly investigate various methods and prodrug techniques employed to overcome the challenging blood-brain barrier. Methods: In this systematic review, conducted in accordance with the PRISMA guidelines, 32 in vivo or in vitro comparative experimental studies were thoroughly investigated. The inclusion criteria encompassed relevant experimental studies conducted in the last fifteen years. Results: Rigorous analysis revealed that modified prodrugs significantly enhance brain exposure compared to their parent drugs, demonstrating their therapeutic superiority. For instance, the prodrug of thyrotropin hormone demonstrated a higher half-life (47 ± 6 min) in the brain compared to its parent drug (4 ± 1 min); ANG1005 with a 12- to 15-fold increase in brain uptake compared to uncovered paclitaxel; and the Abacavir-S2Me4 prodrug, which is 700-fold more potent than its parent drug. These prodrugs, along with many other prodrugs, have the potential to treat a wide range of neurological diseases. Discussion: The success of various experimental studies shows that shortly, the prodrug approach can become one of the irreplaceable strategies in the field of CNS drug design. Conclusion: Despite the encouraging outcomes of many experimental studies, the clinical translation and application of CNS-targeted prodrugs in a clinical setting remains a hurdle. Continued future research is crucial for optimizing prodrug design, for ensuring safety, and efficacy in human models.