The bioavailability enhancement and insight into the action mechanism of poorly soluble natural compounds from co-crystals preparation: Oridonin as an example

Synthesis and Application of a pH-Responsive Functional Metal-Organic Framework: In Vitro Investigation for Delivery of Oridonin in Cancer Therapy

Oridonin (Ori) is a naturally existing diterpenoid substance that mainly exists in the Chinese medicinal plant Rabdosia rubescens. It was previously found to possess intriguing biological properties; however, the quick clearance from plasma and limited solubility in water restricts its use as a drug. Several metal-organic frameworks (MOFs), having big surfaces and large pores, have recently been considered promising drug transporters. The zeolitic imidazolate framework-8 (ZIF-8), a form of MOF consisting of 2-methylimidazole with zinc ions, is structurally stable under physiologically neutral conditions, while it can degrade at low pH values such as in tumor cells. Herein, a nanosized drug delivery system, Ori@ZIF-8, was successfully designed for encapsulating and transporting oridonin to the tumor site. The drug loading of the prepared Ori@ZIF-8 was 26.78%, and the particles' mean size was 240.5 nm. In vitro, the release of Ori@ZIF-8 exhibited acid sensitivity, with a slow release under neutral conditions and rapid release of the drug under weakly acidic conditions. According to the in vitro anti-tumor experiments, Ori@ZIF-8 produced higher cytotoxicity than free Ori and induced apoptosis in A549 cancer cells. In conclusion, Ori@ZIF-8 could be a potential pH-responsive carrier to accurately release more oridonins at the tumor site.

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties

Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.