Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration often remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., estimated to be more than 1200 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevent drug degradation. Moreover, our high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®

Comparison of Planning Quality and Efficiency Between Conventional and Knowledge-based Algorithms in Nasopharyngeal Cancer Patients Using Intensity Modulated Radiation Therapy

The abstract has been presented as a poster presentation at the 57th Annual Meeting of the American Society for Radiation Oncology, San Antonio, TX, 18-21 October 2015.© 2016 Elsevier Inc.Purpose Intensity modulated radiation therapy (IMRT) is widely used to achieve a highly conformal dose and improve treatment outcome. However, plan quality and planning time are institute and planner dependent, and no standardized tool exists to recognize an optimal plan. RapidPlan, a knowledge-based algorithm, can generate constraints to assist optimization and produce high-quality IMRT plans. This report evaluated the quality and efficiency of using RapidPlan in nasopharyngeal carcinoma (NPC) IMRT planning. Methods and Materials RapidPlan was configured using 79 radical IMRT plans for NPC; 20 consecutive NPC patients indicated for radical radiation therapy between October 2014 and May 2015 were then recruited to assess its performance. The ability of RapidPlan to produce acceptable plans was evaluated. For plans that could not achieve clinical acceptance, manual touch-up was performed. The IMRT plans produced without RapidPlan (manual plans) and with RapidPlan (RP-2 plans, including those with manual touch-up) were compared in terms of dosimetric quality and planning efficiency. Results RapidPlan by itself could produce clinically acceptable plans for 9 of the 20 patients; manual touch-up increased the number of acceptable plans (RP-2 plans) to 19. The target dose coverage and conformity were very similar. No difference was found in the maximum dose to the brainstem and optic chiasm. RP-2 plans delivered a higher maximum dose to the spinal cord (46.4 Gy vs 43.9 Gy, P=.002) but a lower dose to the parotid (mean dose to right parotid, 37.3 Gy vs 45.4 Gy; left, 34.4 Gy vs 43.1 Gy; P<.001) and the right cochlea (mean dose, 48.6 Gy vs 52.6 Gy; P=.02). The total planning time for RP-2 plans was significantly less than that for manual plans (64 minutes vs 295 minutes, P<.001). Conclusions This study shows that RapidPlan can significantly improve planning efficiency and produce quality IMRT plans for NPC patients.Link_to_subscribed_fulltex