Chih-Hui Lo

Combination therapies are key to overcoming resistance to androgen receptor (AR) signaling inhibitors in prostate cancer. Current paradigms for developing synergistic therapies, however, remain chronically inefficient and resource‐intensive due to the prohibitive scale of the combinatorial screening space and a lack of computational frameworks for navigating signaling crosstalk. This work introduces a hybrid in silico and in vitro lead discovery platform that integrates knowledge‐augmented large language models (LLMs) with an oxygen‐supplemented 3D spheroid system. By comparing compound mechanism‐of‐action annotations with disease‐relevant signaling crosstalk, the LLM framework nominates drug pairs with predictive performance and interpretable, pathway‐based rationales. This computational pipeline is complemented by an engineered 3D spheroid model that utilizes oxygen supplementation to mitigate artifactual necrosis, a common confounder that masks synergistic signals in standard screening. Using this approach to screen 3,592 natural products, we identified berberine‐enzalutamide as an in vitro combination hit that re‐sensitized resistant prostate cancer cells to AR blockade. Molecular and transcriptomic profiling revealed changes in mTORC1 and AMPK signaling that were consistent with the pathway‐based hypothesis generated by LLM. These results establish proof of concept that mechanism‐informed language modeling coupled with 3D screening can prioritize and experimentally evaluate drug‐combination hypotheses at an early stage of discovery.

The morphology of particles, along with other particle attributes, has been shown to affect the biological fate of particles administered into the body. Particles with collapsed surfaces or shells (dimpled, buckled or crumpled) can have different appearances, under the microscope, that resemble many things encountered in our daily life, such as apples/cherries, doughnuts, and bowls. Recent studies have demonstrated that they are not just particles with interesting geometries, but they can also be used as functional blocks for assembled complex materials. Since previous research focus has been on micron-sized particles and organic solvents were often used, it is of particular interest to synthesize the nanosized counterpart with a buckled surface using a purely aqueous method. Herein we report a facile method for rapid synthesis of buckled silica nanoparticles (SiNPs) based on S-nitrosothiol chemistry in a solvent-free reaction. 3-Mercaptopropyl trimethoxysilane (MPTMS) was used as a single silane source in a one-pot aqueous solution containing sodium nitrite and polyvinyl alcohol (PVA). Upon the addition of HCl, S-nitrosation and condensation of MPTMS occur simultaneously and the reaction system undergoes a rapid transition from a clear solution to a solution containing nanoparticles with a lag time controlled mainly by the amount of HCl added. The experimental parameters were systematically studied to determine the optimal concentration of each component. For a typical reaction, sub-100 nm nanoparticles can be produced in less than 1 h, and the best result can be obtained within 2 to 4 h. Remarkably, the nanoparticle exhibits buckled surface morphologies under TEM and SEM. The average size is about 60 nm in diameter. Moreover, the solid-state Si-NMR data show that T² and T³ silicon species are rapidly evolved with slight dynamic changes over the reaction time. Further, PVA is shown to control the surface buckling as well as to stabilize the formed particles. The as-prepared SiNPs can be used as a nitric oxide (NO) carrier with the potential to release NO in an apparent zero-order manner. In conclusion, the study demonstrates the feasibility of employing an aqueous route for efficiently preparing SiNPs with multifarious surface cavities based on S-nitrosothiol chemistry.