UCLA

In glaucoma treatment, ocular devices such as drug-loaded contact lenses have recently

emerged as preferred candidates over eyedrops. Timolol maleate (TM), a prevalent glaucoma drug, exhibits side effects when excess drug enters the systemic circulation. Unlike eyedrops and drug-soaked lenses, new designs should provide sustained release and minimize undesirable burst release. Current innovations have not addressed the important issue of drug elution from contact lens during wet storage and shipment. Here we present a nanodiamond (ND)-embedded contact lens capable of lysozyme-triggered release of TM for sustained therapy. We found that ND-nanogel containing lysozyme-cleavable polymers enabled the controlled and sustained release of TM in the presence of lysozyme. Nanodiamonds also improved the mechanical properties of the poly-HEMA lenses without compromising on water content, oxygen

permeability and optical clarity. In vitro cell viability assays on primary trabecular meshwork cells revealed that the released TM retained their antioxidant activity – an indicator of timolol efficacy. This successful lysozyme activation of our functionalized ND nanogel can be easily applied to other diseases and drugs where localized triggered release is desired.

Conventional combinatorial treatment for multiple myeloma and other cancers uses an additive approach of maximum tolerated doses from single-drug experiments. These doses do not reflect true drug behavior in combinatorial therapy. A rapid and robust platform is necessary to optimize multiple parameters to find the best drug combination for improved efficacy and safety. Here, we present Response Surface Optimization (RSO) where we rapidly narrowed down our top three myeloma drug candidates (Bortezomib-Panobinostat-Dexamethasone) from a starting list of fourteen drugs in a short span of five months. The mechanism-independent RSO platform screens drugs based on maximizing the therapeutic window – increased healthy cell viability and decreased cancer cell viability. After the 1st iteration experiment, a linear regression

experimental process (equation) is generated using MATLAB to reconcile the empirical data. Undesirable candidates are removed before performing a 2nd screening iteration. In the 3rd iteration, the optimum drug concentrations of the best drug combinations are determined. Response surface maps for output based on multi-parameters are plotted and the optimum is determined. Interestingly, this three-drug combination was the focus of recent myeloma clinical trials for refractory and relapsed patients. While conventional drug screening approaches demand huge time and money investment, the RSO platform rapidly and accurately converges on prime solutions. The RSO is applicable to the clinic where inter-patient variability requires adaptive treatment interventions based on individual clinical outcomes.