Glioblastoma (GBM) is an aggressive and highly lethal malignant brain tumor characterized by extensive molecular heterogeneity. Recent discoveries have revealed GBM can align into two transcriptional subgroups defined by cell of origin, referred to as Type 1 and Type 2. Type 1 are EGFR-driven, a well-studied subgroup already with clinically tested drugs while, conversely, Type 2 GBM have a dependence on human epidermal growth factor receptor 2 (HER2). However, there are currently no HER2-selective tyrosine kinase inhibitors (TKIs) that can effectively cross the blood-brain barrier (BBB) to produce sufficient drug exposure levels to inhibit HER2-dependent GBM. To address this unmet need, we are developing a potent BBB-penetrating HER2 TKI against GBM with the hypothesis that a TKI that is both BBB-penetrant and HER2-selective will lead to effective targeting of HER2-dependent GBM. We will be investigating both the biochemical and functional impacts of novel TKIs by quantifying the efficacy of their HER2 inhibition in SKBR-3 and BT474 HER2+ breast cancer cell lines, common positive controls for HER2 targeting. Moreover, to assess selectivity, we performed the same biochemical and functional assays on MCF7, a non-HER2+ control breast cancer line. We will perform IC50s to measure growth inhibition, western blots to deduce drug-target relationships, and pharmacokinetic assays to determine brain penetrance and drug metabolism. Thus far, we have developed several promising TKIs with various combinations of potent activity, HER2-selectivity, and brain penetrance. Our research will provide insight into the development of a novel, brain-penetrant small molecule inhibitor against GBM to improve patient survivability.

The cycloaddition between 1,2,4,5-tetrazines and cyclopropenes is an important class of bioorthogonal reactions because of its favorable speed and biocompatibility. Due to their desirable bioorthogonal properties, these reactions have found numerous applications in biomedicine, including bioconjugation, clinical diagnostics, molecular imaging of proteins and other biomolecules, etc. However, the utility of tetrazine cycloaddition reactions is hindered by their decreasing in vivo stability as their reaction rate increases. To address such a limitation, we extend upon recent advances in deep-learning-based de novo protein design methods and demonstrate the possibility of computationally designing an enzyme catalyst capable of accelerating stable tetrazine cycloaddition reactions with otherwise limited kinetics. Our enzyme design approach entails using density functional theory to first obtain the transition state structure of a suitable tetrazine cycloaddition reaction, then employing recently developed de novo protein design methods such as deep-learning based protein hallucination algorithms, rotamer interaction field docking, and Rosetta’s fixed-backbone sequence design software to construct proteins with binding pockets that can stabilize the transition state of the tetrazine cycloaddition reaction. The designs with highest predicted transition state stabilization will be experimentally tested by our collaborators, and functional enzymes will be improved by site-saturation mutagenesis. We hope our approach can be adopted in the future to not only improve the utility of tetrazine cycloaddition reactions, but to also create novel enzymes for other reactions in need of an effective catalyst.

Human skeletal myogenesis involves satellite cells that hold distinctive regenerative abilities and can be better understood by using human pluripotent stem cells (hPSCs) to form myogenic lineages in vitro. Although hPSCs can be directed to differentiate into skeletal muscle progenitor cells (SMPCs), the further maturation of hPSC-derived SMPCs into satellite cells (SCs) is still unclear. In this paper, we focused on the addition of hormones, specifically estrogen and glucocorticoids, which are respective of the nuclear receptor transcription factor candidates expressed by SCs that may regulate the maturation of an SMPC into an SC. Multiple differentiations were conducted using different types of hPSCs and directed differentiation protocols and then treated with estradiol and dexamethasone at the end of the differentiation process. We found that there are no significant morphological cellular differences with these hormone treatments. This work provides an analysis of the effect of adding potential maturation regulators to directed differentiation protocols that can hopefully bring a transition from an SMPC into an SC.

As of June 2022, SARS-CoV-2 has resulted in an estimated 6.31 million deaths and 536 million cases. SARS-CoV-2 replication requires two essential proteases: the main protease (Mpro) and the papain-like protease (PLpro). As vaccine-resistant SARS-CoV-2 variants continue to emerge, developing compounds that inhibit Mpro and PLpro is a compelling therapeutic strategy. However, no potent PLpro inhibitors or PLpro + Mpro inhibitor cocktails have been developed and characterized. Consequently, we employed a PLpro activity assay to screen for inhibitors from a custom clinical library and elucidate their inhibitory half maximal effective concentrations (IC50). Two hits were obtained: DDL-701 and 715, with IC50 values of 13 μM and 24 μM respectively. In addition, we tested protease inhibitor combination DDL-701 and DDL-750 (nirmatrelvir of PaxlovidTM) which inhibited both Mpro and PLpro at pharmacologically relevant concentrations. We subsequently employed a human microsome assay and LCMS-MS to elucidate the half lives and microsomal stability of DDL-701, DDL-715, and DDL-701 + 750. DDL-701 (50 μM) and DDL-701 (50 μM) + 750 (0.1 μM) completely inhibit PLpro. These results suggest that DDL 701 + 750 is a potent protease inhibitor cocktail for treatment of SARS-CoV-2. Additionally, we show that DDL-715 exhibits time-dependent inhibition of PLpro.

Infectious diseases disproportionately affect resource-limited settings where access to laboratory tests is low or nonexistent. Point-of-care devices, such as the lateral-flow immunoassay (LFA), provide a promising alternative to laboratory tests due to their ease-of-usse, affordability, and accessibility, but are limited by low sensitivity. To combat this, our lab previously developed the ATPS-automated Concentration and Enhancement of the Lateral-Flow immunoAssay (ACE-LFA), in which the phenomenon of an aqueous two-phase system (ATPS) separating on paper is exploited to simultaneously pre-concentrate the target biomarker while automating the delivery of signal enhancement reagents. This technology improved the LFA’s detection limit, but the ATPS and signal enhancement reagent combination was selected with a trial-and-error approach. This paper summarizes our work in developing an approach that can be used to rationally determine combinations of ATPS and signal enhancement reagent for optimal ACE-LFA performance. Specifically, we first rationalized that the preferential partitioning of the signal enhancement reagent into the phase opposite that of the target biomarker is necessary for maximum test line intensity and minimum background signal. Thus, we developed and validated a molecular thermodynamic model for predicting the partitioning behavior of the signal enhancement reagents 3,3',5,5'-tetramethylbenzidine and 3,3'-diaminobenzidine in a polymer-salt ATPS comprised of poly(ethylene glycol-ran-propylene glycol) and sodium citrate. We also performed experiments to verify that a correlation indeed exists between signal enhancement molecule partitioning and ACE-LFA performance, emphasizing the impact that this model can have on the utilization of ACE-LFA for a wide range of targets moving forward.