Fragment-based screening by protein-detected NMR spectroscopy.

Fragment-based drug discovery (FBDD) identifies low molecular weight compounds that can be developed into ligands with high affinity and selectivity for therapeutic targets. Screening fragment libraries (<10,000 molecules) with biophysical techniques against macromolecules provides information about novel chemical spaces that bind the macromolecule and scaffolds that can be modified to increase potency. A fragment-screening pipeline requires a standardized protocol for target selection, library assembly and maintenance, library screening, and hit validation to ensure hit integrity. Herein, the fundamental aspects of a fragment screening pipeline-focusing on protein-detected NMR data collection and analysis-are discussed in detail for researchers to use as a resource in their FBDD projects. Selected screening targets must undergo rigorous stability and buffer testing by NMR spectroscopy to ensure the protein structure is stable for the entire screen. Biophysical instrumentation that rapidly measures protein thermostability is helpful in buffer screening. Molecules in fragment libraries are analyzed computationally and physically, stored at appropriate temperatures, and multiplexed in well plates for library conservation. The screening protocol is streamlined using liquid handling robotics for sample preparation and customized Python scripts for protein-detected NMR data analysis. Molecules identified from the screen are titrated to determine their binding site(s) and Kd values and confirmed with an orthogonal biophysical assay. This detailed FBDD screening pipeline developed by the Program in Chemical Biology at the Medical College of Wisconsin has successfully screened many unrelated target proteins to identified novel molecules that selectively bind to these target proteins.

Effects of E-Cigarette Flavor Enhancing Capsules on Inhalable Aerosols.

The flavor of inhaled e-cigarette aerosols may be augmented using crushable flavor capsules added to e-cigarettes. For example, Puff Krush contains breakable flavor capsules in a sorbent material. The capsules are crushed, and then, aerosol passes through the saturated sorbent material before inhalation. Herein, we used NMR and GC-MS to identify the capsule medium chain triglyceride (MCT) solvent and flavorants in selected Puff Krush flavor capsules and then determined which molecules from the capsule transfer into aerosols. MCTs from the Puff Krush were not found in the aerosols, and ∼50% of Puff Krush flavorants transferred into the aerosol upon vaping.

Building capacity in quantitative research and data storytelling to enhance knowledge translation: a training curriculum for peer researchers.

BACKGROUND: Many community-based HIV research studies incorporate principles of greater involvement and meaningful engagement of people living with HIV (GIPA/MEPA) by training people with HIV as peer researchers. Unfortunately, there are still some aspects of research (e.g., quantitative data analysis and interpretation) where many projects fall short in realizing GIPA/MEPA principles. To address these gaps, we developed an eight-week training course that aimed to build the capacity of peer researchers around the understanding and interpretation of quantitative data and incorporating lived experience to increase the impact of the knowledge transfer and exchange phase of a study. METHODS: Peer researchers (n = 8) participated from British Columbia, Alberta, and Ontario and lessons learned from the training were implemented throughout the dissemination of research findings from the People Living with HIV Stigma Index study. This paper presents the curriculum and main training components, course evaluation results, and challenges and lessons learned. The manuscript was created in collaboration with and includes the perspectives of both the peer researchers involved in the training, as well the course facilitators. RESULTS: Throughout the course, peer researchers' self-assessed knowledge and understanding of quantitative research and data storytelling improved and, through interactive activities and practice, they gained the confidence to deliver a full research presentation. This improved their understanding of research findings, which was beneficial for discussing results with community partners and study participants. The peer researchers also agreed that learning about integrating lived experience with quantitative data has helped them to make research findings more relatable and convey key messages in a more meaningful way. CONCLUSIONS: Our training curriculum provides a template for research teams to build capacity in areas of research where peer researchers and community members are less often engaged. In doing so, we continue to uphold the principles of GIPA/MEPA and enhance the translation of research knowledge in communities most greatly affected.

Engaging patient groups or community members is commonplace in HIV research where people living with HIV are trained as peer researchers. There are still however some gaps where community members are less engaged, especially in quantitative data analysis. This presents a barrier preventing them from being meaningfully engaged in research about them. To build capacity in these areas, we designed an eight-week online course that taught peer researchers about quantitative data analysis and interpretation with a focus on concepts that would be important for talking about key messages from research findings. This was used to enhance the knowledge translation and dissemination initiatives for the People Living with HIV Stigma Index study­a survey tool containing quantitative measures examining stigma and related health factors. Peer researchers agreed that their knowledge and understanding of the key quantitative data concepts improved significantly throughout the course. This increased understanding helped them discuss quantitative data with community members and study participants, which was important to ensure that research findings reach the affected communities. Peer researchers also agreed that incorporating their new data analysis knowledge with existing lived experience helped them to make findings more relatable and understandable which is critical for translating knowledge to other researchers and policy makers. Overall, our training curriculum gave peer researchers the confidence to talk about quantitative data and improve their capacity to disseminate research. This work also provides guidelines for training peer researchers and ensuring that they are meaningfully engaged in research studies they are a part of.

Kinetics of Aldehyde Flavorant-Acetal Formation in E-Liquids with Different E-Cigarette Solvents and Common Additives Studied by 1H NMR Spectroscopy.

Flavorants, nicotine, and organic acids are common additives found in the e-liquid carrier solvent, propylene glycol (PG) and/or glycerol (GL), at various concentrations. Some of the most concentrated and prevalent flavorants in e-liquids include trans-cinnamaldehyde, vanillin, and benzaldehyde. Aldehyde flavorants have been shown to react with PG and GL to form flavorant-PG and -GL acetals that have unique toxicity properties in e-liquids before aerosolization. However, there is still much that remains unknown about the effects of different e-cigarette solvents, water, nicotine, and organic acids on the rate of acetalization in e-liquids. We used 1H NMR spectroscopy to determine the first-order initial rate constant, half-life, and % acetal formed at equilibrium for flavorant-acetal formation in simulated e-liquids. Herein, we report that acetalization generally occurs at a faster rate and produces greater yields in e-liquids with higher ratios of GL (relative to PG). trans-Cinnamaldehyde acetals formed the fastest in 100% PG-simulated e-liquids, followed by benzaldehyde and vanillin based on their half-lives and rate constants. The acetal yield was greatest for benzaldehyde in PG e-liquids, followed by trans-cinnamaldehyde and vanillin. Acetalization in PG e-liquids containing aldehyde flavorants was inhibited by water and nicotine but catalyzed by benzoic acid. Flavorant-PG acetal formation was generally delayed in the presence of nicotine, even if benzoic acid was present at 2-, 4-, or 10-fold the nicotine concentration, as compared to the PG e-liquids with 2.5 mg/mL flavorant. Thus, commercial e-liquids with aldehyde flavorants containing a higher GL ratio (relative to PG), little water, no nicotine, nicotine with excess organic acids, or organic acids without nicotine would undergo acetalization the fastest and with the highest yield. Many commercial e-liquids must therefore contain significant amounts of flavorant acetals.

Effects of Common e-Liquid Flavorants and Added Nicotine on Toxicant Formation during Vaping Analyzed by 1H NMR Spectroscopy.

A broad variety of e-liquids are used by e-cigarette consumers. Additives to the e-liquid carrier solvents, propylene glycol and glycerol, often include flavorants and nicotine at various concentrations. Flavorants in general have been reported to increase toxicant formation in e-cigarette aerosols, yet there is still much that remains unknown about the effects of flavorants, nicotine, and flavorants + nicotine on harmful and potentially harmful constituents (HPHCs) when aerosolizing e-liquids. Common flavorants benzaldehyde, vanillin, benzyl alcohol, and trans-cinnamaldehyde have been identified as some of the most concentrated flavorants in some commercial e-liquids, yet there is limited information on their effects on HPHC formation. E-liquids containing flavorants + nicotine are also common, but the specific effects of flavorants + nicotine on toxicant formation remain understudied. We used 1H NMR spectroscopy to evaluate HPHCs and herein report that benzaldehyde, vanillin, benzyl alcohol, trans-cinnamaldehyde, and mixtures of these flavorants significantly increased toxicant formation produced during e-liquid aerosolization compared to unflavored e-liquids. However, e-liquids aerosolized with flavorants + nicotine decreased the HPHCs for benzaldehyde, vanillin, benzyl alcohol, and a "flavorant mixture" but increased the HPHCs for e-liquids containing trans-cinnamaldehyde compared to e-liquids with flavorants and no nicotine. We determined how nicotine affects the production of HPHCs from e-liquids with flavorant + nicotine versus flavorant, herein referred to as the "nicotine degradation factor". Benzaldehyde, vanillin, benzyl alcohol, and a "flavorant mixture" with nicotine showed lower HPHC levels, having nicotine degradation factors <1 for acetaldehyde, acrolein, and total formaldehyde. HPHC formation was most inhibited in e-liquids containing vanillin + nicotine, with a degradation factor of ∼0.5, while trans-cinnamaldehyde gave more HPHC formation when nicotine was present, with a degradation factor of ∼2.5 under the conditions studied. Thus, the effects of flavorant molecules and nicotine are complex and warrant further studies on their impacts in other e-liquid formulations as well as with more devices and heating element types.

Ratio of Propylene Glycol to Glycerol in E-Cigarette Reservoirs Is Unchanged by Vaping As Determined by 1H NMR Spectroscopy.

E-cigarette liquids (e-liquids) contain propylene glycol (PG) and/or glycerol (GL) to deliver flavorants/nicotine. It has recently been suggested that the PG:GL ratio in e-cigarette reservoirs changes during vaping, leaving almost entirely GL after aerosolizing much of a 30:70 PG:GL mixture. To evaluate this directly, we analyzed e-liquids from e-cigarettes before and after aerosolization using 4 different coils, and aerosol samples generated using high and low e-liquid levels. The PG:GL ratios of initial and final e-liquids and aerosol samples were comparable. This is important because a large change in e-liquid composition could substantially alter the aerosol profile during a vaping session.

Determination of (R)-(+)- and (S)-(-)-Nicotine Chirality in Puff Bar E-Liquids by 1H NMR Spectroscopy, Polarimetry, and Gas Chromatography-Mass Spectrometry.

Tobacco products generally contain tobacco-derived nicotine (TDN; having ∼99+% (S)-(-)-nicotine). Recent United States regulation has led some producers to transition to synthetic ("tobacco-free") nicotine. For example, Puff Bar is now marketed with tobacco-free nicotine (TFN; presumed to be racemic). To evaluate the claim that these new products contain TFN, we evaluated the presence of the two nicotine optical isomers by 1H NMR spectroscopy, polarimetry, and gas chromatography-mass spectrometry. Older Puff Bars were found to contain (S)-(-)-nicotine, and newer "TFN" Puff Bars were found to contain both (R)-(+) and (S)-(-) isomers-indicating TFN, albeit with slightly more of the (S)-(-)-nicotine form.