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The University of Maryland School of Pharmacy, founded in 1841, is a thriving center for life sciences research and community service. Through its education, research, and service programs, the School of Pharmacy strives to improve the health and well-being of society by aiding in the discovery, development, and use of medicines.
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Capsule 2023University of Maryland, Baltimore. School of Pharmacy, 2023
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Development of an In-Cell Footprinting Method Coupled with MS for the Study of Proteins in Three-Dimensional Cancer ModelsFast photochemical oxidation of proteins (FPOP) is a powerful, mass spectrometry (MS)-based, biophysical method used to probe protein structure, interactions, and conformations. FPOP was recently extended into cells (IC-FPOP) and can modify thousands of proteins in a single experiment, enabling proteome-wide structural biology. Although IC-FPOP can reveal critical structural information in 2D cell culture, the conditions do not emulate an in-vivo environment. To address this, we propose to develop a mass spectrometry-based protein footprinting method that assesses the varying protein heterogeneity in 3D cell culture; Spheroid-FPOP. IC-FPOP on intact spheroids was performed using a patented PIXY platform which brought automation to IC-FPOP. Spheroid-FPOP coupled with serial trypsinization to obtain spatial resolution, revealed modifications in three distinct spheroid regions; the outer inner and core. Native oncogenic pathways were interrogated through this study showing its value in disease pathogenesis and treatment. Though progressive for FPOP, the extension into 3D model systems generated three times the samples and data compared to typical IC- or IV-FPOP experiments. This shed light to FPOP workflow limitations. The research herein responds to those challenges by developing an automated sample preparation workflow by coupling a sample handling robot with Thermo’s sample preparation kit. These modifications robustly improve the workflow by significantly reducing the manual labor, execution time, and variability of samples processed and data acquired. After workflow optimization FPOP, we apply the optimized method more complex biological sample. In our case, 3D bioprinted Huh-7 liver organoids were generated for IC-FPOP. To obtain spatial resolution within the model, we integrated cryosectioning of the top, middle and bottom layers of the organoid. Peptide level analysis revealed differences in the extent of modification for peptides identified in each region of the organoid, which confirms the acquisition of structural information. However further optimization was required to increase proteome depth. By coupling the organoid model with IC-FPOP we aim further validate its implementation for complex proteome-wide structural studies. In all, this research is focused on advancing the applications and processing workflows for IC-FPOP.
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The Effect of Medication Information Delivery Format on Cognitive Load and Knowledge Retention of Informal CaregiversInformal caregivers (IFCs) are tasked with many responsibilities in patient care, including medication management. Many IFCs feel ill-prepared for this responsibility, and it is incumbent on health care professionals to provide education and ensure IFCs competence in medication management. One common strategy is to provide a medication information leaflet to the IFC to prepare them for this role. Designing medication information leaflets using sound educational principles, such as an infographic designed according to the cognitive theory of multimedia learning (CTML), may optimize knowledge retention and decrease cognitive load for IFCs. The purpose of this randomized, experimental study was to investigate the impact of medication information delivery format on immediate retention of medication information and cognitive load of IFCs of patients with a serious illness. Using purposive sampling, 120 IFCs who have provided some element of medication management for patients diagnosed with a serious illness, including patients who may have been receiving hospice or palliative care services were recruited. Study participants were randomly assigned in either the experimental group or the control group. The experimental group viewed an infographic on the medication hydromorphone, followed by a knowledge quiz, and a self-assessment of cognitive load. This was followed by a second infographic on hydroxyzine, the quiz, and cognitive load assessment. The control group went through the same steps but viewed a text-only medication leaflet. Statistical analyses included descriptive statistics, independent samples t-test, one-way analysis of variance, and one-way multivariate analysis of variance. Statistically significantly higher quiz scores were observed among those who viewed the infographics than those who viewed text-only medication leaflets, indicating better immediate knowledge retention of medication information. Those who viewed the infographic also had statistically significantly lower intrinsic and extraneous cognitive load, and higher germane cognitive load. These findings are consistent with the hypothesis that infographics prepared using the CTML result in better and more efficient learning. Limitations of this research include use of nonprobability sampling, examining only two medications that are commonly used in serious illness, and lack of systematic randomization. Additional research is needed to continue determining best practices for instructing and supporting IFCs in medication management.
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The Effects of Graded Versus Ungraded Individual Readiness Assurance Tests on Pharmacy Students’ Assessment Performance and Achievement Goals in a Team-Based Learning ClassroomIndividual readiness assurance tests (iRATs) are frequently graded in team-based learning (TBL) classrooms, with the goal of incentivizing individual pre-class preparation. The purpose of this study was to determine whether shifting to an ungraded iRAT process affects student preparation and learning, as measured using assessment scores, and whether this is accompanied by a change in achievement goals. Using a crossover design in a required second-year Doctor of Pharmacy pharmacotherapy course, students were assigned to one of two iRAT grading sequences: graded/ungraded (G/UG) or ungraded/graded (UG/G). In the G condition iRATs were graded based on correctness and in the UG condition based on completion. Each period consisted of four iRATs and one examination. Students completed the Achievement Goal Questionnaire at the conclusion of each period. A one-way repeated measures multivariate analysis of variance (MANOVA) was used to test within-subject differences of mean iRAT and examination scores across grading conditions. A separate one-way repeated measures MANOVA was used to analyze differences in achievement goal scores. A total of 91 doctor of pharmacy students were included in the study. There was a statistically significant main effect for iRAT grading condition on assessment scores, F(2,88) = 3.851, Wilks’ Λ = .992, p = .025. Univariate testing using one-way analysis of variance with Bonferroni correction demonstrated a significant difference only in iRAT scores, with the mean score higher in the G condition (72.51% versus 67.99%; p = .011). Examination scores were similar in the G and UG conditions (81.07% versus 80.32%, p = .397). There was not a statistically significant difference in achievement goals based on iRAT grading condition, F(4,85) = 1.109, η2 =.050, p = .358. In conclusion, a modest reduction in iRAT performance was observed when shifting from a graded to ungraded iRAT; however, this had no effect on examination performance. Achievement goals were unaffected by the change in iRAT grading condition.
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Development of SILCS kinetics methodology for the determination of ligand dissociation pathways and free energy barriersThe fast and accurate assessment of unbinding kinetics of ligands from proteins remains challenging due to high computational requirements and the lack of the information about the molecular transition states due to limited conformational sampling. Therefore, in the present study we investigate the extension of the site-identification by ligand competitive saturation (SILCS) methodology towards estimation of ligand unbinding kinetics. The proposed SILCS-kinetics (SILCS-KIN) method is implemented to sample the free-energy landscape of drug dissociation pathways. SILCS-KIN methodology will be expected as a potential tool for the discovery and design of drug-like compounds with optimized ligand dissociation properties
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A Robust, Viable, and Resource Sparing HPLC-Based LogP Method Applied to Common Lipophilic Drugs to Help in Expanding in Silico Training DatasetsReliable, experimentally determined partition coefficient P (logP) for most drugs is often unavailable in the literature. Many values are from in silico predictions and may not accurately reflect drug lipophilicity. In this study, a robust, viable, and resource-sparing method to measure logP was developed using reverse-phase high-performance liquid chromatography (RP-HPLC). The logP of twelve common drugs was measured using calibration curves at pH 6 and 9 that were created using reference standards with well- established logP. The HPLC method reported here can be used for high throughput estimation of logP of commonly used drugs. LogP values here showed general agreement with the other few HPLC-based literature logP values available. Additionally, the HPLC-based logP values found here agreed partially with literature logP values found using other methodologies (± 10%). However, there was no strong agreement since there are few experimentally determined literature logP values. This paper shows a facile method to estimate logP without using octanol or computational approaches. This method has excellent promise to provide reliable logP values of commonly used drugs available in the literature. A larger pool of reliable logP values of commonly drugs has the promise to improve the quality of medicinal chemistry, pharmacodynamic, and pharmacokinetic training sets, and models.
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Under-ascertainment and underreporting of adverse events in clinical trialsIntroduction: Clinical trials are widely regarded as the “gold standard” for evaluating different interventions’ adverse events (AEs). However, numerous cases have suggested that AEs are underreported in clinical trials due to inadequate data collection methodology and inconsistent reporting criteria. This study examined 1) the AE ascertainment methodologies used for the marketing approval of new drugs and 2) the diversity and consistency of AE reporting criteria used in trial reports. Methods: We screened drugs approved by the US Food and Drug Administration (FDA) in 2018-2019 and collected publicly available trial documents for all pivotal trials. From these documents, we examined ascertainment methods of adverse events of special interest (AESIs) and newly signaled post-marketing AEs. We also assessed the association between trial characteristics and the AE ascertainment approach using binary logistic regression. Additionally, from the obtained reports, we examined the characteristics of reported AEs and the usage of AE reporting criteria. Then, we assessed the consistency of the number of reported AE types and reporting criteria used across trial publications and other important sources of trial results. Results: 322 AESIs were identified from trial documents for 64 trials reporting 31 drugs approved in 2018-2019. 71% were systematically ascertained, mainly using diagnostic measurement tools and laboratory assessments. 10% were non-systematically ascertained. The ascertainment method of 19% was unclear. The regression analysis did not reveal statistically significant associations between trial characteristics and the use of a systematic ascertainment approach for AESIs. Of the six examined newly signaled post-marketing AEs, one was systematically ascertained. The examined sources utilized various criteria to report both serious and non-serious AEs. Frequency criteria were the most commonly used AE reporting criteria. Furthermore, the examined sources inconsistently reported serious AEs and inconsistently utilized reporting criteria. Conclusions: We were unable to identify the ascertainment methodology for some AEs, even with access to underlying trial documents. Additionally, trial reports applied various criteria that potentially resulted in only a subset of AEs recorded during the trial being reported. The study suggests room for improvement in AE data collection and reporting to aid unbiased harm-benefit assessments of study interventions and informed treatment decisions.
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Determination of Harmful and Potentially Harmful Constituents in E-cigarettes, E-liquids, and Generated AerosolsElectronic nicotine delivery systems (ENDS), including e-cigarettes, are battery-operated devices which are vaped with the intent of delivering nicotine to the user. As these devices have become more prevalent on the market, so has the need to quantify different harmful and potentially harmful constituents (HPHCs) from those e-cigarettes. The FDA CTP has an established list of HPHCs, but my focus in collaboration with other labs was metals and volatile organic compounds (VOCs). Throughout this work, both the unaerosolized e-liquids and generated aerosols were characterized for metals and VOCs. Commercial disposable e-cigarettes were purchased, their e-liquids were extracted, and those e-liquids were analyzed for metal content. Across different batches and lots of the same products, metal levels varied greatly. An early focus of the work was determining where the metals in the e-liquid come from, which necessitated x-raying and deconstructing products to determine where e-liquid contacts materials of construction. To further inform on the metals that may be present in e-cigarette aerosols, we developed and constructed an ENDS aerosolization machine to vape e-cigarette products. The ENDS aerosolization machine is sufficiently adaptable for use in aerosolizing commercially available ENDS products, as well as refillable products, with the aerosol collectable in a variety of manners to permit analysis of metal content, VOCs, and cellular effects. Using this machine, we have determined that metals, such as copper, chromium, nickel, and lead, are transferred from e-liquids into aerosols at different efficiencies depending on the e-liquid carrier vehicle and the type of metal in the starting e-liquid. Similarly, we have developed an effective approach for the characterization of VOCs produced by aerosolization. This necessitated capturing volatile and non-volatile components of the aerosol. Our methodology uses real-time derivatization of the VOCs, namely formaldehyde, acetaldehyde, acrolein, acetone, propionaldehyde, crotonaldehyde and 2-butanone, followed by extraction, and analysis via mass spectrometry. From this body of research, we have developed validated methods to analyze unaerosolized e-liquids and generated aerosols from e-cigarettes in order to better understand the harmful and potentially harmful constituents within.
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A Cost-Effectiveness Analysis Model Framework For Treatments Of Early-Stage Huntington’s Disease Patients In The United StatesHuntington's disease (HD) is a rare neurodegenerative condition caused by a mutation in the huntingtin gene. The emergence of drugs such as Tominersen and AMT-130, which have the potential to treat HD highlights the importance of evaluating their cost-effectiveness. This study aims to fill this gap and evaluate the incremental cost-effectiveness of these treatments compared with the current standard of care for HD. A health state transition Markov model was developed to estimate the costs and benefits of each treatment over a lifetime time horizon from a societal perspective. Our findings showed that Tominersen and AMT-130 were associated with higher costs but also provided greater benefits than the standard of care. AMT-130 was found to be a cost-effective option compared to the standard of care and Tominersen, considering the willingness to pay threshold. This study provides valuable insights into the economic impact of HD which can inform healthcare policy and treatment decisions.
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Evaluation of Evidence in Economic Models Used for Decision-Making: Development of the Data Inputs in Value Economic Evaluations (DIVEE) ChecklistBackground: Value or health technology assessment (V/HTA) is a structured approach for evaluating health interventions to inform coverage and reimbursement decisions. V/HTAs often include model-based economic evaluations (economic models) to gauge economic value. Economic models formally examine costs and consequences of interventions versus alternatives. Data sources and inputs (DSIs) used to populate economic models influence the findings, and inappropriate DSIs can result in misleading economic value appraisals. Thus, it is important health care decision-makers (HCDMs) assess the appropriateness of DSIs used to populate key economic-model parameters to understand whether the model is suitable for use in their decision-making. But many HCDMs lack economic training and may be ill-prepared for assessing DSIs. A tool is needed to help HCDMs efficiently and confidently assess DSIs used in economic models to support informed decision-making. Objective: Create a user-friendly checklist that supports HCDMs in assessing, in a standardized, clear, and consistent manner, the appropriateness of DSIs used in model-based V/HTA economic evaluations. Methods: A checklist was developed in three stages: 1. Need for a checklist was established through assessment of DSIs used in published V/HTA economic model reports and a search for existing guidance on identifying, selecting, or reporting DSIs for economic evaluations. 2. Stakeholder perspectives on evaluation of DSIs were obtained through interviews and applied, along with findings from the previous stage, to create, test, and refine a checklist. 3. HCDM views on the new checklist and its potential use in decision-making were elicited through case reviews and interviews. Findings: The Data Inputs in Value Economic Evaluations (DIVEE) Checklist is comprised of eleven items organized into four domains (transparency, relevance, credibility, and model robustness). HCDMs report the Checklist to be comprehensive, easy to understand, and a useful aid for evaluating economic models. HCDMs support use of the Checklist, but recognize barriers exist to full adoption in current decision-making processes. Conclusions: The DIVEE Checklist is a user-friendly decision-maker aid that facilitates transparent, systematic, and consistent assessment of DSIs used in economic models and, ultimately, better-informed coverage and reimbursement decisions. Further testing in larger groups of HCDMs is needed along with training and educational support.
