The Wet Bridge Transfer System: An Novel In Vitro Tool for Assessing Exogenous Surfactant as a Pulmonary Drug Delivery Vehicle

Background: Due to its complex branching structure, direct drug delivery to the remote areas of the lung is a major challenge. Consequently, most therapies, such as those treating pulmonary infection and inflammation, must utilize large systemic dosing, with the potential for adverse side effects. A novel alternative strategy is to use exogenous surfactant, a material capable of distributing throughout the lung, as a pulmonary drug delivery vehicle. Objective: Utilize an in vitro transferring system to assess exogenous surfactant (BLES) as a pulmonary delivery vehicle for different therapeutics. Methods: An in vitro technique was developed to simultaneously study surfactant delivery and drug efficacy. This Wet Bridge Transfer system consisted of two connected wells in which drugs were instilled into an administration well and function was tested in a remote well. The distal wells were seeded with either bacteria or stimulated macrophages. Then therapeutics were administered to the administration well alone or in combination with BLES. Outcomes involved spot plating for bacterial killing and cytokine analysis for anti-inflammatory effects. Results: Administering any of the antimicrobial or anti-inflammatory drugs alone to the administration well elicited no change for outcomes in the remote well. However, bacterial growth was significantly reduced by several BLES/antibiotic preparations. Similarly, a few BLES/anti-inflammatory mixtures significantly lowered the pro-inflammatory cytokine concentrations in the remote well. Discussion: The Wet Bridge Transfer system can be used to rapidly assess and screen surfactant-based therapies prior to their assessment in vivo. Furthermore, our results indicated that exogenous surfactant was an effective delivery vehicle for many antimicrobial and anti-inflammatory therapeutics

Pulmonary surfactant fortified with Cath-2 as a novel therapy for bacterial pneumonia

Background: Bacterial pneumonia is a leading cause of death worldwide, with high mortality rates persisting even after antibiotic treatment. Current treatments for pneumonia involve administration of antibiotics, however after the bacteria are killed they release toxic substances that induce inflammation and lung dysfunction. Host defense peptides represent a potential solution to this problem through their ability to down regulate inflammation. However, effective delivery to the lung is difficult because of the complex branching structure of the airways. My study addresses this delivery problem by using exogenous surfactant, a pulmonary delivery vehicle capable of improving spreading of these peptides throughout the lungs. We hypothesize that exogenous surfactant fortified with host defense peptides will improve outcomes associated with bacterial pneumonia. Methods: A mouse model of lung inflammation, induced by bacterial toxins from antibiotic-killed bacteria, will be used to test the effects of supplementing antibiotics with host defense peptides. Results: We anticipate that exogenous surfactant fortified with host defense peptides will significantly downregulate pulmonary inflammation when co-administered with antibiotic-killed bacteria. Discussion & Conclusion: Our strategy of utilizing host defense peptides with a spreading agent represents a novel supplementary therapy, that if promising in these animal studies may ultimately be explored in clinical trials. Interdisciplinary Reflection: This project requires interacting with clinicians treating patients with the bacterial pneumonia, physiologists to develop suitable animal models, and biochemists & immunologists to understand the mechanisms of action of our new therapy. Working in this interdisciplinary environment is needed to ultimately make our novel therapy a clinical reality. Key words: Exogenous surfactant, host defense peptides, bacterial pneumonia, pulmonary inflammation, antibiotics, lun

Exogenous Surfactant as a Delivery Vehicle for Intrapulmonary Therapeutics

As an organ system, the lung has unique advantages and disadvantages for direct drug delivery. Its contact with the external environment allows for the airways to be easily accessible to intrapulmonary delivery. However, its complex structure, which divides into more narrow airways with each branch, can make direct delivery to the remote alveoli challenging. The objective of this thesis was to overcome this issue by using exogenous surfactant, a lipoprotein complex used to treat neonatal respiratory distress syndrome, as a carrier for pulmonary therapeutics. It was hypothesized that therapeutics administered with a surfactant vehicle would display enhanced delivery to the deeper regions of the lung. Acute respiratory distress syndrome and bacterial pneumonia were selected as prototypical examples of pulmonary conditions in which surfactant-drug combinations may be beneficial. Consequently, the pharmaceuticals utilized were those with antibacterial or anti-inflammatory activities. To test this hypothesis, the wet bridge transfer system was developed in Chapter 2 as a novel in vitro screening tool for surfactant-based therapeutics. Several antibiotic and anti-inflammatory medications combined with a commercially available exogenous surfactant were screened based on 1) surfactant spreading and 2) the biological efficacy of the transported drug at a remote site. In Chapter 3 this platform, in combination with other in vitro techniques, were utilized to gain the mechanistic insight required for optimizing surfactant vehicle prior to animal studies. Specifically, through these experiments a synthetic surfactant was designed, such that, the antibacterial activity of cathelicidins, a family of potent antimicrobial peptides, was retained when transported to a remote site. Finally, Chapter 4 used a rat model of lung inflammation, to assess the efficacy of this delivery approach for a mainstay anti-inflammatory. Surfactant based delivery was found to downregulate of a wide variety of inflammatory markers across both sexes. To conclude, surfactant-based delivery of antimicrobial and anti-inflammatory therapeutics was found to enhance drug delivery and efficacy at remote sites in vitro as well as in vivo. Based on these findings, it is also suggested that future research expand on the optimization process of this thesis for other surfactant-drug preparations and assess those combinations in clinically relevant animal models

Developing Novel Therapeutics for Bacterial Lung Infections

Background: Bacterial lung infections are leading causes of death worldwide. Unfortunately, increasing resistance to antibiotics and the inflammation often accompanying these infections are leading to poor outcomes despite antibiotic intervention. Complicating treatment further, the tree-like branching structure of the lung makes drug delivery to distal sites of infection difficult. Our research aims to address these challenges by developing new therapeutics and new tools to improve and assess drug delivery, bacterial killing and inflammation. Our therapy combines host defense peptides, which have been shown to kill antibiotic-resistant bacteria and down regulate inflammation, with a pulmonary vehicle, exogenous surfactant, that can improve distribution in the lung. Methods: We have developed the Wet Bridge Transfer system, a new tool which can rapidly assess drug transport, bacterial killing and anti-inflammatory properties of compounds as they spread across a surface. Results: We anticipate that exogenous surfactant will not only increase the transport of host defense peptides, but that the mixture will effectively kill antibiotic-resistant bacteria and reduce inflammation as it spreads. Discussion & Conclusion: Utilizing host defense peptides with a spreading agent represents a novel approach to treating bacterial lung infections. Additionally, the transfer system offers the ability to rapidly screen and examine the next generation of pulmonary therapies. Interdisciplinary Reflection: This project requires interacting with clinicians treating patients with lung infections, biochemists improving the transfer system, and immunologists to understand the underlying mechanism of action for our new therapy. This interdisciplinary environment is essential to making our novel therapy a clinical reality

Chaos-driven dynamics in spin-orbit coupled atomic gases

The dynamics, appearing after a quantum quench, of a trapped, spin-orbit coupled, dilute atomic gas is studied. The characteristics of the evolution is greatly influenced by the symmetries of the system, and we especially compare evolution for an isotropic Rashba coupling and for an anisotropic spin-orbit coupling. As we make the spin-orbit coupling anisotropic, we break the rotational symmetry and the underlying classical model becomes chaotic; the quantum dynamics is affected accordingly. Within experimentally relevant time-scales and parameters, the system thermalizes in a quantum sense. The corresponding equilibration time is found to agree with the Ehrenfest time, i.e. we numerically verify a ~log(1/h) scaling. Upon thermalization, we find the equilibrated distributions show examples of quantum scars distinguished by accumulation of atomic density for certain energies. At shorter time-scales we discuss non-adiabatic effects deriving from the spin-orbit coupled induced Dirac point. In the vicinity of the Dirac point, spin fluctuations are large and, even at short times, a semi-classical analysis fails.Comment: 11 pages, 10 figure

An Evolutionary Reduction Principle for Mutation Rates at Multiple Loci

A model of mutation rate evolution for multiple loci under arbitrary selection is analyzed. Results are obtained using techniques from Karlin (1982) that overcome the weak selection constraints needed for tractability in prior studies of multilocus event models. A multivariate form of the reduction principle is found: reduction results at individual loci combine topologically to produce a surface of mutation rate alterations that are neutral for a new modifier allele. New mutation rates survive if and only if they fall below this surface - a generalization of the hyperplane found by Zhivotovsky et al. (1994) for a multilocus recombination modifier. Increases in mutation rates at some loci may evolve if compensated for by decreases at other loci. The strength of selection on the modifier scales in proportion to the number of germline cell divisions, and increases with the number of loci affected. Loci that do not make a difference to marginal fitnesses at equilibrium are not subject to the reduction principle, and under fine tuning of mutation rates would be expected to have higher mutation rates than loci in mutation-selection balance. Other results include the nonexistence of 'viability analogous, Hardy-Weinberg' modifier polymorphisms under multiplicative mutation, and the sufficiency of average transmission rates to encapsulate the effect of modifier polymorphisms on the transmission of loci under selection. A conjecture is offered regarding situations, like recombination in the presence of mutation, that exhibit departures from the reduction principle. Constraints for tractability are: tight linkage of all loci, initial fixation at the modifier locus, and mutation distributions comprising transition probabilities of reversible Markov chains.Comment: v3: Final corrections. v2: Revised title, reworked and expanded introductory and discussion sections, added corollaries, new results on modifier polymorphisms, minor corrections. 49 pages, 64 reference

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages