Program and Proceedings: The Nebraska Academy of Sciences 1880-2023. 142th Anniversary Year. One Hundred-Thirty-Third Annual Meeting April 21, 2023. Hybrid Meeting: Nebraska Wesleyan University & Online, Lincoln, Nebraska

AERONAUTICS & SPACE SCIENCE Chairperson(s): Dr. Scott Tarry & Michaela Lucas HUMANS PAST AND PRESENT Chairperson(s): Phil R. Geib & Allegra Ward APPLIED SCIENCE & TECHNOLOGY SECTION Chairperson(s): Mary Ettel BIOLOGY Chairpersons: Lauren Gillespie, Steve Heinisch, and Paul Davis BIOMEDICAL SCIENCES Chairperson(s): Annemarie Shibata, Kimberly Carlson, Joseph Dolence, Alexis Hobbs, James Fletcher, Paul Denton CHEM Section Chairperson(s): Nathanael Fackler EARTH SCIENCES Chairpersons: Irina Filina, Jon Schueth, Ross Dixon, Michael Leite ENVIRONMENTAL SCIENCE Chairperson: Mark Hammer PHYSICS Chairperson(s): Dr. Adam Davis SCIENCE EDUCATION Chairperson: Christine Gustafson 2023 Maiben Lecturer: Jason Bartz 2023 FRIEND OF SCIENCE AWARD TO: Ray Ward and Jim Lewi

Proteomics investigation of breast cancer biomarkers in urine and blood for diagnosis and monitoring

Breast cancer (BC) is the most commonly diagnosed cancer among women in the world. Currently, there are no biomarkers available for early diagnosis and monitoring BC progression. Advances in proteomics technology have allowed new insight into cancer biology by allowing us to mine the human proteome. Therefore, the main research objectives for my thesis were to 1) analyse urine and blood, from BC patients and control subjects by proteomics analysis; 2) identify novel proteins which highlight the presence of disease; 3) validate the identified potential biomarkers for diagnosis. After successfully developing a standardised method for urine protein extraction and precipitation using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, it was applied to additional urine samples from BC patients. The study cohort consisted of urine and blood samples from 20 BC patients, 20 healthy control subjects and 6 benign breast disease patients. Blood was collected in 4 different blood tubes (2 serum/2 plasma). Statistical analysis revealed 59 urinary proteins that were significantly different in BC patients compared to the normal healthy control subjects (p3). Thirty-six urinary proteins were exclusively found in specific BC stages, with 24 increasing and 12 decreasing in abundance. Preliminary validation of 3 potential markers ECM1, MAST4 and filaggrin was performed in BC cell lines by Western blotting (WB). One potential marker MAST4, was further validated in human BC tissues as well as human BC urine samples with immunohistochemistry (IHC) and WB, respectively. From my human serum and plasma proteomic analysis, over 100 differentially abundant proteins were identified and the greatest number were identified from the Gold-top serum tubes in the 3-50kDa mass fractions. Normalization of data against the control samples demonstrated 5 secreted proteins, potential markers Clusterin, Vitronectin, LRG1, IGFBP-3 and S100-6 (secreted proteins), as promising to define early stage BC. Preliminary validation, was performed in BC cell lines and blood samples by WB. Two potential marker CLU, IGFBP-1 were further validated in human BC tissues with IHC. In summary, I have identified several important proteins from urine and blood for BC detection and monitoring BC progression. My findings may have a major impact on the prognosis of BC for the thousands of women who die from the disease each year

Machine Learning for Modelling Tissue Distribution of Drugs and the Impact of Transporters

The ability to predict human pharmacokinetics in early stages of drug development is of paramount importance to prevent late stage attrition as well as in managing toxicity. This thesis explores the machine learning modelling of one of the main pharmacokinetics parameters that determines the therapeutic success of a drug - volume of distribution. In order to do so, a variety of physiological phenomena with known mechanisms of impact on drug distribution were considered as input features during the modelling of volume of distribution namely, Solute Carriers-mediated uptake and ATP-binding Cassette-mediated efflux, drug-induced phospholipidosis and plasma protein binding. These were paired with molecular descriptors to provide both chemical and biological information to the building of the predictive models. Since biological data used as input is limited, prior to modelling volume of distribution, the various types of physiological descriptors were also modelled. Here, a focus was placed on harnessing the information contained in correlations within the two transporter families, which was done by using multi-label classification. The application of such approach to transporter data is very recent and its use to model Solute Carriers data, for example, is reported here for the first time. On both transporter families, there was evidence that accounting for correlations between transporters offers useful information that is not portrayed by molecular descriptors. This effort also allowed uncovering new potential links between members of the Solute Carriers family, which are not obvious from a purely physiological standpoint. The models created for the different physiological parameters were then used to predict these parameters and fill in the gaps in the available experimental data, and the resulting merging of experimental and predicted data was used to model volume of distribution. This exercise improved the accuracy of volume of distribution models, and the generated models incorporated a wide variety of the different physiological descriptors supplied along with molecular features. The use of most of these physiological descriptors in the modelling of distribution is unprecedented, which is one of the main novelty points of this thesis. Additionally, as a parallel complementary work, a new method to characterize the predictive reliability of machine learning classification model was proposed, and an in depth analysis of mispredictions, their trends and causes was carried out, using one of the transporter models as example. This is an important complement to the main body of work in this thesis, as predictive performance is necessarily tied to prediction reliability