| Start Date: | 6/10/2021 | Start Time: | 10:00 AM |
| End Date: | 6/10/2021 | End Time: | 12:00 PM |
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Event Description
BIOMED Master's Thesis Defense
Title:
A Computational Approach To Explore the Link Between Serum Biomarkers and Clinical Outcomes in Psoriasis
Speaker:
Aakankschit Nandkeolyar, Master's Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University
Advisor:
Hasan Ayaz, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University
Details:
Psoriasis is a chronic autoimmune skin disease that affects nearly 2% of the world’s population. It is characterized by the formation of red plaques on the skin. Such plaques can cause irritation and pain to patients, not to mention the plaques are a major source of anxiety related to appearance for patients, making psoriasis a disease with high morbidity. While there are currently no cures for psoriasis, anti-inflammatory pharmaceuticals can be used to treat psoriasis by reducing the severity of the symptoms. The efficacy of treatments for psoriasis is measured by improvement in clinical outcomes, the most popular of which is Psoriasis Area Severity Index Score (PASI). These scores are provided by a dermatologist to quantify the severity of psoriasis in a patient. Quantitative Systems Pharmacology (QSP) is a new field in the area of computational disease modeling that allows for quantification and prediction of disease serum biomarkers in virtual patient models. Such prediction can potentially be correlated with clinical outcomes, such as PASI scores, to allow a scientist to predict clinical outcomes in virtual patients. The current study proposes the use of machine learning algorithms to bridge the gap between QSP simulation data for serum biomarkers and clinical outcome measures to predict PASI scores post-treatment to potentially reduce cost and time for clinical trials.
This work specifically aims to develop predictive models for clinical outcome (PASI scores) using serum biomarker data (generated by a QSP model of psoriasis built by GlaxoSmithKline) via an array of machine learning algorithms, which include regularized regression models, Support Vector Machines regression, and ensemble methods (kth-Nearest Neighbor, Random Forest, and Stochastic Gradient Boosting). To construct and test the models, a comprehensive dataset was compiled from across eight different clinical trials that accounted for a total of 2,131 patients, where these trials altogether accounted for three different treatment regimens that target different biomarkers in patients, which included anti-IL-17, anti-IL-23, and anti-TNF-alpha. The dataset contained both PASI scores and their respective serum biomarkers, and was developed via a systematic literature review. To compile the dataset, the average PASI scores across all patients in the clinical trial, recorded every week, were found in the literature and used as a sample PASI score. The serum biomarker data associated with the PASI scores were generated using an “average patient” simulation of the GlaxoSmithKline QSP model. The models were trained and tested using 6-fold cross-validation.
Results indicate that machine learning methods can be utilized to predict clinical outcomes. The best models were generated using a regularized regression algorithm and had a mean square error averaging 14.7 PASI score units (within PASI score range of 0-72), with an R-squared value of 0.75. This is the first attempt to develop a predictive model of psoriasis from serum biomarker data, generated using QSP simulation data to clinical state. The significance of this research is based on the novel approach for using computational methods to potentially reduce the cost and time of clinical trials, and aid in faster development of treatments for different diseases. |
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Audience:
Undergraduate StudentsGraduate StudentsFacultyStaff |
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