Mathematical models for data mining and system dynamics to study head and neck cancer progression and chemoprevention

Head and neck squamous cell carcinoma (HNSCC) is the 6th most prevalent cancer worldwide, and more than 12,000 deaths from this disease are anticipated in 2015 in the U.S. alone. The advent of the “Big Data” era for biomedicine, through the widespread use of genomic, transcriptomic, and other –omic data acquisition technologies, has enabled deeper exploration of the molecular-level mechanisms behind HNSCC development and progression. This knowledge in turn can lead to earlier diagnosis and better treatment strategies, resulting overall in better patient outcomes. However, the volume and complexity of –omic data present a major obstacle to fully realizing its potential to accelerate and enable basic and translational research for HNSCC. The goal of this Ph.D. dissertation is to address several key technical challenges related to harnessing –omic data for clinical HNSCC research. These are (1) the lack of knowledge-driven modeling tools and systems for discovering biomarkers at the protein and metabolite levels; (2) the lack of effective strategies for integrating heterogeneous types of –omic data for prediction; and (3) the lack of systems-level representations of biomarker knowledge for effectively predicting responses to bioactive agents. This dissertation addresses these challenges through three specific aims: 1. Knowledge-driven Data Mining: To develop modeling tools to mine –omic datasets in HNSCC for biomarker discovery by harnessing existing knowledge 2. Integrated –Omic Modeling: To develop supervised learning models for predicting HNSCC progression through integration of –omic datasets 3. System Modeling: To develop dynamic system models for predicting response to combinations of multi-target agents against HNSCC. The research in this dissertation was completed in collaboration with the Winship Cancer Institute and Georgia Institute of Technology. The models and tools developed have been systematically evaluated and validated using a variety of –omic data types. These results and associated case studies demonstrate the contribution of this work and its future potential in computational HNSCC research.Ph.D

A robust computational pipeline for model-based and data-driven phenotype clustering

Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non‐neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular‐level, cellular‐level, and organ‐level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient‐specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat

Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-na\uefve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population

Integrated quantitative systems pharmacology (QSP) model of lysosomal diseases provides an innovative computational platform to support research and therapeutic development for the sphingolipidoses

Integrated quantitative systems pharmacology (QSP) model of lysosomal diseases provides an innovative computational platform to support research and therapeutic development for the sphingolipidose