Despite advances in therapeutic options for head and neck cancer (HNC), treatment-associated toxicities and overall clinical outcomes have remained disappointing. Even with radiation therapy (RT), which remains the primary curative modality for HNC, the most effective regimens achieve local control rates of 45-55%, with disease-free survival rates of only 30-40%. Thus, the development of novel strategies to enhance tumor cell killing, while minimizing damage to the surrounding normal tissues, is critical for improving cure rates with RT. Accordingly, we sought to identify novel radiosensitizing therapies for HNC, exploiting a high-throughput screening (HTS) approach. Initially, a cell-based phenotype-driven HTS of ~2,000 commercially available natural products was conducted, utilizing the short-term MTS cell viability assay. Cetrimonium bromide (CTAB) was identified as a novel anti-cancer agent, exhibiting in vitro and in vivo efficacy against several HNC models, with minimal effects on normal fibroblasts. Two major limitations of our findings, however, were that CTAB did not synergize with radiation, nor was its precise cellular target(s) elucidated. Consequently, an alternative strategy was proposed involving a target-driven RNAi-based HTS. Since the colony formation assay (CFA) is the gold standard for measuring cellular effects of radiation in vitro, an automated high-throughput colony-formation read-out was developed as a more appropriate end-point for radiosensitivity. Although successful as a tool for the discovery of potent anti-cancer cytotoxics, a technical drawback was its limited dynamic range. Thus, the BrdU incorporation assay, which measures replicative DNA synthesis and is a viable CFA alternative, was employed. From an RNAi-based screen of ~7000 human genes, uroporphyrinogen decarboxylase (UROD), a key regulator of heme biosynthesis, was identified as a novel tumor-selective radiosensitizing target against HNC in vitro and in vivo. Radiosensitization appeared to be mediated via tumor-selective enhancement of oxidative stress from perturbation of iron homeostasis and increased ROS production. UROD was significantly over-expressed in HNC patient biopsies, wherein lower pre-RT UROD levels correlated with improved disease-free survival, suggesting that UROD expression could also be a potential predictor for radiation response. Thus, employing a HTS approach, this thesis identified two novel therapeutic strategies with clinical potential in the management of HNC.

Despite advances in therapeutic options for head and neck cancer (HNC), treatment-associated toxicities and overall clinical outcomes have remained disappointing. Even with radiation therapy (RT), which remains the primary curative modality for HNC, the most effective regimens achieve local control rates of 45-55%, with disease-free survival rates of only 30-40%. Thus, the development of novel strategies to enhance tumor cell killing, while minimizing damage to the surrounding normal tissues, is critical for improving cure rates with RT. Accordingly, we sought to identify novel radiosensitizing therapies for HNC, exploiting a high-throughput screening (HTS) approach. Initially, a cell-based phenotype-driven HTS of ~2,000 commercially available natural products was conducted, utilizing the short-term MTS cell viability assay. Cetrimonium bromide (CTAB) was identified as a novel anti-cancer agent, exhibiting in vitro and in vivo efficacy against several HNC models, with minimal effects on normal fibroblasts. Two major limitations of our findings, however, were that CTAB did not synergize with radiation, nor was its precise cellular target(s) elucidated. Consequently, an alternative strategy was proposed involving a target-driven RNAi-based HTS. Since the colony formation assay (CFA) is the gold standard for measuring cellular effects of radiation in vitro, an automated high-throughput colony-formation read-out was developed as a more appropriate end-point for radiosensitivity. Although successful as a tool for the discovery of potent anti-cancer cytotoxics, a technical drawback was its limited dynamic range. Thus, the BrdU incorporation assay, which measures replicative DNA synthesis and is a viable CFA alternative, was employed. From an RNAi-based screen of ~7000 human genes, uroporphyrinogen decarboxylase (UROD), a key regulator of heme biosynthesis, was identified as a novel tumor-selective radiosensitizing target against HNC in vitro and in vivo. Radiosensitization appeared to be mediated via tumor-selective enhancement of oxidative stress from perturbation of iron homeostasis and increased ROS production. UROD was significantly over-expressed in HNC patient biopsies, wherein lower pre-RT UROD levels correlated with improved disease-free survival, suggesting that UROD expression could also be a potential predictor for radiation response. Thus, employing a HTS approach, this thesis identified two novel therapeutic strategies with clinical potential in the management of HNC.

Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.