Abstract: Multidrug resistance (MDR) is a major cause of chemotherapy failure in cancer and a major public health concern. Often, MDR cancers are aggressive, metastatic, and have poor prognoses. In addition, MDR cancer is highly resistant to treatments that induce conventional programmed cell death, such as chemotherapy and radiation. For the purpose of combating apoptosis mediated MDR, new drug discoveries are being directed towards therapies that induce apoptosis-inhibiting processes, such as necroptosis, autophagy, paraptosis, methuosis, and ferroptosis. When optimizing new chemical molecules during the early phases of drug discovery, two key questions need to be addressed: a) the ability of the drug to kill cancer cells, and b) the mechanism by which the drug kills cancer cells. At present, conventional biochemical assays and high-definition imaging are the only methods for studying these processes, but they are time consuming, costly, and require skilled experts, thus limiting their utility to a small number of laboratories. We propose a paradigm-altering phenotypic screening tool that identifies cell death mechanisms in real time using high-resolution widefield microscopy coupled with deep learning. First, we propose to develop a low-cost widefield holographic microscope, with multi-wavelength illumination, including ultraviolet (UV), to enable high content screening without external labeling, at high resolution that exceeds the diffraction limit. We will achieve this by integrating lens-less holographic microscopy with microparticle array-based imaging substrates that will allow us to image thousands of live cells per test. UV illumination may provide extra information about nuclei and other organelles of cells, even though it is used sparingly. Using time lapse images of cancer cells, a 3D- convolutional neural network will be trained to identify different morphological features, such as shrinking, blebbing, vacuoles and membrane ruptures, associated with different cell death processes. During the incubation step, results will be obtained in real time, using the pre-trained network for automated classification of the cell death process. Using this approach, new anti-cancer drug molecules and their intermediates can be screened at high-throughput without requiring any further processing or labeling. A successful completion of this project will result in an affordable, compact, high-content screening tool that can be used for many different applications, in addition to phenotypic screening. In particular, during this Covid-19 crisis, which has highlighted the need for high throughput diagnostics, drug screening, and therapy tools. By implementing this AREA award, we will significantly strengthen University of Toledo's research climate and provide undergraduate students with a unique interdisciplinary training experience in biophysics, microscopy, imaging, deep learning, cell biology, and pharmacology.

Narrative: Developing new anticancer drugs relies on bypassing the conventional proapoptotic (i.e., programmed cell death) process and focusing instead on non-apoptotic pathways that are difficult to study due to lack of simple, cost-effective yet powerful tools. We present a novel approach for detecting toxicity, selectivity, and efficacy of new drugs based on the phenotypic cell-death mechanism using a low-cost, high-resolution, high-throughput microscope powered by Artificial Intelligence. Using this technology, drug discovery will be significantly sped up, reducing the costs and time associated with developing new cancer drugs.