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Principal Investigator: Wei Zheng
Organization: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
Fiscal Year: 2024
Award: $2,200,000
Funding agency: National Center for Advancing Translational Sciences
In collaboration with NIH and external academic and industry researchers, we have carried out assay development and performed drug repurposing screens for multiple projects across a range of therapeutic areas, including COVID-19, rare genetic disorders and rare drug resistant cancers.
DISEASE MODELING USING PATIENT IPSC-DERIVED CELLS: Disease modeling using patient cells is an effective approach to enable establishment of new alternative / non-animal disease models. Human induced pluripotent stem cells (iPSCs) are generated from patient samples that can be further differentiated to various cell types such as neuronal cells, cardiomyocytes, and hepatocytes for disease modeling. These cell-based models are particularly useful for drug discovery and development for rare genetic diseases, as animal models are not always available. The patient derived iPSCs share the same genetic background with patients and have the same cellular disease phenotypes that are suitable for assay development to screen compounds. We have generated over 150 patient-derived iPSC lines for rare genetic diseases, gaining extensive experience and expertise in this field. We have also published a comprehensive protocol for the culture and characterization of iPS cells, serving as a valuable resource for researchers working with these cells. (Cheng YS et al., Curr Protoc 3, e866. PMID: 37610273). In this period, we generated several iPSC lines from samples of Alagille Syndrome and healthy donors (Hatim O et al., 2023. Stem Cell Res73, 103231. PMID: 37890331, and Stem Cell Res 77: 103429. PMID: 38703666). The iPSCs have been characterized and stably passaged over 10 passages. These iPSCs were differentiated to support generation of hepatocytes and liver organoids for ALGS disease modeling and assay development for repurposing screens. In addition, we have developed brain organoids from iPS cells that have been characterized for disease modeling (Pavlinov I et al, 2023, PLoS One 18, e0292926. PMID: 37862312). The iPS cell derived organoids models can be served as a disease relevant model system for evaluating drug efficacy and toxicity.
DRUG-RESISTANT CANCER: Cancer drug resistance is a serious clinical issue that often leads to patient death. We conducted drug repurposing screens on drug-resistant colon cancer cell lines that are unresponsive to multiple standard chemotherapies, including platinum drugs and 5FU. Several approved drugs have been identified that either directly suppress the drug-resistant cancer cells or re-sensitize them to the anticancer effects of standard treatments. We are currently analyzing the data and confirming the primary compound screen hits. These findings will contribute to further studies on the mechanisms of drug resistance and the identification of cancer targets for new drug development. In addition, we are preparing two manuscripts for publication, which will be submitted once data analysis is complete.
SARS-CoV-2 and COVID-19: In our previous work, we identified that anti-heparan sulfate compounds can inhibit the entry of the SARS-CoV-2 virus into cells. Continuing our collaboration with the Yihong Ye lab at NIDDK/NIH, we discovered that host heparan sulfate facilitates the binding of the SARS-CoV-2 spike protein to the host ACE2 receptor (Zhang Q et al., 2023, Nature Communications 14, 5777. PMID: 37723160). Based on drug repurposing screens and mechanistic studies, we have proposed a novel antiviral drug development approach that targets the interaction between heparan sulfate and viral proteins, thereby blocking viral entry into cells. This type of antiviral drug could potentially inhibit a broad range of RNA viruses, offering a wider antiviral spectrum (Zhang Q. et al., 2024, Frontiers in Medicine. 11:1364657. PMID: 38618194).
NGLY1 DEFICIENCY: This is a low incidence genetic disease that primarily affects the neuronal system, resulting in neuromotor impairment, intellectual disability, and neuropathy. We have generated brain organoids using the NGLY1 patient iPSCs for disease modeling. We found that the patient midbrain organoids show altered neuronal development and reduced dopaminergic neurons compared to wild type organoids (Abbott J. et al. 2023. Front Cell Dev Biol. 11: 1039182. PMID: 36875753). We also worked on a neuromuscular junction platform using the patient iPSC-derived motor neurons to study the pathogenesis of disease. We observed reduced axon length, increased and shortened axon branches, motor neuron action potential bursting, and decreased action potential firing rate and amplitude in this system (Sasserath T. et al. 2022. Adv Ther 5:2200009, PMID: 36589922). The NGLY1 brain organoids and motor neurons are useful models for the study of disease mechanisms and evaluation of therapeutic candidates.
AI-BASED MODELING AND VIRTUAL SCREENING: Recent advancements in artificial intelligence (AI)- based modeling have enabled virtual compound screening for lead compound identification. The advantages of this approach for drug discovery are (1) a reduction in physical compound screening experiments that significantly decreases project costs; and (2) a significantly increased screening capacity, as virtual screens can cover in-house compound collections as well as large commercial collections (i.e., millions of compounds), both of which save time during the lead identification process. We have developed and optimized several AI-based compound screening tools that have been applied to several projects. The experimental screening data from a small compound collection (such as the collection of 2800 approved drugs available at NCATS) is typically used to establish, train, and optimize a computer model for a given project that is then employed in a virtual compound screen of larger compound collections (up to millions of compounds) to identify hits. After computerized hit selection and confirmation, the top compounds (typically 50 to 200) are selected and ordered for laboratory experiments to evaluate their activities. We have published the method of AI-based modeling and virtual screening (Gao P et al., 2023. J Med Chem. 66, 15084-93. PMID: 37937963), and three manuscripts are currently in the submission stage.
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