Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Synthetic and Natural Inhibitors of Mortalin for Cancer Therapy.

Upregulation of stress chaperone Mortalin has been closely linked to the malignant transformation of cells, tumorigenesis, the progression of tumors to highly aggressive stages, metastasis, drug resistance, and relapse. Various in vitro and in vivo assays have provided evidence of the critical role of Mortalin upregulation in promoting cancer cell characteristics, including proliferation, migration, invasion, and the inhibition of apoptosis, a consistent feature of most cancers. Given its critical role in several steps in oncogenesis and multi-modes of action, Mortalin presents a promising target for cancer therapy. Consequently, Mortalin inhibitors are emerging as potential anti-cancer drugs. In this review, we discuss various inhibitors of Mortalin (peptides, small RNAs, natural and synthetic compounds, and antibodies), elucidating their anti-cancer potentials.

Donepezil Nanoemulsion Induces a Torpor-like State with Reduced Toxicity in Nonhibernating Xenopus laevis Tadpoles.

Achieving a reversible decrease of metabolism and other physiological processes in the whole organism, as occurs in animals that experience torpor or hibernation, could contribute to increased survival after serious injury. Using a Bayesian network tool with transcriptomic data and chemical structure similarity assessments, we predicted that the Alzheimer's disease drug donepezil (DNP) could be a promising candidate for a small molecule drug that might induce a torpor-like state. This was confirmed in a screening study with Xenopus laevis tadpoles, a nonhibernator whole animal model. To improve the therapeutic performance of the drug and minimize its toxicity, we encapsulated DNP in a nanoemulsion formulated with low-toxicity materials. This formulation is composed of emulsified droplets <200 nm in diameter that contain 1.250 mM DNP, representing ≥95% encapsulation efficiency. The DNP nanoemulsion induced comparable torpor-like effects to those produced by the free drug in tadpoles, as indicated by reduced swimming motion, cardiac beating frequency, and oxygen consumption, but with an improved biodistribution. Use of the nanoemulsion resulted in a more controlled increase of DNP concentration in the whole organism compared to free DNP, and to a higher concentration in the brain, which reduced DNP toxicity and enabled induction of a longer torpor-like state that was fully reversible. These studies also demonstrate the potential use of Xenopus tadpoles as a high-throughput in vivo screen to assess the efficacy, biodistribution, and toxicity of drug-loaded nanocarriers.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Importance: Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. Objectives: To test if different statins differ in their ability to exert protective effects based on molecular computational predictions and electronic medical record analysis. Main Outcomes and Measures: A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Results: Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Conclusions and Relevance: Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.