Characterization of a multidisciplinary team's role in hospital discharge for patients receiving hypomethylating agents with venetoclax as induction therapy for acute myeloid leukemia.

PURPOSE: Venetoclax combined with a hypomethylating agent (HMA) has become the standard of care for elderly/unfit patients with newly diagnosed acute myeloid leukemia (AML). This study is aimed at characterizing the impact of an interdisciplinary team on the length of stay (LOS) of patients with newly diagnosed AML receiving venetoclax with an HMA. METHODS: This retrospective observational study included patients with AML who received HMA with venetoclax as an initial treatment between December 2015 and July 2021. The primary outcome was the median LOS during induction stratified by HMA. Secondary outcomes included barriers to hospital discharge, incidence of tumor lysis syndrome (TLS), response rates, and utilization of the institution's prescription assistance program (PAP). RESULTS: Seventy-eight patients were included in our analysis: 51 received azacitidine/venetoclax, and 27 received decitabine/venetoclax. The median LOS from therapy initiation was eight days (range 7-38) for the azacitidine group and six days (range 5-26) for the decitabine group. The most common barriers to discharge were transfusion dependence (33 patients, 42.3%) and insurance coverage (12 patients, 15.4%). Twelve patients (15.3%) had tumor lysis syndrome during hospital admission, and 20 (25.6%) were readmitted during induction. Twenty-three patients (29.5%) required financial assistance for AML care, and a pharmacy-led PAP generated approximately $342,646 in cost savings. CONCLUSION: The utilization of an interdisciplinary AML team to target early hospital discharge proved to be safe and effective and led to a reduction in costs for the health system. Future research may identify select patients who may be suitable for earlier discharge or outpatient induction.

COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19.

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

Knowledge-based approaches to drug discovery for rare diseases.

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19.

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.