scholarly journals AI Enabled Precision Medicine: Patient Stratification, Drug Repurposing and Combination Therapies

2020 ◽  
Author(s):  
Steve Gardner ◽  
Sayoni Das ◽  
Krystyna Taylor
Keyword(s):  
Precision Medicine ◽  
Drug Repurposing ◽  
Patient Stratification ◽  
Combination Therapies

Model-based Computational Precision Medicine to develop combination therapies for autoimmune diseases

2021 ◽  
Author(s):  
Emiko Desvaux ◽  
Audrey Aussy ◽  
Sandra Hubert ◽  
Florence Keime-Guibert ◽  
Alexia Blesius ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Precision Medicine ◽  
Combination Therapies ◽  
Model Based

scholarly journals Screening a library of FDA-approved and bioactive compounds for antiviral activity against SARS-CoV-2

2020 ◽  
Author(s):  
Scott B. Biering ◽  
Erik Van Dis ◽  
Eddie Wehri ◽  
Livia H. Yamashiro ◽  
Xammy Nguyenla ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Combination Therapy ◽  
Global Health ◽  
Antiviral Activity ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Screening Strategy ◽  
Combination Therapies ◽  
Pulmonary Epithelial Cells ◽  
Compound Screening

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has emerged as a major global health threat. The COVID-19 pandemic has resulted in over 80 million cases and 1.7 million deaths to date while the number of cases continues to rise. With limited therapeutic options, the identification of safe and effective therapeutics is urgently needed. The repurposing of known clinical compounds holds the potential for rapid identification of drugs effective against SARS-CoV-2. Here we utilized a library of FDA-approved and well-studied preclinical and clinical compounds to screen for antivirals against SARS-CoV-2 in human pulmonary epithelial cells. We identified 13 compounds that exhibit potent antiviral activity across multiple orthogonal assays. Hits include known antivirals, compounds with anti-inflammatory activity, and compounds targeting host pathways such as kinases and proteases critical for SARS-CoV-2 replication. We identified seven compounds not previously reported to have activity against SARS-CoV-2, including B02, a human RAD51 inhibitor. We further demonstrated that B02 exhibits synergy with remdesivir, the only antiviral approved by the FDA to treat COVID-19, highlighting the potential for combination therapy. Taken together, our comparative compound screening strategy highlights the potential of drug repurposing screens to identify novel starting points for development of effective antiviral mono- or combination therapies to treat COVID-19.

scholarly journals MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Gilmer Valdes ◽  
José Marcio Luna ◽  
Eric Eaton ◽  
Charles B. Simone ◽  
Lyle H. Ungar ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Patient Stratification

scholarly journals New Insights into Therapeutic Targets in Myeloma

Hematology ◽  
2011 ◽  
Vol 2011 (1) ◽  
pp. 184-190 ◽  
Author(s):  
Kenneth C. Anderson
Keyword(s):  
Rapid Evolution ◽  
Proteasome Inhibitors ◽  
Complete Response ◽  
Patient Stratification ◽  
Immunomodulatory Drugs ◽  
Combination Therapies ◽  
Number Of Patients ◽  
Treatment Paradigm ◽  
Novel Targets ◽  
And Personalized Medicine

Abstract Patient outcome in multiple myeloma (MM) has been remarkably improved due to the use of combination therapies including proteasome inhibitors and immunomodulatory drugs, which target the tumor in its BM microenvironment. Ongoing efforts to improve the treatment paradigm even further include using oncogenomics to better characterize molecular pathogenesis and to develop refined patient stratification and personalized medicine in MM; using models of MM in its BM milieu to identify novel targets and to validate next-generation therapeutics directed at these targets; developing immune-based therapies including mAbs, immunotoxins targeting MM cells and cytokines, and novel vaccine strategies; and using functional oncogenomics to inform the design of novel combination therapies. With continued rapid evolution of progress in these areas, MM will be a chronic illness with sustained complete response in a significant number of patients.

scholarly journals Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development (Preprint)

2020 ◽  
Author(s):  
Enrico Santus ◽  
Nicola Marino ◽  
Davide Cirillo ◽  
Emmanuele Chersoni ◽  
Arnau Montagud ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Health Care ◽  
Research And Development ◽  
Precision Medicine ◽  
Patient Outcomes ◽  
Medical Literature ◽  
Drug Repurposing ◽  
Public Health Systems ◽  
Improve Patient

UNSTRUCTURED Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

scholarly journals Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach

2018 ◽  
pp. 1-17 ◽  
Author(s):  
Alessandro Laganà ◽  
Itai Beno ◽  
David Melnekoff ◽  
Violetta Leshchenko ◽  
Deepu Madduri ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Precision Medicine ◽  
Rna Sequencing ◽  
Plasma Cells ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Precision Oncology ◽  
Dna Mutation ◽  
Dna And Rna ◽  
Disease Marker

Purpose Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. Methods We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. Results We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. Conclusion Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

scholarly journals Combinatorial clinically driven blood biomarker functional genomics significantly enhances genotype-phenotype resolution and diagnostics in neuromuscular disease

2021 ◽  
Author(s):  
Samya Chakravorty ◽  
Kiera Berger ◽  
Laura Rufibach ◽  
Logan Gloster ◽  
Sarah Emmons ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Protein Expression ◽  
Functional Genomics ◽  
Precision Medicine ◽  
Molecular Diagnosis ◽  
Neuromuscular Disease ◽  
Patient Stratification ◽  
Rna Seq ◽  
Patient Cohort ◽  
Blood Biomarker

ABSTRACTPurpose50-60% of neuromuscular-disease patients remain undiagnosed even after extensive genetic testing that hinders precision-medicine/clinical-trial-enrollment. Importantly, those with DNA-based molecular diagnosis often remain without known molecular mechanism driving different degrees of disease severity that hinders patient stratification and trial-readiness. These are due to: a) clinical-genetic-heterogeneity (eg: limb-girdle-muscular-dystrophies(LGMDs)>30-subtypes); b) high-prevalence of variants-of-uncertain-significance (VUSs); (c) unresolved genotype-phenotype-correlations for patient stratification, and (d) lack of minimally-invasive biomarker-driven-assays. We therefore implemented a combinatorial phenotype-driven blood-biomarker functional-genomics approach to enhance diagnostics and trial-readiness by elucidating disease mechanisms of a neuromuscular-disease patient-cohort clinically-suspected of Dysferlinopathy/related-LGMD, the second-most-prevalent LGMD in the US.MethodsWe used CD14+monocyte protein-expression-assay on 364 Dysferlinopathy/related-LGMD-suspected patient-cohort without complete molecular-diagnosis or genotype-phenotype correlation; and then combined with blood-based targeted-transcriptome-sequencing (RNA-Seq) with tiered-analytical-algorithm correlating with clinical-measurements for a subset of patients.ResultsOur combinatorial-approach significantly increased the diagnostic-yield from 25% (N=326; 18%-27%; 95%CI) to 82% (N=38; 69.08% to 84.92%; 95% CI) by combining monocyte-assay with enhanced-RNA-Seq-analysis and clinical-correlation, following ACMG-AMP-guidelines. The tiered-analytical-approach detected aberrant-splicing, allele-expression-imbalance, nonsense-mediated-decay, and compound-heterozygosity without parental/offspring-DNA-testing, leading to VUS-reclassifications, identification of variant-pathomechanisms, and enhanced genotype-phenotype resolution including those with carrier-range Dysferlin-protein-expression and milder-symptoms, allowing patient-stratification for better trial-readiness. We identified uniform-distribution of pathogenic-variants across DYSF-gene-domains without any hotspot suggesting the relevance of upcoming gene-(full-DYSF-cDNA)-therapy trials.ConclusionOur results show the relevance of using a clinically-driven multi-tiered-approach utilizing a minimally-invasive biomarker-functional-genomic platform for precision-medicine-diagnostics, trial-recruitment/monitoring, elucidating pathogenic-mechanisms for patient stratification to enhance better trial outcomes, which in turn, will guide more rational use of current-therapeutics and development of novel-interventions for neuromuscular-disorders, and applicable to other genetic-disorders.

scholarly journals Learning and Mapping Lyme Disease Patient Trajectories from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response

2017 ◽  
Author(s):  
Osamu Ichikawa ◽  
Benjamin S. Glicksberg ◽  
Brian Kidd ◽  
Li Li ◽  
Joel T. Dudley
Keyword(s):  
Lyme Disease ◽  
Precision Medicine ◽  
Disease Risk ◽  
Disease Patient ◽  
Tear Film ◽  
Drug Repurposing ◽  
Not Otherwise Specified ◽  
Precise Understanding ◽  
New Findings ◽  
Machine Learning Approach

ABSTRACTBackgroundLyme disease (LD) is an epidemic, tick-borne illness with approximately 329,000 incidences diagnosed each year in United States. Long-term use of antibiotics is associated with serious complications, including post-treatment Lyme disease syndrome (PTLDS). The landscape of comorbidities and health trajectories associated with LD and associated treatments is not fully understood. Consequently, there is an urgent need to improve clinical management of LD based on a more precise understanding of disease and patient stratification.MethodsWe used a precision medicine machine-learning approach based on high-dimensional electronic medical records (EMRs) to characterize the heterogeneous comorbidities in a LD population and develop systematic predictive models for identifying medications that influence the risk of subsequent comorbidities.FindingsWe identified 3, 16, and 17 comorbidities at broad disease categories associated with LD within 2, 5, and 10 years of diagnosis, respectively. At higher resolution of ICD-9 levels, we pinpointed specific co-morbid diseases on a timescale that matched the symptoms associated with PTLDS. We identified 7, 30, and 35 medications that influenced the risks of the reported comorbidities within 2, 5, and 10 years, respectively. These medications included six previously associated with the identified comorbidities and 29 new findings. For instance, the first-line antibiotic doxycycline exhibited a consistently protective effect for typical symptoms of LD, including ‘backache Not Otherwise Specified (NOS)’ and ‘chronic rhinitis’, but consistently increased the risk of ‘cataract NOS’, ‘tear film insufficiency NOS’, and ‘nocturia’.InterpretationOur approach and findings suggest new hypotheses for precision medicine treatments regimens and drug repurposing opportunities tailored to the phenotypic profiles of LD patients.FundingThe Steven & Alexandra Cohen Foundation

Sign in / Sign up

Export Citation Format

Share Document