scholarly journals sierra-local: A lightweight standalone application for secure HIV-1 drug resistance prediction

2018 ◽  
Author(s):  
Jasper C Ho ◽  
Garway T Ng ◽  
Mathias Renaud ◽  
Art FY Poon
Keyword(s):  
Clinical Management ◽  
Data Provenance ◽  
Genotypic Resistance ◽  
Web Based ◽  
Network Connection ◽  
Legal Obligations ◽  
Drug Resistance Prediction ◽  
Development And Validation ◽  
Hiv 1 ◽  
Interpretation Systems

AbstractGenotypic resistance interpretation systems for the prediction and interpretation of HIV-1 antiretroviral resistance are an important part of the clinical management of HIV-1 infection. Current interpretation systems are generally hosted on remote webservers that enable clinical laboratories to generate resistance predictions easily and quickly from patient HIV-1 sequences encoding the primary targets of modern antiretroviral therapy. However they also potentially compromise a health provider’s ethical, professional, and legal obligations to data security, patient information confidentiality, and data provenance. Furthermore, reliance on web-based algorithms makes the clinical management of HIV-1 dependent on a network connection. Here, we describe the development and validation of sierra-local, an open-source implementation of the Stanford HIVdb genotypic resistance interpretation system for local execution, which aims to resolve the ethical, legal, and infrastructure issues associated with remote computing. This package reproduces the HIV-1 resistance scoring by the web-based Stanford HIVdb algorithm with a high degree of concordance (99.997%) and a higher level of performance than current methods of accessing HIVdb programmatically.

scholarly journals Discordance between Etravirine Phenotype and Genotype-Based Predicted Phenotype for Subtype C HIV-1 from First-Line Antiretroviral Therapy Failures in South Africa

2020 ◽  
Vol 64 (5) ◽  
Author(s):  
Kevin D. McCormick ◽  
Kerri J. Penrose ◽  
Chanson J. Brumme ◽  
P. Richard Harrigan ◽  
Raquel V. Viana ◽  
...  
Keyword(s):  
Virologic Failure ◽  
Transcriptase Inhibitor ◽  
Subtype C ◽  
Genotypic Resistance ◽  
Low Level ◽  
Nnrti Resistance ◽  
Subtype B ◽  
Treatment Naïve ◽  
Hiv 1 ◽  
Interpretation Systems

ABSTRACT Etravirine (ETR) is a nonnucleoside reverse transcriptase inhibitor (NNRTI) used in treatment-experienced individuals. Genotypic resistance test-interpretation systems can predict ETR resistance; however, genotype-based algorithms are derived primarily from HIV-1 subtype B and may not accurately predict resistance in non-B subtypes. The frequency of ETR resistance among recombinant subtype C HIV-1 and the accuracy of genotypic interpretation systems were investigated. HIV-1LAI containing full-length RT from HIV-1 subtype C-positive individuals experiencing virologic failure (>10,000 copies/ml and >1 NNRTI resistance-associated mutation) were phenotyped for ETR susceptibility. Fold change (FC) was calculated against a composite 50% effective concentration (EC50) from treatment-naive individuals and three classifications were assigned: (i) <2.9-FC, susceptible; (ii) ≥2.9- to 10-FC, partially resistant; and (iii) >10-FC, fully resistant. The Stanford HIVdb-v8.4 was used for genotype predictions merging the susceptible/potential low-level and low-level/intermediate groups for 3 × 3 comparison. Fifty-four of a hundred samples had reduced ETR susceptibility (≥2.9-FC). The FC correlated with HIVdb-v8.4 (Spearman’s rho = 0.62; P < 0.0001); however, 44% of samples were partially (1 resistance classification difference) and 4% completely discordant (2 resistance classification differences). Of the 34 samples with an FC of >10, 26 were HIVdb-v8.4 classified as low-intermediate resistant. Mutations L100I, Y181C, or M230L were present in 27/34 (79%) of samples with an FC of >10 but only in 2/46 (4%) of samples with an FC of <2.9. No other mutations were associated with ETR resistance. Viruses containing the mutation K65R were associated with reduced ETR susceptibility, but 65R reversions did not increase ETR susceptibility. Therefore, genotypic interpretation systems were found to misclassify ETR susceptibility in HIV-1 subtype C samples. Modifications to genotypic algorithms are needed to improve the prediction of ETR resistance for the HIV-1 subtype C.

scholarly journals Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

PLoS ONE ◽  
2010 ◽  
Vol 5 (7) ◽  
pp. e11505 ◽  
Author(s):  
Dineke Frentz ◽  
Charles A. B. Boucher ◽  
Matthias Assel ◽  
Andrea De Luca ◽  
Massimiliano Fabbiani ◽  
...  
Keyword(s):  
Resistance Test ◽  
Test Interpretation ◽  
Genotypic Resistance ◽  
Genotypic Resistance Test ◽  
Virological Outcomes ◽  
Hiv 1 ◽  
Over Time ◽  
Interpretation Systems

scholarly journals Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting

2009 ◽  
Vol 64 (3) ◽  
pp. 616-624 ◽  
Author(s):  
Maurizio Zazzi ◽  
Mattia Prosperi ◽  
Ilaria Vicenti ◽  
Simona Di Giambenedetto ◽  
Annapaola Callegaro ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
Antiretroviral Treatment ◽  
Genotypic Resistance ◽  
Drug Potency ◽  
Hiv 1 ◽  
Interpretation Systems

Development and Validation of Plasma miRNA Biomarker Signature Panel for the Detection of Early HIV-1 Infection

2018 ◽  
Author(s):  
Santanu Biswas ◽  
Mohan Haleyurgirisetty ◽  
Sherwin Lee ◽  
Indira Hewlett ◽  
Krishnakumar Devadas
Keyword(s):  
Biomarker Signature ◽  
Development And Validation ◽  
Plasma Mirna ◽  
Hiv 1

The place of raltegravir in the clinical management of HIV-1 infection

2013 ◽  
Vol 10 (4) ◽  
pp. 427-438 ◽  
Author(s):  
Ruxandra Calin ◽  
Christine Katlama
Keyword(s):  
Clinical Management ◽  
Hiv 1

Concordance of genotypic resistance interpretation algorithms in HIV-1 infected patients: An exploratory analysis in Greece

2021 ◽  
Vol 137 ◽  
pp. 104779
Author(s):  
Maria Kantzanou ◽  
Maria A. Karalexi ◽  
Anduela Zivinaki ◽  
Elena Riza ◽  
Helen Papachristou ◽  
...  
Keyword(s):  
Exploratory Analysis ◽  
Genotypic Resistance ◽  
Hiv 1

scholarly journals Clinical management of tuberculosis and HIV-1 co-infection

2010 ◽  
Vol 36 (6) ◽  
pp. 1460-1481 ◽  
Author(s):  
C. Schutz ◽  
G. Meintjes ◽  
F. Almajid ◽  
R. J. Wilkinson ◽  
A. Pozniak
Keyword(s):  
Clinical Management ◽  
Hiv 1

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 123-124
Keyword(s):  
Model Development ◽  
Lung Nodule ◽  
Dynamic Bayesian Network ◽  
Web Based ◽  
National Lung Screening Trial ◽  
Catastrophic Illness ◽  
Development And Validation ◽  
Low Dose Computed Tomography ◽  
Lung Screening ◽  
Heart Sound Segmentation

Arnold CW, Wallace WD, Chen S, Oh A, Abtin F, Genshaft S, Binder S, Aberle D, Enzmann D. RadPath: A web-based system for integrating and correlating radiology and pathology findings during cancer diagnosis. Acad Radiol 2016 Jan;23(1):90-100 http://escholarship.org/uc/item/22x4021q Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 2016 Dec;30(6):875-88 https://link.springer.com/article/10.1007%2Fs10877-015-9788-2 Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H, Tan Y, Gillies R, Napel S. A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging 2016 Aug;29(4):476-87 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942386/ Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD, Moorman JR. Signatures of subacute potentially catastrophic illness in the ICU: model development and validation. Crit Care Med 2016 Sep;44(9):1639-48 https://insights.ovid.com/pubmed?pmid=27452809 Petousis P, Han SX, Aberle D, Bui AA. Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artif Intell Med 2016 Sep;72:42-55 https://linkinghub.elsevier.com/retrieve/pii/S0933-3657(16)30106-3 Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32 http://ieeexplore.ieee.org/document/7234876/

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