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Arslan N, Eggeling R, Reuter B, Van Leathem K, Pingarilho M, Gomes P, Sönnerborg A, Kaiser R, Zazzi M, Pfeifer N. HIV multidrug class resistance prediction with a time sliding anchor approach. BIOINFORMATICS ADVANCES 2025; 5:vbaf099. [PMID: 40421422 PMCID: PMC12104520 DOI: 10.1093/bioadv/vbaf099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2025] [Revised: 04/16/2025] [Accepted: 04/25/2025] [Indexed: 05/28/2025]
Abstract
Motivation The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) is a rare but significant challenge in antiretroviral therapy (ART). MDR, which may arise from prolonged drug exposure, treatment failures, or transmission of resistant strains, accelerates disease progression and poses particular challenges in resource-limited settings with restricted access to resistance testing and advanced therapies. Early prediction of future MDR development is important to inform therapeutic decisions and mitigate its occurrence. Results In this study, we employ various machine learning classifiers to predict future resistance to all four major antiretroviral drug classes using features extracted from clinical HIV sequence data. We systematically explore several variations of the problem that differ in the pre-existing resistance level and the temporal gap between sample collection and observed MDR occurrence. Our models show the ability to predict multidrug class resistance even in the most challenging variations, albeit at a reduced accuracy. Feature importance analysis reveals that our models primarily utilize known drug resistance mutations for easier classification tasks, but rely on new mutations for the difficult task of distinguishing four class drug resistance from three class drug resistance. Availability and implementation All analysis was performed using the Euresist Integrated DataBase (EIDB). Researchers wishing to reproduce, validate or extend these findings can request access to the latest EIDB release via the Euresist Network.
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Affiliation(s)
- Nurhan Arslan
- Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Tuebingen 72076, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen 72076, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Tuebingen 72076, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen 72076, Germany
| | - Bernhard Reuter
- Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Tuebingen 72076, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen 72076, Germany
| | - Kristel Van Leathem
- Laboratory of Clinical and Epidemiological Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Marta Pingarilho
- Global Health and Tropical Medicine, GHTM, Associate Laboratory in Translation and Innovation towards Global Health, LA-REAL, Instituto de Higiene e Medicina Tropical, IHMT, Universidade NOVA de Lisboa, Lisbon 1349-008, Portugal
| | - Perpétua Gomes
- Laboratório de Biologia Molecular, LMCBM, SPC, Unidade Local de Saúde Lisboa Ocidental, Hospital Egas Moniz, Caparica 2829-511, Portugal
- Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health and Science, Lisbon, Almada 1349-019, Portugal
| | - Anders Sönnerborg
- Department of Medicine Huddinge, Karolinska University Hospital, Stockholm 14186, Sweden
- Division of Infectious Diseases, Department of Clinical Microbiology, Karolinska Institutet, Stockholm 14152, Sweden
| | - Rolf Kaiser
- Institute of Virology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne 50935, Germany
| | - Maurizio Zazzi
- Department of Medical Biotechnology, University of Siena, Siena 53100, Italy
| | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Tuebingen 72076, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen 72076, Germany
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Di Teodoro G, Siciliano F, Guarrasi V, Vandamme AM, Ghisetti V, Sönnerborg A, Zazzi M, Silvestri F, Palagi L. A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1. Comput Med Imaging Graph 2025; 120:102484. [PMID: 39808870 DOI: 10.1016/j.compmedimag.2024.102484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Revised: 11/16/2024] [Accepted: 12/23/2024] [Indexed: 01/16/2025]
Abstract
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion. Our model uses both tabular data about genetic sequences and a knowledge base derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence. By leveraging this knowledge base structured as a graph, the GNN component enables our model to adapt to imbalanced data distributions and account for Out-of-Distribution (OoD) drugs. We evaluated these models' robustness against OoD drugs in the test set. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in contributing in more informed clinical decisions with limited data availability. The source code is available at https://github.com/federicosiciliano/graph-ood-hiv.
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Affiliation(s)
- Giulia Di Teodoro
- Sapienza University of Rome, Department of Computer Control and Management Engineering Antonio Ruberti, 00185, Rome, Italy; EuResist Network, 00152, Rome, Italy.
| | - Federico Siciliano
- Sapienza University of Rome, Department of Computer Control and Management Engineering Antonio Ruberti, 00185, Rome, Italy.
| | - Valerio Guarrasi
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, 00128, Rome, Italy.
| | - Anne-Mieke Vandamme
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium; Center for Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, 1349-008, Lisbon, Portugal.
| | - Valeria Ghisetti
- Molecular Biology and Microbiology Unit, Amedeo di Savoia Hospital, ASL Città di Torino, 10128, Turin, Italy.
| | - Anders Sönnerborg
- Karolinska Institutet, Division of Infectious Diseases, Department of Medicine Huddinge, 14152, Stockholm, Sweden; Karolinska University Hospital, Department of Infectious Diseases, 14186, Stockholm, Sweden.
| | - Maurizio Zazzi
- Department of Medical Biotechnologies, University of Siena, 53100, Siena, Italy.
| | - Fabrizio Silvestri
- Sapienza University of Rome, Department of Computer Control and Management Engineering Antonio Ruberti, 00185, Rome, Italy.
| | - Laura Palagi
- Sapienza University of Rome, Department of Computer Control and Management Engineering Antonio Ruberti, 00185, Rome, Italy.
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Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S. Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV. INTEGRATED PHARMACY RESEARCH AND PRACTICE 2025; 14:1-16. [PMID: 39872224 PMCID: PMC11766232 DOI: 10.2147/iprp.s492422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Accepted: 01/14/2025] [Indexed: 01/30/2025] Open
Abstract
Background In the realm of Evidence-Based Medicine, introduced by Gordon Guyatt in the early 1990s, the integration of machine learning technologies marks a significant advancement towards more objective, evidence-driven healthcare. Evidence-Based Medicine principles focus on using the best available scientific evidence for clinical decision-making, enhancing healthcare quality and consistency by integrating this evidence with clinician expertise and patient values. Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively. Purpose The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV. Patients and Methods Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system. Results The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. Notably, our ALGOPROMIA ML model achieved the highest predictive accuracy for questionnaires such as MOS30 VIH (Adj. R² = 0.984), ESTAR (Adj. R² = 0.963), and BERGER (Adj. R² = 0.936). Moderate performance was observed for the P3CEQ (Adj. R² = 0.753) and TSQM (Adj. R² = 0.698), reflecting variability in model accuracy across instruments. Additionally, the model demonstrated strong reliability in maintaining standardized prediction errors below 0.2 for most instruments, with probabilities of achieving this threshold being 96.43% for WHOQoL HIV Bref and 88.44% for ESTAR, while lower probabilities were observed for TSQM (44%) and WRFQ (51%). Conclusion The results from our machine learning algorithms are promising for predicting PROMs and PREMs in AIDS settings. This work highlights how integrating ML technologies can enhance clinical pharmaceutical decision-making and support personalized treatment strategies within a multidisciplinary integration framework. Furthermore, leveraging platforms like NAVETA for deploying these models presents a scalable approach to implementation, fostering patient-centered, value-based care.
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Affiliation(s)
- Gabriel Mercadal-Orfila
- Pharmacy Department, Hospital Mateu Orfila, Maón, Spain
- Department of Biochemistry and Molecular Biology, Universitat de Les Illes Balears (UIB), Palma de Mallorca, Spain
| | | | - Melchor Riera-Jaume
- Unidad de Enfermedades Infecciosas, Servicio de Medicina Interna, Hospital Universitario Son Espases, Palma de Mallorca, Spain
| | - Salvador Herrera-Perez
- Facultad de Ciencias de la Salud, Universidad Internacional de Valencia, Valencia, España
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Di Teodoro G, Pirkl M, Incardona F, Vicenti I, Sönnerborg A, Kaiser R, Palagi L, Zazzi M, Lengauer T. Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1. Bioinformatics 2024; 40:btae327. [PMID: 38775719 PMCID: PMC11153833 DOI: 10.1093/bioinformatics/btae327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 05/10/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
MOTIVATION In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). RESULTS The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. AVAILABILITY AND IMPLEMENTATION This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network.
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Affiliation(s)
- Giulia Di Teodoro
- Department of Computer Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome 00185, Italy
- EuResist Network, Rome 00152, Italy
| | - Martin Pirkl
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50935, Germany
- German Center for Infection Research (DZIF), Cologne 50935, Germany
| | - Francesca Incardona
- EuResist Network, Rome 00152, Italy
- Department of Medical Biotechnologies, University of Siena, Siena 53100, Italy
| | | | - Anders Sönnerborg
- Department of Medicine Huddinge, Karolinska Institutet, Division of Infectious Diseases, Stockholm 14152, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm 14186, Sweden
| | - Rolf Kaiser
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50935, Germany
- German Center for Infection Research (DZIF), Cologne 50935, Germany
| | - Laura Palagi
- Department of Computer Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome 00185, Italy
| | | | - Thomas Lengauer
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50935, Germany
- Computational Biology, Max Planck Institute for Informatics, Saarbrücken 66123, Germany
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Paremskaia AI, Rudik AV, Filimonov DA, Lagunin AA, Poroikov VV, Tarasova OA. Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets. Viruses 2023; 15:2245. [PMID: 38005921 PMCID: PMC10674809 DOI: 10.3390/v15112245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.
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Affiliation(s)
- Anastasiia Iu. Paremskaia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia;
- Live Sciences Research Center, Moscow Institute of Physics and Technology, National Research University, Institutsky Lane 9, Dolgoprudny 141700, Russia
| | - Anastassia V. Rudik
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Dmitry A. Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Alexey A. Lagunin
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia;
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Vladimir V. Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Olga A. Tarasova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
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Tarasova O, Rudik A, Kireev D, Poroikov V. RHIVDB: A Freely Accessible Database of HIV Amino Acid Sequences and Clinical Data of Infected Patients. Front Genet 2021; 12:679029. [PMID: 34178036 PMCID: PMC8222909 DOI: 10.3389/fgene.2021.679029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/14/2021] [Indexed: 11/13/2022] Open
Abstract
Human immunodeficiency virus (HIV) infection remains one of the most severe problems for humanity, particularly due to the development of HIV resistance. To evaluate an association between viral sequence data and drug combinations and to estimate an effect of a particular drug combination on the treatment results, collection of the most representative drug combinations used to cure HIV and the biological data on amino acid sequences of HIV proteins is essential. We have created a new, freely available web database containing 1,651 amino acid sequences of HIV structural proteins [reverse transcriptase (RT), protease (PR), integrase (IN), and envelope protein (ENV)], treatment history information, and CD4+ cell count and viral load data available by the user's query. Additionally, the biological data on new HIV sequences and treatment data can be stored in the database by any user followed by an expert's verification. The database is available on the web at http://www.way2drug.com/rhivdb.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Anastasia Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Dmitry Kireev
- Central Research Institute of Epidemiology, Moscow, Russia
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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8
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Tarasova O, Biziukova N, Kireev D, Lagunin A, Ivanov S, Filimonov D, Poroikov V. A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy. Int J Mol Sci 2020; 21:ijms21030748. [PMID: 31979356 PMCID: PMC7037494 DOI: 10.3390/ijms21030748] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 02/01/2023] Open
Abstract
Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P1) or did not belong (P0) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P1 and P0 values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09).
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Correspondence:
| | - Nadezhda Biziukova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Dmitry Kireev
- Central Research Institute of Epidemiology, 111123 Moscow, Russia;
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
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9
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Capetti A, Rizzardini G. Choosing appropriate pharmacotherapy for drug-resistant HIV. Expert Opin Pharmacother 2019; 20:667-678. [DOI: 10.1080/14656566.2019.1570131] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Amedeo Capetti
- Divisione Malattie Infettive, Aziende Socio Sanitarie Territoriale Fatebenefratelli Sacco, Milano, Italy
| | - Giuliano Rizzardini
- Divisione Malattie Infettive, Aziende Socio Sanitarie Territoriale Fatebenefratelli Sacco, Milano, Italy
- Faculty of Health Sciences, School of Clinical Medicine, Whitwaterstrand University, Johannesburg, South Africa
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10
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Döring M, Büch J, Friedrich G, Pironti A, Kalaghatgi P, Knops E, Heger E, Obermeier M, Däumer M, Thielen A, Kaiser R, Lengauer T, Pfeifer N. geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data. Nucleic Acids Res 2018; 46:W271-W277. [PMID: 29718426 PMCID: PMC6031006 DOI: 10.1093/nar/gky349] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/13/2018] [Accepted: 04/24/2018] [Indexed: 01/29/2023] Open
Abstract
Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.
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Affiliation(s)
- Matthias Döring
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Joachim Büch
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Georg Friedrich
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Alejandro Pironti
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Prabhav Kalaghatgi
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Elena Knops
- Institute of Virology, University of Cologne, Fürst-Pückler-Str. 56, 50935 Cologne, Germany
| | - Eva Heger
- Institute of Virology, University of Cologne, Fürst-Pückler-Str. 56, 50935 Cologne, Germany
| | - Martin Obermeier
- MVZ Medizinisches Infektiologiezentrum Berlin (MIB), Oudenarder Str. 16, 13353 Berlin, Germany
| | | | | | - Rolf Kaiser
- Institute of Virology, University of Cologne, Fürst-Pückler-Str. 56, 50935 Cologne, Germany
| | - Thomas Lengauer
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Nico Pfeifer
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Medical Faculty, University of Tübingen, Geissweg 5, 72076 Tübingen, Germany
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Herbert S, Chung E. Clinical round-up. Sex Transm Infect 2017. [DOI: 10.1136/sextrans-2017-053404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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