Percorrer por autor "Zazzi, Maurizio"
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- HIV multidrug class resistance prediction with a time sliding anchor approachPublication . Arslan, Nurhan; Eggeling, Ralf; Reuter, Bernhard; Leathem, Kristel Van; Pingarilho, Marta; Gomes, Perpétua; Sönnerborg, Anders; Kaiser, Rolf; Zazzi, Maurizio; Pfeifer, Nico; EuResist Network Study GroupMotivation: 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.
- The role of late presenters in HIV-1 transmission clusters in EuropePublication . Miranda, Mafalda N. S.; Pimentel, Victor; Gomes, Perpétua; Martins, Maria do Rosário O.; Seabra, Sofia G.; Kaiser, Rolf; Böhm, Michael; Seguin-Devaux, Carole; Paredes, Roger; Bobkova, Marina; Zazzi, Maurizio; Incardona, Francesca; Pingarilho, Marta; Abecasis, Ana B.Background: Investigating the role of late presenters (LPs) in HIV-1 transmission is important, as they can contribute to the onward spread of HIV-1 virus before diagnosis, when they are not aware of their HIV status. Objective: To characterize individuals living with HIV-1 followed up in Europe infected with subtypes A, B, and G and to compare transmission clusters (TC) in LP vs. non-late presenter (NLP) populations. Methods: Information from a convenience sample of 2679 individuals living with HIV-1 was collected from the EuResist Integrated Database between 2008 and 2019. Maximum likelihood (ML) phylogenies were constructed using FastTree. Transmission clusters were identified using Cluster Picker. Statistical analyses were performed using R. Results: 2437 (91.0%) sequences were from subtype B, 168 (6.3%) from subtype A, and 74 (2.8%) from subtype G. The median age was 39 y/o (IQR: 31.0–47.0) and 85.2% of individuals were males. The main transmission route was via homosexual (MSM) contact (60.1%) and 85.0% originated from Western Europe. In total, 54.7% of individuals were classified as LPs and 41.7% of individuals were inside TCs. In subtype A, individuals in TCs were more frequently males and natives with a recent infection. For subtype B, individuals in TCs were more frequently individuals with MSM transmission route and with a recent infection. For subtype G, individuals in TCs were those with a recent infection. When analyzing cluster size, we found that LPs more frequently belonged to small clusters (<8 individuals), particularly dual clusters (2 individuals). Conclusion: LP individuals are more present either outside or in small clusters, indicating a limited role of late presentation to HIV-1 transmission.
- Spectrum of non-nucleoside reverse transcriptase inhibitor-associated drug resistance mutations in persons living with HIV-1 receiving rilpivirinePublication . Nagarajan, Pavithra; Zhou, Jinru; Teodoro, Giulia Di; Incardona, Francesca; Seguin-Devaux, Carole; Kaiser, Rolf; Abecasis, Ana B.; Gomes, Perpétua; Tao, Kaiming; Zazzi, Maurizio; Shafer, Robert W.; on behalf of the EuResist NetworkIntroduction: Few data are currently available on the nonnucleoside reverse transcriptase (RT) inhibitors (NNRTI) resistance mutations selected in persons living with HIV-1 (PLWH) who develop virological failure while receiving rilpivirine (RPV). Methods: We analyzed pooled HIV-1 RT genotypic data from 280 PLWH in the multicenter EuResist database and 115 PLWH in the Stanford HIV Drug Resistance Database (HIVDB) who received RPV as their only NNRTI. Results: Among the 395 PLWH receiving RPV, 180 (45.6%) had one or more NNRTI-associated DRMs. Overall, 44 NNRTI-associated DRMs were identified, including 26 that occurred in two or more PLWHs. Seven mutations had a prevalence ≥10% among the 180 PLWH with one or more NNRTI-associated DRM: E138K (32.2%), V90I (25.0%), K101E (17.8%), Y181C (17.2%), E138A (13.9%), H221Y (12.2%), and K103N (10.6%). Y181C was significantly more likely to co-occur with K101E, V179F, H221Y, and M230L. Ten novel non-polymorphic mutations at known NNRTI-associated mutation positions were also identified, usually in just one PLWH: L100F, V108A, T139I, P225S, M230V, Y232C, and T240A/I/M/S. Conclusions: Our analysis extends the spectrum of mutations emerging in PLWH receiving RPV. Additional phenotypic characterization of RPV-selected mutations is necessary to better understand their biological and possible clinical significance.
- Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation toolPublication . Pironti, Alejandro; Pfeifer, Nico; Walter, Hauke; Jensen, Björn-Erik O.; Zazzi, Maurizio; Gomes, Perpétua; Kaiser, Rolf; Lengauer, ThomasAntiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.