Croi

Plasma CNS Tissue Markers in Heavily Treatment-Experienced PWH in the PRESTIGIO Registry

Abstracts

Neuro Prestigio Cro I2025

CD4 T cell counts are inversely correlated with anti-cluster A antibodies in antiretroviral therapy-treated PLWH

The Genetic Barrier to Resistance to LEN Is Not Affected by Non-B Subtypes or Treatment Exposure

Glasgow

Two-fold increased risk of cardiovascular events in people with multidrug-resistant HIV: a matched cohort analysis with data from the PRESTIGIO Registry

Evaluation of HIV-DNA resistance evolution in highly treatment-experienced and multi-resistant individuals under virological control: a longitudinal study from the PRESTIGIO registry

Virological Efficacy of Dolutegravir Plus Darunavir in Multi-Drug-Resistant HIV Patients: A Real-World Cohort Study with data from the PRESTIGIO Registry

Aids 2024

Persistence of CXCR4-tropic virus and incidence of clinical events in persons living with four-class drug resistant HIV: data from the PRESTIGIO Registry

Background: Multiple posttranslational modifications (PTM) of HIV-1 integrase such as acetylation, phosphorylation, sumoylation and ubiquitination have been described. Variations in PTM sites might impact viral fitness but nothing is known about variability of PTM sites in vivo and their association with drug-resistance. The aim of this study was to characterize integrase PTM in heavily treatment experienced (HTE) multidrug resistant (MDR) individuals compared to individuals never exposed to antiretrovirals or to those treated but never exposed to integrase inhibitors (INI).
Material and methods: HIV-1 Integrase sequences from plasma RNA specimens of viremic HTE MDR individuals from the PRESTIGIO registry were compared to sequences from drug-experienced INI na&iuml;ve and drug-na&iuml;ve individuals, all harboring subtype B virus. The MusiteDeep tool (https://www.musite.net/) was used to evaluate known PTM sites for acetylation, phosphorylation, sumoylation and ubiquitination. Associations between CD4 count, viremia and integrase resistance (HIVdb ver 9.5) and the number and type of PTM sites were evaluated.
Results: 38 integrase sequences from distinct individuals were compared with 48 and 76 sequences from INI-naive and drug-na&iuml;ve individuals, respectively (Table 1). PTM frequency for each group is reported in Figure 1. All acetylation sites were fully conserved in 99.4% of individuals. No significant differences in the number of phosphorylation sites were observed among the three groups. By contrast, the number of sumoylation sites was lower in HTE MDR individuals compared to those drug-na&iuml;ve or INI-na&iuml;ve. This was driven by a lower frequency of K136 and K244 PTM sites in HTE MDR individuals. The number of ubiquitination sites was higher in HTE MDR individuals than in other groups (P=0.043). CD4 counts and viremia levels were not significantly different according to PTM sites within treatment groups. Concerning INI resistance in HTE MDR, individuals with cross resistance to all INI showed less than three phosphorylation sites as opposed to HTE individuals with lower-level or no resistance (P=0.001, Figure 2A). The proportion of individuals with intermediate or high-level resistance to bictegravir/dolutegravir increased with increasing numbers of sumoylation sites (P=0.021, Figure 2B).
Conclusions: The number of integrase phosphorylation, sumoylation and ubiquitination sites seems to be affected by a complex and prolonged treatment history associated with multidrug resistance. The presence of high-level resistance to second generation INI seems to impact phosphorylation and sumoylation sites in integrase. Further longitudinal studies are needed to unveil the nature and implications of these correlations.

Ica R2024

Post-translational HIV-1 integrase modification sites might be affected by complex and prolonged treatment history associated with multidrug resistance: a proof of concept study from the PRESTIGIO registry

Background: In highly treatment-experienced people with HIV (PWH) mutations interact within a complex system. To date, network-based approach has not been used to study HIV mutations. This study aims to identify communities of mutations archived in proviral DNA in PWH with 4-class drug resistance (PWH-4DR) under virological suppression.
Methods: HIV-1 DNA next-generation sequencing (Illumina MiSeq) was performed with a 5% cutoff; we included major mutations (based on Stanford HIV Drug Resistance Database) detected in suppressed individuals from the PRESTIGIO Registry. A correlation network was constructed by calculating Spearman coefficients between each pair of mutations (nodes). Weighted edges represent significant correlations (adjusted Benjamini-Hochberg p&lt;0.001) with thresholds set at the 5th and 95th percentiles for negative and positive values, respectively. The analysis was repeated including other mutations (accessory and polymorphisms non-resistance related) detected in &ge;10% of study population.
Results: Overall 91 PWH-4DR, maintaining HIV-RNA&lt;50 cps/mL for a median of 3.2 (IQR=1.7-5.0) years, included: at sampling 70 (77%) males, median (IQR) age 54 (50-59) years, on ART for 23 (21-25) years, on the current ART regimen for 2.6 (1.5-3.6) years and CD4+ 655 (484-890) cells/mm3. Out of 1226 mutations detected, 71 majors [25 for protease inhibitors (PI), 13 for nucleoside reverse transcriptase inhibitors (NRTI), 18 for non-nucleoside reverse transcriptase inhibitors (NNRTI), 15 for integrase strand transfer inhibitors (INSTI)] were selected for the above criteria. Network analysis revealed 3 connected components that are biologically relevant (Figure 1). The largest one (Figure 1, A-component) is characterized by 2 distinct communities, connected by a negative correlation among thymidine analog mutations (TAMs), indicating mutually exclusive mutation patterns; type 2 TAMs are associated with a cluster of PI major mutations, while type 1 are linked to L90M for PIs and Y188L for NNRTIs. The B component in Figure 1 is characterized by a pattern of INSTI major mutations, where Q148K is the connector node between 3 groups, composed of NNRTI and major mutations from other drug classes. The second analysis including all mutations (71 majors, 125 minors, 6 stop codons) confirms the structure of these components (Figure 2: A-, B-, C-component), suggesting that not only major mutations may play a role in generating the network. Regarding potentially defective reservoir in proviral DNA, the C-component in Figure 2 presents a cluster of stop codons associated with APOBEC-related context drug resistance mutations.
Conclusions: In PWH-4DR network analysis allows the identification of distinct clusters of major mutations across different drug classes; it also highlights the direction and strength of the association between them. This innovative approach may outline the complex system of relations between proviral DNA mutations.

Network analysis of proviral DNA mutations in People with 4-class-resistant HIV-1: Data from the PRESTIGIO Registry

Background: DNA methylation (DNAm) predicts disease progression, frailty, and mortality in people with HIV (PWH). No data on this topic are available in PWH with 4-class drug resistance (4DR), characterized by a high burden of disease. Our aim was to investigate DNAm in PWH-4DR, as compared to PWH without drug resistance (no-DR).
Methods: Cross-sectional, propensity-score-matched cohort study on PWH on antiretroviral treatment (ART), with viral load (VL) &lt;200 copies/mL, classified as: i) 4DR (defined as documented resistance to NRTIs, NNRTIs, PIs, and INSTIs) from the PRESTIGIO Registry (n=27); ii) no-DR (n=27). Groups matched by age (&plusmn;1.5 years) and sex. Genome-wide methylation patterns were determined by reduced representation bisulfite sequencing from peripheral blood mononuclear cells. Differential DNAm evaluated on cytosines covered by &ge;10 reads in &ge;10 PWH for each group: bases with a minimum difference of 25% in medium methylation and a false discovery rate &lt;0.01 were identified as differentially methylated. The number of significant cytosines per chromosomes (chrs) and genes were normalized according to chr and gene length, respectively. Enrichment analysis was performed for genes with &ge;10 differentially methylated cytosines in absolute count and &ge;0.005% after normalization for gene length.
Results: Overall, 54 individuals evaluated: characteristics reported in Table 1. Comparing PWH-4DR with PWH-no-DR, there were 27060 differentially methylated cytosines distributed throughout all chrs, with a notable enrichment in chr 19 (Figure 1). The top 10 hypermethylated genes in PWH-4DR included 5 long non-coding RNAs (lncRNAs), 2 pseudogenes, C19orf13, IDH3B (coding for a subunit of isocitrate dehydrogenase, known to be downregulated in striatal cells of murine models of HIV), and MGAT4B (coding for a glycosyltransferase); the top 10 hypomethylated genes included 4 lncRNAs, 2 microRNAs, 2 pseudogenes, BHLHE22 (coding for a transcription factor involved in neural and retinal development, known to be upregulated in macrophages with integrated HIV proviruses), and B3GALT6 (coding for a galactosyltransferase involved in synthesis of glycosaminoglycans; it is a ligand of VISTA, a novel monocytic checkpoint regulator involved in immune tolerance and increased in HIV infection). Enrichment analysis of differentially methylated genes highlighted two pathways: &lsquo;estrogen response early&rsquo; and &lsquo;serine/threonine kinases&rsquo;; the last one was confirmed in the analysis with hypomethylated genes only, suggesting a possible upregulation of the genes in the pathway in PWH-4DR.
Conclusions: Even when HIV replication is controlled by ART, multidrug resistance is associated with a different DNAm, a proxy for different gene expression. DNAm might play a role in the high inflammation and disease burden observed in the fragile population with 4DR.

Multidrug resistance is associated with different gene methylation in people with HIV: a matched cohort study with data from the PRESTIGIO Registry

Introduction: Individuals with HIV vertical transmission (VT) are exposed to both HIV and combination antiretroviral therapy (cART) from young age. In this setting, suboptimal adherence to cART may lead to viral failure and therapeutic burden (HTE, Heavily Treatment Experienced individuals), which drive immune impairment. Indeed, unfavourable clinical and viro-immunologic outcomes have also been described in HTE without VT. Whether HTE with VT feature disrupted T-cell homeostasis is unknown. We therefore investigated T-cell dysfunction in HTE with and without VT, also according to viral load (VL).
Methods: HTE VT and matched HTE no-VT from the Prestigio Registry were enrolled and classified according to undetectable (VL&lt;50 cp/mL) or detectable viremia (VL&gt;200 cp/mL). We measured senescence (CD57), activation (HLA-DR/CD38) and exhaustion (PD-1/TIGIT) in CD4/CD8 T-cells by flow cytometry. Age/sex-matched HIV-individuals were enrolled as controls. Kruskal-Wallis and Mann-Whitney test were used for statistics.
Results: We evaluated 16 VT and 16 no-VT HIV individuals. In each group, 50% were viremic and 50% were aviremic . VT were younger than no-VT (31yrs, IQR 27-33 vs 56, IQR 54-59; p&lt;0.0001), yet duration of HIV infection was comparable in the two groups (31yrs, IQR 27-33 vs 30yrs, IQR 25-32; p=0.7). VT and no-VT HTE showed similar senescent, activated and exhausted T-cells. Interestingly, compared to HIV- controls, both VT and no-VT displayed higher: i) activated CD8+HLA-DR+CD38+ (HIV-: 0.4%, IQR 0.2-0.9; VT: 1.9%, IQR 1.1-8.5; p=0.001; no-VT: 1.7%, IQR 1.3-4.8; p=0.001, Fig.1A); ii) exhausted PD-1+TIGIT+ CD4+ (HIV-: 0.5%, IQR 0.3-0.9; VT: 2.4%, IQR 0.7-4.7; p=0.009; no-VT: 1.4%, IQR 0.9-2; p=0.02, Fig.1B) and CD8+ (HIV-: 0.3%, IQR 0.1-0.5; VT: 1.1%, IQR=0.4-3.4; p=0.02; no-VT: 1%, IQR 0.7-3; p=0.01, Fig.1C). In contrast, senescent CD57+CD4+ were increased only in no-VT compared to controls (7.8%, IQR 5.4-15.4 vs 3.4%, IQR 2.6-7.9, p=0.03, Fig.1D). When stratifying by viremia, these differences were retained in the viremic VT and no-VT groups, whereas no differences were registered in their aviremic counterparts, except for activated (1.6%, IQR 0.9-1.7 vs 0.4%, IQR 0.2-0.9, p=0.003, Fig.1E) and exhausted (1% IQR 0.7-1.2 vs 0.3%, IQR 0.1-0.5, p=0.04, Fig.1F) CD8 T-cells which were consistently higher in no-VTs than in HIV-.
Conclusions: Irrespective of HIV transmission mode, compared to HIV-, HTE show greater T-cell senescence, activation and exhaustion which appear to be driven by uncontrolled viremia. Despite no differences according to transmission modality, our findings of higher CD8+HLA-DR+CD38+ and CD8+PD-1+TIGIT+ in no-VT with suppressed viral load compared to HIV-, point to greater immunological dysfunction in HTE without vertical transmission, that might possibly reflect their older age. The link(s) between age-related immunesenescence, HIV mode of infection and persistent viral replication in the pathogenesis of HTE merits further studies.

HIV acquisition at birth does not drive T-cell dysfunction in Heavily Treatment Experienced (HTE): data from the PRESTIGIO Registry

Background: Broadly neutralizing antibodies (bNAbs) are being investigated as long-acting antiviral therapies, but sensitivity to bNAbs in persons with multidrug-resistant HIV is unknown. Here, we characterized sensitivity to teropavimab (GS-5423; 3BNC117-LS; TAB) and zinlirvimab (GS-2872; 10-1074-LS; ZAB) in people living with 4-class drug-resistant HIV (4DR-PWH). Methods: Multicenter, observational study using plasma or peripheral blood mononuclear cells collected from 50 4DR-PWH (25 with HIV-1 RNA &gt; 1000 copies/mL matched by age, sex, nadir CD4+ and years on ART to 25 virologically suppressed [HIV-1 RNA &lt; 50 copies/mL]) enrolled in the PRESTIGIO Registry (NCT04098315) with a documented 4DR (NRTI, NNRTI, PI and INSTI). Phenotypic sensitivity to bNAbs was determined using the PhenoSense Monoclonal Antibody assay (Monogram), with susceptibility defined as IC90 &le;2 &micro;g/mL. Descriptive statistics are used to present results. Spearman's rank test used for associations between phenotypic susceptibility and clinical variables. Results: Characteristics of included individuals with analyzed samples were indicative of extensive treatment history (Table1). Of 46/50 (92%) participants with PhenoSense mAb assay results, 35 (76%) were phenotypically sensitive to TAB, 23 (50%) to ZAB, and 19 (41%) to both bNAbs; 7 (15%) had phenotypic resistance to both bNAbs. Of 22 viremic participants, 19 (86%)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; were phenotypically sensitive to TAB, 10 (45%) to ZAB, 9 (41%) to both bNAbs, and 2 (9%) to neither. Of 24 participants with virologic suppression, 67% were phenotypically sensitive to TAB, 54% to ZAB, 42% to both bNAbs, and 5 (21%) to neither. The proportion of participants with sensitivity to both bNAbs was similar (p=0.99) in viremic participants (9/22 [41%]) compared to those with virologic suppression (10/24 [42%]). Nonsignificant correlations between phenotypic sensitivity to bNAbs and age, years of ART, CD4+ cell count, HIV-RNA, type of ART regimen at the sample collection, viral tropism and HIV subtype. There were marginal correlations between phenotypic sensitivity to TAB and years since HIV diagnosis (Spearman r= 0.287, p=0.053) and phenotypic sensitivity to ZAB and CD8+cell count (Spearman r= -0.317, p=0.049). Conclusion: A significant number of the analyzed 4DR-PWH were found to have virus susceptible to TAB and ZAB. These data provide proof-of-concept that selected multidrug-resistant PWH may be candidates for future trials investigating bNAbs-containing regimens to achieve or maintain virologic suppression.

Teropavimab and Zinlirvimab Sensitivity in People Living With MDR HIV-1: PRESTIGIO Registry Data

Purpose: To evaluate the risk of virological failure (VF) after drug burden reduction (DBR) in non-viremic people living with documented 4-class drug-resistant HIV (4DR-PLWH).
Methods: Cohort study on 4DR-PLWH from the PRESTIGIO Registry, on antiretroviral therapy (ART), with stable viral suppression. In case of ART switch (defined as DBR when number of drugs or dosage was reduced for any reason), only the first change was considered. Follow-up accrued from the second VL&lt;50 copies/mL (baseline) until VF [2 consecutive VLs&ge;200 (VF200) or 50 copies/mL (VF50)] or the second ART switch or the last available VL measurement. Time to VF200/VF50 estimated using the Simon-Makuch method. Cox regression model applied to assess predictors of risk of VF200; type of ART switch considered as time-dependent variable.
Results: Overall, 166 4DR-PLWH evaluated (Table1). After a median follow-up of 3.75 (IQR=2.26-5.59) years, 34 (20.5%) individuals had VF200. DBR occurred in 51 (30.7%) PLWH after 1.98 (0.6-3.42) years [10/51 (19.6%) had VF200], other ART switches in 28 (16.9%) after 1.82 (1.09-3.32) years [4/28 (14.3%) had VF200]. Risk of VF200 tended to be higher after DBR (Figure1).
Fifty-one (30.7%) PLWH had VF50; DBR in 45 (27.1%) individuals [8/45 (17.8%) had VF50], other ART switches in 27 (16.3%) [8/27 (29.6%) had VF50]. Compared to no ART change, DBR did not lead to a higher risk of VF50, differently from other switches (Figure2). At multivariable analysis, a higher risk of VF200 was significantly associated with a lower baseline CD4+/CD8+, but with neither DBR nor other ART switches (Table2).
Conclusions: Drug burden reduction showed a potential impact on the risk of VF &ge;200 copies/mL in non-viremic 4DR-PLWH. Baseline immunological impairment could also affect the risk of VF. Investigation of new tools to identify 4DR-PLWH at higher risk of VF after simplification is required, given the critical need for DBR in this fragile population.
Key Words: HIV; multidrug resistance; antiretroviral therapy; simplification; virological failure.
Category: B1 (Clinical science &ndash; Antiretroviral therapy)
Subcategory: HIV drug resistance
Table 1.
<table style="width: 100%;" cellspacing="0" cellpadding="0">
<thead>
<tr>
<th>Baseline characteristics</th>
<th>Overall (n=166)</th>
<th>No ART change (n=87)</th>
<th>Drug burden reduction (n=51)</th>
<th>Other ART switches (n=28)</th>
<th nowrap="nowrap">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td>Age (years), median (IQR)</td>
<td>51.3 (46.8-56.3)</td>
<td>52.7 (47.1-57.2)</td>
<td>50.2 (46.1-56.5)</td>
<td>50.4 (47.8-54.8)</td>
<td>0.48</td>
</tr>
<tr>
<td>Female gender, n (%)</td>
<td>47 (28.3%)</td>
<td>23 (26.4%)</td>
<td>17 (33.3%)</td>
<td>7 (25.0%)</td>
<td>0.63</td>
</tr>
<tr>
<td>ART duration (years), median (IQR)</td>
<td>19.9 (16.0-23.0)</td>
<td>21.3 (16.5-23.9)</td>
<td>19.8 (15.7-21.8)</td>
<td>19.0 (16.2-21.1)</td>
<td>0.07</td>
</tr>
<tr>
<td>CD4+/CD8+ ratio, median (IQR)</td>
<td>0.48 (0.31-0.71)</td>
<td>0.49 (0.32-0.73)</td>
<td>0.48 (0.32-0.60)</td>
<td>0.54 (0.27-0.83)</td>
<td>0.52</td>
</tr>
<tr>
<td>Number of ongoing antiretroviraldrugs, n (%)</td>
<td>3.0 (3.0-4.0)</td>
<td>3.0 (2.0-4.0)</td>
<td>3.00 (3.00-4.50)</td>
<td>3.0 (3.0-4.0)</td>
<td>0.01</td>
</tr>
<tr>
<td>PI-containing regimen, n (%)</td>
<td>146 (88.0%)</td>
<td>76 (87.4%)</td>
<td>46 (90.2%)</td>
<td>24 (85.7%)</td>
<td>0.81</td>
</tr>
<tr>
<td>INSTI-containing regimen, n (%)</td>
<td>147 (88.6%)</td>
<td>79 (90.8%)</td>
<td>45 (88.2%)</td>
<td>23 (82.1%)</td>
<td>0.45</td>
</tr>
<tr>
<td>Entry inhibitor (MVC, T20, FTR and/or IBA) -containing regimen, n (%)</td>
<td>57 (34.3%)</td>
<td>24 (27.6%)</td>
<td>23 (45.1%)</td>
<td>10 (35.7%)</td>
<td>0.11</td>
</tr>
<tr>
<td>Genotypic susceptibility score, median (IQR)</td>
<td>2.0 (1.5-2.0)</td>
<td>2.0 (1.5-2.0)</td>
<td>2.0 (1.5-2.12)</td>
<td>2.0 (1.5-2.5)</td>
<td>0.36</td>
</tr>
</tbody>
</table>
&nbsp;
Table 2.
<table style="width: 100%;" cellspacing="0" cellpadding="0">
<thead>
<tr>
<th>Characteristics</th>
<th>Category</th>
<th>Adjusted Hazard ratio of VF200 (95% confidence interval)</th>
<th>P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td>DBR</td>
<td>Yes vs No</td>
<td>1.95 (0.73-5.17)</td>
<td>0.17</td>
</tr>
<tr>
<td>Other ART switches</td>
<td>Yes vs No</td>
<td>1.92 (0.56-6.61)</td>
<td>0.30</td>
</tr>
<tr>
<td>Gender</td>
<td>Female vs Male</td>
<td>0.67 (0.27-1.63)</td>
<td>0.37</td>
</tr>
<tr>
<td>Age at baseline</td>
<td>Per 1-year older</td>
<td>0.96 (0.91-1.01)</td>
<td>0.09</td>
</tr>
<tr>
<td>CD4+/CD8+ ratio at baseline</td>
<td>Per 0.1 higher</td>
<td>0.84 (0.72-0.99)</td>
<td>0.03</td>
</tr>
<tr>
<td>ART duration at baseline</td>
<td>Per 1-year higher</td>
<td>0.97 (0.92-1.02)</td>
<td>0.26</td>
</tr>
</tbody>
</table>

Eacs

Risk of virological failure after drug burden reduction in non-viremic people with 4-class drug-resistant HIV: data from the PRESTIGIO Registry

Purpose: To clarify whether next-generation sequencing (NGS) can be useful for resistance assessment in virologically suppressed highly treatment-experienced (HTE) individuals with multi-drug resistance (MDR).
Methods: Ninety-one HTE MDR individuals from the PRESTIGIO registry were analysed. NGS on HIV-DNA was performed through Illumina-MiSeq. Major resistance mutations (MRM) and APOBEC editing were evaluated by the HIVdb system. NGS cut-offs at &ge;1%, &ge;5% and &ge;20% were tested. Minority MRM were compared to historical-GRT (h-GRT). The probability of virological rebound (VR) after NGS resistance assessment was estimated through Kaplan-Meyer curves. Discontinuation of FTR, IBA or LEN was defined as interruption of the drug for any cause. Follow-up (FU) started from the date of FTR, LEN or IBA start (baseline; BL) until discontinuation of FTR/IBA/LEN or death/freezing date (30/June/2023). Descriptions by median (IQR) or frequency (%). Genotypic susceptibility score for the optimized background therapy introduced with FTR/LEN/IBA was estimated according to the cumulative data of the available plasma genotyping resistance tests recorded for each patient.
Results: At NGS-GRT, individuals had a long and complex treatment history and were in virological suppression since 3 (2-5) years (Table 1). Around half of MRM detected with a considerable frequency in HIV-DNA were already found in h-GRT. However, 30% of those detected at lower frequency were never observed before (Figure 1 A). The highest detection rate of historical MRM was reached with NGS set at 1% (Figure 1 B) but unusual substitutions and extensive APOBEC hypermutation (Figure 1 C) suggest technical issues and poor clinical relevance at this threshold. After NGS-GRT, 21 individuals experienced VR with a median (IQR) viremia of 365 (98-7,840) copies/mL. Among them, the number of minority resistant variants (frequency 5-20%, mv5%) detected exclusively by NGS-GRT was higher compared to those who maintained virological suppression (p=0.030, Figure 2 A) and positively correlated with viremia levels at rebound (Figure 2 B). By 3 years after NGS-GRT, the probability of VR was 21.6%. Having &ge;2 mv5% exclusively detected by NGS without APOBEC signal was associated with VR (Figure 2 C).
Conclusions: In this cohort of virologically suppressed MDR HTE subjects, HIV-DNA NGS set at 5% reliably detected around 70% of historical MRM and a considerable amount of new resistance. Minority resistance exclusively detected by NGS might predict loss of virological control.

Do minority resistant variants in HIV-DNA predict virological rebound in highly treatment experienced PLWH under virological control: Data from the PRESTIGIO Registry

Purpose: To describe the efficacy and durability of fostemsavir (FTR), ibalizumab (IBA) or lenacapavir (LEN)-including regimens in people living with 4-drug class resistant HIV (4DR-PLWH) in a real-life setting.
Methods: We included 4DR-PLWH from the PRESTIGIO Registry, treated with a FTR-, IBA- or LEN-including regimen and with a documented resistance to NRTIs/NNRTIs/PIs/INSTIs.
Discontinuation of FTR, IBA or LEN was defined as interruption of the drug for any cause. Follow-up (FU) started from the date of FTR, LEN or IBA start (baseline; BL) until discontinuation of FTR/IBA/LEN or death/freezing date (30/June/2023). Descriptions by median (IQR) or frequency (%). Genotypic susceptibility score for the optimized background therapy introduced with FTR/LEN/IBA was estimated according to the cumulative data of the available plasma genotyping resistance tests recorded for each patient.
Results: Among 35 4DR-PLWH, we considered 27, 11 and 10 FTR-, IBA- and LEN-including regimens, respectively (Table 1). FTR-including regimens: at the end of FU [median duration 18.7 (5.6-82.7) months], virological efficacy (VE; HIV-RNA &lt;200 copies/mL) was 66.7% (18/27); 8/27 (29.6%) regimens had been discontinued. Among those still in a FTR-including regimen, the median change in CD4+ was +97 (-38/+182) cells/mm3(p=0.064). IBA-including regimens: median FU 14.1 (5.6 -39.1) months, VE was 63.6% (7/11); 4/11 (36%) regimens had been discontinued. Median CD4+ change, in those still on treatment with IBA, was +63 (-38/+122) cells/mm3(p=0.109). LEN including regimens: median FU 30.3 (18.7-33.5) months, VE was 90% (9/10); 2/10 (20%) regimens had been discontinued. Median CD4+ change was +5 (-48/+101) cells/mm3(p=0.742). Regimens including a concomitant administration of FTR, IBA or LEN (n=4) are described in Table 2.
Conclusions: In 4DR-PLWH, the overall efficacy and durability of FTR, IBA or LEN-including regimens were good. Efficacy and safety data on combination regimens including entry and capsid inhibitors are urgently needed in this fragile population.

Efficacy and durability of Fostemsavir-, Ibalizumab-, or Lenacapavir-including regimens in people living with a Multidrug Resistant HIV: results from the PRESTIGIO Registry

Objectives: Assessment of anticholinergic burden (ACB) has emerged as a clinical problem, especially in people with HIV (PWH) undergoing polypharmacy. People with HIV and a resistant virus to all 4 classes of antiretrovirals (4DR-PWH) often require complex treatment regimens, potentially causing drug-drug interactions (DDIs). Our objectives were to evaluate polypharmacy, ACB and DDIs in this population.
Methods: This is a cross-sectional study, including 4DR-PLWH of the PRESTIGIO Registry taking at least one non-antiretroviral drug. Polypharmacy was defined as taking &ge; 5 non-antiretroviral drugs/person. The cumulative ACB of one or more drugs with anticholinergic activity, was calculated using the ACB scale: 0= no AC effect, 1-2= low/moderate risk, &ge;3= high anticholinergic risk. Characteristics of people classified according to ACB score were compared using the Kruskal-Wallis test; Spearman's correlation coefficient calculated to assess linear relationship. DDIs evaluated from the Liverpool database.5
Results: Overall, 172 4DR-PLWH were evaluated: 62 (27.1%) on polypharmacy, 109 (63.4%) treated with an INSTI-based regimen, 124 (72.1%) using a boosting agent and 72 (41.8%) with 4 or more antiretrovirals. Other characteristics in Table 1. Based on ACB, 33/172 (19.2%) and 11/172 (6.4%) had a low/moderate and high AC, respectively. The most common drugs for ACB are reported in Table. 2. The high ACB was significantly related to the number of drugs/person (r=0.327, p=&lt;.0001) and the number of previous clinical event (r=0.222, p=0.004). Overall, we found 258 DDIs between ARVs and comedications in 115 (66.8%) PWH, and 14 (8.1%) PWH received contraindicated drug combinations (Figure 1).
Conclusions: In 4DR-PLWH, polypharmacy, and proportion of people with moderate/high AC burden were high, as well as the number of DDIs detected. As the use of boosted agents is often not avoidable in PWH with multidrug resistance, a multidisciplinary approach among specialists of different fields is strongly encouraged.
Figure 1.Summary of DDIs in the PRESTIGIO Registry
<table style="width: 100%;" cellspacing="0" cellpadding="0">
<thead>
<tr>
<th style="background-color: unset;" colspan="1">&nbsp;</th>
<th style="background-color: unset;" colspan="2">Overall</th>
<th style="background-color: yellow;" colspan="2">Potential weak</th>
<th style="background-color: orange;" colspan="2">Potential</th>
<th style="background-color: red;" colspan="2">Do not coadminister</th>
</tr>
</thead>
<tbody>
<tr>
<td>Antiretroviral causing DDIs</td>
<td>n= 258</td>
<td>%</td>
<td>n</td>
<td>%</td>
<td>n</td>
<td>%</td>
<td>n</td>
<td>%</td>
</tr>
<tr>
<td>Ritonavir</td>
<td>115</td>
<td>44,6</td>
<td>42</td>
<td>16,5</td>
<td>65</td>
<td>25,2</td>
<td>8</td>
<td>3,1</td>
</tr>
<tr>
<td>Cobicistat</td>
<td>79</td>
<td>30,6</td>
<td>28</td>
<td>11,0</td>
<td>47</td>
<td>18,2</td>
<td>4</td>
<td>1,6</td>
</tr>
<tr>
<td>Etravirine</td>
<td>27</td>
<td>10,5</td>
<td>14</td>
<td>5,5</td>
<td>13</td>
<td>5,0</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>TAF/FTC or TDF/FTC</td>
<td>7</td>
<td>2,7</td>
<td>6</td>
<td>2,4</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Dolutegravir</td>
<td>8</td>
<td>3,1</td>
<td>2</td>
<td>0,8</td>
<td>6</td>
<td>2,3</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Bictegravir</td>
<td>3</td>
<td>1,2</td>
<td>0</td>
<td>0,0</td>
<td>3</td>
<td>1,2</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Fostemsavir</td>
<td>7</td>
<td>2,7</td>
<td>2</td>
<td>0,8</td>
<td>5</td>
<td>1,9</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Doravirine</td>
<td>1</td>
<td>0,4</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Rilpivirine</td>
<td>4</td>
<td>1,6</td>
<td>2</td>
<td>0,8</td>
<td>2</td>
<td>0,8</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Lamivudine</td>
<td>2</td>
<td>0,8</td>
<td>2</td>
<td>0,8</td>
<td>0</td>
<td>0,0</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Atazanavir</td>
<td>3</td>
<td>1,2</td>
<td>0</td>
<td>0,0</td>
<td>1</td>
<td>0,4</td>
<td>2</td>
<td>0,8</td>
</tr>
<tr>
<td>Maraviroc</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
</tr>
<tr>
<td>Zidovudina</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
<td>1</td>
<td>0,4</td>
<td>0</td>
<td>0,0</td>
</tr>
</tbody>
</table>
&nbsp;
Table 1. Demographic, immunologic, virologic characteristics according to ACB score.
<table style="width: 100%;" cellspacing="0" cellpadding="0">
<thead>
<tr>
<th>Variable</th>
<th>Overall (N=172)</th>
<th>Score ACB &ge;3 (n=11)</th>
<th>Score ACB=1-2 (n=33)</th>
<th>Score ACB=0 (n=128)</th>
<th>P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td>Age (years)</td>
<td>49.95 (45.62 &ndash; 56)</td>
<td>51.25 (45.34 &ndash; 55.2)</td>
<td>49.87 (46.28 &ndash; 56)</td>
<td>49.82 (45.47 &ndash; 54)</td>
<td>0.726</td>
</tr>
<tr>
<td>Male Gender</td>
<td>130 (75.6%)</td>
<td>10 (90.9%)</td>
<td>26 (78.8%)</td>
<td>94 (73.4%)</td>
<td>0.386</td>
</tr>
<tr>
<td>ART duration (years)</td>
<td>25.76 (22.34 &ndash; 28.7)</td>
<td>26.16 (19.45 &ndash; 29)</td>
<td>26.25 (22.34 &ndash; 29)</td>
<td>25.72 (22.27 &ndash; 28.4)</td>
<td>0.744</td>
</tr>
<tr>
<td>CD4+ cell count (cells/&micro;L)</td>
<td>537 (331.5 &ndash; 818)</td>
<td>574 (372 - 628)</td>
<td>414 (326 - 740)</td>
<td>537 (338.5 &ndash; 843)</td>
<td>0.657</td>
</tr>
<tr>
<td>HIV-RNA &lt;50 (copies/mL)</td>
<td>121 (70.3%)</td>
<td>9 (81.8%)</td>
<td>25 (75.8%)</td>
<td>87 (68%)</td>
<td>0.187</td>
</tr>
<tr>
<td>History of MACEs</td>
<td>22 (12.9%)</td>
<td>1 (9.1%)</td>
<td>10 (30.2%)</td>
<td>11 (8.7%)</td>
<td>0.114</td>
</tr>
<tr>
<td>History of Malignances</td>
<td>25 (14.5%)</td>
<td>3 (27.3%)</td>
<td>5 (15.2%)</td>
<td>17 (13.3%)</td>
<td>0.126</td>
</tr>
<tr>
<td>History of Hypertension</td>
<td>35 (20.3%)</td>
<td>2 (18.2%)</td>
<td>7 (21.2%)</td>
<td>26 (20.3%)</td>
<td>0.977</td>
</tr>
<tr>
<td>History of Neuropsychiatric diseases</td>
<td>39 (22.7%)</td>
<td>9 (81.8%)</td>
<td>7 (21.2%)</td>
<td>23 (18%)</td>
<td>0.0008</td>
</tr>
<tr>
<td>History of Dyslipidemia</td>
<td>48 (27.9%)</td>
<td>3 (27.3%)</td>
<td>10 (30.3%)</td>
<td>35 (27.3)</td>
<td>0.943</td>
</tr>
</tbody>
</table>
&nbsp;
Table 2. Description of polypharmacy/comedication according to ACB score.
<table style="width: 100%;" cellspacing="0" cellpadding="0">
<thead>
<tr>
<th>Variable</th>
<th>Overall (N=172)</th>
<th>Score ACB &ge;3 (n=11)</th>
<th>Score ACB=1-2 (n=33)</th>
<th>Score ACB=0 (n=128)</th>
<th>P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td>Antidepressants</td>
<td>26 (15.1%)</td>
<td>10 (90.9%)</td>
<td>5 (15.1%)</td>
<td>11 (8.6%)</td>
<td>&lt;0.0001</td>
</tr>
<tr>
<td>Hypercholesterolemia/ hyperlipidemia drugs</td>
<td>68 (39.5%)</td>
<td>6 (54.5%)</td>
<td>12 (36.4%)</td>
<td>50 (39.1%)</td>
<td>0.552</td>
</tr>
<tr>
<td>Beta-blockers</td>
<td>51 (29.7%)</td>
<td>4 (36.4%)</td>
<td>17 (51.5%)</td>
<td>30 (23.4%)</td>
<td>0.006</td>
</tr>
<tr>
<td>Diuretics</td>
<td>23 (13.3%)</td>
<td>1 (9.1%)</td>
<td>14 (42.4%)</td>
<td>8 (6.3%)</td>
<td>&lt;.0001</td>
</tr>
<tr>
<td>Other Hypolipidemic Drugs</td>
<td>26 (15.1%)</td>
<td>2 (18.2%)</td>
<td>6 (18.2%)</td>
<td>18 (14%)</td>
<td>0.924</td>
</tr>
<tr>
<td>Antiplatelets</td>
<td>40 (23.4%)</td>
<td>3 (27.3%)</td>
<td>11 (33.3%)</td>
<td>26 (20.3)</td>
<td>0.053</td>
</tr>
<tr>
<td>Ace-inhibitors</td>
<td>42 (24.4%)</td>
<td>2 (18.2%)</td>
<td>6 (18.2%)</td>
<td>34 (26.6%)</td>
<td>0.536</td>
</tr>
<tr>
<td>Sartans</td>
<td>17 (9.9%)</td>
<td>1 (9.1%)</td>
<td>4 (12.1%)</td>
<td>12 (9.4%)</td>
<td>0.891</td>
</tr>
<tr>
<td>Calcium channel blockers</td>
<td>20 (11.6%)</td>
<td>0 (0%)</td>
<td>7 (21.2%)</td>
<td>13 (10.2%)</td>
<td>0.097</td>
</tr>
</tbody>
</table>

Polypharmacy, anticholinergic burden, and drug-drug interaction assessment in people living with HIV harboring a 4-class resistance: data from the PRESTIGIO Registry

Background:&nbsp; Ineffective viral control in Heavily Treatment-Experienced (HTE) fuels inflammation which may be increased in HTE with vertical transmission (VT), given that viral acquisition at birth may alter immune homeostasis. We investigated inflammation in HTE with and without VT, according to viral load (VL) status.
Methods: Matched cohort study including individuals of the Prestigio Registry with resistance to NRTIs, NNRTIs, PIs and INSTIs, with or without VT, VL &lt;50 copies/mL or VL&gt;200 copies/mL. GM-CSF, IFN-&alpha;, IFN-&gamma;, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-17A, TNF-&alpha; (Cytometric Bead Array) and sCD14 (ELISA) were determined. 208 Author Index Late-breaker abstracts Oral abstracts E-posters Poster exhibition ias2023.org Abstract book Propensity score was used to match VT and no-VT for sex, HIV duration, CD4 nadir and VL at plasma sampling. Mann-Whitney test and Spearman&rsquo;s correlation were calculated.
Results:&nbsp;We evaluated 16 VT and 16 no-VT (Table); in each group, 8/16 had VL&lt;50 copies/mL and 8/16 VL&gt;200 copies/ mL. VT were younger than no-VT (p&lt;0.0001). TV and noTV showed comparable cytokines, with the exception of lower IL-6 in VT (median=0.526 pg/ml, IQR=0-2.85) vs 2.198 pg/ml, IQR=0.94-6.17; p=0.04; 1A) and a trend to lower IL-10 (p=0.08). IL-6 did not differ between VT and no-VT, within viremia strata (1B-C), while a tendency to lower IL-10 was retained in viremic VT (p=0.06; Figure1, D). IL-6 correlated with age (r=0.362, p=0.04; 1E); no other correlations between cytokines and demographic/viro-immunologic parameters were found.
Conclusions: HTE with VT feature comparable inflammation to that of HTE without VT, regardless of VL. The correlation between IL-6 and age suggests that age, rather than mode of transmission and viremia, drives inflammation in treated HIV.&nbsp;

Heavily Treatment-Experienced (HTE) HIV-Infected Individuals with Vertical Transmission and Detectable Viremia do not Display Increased Peripheral Inflammation Markers: Data from the PRESTIGIO Registry

Background: This study aimed to clarify whether NGS might be useful for resistance assessment in virologically suppressed highly treatment-experienced (HTE) individuals with multidrug resistance (MDR).
Methods: Ninety-one HTE MDR individuals from the PRESTIGIO registry were analysed. HIV-1 DNA PR/RT/IN and V3 sequences were obtained through NGS on MiSeq platform. Major resistance mutations (MRM) and APOBEC editing estimation (APOBEC mutations [APO-M]; stop codons) were evaluated through HIVdb algorithm. NGS cut-offs at &ge;1%, &ge;5% and &ge;20% were tested. Minority MRM with frequency ranging 1-5% (mV1%) and 5-20% (mV5%) and majority MRM (frequency &gt;20%, mV20%) were compared to historical-GRT (H-GRT). Variants distribution was compared between individuals who experienced virological rebound after NGS-GRT and those who maintained virological control.
Results:&nbsp;At NGS-GRT, individuals had a median (IQR) cART exposure of 23 (21-25) years, had been virologically suppressed since 3 (2-5) years and had a total HIV-DNA of 2,377 (1,274-4,949) copies/106 CD4+ cells. X4 tropism was detected in 61.5% of individuals. A total of 1,772 MRM were detected. Around half of MRM detected by NGS were already found in H-GRT; on the other hand, NGS-GRT detected a considerable number of additional mutations never observed before (Figure 1). The highest detection rate of historical MRM was obtained by setting NGS at 1% (Figure 2). NGS set at 1% showed poor reliability, although associated with the highest detection rate of historical MRM. In fact, mV1% (N=337) were frequently detected in samples with stop codons (94.4%) or APO-M (97.4%) providing potential misleading resistance assessment. Differently, among mV5% (N=370), a substantial proportion of cases was not affected by APOBEC editing and contributed in expanding detection of historical MRM (25.9%) or detecting new MRM (18.6%). Regarding majority variants, mV20% (N=704) were marginally detected in samples with stop codons (2.9%) or APO-M (5.3%), and mostly contributed to detect (69.4%) historical MRM or detect new MRM (25.4%). After NGS-GRT, 21 individuals underwent virological rebound with a median (IQR) viremia of 365 (98-7,840) copies/mL. Among them, only the median (IQR) number of mV5% detected exclusively by NGS-GRT was higher (2 [1-3]) compared to those who maintained virological control (1 [0-2], p=0.030, Figure 3). The number of mV5% newly detected by NGS in failing individuals positively correlated with plasma HIV-RNA levels detected at virological rebound (Spearman test, Rho=0.474, P=0.030).
Conclusions:&nbsp;In HTE MDR virologically suppressed individuals, NGS-GRT on HIV-1 DNA allows detection of around 60-70% historical MRM and detects considerable new resistance. Our results confirm that setting NGS at 5% might be a good choice to obtain reliable sequence data. At this setting, an increased number of minority species correlates with loss of virological control and with viremia levels at virological rebound.

Icar

Does NGS on HIV-1 DNA improve resistance assessment in highly treatment-experienced and multi-resistant individuals under virological control? An experience from the PRESTIGIO Registry

Background: Aim of the current study was to explore the incidence of malignancies and major adverse cardiovascular events (MACEs) in people living with 4-class drug-resistant HIV (4DR-PLWH).
Methods: Cohort study on PLWH with documented resistance to NRTIs, NNRTIs, PIs, and INSTIs, from the PRESTIGIO Registry. The primary outcome was to evaluate the incidence rates (IRs) of malignancies and MACEs occurred after 4DR evidence (baseline, BL). Malignancies included both AIDS- and non-AIDS-related cancers. MACEs included stroke, acute myocardial infarction, coronary or peripheral artery disease requiring revascularization, and acute congestive heart failure. Follow-up accrued from BL until death/loss-to-follow-up/freezing date (28th February, 2023). Descriptions by median (interquartile range, IQR) or frequency (%). Poisson regression modelled IRs and 95% confidence intervals (95%CIs). Kaplan-Meier curves estimated cumulative probabilities of the first incident: 1) cancer; and 2) MACE.
Results: Overall, 229 4DR-PLWH included: BL characteristics reported in Table 1. During a median follow-up of 7.7 (4.8-10.3) years [1760 person-years-of-follow-up (PYFU)], 28 (12.2%) 4DR-PLWH developed &ge;1 incident cancer (n=30, Figure 1): IR=1.7 (95%CI=1.1-2.3)/100 PYFU. Specifically, 24 (80%) malignancies were non-AIDS-related: 7 HPV-related anal neoplasms, 4 Hodgkin lymphomas, 4 skin cancers, 2 hepatocellular carcinomas, 2 laryngeal carcinomas, 1 breast cancer, 1 lung cancer, 1 urothelial carcinoma, 1 cholangiocarcinoma, 1 conjunctival squamous cell carcinoma. Only 6 (20%) tumors were AIDS-defining: 4 high-grade non-Hodgkin lymphomas, and 2 Kaposi&rsquo;s sarcomas. Notably, 2 individuals developed 2 different incident malignancies: one person developed Hodgkin lymphoma followed by non-Hodgkin lymphoma, the other a skin cancer followed by an HPV-related anal neoplasm. Furthermore, an individual who developed Kaposi&rsquo;s sarcoma, had a prevalent HPV-related anal cancer at baseline. Twenty-two (9.6%) 4DR-PLWH developed &ge;1 MACE (n=31, Figure 1): IR=1.8 (95%CI=1.1-2.4)/100 PYFU. In particular, 15 myocardial infarctions, 8 acute congestive heart failures, 4 coronary diseases requiring revascularization, 3 strokes, and 1 peripheral artery disease requiring revascularization occurred. Remarkably, 5 individuals developed &ge;2 incident MACEs: 2 events occurred in 2 individuals, 3 events in 2, 4 events in 1. Cumulative probabilities of the first incident cancer and the first incident MACE reported in Figure 2. Among 4DR-PLWH who experienced &ge;1 incident malignancy or &ge;1 incident MACE, 11 died: 8 after &ge;1 tumor, 2 after &ge;1 MACE, and 1 after both 1 malignancy and 1 MACE (Figure 1).
Conclusions: Due to high incidence of malignancies and MACEs in people living with multidrug-resistant HIV, screening and prevention strategies are strongly recommended in this fragile population.

Malignancies and MACEs in the PRESTIGIO Registry

Background: We explored the incidence of hospitalization in male and female people living with 4-drug class resistant HIV (4DR-PLWH) enrolled in the PRESTIGIO study.
Methods: We included PLWH with a documented resistance to NRTIs, NNRTIs, PIs and INSTIs. Hospitalization was defined as a hospital admission for any reason with &ge;1 overnight stay. Follow-up started from the date of the first 4DR evidence (baseline) until death/loss-to-follow-up/freezing date (December 31st, 2022). Data were reported as median (IQR) or frequency (%). Poisson regression was used to model incidence rates (IR) with 95% confidence intervals (CI).
Results: Overall, 178 4DR-PLWH were included; baseline characteristics, according to gender, are reported in Table 1. During 1294 PYFU, 122 hospitalizations (28 in females) occurred in 60 4DR-PLWH: 29/60 (48.3%) had &gt;1 hospitalization. The overall IR for hospitalization was 9.43/100 PYFU (95%CI=7.76-11.10), without a significant difference between males and females (Incidence rate ratio (F/M)=0.77, 95%CI=0.51-1.17, p=0.223). At 7 years after baseline, 34.7% were estimated to have had &gt;=1 hospitalization. The median duration of hospitalization was 8 days (IQR 4-20). The most frequent causes were non-AIDS-defining infections (n=28, 23%) and major adverse cardiovascular events (MACEs, n=19, 15.6%). Females had no hospitalizations for AIDS-defining events compared to males (0% vs 11.7% (n=11), p=0.049); gender differences appeared with regard to hospitalizations for non-AIDS-defining events (MACEs: 17 (18.1%) and 2 (7.1%) in males vs females, p=0.097; non-AIDS-defining infections: 19 (20.2%) and 9 (32.1%) in males vs females, p=0.084; Figure1).
Conclusion: Women living with HIV multidrug resistance seem to have a lower incidence of AIDS-defining events hospitalizations; women tended to have fewer hospitalizations for MACEs and more hospitalizations for non-AIDS-defining infections than males.

Abstract from CROI 2025 - Sun Mar 09 2025

Plasma CNS Tissue Markers in Heavily Treatment-Experienced PWH in the PRESTIGIO Registry

Authors: A.Calcagno, M.Strano, R.Lolatto, R.Vercesi, A.Mandelli, M.Antonucci, V.Spagnuolo, T.Clemente, D.Francisci, G.Cenderello, O.Schioppa, L.Comi, R.Furlan, A.Castagna, P.Cinque

Affiliations: .

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