Serendipitous Identification of Azine Anticancer Agents Using a Privileged Scaffold Morphing Strategy.

The use of privileged scaffolds as a starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that enable the modification of the chosen privileged framework and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized using a scaffold morphing approach consisting of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells, showing in five cases acceptable to good anticancer activity associated with low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

PRESTIGIO RING: "An 80-year-old man living with HIV resistant to all four antiretroviral classes and desiring treatment simplification".

People aging with 4 antiretroviral class resistant HIV are a very challenging population. It is difficult to build up a fully suppressive regimen, and the high prevalence of comorbidities and polypharmacy may cause drug-drug interactions and put adherence at risk. We herein present the case of an 80-year-old man, participating in the PRESTIGIO registry, asking for a reduction in his antiretroviral burden while on polypharmacy for his comorbidities.

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV.

OBJECTIVE: Clinical data's confidential nature often limits the development of machine learning models in healthcare. Generative adversarial networks (GANs) can synthesise realistic datasets, but suffer from mode collapse, resulting in low diversity and bias towards majority demographics and common clinical practices. This work proposes an extension to the classic GAN framework that includes a variational autoencoder (VAE) and an external memory mechanism to overcome these limitations and generate synthetic data accurately describing imbalanced class distributions commonly found in clinical variables. METHODS: The proposed method generated a synthetic dataset related to antiretroviral therapy for human immunodeficiency virus (ART for HIV). We evaluated it based on five metrics: (1) accurately representing imbalanced class distribution; (2) the realism of the individual variables; (3) the realism among variables; (4) patient disclosure risk; and (5) the utility of the generated dataset for developing downstream machine learning models. RESULTS: The proposed method overcomes the issue of mode collapse and generates a synthetic dataset that accurately describes imbalanced class distributions commonly found in clinical variables. The generated data has a patient disclosure risk of 0.095%, lower than the 9% threshold stated by Health Canada and the European Medicines Agency, making it suitable for distribution to the research community with high security. The generated data also has high utility, indicating the potential of the proposed method to enable the development of downstream machine learning algorithms for healthcare applications using synthetic data. CONCLUSION: Our proposed extension to the classic GAN framework, which includes a VAE and an external memory mechanism, represents a promising approach towards generating synthetic data that accurately describe imbalanced class distributions commonly found in clinical variables. This method overcomes the limitations of GANs and creates more realistic datasets with higher patient cohort diversity, facilitating the development of downstream machine learning algorithms for healthcare applications.

Inflammation burden score in multidrug-resistant HIV-1 infection.

OBJECTIVES: Four-class drug-resistant (4DR) people living with HIV (PLWH) are a fragile population with a high burden of disease. No data on their inflammation and T-cell exhaustion markers are currently available. METHODS: Inflammation, immune activation and microbial translocation biomarkers were measured through ELISA in 30 4DR-PLWH with HIV-1 RNA ≥ 50 copies/mL, 30 non-viremic 4DR-PLWH and 20 non-viremic non-4DR-PLWH. Groups were matched by age, gender and smoking habit. T-cell activation and exhaustion markers were assessed by flow cytometry in 4DR-PLWH. An inflammation burden score (IBS) was calculated from soluble marker levels and associated factors were estimated through multivariate regression. RESULTS: The highest plasma biomarker concentrations were observed in viremic 4DR-PLWH, the lowest ones in non-4DR-PLWH. Endotoxin core immunoglobulin G showed an opposite trend. Among 4DR-PLWH, CD38/HLA-DR and PD-1 were more expressed on CD4+ (p = 0.019 and 0.034, respectively) and CD8+ (p = 0.002 and 0.032, respectively) cells of viremic compared to non-viremic subjects. An increased IBS was significantly associated with 4DR condition, higher values of viral load and a previous cancer diagnosis. CONCLUSIONS: Multidrug-resistant HIV infection is associated with a higher IBS, even when viremia is undetectable. Therapeutic approaches aimed to reduce inflammation and T-cell exhaustion in 4DR-PLWH need to be investigated.

Efficacy of Licensed Monoclonal Antibodies and Antiviral Agents against the SARS-CoV-2 Omicron Sublineages BA.1 and BA.2.

Newly emerging SARS-CoV-2 variants may escape monoclonal antibodies (mAbs) and antiviral drugs. By using live virus assays, we assessed the ex vivo inhibition of the B.1 wild-type (WT), delta and omicron BA.1 and BA.2 lineages by post-infusion sera from 40 individuals treated with bamlanivimab/etesevimab (BAM/ETE), casirivimab/imdevimab (CAS/IMD), and sotrovimab (SOT) as well as the activity of remdesivir, nirmatrelvir and molnupiravir. mAbs and drug activity were defined as the serum dilution (ID50) and drug concentration (IC50), respectively, showing 50% protection of virus-induced cytopathic effect. All pre-infusion sera were negative for SARS-CoV-2 neutralizing activity. BAM/ETE, CAS/IMD, and SOT showed activity against the WT (ID50 6295 (4355-8075) for BAM/ETE; 18,214 (16,248-21,365) for CAS/IMD; and 456 (265-592) for SOT) and the delta (14,780 (ID50 10,905-21,020) for BAM/ETE; 63,937 (47,211-79,971) for CAS/IMD; and 1103 (843-1334) for SOT). Notably, only SOT was active against BA.1 (ID50 200 (37-233)), whereas BA.2 was neutralized by CAS/IMD (ID50 174 (134-209) ID50) and SOT (ID50 20 (9-31) ID50), but not by BAM/ETE. No significant inter-variant IC50 differences were observed for molnupiravir (1.5 ± 0.1/1.5 ± 0.7/1.0 ± 0.5/0.8 ± 0.01 µM for WT/delta/BA.1/BA.2, respectively), nirmatrelvir (0.05 ± 0.02/0.06 ± 0.01/0.04 ± 0.02/0.04 ± 0.01 µM) or remdesivir (0.08 ± 0.04/0.11 ± 0.08/0.05 ± 0.04/0.08 ± 0.01 µM). Continued evolution of SARS-CoV-2 requires updating the mAbs arsenal, although antivirals have so far remained unaffected.

Viral resistance burden and APOBEC editing correlate with virological response in heavily treatment-experienced people living with multi-drug resistant HIV.

BACKGROUND: The impact of drug resistance mutational load and APOBEC editing in heavily treatment-experienced (HTE) people living with multidrug-resistant HIV has not been investigated. MATERIAL AND METHODS: This study explored the HIV-DNA and HIV-RNA mutational load of drug resistance and APOBEC-related mutations through next-generation sequencing (NGS, Illumina MiSeq) in 20 failing HTE participants enrolled in the PRESTIGIO registry. RESULTS: The patients showed high levels of both HIV-DNA (4.5 [4.0-5.2] log10 copies/106 T-CD4+ cell) and HIV-RNA (4.5 [4.1-5.0] log10 copies/mL) with complex resistance patterns in both compartments. Among the 255 drug-resistant mutations found, 66.3% were concordantly detected in both HIV-DNA and HIV-RNA; 71.3% of mutations were already present in historical Sanger genotypes. At an intra-patient frequency > 5%, a considerable proportion of mutations detected through DNA-NGS were found in historical genotypes but not through RNA-NGS, and few patients had APOBEC-related mutations. Of 14 patients who switched therapy, the five who failed treatment had DNA resistance with higher intra-patient frequency and higher DNA/RNA mutational load in a context of tendentially less pronounced APOBEC editing compared with those who responded. CONCLUSIONS: Using NGS in HIV-DNA and HIV-RNA together with APOBEC editing evaluation might help to identify HTE individuals with MDR who are more prone to experience virological failure.

Sofosbuvir Selects for Drug-Resistant Amino Acid Variants in the Zika Virus RNA-Dependent RNA-Polymerase Complex In Vitro.

The nucleotide analog sofosbuvir, licensed for the treatment of hepatitis C, recently revealed activity against the Zika virus (ZIKV) in vitro and in animal models. However, the ZIKV genetic barrier to sofosbuvir has not yet been characterized. In this study, in vitro selection experiments were performed in infected human hepatoma cell lines. Increasing drug pressure significantly delayed viral breakthrough (p = 0.029). A double mutant in the NS5 gene (V360L/V607I) emerged in 3 independent experiments at 40-80 µM sofosbuvir resulting in a 3.9 ± 0.9-fold half- maximal inhibitory concentration (IC50) shift with respect to the wild type (WT) virus. A triple mutant (C269Y/V360L/V607I), detected in one experiment at 80 µM, conferred a 6.8-fold IC50 shift with respect to the WT. Molecular dynamics simulations confirmed that the double mutant V360L/V607I impacts the binding mode of sofosbuvir, supporting its role in sofosbuvir resistance. Due to the distance from the catalytic site and to the lack of reliable structural data, the contribution of C269Y was not investigated in silico. By a combination of sequence analysis, phenotypic susceptibility testing, and molecular modeling, we characterized a double ZIKV NS5 mutant with decreased sofosbuvir susceptibility. These data add important information to the profile of sofosbuvir as a possible lead for anti-ZIKV drug development.

SARS-CoV-2 RNA-dependent RNA polymerase as a therapeutic target for COVID-19.

Introduction: The current SARS-CoV-2 pandemic urgently demands for both prevention and treatment strategies. RNA-dependent RNA-polymerase (RdRp), which has no counterpart in human cells, is an excellent target for drug development. Given the time-consuming process of drug development, repurposing drugs approved for other indications or at least successfully tested in terms of safety and tolerability, is an attractive strategy to rapidly provide an effective medication for severe COVID-19 cases.Areas covered: The currently available data and upcominSg studies on RdRp which can be repurposed to halt SARS-CoV-2 replication, are reviewed.Expert opinion: Drug repurposing and design of novel compounds are proceeding in parallel to provide a quick response and new specific drugs, respectively. Notably, the proofreading SARS-CoV-2 exonuclease activity could limit the potential for drugs designed as immediate chain terminators and favor the development of compounds acting through delayed termination. While vaccination is awaited to curb the SARS-CoV-2 epidemic, even partially effective drugs from repurposing strategies can be of help to treat severe cases of disease. Considering the high conservation of RdRp among coronaviruses, an improved knowledge of its activity in vitro can provide useful information for drug development or drug repurposing to combat SARS-CoV-2 as well as future pandemics.

Burden of Disease in PWH Harboring a Multidrug-Resistant Virus: Data From the PRESTIGIO Registry.

BACKGROUND: Currently, no data are available on the burden of morbidity and mortality in people with HIV-1 (PWH) harboring a 4-class drug-resistant (4DR) virus (nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, protease inhibitors, integrase strand transfer inhibitors). The study aimed to assess the incidence of clinical events and death in this population. METHODS: This was a cohort study on PWH from the PRESTIGIO Registry with a documented 4DR virus. Burden of disease was defined as the occurrence of any new event including an AIDS-defining event (ADE) or non-AIDS-defining event (NADE) or death from any cause after 4DR evidence (baseline). Cox regression models evaluated factors associated with the risk of new clinical events/death. RESULTS: Among 148 PWH followed for a median (interquartile range) of 47 (32-84) months after 4DR evidence, 38 PWH had 62 new events or died from any cause (incidence rate, 9.12/100 person-years of follow-up; 95% CI = 6.85-11.39): 12 deaths (6 AIDS-related and 6 non-AIDS-related), 18 ADEs, 32 NADEs; 20 of the 38 NADEs (45%) of the incident clinical events were malignancies. The 4-year cumulative incidence of death was 6% (95% CI, 3%-13%), and that of ≥1 event or death was 22% (95% CI, 16%-31%). A higher risk of new clinical events/death was more likely in PWH with previous clinical events (adjusted hazard ratio [aHR], 2.67; 95% CI, 1.07-6.67) and marginally associated with lower baseline CD4+/CD8+ ratio (aHR, 0.82; 95% CI, 0.65-1.02). CONCLUSIONS: PWH harboring 4DR have a high burden of disease with a worrying incidence of malignancies, strongly advising for close prevention and monitoring interventions as well as access to innovative therapeutic strategies, especially in people with a history of clinical events and low CD4+/CD8+ ratio.

Unique Domain for a Unique Target: Selective Inhibitors of Host Cell DDX3X to Fight Emerging Viruses.

Emerging viruses like dengue, West Nile, chikungunya, and Zika can cause widespread viral epidemics. Developing novel drugs or vaccines against specific targets for each virus is a difficult task. As obligate parasites, all viruses exploit common cellular pathways, providing the possibility to develop broad-spectrum antiviral agents targeting host factors. The human DEAD-box RNA helicase DDX3X is an essential cofactor for viral replication but dispensable for cell viability. Herein, we exploited the presence of a unique structural motif of DDX3X not shared by other cellular enzymes to develop a theoretical model to aid in the design of a novel class of highly selective inhibitors acting against such specific targets, thus limiting off-targeting effects. High-throughput virtual screening led us to identify hit compound 5, endowed with promising antienzymatic activity. To improve its aqueous solubility, 5 and its two enantiomers were synthesized and converted into their corresponding acetate salts (compounds 11, 12, and 13). In vitro mutagenesis and biochemical and cellular assays further confirmed that the developed molecules were selective for DDX3X and were able to suppress replication of West Nile and dengue viruses in infected cells in the micromolar range while showing no toxicity for uninfected cells. These results provide proof of principle for a novel strategy in developing highly selective and broad-spectrum antiviral molecules active against emerging and dangerous viral pathogens. This study paves the way for the development of larger focused libraries targeting such domain to expand SAR studies and fully characterize their mode of interaction.

Synthesis and Antiviral Activity of Novel 1,3,4-Thiadiazole Inhibitors of DDX3X.

The human ATPase/RNA helicase X-linked DEAD-box polypeptide 3 (DDX3X) emerged as a novel therapeutic target in the fight against both infectious diseases and cancer. Herein, a new family of DDX3X inhibitors was designed, synthesized, and tested for its inhibitory action on the ATPase activity of the enzyme. The potential use of the most promising derivatives it has been investigated by evaluating their anti-HIV-1 effects, revealing inhibitory activities in the low micromolar range. A preliminary ADME analysis demonstrated high metabolic stability and good aqueous solubility. The promising biological profile, together with the suitable in vitro pharmacokinetic properties, make these novel compounds a very good starting point for further development.

The effect of primary drug resistance on CD4+ cell decline and the viral load set-point in HIV-positive individuals before the start of antiretroviral therapy.

OBJECTIVE: To evaluate the effect of primary resistance and selected polymorphic amino-acid substitutions in HIV reverse transcriptase and protease on the CD4 cell count and viral load set point before the start of antiretroviral treatment. DESIGN: Prospective cohort study. METHODS: A total of 6180 individuals with a resistance test prior to starting antiretroviral treatment accessing care in HIV clinics across Europe who had at least one viral load and one CD4+ test available were included in the analysis. The impact of amino-acid substitutions variants on viral load and CD4+ trends was investigated using linear mixed models. Clusters of mutations were studied using principal component analysis. RESULTS: Overall, the detection of any primary resistance was not associated with either the speed of CD4+ cell decline or the viral load set point. However, transmitted nucleoside reverse transcriptase inhibitor and protease inhibitor resistance appeared to be weakly associated with lower viral load set points, as were the polymorphic G16E or Q92K protease mutations. There was some evidence suggesting that these effects varied according to HIV subtype, with the effects of transmitted nucleoside reverse transcriptase inhibitor and protease resistance being particularly marked among individuals with a subtype B virus. A cluster of five polymorphic protease substitutions at position 20, 13, 36, 69 and 89 was associated with less steep CD4+ cell declines and lower viral load set points. CONCLUSION: Although we found little evidence for an association between primary resistance and CD4+ speed of decline and viral load set point, the potential role of polymorphic protease (alone or in clusters) and their interplay with HIV subtype needs to be further evaluated.

Comparative analysis of different cell systems for Zika virus (ZIKV) propagation and evaluation of anti-ZIKV compounds in vitro.

A strong correlation between Zika virus (ZIKV) infection and severe neurological disease in newborns and occasionally adults has emerged in the Brazilian outbreak. Efficient human cell-based assays are required to test candidate inhibitors of ZIKV replication. The aim of this work was to investigate ZIKV propagation and quantification in different cell lines. The human (U87, A549, Huh7), mosquito (C6/36) and monkey (VERO E6) cell lines tested were all permissive to ZIKV infection. When assessed by plaque forming units (PFU) in three different target cell lines, the maximal production of ZIKV was achieved in Huh7 at day 3 post-infection (6.38±0.44 log10PFU/ml). The C6/36 cell line showed a low and slow production of virus when compared with other cell lines. A549 readout cells generated a larger number of plaques compared to Huh7 but not to VERO E6 cells. ZIKV PFU and RNA titers showed the highest correlation when Huh7 and A549 were used as the producer and readout cells, respectively. Also, U87 cells produced ZIKV RNA titers which were highly correlated with PFU independently from the readout cell line. Using the best virus-cell system, sofosbuvir and ribavirin EC50 were 1.2µM and 1.1µM when measured through plaque assay, and 4.2µM and 5.2µM when measured by quantitative real time PCR (qRT-PCR), respectively. In summary, ZIKV can efficiently infect different human cell lines and rapidly reach peak viral titers. Overall, A549 cells appear to be as efficient as the VERO E6 gold standard for plaque assay allowing the use of human, rather than simian, cells for evaluating candidate anti-ZIKV compounds by the reference assay. The possibility to replace the labor-intensive plaque assay with the more rapid and easy-to-perform qRT-PCR is appealing and warrants further investigation.

Human DDX3 protein is a valuable target to develop broad spectrum antiviral agents.

Targeting a host factor essential for the replication of different viruses but not for the cells offers a higher genetic barrier to the development of resistance, may simplify therapy regimens for coinfections, and facilitates management of emerging viral diseases. DEAD-box polypeptide 3 (DDX3) is a human host factor required for the replication of several DNA and RNA viruses, including some of the most challenging human pathogens currently circulating, such as HIV-1, Hepatitis C virus, Dengue virus, and West Nile virus. Herein, we showed for the first time, to our knowledge, that the inhibition of DDX3 by a small molecule could be successfully exploited for the development of a broad spectrum antiviral agent. In addition to the multiple antiviral activities, hit compound 16d retained full activity against drug-resistant HIV-1 strains in the absence of cellular toxicity. Pharmacokinetics and toxicity studies in rats confirmed a good safety profile and bioavailability of 16d. Thus, DDX3 is here validated as a valuable therapeutic target.

A novel methodology for large-scale phylogeny partition.

Understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny of 11,541 human immunodeficiency virus-1 subtype B pol gene sequences from a large Italian cohort. Molecular transmission chains were characterized by means of different clinical/demographic factors, such as the interaction between male homosexuals and male heterosexuals. Our method takes an advantage of a flexible notion of transmission cluster and can become a general framework to analyse other epidemics.

Advantages of predicted phenotypes and statistical learning models in inferring virological response to antiretroviral therapy from HIV genotype.

BACKGROUND: Inferring response to antiretroviral therapy from the viral genotype alone is challenging. The utility of an intermediate step of predicting in vitro drug susceptibility is currently controversial. Here, we provide a retrospective comparison of approaches using either genotype or predicted phenotypes alone, or in combination. METHODS: Treatment change episodes were extracted from two large databases from the USA (Stanford-California) and Europe (EuResistDB) comprising data from 6,706 and 13,811 patients, respectively. Response to antiretroviral treatment was dichotomized according to two definitions. Using the viral sequence and the treatment regimen as input, three expert algorithms (ANRS, Rega and HIVdb) were used to generate genotype-based encodings and VircoTYPE() 4.0 (Virco BVBA, Mechelen, Belgium) was used to generate a predicted -phenotype-based encoding. Single drug classifications were combined into a treatment score via simple summation and statistical learning using random forests. Classification performance was studied on Stanford-California data using cross-validation and, in addition, on the independent EuResistDB data. RESULTS: In all experiments, predicted phenotype was among the most sensitive approaches. Combining single drug classifications by statistical learning was significantly superior to unweighted summation (P<2.2x10(-16)). Classification performance could be increased further by combining predicted phenotypes and expert encodings but not by combinations of expert encodings alone. These results were confirmed on an independent test set comprising data solely from EuResistDB. CONCLUSIONS: This study demonstrates consistent performance advantages in utilizing predicted phenotype in most scenarios over methods based on genotype alone in inferring virological response. Moreover, all approaches under study benefit significantly from statistical learning for merging single drug classifications into treatment scores.