General Mechanisms of Antiviral Resistance

Uses and Challenges of Antiviral Polyclonal and Monoclonal Antibody Therapies

Viral diseases represent a major public health concerns and ever-present risks for developing into future pandemics. Antiviral antibody therapeutics, either alone or in combination with other therapies, emerged as valuable preventative and treatment options, including during global emergencies. Here we will discuss polyclonal and monoclonal antiviral antibody therapies, focusing on the unique biochemical and physiological properties that make them well-suited as therapeutic agents. We will describe the methods of antibody characterization and potency assessment throughout development, highlighting similarities and differences between polyclonal and monoclonal products as appropriate. In addition, we will consider the benefits and challenges of antiviral antibodies when used in combination with other antibodies or other types of antiviral therapeutics. Lastly, we will discuss novel approaches to the characterization and development of antiviral antibodies and identify areas that would benefit from additional research.

Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2

Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.

Discovery of a Novel Class of Norovirus Inhibitors with High Barrier of Resistance

Human noroviruses (HuNoVs) are the most common cause of viral gastroenteritis resulting in ~219,000 deaths annually and a societal cost of ~USD60 billion. There are no antivirals or vaccines available to treat and/or prevent HuNoV. In this study, we performed a large-scale phenotypical antiviral screening using the mouse norovirus (MNV), which included ~1000 drug-like small molecules from the Drug Design and Synthesis Centre (Sapienza University, Rome). Compound 3-((3,5-dimethylphenyl)sulfonyl)-5-chloroindole-N-(phenylmethanol-4-yl)-2.carboxamide (compound 1) was identified as an inhibitor of MNV replication with an EC₅₀ of 0.5 ± 0.1 µM. A series of 10 analogs were synthesized of which compound 6 showed an improved potency/selectivity (EC₅₀ 0.2 ± 0.1 µM) against MNV; good activity was also observed against the HuNoV GI replicon (EC₅₀ 1.2 ± 0.6 µM). Time-of-drug-addition studies revealed that analog 6 acts at a time point that coincides with the onset of viral RNA replication. After six months of selective pressure, two compound 6^res variants were independently selected, both harboring one mutation in VPg and three mutations in the RdRp. After reverse engineering S131T and Y154F as single mutations into the MNV backbone, we did not find a markedly compound 6^res phenotype. In this study, we present a class of novel norovirus inhibitors with a high barrier to resistance and in vitro antiviral activity.

Smart and Sustainable Nanotechnological Solutions in a Battle against COVID-19 and Beyond: A Critical Review

The variety of available biocidal features make nanomaterials promising for fighting infections. To effectively battle COVID-19, categorized as a pandemic by the World Health Organization (WHO), materials scientists and biotechnologists need to combine their knowledge to develop efficient antiviral nanomaterials. By design, nanostructured materials (spherical, two-dimensional, hybrid) can express a diverse bioactivity and unique combination of specific, nonspecific, and mixed mechanisms of antiviral action. It can be related to the material's specific features and their multiple functionalization strategies. This is a complex guiding approach in which an interaction target is constantly moving and quickly changing. On the other hand, in such a rush, sustainability may be put aside. Therefore, to elucidate the most promising nanotechnological solutions, we critically review available data within the frame of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other types of viruses. We highlight solutions that are, or could be, more sustainable and less toxic. In this regard, reduction of the number of synthetic routes, organic solvents, byproducts, and residues is highly recommended. Such efficient, green solutions may be further used for the prevention of virion-host interactions, treatment of the already developed infection, reducing inflammation, and finally, protecting healthcare professionals with masks, fabrics, equipment, and in other associated areas. Further translation into the market needs putting on the fast track with respect to principles of green chemistry, feasibility, safety, and the environment.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.