Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

Evolution of viruses and the emergence of SARS-CoV-2 variants

Life implies adaptation. This is one of the fundamental principles that has permitted most living species to survive through ages in an ever-changing environment. Spontaneously occurring events have shaped also virus populations and their fitness. Thanks to their plasticity, viruses have thrived in extremely dissimilar conditions. Unsurprisingly, SARS-CoV-2, the etiological agent of COVID-19, is no exception. Thanks to an unprecedented rate of molecular tracing and sequence scrutiny, the virus was followed in all its changes and shown to evolve in such a way as to possibly determine subsequent waves of infection after the first global and massive outbreak. This review illustrates the major modifications occurred to the virus since its discovery. We describe the potential advantages that these changes conveyed as regards SARS-CoV-2 transmissibility, resistance to host innate and adaptive barriers and molecular diagnosis

Protection of Stem Cell-Derived Lymphocytes in a Primate AIDS Gene Therapy Model after In Vivo Selection

Background: There is currently no effective AIDS vaccine, emphasizing the importance of developing alternative therapies. Recently, a patient was successfully transplanted with allogeneic, naturally resistant CCR5-negative (CCR5 delta 32) cells, setting the stage for transplantation of naturally resistant, or genetically modified stem cells as a viable therapy for AIDS. Hematopoietic stem cell (HSC) gene therapy using vectors that express various anti-HIV transgenes has also been attempted in clinical trials, but inefficient gene transfer in these studies has severely limited the potential of this approach. Here we evaluated HSC gene transfer of an anti-HIV vector in the pigtailed macaque (Macaca nemestrina) model, which closely models human transplantation. Methods and Findings: We used lentiviral vectors that inhibited both HIV-1 and simian immunodeficiency virus (SIV)/HIV-1 (SHIV) chimera virus infection, and also expressed a P140K mutant methylguanine methyltransferase (MGMT) transgene to select gene-modified cells by adding chemotherapy drugs. Following transplantation and MGMT-mediated selection we demonstrated transgene expression in over 7% of stem-cell derived lymphocytes. The high marking levels allowed us to demonstrate protection from SHIV in lymphocytes derived from gene-modified macaque long-term repopulating cells that expressed an HIV-1 fusion inhibitor. We observed a statistically significant 4-fold increase of gene-modified cells after challenge of lymphocytes from one macaque that received stem cells transduced with an anti-HIV vector (p<0.02, Student's t-test), but not in lymphocytes from a macaque that received a control vector. We also established a competitive repopulation assay in a second macaque for preclinical testing of promising anti-HIV vectors. The vectors we used were HIV-based and thus efficiently transduce human cells, and the transgenes we used target HIV-1 genes that are also in SHIV, so our findings can be rapidly translated to the clinic. Conclusions: Here we demonstrate the ability to select protected HSC-derived lymphocytes in vivo in a clinically relevant nonhuman primate model of HIV/SHIV infection. This approach can now be evaluated in human clinical trials in AIDS lymphoma patients. In this patient setting, chemotherapy would not only kill malignant cells, but would also increase the number of MGMTP140K-expressing HIV-resistant cells. This approach should allow for high levels of HIV-protected cells in AIDS patients to evaluate AIDS gene therapy

Harnessing adaptive novelty for automated generation of cancer treatments

© 2020 The Authors Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice

The Structural Basis for the Interdependence of Drug Resistance in the HIV-1 Protease

The human immunodeficiency virus type 1 (HIV-1) protease (PR) is a critical drug target as it is responsible for virion maturation. Mutations within the active site (1°) of the PR directly interfere with inhibitor binding while mutations distal to the active site (2°) to restore enzymatic fitness. Increasing mutation number is not directly proportional to the severity of resistance, suggesting that resistance is not simply additive but that it is interdependent. The interdependency of both primary and secondary mutations to drive protease inhibitor (PI) resistance is grossly understudied. To structurally and dynamically characterize the direct role of secondary mutations in drug resistance, I selected a panel of single-site mutant protease crystal structures complexed with the PI darunavir (DRV). From these studies, I developed a network hypothesis that explains how mutations outside the active site are able to perpetuate changes to the active site of the protease to disrupt inhibitor binding. I then expanded the panel to include highly mutated multi-drug resistant variants. To elucidate the interdependency between primary and secondary mutations I used statistical and machine-learning techniques to determine which specific mutations underlie the perturbations of key inter-molecular interactions. From these studies, I have determined that mutations distal to the active site are able to perturb the global PR hydrogen bonding patterns, while primary and secondary mutations cooperatively perturb hydrophobic contacts between the PR and DRV. Discerning and exploiting the mechanisms that underlie drug resistance in viral targets could proactively ameliorate both current treatment and inhibitor design for HIV-1 targets

Characterization of the Hemagglutinin Cleaving Transmembrane Serine Proteases Matriptase and TMPRSS2

Influenza is one of the commonest infectious diseases affecting millions of people every year including 290,000 – 650,000 heavy casualties. Influenza viruses undergo constant genetic changes and every 10 – 50 years new influenza virus strains emerge that potentially cause a severe pandemic. In this modern interconnected world, experts believe the next influenza pandemic will be a “devastating global health event with far-reaching consequences” [1]. Novel effective anti-influenza drugs are in need. One strategy of influenza research is to focus on host-specific proteases that are essential for virus activation and spread. Trypsin-like serine proteases are crucial for influenza activation by mediating the cleavage of the viral surface glycoprotein HA and hence promoting the fusion potential of the virus. Therefore, their inhibition provides a promising therapeutic approach. The present work focused on the characterization of two relevant HA cleaving type-II transmembrane serine proteases matriptase and TMPRSS2. Chapter 3 and chapter 4 of this thesis engaged with the recombinant production of matriptase (chapter 3) in order to obtain a pure functional enzyme of high quality for a SAR study with novel monobasic (hence potentially bioavailable) matriptase inhibitors of the 3-amidinophenylalanine type (chapter 4). Adequate amounts of high-quality matriptase enzymes were isolated using a new expression system and in total 5 matriptase crystals were available at the end of this thesis for structural analysis. The matriptase inhibitor design in this thesis focused on matriptase-affine compounds with a fair selectivity profile against the blood coagulation enzymes thrombin and fXa. In total, 18 new monobasic and potentially bioavailable, as well as four new dibasic compounds of the 3-amidinophenylalanine types were tested. Based on the last published crystal structure of this inhibitor type in complex with matriptase from 2006 (PDB code 2GV6) docking was used as a structure-based virtual screening method for lead optimization of the compounds N-terminus. Selected compounds were suggested to interact with the carbonyl side chain of Gln175 of matriptase to achieve a higher affinity of matriptase compared to fXa. The 4-tert-butylureido-piperidine could be identified as suitable C-terminus in combination with 3-fluoro-4-hydroxymethyl biphenylsulphonyl N-terminally in order to obtain excellent selectivity over thrombin. The binding mode of this compound (compound 55) was crystallographically determined in complex with matriptase as well as trypsin. Trypsin proved as a suitable alternative to matriptase for detailed binding mode analysis of the compounds N-terminus. However, different preferences were detected for the C-terminus. Dibasic compounds showed higher matriptase affinity and selectivity in comparison with the monobasic analogues. However, the tested monobasic compounds were still decent matriptase inhibitors that are additionally suitable for cell culture and animal studies in their benzamidine prodrug forms, which are well established from related inhibitors of thrombin. In addition, selected monobasic as well as dibasic compounds demonstrated strong suppression of the replication of certain H9N2 influenza viruses in a matriptase-expressing MDCK II cell model. These matriptase inhibitors could be potential lead structures for the development of new drugs against H9 strains for influenza. TMPRSS2 is widely discussed for its role in influenza activation. With a TMPRSS2 dependancy of HA-activation of certain subtypes, the characterization of this protease is an important prerequisite for being available as a target for influenza drug design. However, only little is known about the physiological function of TMPRSS2 and no experimental structure data are available at the moment to enable a structure-based drug development. Therefore, chapter 5 of this thesis focused on the characterization of TMPRSS2 in order to develop a strategy for the isolation of proteolytically active TMPRSS2 from cell culture. Even though, no functional TMPRSS2 could be recovered at the end of this work some new structural characteristics of TMPRSS2 were identified as crucial for functionality insight the cell. In general, TMPRSS2 without the cytosolic part, the transmembrane domain and the LDLRA domain is able to undergo autocatalytically activation if an artificial signal peptide was added N-terminal to enable entry into the endoplasmic reticulum. The presence of the cysteine-rich SRCR domain and the presence of the disulfide chain that connects the SPD and the stem region after activation cleavage have been identified as crucial for activity. N-terminal truncation of TMPRSS2 did not result in obvious dislocation within the cell: as the full-length positive control truncated TMPRSS2 was exclusively found in cell compartments surrounding the nucleus in immunofluorescence experiments. However, a reduced proteolytic cleavage activity towards H3-HA in co-expression experiments has been observed and might be a result of dislocation, since truncated TMPRSS2 is not bound to the biomembrane anymore. In addition, TMPRSS2 has been identified as a potential substrate of matriptase in vitro, which suggests possible participation in several zymogen cascades

Engineering and evolving helical proteins that improve in vivo stability and inhibit entry of Enfuvirtide-resistant HIV-1

2019 Spring.Includes bibliographical references.Methods for the stabilization of well-defined helical peptide drugs and basic research tools have received considerable attention in the last decade. Enfuvirtide is a 36-residue chemically synthesized helical peptide that targets the viral gp41 protein and inhibits viral membrane fusion. Enfuvirtide-resistant HIV, however, has been prolific, and this peptide therapy requires daily injection due to proteolytic degradation. In this dissertation I have developed a method for stabilizing helical peptide therapeutics termed helix-grafted display proteins. These consist of the HIV-1 gp41 C-peptide helix grafted onto Pleckstrin Homology domains. Some of these earlier protein biologics inhibit HIV-1 entry with modest and variable potencies (IC50 190 nM - >1 μM). After optimization of the scaffold and the helix, our designer peptide therapeutic potently inhibited HIV-1 entry in a live-virus assay (IC50 1.9-12.4 nM). Sequence optimization of solvent-exposed helical residues using yeast display as a screening method led to improved biologics with enhanced protein expression in Escherichia coli (E. coli, a common bio-expression host), with no appreciable change in viral membrane fusion suppression. Optimized proteins suppress the viral entry of a clinically-relevant double mutant of HIV-1 that is gp41 C-peptide sensitive and Enfuvirtide-resistant. Protein fusions engineered for serum-stability also potently inhibit HIV-1 entry