A phase 1/2 clinical trial of invariant natural killer T cell therapy in moderate-severe acute respiratory distress syndrome.

Invariant natural killer T (iNKT) cells, a unique T cell population, lend themselves for use as adoptive therapy due to diverse roles in orchestrating immune responses. Originally developed for use in cancer, agenT-797 is a donor-unrestricted allogeneic ex vivo expanded iNKT cell therapy. We conducted an open-label study in virally induced acute respiratory distress syndrome (ARDS) caused by the severe acute respiratory syndrome-2 virus (trial registration NCT04582201). Here we show that agenT-797 rescues exhausted T cells and rapidly activates both innate and adaptive immunity. In 21 ventilated patients including 5 individuals receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO), there are no dose-limiting toxicities. We observe an anti-inflammatory systemic cytokine response and infused iNKT cells are persistent during follow-up, inducing only transient donor-specific antibodies. Clinical signals of associated survival and prevention of secondary infections are evident. Cellular therapy using off-the-shelf iNKT cells is safe, can be rapidly scaled and is associated with an anti-inflammatory response. The safety and therapeutic potential of iNKT cells across diseases including infections and cancer, warrants randomized-controlled trials.

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922).

Molecular mechanism of phosphopeptide neoantigen immunogenicity.

Altered protein phosphorylation in cancer cells often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen, pMLL747-755 (EPR(pS)PSHSM), is recognized by a cognate T cell receptor named TCR27, a candidate for cancer immunotherapy. We show that the replacement of phosphoserine P4 with serine or phosphomimetics does not affect pMHC conformation or peptide-MHC affinity but abrogates TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. Here we describe the crystal structures for TCR27 and cognate pMHC, map of the interface produced by nuclear magnetic resonance, and a ternary complex generated using information-driven protein docking. Our data show that non-covalent interactions between the epitope phosphate group and TCR27 are crucial for TCR specificity. This study supports development of new treatment options for cancer patients through target expansion and TCR optimization.

Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations.

The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG bind . This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).

TCR Fingerprinting and Off-Target Peptide Identification.

Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C259, which recognizes the peptide epitope SLLMWITQC presented by HLA-A*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESOc259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESOc259 TCR recognition of 336,921 predicted human HLA-A*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESOc259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the "TCR fingerprint" and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.

Prevalence of Anal Fistulas in Europe: Systematic Literature Reviews and Population-Based Database Analysis.

INTRODUCTION: Despite the fact that perianal fistulas are associated with significant morbidity and impaired quality of life, their prevalence in Europe is unknown. The aim of this study was to estimate the prevalence of perianal fistulas in Europe, overall and according to etiology. METHODS: Two independent literature reviews were performed using different search strategies to maximize the identification of potentially relevant studies. Data from relevant articles were used to estimate the prevalence of perianal fistulas in Europe. The robustness of the estimate was evaluated using data from a large population-based database from the UK. RESULTS: A total of 26 studies provided epidemiological data on perianal fistulas, of which 16 provided suitable data to estimate the prevalence. Estimations using these data yielded a total prevalence of 1.69 per 10,000 population. Cryptoglandular infection and Crohn's disease (CD) were the predominant etiologies, with prevalence rates at 0.86 and 0.76 per 10,000 population, respectively. Comparison of prevalence data from the UK population-based database with the European population resulted in a standardized prevalence estimate of all perianal fistulas of 1.83 per 10,000 population, confirming the robustness of the literature-based estimate. CONCLUSION: Although in terms of incidence cryptoglandular fistulas were clearly predominant, the prevalence of fistulas in CD and cryptoglandular infection appeared more balanced. This is due to the longer duration and higher frequency of relapses of fistulas in CD. The estimated prevalence implies that perianal fistulas meet the criteria to be considered as a rare condition in Europe (prevalence less than 5 per 10,000 population). FUNDING: This study was funded by Takeda Pharmaceutical U.S.A., Inc. and TiGenix SAU.

Humanized Monoclonal Antibody Blocking Carbonic Anhydrase 12 Enzymatic Activity Leads to Reduced Tumor Growth In Vitro.

BACKGROUND/AIM: Carbonic anhydrase 12 (CA12) is a membrane-associated enzyme that is highly expressed on many human cancers. It is a poor prognostic marker and hence an attractive target for cancer therapy. This study aimed to develop a humanized CA12-antibody with anti-cancer activity. MATERIALS AND METHODS: Antibody libraries were constructed and screened by the Retrocyte display®. Antibody binding and blocking properties were determined by ELISA, flow cytometry and enzymatic activity assays. Spheroid viability was determined by Cell-Titer-Fluor assay. RESULTS: We developed a novel humanized CA12-specific antibody, 4AG4, which recognized CA12 as an antigen and blocked CA12 enzymatic activity. Our humanized CA12-antibody significantly inhibited spheroid growth of lung adenocarcinoma A549-cells in vitro by blocking CA12 enzymatic activity. Similar anti-tumor effects were recapitulated with CA12-gene knockout of A549-cells. CONCLUSION: Our newly identified humanized CA12-antibody with anti-cancer activity, represents a new tool for the treatment of CA12-positive tumors.

A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation.

Binding free energy (ΔGbind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔGbind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard single-trajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔGbind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔGbind calculations.

Toxicological and pharmacological assessment of AGEN1884, a novel human IgG1 anti-CTLA-4 antibody.

CTLA-4 and CD28 exemplify a co-inhibitory and co-stimulatory signaling axis that dynamically sculpts the interaction of antigen-specific T cells with antigen-presenting cells. Anti-CTLA-4 antibodies enhance tumor-specific immunity through a variety of mechanisms including: blockade of CD80 or CD86 binding to CTLA-4, repressing regulatory T cell function and selective elimination of intratumoral regulatory T cells via an Fcγ receptor-dependent mechanism. AGEN1884 is a novel IgG1 antibody targeting CTLA-4. It potently enhanced antigen-specific T cell responsiveness that could be potentiated in combination with other immunomodulatory antibodies. AGEN1884 was well-tolerated in non-human primates and enhanced vaccine-mediated antigen-specific immunity. AGEN1884 combined effectively with PD-1 blockade to elicit a T cell proliferative response in the periphery. Interestingly, an IgG2 variant of AGEN1884 revealed distinct functional differences that may have implications for optimal dosing regimens in patients. Taken together, the pharmacological properties of AGEN1884 support its clinical investigation as a single therapeutic and combination agent.

Inter-individual Variability in Activity of the Major Drug Metabolizing Enzymes in Liver Homogenates of 20 Individuals.

BACKGROUND: Inter-individual variability in hepatic drug metabolizing enzyme (DME) activity is a major contributor to heterogeneity in drug clearance and safety. Accurate data on expression levels and activities of DMEs is an important prerequisite for in vitro-in vivo extrapolation and in silico based predictions. Characterization and assessment of inter-correlations of the major DMEs cytochrome P450s (CYPs) and UDP-glucuronosyltransferases (UGTs) have been extensively documented, but simultaneous quantification including other major DMEs has been lacking. OBJECTIVE: Assessment of inter-donor variability and inter-correlations of CYPs, UGTs, sulfotransferases (SULTs), glutathione S-transferases (GSTs), NAD(P)H:quinone oxidoreductase 1 (NQO1) and NRH: quinone oxidoreductase 2 (NQO2) in a set of 20 individual liver homogenates. METHOD: The main drug metabolizing isoforms of CYP and UGT have been reaction phenotype in individual liver microsomes and NQO1, NQO2, GSTT1 and GSTT2 in corresponding cytosol. In addition, we assessed overall SULT activity in liver cytosol using acetaminophen and 7-hydroxycoumarin as non-selective substrates and cytosolic GST activity using the non-selective substrate 1-chloro-2,4-dinitrobenzene (CDNB). Expression of GST isoforms was also assessed. RESULTS AND CONCLUSION: While hepatic NQO1 activity was highly variable, NQO2 activity was more conserved. In addition, we found that of the hepatic GST isoforms, the variation in GSTM3 levels, which is poorly studied, was highest. The majority of significant correlations were found amongst CYP and UGT enzyme activities. The dataset presented provides the absolute quantification of the largest number of hepatic DME activities so far and constitute an essential resource for in silico toxicokinetic and metabolic modelling studies.

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2.

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

Drug toxicity profiling of a Saccharomyces cerevisiae deubiquitinase deletion panel shows that acetaminophen mimics tyrosine.

Post-translational protein modification by addition or removal of the small polypeptide ubiquitin is involved in a range of critical cellular processes, like proteasomal protein degradation, DNA repair, gene expression, internalization of membrane proteins, and drug sensitivity. We recently identified genes important for acetaminophen (APAP) toxicity in a comprehensive screen and our findings suggested that a small set of yeast strains carrying deletions of ubiquitin-related genes can be informative for drug toxicity profiling. In yeast, approximately 20 different deubiquitinating enzymes (DUBs) have been identified, of which only one is essential for viability. We investigated whether the toxicity profile of DUB deletion yeast strains would be informative about the toxicological mode of action of APAP. A set of DUB deletion strains was tested for sensitivity and resistance to a diverse series of compounds, including APAP, quinine, ibuprofen, rapamycin, cycloheximide, cadmium, peroxide and amino acids and a cluster analysis was performed. Most DUB deletion strains showed an altered growth pattern when exposed to these compounds by being either more sensitive or more resistant than WT. Toxicity profiling of the DUB strains revealed a remarkable overlap between the amino acid tyrosine and acetaminophen (APAP), but not its stereoisomer AMAP. Furthermore, co-exposure of cells to both APAP and tyrosine showed an enhancement of the cellular growth inhibition, suggesting that APAP and tyrosine have a similar mode of action.

eTOX ALLIES: an automated pipeLine for linear interaction energy-based simulations.

BACKGROUND: Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein-ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. RESULTS: We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling. CONCLUSIONS: Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors.

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.

Esterase activity of carbonic anhydrases serves as surrogate for selecting antibodies blocking hydratase activity.

Carbonic anhydrase 9 (CA9) and carbonic anhydrase 12 (CA12) were proposed as potential targets for cancer therapy more than 20 years ago. However, to date, there are only very few antibodies that have been described to specifically target CA9 and CA12 and also block the enzymatic activity of their targets. One of the early stage bottlenecks in identifying CA9- and CA12-inhibiting antibodies has been the lack of a high-throughput screening system that would allow for rapid assessment of inhibition of the targeted carbon dioxide hydratase activity of carbonic anhydrases. In this study, we show that measuring the esterase activity of carbonic anhydrase offers a robust and inexpensive screening method for identifying antibody candidates that block both hydratase and esterase activities of carbonic anhydrase's. To our knowledge, this is the first implementation of a facile surrogate-screening assay to identify potential therapeutic antibodies that block the clinically relevant hydratase activity of carbonic anhydrases.

Mass spec studio for integrative structural biology.

The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS and tandem MS (MS(2)) data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-MS(2) and data-independent HX-MS(2). The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Retrocyte Display® technology: generation and screening of a high diversity cellular antibody library.

Over the last nearly three decades in vitro display technologies have played an important role in the discovery and optimization of antibodies and other proteins for therapeutic applications. Here we describe the use of retroviral expression technology for the display of full-length IgG on B lineage cells in vitro with a hallmark of a tight and stable genotype to phenotype coupling. We describe the creation of a high-diversity (>1.0E09 different heavy- and light-chain combinations) cell displayed fully human antibody library from healthy donor-derived heavy- and light-chain gene libraries, and demonstrate the recovery of high affinity target-specific antibodies from this library by staining of cells with a labeled target antigen and their magnetic- and flow cytometry-based cell sorting. The present technology represents a further evolution in the discovery of full-length, fully human antibodies using mammalian display, and is termed Retrocyte Display® (Retroviral B lymphocyte Display).

Solvated protein-DNA docking using HADDOCK.

Interfacial water molecules play an important role in many aspects of protein-DNA specificity and recognition. Yet they have been mostly neglected in the computational modeling of these complexes. We present here a solvated docking protocol that allows explicit inclusion of water molecules in the docking of protein-DNA complexes and demonstrate its feasibility on a benchmark of 30 high-resolution protein-DNA complexes containing crystallographically-determined water molecules at their interfaces. Our protocol is capable of reproducing the solvation pattern at the interface and recovers hydrogen-bonded water-mediated contacts in many of the benchmark cases. Solvated docking leads to an overall improvement in the quality of the generated protein-DNA models for cases with limited conformational change of the partners upon complex formation. The applicability of this approach is demonstrated on real cases by docking a representative set of 6 complexes using unbound protein coordinates, model-built DNA and knowledge-based restraints. As HADDOCK supports the inclusion of a variety of NMR restraints, solvated docking is also applicable for NMR-based structure calculations of protein-DNA complexes.