Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial.

High-risk large B-cell lymphoma (LBCL) has poor outcomes with standard first-line chemoimmunotherapy. In the phase 2, multicenter, single-arm ZUMA-12 study (ClinicalTrials.gov NCT03761056) we evaluated axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, as part of first-line treatment in 40 patients with high-risk LBCL. This trial has completed accrual. The primary outcome was complete response rate (CRR). Secondary outcomes were objective response rate (ORR), duration of response (DOR), event-free survival (EFS), progression-free survival (PFS), overall survival (OS), assessment of safety, central nervous system (CNS) relapse and blood levels of CAR T cells and cytokines. The primary endpoint in efficacy-evaluable patients (n = 37) was met, with 78% CRR (95% confidence interval (CI), 62-90) and 89% ORR (95% CI, 75-97). As of 17 May 2021 (median follow-up, 15.9 months), 73% of patients remained in objective response; median DOR, EFS and PFS were not reached. Grade ≥3 cytokine release syndrome (CRS) and neurologic events occurred in three patients (8%) and nine patients (23%), respectively. There were no treatment-related grade 5 events. Robust CAR T-cell expansion occurred in all patients with a median time to peak of 8 days. We conclude that axi-cel is highly effective as part of first-line therapy for high-risk LBCL, with a manageable safety profile.

KTE-X19 for relapsed or refractory adult B-cell acute lymphoblastic leukaemia: phase 2 results of the single-arm, open-label, multicentre ZUMA-3 study.

BACKGROUND: Despite treatment with novel therapies and allogeneic stem-cell transplant (allo-SCT) consolidation, outcomes in adult patients with relapsed or refractory B-precursor acute lymphoblastic leukaemia remain poor, underlining the need for more effective therapies. METHODS: We report the pivotal phase 2 results of ZUMA-3, an international, multicentre, single-arm, open-label study evaluating the efficacy and safety of the autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy KTE-X19 in adult patients with relapsed or refractory B-precursor acute lymphoblastic leukaemia. Patients were enrolled at 25 sites in the USA, Canada, and Europe. Eligible patients were aged 18 years or older, with Eastern Cooperative Oncology Group performance status of 0-1, and morphological disease in the bone marrow (>5% blasts). After leukapheresis and conditioning chemotherapy, patients received a single KTE-X19 infusion (1 × 106 CAR T cells per kg bodyweight). The primary endpoint was the rate of overall complete remission or complete remission with incomplete haematological recovery by central assessment. Duration of remission and relapse-free survival, overall survival, minimal residual disease (MRD) negativity rate, and allo-SCT rate were assessed as secondary endpoints. Efficacy and safety analyses were done in the treated population (all patients who received a dose of KTE-X19). This study is registered with ClinicalTrials.gov, NCT02614066. FINDINGS: Between Oct 1, 2018, and Oct 9, 2019, 71 patients were enrolled and underwent leukapheresis. KTE-X19 was successfully manufactured for 65 (92%) patients and administered to 55 (77%). The median age of treated patients was 40 years (IQR 28-52). At the median follow-up of 16·4 months (13·8-19·6), 39 patients (71%; 95% CI 57-82, p<0·0001) had complete remission or complete remission with incomplete haematological recovery, with 31 (56%) patients reaching complete remission. Median duration of remission was 12·8 months (95% CI 8·7-not estimable), median relapse-free survival was 11·6 months (2·7-15·5), and median overall survival was 18·2 months (15·9-not estimable). Among responders, the median overall survival was not reached, and 38 (97%) patients had MRD negativity. Ten (18%) patients received allo-SCT consolidation after KTE-X19 infusion. The most common adverse events of grade 3 or higher were anaemia (27 [49%] patients) and pyrexia (20 [36%] patients). 14 (25%) patients had infections of grade 3 or higher. Two grade 5 KTE-X19-related events occurred (brain herniation and septic shock). Cytokine release syndrome of grade 3 or higher occurred in 13 (24%) patients and neurological events of grade 3 or higher occurred in 14 (25%) patients. INTERPRETATION: KTE-X19 showed a high rate of complete remission or complete remission with incomplete haematological recovery in adult patients with relapsed or refractory B-precursor acute lymphoblastic leukaemia, with the median overall survival not reached in responding patients, and a manageable safety profile. These findings indicate that KTE-X19 has the potential to confer long-term clinical benefit to these patients. FUNDING: Kite, a Gilead Company.

Inhibition of IRF5 cellular activity with cell-penetrating peptides that target homodimerization.

The transcription factor interferon regulatory factor 5 (IRF5) plays essential roles in pathogen-induced immunity downstream of Toll-, nucleotide-binding oligomerization domain-, and retinoic acid-inducible gene I-like receptors and is an autoimmune susceptibility gene. Normally, inactive in the cytoplasm, upon stimulation, IRF5 undergoes posttranslational modification(s), homodimerization, and nuclear translocation, where dimers mediate proinflammatory gene transcription. Here, we report the rational design of cell-penetrating peptides (CPPs) that disrupt IRF5 homodimerization. Biochemical and imaging analysis shows that IRF5-CPPs are cell permeable, noncytotoxic, and directly bind to endogenous IRF5. IRF5-CPPs were selective and afforded cell type- and species-specific inhibition. In plasmacytoid dendritic cells, inhibition of IRF5-mediated interferon-α production corresponded to a dose-dependent reduction in nuclear phosphorylated IRF5 [p(Ser462)IRF5], with no effect on pIRF5 levels. These data support that IRF5-CPPs function downstream of phosphorylation. Together, data support the utility of IRF5-CPPs as novel tools to probe IRF5 activation and function in disease.

Case-control Indian buffet process identifies biomarkers of response to Codrituzumab.

BACKGROUND: Codrituzumab, a humanized monoclonal antibody against Glypican-3 (GPC3), which is expressed in hepatocellular carcinoma (HCC), was tested in a randomized phase II trial in advanced HCC patients who had failed prior systemic therapy. Biomarker analysis was performed to identify a responder population that benefits from treatment. METHODS: A novel statistical method based on the Indian buffet process (IBP) was used to identify biomarkers predictive of response to treatment with Codrituzumab. The IBP is a novel method that allows flexibility in analysis design, and which is sensitive to slight, but meaningful between-group differences in biomarkers in very complex datasets RESULTS: The IBP model identified several subpopulations of patients having defined biomarker values. Tumor necrosis and viable cell content in the tumor were identified as prognostic markers of disease progression, as were the well-known HCC prognostic markers of disease progression, alpha-fetoprotein and Glypican-3 expression. Predictive markers of treatment response included natural killer (NK) cell surface markers and parameters influencing NK cell activity, all related to the mechanism of action of this drug CONCLUSIONS: The Indian buffet process can be effectively used to detect statistically significant signals with high sensitivity in complex and noisy biological data TRIAL REGISTRATION: NCT01507168 , January 6, 2012.

Targeting NOTCH activation in small cell lung cancer through LSD1 inhibition.

Small cell lung cancer (SCLC) is a recalcitrant, aggressive neuroendocrine-type cancer for which little change to first-line standard-of-care treatment has occurred within the last few decades. Unlike nonsmall cell lung cancer (NSCLC), SCLC harbors few actionable mutations for therapeutic intervention. Lysine-specific histone demethylase 1A (LSD1 also known as KDM1A) inhibitors were previously shown to have selective activity in SCLC models, but the underlying mechanism was elusive. Here, we found that exposure to the selective LSD1 inhibitor ORY-1001 activated the NOTCH pathway, resulting in the suppression of the transcription factor ASCL1 and the repression of SCLC tumorigenesis. Our analyses revealed that LSD1 bound to the NOTCH1 locus, thereby suppressing NOTCH1 expression and downstream signaling. Reactivation of NOTCH signaling with the LSD1 inhibitor reduced the expression of ASCL1 and neuroendocrine cell lineage genes. Knockdown studies confirmed the pharmacological inhibitor-based results. In vivo, sensitivity to LSD1 inhibition in SCLC patient-derived xenograft (PDX) models correlated with the extent of consequential NOTCH pathway activation and repression of a neuroendocrine phenotype. Complete and durable tumor regression occurred with ORY-1001-induced NOTCH activation in a chemoresistant PDX model. Our findings reveal how LSD1 inhibitors function in this tumor and support their potential as a new and targeted therapy for SCLC.

Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping.

Background: The tumor microenvironment is an important factor in cancer immunotherapy response. To further understand how a tumor affects the local immune system, we analyzed immune gene expression differences between matching normal and tumor tissue.Methods: We analyzed public and new gene expression data from solid cancers and isolated immune cell populations. We also determined the correlation between CD8, FoxP3 IHC, and our gene signatures.Results: We observed that regulatory T cells (Tregs) were one of the main drivers of immune gene expression differences between normal and tumor tissue. A tumor-specific CD8 signature was slightly lower in tumor tissue compared with normal of most (12 of 16) cancers, whereas a Treg signature was higher in tumor tissue of all cancers except liver. Clustering by Treg signature found two groups in colorectal cancer datasets. The high Treg cluster had more samples that were consensus molecular subtype 1/4, right-sided, and microsatellite-instable, compared with the low Treg cluster. Finally, we found that the correlation between signature and IHC was low in our small dataset, but samples in the high Treg cluster had significantly more CD8+ and FoxP3+ cells compared with the low Treg cluster.Conclusions: Treg gene expression is highly indicative of the overall tumor immune environment.Impact: In comparison with the consensus molecular subtype and microsatellite status, the Treg signature identifies more colorectal tumors with high immune activation that may benefit from cancer immunotherapy. Cancer Epidemiol Biomarkers Prev; 27(1); 103-12. ©2017 AACR.

Identification of Short Hydrophobic Cell-Penetrating Peptides for Cytosolic Peptide Delivery by Rational Design.

Cell-penetrating peptides (CPPs) enhance the cellular uptake of membrane-impermeable molecules. Most CPPs are highly cationic, potentially increasing the risk of toxic side effects and leading to accumulation in organs such as the liver. As a consequence, there is an unmet need for less cationic CPPs. However, design principles for effective CPPs are still missing. Here, we demonstrate a design principle based on a classification of peptides according to accumulated side-chain polarity and hydrophobicity. We show that in comparison to randomly selected peptides, CPPs cover a distinct parameter space. We designed peptides of only six to nine amino acids with a maximum of three positive charges covering this property space. All peptides were tested for cellular uptake and subcellular distribution. Following an initial round of screening we enriched the collection with short and hydrophobic peptides and introduced d-amino acid substitutions and lactam bridges which increased cell uptake, in particular for long-term incubation. Using a GFP complementation assay, for the most active peptides we demonstrate cytosolic delivery of a biologically active cargo peptide.

Detecting Cytosolic Peptide Delivery with the GFP Complementation Assay in the Low Micromolar Range.

Transfection of cells with a plasmid encoding for the first ten strands of the GFP protein (GFP1-10) provides the means to detect cytosolic peptide import at low micromolar concentrations. Cytosolic import of the eleventh strand of the GFP protein either by electroporation or by cell-penetrating peptide-mediated import leads to formation of the full-length GFP protein and fluorescence. An increase in sensitivity is achieved through structural modifications of the peptide and the expression of GFP1-10 as a fusion protein with mCherry.

Quantifying the probability of clinical trial success from scientific articles.

We sought to analyze how the number and quality of publications predict clinical trial success for a set of gene-disease associations. Limiting the scope of our analysis to genes in the protein kinase family and to oncology indications, we extracted gene-disease relationships from more than 12 million article titles and abstracts published between 1992 and 2012. We integrated these data with clinical trial information for FDA-approved kinase inhibitors and kinase inhibitors that failed owing to lack of efficacy. We found that, up until the year when a compound enters clinical trials, the cumulative number of publications about a gene-disease relationship corresponding to the compound's mechanism of action is, at the median, 30 for approved compounds but only four for failed compounds.

Chemogenomics in drug discovery: computational methods based on the comparison of binding sites.

Novel computational methods for understanding relationships between ligands and all possible biological targets have emerged in recent years. Proteins are connected to each other based on the similarity of their ligands or based on the similarity of their binding sites. The assumption is that compounds sharing chemical similarity should share targets and that targets with a similar binding site should also share ligands. A large number of computational techniques have been developed to assess ligand and binding site similarity, which can be used to make quantitative predictions of the most probable biological target of a given compound. This review covers the recent advances in new computational methods for relating biological targets based on the similarity of their binding sites. Binding site comparisons are used for the prediction of their most likely ligands, their possible cross reactivity and selectivity. These comparisons can also be used to infer the function of novel uncharacterized proteins.

Cell-penetrating peptides: classes, origin, and current landscape.

With more than ten new FDA approvals since 2001, peptides are emerging as an important therapeutic alternative to small molecules. However, unlike small molecules, peptides on the market today are limited to extracellular targets. By contrast, cell-penetrating peptides (CPPs) can target intracellular proteins and also carry other cargoes (e.g. other peptides, small molecules or proteins) into the cell, thus offering great potential as future therapeutics. In this review I present a classification scheme for CPPs based on their physical-chemical properties and origin, and I provide a general framework for understanding and discovering new CPPs.

Targeted kinase selectivity from kinase profiling data.

Kinase selectivity plays a major role in the design strategy of lead series and in the ultimate success of kinase drug discovery programs. Although profiling compounds against a large panel of protein kinases has become a standard part of modern drug discovery, data accumulated from these kinase panels may be underutilized for new kinase projects. We present a method that can be used to optimize the selectivity profile of a compound using historical kinase profiling data. This method proposes chemical transformations based on pairs of very similar compounds, which are both active against a desired target kinase and differ in activity against another kinase. We show that these transformations are transferable across scaffolds, thus making this tool valuable to exploit kinase profiling data for unrelated series of compounds.

Progress in the prediction of pKa values in proteins.

The pK(a) -cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pK(a) values and protein electrostatics in general. The first round of the pK(a) -cooperative, which challenged computational labs to carry out blind predictions against pK(a) s experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This article serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here, we briefly outline existing approaches for pK(a) calculations, emphasizing methods that were used by the participants in calculating the blind pK(a) values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pK(a) calculations.

Extending pKa prediction accuracy: high-throughput pKa measurements to understand pKa modulation of new chemical series.

We have recently developed a tool, MoKa, to predict the pK(a) of organic compounds using a large dataset of over 26,500 literature pK(a) values as a training set. However, predicting accurately pK(a) (<0.5 pH units) remains challenging for novel series, and this can be a drawback in the optimization of activity and ADME properties of lead compounds. To address this issue it is important to expand our knowledge of pK(a) determinants, therefore we have conducted high-throughput pK(a) measurements by using Spectral Gradient Analysis (SGA) on novel series of compounds selected from vendor databases. Here we report our findings on the effect of specific chemical groups and steric constraints on the pK(a) of common functionalities in medicinal chemistry, such as amines, sulfonamides, and amides. Furthermore, we report the pK(a) of ionizable groups that were not well represented in the database of literature pK(a) of MoKalpha, such as hydrazide derivatives. These findings helped us to enhance MoKalpha, which is here benchmarked on a set of experimental pK(a) values from the Roche in-house library (N = 5581; RMSE = 1.09; R2 = 0.82). The accuracy of the predictions was greatly improved (RMSE = 0.49, R2 = 0.96) after training the software by using the automated tool Kibitzer with 6226 pK(a) values taken from a different set of Roche compounds appropriately selected, and this demonstrates the value of using high-throughput pK(a) measurements to expand the training set of pK(a) values used by the software MoKalpha.

Predicting polypharmacology by binding site similarity: from kinases to the protein universe.

Polypharmacology is receiving increasing attention in the pharmaceutical industry, since finding new targets of a compound is useful not only for anticipating possible side effects but also for opening new therapeutic opportunities. Thus, while system biology and personalized medicine are becoming increasingly important, there is an urgent need to map the inhibition profile of a compound on a large panel of targets by using both experimental and computational methods. This is especially important for kinase inhibitors, given the high similarity at the binding site level for the 518 kinases in the human genome. In this paper, we propose and validate a new method to predict the inhibition map of a compound by comparison of binding pockets. We used a subset of the Ambit panel for the validation-17 inhibitors with K(d) measured on 189 kinases-and found that on average 37% of kinases inhibited with K(d) < 10 microM were retrieved at 10% ROC enrichment. These results make this method particularly suitable to rationalize and optimize the selectivity profile of a compound. In addition, the method was extended to explore all the proteins in the PDB by using as queries pockets occupied by compounds of biological interest (ATP and various marketed drugs). The profiling of compounds against the protein universe revealed that striking structural similarities at the subpocket level (RMSD < 0.5 A) may also occur among targets with different folds, which can be exploited not only to predict off-target effects but also to design novel inhibitors for the target of interest.

Tautomer preference in PDB complexes and its impact on structure-based drug discovery.

Tautomer enrichment is a key step of ligand preparation prior to virtual screening. In this paper, we have investigated how tautomer preference in various media (water, gas phase, and crystal) compares to tautomer preference at the active site of the protein by analyzing the different possible H-bonding contacts for a set of 13 tautomeric structures. In addition, we have explored the impact of four different protocols for the enumeration of tautomers in virtual screening by using Flap, Glide, and Gold as docking tools on seven targets of the DUD data set. Excluding targets in which the binding does not involve tautomeric atoms (HSP90, p38, and VEGFR2), we found that the average receiver operating characteristic curve enrichment at 10% was 0.25 (Gold), 0.24 (Glide), and 0.50 (Flap) by considering only tautomers predicted to be unstable in water versus 0.41 (Gold), 0.56 (Glide), 0.51 (Flap) by limiting the enumeration process only to the predicted most stable tautomer. The inclusion of all tautomers (stable and unstable) yielded slightly poorer results than considering only the most stable form in water.

In silico pKa prediction and ADME profiling.

Improving the ADME profile of drug candidates is a critical step in lead optimization, and because pKa affects most ADME properties such as lipophilicity, solubility, and metabolism, it is extremely advantageous to predict pKa in order to guide the design of new compounds. However, accurately (<0.5 log units) predicting pKa by empirical methods can be challenging especially for novel series, because of lack of knowledge on determinants of pKa (steric effects, ring effects, H-bonding, etc.), and because of limited experimental data on the effects of specific chemical groups on the ionization of an atom. To address these issues, we recently developed the computational package MoKa, which integrates graphical and command line tools designed for computational and medicinal chemists to predict the pKa values of organic compounds. Here, we present the major issues considered when we developed MoKa, such as the accurate selection of training data, the fundamentals of the methodology (which has also been extended to predict protein pKa), the treatment of multiprotic compounds, and the selection of the dominant tautomer for the calculation. Last, we illustrate some specific applications of MoKa to predict solubility, lipophilicity, and metabolism.

Predicting protein pK(a) by environment similarity.

A statistical method to predict protein pK(a) has been developed by using the 3D structure of a protein and a database of 434 experimental protein pK(a) values. Each pK(a) in the database is associated with a fingerprint that describes the chemical environment around an ionizable residue. A computational tool, MoKaBio, has been developed to identify automatically ionizable residues in a protein, generate fingerprints that describe the chemical environment around such residues, and predict pK(a) from the experimental pK(a) values in the database by using a similarity metric. The method, which retrieved the pK(a) of 429 of the 434 ionizable sites in the database correctly, was crossvalidated by leave-one-out and yielded root mean square error (RMSE) = 0.95, a result that is superior to that obtained by using the Null Model (RMSE 1.07) and other well-established protein pK(a) prediction tools. This novel approach is suitable to rationalize protein pK(a) by comparing the region around the ionizable site with similar regions whose ionizable site pK(a) is known. The pK(a) of residues that have a unique environment not represented in the training set cannot be predicted accurately, however, the method offers the advantage of being trainable to increase its predictive power.