Preclinical efficacy of the potent, selective menin-KMT2A inhibitor JNJ-75276617 (bleximenib) in KMT2A- and NPM1-altered leukemias.

The interaction between menin and histone-lysine N-methyltransferase 2A (KMT2A) is a critical dependency for KMT2A- or nucleophosmin 1 (NPM1)-altered leukemias and an emerging opportunity for therapeutic development. JNJ-75276617 (bleximenib) is a novel, orally bioavailable, potent, and selective protein-protein interaction inhibitor of the binding between menin and KMT2A. In KMT2A-rearranged (KMT2A-r) and NPM1-mutant (NPM1c) acute myeloid leukemia (AML) cells, JNJ-75276617 inhibited the association of the menin-KMT2A complex with chromatin at target gene promoters, resulting in reduced expression of several menin-KMT2A target genes, including MEIS1 and FLT3.

Synergy detection: A practical guide to statistical assessment of potential drug combinations.

Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e.

Differential detection workflows for multi-sample single-cell RNA-seq data.

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected.

Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures.

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects.

Statistical detection of synergy: New methods and a comparative study.

Combination therapies are increasingly adopted as the standard of care for various diseases to improve treatment response, minimise the development of resistance and/or minimise adverse events. Therefore, synergistic combinations are screened early in the drug discovery process, in which their potential is evaluated by comparing the observed combination effect to that expected under a null model. Such methodology is implemented in the BIGL R-package which allows for a quick screening of drug combinations.

Discovery and Pharmacological Characterization of JNJ-64619178, a Novel Small-Molecule Inhibitor of PRMT5 with Potent Antitumor Activity.

The protein arginine methyltransferase 5 (PRMT5) methylates a variety of proteins involved in splicing, multiple signal transduction pathways, epigenetic control of gene expression, and mechanisms leading to protein expression required for cellular proliferation. Dysregulation of PRMT5 is associated with clinical features of several cancers, including lymphomas, lung cancer, and breast cancer. Here, we describe the characterization of JNJ-64619178, a novel, selective, and potent PRMT5 inhibitor, currently in clinical trials for patients with advanced solid tumors, non-Hodgkin's lymphoma, and lower-risk myelodysplastic syndrome.

Controlling the Reproducibility of AC50 Estimation during Compound Profiling through Bayesian β-Expectation Tolerance Intervals.

During drug discovery, compounds/biologics are screened against biological targets of interest to find drug candidates with the most desirable activity profile. The compounds are tested at multiple concentrations to understand the dose-response relationship, often summarized as AC50 values and used directly in ranking compounds. Differences between compound repeats are inevitable because of experimental noise and/or systematic error; however, it is often desired to detect the latter when it occurs.

Analyzing magnetic bead QuantiGene® Plex 2.0 gene expression data in high throughput mode using QGprofiler.

Background: The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This technology allows multiplexing in a single well of a 96 or 384 multi-well plate and can thus be used in high throughput drug discovery mode.

BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism.

Clinical efficacy regularly requires the combination of drugs. For an early estimation of the clinical value of (potentially many) combinations of pharmacologic compounds during discovery, the observed combination effect is typically compared to that expected under a null model. Mechanistic accuracy of that null model is not aspired to; to the contrary, combinations that deviate favorably from the model (and thereby disprove its accuracy) are prioritized.

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development.

The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles.

Daratumumab depletes CD38+ immune regulatory cells, promotes T-cell expansion, and skews T-cell repertoire in multiple myeloma.

Daratumumab targets CD38-expressing myeloma cells through a variety of immune-mediated mechanisms (complement-dependent cytotoxicity, antibody-dependent cell-mediated cytotoxicity, and antibody-dependent cellular phagocytosis) and direct apoptosis with crosslinking. These mechanisms may also target nonplasma cells that express CD38, which prompted evaluation of daratumumab's effects on CD38-positive immune subpopulations. Peripheral blood (PB) and bone marrow (BM) from patients with relapsed/refractory myeloma from 2 daratumumab monotherapy studies were analyzed before and during therapy and at relapse.

A web application for sample size and power calculation in case-control microbiome studies.

Unlabelled: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the 'HMP' R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances.

QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles.

Background: Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth ("deep sequencing"), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.

Integrating High-Dimensional Transcriptomics and Image Analysis Tools into Early Safety Screening: Proof of Concept for a New Early Drug Development Strategy.

During drug discovery and development, the early identification of adverse effects is expected to reduce costly late-stage failures of candidate drugs. As risk/safety assessment takes place rather late during the development process and due to the limited ability of animal models to predict the human situation, modern unbiased high-dimensional biology readouts are sought, such as molecular signatures predictive for in vivo response using high-throughput cell-based assays. In this theoretical proof of concept, we provide findings of an in-depth exploration of a single chemical core structure.

Principal bicorrelation analysis: Unraveling associations between three data sources.

In this article, we propose a statistical explorative method for data integration. It is developed in the context of early drug development for which it enables the detection of chemical substructures and the identification of genes that mediate their association with the bioactivity (BA). The core of the method is a sparse singular value decomposition for the identification of the gene set and a permutation-based method for the control of the false discovery rate.

Performance assessment of the Illumina massively parallel sequencing platform for deep sequencing analysis of viral minority variants.

Massively parallel sequencing (MPS) technology has opened new avenues to study viral dynamics and treatment-induced resistance mechanisms of infections such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV). Whereas the Roche/454 platform has been used widely for the detection of low-frequent drug resistant variants, more recently developed short-read MPS technologies have the advantage of delivering a higher sequencing depth at a lower cost per sequenced base. This study assesses the performance characteristics of Illumina MPS technology for the characterization of genetic variability in viral populations by deep sequencing.

ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering.

Background: Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing.

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds.

VirVarSeq: a low-frequency virus variant detection pipeline for Illumina sequencing using adaptive base-calling accuracy filtering.

Motivation: In virology, massively parallel sequencing (MPS) opens many opportunities for studying viral quasi-species, e.g. in HIV-1- and HCV-infected patients.

5-sulfonyl-benzimidazoles as selective CB2 agonists-part 2.

In a previous communication, the SAR of a series of potent and selective 5-sulfonyl-benzimidazole CB2-receptor agonists was described. The lack of in vivo activity of compounds from this series was attributed to their poor solubility and metabolic stability. In this Letter, we report on the further optimization of this series, leading to the relatively polar and peripherically acting CB2 agonists 41 and 49.

5-Sulfonyl-benzimidazoles as selective CB2 agonists.

A novel series of benzimidazole CB2-receptor agonists was synthesized and the structure-activity relationship explored. The results showed agonistic activities with an EC(50) up to 0.5 nM and excellent selectivity (>4000-fold) over the CB1 receptor.