Bie Verbist

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 daratumumabs 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. Regulatory B cells and myeloid-derived suppressor cells, previously shown to express CD38, were evaluated for immunosuppressive activity and daratumumab sensitivity in the myeloma setting. A novel subpopulation of regulatory T cells (Tregs) expressing CD38 was identified. These Tregs were more immunosuppressive in vitro than CD38-negative Tregs and were reduced in daratumumab-treated patients. In parallel, daratumumab induced robust increases in helper and cytotoxic T-cell absolute counts. In PB and BM, daratumumab induced significant increases in CD8(+):CD4(+) and CD8(+):Treg ratios, and increased memory T cells while decreasing naïve T cells. The majority of patients demonstrated these broad T-cell changes, although patients with a partial response or better showed greater maximum effector and helper T-cell increases, elevated antiviral and alloreactive functional responses, and significantly greater increases in T-cell clonality as measured by T-cell receptor (TCR) sequencing. Increased TCR clonality positively correlated with increased CD8(+) PB T-cell counts. Depletion of CD38(+) immunosuppressive cells, which is associated with an increase in T-helper cells, cytotoxic T cells, T-cell functional response, and TCR clonality, represents possible additional mechanisms of action for daratumumab and deserves further exploration.

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. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.

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. The reported results from MPS experiments comprising HIV and HCV plasmids demonstrate that a 0.5-1% lower limit of detection can be achieved readily with Illumina MPS while retaining good accuracy also at low frequencies. Deep sequencing of a set of clinical samples (12 HIV and 9 HCV patients), designed at a similar budget for both MPS platforms, reveals a comparable lower limit of detection for Illumina and Roche/454. Finally, this study shows the possibility to apply Illuminas paired-end sequencing as a strategy to assess linkage between different mutations identified in individual viral subspecies. These results support the use of Illumina as another MPS platform of choice for deep sequencing of viral minority species.

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. This is essential for understanding pathways to resistance, which can substantially improve treatment. Although MPS platforms allow in-depth characterization of sequence variation, their measurements still involve substantial technical noise. For Illumina sequencing, single base substitutions are the main error source and impede powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores (Qs) that are useful for differentiating errors from the real low-frequency mutations. RESULTS A variant calling tool, Q-cpileup, is proposed, which exploits the Qs of nucleotides in a filtering strategy to increase specificity. The tool is imbedded in an open-source pipeline, VirVarSeq, which allows variant calling starting from fastq files. Using both plasmid mixtures and clinical samples, we show that Q-cpileup is able to reduce the number of false-positive findings. The filtering strategy is adaptive and provides an optimized threshold for individual samples in each sequencing run. Additionally, linkage information is kept between single-nucleotide polymorphisms as variants are called at the codon level. This enables virologists to have an immediate biological interpretation of the reported variants with respect to their antiviral drug responses. A comparison with existing SNP caller tools reveals that calling variants at the codon level with Q-cpileup results in an outstanding sensitivity while maintaining a good specificity for variants with frequencies down to 0.5%. AVAILABILITY The VirVarSeq is available, together with a users guide and test data, at sourceforge: http://sourceforge.net/projects/virtools/?source=directory.

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. Although both compounds were not active in acute pain models, the less selective compound 41 displayed good, sustained activity in a chronic model of neuropathic pain without the tolerance observed with morphine. In addition, both 41 and 49 delayed the onset of clinical symptoms in an experimental model for Multiple sclerosis.

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

BackgroundDeep-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. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses.ResultsUsing mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step.ConclusionsViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection.

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. Via transcriptional profiling, we identified a subset of close analogues that commonly downregulate multiple tubulin genes across cellular contexts, suggesting possible spindle poison effects. Confirmation via a qualified toxicity assay (in vitro micronucleus test) and the identification of a characteristic aggregate-formation phenotype via exploratory high-content imaging validated the initial findings. SAR analysis triggered the synthesis of a new set of compounds and allowed us to extend the series showing the genotoxic effect. We demonstrate the potential to flag toxicity issues by utilizing data from exploratory experiments that are typically generated for target evaluation purposes during early drug discovery. We share our thoughts on how this approach may be incorporated into drug development strategies.

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. Arguably the most popular null model is the Loewe Additivity model, which conceptually maps any assay under study to a (virtual) single-step enzymatic reaction. It is easy-to-interpret and requires no other information than the concentration-response curves of the individual compounds. However, the original Loewe model cannot accommodate concentration-response curves with different maximal responses and, by consequence, combinations of an agonist with a partial or inverse agonist. We propose an extension, named Biochemically Intuitive Generalized Loewe (BIGL), that can address different maximal responses, while preserving the biochemical underpinning and interpretability of the original Loewe model. In addition, we formulate statistical tests for detecting synergy and antagonism, which allow for detecting statistically significant greater/lesser observed combined effects than expected from the null model. Finally, we demonstrate the novel method through application to several publicly available datasets.

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

Abstract 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. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.

Principal bicorrelation analysis: Unraveling associations between three data sources

ABSTRACT 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. The method is illustrated using a real dataset, and its properties are empirically evaluated by means of a simulation study. Quantitative Structure Transcriptional Activity Relationship (QSTAR, www.qstar-consortium.org) is a new paradigm in early drug development that extends QSAR by not only considering data on the chemical structure of the compounds and on the compound-induced BA, but by simultaneously using transcriptomics data (gene expression). This approach enables, for example, the detection of chemical substructures that are associated with BA, while at the same time a gene set is correlated with both these substructures and the BA. Although causal associations cannot be formally concluded, these associations may suggest that the compounds act on the BA through a particular genomic pathway.