Preclinical efficacy of potent and selective menin-KMT2A inhibitor JNJ-75276617 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 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) 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. JNJ-75276617 displayed potent anti-proliferative activity across several AML and ALL cell lines and patient samples harboring KMT2A- or NPM1-alterations in vitro. In xenograft models of AML and ALL, JNJ-75276617 reduced leukemic burden and provided a significant dose-dependent survival benefit accompanied by expression changes of menin-KMT2A target genes. JNJ-75276617 demonstrated synergistic effects with gilteritinib in vitro in AML cells harboring KMT2A-r. JNJ-75276617 further exhibited synergistic effects with venetoclax and azacitidine in AML cells bearing KMT2A-r in vitro, and significantly increased survival in mice. Interestingly, JNJ-75276617 showed potent anti-proliferative activity in cell lines engineered with recently discovered mutations (MEN1M327I or MEN1T349M) that developed in patients refractory to the menin-KMT2A inhibitor revumenib. A co-crystal structure of menin in complex with JNJ-75276617 indicates a unique binding mode distinct from other menin-KMT2A inhibitors, including revumenib. JNJ-75276617 is being clinically investigated for acute leukemias harboring KMT2A or NPM1 alterations, as a monotherapy for relapsed/refractory (R/R) acute leukemia (NCT04811560), or in combination with AML-directed therapies (NCT05453903).

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., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.

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. We extend the meanR and maxR tests from this package by allowing non-constant variance of the responses and by extending the list of null models (Loewe, Loewe2, HSA, Bliss). These new tests are evaluated in a comprehensive simulation study under various models for additivity and synergy, various monotherapeutic dose-response models (complete, partial and incomplete responders) and various types of deviation from the constant variance assumption. In addition, the BIGL package is extended with bootstrap confidence intervals for the individual off-axis points and for the overall synergy strength, which were demonstrated to have reliable coverage and can complement the existing tests. We conclude that the differences in performance between the different null models are small and depend on the simulation scenario. As a result, the choice of null model should be driven by expert knowledge on the particular problem. Finally, we demonstrate the new features of the BIGL package and the difference between the synergy models on a real dataset from drug discovery. The BIGL package is available at CRAN (https://CRAN.R-project.org/package=BIGL) and as a Shiny app (https://synergy.openanalytics.eu/app).

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. Data interpretation follows a three-step normalization/transformation flow in which raw median fluorescent gene signals are transformed to fold change values with the use of proper housekeeping genes and negative controls. Clear instructions on how to assess the data quality and tools to perform this analysis in high throughput mode are, however, currently lacking. RESULTS: In this paper we introduce QGprofiler, an open source R based shiny application. QGprofiler allows for proper QuantiGene® Plex 2.0 assay optimization, choice of housekeeping genes and data pre-processing up to fold change, including appropriate QC metrics. In addition, QGprofiler allows for an Akaike information criterion based dose response fold change model selection and has a built-in tool to detect the cytotoxic potential of compounds evaluated in a high throughput screening campaign. CONCLUSION: QGprofiler is a user friendly, open source available R based shiny application, which is developed to support drug discovery campaigns. In this context, entire compound libraries/series can be tested in dose response against a gene signature of choice in search for new disease relevant chemical entities. QGprofiler is available at: https://qgprofiler.openanalytics.eu/app/QGprofiler.

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. 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.

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. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power. AVAILABILITY AND IMPLEMENTATION: The web interface is written in R code using Shiny (RStudio Inc., 2016) and it is available at https://fedematt.shinyapps.io/shinyMB The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP CONTACT: Federico.Mattiello@UGent.be.

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. 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.

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 user's guide and test data, at sourceforge: http://sourceforge.net/projects/virtools/?source=directory.

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. 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.

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.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset. RESULTS: For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNV(D)). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNV(HS)). To also increase specificity, SNVs called were overruled when their frequency was below the 80(th) percentile calculated on the distribution of error frequencies (QQ-SNV(HS-P80)). When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNV(D) performed similarly to the existing approaches. QQ-SNV(HS) was more sensitive on all test sets but with more false positives. QQ-SNV(HS-P80) was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5%, QQ-SNV(HS-P80) revealed a sensitivity of 100% (vs. 40-60% for the existing methods) and a specificity of 100% (vs. 98.0-99.7% for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets. Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5% were consistently detected by QQ-SNV(HS-P80) from different generations of Illumina sequencers. CONCLUSIONS: We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data.

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. 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. RESULTS: Using 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. CONCLUSIONS: ViVaMBC 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.

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. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays.

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. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

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.

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. JNJ-64619178 demonstrated a prolonged inhibition of PRMT5 and potent antiproliferative activity in subsets of cancer cell lines derived from various histologies, including lung, breast, pancreatic, and hematological malignancies. In primary acute myelogenous leukemia samples, the presence of splicing factor mutations correlated with a higher ex vivo sensitivity to JNJ-64619178. Furthermore, the potent and unique mechanism of inhibition of JNJ-64619178, combined with highly optimized pharmacological properties, led to efficient tumor growth inhibition and regression in several xenograft models in vivo, with once-daily or intermittent oral-dosing schedules. An increase in splicing burden was observed upon JNJ-64619178 treatment. Overall, these observations support the continued clinical evaluation of JNJ-64619178 in patients with aberrant PRMT5 activity-driven tumors.

Controlling the Reproducibility of AC50 Estimation during Compound Profiling through Bayesian ß-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. To address this, the ß-expectation tolerance interval is proposed in this article. Besides the classical acceptance criteria on assay performance, based on control compounds (e.g., quality control samples), this metric permits us to compare new estimates against historical estimates of the same study compound. It provides a measure that detects whether observed differences are likely due to systematic error. The challenge here is that limited information is available to build such compound-specific acceptance limits. To this end, we propose the use of Bayesian ß-expectation tolerance intervals to validate agreement between replicate potency estimates for individual study compounds. This approach allows the variability of the compound-testing process to be estimated from reference compounds within the assay and used as prior knowledge in the computation of compound-specific intervals as from the first repeat of the compound and then continuously updated as more information is acquired with subsequent repeats. A repeat is then flagged when it is not within limits. Unlike a fixed threshold such as 0.5log, which is often used in practice, this approach identifies unexpected deviations on each compound repeat given the observed variability of the assay.

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. The size of the substituent on the 2-position determined the level of agonism, ranging from inverse agonism to partial agonism to full agonism, which was more pronounced for the rat CB2 receptor. A wide variation of sulfonyl substituents at the benzimidazole 5-position was tolerated, which was used to optimize the drug-like properties. This resulted into lead compound 14j that can be used to investigate the potential of a selective, peripherically acting CB2 agonist. The in vitro profile of key compounds is displayed using pie bar charts (VlaaiVis).

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.

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 Illumina's 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.