A phase 1 healthy male volunteer single escalating dose study of the pharmacokinetics and pharmacodynamics of risdiplam (RG7916, RO7034067), a SMN2 splicing modifier.

AIMS: Risdiplam (RG7916, RO7034067) is an orally administered, centrally and peripherally distributed, survival of motor neuron 2 (SMN2) mRNA splicing modifier for the treatment of spinal muscular atrophy (SMA). The objectives of this entry-into-human study were to assess the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics of risdiplam, and the effect of the strong CYP3A inhibitor itraconazole on the PK of risdiplam in healthy male volunteers. METHODS: Part 1 had a randomized, double-blind, adaptive design with 25 subjects receiving single ascending oral doses of risdiplam (ranging from 0.6-18.0 mg, n = 18) or placebo (n = 7). A Bayesian framework was applied to estimate risdiplam's effect on SMN2 mRNA. The effect of multiple doses of itraconazole on the PK of risdiplam was also assessed using a two-period cross-over design (n = 8). RESULTS: Risdiplam in the fasted or fed state was well tolerated. Risdiplam exhibited linear PK over the dose range with a multi-phasic decline with a mean terminal half-life of 40-69 h. Food had no relevant effect, and itraconazole had only a minor effect on plasma PK indicating a low fraction of risdiplam metabolized by CYP3A. The highest tested dose of 18.0 mg risdiplam led to approximately 41% (95% confidence interval 27-55%) of the estimated maximum increase in SMN2 mRNA. CONCLUSIONS: Risdiplam was well tolerated and proof of mechanism was demonstrated by the intended shift in SMN2 splicing towards full-length SMN2 mRNA. Based on these data, Phase 2/3 studies of risdiplam in patients with SMA are now ongoing.

Selective pharmacological inhibition of DDR1 prevents experimentally-induced glomerulonephritis in prevention and therapeutic regime.

BACKGROUND: Discoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase extensively implicated in diseases such as cancer, atherosclerosis and fibrosis. Multiple preclinical studies, performed using either a gene deletion or a gene silencing approaches, have shown this receptor being a major driver target of fibrosis and glomerulosclerosis. METHODS: The present study investigated the role and relevance of DDR1 in human crescentic glomerulonephritis (GN). Detailed DDR1 expression was first characterized in detail in human GN biopsies using a novel selective anti-DDR1 antibody using immunohistochemistry. Subsequently the protective role of DDR1 was investigated using a highly selective, novel, small molecule inhibitor in a nephrotoxic serum (NTS) GN model in a prophylactic regime and in the NEP25 GN mouse model using a therapeutic intervention regime. RESULTS: DDR1 expression was shown to be mainly limited to renal epithelium. In humans, DDR1 is highly induced in injured podocytes, in bridging cells expressing both parietal epithelial cell (PEC) and podocyte markers and in a subset of PECs forming the cellular crescents in human GN. Pharmacological inhibition of DDR1 in NTS improved both renal function and histological parameters. These results, obtained using a prophylactic regime, were confirmed in the NEP25 GN mouse model using a therapeutic intervention regime. Gene expression analysis of NTS showed that pharmacological blockade of DDR1 specifically reverted fibrotic and inflammatory gene networks and modulated expression of the glomerular cell gene signature, further validating DDR1 as a major mediator of cell fate in podocytes and PECs. CONCLUSIONS: Together, these results suggest that DDR1 inhibition might be an attractive and promising pharmacological intervention for the treatment of GN, predominantly by targeting the renal epithelium.

Gene expression analysis in biomarker research and early drug development using function tested reverse transcription quantitative real-time PCR assays.

The identification of new biomarkers is essential in the implementation of personalized health care strategies that offer new therapeutic approaches with optimized and individualized treatment. In support of hypothesis generation and testing in the course of our biomarker research an online portal and respective function-tested reverse transcription quantitative real-time PCR assays (RT-qPCR) facilitated the selection of relevant biomarker genes. We have established workflows applicable for convenient high throughput gene expression analysis in biomarker research with cell lines (in vitro studies) and xenograft mouse models (in vivo studies) as well as formalin-fixed paraffin-embedded tissue (FFPET) sections from various human research and clinical tumor samples. Out of 92 putative biomarker candidate genes selected in silico, 35 were shown to exhibit differential expression in various tumor cell lines. These were further analysed by in vivo xenograft mouse models, which identified 13 candidate genes including potential response prediction biomarkers and a potential pharmacodynamic biomarker. Six of these candidate genes were selected for further evaluation in FFPET samples, where optimized RNA isolation, reverse transcription and qPCR assays provided reliable determination of relative expression levels as precondition for differential gene expression analysis of FFPET samples derived from projected clinical studies. Thus, we successfully applied function tested RT-qPCR assays in our biomarker research for hypothesis generation with in vitro and in vivo models as well as for hypothesis testing with human FFPET samples. Hence, appropriate function-tested RT-qPCR assays are available in biomarker research accompanying the different stages of drug development, starting from target identification up to early clinical development. The workflow presented here supports the identification and validation of new biomarkers and may lead to advances in efforts to achieve the goal of personalized health care.

Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics.

The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.

Identification of Natural Antisense Transcripts in Mouse Brain and Their Association With Autism Spectrum Disorder Risk Genes.

Genome-wide sequencing technologies have greatly contributed to our understanding of the genetic basis of neurodevelopmental disorders such as autism spectrum disorder (ASD). Interestingly, a number of ASD-related genes express natural antisense transcripts (NATs). In some cases, these NATs have been shown to play a regulatory role in sense strand gene expression and thus contribute to brain function. However, a detailed study examining the transcriptional relationship between ASD-related genes and their NAT partners is lacking. We performed strand-specific, deep RNA sequencing to profile expression of sense and antisense reads with a focus on 100 ASD-related genes in medial prefrontal cortex (mPFC) and striatum across mouse post-natal development (P7, P14, and P56). Using de novo transcriptome assembly, we generated a comprehensive long non-coding RNA (lncRNA) transcriptome. We conducted BLAST analyses to compare the resultant transcripts with the human genome and identified transcripts with high sequence similarity and coverage. We assembled 32861 de novo antisense transcripts mapped to 12182 genes, of which 1018 are annotated by Ensembl as lncRNA. We validated the expression of a subset of selected ASD-related transcripts by PCR, including Syngap1 and Cntnap2. Our analyses revealed that more than 70% (72/100) of the examined ASD-related genes have one or more expressed antisense transcripts, suggesting more ASD-related genes than previously thought could be subject to NAT-mediated regulation in mice. We found that expression levels of antisense contigs were mostly positively correlated with their cognate coding sense strand RNA transcripts across developmental age. A small fraction of the examined transcripts showed brain region specific enrichment, indicating possible circuit-specific roles. Our BLAST analyses identified 110 of 271 ASD-related de novo transcripts with >90% identity to the human genome at >90% coverage. These findings, which include an assembled de novo antisense transcriptome, contribute to the understanding of NAT regulation of ASD-related genes in mice and can guide NAT-mediated gene regulation strategies in preclinical investigations toward the ultimate goal of developing novel therapeutic targets for ASD.

Insights into effective RNAi gained from large-scale siRNA validation screening.

Transfection of chemically synthesized short interfering RNAs (siRNAs) enables a high level of sequence-specific gene silencing. Although siRNA design algorithms have been improved in recent years, it is still necessary to prove the functionality of a given siRNA experimentally. We have functionally tested several thousand siRNAs for target genes from various gene families including kinases, phosphatases, and cancer-related genes (e.g., genes involved in apoptosis and the cell cycle). Some targets were difficult to silence above a threshold of 70% knockdown. By working with one design algorithm and a standardized validation procedure, we discovered that the level of silencing achieved was not exclusively dependent on the siRNA sequences. Here we present data showing that neither the gene expression level nor the cellular environment has a direct impact on the knockdown which can be achieved for a given target. Modifications of the experimental setting have been investigated with the aim of improving knockdown efficiencies for siRNA-target combinations that show only moderate knockdown. Use of higher siRNA concentrations did not change the overall performance of the siRNA-target combinations analyzed. Optimal knockdown at the mRNA level was usually reached 48-72 hours after transfection. Target gene-specific characteristics such as the accessibility of the corresponding target sequences to the RNAi machinery appear to have a significant influence on the knockdown observed, making certain targets easy or difficult to knock down using siRNA.

Longitudinal characterization of biomarkers for spinal muscular atrophy.

OBJECTIVE: Recent advances in understanding Spinal Muscular Atrophy (SMA) etiopathogenesis prompted development of potent intervention strategies and raised need for sensitive outcome measures capable of assessing disease progression and response to treatment. Several biomarkers have been proposed; nevertheless, no general consensus has been reached on the most feasible ones. We observed a wide range of measures over 1 year to assess their ability to monitor the disease status and progression. METHODS: 18 SMA patients and 19 healthy volunteers (HV) were followed in this 52-weeks observational study. Quantitative-MRI (qMRI) of both thighs and clinical evaluation of motor function was performed at baseline, 6, 9 and 12 months follow-up. Blood samples were taken in patients for molecular characterization at screening, 9 and 12 month follow-up. Progression, responsiveness and reliability of collected indices were quantified. Correlation analysis was performed to test for potential associations. RESULTS: QMRI indices, clinical scales and molecular measures showed high to excellent reliability. Significant differences were found between qMRI of SMA patients and HV. Significant associations were revealed between multiple qMRI measures and functional clinical scales. None of the qMRI, clinical, or molecular measures was able to detect significant disease progression over 1 year. INTERPRETATION: We probed a variety of quantitative measures for SMA in a slowly-progressing disease population over 1 year. The presented measures demonstrated potential to provide a closer link to underlying disease biology as compared to conventional functional scales. The proposed biomarker framework can guide implementation of more sensitive endpoints in future clinical trials and prove their utility in search for novel disease-modifying therapies.

Drug Target Identification and Validation: Global Pharmaceutical Industry Experts on Challenges, Best Strategies, Innovative Precompetitive Collaboration Concepts, and Future Areas of Industry Precompetitive Research and Development.

Focused interviews were conducted with global pharmaceutical company representatives in order to derive a consistent view on drug target identification/validation challenges, collaborative strategies, and future developments in a precompetitive space. Analysis revealed translation into clinical utility as a major hurdle of novel drug target validation, originating from lack of biological understanding, irreproducibility of published results, and lack of valid animal models. Direct and close collaborations with academia are the preferred model to tackle basic research on novel drug targets in high-risk projects. Efforts to conduct target identification in large precompetitive consortia are acknowledged with some doubts about the pace of progress and data-sharing policies, while concept to extend the precompetitive space to target validation in phase II trials was curtailed to niche indications together with a revision of current intellectual property (IP) practice. Public-private partnerships in established areas are forecasted to increase. Novel emerging themes are toxicology data sharing, joint genetic patient data analysis, and reimbursement concepts.

Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression.

Blood consists of different cell populations with distinct functions and correspondingly, distinct gene expression profiles. In this study, global miRNA expression profiling was performed across a panel of nine human immune cell subsets (neutrophils, eosinophils, monocytes, B cells, NK cells, CD4 T cells, CD8 T cells, mDCs and pDCs) to identify cell-type specific miRNAs. mRNA expression profiling was performed on the same samples to determine if miRNAs specific to certain cell types down-regulated expression levels of their target genes. Six cell-type specific miRNAs (miR-143; neutrophil specific, miR-125; T cells and neutrophil specific, miR-500; monocyte and pDC specific, miR-150; lymphoid cell specific, miR-652 and miR-223; both myeloid cell specific) were negatively correlated with expression of their predicted target genes. These results were further validated using an independent cohort where similar immune cell subsets were isolated and profiled for both miRNA and mRNA expression. miRNAs which negatively correlated with target gene expression in both cohorts were identified as candidates for miRNA/mRNA regulatory pairs and were used to construct a cell-type specific regulatory network. miRNA/mRNA pairs formed two distinct clusters in the network corresponding to myeloid (nine miRNAs) and lymphoid lineages (two miRNAs). Several myeloid specific miRNAs targeted common genes including ABL2, EIF4A2, EPC1 and INO80D; these common targets were enriched for genes involved in the regulation of gene expression (p<9.0E-7). Those miRNA might therefore have significant further effect on gene expression by repressing the expression of genes involved in transcriptional regulation. The miRNA and mRNA expression profiles reported in this study form a comprehensive transcriptome database of various human blood cells and serve as a valuable resource for elucidating the role of miRNA mediated regulation in the establishment of immune cell identity.

Analysis of putative miRNA binding sites and mRNA 3' ends as targets for siRNA-mediated gene knockdown.

Tremendous efforts have been made to develop short-interfering RNA (siRNA) design algorithms that generate highly functional siRNAs for gene knockdown. Nevertheless, "difficult-to-silence" target messenger RNAs (mRNAs) still exist for which no functionally validated siRNAs are available. MicroRNA (miRNA) sites in the mRNA 3'UTR, which interact with miRNA-loaded RNA-induced silencing complex (miRISC) for posttranscriptional gene regulation, provide alternative potentially accessible sites for siRNA. To investigate this, we designed siRNAs directed against single putative miRNA sites (misiRNAs) as predicted by miRNA target databases as well as siRNAs against other regions within the 3'UTR of "difficult-to-silence" targets in this proof-of-principle study. Although their design was not fully optimized, the misiRNAs generally caused higher knockdown than previously designed siRNAs for these targets. In general, knockdown by misiRNAs targeting the miRNA seed region was specific for the target mRNA, and misiRNAs targeting 1 nt upstream of miRNA seed region were similarly potent. We also systematically screened the 3'UTR of two mRNA targets using siRNA-tiling experiments. 5' and 3' regions of the p21-activated kinase 6 (PAK6) 3'UTR were found accessible, whereas the middle portion was largely inaccessible for siRNA knockdown. In ribosomal protein S6 kinase (RPS6KB1) 3'UTR, however, only the 5' region was accessible for siRNA knockdown. Detailed analysis of 10 further "difficult-to-silence" targets revealed that siRNA accessibility at the mRNA 3' end is not a general phenomenon, at least in "difficult-to-silence" targets, as we could not detect significant knockdown by siRNAs directed against this region.

A probability-based approach for the analysis of large-scale RNAi screens.

We describe a statistical analysis methodology designed to minimize the impact of off-target activities upon large-scale RNA interference (RNAi) screens in mammalian cells. Application of this approach enhances reconfirmation rates and facilitates the experimental validation of new gene activities through the probability-based identification of multiple distinct and active small interfering RNAs (siRNAs) targeting the same gene. We further extend this approach to establish that the optimal redundancy for efficacious RNAi collections is between 4-6 siRNAs per gene.