Leveraging transcriptomics for antisense oligonucleotide drug discovery

Advances in massive parallel sequencing provide an unprecedented ability to survey the transcriptome. Sequencing-based approaches allow studying globally not only RNA abundance and splice isoforms, but also structure and accessibility, binding of proteins, as well as rates of turnover—all of which are major factors influencing potency and efficacy of antisense oligonucleotides targeting RNA. It is our view that many long-standing challenges in the therapeutic application of antisense oligonucleotides may be resolved by mapping out

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the druggable transcriptome.

We are currently reviewing recent studies (besides our own) applying transcriptomics for investigating therapeutic oligonucleotides. Do you work in this field and have soon-to-be-published contributions you would

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Posted in COAT, Oligoinformatics, Progress update, Research

Antisense oligonucleotides can bind too strongly

Central to the development of RNase H-recruiting antisense oligonucleotides for therapeutics lies efforts in medicinal chemistry to improve oligonucleotide stability,

biodistribution, as well as RNA binding affinity. With the introduction of high-affinity locked nucleic acids (LNAs), oligonucleotides as short as 12nt in length have been reported to achieve sufficient binding affinity to potently silence their targets.

Relationship between calculated affinity and experimental knockdown of ApoB mRNA by a range of LNA-containing oligonucleotides (From Pedersen et al)

Indeed it has been generally observed that high-affinity modifications improve the potency of the oligonucleotide compared to the same sequence with lower-affinity modifications. With such an apparent proportionality between the affinity and potency of antisense oligonucleotides, it could be expected that longer oligonucleotides tended to have higher potency than shorter ones, since more nucleotides increase affinity by allowing more hydrogen bonds and additional base stackings.

But this simple expectation is contradicted by experimental observations.

Potency has been evaluated and reported for LNA-modified oligonucleotides between 12 and 20nt in length with between 2 and 5 LNAs in the flanks. When stratifying by length, it has in some cases been observed that shorter oligonucleotides targeting the same target site has increased potency compared with longer versions. So far, no mechanism for this seemingly counterintuitive increase in potency with decreases in length and affinity has been demonstrated. Suggested explanations include variations in gapsize, less tendency to self-complementarity or improved pharmacokinetics of shorter oligonucleotides.

Scientists from COAT and Santaris Pharma now provide an alternative explanation that invokes only the kinetics behind oligonucleotide-mediated cleavage of RNA targets. Recently, we published a kinetic model based

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on the law of mass action in Molecular Therapy – Nucleic Acids, a companion journal to the well-established high impact journal, Molecular Therapy (Pedersen et al., 2014), which predict the existence of an optimal binding affinity. This prediction was confirmed by experiments across multiple RNA targets. Intuitively put, too high affinity between the target RNA and the oligonucleotide decreases the rate that at which the oligonucleotide leaves the cleaved target, which limits the catalytic cycle and therefore the potency of target degradation.

Thus, exaggerated affinity, and not length per se, is detrimental to potency. This finding clarifies how to optimally apply high-affinity modifications in the discovery of potent antisense oligonucleotide drugs.

Posted in Oligoinformatics, Research

Installing RStudio Server on Mac OS X Mavericks

RStudio Server provides a browser based interface to a version of R running on a remote computer. It is only supported on linux, though. We have just ordered the new Mac Pro and want to run RStudio Server from that computer. The tutorial in this link works great (on our iMac), except that for Mavericks specifically, Ant (a tool to help build Java applications) has to be installed “manually”, before step 4 in the tutorial will work (specifically sudo make install).

To

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do this, type

sudo port install apache-ant

in the terminal with macports installed.

 

Posted in Education, Oligoinformatics

Predictive modeling in drug discovery

Predictive modeling, that is, the process of developing a mathematical tool or model that generates an accurate prediction, is the topic in a new book just published by Springer titled Applied Predictive Modeling, by Kuhn and Johnson. Pre-clinical drug discovery and development is focused on predicting the extent to which a chemical will be safe and efficacious once dosed in man. Many of the predictive methods normally used involve measurements of responses in biological assays such as cell cultures or animal models. As more and more experimental data are generated in this manner, it may in some cases be possible to capture the relation between stimuli (e.g. dosing of a chemical), and the experimentally measured responses, in a mathematical model (we recently developed a predictive model for oligonucleotide-induced hepatotoxicity in mice, which you can read about here if interested). The book by Kuhn and Johnson therefore seems to be an interesting read for mathematically-inclined drug hunters. While predictive modelling naturally cover hallmark bioinformatics disciplines such as machine learning, pattern recognition, and data mining, which have been treated in many books already, in this book, the entire process is

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the focus, which seems interesting. Naturally, the statistical programming language chosen for the examples is R. Happy Christmas vacation reading – we’ll hopefully update this blog entry in the new year with our reading impressions (there is also a nice review of the book on R-bloggers).

Posted in Education

How to install the VienneRNA package 2 on mac OSX 10.8.5

The ViennaRNA package provides a nice set of tools to predict secondary structures of RNA. On mac, the source code needs to be compiled locally. Below follows step-by-step instructions for how to do this. This has been tested on both iMac and MacBook pro.

  1. From the mac app store, get Xcode , which is Apples toolset for building OS X and iOS applications.
  2. In Xcode, choose preferences -> downloads, and download the command line tools.
  3. Log out and back in again to make sure the tools are available in the Terminal.
  4. From http://www.tbi.univie.ac.at/~ronny/RNA/index.html, retrieve the source code for the latest version of ViennaRNA, and unpack.
  5. Open a terminal, and cd to the just unpacked ViennaRNA folder.
  6. Type

 ./configure --without-perl --disable-openmp make sudo make install
This will throw a lot of warnings but still produce a working set of command line tools in the end. When done like this there will be no perl interface support. We have not been able to compile the package with this support not switched off. Also, if you play around with the switches and try adding, e.g. export ARCHFLAGS=”-arch x86_64″, as mentioned in the “Notes for macOS X users” on the ViennaRNA homepage, but get an error message, you may need to remove the folder and re-unpack, before trying again. For those interested in integrating ViennaRNA tools with R, here is a basic R wrapper for RNAplfold, which returns the accessibilities in a matrix object
RNAplfoldR <- function (seq.char, L.in = 40, W.in = 80, u.in = 16) { seq.char <- as.character(seq.char) cmmnd <- paste("RNAplfold -L", L.in, "-W", W.in, "-u", u.in) cat(seq.char, file = paste("|", cmmnd, sep = "")) acc.tx <- read.delim("plfold_lunp", as.is = T, skip = 2, header = F, row.names = 1) acc.tx <- acc.tx[, colSums(is.na(acc.tx)) != nrow(acc.tx)] colnames(acc.tx) <- 1:ncol(acc.tx) file.remove("plfold_lunp") file.remove("plfold_dp.ps") return(acc.tx) }

Posted in Education, Oligoinformatics Tagged with:

RNA antisense drugs in the pipelines

We give an overview of the antisense drug discovery and development programs that are in the pipeline for RNA targeted medicine. It is based on manual collection of data from company websites and clinicaltrials.gov. Main author is Stine Møllerud.

A data table is available here as a google spreadsheet. You are welcome improve the table if you find mistakes or have additional info. Below we do descriptive statistics on it. In later posts we will follow up on specific aspects and more analysis.

Approved drugs

Antisense technology has been pursued for more than two decades but despite the heavy research in the field, only two RNA therapeutics have been approved. The first antisense oligonucleotide to reach the market was fomivirsen, which was approved by the Food and Drug Administration (FDA) in 1998 for the treatment of cytomegalovirus retinitis in patients with AIDS. It was discontinued by the marketing authorisation holder in 2002 for commercial concerns. Recently ISIS Pharmaceuticals and Sanofi have achieved approval from FDA for kynamro (generic name mipomersen), an antisense oligonucleotide targeting apolipoprotein B100, for the treatment of homozygous familial hypercholesterolemia.

The pipelines

More than a hundred oligonucleotides are in the pipelines of more than 30 companies. The table below lists the antisense drug candidates that, according to company homepages and clinicaltrials.gov currently, are under development.

Approximately half of the listed drug candidates have reached the state of clinical development, five of which are in phase III clinical trials, including the recently approved Kynamro.

When it comes to the mechanism of action, single stranded antisense and siRNA dominate the clinical pipelines, whereas a large number of antimiR programs appears in preclinical research.

plot of chunk unnamed-chunk-2

The field has many players and the table contains 46 pharmaceutical or biotech companies which are either the developing company or a partner. Almost one third of these companies are involved in 4 or more projects. ISIS Pharmaceuticals is the dominating player, as they are involved in nearly one of four drug candidates in the pipeline (26 out of 109) or almost every third of those in clinical trials (18 out of 58).

Phase I Phase Ib Phase II Phase IIb Phase III pre-clinical
Alnylam 3 0 2 0 0 6
Antisense Pharma 0 0 1 0 0 0
Antisense Therapeutics 0 0 0 0 0 2
Arrowhead Research 0 0 0 0 0 1
Atlantic Pharmaceuticals 0 0 1 0 0 0
Bio-Path Holdings 1 0 0 0 0 1
Calando Pharmaceuticals 0 1 0 0 0 0
Enzon Pharmaceuticals 3 0 0 0 0 0
Gene Signal 0 0 0 0 1 0
Geron 0 0 1 0 0 0
GlaxoSmithKline 0 0 1 0 1 0
Gradalis 1 0 0 0 0 1
iCo Therapheutics 0 0 1 0 0 0
ISIS Pharmaceuticals 4 0 8 0 3 8
Lorus Therapeutics 0 0 1 0 0 0
Marina Biotech 1 0 0 0 0 2
miRagen Therapeutics 0 0 0 0 0 6
Mirna Therapeutics 1 0 0 0 0 0
OncoGenex 0 0 1 0 0 1
Pharmaxis 0 0 1 0 0 0
Prosensa 0 0 1 0 0 3
Quark 2 0 2 1 0 0
Regulus therapeutics 0 0 0 0 0 6
Rexhan Pharmaceuticals 0 0 1 0 0 0
RXi Pharmaceuticals 1 0 0 0 0 3
Santaris 1 0 1 0 0 1
Sarepta Therapeutics 2 0 1 0 0 3
Silence Therapeutics 1 0 0 0 0 1
Silenseed 0 0 1 0 0 0
Sirnaomics 0 0 0 0 0 4
Sylentis 1 0 1 0 0 0
Tekmira 2 0 0 0 0 2
TransDerm Inc 1 0 0 0 0 0
VasGene 0 0 1 0 0 0

Indications

Cancers are the prevailing indication for the drug candidates in the pipeline (31 out of 109) followed by cardiovascular and related diseases (14), ocular disorders (10), and muscle dystrophies (10). Antisense oligonucleotides to treat cancers are also the disease area with the most involved companies.

The drug candidates in phase III clinical trials have the indications: Hypercholesterolemia, transthyretin amyloidosis, prostate cancer, Duchenne’s muscular dystrophy, and corneal neovascularisation.

Formulations

One of the challenges for oligonucleotide based therapy is to deliver the drugs to the relevant tissue and achieve a satisfactory PK-profile. A multitude of chemical modifications, conjugations and formulation techniques are being used.

plot of chunk unnamed-chunk-4

Posted in business, drugs

Toxicity of antisense oligonucleotides can be predicted

When antisense oligonucleotides are dosed systemically in rodents, injury to the liver is sometimes seen. The liver is one of the organs that accumulate the most oligonucleotides, and this may be part of the reason. However, some oligonucleotides can accumulate to very high concentrations in the liver without any hepatotoxic reactions, whereas others elicit hepatotoxicity at much lower levels. This indicates that each oligonucleotide has an inherent hepatotoxic potential; the lower this potential, the higher the dose needed to elicit a hepatotoxic reaction, and vice versa.

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p>When considering the dose at which any drug starts causing toxicity, this of course has to be related to the dose at which it has beneficial therapeutic

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effect. Still, it is uncommon for drug developers to continue development with products that elicit overt hepatic toxicity early in the animal testing, and oligonucleotides with high hepatotoxic potential as evaluated in rodents are usually not progressed to clinical testing in humans. It is therefore very important to

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be able to identify and exclude oligonucleotides with high hepatotoxic potential early in the discovery process.

Below is shown a histogram stratifying oligonucleotides according to their hepatotoxic potential as measured in mice.

Distribution of average alanine-aminotransferase (ALT) levels, a marker of hepatic injury, relative to intrastudy average saline control mice for 236 oligonucleotides (adapted from Hagedorn et al., 2013, Fig. 1a).

For the past three years, scientists from COAT and Santaris Pharma have been exploring ways to decompose the chemical structure of RNAse H-recruiting oligonucleotides in a manner that allows the hepatotoxic potential to be predicted by a computer algorithm. Recently, we published a study in Nucleic Acid Therapeutics, the official journal of the Oligonucleotide Therapeutics Society (Hagedorn et al., 2013), which shows that this is indeed possible. A press release from Mary Ann Liebert, Inc., publishers can be found here. By decomposing the oligonucleotide sequence and modification pattern into dinucleotide counts, we demonstrate that a random forests classifier distinguish between oligonucleotides with high- and low hepatotoxic potential with more than 80% accuracy.

Since tens of thousands of RNAse H-recruiting oligonucleotides can be designed against an RNA target (see previous blog entry) such a hepatotoxicity predictor can for example be applied at the design phase, to ensure that most of the oligonucleotides that are actually synthesized and evaluated in vitro and in vivo, have a low hepatotoxic potential. Using the classifier, we furthermore demonstrate how an oligonucleotide with otherwise high hepatotoxic potential can be efficiently redesigned to abate hepatotoxic potential. So, alternatively, the hepatoxicity predictor can be used for lead optimization once a highly potent oligonucleotide has been identified.

ALT levels (on log-scale) for original and redesigned oligonucleotide as screened in mice. LNA shown in bold upper-case letters, DNA in lower-case letters (adapted from Hagedorn et al., 2013, Fig. 4c).

As seen from the example shown in the figure above, the study supports the view that the hepatotoxicity profile of an oligonucleotide is unique to the specific oligonucleotide compound, and slight alterations may result in a profoundly different hepatotoxicity profile. Since the training and validation of the classifier was based on screening data for 236 locked nucleic acid (LNA)- modified, RNAse H-recruiting, antisense oligonucleotides, it will be interesting to see whether the same kind of predictive performance can be achieved with other types of chemical modifications or therapeutic mechanisms.

This work will also be presented at 9th Annual Meeting of the Oligonucleotide Therapeutics Society in Naples, Italy (October 5th-8th, 2013) as a short talk (Session IV) and poster.

Posted in COAT, drugs, Oligoinformatics, Research

PhD positions at the Department of Biology, University of Copenhagen

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now hiring generic viagra two new PhD students to join the COAT viagra generic team in the Section for Bioinformatics and RNA Biology PhD at the Department of Biology, University of Copenhagen.

You http://viagraonline-4rxpharmacy.com/ can find the announcement here.

Posted in COAT, Education, Research, Sequencing Tagged with: , ,

CSHL 2013 Conference on RNA and Oligonucleotide Therapeutics

To identify a therapeutic oligonucleotide with the right drug-like properties, a large number of oligonucleotides are usually screened. However, many more oligonucleotides can be designed against a target than can practically be screened (see previous blog). So how should we select oligonucleotides for a screening campaign among the vast number of possibilities?

In early April, the Cold Spring Harbor Laboratory will host a conference on RNA and oligonucleotide therapeutics. Some of the research done in COAT to address this issue will be presented at the conference.

Morten will be giving a talk, where he demonstrate that the sequence-nature of oligonucleotides, combined with data from a large number of screening

campaigns, may contain so much information that a systematic analysis can yield predictive models of affinity, potency, specificity, and toxicity. Two examples of this approach will be presented in-depth on posters. Lykke will present a kinetic model of enzyme recruiting oligonucleotides, and demonstrate that it predicts an optimal binding affinity between oligonucleotide and RNA target. This explains why shorter and less affine oligonucleotides may sometimes be more potent. And Peter will present how the hepatotoxic potential of oligonucleotides can be predicted from their sequence and modification pattern using machine learning.

All abstracts for the meeting can be seen here. We look forward to the talk by our colleague Dr. Susanna Obad from Santaris Pharma, presenting data on the treatment of hepatitis C infected patients with the antimiR oligonucleotide Miravirsen.

We are excited to participate and contribute with some of our research at the meeting. See you there!

Posted in Conferences, Oligoinformatics

How to efficiently read in large bedgraph files in R

This post is a bit R programming-technical, but I have spent quite some time finding a solution, so I thought I would share it anyway.

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p>First some background. One of the factors

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that might affect the potency of an antisense oligonucleotide is whether or not there are proteins binding to the same region of the transcript that the oligo binds to. Should such potential protein binding increase or decrease potency? Well, it could go both ways, I guess. A large protein complex might hinder the oligo in getting near its target region. On the other hand, since the oligo is expected to bind much more strongly than the protein, in a thermal and stochastic microworld, the presumed binding/release events between protein and transcript might serve to actually make the target region more accessible to the oligo. In the end, it might simply depend on the type of protein, whether the potency goes up or down.

In a recent paper by Baltz et al. (2012) from the Landthaler lab, the mRNA-bound proteome and its global occupancy profile on protein-coding transcripts is identified by UV crosslinking, proteomics, and sequencing. The data for this paper includes bedgraph files of (genome-wide) protein occupancy (GSE38355). These bedgraph files are basically tab-separated text files with 4 columns (chromosome, start position, end position, occupancy value).

With such data, we may be able to evaluate whether protein binding affect oligo potency. For a given oligo binding region, a simple approach would be to extract the occupancy values for that region, and associate that with the observed oligo potency.

However, in my case, I really like to do these analyses in the R language, and the bedgraph files are >1GB in size. A simple read.table command takes longer to complete than I had patience to wait (probably hours). Googling around, this size/speed-limitation of read.table is a known problem. One solution, I found, is to use the scan command instead. Here is a simple function based on that

read.bedgraph <- function(file) { dat <- scan(file=file,

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what=list(character(),integer(),integer(),numeric()), sep="\t", skip=1) dat <- data.frame(chr=dat[[1]], start=dat[[2]], end=dat[[3]], val=dat[[4]]) return(dat) }

With the read.bedgraph command, it takes 189s to read in a 1.28GB bedgraph file. Inspection of the data frame reveals that there are over 45 million rows. Reading in that many rows in a bit more than 3 minutes is OK I think. An additional improvement, however, comes when using the save command to store the bedgraph data frame as an R object in binary format (.rdata). This reduces the size of the saved file to 266MB, and when using the load command to retrieve the data frame, it now takes only 6.1s.

So, using these tricks, getting a very big data file into R can be done in a few seconds.

Of course, this solution is dependent on holding the entire table in memory. What if the bedgraph file was, say 50GB? On my 8GB laptop, the in-memory solution will not work then. In this case, I found a solution where the data is first

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read into a temporary SQL database. From this database, the relevant data are selected and returned in a data frame. As an example, to extract occupancy data from between position 60K and 100K on chromosome 10 from the above mentioned bedgraph files, the R code (which depends on the package sqldf) looks like this

require(sqldf) f <- file(filename) d <- sqldf("select * from f where V1='chr10' and V2>60000 and V3<100000", dbname = tempfile(), file.format = list(header = F, row.names = F, sep="\t", skip=1)))

I have tested this on a 1.35GB bedgraph file, and here it took 250s (4.2 minutes). This is OK. If it actually works, in reasonably time, on a >10GB (larger than in-memory) file, would be interesting to know. Anyone know of oligo-relevant bedgraph files of that size?

Posted in Education, Oligoinformatics, Sequencing