November 1, 2010 (Vol. 30, No. 19)
Corroborative Studies and New Data Analysis Approaches Overcome Pervasive Drawbacks
In the decade since the discovery of siRNA, the use of these molecules as therapeutics has received ample R&D investment and delivered a lot of promise. In the past two and a half months alone, several companies have reported positive clinical trial results for RNAi drug candidates.
The development of RNAi therapeutics faces all the challenges one would expect with gene-based therapies: delivery, localization of activity, toxicity, and drug-stability—as Tekmira, for instance, might acknowledge. The company recently announced that it will delay a Phase I–II trial of its TKM-ApoB candidate to treat high low-density-lipid cholesterol patients, because the compound had “not met the company’s expectations” in this particular application.
Herein lies one of the particularly interesting aspects of RNAi: its function as both an analytical tool and potential therapeutic, such that it may be employed as a workhorse component even toward its own eventual development into a pharmaceutical compound.
Drug delivery, for example, is a critical challenge for all drug candidates in development. But given the generally short time windows of biological efficiency of siRNAs, and their potential for unwanted systemic effects (they are, after all, systemically delivered agents of gene knockdown), identification of targeted delivery is particularly critical. Basic research, at medical centers and in company laboratories, is seeking to overcome this challenge.
Focusing on kidney injuries associated with ischemia and exposure to nephrotoxins, Bruce Molitoris, M.D., director of nephrology, and his team at Indiana University’s School of Medicine, have determined that intravenously injected siRNA can protect kidney function and, specifically, the proximal tubule cells that are the primary cellular injury sites following ischemic or nephrotoxic events.
“siRNA, when injected intravenously, is filtered rapidly across the glomerulus and into proximal tubule cells, which pick it up like candy,” Dr. Molitoris describes. “siRNA is inherently targeted to the kidney, from where it can be transported to the cytosol to effect its therapeutically intended function.”
Dr. Molitoris’ conclusions were based on analysis drawn from different datasets. His team assessed siRNA-based phenotypic nephroprotective effects in several models of injury and tracked these nephroprotective effects to mRNA expression of p53 (associated with apoptotic effects in tissues following ischemia and other insults). With RNAi essentially a means of gene knockdown, the requirement of corroborative data is inherent for meaningful conclusions in RNAi therapeutics development.
Adjunct Data Required
Amgen understands the value, and limitations, of RNAi analysis. “Genetic approaches are ideally and uniquely suited to identifying the genes essential for, say, cancer cell survival and proliferation,” says Kim Quon, Ph.D., principal scientist. But prior to the advent of RNAi technologies, he notes, this was not possible, “because the tools needed to efficiently apply loss-of-function genetics to human cancer cells did not exist.”
Amgen is conducting siRNA screens of panels of cancer cells lines. As Dr. Quon says, the approach sounds great, on paper.
“It is not so simple to implement in practice. False positives and false negatives abound, making very general phenotypes such as viability and proliferation particularly difficult to interpret.”
Dr. Quon’s research has focused on parsing out meaningful conclusions from the spectra of phenotypes that result from differential siRNA knockdown efficiencies. His research team has suggested that siRNAs be considered as series of “hypomorphic alleles” instead of knockout agents, and they have used this approach to develop a means of quantifying siRNA data.
“The long-term potential of RNAi is huge,” agrees Francesca Santini, Ph.D., cellular discovery biology leader at Merck. “The technology provides access to an enormous wealth of disease targets that were previously inaccessible with traditional small molecule drugs.”
Merck acknowledges the need of combined areas of expertise here, noting that it conducts adjunct substantiating studies throughout its RNAi drug development process. Safety assessments include microarray gene-expression studies for assessing off-target effects of siRNA drug candidates, and preclinical toxicology routinely involves monitoring of immune system activation.
Since its $1 billion-plus acquisition of Sirna Therapeutics in 2006, the company has made few announcements regarding its siRNA R&D, though it has presented data at meetings in recent years regarding identification of modulators of hypoxia-induced factor (HIF). Primary siRNA knockdown screens were coupled with gene-expression and cell-imaging assays and network-based analyses to more clearly elucidate specific HIF pathway activity.
“At Merck, we have a mid-term goal of using RNAi to improve decision-making and increase the probability of success of our traditional therapeutics. We are using RNAi to validate disease-specific targets and pathways, as well as generate in vitro disease models. In the preclinical setting, RNAi technology can be used to assess the effect of knocking down specific target genes in model systems. Data from such experiments will help us decide whether to pursue novel targets for small molecule or biologics development,” Dr. Santini says.
For compounds in later clinical development, the analytical potential of siRNA is further supportive. “In the clinic, RNAi could potentially be given to humans to measure the effect of knocking down target genes on well-established biomarkers such as blood glucose levels or LDL cholesterol. Obtaining human proof-of-concept for targets with RNAi could increase the probability of success of small molecule or biologic therapeutics once they reach the clinic.”
For certain treatment-recalcitrant viral infections, RNAi has been a particularly beneficial analytical tool. “To target HIV specifically, given the high sequence variation of HIV isolates, is always a challenge,” notes Renate Koenig, Ph.D., research assistant professor in the infectious and inflammatory disease center of the Burnham Institute for Medical Research. “Only siRNA has made it possible to knockdown each gene in the human genome, enabling us to gain a whole snapshot of the life cycle of HIV and an understanding of the virus’ pathways in detail.”
Through RNAi analysis, she adds, her team “determined several previously undescribed virus-host interactions that likely occur in concert to facilitate the early stages of HIV infection. For instance, nuclear import of the virus and integration of the virus seem to be coupled processes mediated by nuclear porins, karyopherins, and other soluble transport factors.”
Dr. Koenig and her colleagues are conducting whole-genome siRNA screening, with the goal of discovering new antiviral targets, for HIV and influenza virus. “It is the main research tool we are using, but it is inherently prone to false negatives and positives.”
Dr. Koenig’s team thus uses these approaches, in conjunction with other methodologies, to “rank” the data mined from the RNAi screens more effectively. The researchers combined their RNAi results with interrogation of “human interactome” databases, and assessment of protein-protein interactions, mRNA expression, and gene ontology.
The end result is a functional map that includes subnetworks of interacting pathogen-host factors, each of which may be a potential drug target. Such RNAi analyses can contribute substantially to greater efficacy of eventual pharmaceutical treatment regimens. For complex systemic infections such as HIV, for instance, eventual therapeutics based on RNAi may seemingly do well to target both host and pathogen pathways.
“Given the changeability of viral DNA, therapeutic targeting of virus genes is always a moving target. But to target host cellular proteins in addition to the viral proteins adds a fixed drug target,” Dr. Koenig notes. “Administration of multiple RNAi drugs, some targeting host processes and others viral processes, may be the most effective.”
Educated as a mathematician, Auguste Genovesio, Ph.D., head of the image-mining group at Institut Pasteur Korea, brings an understanding of the necessity of progressive data visualization in RNAi analysis. Citing the enormous variability of results in RNAi HIV screens (there may be as low as 7% overlap across results of different studies), he says single-gene knockdown studies by siRNA can be “a nightmare” in practice.
This variability of results is due to the usual statistical methods applied in compound screens, according to Dr. Genovesio, who suggests that these methods are not strictly applicable to RNAi screens (given the high variation of phenotypes that results from siRNA genome-wide knockdowns). With more than 20,000 genes in the human genome and the de facto inclusion of the numerous false-positive and false-negative results to be expected from the systematic knockdown of all gene functions within a genome, RNAi studies require multiple experiments at high resolution for meaningful conclusions to be drawn.
This translates to the need for massively high-throughput and high-content studies conducted several times over, creating anew for the biopharma field the requirement of high-throughput capacities on a whole new level. As Dr. Genovesio describes it, one must ensure “very sensitive but robust selection” with primary screens on multiple criteria, and hence, his team’s approach—high-throughput imaging.
Honing cellular microarray technology developed by David Sabatini at Whitehead Institute, Dr. Genovesio says his team has developed a platform to visually screen half a million siRNA experiments in two weeks. He stresses that the “visual” aspect of these experiments (allowing the cells themselves to be seen and phenotypes to be properly assessed) is a novel development. Similar screening by traditional methods would take up to four months and be very cost-intensive, he asserts.
Dr. Genovesio’s research team has adopted a two-pronged approach to identifying novel viral-infection targets: high-throughput siRNA cellular microarrays and a high-throughput visualization platform, with accompanying computer algorithms to parse out results from data noise.
“We’ve increased performance in two dimensions simultaneously, that is, for both imaging and the number of experiments possible. This is essential for siRNA studies. Each time we do a genome-wide screen, we conduct the same experiments as many as seven or eight times; this can mean, at the microarray level, as many as 200,000 experiments in total that need to be visualized, and their data assessed, for a single genome-wide assay.
“We have developed algorithms that can identify the experiments associated with promising drug targets through actual pictures. This is very useful for RNAi screens, because we can recover meaning more precisely: describing each experiment with lots of texture- and shape-based quantitative descriptions computed on the nuclei and cytoplasm of cells.
“We can then take these selected experiments further, and know more conclusively that, for example, in the case of infectivity studies, the cells whose phenotypes appear normal are actually representative of repressed infectivity and not just false positives. The robustness is much higher.”
Dr. Genovesio’s team at Institut Pasteur Korea is further developing its analysis platform for what he has dubbed “target deconvolution” that will essentially reverse the drug-discovery process. The approach begins with currently known effective pharmaceutical agents and “back-analyses,” through RNAi analysis and ultrahigh-content and ultrahigh-throughput visualized screening, to precisely determine networks and pathways involved in drug activity. New drug targets and their specific activity within subcellular networks are thus identified with degrees of inherent validation.
Pluses Outweigh Minuses
1 RNAi holds enormous potential as an analytical tool for drug development but is associated with numerous challenges—in particular, parsing out meaningful results from the high percentage of false positives and false negatives in whole-genome screens.
2 Adjunct data is generally needed to corroborate whole-genome RNAi analyses, such as from protein-protein interactions, gene-expression analyses, pathway analyses, and cell-imaging assays.
3 Imaging and data analysis remain bottlenecks in ultrahigh throughput and ultrahigh content whole-genome studies with RNAi knockdowns. Research groups worldwide are tackling this challenge and making headway into developing therapeutics and prophylactics, particularly against recalcitrant conditions such as viral infections (HIV and influenza) and cancer.
4 Despite certain inherent limitations, RNAi remains a powerful drug development tool for both target identification (pathways and proteins) and target validation, as well as a potential therapeutic agent.