Acute Neurotoxicity of Antisense Oligonucleotides After Intracerebroventricular Injection Into Mouse Brain Can Be Predicted from Sequence Features.

Antisense oligonucleotides are a relatively new therapeutic modality and safety evaluation is still a developing area of research. We have observed that some oligonucleotides can produce acute, nonhybridization dependent, neurobehavioral side effects after intracerebroventricular (ICV) dosing in mice. In this study, we use a combination of in vitro, in vivo, and bioinformatics approaches to identify a sequence design algorithm, which can reduce the number of acutely toxic molecules synthesized and tested in mice. We find a cellular assay measuring spontaneous calcium oscillations in neuronal cells can predict the behavioral side effects after ICV dosing, and may provide a mechanistic explanation for these observations. We identify sequence features that are overrepresented or underrepresented among oligonucleotides causing these reductions in calcium oscillations. A weighted linear combination of the five most informative sequence features predicts the outcome of ICV dosing with >80% accuracy. From this, we develop a bioinformatics tool that allows oligonucleotide designs with acceptable acute neurotoxic potential to be identified, thereby reducing the number of toxic molecules entering drug discovery pipelines. The informative sequence features we identified also suggest areas in which to focus future medicinal chemistry efforts.

Hepatotoxic potential of therapeutic oligonucleotides can be predicted from their sequence and modification pattern.

Antisense oligonucleotides that recruit RNase H and thereby cleave complementary messenger RNAs are being developed as therapeutics. Dose-dependent hepatic changes associated with hepatocyte necrosis and increases in serum alanine-aminotransferase levels have been observed after treatment with certain oligonucleotides. Although general mechanisms for drug-induced hepatic injury are known, the characteristics of oligonucleotides that determine their hepatotoxic potential are not well understood. Here, we present a comprehensive analysis of the hepatotoxic potential of locked nucleic acid-modified oligonucleotides in mice. We developed a random forests classifier, in which oligonucleotides are regarded as being composed of dinucleotide units, which distinguished between 206 oligonucleotides with high and low hepatotoxic potential with 80% accuracy as estimated by out-of-bag validation. In a validation set, 17 out of 23 oligonucleotides were correctly predicted (74% accuracy). In isolation, some dinucleotide units increase, and others decrease, the hepatotoxic potential of the oligonucleotides within which they are found. However, a complex interplay between all parts of an oligonucleotide can influence the hepatotoxic potential. Using the classifier, we demonstrate how an oligonucleotide with otherwise high hepatotoxic potential can be efficiently redesigned to abate hepatotoxic potential. These insights establish analysis of sequence and modification patterns as a powerful tool in the preclinical discovery process for oligonucleotide-based medicines.