THERAPEUTIC OLIGONUCLEOTIDE METHODS
The invention provides systems and methods for discovering candidate therapies for genetic conditions and also for screening those therapies in vitro for evidence of neurotoxicity. Where a medical condition is a consequence of a genetic target such as a mutated gene, the disclosure provides in silico methods to generate lists of candidate sequences for antisense oligonucleotides (ASOs) that will potentially bind to the gene or transcripts from the gene in vivo and treat the associated condition by restoring a healthy phenotype of gene expression. The invention provides in vitro methods for screening candidate ASO sequences for symptoms of neurotoxicity in vivo. For example, candidate sequences that are output by the in silico analytical pipeline can be synthesized and assayed against live cells in vitro.
1 . A method comprising:
generating a list of oligonucleotide sequences that are substantially complementary to a genetic target implicated in a disorder;
analyzing the sequences via in silico operations that remove sequences from the list according to pre-determined criteria, leaving a filtered list;
obtaining oligonucleotides made with sequences from the filtered list; and
exposing one or more live cells to the oligonucleotides in vitro to identify candidate therapeutic oligonucleotides that do not induce an adverse phenotype in the live cells.
2 - 3 . (canceled)
4 . The method of claim 1 , wherein the in silico operations include comparing each oligonucleotide sequence to a genome and removing ones that are substantially complementary to a sub-sequence in the genome outside of the genetic target.
5 . The method of claim 1 , wherein the in silico operations include removing sequences from the list for which binding affinity to its intended target is insufficiently favorable.
6 . The method of claim 5 , wherein the in silico operations include a software module that models duplex formation and associated Gibbs free energy changes to exclude sequences that: form dimers, form hairpins, or bind off-target.
7 . The method of claim 1 , wherein the in silico operations include comparing the list of oligonucleotide sequences or the genetic target to a genome of a non-human model organism to identify a genetic target with homologous target in the non-human model organism.
8 . (canceled)
9 . The method of claim 1 , wherein the live cells comprise stem-cell derived neurons in vitro.
10 . The method of claim 9 , wherein at least one of the neurons comprises an optical reporter of membrane potential, and the method includes using a light detector or sensor to read a neural activity phenotype of the neuron when exposed at least one of the oligonucleotides.
11 . The method of claim 10 , wherein the neurons include a light-gated ion channel.
12 . The method of claim 9 , wherein the neural activity phenotype is analyzed against a data store of phenotypes.
13 . The method of claim 12 , wherein the analysis is performed by a machine learning system trained on the data store, wherein phenotypes in the data store are associated with condition labels.
14 . The method of claim 13 , wherein the phenotypes in the data store are labeled by neurological conditions that include one or more of epilepsy, autism, and Alzheimer's disease.
15 . (canceled)
16 . The method of claim 1 , wherein the in silico operations include predicting the performance of the oligonucleotide sequences as gapmers that will mediate enzymatic degradation of an RNA.
17 . The method of claim 16 , wherein the genetic target is a gene for a sodium channel.
18 - 19 . (canceled)
20 . The method of claim 1 , wherein the in silico operations include predicting the performance of the oligonucleotide sequences as splice-modulating oligonucleotides that promote splicing of a pre-RNA to form a preferred isoform of an RNA.
21 . The method of claim 1 , wherein the in silico operations include predicting the performance of the oligonucleotide sequences as steric blocking oligonucleotides that inhibit the function of a micro-RNA.
22 . The method of claim 1 , wherein the in silico operations include presenting the oligonucleotide sequences to a predictive module that predicts target-binding by comparison to results from transcriptomic analysis assays performed with test oligonucleotides.
23 . The method of claim 22 , wherein the predictive module uses a machine learning system to predict expression modulation of off-target genes for each oligonucleotide sequence, the machine learning system trained on results of expression analysis for a plurality of antisense oligonucleotides.
24 . The method of claim 1 , wherein the in silico operations include the application of sequence distance rules to avoid off-target effects, wherein the rules exclude sequences for which the genome includes a non-target region that aligns to the sequence with an exact match, 1 mismatch, or at least a threshold number of consecutive matches.
25 . The method of claim 1 , wherein the in silico operations include software packages that perform a pairwise alignment of each of the oligonucleotide sequences to a human genome or to a primary transcript sequence for a gene that includes the genetic target to exclude sequences with off-target binding affinity.
26 - 27 . (canceled)
28 . The method of claim 1 , wherein the in silico operations include evaluating, for each oligonucleotide sequence, accessibility of a binding site in the genetic target wherein accessibility is evaluated by a software module that predicts secondary structure or binding protein occupancy in an RNA transcript of the genetic target.
29 - 43 . (canceled)