Machine methods to determine neoepitope payload toxicity

Patent metadata

Field	Value
Publication number	US-12545928-B2
Application number	US-202017633876-A
Country	US
Kind code	B2
Filing date	Jul 24, 2020
Priority date	Aug 9, 2019
Publication date	Feb 10, 2026
Grant date	Feb 10, 2026

Abstract

Systems and methods are presented that allow for determination and prediction of payload toxicity in therapeutic viruses. Contemplated methods of determining payload toxicity of an expressed polypeptide in a cell may comprise the steps of generating or procuring a plurality of expression vectors, each containing a different recombinant nucleic acid sequence that encodes a corresponding recombinant polypeptide; expressing the recombinant nucleic acid sequence in a plurality of host cells while culturing the host cells; sequencing the plurality of expression vectors after culturing the host cells; and correlating at least portions of the recombinant nucleic acid sequence with a toxicity measure.

First claim

What is claimed is:

1 . A method of determining payload toxicity of an expressed polypeptide in a cell, comprising:
generating or procuring a plurality of expression vectors, each containing a different recombinant nucleic acid sequence that encodes a corresponding recombinant polypeptide; expressing the recombinant nucleic acid sequence in a plurality of host cells while culturing the host cells; sequencing the plurality of expression vectors after culturing the host cells; and correlating at least portions of the recombinant nucleic acid sequence with a toxicity measure.

2 . The method of claim 1 , wherein the expression vectors are viral expression vectors.

3 . The method of claim 1 , wherein the expression vectors are recombinant genomes of respective therapeutic viruses.

4 . The method of claim 1 , wherein the recombinant polypeptide is a polytope comprising a plurality of neoantigens.

5 . The method of claim 4 , wherein at least two of the neoantigens are separated by a linker peptide.

6 . The method of claim 4 , wherein each of the plurality of the neoantigens have a length of between 8-50 amino acids.

7 . The method of claim 4 , wherein the polytope has at least 200 amino acids.

8 . The method of claim 1 , wherein the recombinant nucleic acid sequence is monoclonally expressed in the plurality of host cells.

9 . The method of claim 1 , wherein the recombinant nucleic acid sequence is polyclonally expressed in the plurality of host cells.

10 . The method of claim 1 , wherein the plurality of expression vectors are individually sequenced.

11 . The method of claim 1 , wherein the plurality of expression vectors are sequenced in a mixture of expression vectors.

12 . The method of claim 1 , wherein the toxicity measure is observed in the host cells.

13 . The method of claim 12 , wherein the toxicity measure in the host cells is cell death, cell stress, reduced cell division, and reduced virus production.

14 . The method of claim 1 , wherein the toxicity measure is observed in the recombinant nucleic acid sequence of the virus.

15 . The method of claim 14 , wherein the toxicity measure in the recombinant nucleic acid sequence of the virus is a nonsense mutation, a missense mutation, and a deletion.

16 . The method of claim 1 , wherein the step of correlating uses machine learning.

17 . The method of claim 16 , wherein the machine learning uses a classifier selected from the group consisting of a linear classifier, an NMF (Non-negative Matrix Factorization)-based classifier, a graphical-based classifier, a tree-based classifier, a Bayesian-based classifier, a rules-based classifier, a net-based classifier, and a kNN (k-nearest neighbor) classifier.

18 . The method of claim 16 , wherein the machine learning uses an autoencoder.

19 . The method of claim 16 , wherein the machine learning uses a secondary aspect of the recombinant polypeptide.

20 . The method of claim 19 , wherein the secondary aspect is a folding pattern of the polypeptide, a secondary structure of the polypeptide, a polarity domain, a charged domain, a hydrophobic domain, a hydrophilic domain, and/or aggregation of the polypeptide.

Assignees

• Nantomics Llc
• Nantbio Inc

Inventors

• Wnuk Kamil
• Geissert Lise
• Sudol Jeremi
• Vaske Charles
• Benz Stephen Charles
• Tsai Connie
• Niazi Kayvan
• Szeto Christopher W

Classifications

• C12N2710/10343

  viral genome or elements thereof as genetic vector · CPC title

• C12N15/86 Primary

  Viral vectors · CPC title