Multi-stage personalized longevity therapeutics

Abstract

A method of treating senescence in a subject can include applying a senoremediation drug treatment protocol to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment protocol is derived from a computational transcriptome analysis of the tissue or organ of the subject. The method can include applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject. The method can include introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ. The method can include carrying out a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ.

1 . A method of providing a treatment of senescence in a subject, the method comprising:
 (a) receiving a biological data signature derived from a biological sample of the subject;   (b) creating input vectors based on the biological data signature;   (c) inputting the input vectors into a machine learning platform;   (d) generating a predicted treatment of senescence in the subject based on the input vectors by the machine learning platform, wherein the predicted treatment of senescence is specific to the subject;   (e) preparing a report that identifies the predicted treatment of senescence in the subject; and   (f) providing the report to the subject for performance of the treatment of senescence by the subject.   
     
     
         2 . The method of  claim 1 , further comprising applying a senoremediation drug treatment protocol of the treatment of senescence to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment protocol is derived from the machine learning platform for the subject, wherein the senoremediation drug treatment includes administering to the subject at least one senoremediation drug in an amount sufficient to rescue the one or more first cells in the subject, wherein the senoremediation drug increases an amount of the one or more first cells that are presenescent cells that are healthy. 
     
     
         3 . The method of  claim 1 , further comprising applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject, wherein the senolytic drug treatment protocol is derived from the machine learning platform for subject, wherein the senolytic drug treatment includes administering to the subject at least one senolytic drug in an amount sufficient to kill the one or more second cells in the subject, wherein the senolytic drug decreases an amount of the one or more second cells that are senescent cells that are unhealthy. 
     
     
         4 . The method of  claim 1 , further comprising applying a stem cell treatment to the subject, the stem cell treatment including introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ, wherein the introduced stem cells are from a stem cell treatment protocol is derived from the machine learning platform for subject. 
     
     
         5 . The method of  claim 1 , further comprising carrying out a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ, wherein the reinforcement step is derived from the machine learning platform for the subject, wherein the one or more actions include administering to the subject at least one reinforcement drug in an amount sufficient to inhibit senescence or degradation of the tissue or organ, wherein the reinforcement drug is selected from an immunomodulation drug, a cytoprotection drug, or macrophage stimulating drug. 
     
     
         6 . The method of  claim 1 , the method comprising:
 (g) applying a senoremediation drug treatment protocol to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment includes administering to the subject at least one senoremediation drug in an amount sufficient to rescue the one or more first cells in the subject, wherein the senoremediation drug increases an amount of the one or more first cells that are presenescent cells that are healthy;   (h) applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject, wherein the senolytic drug treatment includes administering to the subject at least one senolytic drug in an amount sufficient to kill the one or more second cells in the subject, wherein the senolytic drug decreases an amount of the one or more second cells that are senescent cells that are unhealthy;   (i) introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ; and   (j) carrying out a reinforcement step that includes one or more actions that inhibit further senescence or degradation of the tissue or organ, wherein the one or more actions include administering to the subject at least one reinforcement drug in an amount sufficient to inhibit senescence or degradation of the tissue or organ, wherein the reinforcement drug is selected from an immunomodulation drug, a cytoprotection drug, or macrophage stimulating drug.   
     
     
         7 . The method of  claim 6 , wherein at least one of:
 the first cells that are rescued are characterized as pre-senescent cells; or   the second cells that are removed are characterized as senescent cells.   
     
     
         8 . The method of  claim 6 , further comprising repeating at least one of (g) (h) (i) or (j) at least once. 
     
     
         9 . The method of  claim 6 , wherein:
 the senoremediation drug is selected from the group consisting of nicotinamide mononucleotide, Withaferin A, Lavendustin, and Sulforaphane;   the senolytic drug is selected from the group consisting of navitoclax, tocotrienols, quercetin, Withaferin A, Argatroban, Flavopiridol, Linifanib, dasatinib, and quercetin;   the stem cells are mesenchymal or epithelial stem cells or both; and   the reinforcement drug includes at least one of: Insulin receptor substrate (Tyr608) peptide; 740 Y-P; Sapanisertib; Dactolisib; GSK2334470; MP7; Dasantinib; Quercitin; Flavopiridol; Linifanib; Argatroban; Sorafenib; Tucaresol; Methotrexate; Tacrolimus; Curcumin; Withaferin A; Sulphoraphane; Lavendustin A; naturally occurring flavonoids; or intravenous immunoglobulin.   
     
     
         10 . The method of  claim 1 , the method comprising the machine learning platform generating:
 a senoremediation drug treatment protocol of the treatment of senescence to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment includes administering to the subject at least one senoremediation drug in an amount sufficient to rescue the one or more first cells in the subject, wherein the senoremediation drug increases an amount of the one or more first cells that are presenescent cells that are healthy.   
     
     
         11 . The method of  claim 1 , the method comprising the machine learning platform generating:
 a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject, wherein the senolytic drug treatment includes administering to the subject at least one senolytic drug in an amount sufficient to kill the one or more second cells in the subject, wherein the senolytic drug decreases an amount of the one or more second cells that are senescent cells that are unhealthy.   
     
     
         12 . The method of  claim 1 , the method comprising the machine learning platform generating:
 a stem cell treatment protocol in the subject that includes introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ.   
     
     
         13 . The method of  claim 1 , the method comprising the machine learning platform generating:
 a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ, wherein the one or more actions include administering to the subject at least one reinforcement drug in an amount sufficient to inhibit senescence or degradation of the tissue or organ, wherein the reinforcement drug is selected from an immunomodulation drug, a cytoprotection drug, or macrophage stimulating drug.   
     
     
         14 . The method of  claim 1 , the method comprising:
 (i) receiving a first biological data signature derived from the biological sample of the subject;   (ii) receiving a second biological data signature derived from a baseline;   (iii) creating a difference matrix, in a computer with a model or neural network or machine learning, using the first biological data signature of (i) and the second biological data signature of (ii);   (iv) receiving a cellular signature library;   (v) receiving a drug therapeutic use library;   (vi) using the difference matrix created in (iii), the cellular signature library of (iv), and the drug therapeutic use library of (v) to provide input vectors to the machine learning platform;   (vii) outputting from the machine learning platform classification vectors on one or more drugs, and   (viii) determining the treatment of senescence with the classification vectors.   
     
     
         15 . The method of  claim 14 , the method comprising obtaining the second biological data signature derived from the baseline by a biological data signature of a non-senescent tissue or organ of the subject or from a different subject. 
     
     
         16 . The method of  claim 1 , the method comprising:
 (a-i) identifying a gene expression signature of the subject, wherein the biological data signature includes the gene expression signature;   (a-ii) defining a patient drug score for each gene expression signature taken from one or more patient biological samples and the subject;   (a-iii) selecting a treatment of senescence to include at least one drug treatment for the subject based on the (ai) and (aii); and   (a-iv) defining a lowest effective combination for each drug in the at least one drug treatment, wherein at least one of:
 each gene expression signature is based on a signature signaling pathway activation network analysis, 
 each gene expression signatures is based on an in silico signaling pathway activation network decomposition, or 
 each gene expression signature comprises a transcriptome Pearson correlation matrix. 
   
     
     
         17 . The method of  claim 1 , the method comprising deriving the biological data signature by:
 processing, with a computer with machine learning platform, biological data from the biological sample of the subject, wherein the biological sample is from a non-senescent tissue or organ of the subject or a different subject, wherein the machine learning platform comprises: at least two generative adversarial networks, an adversarial autoencoder architecture, and/or one or more deep neural networks.   
     
     
         18 . The method of  claim 1 , the method comprising performing:
 (b-i) a transcriptomic similarity search on transcriptomic tissue-specific aging datasets to identify tissue-specific cellular senescence pathway markers;   (b-ii) a protein target based search to identify target genes involved in the action of previously identified senolytic compounds, a list of target genes that are enriched by proteins likely to interact with these compounds is generated using a human drug-target interaction database, and compounds are identified which specifically bind the proteins of identified target genes with highest affinity;   (b-iii) a structural similarity search based on known compounds with senolytic properties based on importance weights of chemical groups;   (b-iv) a transcriptomic signature screening with cellular data of cell lines before and after treatment with a plurality of different compounds; and   (b-v) deep neural network based search for classifying a compound, wherein the deep neural network is trained on a plurality of compounds, wherein training data includes structural data, transcriptomic response data, pathway activation network decomposition analysis data, and drug-target interaction data.   
     
     
         19 . One or more non-transitory computer readable media storing instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising the method of  claim 1 . 
     
     
         20 . A computer system comprising:
 one or more processors; and   one or more non-transitory computer readable media storing instructions that in response to being executed by the one or more processors, cause the computer system to perform operations, the operations comprising the method of  claim 1 .