Smart multidosing

A method for dosing multiple drugs for an individual patient and treating the patient with a gene editor treatment, including the steps of:

• a healthcare professional collecting and inputting data from the individual patient including drugs to be taken by the patient and genetic testing information into a database with a central artificial intelligence (Al) stored on non-transitory computer readable media, wherein the Al has access to otherwise private outside data, the collecting step including the healthcare professional collecting a sample from the individual patient chosen from the group consisting of spit, blood, plasma, urine, and tissue and obtaining genetic data and determining that the individual patient has cancer or a virus;
• after receiving the individual patient data, the central Al analyzing the individual patient data in view of dosing criteria established based on the outside data including clinical trial data from outside databases of clinics, electronic medical records, pharmaceutical companies, private databases, and contract research organizations (CROs);
• the central Al performing the analyzing step by the Al extracting all features relating variables that affect drug metabolism and creating a model relating dosing to patient condition and effect of drugs on the condition that affect efficacy and toxicity of all drugs taken, wherein the variables include age of patient, weight of patient, known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50 and dose response points of interest, efficacy ranges, and chronic treatment effect versus acute treatment, the Al conducting natural language processing on unstructured data and extracting features from unstructured text of patient notes and notes from informal exams,
• after extracting all features and creating a model, the Al identifying nearest neighbors of persons from the outside data having similar patient data and genetic data and/or underwent a treatment plan with similar drug combinations and identifies related study and trial data with a K- Nearest Neighbor algorithm that calculates a distance between the patient and a neighbor patient using both continuous data and discrete data that is assigned a value and manually supplied to the database,
• after identifying nearest neighbors, the Al comparing patient data to neighboring patient data with weighting schemes, and wherein genetic testing information generates ethnicity and demographics features for the K-Nearest Neighbor algorithm, and the Al running the model simultaneously across all possible dosage ranges;
• after the central Al performing the analyzing step, the central Al determining doses of multiple drugs for each drug taken by the individual patient and maximizing therapeutic effect while minimizing adverse effects for the interaction of specific drugs or drug classes taken while minimizing cost to the patient, taking into account insurance coverage, patient preferences, and availability of the drugs, and displaying the dose in a readable report for a practitioner;
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• based on the readable report, a healthcare professional providing a personalized gene editor editing treatment to the individual patient with dosing and targeting information based on the genetic testing information input into the central Al;
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• designing gRNAs for use with the gene editor treatment for a virus or cancer based on the genetic testing information, wherein the gene editor is chosen from the group consisting of zinc finger nuclease, TALENs (transcription activator-like effector nucleases), argonaute protein, human WRN, C2c2, C2c1, C2c3, CRISPR Cas9, CRISPR/Cpf1, CRISPR/TevCas9, Archaea Cas9 (ARMAN 1, ARMAN 4), CasX, and CasY, and treating the individual patient with the gene editor treatment.