Selection and Monitoring Methods for Xenotransplantation

• Main Authors: ROGERS, Kaitlyn, PTITSYN, Andrey, BROWN, Travis, HOLZER, Paul, ADKINS, Jon, CHANG, Elizabeth, Monroy, Rodney L
• Format: Patent
• Language: English
• Published: 09.12.2021
• Subjects: BEER; BIOCHEMISTRY; CALCULATING; CHEMISTRY; COMPOSITIONS THEREOF; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; CULTURE MEDIA; ENZYMOLOGY; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; METALLURGY; MICROBIOLOGY; MICROORGANISMS OR ENZYMES; MUTATION OR GENETIC ENGINEERING; PHYSICS; PROPAGATING, PRESERVING OR MAINTAINING MICROORGANISMS; SPIRITS; VINEGAR; WINE

A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance. The method includes designing candidate samples derived from the non-human donor using the selected subset of sequences. The method includes measuring a respective in silico performance of each designed candidate sample. The method includes selecting a designed candidate sample for manufacture based on the respective in silico performance of each designed candidate sample.