Cell reprogramming offers a path to comprehensive rejuvenation but has a goldilocks problem. Too little and you have no rejuvenation, too much and you risk cancer. To fulfil its potential (comprehensive reversal of biological age, avoidance/mitigation of age linked disease), cell-reprogramming must be made safe. Based on a novel application of machine learning, we have the opportunity to tame cell reprogramming and safely reset cells and tissues back to a youthful state.
We are using public and proprietary gene expression data from cell reprogramming studies to identify the contributions that different genes make to the rejuvenation process. We have developed a machine learning framework using genes – the Shift DC1 driver clock - with meaningful accuracy. Our approach enables a more complete understanding of the causes of cellular rejuvenation during cellular reprogramming and we are now validating gene causality and safety, the final step before interventions.
We are ready to test whether a combination of putative safe rejuvenation genes can rejuvenate a human cell line without triggering pluripotency. We have first access to a cell line capable of rapid aging (76 DNAm aging days per chronological day) and rapid rejuvenation (DNAm 60 to 0 years in <1 week). We are ready to synthesise 30 genes for combinatorial testing in Spring 2022. This testing is intended to inform iteration/development of our machine learning method/gene predictions, and we expect to run 6-week cycles (results>method development>gene prediction>testing) over a period of 2 years until gene predictions fully stabilise. At this point we will claim mRNA drug IP targeting safe cellular rejuvenation genes, and if applicable, drug-target IP on gene products. It is worth highlighting that we may validate safe rejuvenation genes and be in the position to claim IP as soon as summer 2022. However, we would prefer to postpone such claims until an exhaustive analysis has been completed.