Cycles matters in AI biotech, but not only!

It is the great circle of life. Not me saying, but Pumbaa in the Lion King, probably the best Disney of all times of at least of the time where Disney were really funny and when it was worth going to McDonalds right after to collect the collector 100% BPA plastic gifts.

What did Pumbaa (or maybe was it Simba. Or Nala. Or Aristotes. Whatever.) mean was that - everything ends where it starts. Right, but only after a cycle if "completed". Everyone universe is a series of concentric cycles. Life, Death, friendship, certains would say love, work, projects. Certains last longer than others (death is unfortunately the longest of all the latter - but we have hope that all the AI enabled longevity companies, Metformine, NAD+ or the "new" Jeff Bezos - powered by octopus in the morning based diets - investments will make this one a little shorter).

Is this trivial? I am not sure it is. There is temptation to force some circles lasting too long (friendship if by definition quite cyclic for some relations, and it seem important to not fight those ones) - getting married 7 times like in the great book for JC Ruffin (a great french writer who wrote amazing books and, by taste of Ferrero Rocher, became Ambassador) "Les Sept Mariages d'Edgar and Ludmilla" helps understanding at each mariage which are the friend that matter around us. 7 times during their roller coaster style relationship, Edgar and Ludmila rebuilt their empire, their love, their friends, and start of a new cycle.

Passions are cyclic too, whatever you are passionated about, don't change before this perception of 'finishing the passion cycle is strong". Unless you are passionated about using #generative ai to create images hot dogs, and here you can probably change right away.

The IVIY leagues seem to understand that very well in their recruitment, giving priority fo students that go far in a passion cycle, versus the one that butterfly from one activity to one another (slasher not welcome!), and above the non profit experiences that most student were taking in countries under development before applying. "cycle" Completed passion seem key to success!

Changing work experience si fundamental to grow. but not before finishing a PhD, a degree, a rotation, a course, cutting the corner of experiences and not finishing a cycle could remain an impression of un achievement later in life. Whahou, really feeling I am an old sage giving lessons to the possible 2 readers of this blog. Simba? Nope. Rafiki rather.

I personally favor colleagues that have many different experiences, side projects, are sport champions! (unlike me), and did not stop abruptly their studies and previous work unless they do explain why this was, in fact, the end of one cycle. Projects are cycle that need to be finished. Creating projects that are challenging but doable is a big part of happiness, and not "doing' them is a source of disappointment.

Finally, on the AI space, cycles matter as much. (I probably took too long to get there). At OWKIN , we really believe that an industry needs to be reinvented, not remodeled vertical by vertical, which is the approach of the AI industry those day in heath. This is why we have decided to bring precision medicine by design, using our federated patients and samples worldwide data platform to discover new breakthrough in medicine in the form of new high value subgroups of patients.

1. The multimodal biomarkers that describe these subgroups not only feed a precision Ai enabled drug discovery with new targets that math these subgroups, but also :
2. Augment and accelerate clinical trials using AI adjustment of the covariate and variance reduction, development of the drugs in the subgroup, better matched federated synthetic control arms, eventually doubling the probably of success, which is our goal.
3. These subgroups can be deployed in our network of tier 1 academic hospitals to detect the patients to be treated as AI enabled companion diagnosis tools, augmenting precision medicine at scale.

From patients to patients, we took the bet to work from discovery to diagnosis, finding the right patients data, derived mulitmodal causal biomarkers and understood how to integrate them in the molecule life cycle. Every step cross fertilize, synergies, and bring the full value of the platform that can defining a new category of biotech. The one of the future.

We won't stop before completing these circle in every unmet disease, bringing the power of AI biology, and hopeful participating, us too, in the circle of more and more patients life.