Discover and read the best of Twitter Threads about #iPSC
Most recents (15)
1/@LineageCell announced it entered an exclusive option & license agreement with $ERNA for the development of novel B2M deficient induced pluripotent #stem #cell #iPSC by using Eterna’s #GeneEditing technology thus developing therapies for #CNS diseases. #BioTech #CRISPR $LCTX 
2/The new cell lines - to be developed by $ERNA, will support the creation of additional product candidates at $LCTX and specifically for the treatment of central #nervoussystem #CNS disorders & other #neurology targets via Eterna’s IP #CRISPR collaboration with @FactorBio
3/This agreement will also allow @LineageCell to enter the #CRISPR field and will provide $LCTX an option to obtain an exclusive license to utilize & sublicense $ERNA novel #GeneEditing cell lines for #preclinical, #clinical, & commercial purposes in the field of CNS diseases.
🧵You can own the Future of Tomorrow Today.
I have heard many people try to call this crash in biotech the end of the #Biotechnology sector. A sector that has been around for decades. One of the most necessary sectors in the entire market.
I have heard many people try to call this crash in biotech the end of the #Biotechnology sector. A sector that has been around for decades. One of the most necessary sectors in the entire market.
1/ Here I am going to lay out for you the opposite case. I have been investing in tech and biotech for 27 years. I have seen so much technology that excites me about the future, and I see it today.
2/ The biggest question I always get in biotech investing is when will we run out of things to discover or great new technologies to invent. I my response is we will never run out until we cure every disease.
🧵My Top Science Themes for Biotech:
1. #AI/#ML and #Automation in Drug Discovery
2. #CRISPR
3. #Genomics
4. #SyntheticBiology
5. #iPSC
6. #TargetedTherapies
1. #AI/#ML and #Automation in Drug Discovery
2. #CRISPR
3. #Genomics
4. #SyntheticBiology
5. #iPSC
6. #TargetedTherapies
🧵The greatest sign I see in this #biotech market is the deafening quiet. All those rock stars of the 2021 biotech bubble are now gone. Their podcasts are gone, their Youtube channels are gone, and their $400 subscription services are gone.
1/ The only people left talking about biotech are few and far between assuming they can even endure the myriads of scoffers and skeptics. See, real investors are not fair weather investors.
2/ They aren't pounding the table at the peaks and disappearing back to their day jobs in the crash. The real investors are hear every day. They are the ones picking up the pieces left behind.
If you are looking for cheap biotech ideas, I suggest looking to these key themes. I call them my technology of the future themes. I will even throw in some names. Just be sure to do your own DD as this is spec biotech which is risky.
Looking at a little Strategy: 🧵
I hope putting out my game plan will help other get ideas on how to do a strategy for investing to maximize returns.
I hope putting out my game plan will help other get ideas on how to do a strategy for investing to maximize returns.
1/ I am finally getting comfortable with my strategy. I have a set amount now that I am contributing to each of my portfolios for #biotech, #tech and #crypto. My plan is to use the any rallies to fade some of the companies I want get rid of that disappointed me.
2/ Then I want to use that cash to accumulate more of the companies I like. The max tax loss I can take for a year about $3,000. That would reduce my taxes and get me back about 25% or $750 in tax savings. It will work out like getting some cash back on those losers.
The most impressive science in biotech right now is:
1. #iPSC
2. #AI/#ML in biotech
3. #SynBio
4. #CRISPR
5. #TPD
1. #iPSC
2. #AI/#ML in biotech
3. #SynBio
4. #CRISPR
5. #TPD
#iPSC has to be doing some of the most amazing science in the entire sector right now. The funny part is they don't get all the hype as the other hot science spaces. They are completely engineering cells that have many new functions and remove many old functions.
They are making all kinds of cells from stem cells into eye, heart, liver, pancreas and immune cells. They don't get much love because of the bad data of the allogeneic CAR-T programs, but some of these companies are completely disruptive.
1/ I have been spending a lot of time with the market selling off trying to share and teach about investing and how to build a portfolio to diversify across assets and risks.
2/ I think its good to take time when the market is down to focus on improving our game for the next time around. Since I am a biotech focused investor, I would like to share with you the trends within the biotech space I think could propel the next decade of innovation.
3/ If you know me already, these won't be very new to you. My first big concept is using tech in biotech with developing #AI, #ML and #Automation to drive down the costs and improve the success of clinical development.
1/ I am planning some threads on cell therapies. I want to start with a history of cell therapies with Autologous CAR-T with companies like Kite, Juno and $BLUE. Get into the limitations.
2/ Then cover the transition to Allogeneic with companies like $CRSP, $ALLO, $DTIL, $ATRA, $TCRR, $CRBU. Cover the limitations and benefits over Autologous.
3/ Then I want to cover TCR therapies and how they address the unique issue of intra vs extra cellular antigens with $ADAP and $IMTX.
Taking a Look at #CRISPR gene editing:
This has been a space that really needed new technology after decades of older technology failing to work. The need for precise edits without off target effects still exists.
This has been a space that really needed new technology after decades of older technology failing to work. The need for precise edits without off target effects still exists.
1/ The top pick here is clearly $BEAM with their base editing technology. They are using a modified CAS9 that only has 1 cutting domain. This allows them to use a deaminase to modify a single base and cut the opposite strand for repair.
2/ This technology has limitations as it can only do ABE and CBE modifications. It can't do insertions. There is still a ton of potential across hundreds of genetic disorders for this technology. They could do billions in revenues if the science works.
Drug discovery for complex diseases like #Parkinsons (#PD) is challenging - we need screenable cellular phenotypes to move faster. Today in @NatureComms, we present an #AI-driven phenotyping platform that identifies #PD hallmarks in patient cells: nature.com/articles/s4146… 🧵(1/11)
By combining our cell culture #automation, #CellPainting, high-content imaging, and #DeepLearning methods, we built a robust platform for phenotypic profiling and collaborated with @GoogleAI to screen fibroblasts from 91 #Parkinsons patients and matched healthy controls (2/11)
We profiled images of nearly 6 million fibroblasts, creating the largest publicly available #CellPainting dataset to date at 48 terabytes (3/11)
1/ Bargain Hunting in #iPSC:
These are the companies that rank in my top picks for people to use as a jumping off point to do their own research.
These are the companies that rank in my top picks for people to use as a jumping off point to do their own research.
2/ My top pick here would be $FATE. They have been the leader in this space for years. They have strong management and strong science. They are the only company to have early phase 1 data so far.
3/ The data has been very encouraging, but it still needs to fully develop to know where it will fit into the treatment landscape. They have multiple programs across blood cancers and now moving into solid tumors.
Ranking my Companies by Management:
By science theme and management.
By science theme and management.
The top science themes in the $XBI and the companies to follow.
#AI/#ML based Drug Discovery
#ProteinDegraders
#iPSC
#SyntheticBiology
#PersonalizedMedicine
#Sequening
#CRISPR
#AI/#ML based Drug Discovery
#ProteinDegraders
#iPSC
#SyntheticBiology
#PersonalizedMedicine
#Sequening
#CRISPR
Under #AI/#ML based Drug Discovery
$SDGR, $RXRX, $RLAY, $EXAI and $ABCL
All are using AI, ML, automation and/or deep learning to drive drug discovery to lower costs and improve success.
$SDGR, $RXRX, $RLAY, $EXAI and $ABCL
All are using AI, ML, automation and/or deep learning to drive drug discovery to lower costs and improve success.
Under #ProteinDegraders
$ARVN, $KYMR, $CCCC and $GLUE
All are using targeted protein degradation to target old and new targets. Could be far safer then TKI and other approaches.
$ARVN, $KYMR, $CCCC and $GLUE
All are using targeted protein degradation to target old and new targets. Could be far safer then TKI and other approaches.
Excited to present a new preprint on how to model E/I balance using #ipsc derived neuronal models. Great work by @brittmossink and nice collaboration with @SchubiNeuro
biorxiv.org/content/10.110…
biorxiv.org/content/10.110…
We describe a new and easy to implement protocol to efficiently generate SST+ and PV+-GABAergic neurons from human iPSCs.
After 7 weeks in culture these GABAergic neurons effectively exert inhibitory control over glutamatergic neurons – at the single neuron and neuronal network level.
