I have been a massive bear on biotech all year. I have been calling it a bubble and calling for the $XBI to go to $112 ish. Now that were are hear, I am see hope for the future of biotech.
1/ When I look at the themes I have set my self up for going into the new year, I see areas of the science where next year will unfold data that will either validate or break these themes. That is part of the game of picking biotech stocks.
2/ If these themes work out, then this bear market, we experienced this year, will be a buying opportunity for a life time. I can't say they will work out, but I am very hopeful. Most of these companies are down now 50% to 70% from their peaks.
3/ If this isn't a good time to start buying, then when will it ever be? I look at those themes like AI base drug development, Protein Degraders, Base Editing, iPSC and Synthetic Biology. I see areas of the future that will begin to unfold in 2022 with data.
4/ While everyone else is bearish, I am turning hopeful. I say take the time to do some buying in those companies that will lead the future of biotech while they are beaten down. It might not be the exact bottom here at $112, but its close enough for me.
5/ I have been constantly and consistently deploying cash into any down days for the $XBI. I think there is a lot of bad science out there that looks cheap that might lure you in. I am not here to tell you what to buy just that I think its a good time to do some buying.
6/ I stay stick to the themes that are leading the future. Those are the ones that will lead us out of the bear market first. I personally like:
1. AI based Drug Development 2. Oncology Pathways 3. Protein Degraders 4. iPSC 5. Synthetic Biology 6. Genomics
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Incase you missed anything. Here I am going to link my landscape posts. I think its critical to focus on the long term fundamentals of the companies when the market falls apart and throws us a sale.
Looking at the AI based Drug Development landscape.
This is one area of biotech many investors do not agree with me on. The blending together of tech with biotech to reduce costs and increase the level of success for clinical development.
1/ It takes over $1 billion to develop a new drug and 90% of those drugs will fail to ever reach commercial success. The use of tech in biotech can help reduce the cost and increase the success rate.
2/ There are many ways in which tech can help from understanding genomic data for developing new drug targets to screening many potential targets for the one that offers the best chance of success.
This is another area that has gotten crushed in the biotech sell off. It was another space that was over loved and now its just getting wiped out as panic takes over.
1/ I just recently started buying into the synthetic biology space. Even after so much carnage, its been painful to watch with the small positions I have. I personally think that synthetic biology plays a key role in the future of biotech.
2/ Synthetic Biology is all about programming cells like bacteria or yeast to turn them into factories to produce ingredients for other products. Its the blending together gene editing and cell engineering.
This is a sector that went from hot to not in a real hurry lately as the biotech sector has collapsed. Here I am going to go over the sector and where I think the opportunities are.
1/ I hear people talking about how far the CRISPR space has fallen. Some think its too far and some think its not far enough. What I do know is that this space has developed a lot since its bottom in 2020.
2/ We still have very strong data from $CRSP in SCD. Their data is second to none. This is a very huge indication where the only limit is capacity. We got very promising early data from $NTLA in in-vivo gene knockout.
This is a company I recently got into. I really think what they are doing is very cool. I have been taking my time and going through everything I could about them. Now I am here to share it all with you.
1/ This company is all about using robots to do millions of experiments and record the data. They feed all that data into a super computer and turn it into a platform that can develop drugs.
2/ The statistics show that it takes well over $1 billion per drug for development and 90% of those drugs fail to ever reach commercial. That cost of drug development is way too high. Using AI and machine learning, they can reduce those costs and increase the success.