Observation 1: The future of biotechnology is both a technological breakthrough and a commercial application

In comparison with materials and chemicals, biotechnology has seen rapid technological advances in the last decade, and many significant applications and breakthroughs have been made, such as mRNA vaccines, CRISPR R&D tools, Google’s protein structure prediction tools, computational pharmaceuticals, synthetic biology, and so on. Taking wearable biosensors as an example, some people predict that Apple Watch will add a biosensor capable of measuring human signals every year; currently, it can measure heart rate and blood oxygen concentration, but in the future it may be able to measure blood pressure, blood sugar, and the like. Numerous technological advances have been made in the past few years, and these technological advances have also been converted into actual products over the past few years. As a result of the epidemic, we developed an mRNA vaccine within a year and began deploying it globally on a large scale. This rapid transition from technology to product is unprecedented in the history of mankind.

We can look to the future with the possibility of utilizing underlying technologies, such as mRNA, to develop other products, such as vaccines for HIV prevention and cancer treatment. This will enable platform breakthroughs to become more widely available and, consequently, increase productivity more broadly. In the case of CRISPR, it is currently mainly used for research and development. It has been tested in in vivo clinical trials last year and may be able to be used for clinical trials in the coming decade. As we have solved major technical aspects of protein structure prediction and are able to clearly relate the protein structure to its sequence, the next step is to look more into how the structure can be translated into function in enzymes, peptides, antibodies, etc. AI pharmaceuticals still face bottlenecks in several segments, but several pipelines have entered phase 1 to phase 3 clinical trials.

This is our first observation, that there are both big breakthroughs in biotechnology and many commercial applications, which will give us a lot of imagination in the next decade.

Observation 2: The 14th Five-Year Plan for the Development of Bio-Economy and the Penetration of Biology into Various Industries

A second observation is about moving from the digital world to the real world, since all the innovations in the last 20 years have been in the digital world; as for whether the digital world innovations can affect all industries, biotechnology plays an important role. As part of its 14th Five-Year Plan for economic development, China has unified agriculture, forestry, energy, environmental protection, materials and medical care around biotechnology. This plan also mentions several key directions. The new trend in medicine is “health-centered” instead of “disease-centered”. Agriculture’s plan proposes a shift from “solving problems regarding lack of food and clothing” to “diversifying nutrition intake” for citizens. In manufacturing, it is proposed to shift away from “pursuing production capacity and efficiency” to “insisting on ecological priority”. This is a list of directions that the biotechnology can take in the future.

Observation 3: New biology and cutting-edge tools produces new therapies

New data and measurement methods allow us to see in real time the interactions between proteins in cells and their dynamics. Using super-resolution microscopy, microfluidic sorting, and other techniques, we can create more complex conceptual models than the original central law. Originally, we only knew the translational correspondence between protein and RNA, and the coding correspondence between RNA and DNA. We now understand more complex physical models of proteins, such as how protein behaves in the cell and, how it folds, etc. Furthermore, we have new tools, such as new computational methods for designing sequences that are accounted for, and new ways to modify and deliver new therapies. In the near future, there will be a greater emphasis on new therapies, especially ones that are more programmable and targeted, more in vivo therapies. In addition, there are drugs that use biological self-regulation mechanisms, such as PROTAC, molecular gels, dual antibodies, etc. The use of coupling, such as ADC, PDC, AOC, etc., is another possibility.

Observation 4: R&D and measurement tools are becoming more popular

A 2019 report by Nature Research described the technologies that it believes will drive progress towards advancing biology in the future. There are three main categories in which these technologies can be classified: first, how to view/measure more precisely. Following this would be how to regulate more precisely. Finally, how to count more accurately. Many of these highly developed tools are now available to us. Before, people could only invest in innovative drugs and innovative treatments in the biomedical field, but now R&D tools (RX Tools) are a very hot area for investment.

Biological systems/biological processes

Based on the above observations and analyses, we believe that biotechnology will be a future trend, thanks to its ability to digitize and penetrate into different industries, in addition to the lasting changes it brings. In essence, biomedical problems are data problems. For example, if we have all the relevant data on the interactions between DNA, RNA, and proteins, all biomedical problems can be solved. However, we are only at the beginning stages of datafication. The first type of datatization is the measurement of components, such as the measurement of the proteome, which studies the correspondence between proteins and genes. This top-down approach, known as systems biology, is based on studying the interaction between network components.

The second type of datafication involves interactions, such as how molecules form solids with each other, whether they bind, etc. It is referred to as AI pharmaceuticals. Synthetic biology is bottom-up research that examines the regulatory elements of a string of proteins and the relationships between them as complexity increases. In terms of complexity, it would make sense to invest first in gene sequencing, then proteomics, then gene pharma, and then synthetic biology.

In an early stage investment fund like Frees Fund, the matrix layer, which is really a data-driven and tool-based platform for biology, is the bottom layer of the investment logic. The second layer is the efficiency layer, using computation and artificial intelligence to understand biology. The top layer is the application layer, where we apply new technologies to various different fields after understanding what data means. the data means. Our investment in bioscience involves computationally driven drug design (AI), synthetic biology, brain science, and carbon neutrality.