Biotecnologia

Multiomica e IA nella Sanità: Una Nuova Frontiera per la Scoperta di Farmaci

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La medicina di precisione è il futuro dell’assistenza sanitaria

When it comes to applied hard sciences, a source of progress has been more precise measurements and tools. This is especially true in physics and chemistry, with analytical tools now able to observe individual atoms reactions to experiments, driving quick progress in energia pulita, scienze dei materiali, nanotecnologia, produzione, and informatica (follow the links for articles on these topics).

However, one scientific field has proven to be a tougher nut to crack for precise measurements: biology. This is because living organisms are not “simple” materials made of a few elements but ultra-complex molecular machinery made of millions, if not billions, of different parts.

So, a true representation of a single human cell would be almost incomprehensibly complex, as illustrated by a computer-generated picture of a single human cell that went viral a few years ago.

Fonte: Newsweek

This has made a true understanding of biological and biochemical phenomena a persistent challenge.

Genomics has been an important first step, in explaining the template/instructions used by the cells to build their internal components. Another growing factor is the use of AI, as advanced neural networks are able to handle the massive amount of data better than the human mind.

Together, this will drive the transformation in healthcare, with the emergence of truly personalized precision medicine, tailored to each individual’s unique makeup of genes, metabolism, medical history, etc.

Currently, la medicina di precisione è un mercato da 500 miliardi di dollari, and includes anticorpi monoclonali, (un mercato da 222 miliardi di dollari nel 2023, as well as most advanced cancer treatments like terapie CAR-T.

Che cos’è la Multiomica?

The sheer complexity of living systems has led to the emergence of multiomics, a field merging together all the -omics sub-segments of biological sciences and touted as the next step in biotechnology:

  • Genomica: l’analisi della sequenza del DNA nel nucleo delle cellule.
  • Trascrittomica: l’analisi dell’mRNA che trasporta le istruzioni del DNA.
  • Epigenomica: la modifica del genoma senza alterare la sequenza genetica, o “epigenetica”.
  • Proteomica: l’analisi delle proteine, inclusa la modifica delle proteine con zuccheri (“post-traslazionale”).
  • Metabolomica: l’analisi dei composti chimici e del metabolismo.
  • Microbiomica: l’analisi di tutti i microbi che vivono dentro o sul corpo.
  • Multiomica a cellula singola: l’analisi multiomica su singole cellule.
  • Biologia spaziale: analisi in 3D della posizione di specifici mRNA, proteine o cellule.

New fields are emerging, like, for example, Agrigenomica (genomica per migliorare i rendimenti agricoli), Genomica ecologica (Valutazione accurata della salute di un ecosistema e la sua diversità genetica), or Biologia sintetica (creazione di nuovi geni, tratti o interi organismi con uno scopo specifico).

Each of these fields has made tremendous advances thanks to new & improved analytical methods, in large part carried by revolutionary progress in nanotechnology, optics, semiconductor technology, and computing power.

Unfortunately, biological sciences and medicine have started to struggle to deal with all these new data, and even more to make sense of the complex interactions of every possible -omics field with each other.

In large part, this is due to literally trillions or maybe quadrillions of potential cross-interactions between individual genes, proteins, biomolecules, bacteria, etc.

And in theory, for truly personalized medicine, this data would be collected for every single individual, and put in connection to the records of their digital health data.

Costi in diminuzione e un’inondazione di dati

The technological improvement in analytical tools has collapsed the costs of collecting new data. This is, for example, true for sequencing a full human genome, whose costs have been divided by more than a million in 30 years, or DNA synthesis is now 10,000x cheaper.

It means that when it initially cost $450,000 to sequence only one genome in 2001, we now can sequence 1.4 billion genomes for the same price, or 17% of the world’s population.

As a result, genomics and other -omics data have been flooding biologists.

For example, the UK Biobank, the largest publicly-available genomics database, contains 27x times the data powering one of the largest LLM (Large Language Model), the AI Llama 405b built by Meta (FB ).

If every newborn in the world had its genome sequenced, a likely practice in the coming years, this would generate 10,000x the data used by Llama every year.

Luckily, while the amount of data is multiplying by the thousands, so is the efficiency of digital analysis tools, especially AI, becoming 1,000x more powerful for the same cost.

Applicazioni nella scoperta di farmaci

Cellule virtuali

Until recently, to know the effect of a potential new drug, or how a protein interacts with another, biologists needed to run the experiment manually, an expensive and time-consuming task.

This naturally slowed down new drug discovery and increased healthcare costs for innovative treatments. It could either be done in-vitro (in a lab) or in-vivo (in a living organism, usually an animal).

A new option has recently appeared, the in-silico approach, where one or several virtual cells are simulated in a computer. These virtual cells are then exposed to the potential new treatment and the simulation calculates how they would react.

Simulazione migliorata

Besides full genome and transcriptome data, another tool is now making its way into in-silico simulation: protein folding simulators like Google’s AI AlphaFold (GOOGL ).

Many medicines based on proteins are dependent on the interactions between the drug and a receptor in the cells of the body, or the surface of targeted bacteria or cancer cells.

Correctly predicting in-silico the protein’s 3D configuration (folding) will radically improve the success rate and speed of drug development, driving costs down.

As AlphaFold improves by up to 500x since 2018, in-silico simulations will become a must-have technology for most biotech companies.

Other companies are also working on similar technology to AlphaFold but for non-protein molecules, like Schrödinger (SDGR ) , which we covered in “Top 5 AI & Digital Biotech Companies”.

Rilevamento del cancro

The early detection of cancer, even when it is not visible in an MRI scanner, can often be a matter of life or death.

A new technique called liquid biopsy carries the promise of detecting early such invisible cancers. How it works is that it uses genome sequencing tools to detect DNA sequences typical of cancer in the patient’s blood, even if these sequences are ultra-rare.

Liquid biopsies are much less invasive than actual biopsies, requiring only a blood sample. They could also be used to look for multiple potential cancers at the same time.

In giugno 2024, Guardant Health (GH ) saw its “Shield” colorectal cancer screening test approved by the FDA. Grail (GRAL ), the recent spin-off from Illumina (ILMN ) is also a company developing such a test.

Besides liquid biopsy, Minimal residual disease (MRD) testing is also becoming increasingly common for checking the remission of cancer patients. MRDs can detect the recurrence of cancer up to 20 months earlier than traditional imaging.

For both MRD and liquid biopsy, reimbursement by private and national health insurance systems will be key in speeding up their adoptions.

Laboratorio automatizzato

As more and more data are needed, and the detection tools get cheaper, it is increasingly the labor from humans with PhD-level qualifications that become the limiting factors (both in costs and capacity) for generating more multiomics data.

This is true both for the manual work itself, like extracting samples, and for the design of the experiments.

An emerging alternative method is the self-driving lab (SDL). This combines robotics and automation to replace tedious and slower manual labor, creating high-throughput experimentation. It also adds LLMs to analyze data and design the next set of experiments.

A leader in this new drug discovery method is Recursion Pharmaceuticals (RXRX ) (more on this company below).

Approfondimenti sugli investimenti

While there are many companies innovating at the intersection of AI and multiomics, a few stand out by either their importance or their ambitions.

Recursion Pharmaceuticals

RXRX Grafico dei prezzi

Recursion is a company that, from its inception, was focused on using AI to accelerate new drug discovery. To do so, it combines dry lab (in-silico) and wet lab (biological samples) with:

  • Una libreria di 1,7 milioni di piccole molecole.
  • Colture cellulari, editing genico CRISPR, fattori solubili, virus vivi, ecc.
  • Un flusso di lavoro robotico di laboratorio automatizzato che consente fino a 2,2 milioni di esperimenti a settimana.
  • Microscopi ad alto rendimento e sistemi di sequenziamento.
  • Flussi video continui dalle telecamere, che registrano misurazioni olistiche dei comportamenti animali.
  • Risorse computazionali avanzate, che hanno generato >21 petabyte di dati proprietari ad alta dimensionalità.
  • Dati ADMET (assorbimento, distribuzione, metabolismo, escrezione e tossicologia).

Recursion also owns one of the world’s fastest supercomputers to train their LLMs and AIs for drug discovery. The AI models were trained on a library of more than 2 billion images and inferred 6 trillion relationships between all possible combinations of genes and compounds.

In agosto 2024, Recursion merged with Exscientia, a company focused on precision therapies and using its own “comprehensive robotic automation across the entire experimentation cycle”. Recursion also acquired in maggio 2023 the drug chemistry-focused preclinical startups, Cyclica and Valance, for a total of $87.5M.

With these newly acquired companies merging with Recursion’s core datasets, the company is now a fully integrated biotech company, handling everything from target identification, in-silico predictions, in-vivo validation, and clinical trials.

Fonte: Recursion

Recursion has 20+ molecules at various stages of development in its R&D pipeline, of which 7 are in stage 1/2 of clinical trials, mostly in oncology (cancer) and rare diseases.

These programs include 10+ partnered programs with up to $20B in potential payment for R&D milestones, of which $450M have already been paid.

Overall, the new Recursion, grown through acquisitions and an early move in leveraging AI for drug discovery, is shaping to become a key partner for large pharmaceutical companies looking to replenish their R&D pipeline.

Illumina

While the other -omics are important, almost all articulate one way or another around genomics, the core “instruction manual” of every living cell.

And by far, the largest producer of genome sequencing machines is Illumina. The company is focused on short genetic sequence reading, the one used for cancer detection. It currently has 22,000+ installed sequencers in 165 countries.

Around half of Illumina’s sequencing machines’ consumables are used in clinical applications, with the other half used in public and private research labs. In clinical applications, half of the demand comes from oncology.

Fonte: Illumina

As genomics and multiomics become the center of the drug discovery process, as well as cancer diagnostics, Illumina’s equipment is expected to be in high demand. The company expects the demand for NGS (Next Generation Sequencing) to grow by 18% CAGR for clinical applications and 6% CAGR for research, boosting the sector’s total addressable market (TAM) from $100B for clinical and to $25B for research by 2033.

Fonte: Illumina

Illumina had a complicated history with liquid biopsy company Grail (GRAL ), which was a spin-off from Illumina, later reacquired, and now forced back into a spin-off by competition authorities in the US and the EU.

With this trouble out of the way, Illumina might resume its long-term growth and stock price rise, especially as ultimately, Grail’s liquid biopsy tests will likely rely on Illumina sequencers.

Ginkgo Bioworks

Most of the multiomics-focused companies are present in the pharmaceutical/biotech space due to the potential profitability of blockbuster cancer treatments or curing rare diseases.

But this would be ignoring the incredible potential of biosystems for countless other applications, including chemical production, agriculture, materials, biofuels, etc.

This is exactly the focus of Gingko Bioworks, with an innovative model of building “organisms on demand” to answer specific industrial needs, making it a leader in the emerging field of synthetic biology (see “Top 5 Synthetic Biology Public Companies”).

This way, Gingko can offer any level of collaboration, from simply selling the tools to produce new organisms to contracting its solutions to full-fledged partnerships.

The company’s research hardware is structured around the Reconfigurable Automation Carts (RACs), which form the modules that can integrated together into entire research labs for high-throughput biological data generation.

Gingko had to reform its business model in 2024, after a period of expansion and too many geographical locations. This has allowed the company to cut its operation expenditure (opex) from $515M per quarter to $375M, mostly by dividing overhead costs in half.

This should bring it closer to profitability, as well as further progress on research agreement with their corresponding payment milestones.

Sfide

Privacy

If the era of multiomics analyses and personalized medicine is coming soon, it is not without challenges. The very first one is the question of data privacy, an especially sensitive topic when the data are not just our digital life but our very own bodies.

It is clear that good security, access only to authorized personnel, and anonymization of data, as well as avoiding such data to be leveraged to deny medical care or insurance will be a must for people to embrace this technological revolution.

Concern about universally collected genetic data, especially if it is accessible by non-medical specialists (for example, members of the police or state apparatus) will also need to be addressed.

Regolamentazioni

Due to the sensitive nature of biological data, increased regulations are to be expected. This covers first the question of privacy, but also topics other topics like:

  • Il rischio di emergere monopoli, con una o poche aziende che progressivamente prendono il controllo di tutti i nostri dati biologici, poiché più dati, più efficienti saranno le analisi basate sull’IA.
  • Gestire un equilibrio tra la comprensione dei rischi per individui o intere popolazioni, evitando discriminazioni o pratiche commerciali ingiuste.
  • Trattamento equo della medicina personalizzata indipendentemente dalla ricchezza, ma anche gestire i potenziali costi collettivi.

Accessibilità

Because personalized medicine and multiomics data are extremely complex, they will be difficult to explain to non-scientists or doctors. Combined with the risks of unfairness and privacy concerns, it is likely that some resistance or even backlash will occur.

In the same way that the policy of mass vaccination with mRNA vaccine against Covid was highly politicized, such an outcome is not unlikely for multiomics and AI-driven medicine.

Similarly, such technology might at times be initially costly, and it will be important to avoid wealth inequality becoming a biological and health divide as well.

Il futuro dell’assistenza sanitaria guidata dall’IA

The combination of a growing volume of medical data, powerful AI, automated biological labs, in-silico simulations of proteins, and even whole cells are creating entirely new fields of medicine and medical research.

It is also likely to be only the beginning, as more progress is piling up to speed up innovations even further:

  • IA migliore, con il miglioramento radicale nel calcolo dell’open source DeepSeek come esempio più recente, così come AlphaFold e il corso aperto OpenCRISPR-1.
  • Capacità computazionale migliorata, con il quantum computing probabilmente uno strumento potente per risolvere problemi come la configurazione 3D delle proteine.
  • Strumenti analitici migliori, dal Next-Generation Sequencing (NGS) alla biologia spaziale.
  • Strumenti di manipolazione genica più precisi e sicuri, inclusi CRISPR, evoluzione diretta e biologia sintetica e programmabile.
  • Nuove applicazioni creano nuove economie di scala per le analisi multiomiche, come la biopsia liquida e il rilevamento precoce del cancro.

Ultimately, in the not-so-distant future, most of medicine might be tailored to our individual genetic makeup, we might run a yearly foolproof cancer checkup from a blood test, and be more healthy and energetic thanks to the perfect balance of our metabolism, microbiome, and genome.

From an investment point, it is likely that the main winners in the AI-biotech race will be companies with the ability to create large biological datasets, more than having a unique proprietary algorithm or computation power, as AI technology is moving very fast beyond ultra-large datacenters providing a durable advantage, and more toward a decentralized open-source model.

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Jonathan è un ex ricercatore biochimico che ha lavorato nell'analisi genetica e nelle sperimentazioni cliniche. Ora è un analista di titoli e scrittore finanziario con un focus su innovazione, cicli di mercato e geopolitica nella sua pubblicazione 'The Eurasian Century'.