Your Edge In Biotech.
6
Jan
2025

AI Needs Natural Language to Give Structure to Biology

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06 Jan 2025

Sam Rodriques, co-founder and CEO, FutureHouse

The word of the day, at least in the AI for Biology community, is foundation models. Everyone wants bigger data on more things to throw into bigger models.

Virtual cell models will enable us to predict how cell states will change in response to chemical perturbations. Protein language models will enable us to identify better enzymes for degrading plastics or protein binders that have more drug-like properties. These layers are on top of increasingly accessible genomic data. The future is bright.

Real biology discoveries look somewhat different, though, and I think it is telling that there are not many actual biologists at AI biology meetings like NeurIPS, a conference on Neural Information Processing Systems. which I attended last month in Vancouver BC.

Contrast these dreams of foundation models driving biological discovery with the latest table of contents from Science or Nature:

I struggle to imagine how any of these discoveries could fall out of a multimodal biology foundation model.

This is not intended to be a straw man argument. Surely, a foundation model could potentially identify the lncRNA from the first paper, but I am not sure how such a foundation model would associate it with chromatin remodeling.

A multimodal foundation model with enough data could also potentially identify metabolic changes associated with melanoma cells subjected to certain kinds of treatments, but I don’t see how that foundation model could identify the effect of those metabolites in preventing CD8+ T cell activation. Indeed, I do not think that any of the foundation models that are being developed today would be capable of generating rich new biological insights of the kind described in these papers. And yet, these are the kinds of insights that new therapies are made from.

The issue, I think, is that machine learning models work extremely well on structured data, and so all the foundation models that are being built are highly structured. Take a protein sequence as input and produce a protein sequence as output. Take a cell state and a chemical perturbation as input and produce a new cell state as output.

Biology, however, is poorly structured. The lncRNA insight is a good example: what structured representation can we use for the action of the lncRNA in modulating chromatin architecture? Protein models cannot represent it; DNA models cannot represent it; virtual cell models cannot represent it. Perhaps a model that incorporates RNA expression and 3D genome state could represent it, but then how would that model represent the lipid modulation of the monocytes?

I worry that every discovery may need its own representation space. Indeed, the nature of biology is such that there likely is no representation, short of an atomic-resolution real-space model of the entire organism, that is sufficient to represent the diversity of biological phenomena that are relevant for disease. Such a whole-organism model is far off – we still don’t have a computer model that fully represents the complexity of a single living cell.

Except, of course, for natural language, which has evolved to represent all concepts that humans are capable of contemplating. Indeed, I think natural language is ultimately unavoidable for discovery in biology, insofar as it is the only medium we know of that is sufficiently structured for machine learning and sufficiently flexible to represent the full diversity of biological concepts.

One way to combine language and biology is to use agents, like the ones we build at FutureHouse, a non-profit AI lab that I run in San Francisco. Language agents are language models – like ChatGPT – that can use literature search tools (e.g. PubMed), protein structure prediction tools (e.g. AlphaFold), DNA analysis tools (e.g. BLAST), and so on to analyze biological data in the same way humans do, but much faster and at much larger scale. We recently deployed an agent we built, PaperQA2, to search the literature and write an accurate and cited Wikipedia-style article for nearly every protein-coding gene in the human genome. In the future, language agents will be able to automatically analyze experimental data and clinical reports to provide detailed biological hypotheses similar to those in the Nature and Science papers above.

There are other ways to combine language and biology as well. Training models that combine natural language with protein, DNA, transcriptomics, and so on will also be extremely productive, provided the addition of the structured datatypes does not restrict their ability to represent unstructured concepts.

The history of biology is built on tools that we have found in nature to study biological phenomena. CRISPR is one powerful recent example. As all biologists know, trying to engineer things from scratch (almost) never works; what works is finding things in nature and repurposing them. It will be aesthetically pleasing if it turns out that our engineered representations are yet again insufficient for studying biology, and that good old natural language is simply another such tool that we have found in nature that must be applied to unravel the mysteries of biology.

2
Jan
2025

Investing in Biotech: David Schenkein on The Long Run

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02 Jan 2025

David Schenkein is today’s guest on The Long Run.

David is a general partner with GV. It’s the non-strategic corporate venture firm formerly known as Google Ventures. GV is backed by a single limited partner, Alphabet, and has $10 billion in assets under management in 400 active portfolio companies working in tech and life sciences.

David Schenkein, general partner, GV.

In this conversation, we talk about turning points in David’s career, the opportunities he sees at the nexus of technology and biology, and how he thinks about company culture. This last point is especially important to David, and deserves more public discussion.

Please join me and David Schenkein on The Long Run.

12
Dec
2024

The Past, Present and Future of RNAi Therapies: Kevin Fitzgerald on The Long Run

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12 Dec 2024

Today’s guest on The Long Run is Kevin Fitzgerald.

Kevin is the chief scientific officer of Cambridge, Mass.-based Alnylam Pharmaceuticals. He joined the company way back in 2005, when it was aspiring to create a new class of RNA interference medicines. These are sometimes referred to as “gene silencing” drugs. They are designed to shut down the production of disease-related proteins.

Kevin Fitzgerald, chief scientific officer, Alnylam Pharmaceuticals

Kevin has been through a roller coaster ride of events, as it worked through years of challenges on how to effectively deliver these promising molecules into cells. Alnylam now has four FDA approved medicines for rare diseases based on this technology. A fifth drug from Alnylam’s platform, inclisiran, is now marketed by Novartis as Leqvio for lowering LDL cholesterol, and reducing people’s risk of cardiovascular disease.

In this episode, we talk about how Kevin found his way into science and ultimately, on the front wave of a revolution in RNA-targeted medicines. He’s stayed around 20 years, he says, in large part because the technology keeps improving and opening up new possibilities to treat patients with both rare and common diseases. He also discussed why patients might choose RNA medicines when given a variety of other options with gene editing and gene therapy.

Please join me and Kevin Fitzgerald on The Long Run.

5
Dec
2024

Computational Techniques Are Driving a Tidal Shift in Therapeutic Protein Design

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05 Dec 2024

Simon Barnett, research director, Dimension

I recently attended the Molecular Machine Learning (MoML) Conference sponsored by the MIT Jameel Clinic, an institute focused on cutting-edge machine learning (ML) techniques in the life sciences. Computational approaches to drug discovery and development were the centerpiece of the symposium. 

Investor sentiment for ML-centric discovery biotechs has been turbulent recently, but researchers at MoML were widely and consistently enthusiastic. Specifically, in silico therapeutic protein design stands apart as an area with enormous, near-term disruptive potential. 

Computational techniques may materially compress the costs and timelines associated with therapeutic protein discovery, spawning new business paradigms along the way. How these technologies saturate the pharmaceutical ecosystem to capture value is still nebulous. 

Should this thesis play out, adjacent phases of drug development (e.g., clinical translation) will become bottlenecked. We should be grappling with how to solve these challenges today because this future is not as far off as it seems. 

Monoclonal Antibody Development is Poised for Disruption

Monoclonal antibodies (mAbs) are a cornerstone of the pharmaceutical industry. Comprising both in vitro and in vivo approaches, the screening technologies scientists use to discover and optimize mAbs have matured over several decades. 

Researchers can inject a drug target (e.g., an antigen) into a mouse, rabbit, or other mammal, leveraging these species’ immune systems to produce antibody candidates. Alternatively, scientists can use a method called biopanning. This involves expressing antibody libraries on the surfaces of microorganisms and testing to see which candidates bind to a target antigen. 

Both screening paradigms are effective, having produced dozens of approved mAbs. Over the years, both approaches have undergone steady improvements, though I’d argue these have been incremental. At MoML, some even claimed that mAb discovery is effectively solved since very few drug campaigns fail because a high-affinity antibody couldn’t be produced. 

I agree—to an extent. Immunization and biopanning are still relatively cost and time-intensive, especially compared to the promise of in silico antibody design. Computational approaches could meaningfully alter the economics of early-stage antibody discovery, enable multi-objective optimization (e.g., affinity and developability) in a manner that chips away at downstream program failures, and contend with the exploding complexity of next-generation biologics (e.g., multi-specific antibodies).

Machine Learning Can Supercharge Protein Design

Drug discovery’s core challenge is traversing between a molecule’s structure (or sequence) and its activity. This relationship is often complex and non-linear. Another hallmark of modern drug discovery is that the design space is orders of magnitude larger than our experimental screening technologies can contend with—creating search bottlenecks.

In silico design is tantalizing because it offers an ostensible bridge between inputs and outputs that isn’t reliant on the throughput of physical laboratories. What if there were a world where researchers could condition ML models to generate a small number of high-quality candidate designs for pennies? This might allow scientists to reconfigure physical assays for the purpose of validation rather than discovery. 

This is fiction in 2024, but it may not be in 2030. Progress won’t be uniform, however. My sense is that digital tooling for therapeutic protein design is the most advanced and rapidly improving category.

Proteins have several advantages that other established therapeutic modalities (e.g., small molecules) do not when it comes to the viability of contemporary ML techniques. 

Firstly, ML models are only as good as the data they’re trained on. Very large, diverse, well-annotated bodies of data make for the most performant models. These are few and far between in the public domain. Researchers can leverage open repositories like UniProt that are replete with matched protein sequence and function data. Antibody-specific databases like OAS also contain over a billion, highly relevant datapoints. 

Small molecules don’t have this advantage. Protein-ligand structural databases, such as the Protein Data Bank (PDB), contain a fraction of the data we have on proteins in the public domain. Though the PDB has given rise to invaluable ML models like the AlphaFold series for structure prediction, I’m convinced that other enabling technologies (e.g., neural network potentials) are required for bridging the structure-activity chasm of small molecules. 

Secondly, ML models are attuned to decipher the rich, evolutionary signal embedded in protein sequence data. During training, models extract how natural selection has etched patterns linking sequence motifs with functional attributes. This is how in silico protein models can gain multi-objective capabilities, enabling the simultaneous optimization of affinity as well as other intrinsic properties like aggregation potential, thermostability, and more. 

Finally, it’s much easier to physically express and analyze protein sequences than it is for small molecules. Researchers can choose from a host of highly optimized expression chassis and leverage next-generation sequencing (NGS) to map sequence to functional data. This allows labs to establish active learning loops that marry together wet- and dry-lab capabilities. 

Where Will Generative Protein Design Be in a Few Years?

Over the next few years, I predict that state-of-the-art computational models will be able to generate 10s of candidate antibody sequences with nanomolar affinity towards specific target epitopes. It’s likely that these models will be multi-objective, enabling co-optimization of multiple, desirable properties. Certain downstream properties with miniscule data (e.g., manufacturing titer) will prove challenging, however.

There’s also a world where specialized or otherwise fine-tuned models excel in adjacent biologics categories, such as multi-specific antibodies, T-cell engagers, minibinders, and more. 

What happens in this new, potential reality? It’s true that molecular discovery only represents a fraction of the total cost, time, and failure risk of a drug program. Bookending molecular discovery are target nomination and clinical translation. Both of these are challenging domains that aren’t subject for disruption by even the most sophisticated protein ML models today. Even so, with the rise of potent generative models, several industry aftershocks may occur. 

Industry Implications and the Future

Large pharmaceutical companies will seek to maintain their positions. Recently, large pharma has outsourced innovation via M&A of smaller, agile biotechs. This is likely to persist.  Genentech’s acquisition of ML-pioneer Prescient Design in Aug. 2021 is an example. I wouldn’t be surprised if most large pharma companies seek to acquire similar, emerging computational platforms. 

Established and growing biopharma alike will also outsource biologics R&D to specialized development partners who themselves increasingly lean on computational approaches. San Mateo, Calif.-based BigHat Biosciences and Boston, Mass.-based Generate:Biomedicines are both exceptional protein drug discovery platform companies with burgeoning pipelines.

Other companies will try to transform their discovery engines by purchasing and integrating a wave of ML-native protein design tools from companies like Cradle, Latent Labs, Chai Discovery, and more. The speed of progress is astounding, as evidenced by the launch and open-sourcing of several competitive structure prediction models just 12 months after AlphaFold3’s introduction.

Next-generation antibody development partners may have totally different unit economics. They may have very small physical footprints, and low fixed labor costs, while supporting an equivalent or greater number of campaigns compared to current players. Whether they try to undercut existing vendors or retain the margin to morph into a new type of company is still unclear. 

If the cost and time associated with molecular discovery collapses to near-zero, it will place immense pressure on the up and downstream phases of drug development. Do we have enough sound drug targets to prosecute? Do we have the translational infrastructure necessary to deliver these new molecules to patients? 

Investing in the entire stack, from target biology to regulatory affairs, is Dimension’s modus operandi (TR coverage, Jan. 2023). While we expect generative protein models to supplant existing discovery techniques, innovative methods will need to saturate the entire ecosystem to achieve tidal shifts in the aggregate burden of bringing new medicines to patients. The potential exists to make drug discovery faster, cheaper, and better. We’re excited about the future and there’s still so much to build. 

Simon Barnett is Research Director at Dimension.

Disclosure: Dimension is an investor in Chai Discovery. 

27
Nov
2024

What Should We Pay for a Good Death?

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27 Nov 2024

The intensive care unit in a hospital is a place where hope and despair whisper back and forth in the air.

For Richard, the emotional seesaw was becoming all too familiar. This was his third ICU stay in a month, and the ninth brush with life and death from pulmonary arterial hypertension. No matter how hard the day was, he remained determined to live happily. He’d belt out Rolling Stones songs at full volume, drowning out the hum of machines.

Dr. Lindsey Ulin, palliative care fellow, Massachusetts General Hospital and Dana Farber Cancer Institute.

I could never quite bring myself to disturb him, so I’d gently slide the door closed, not wanting to disrupt his joy.

His prognosis was grim. High-flow oxygen and a constant Remodulin infusion were his companions. Even so, when he got out of the hospital, he would quickly hop on planes and trains to see the world. His will to live a good life was powerful. So when it came time to address his recent escalation in medical care, I wasn’t surprised that Richard put up another fight. 

Pulmonary arterial hypertension (PAH) is a terminal illness, manageable through medications like treprostinil in the injectable or inhalable forms (Remodulin) and (Tyvaso). But these drugs only buy some time. The disease is irreversible without a lung transplant. For Richard, his age (80s) and medical history of other co-morbid conditions meant he was no longer a candidate for a transplant. Time was running out. We began discussing hospice care.

Dr. Jingyi Liu, a hospitalist and biopharma investor in New York City.

Hospice is both a philosophy of care and an insurance benefit. The focus of care changes from curative therapies to maximizing comfort and quality of life with an interdisciplinary team of clinicians, social workers, and chaplains. It’s an option for adults and children with a life expectancy of less than six months, but it requires making a difficult decision.

Receiving hospice care requires stopping medical treatments to help them live longer, which also may make them feel better. 

This is where the H.R. 9803 Hospice Care Accountability, Reform, and Enforcement (CARE) Act, currently introduced to the US House of Representatives, comes into play.

Currently, under Medicare, hospice services are paid for at a per diem rate (~$200 per day for hospice-at-home) which must cover everything ranging from nursing care to medications to equipment.

This payment structure hasn’t evolved since the 1980s. Payments were based on a couple of assumptions. Cancer patients, for example, tended to decline and die fairly quickly. Second, there was a clear line between drugs that extend life (e.g. chemotherapy) and treatments that alleviate symptoms (e.g. opioids for pain and shortness of breath). 

Advances in medicine have made this payment structure outdated. Medications like Remodulin can both reduce symptoms and extend life. It’s not a cure. Remodulin is one of a handful of treatments that isn’t covered by Medicare’s hospice payment structure because it extends life for a while, without offering hope for a cure. Inotropes and diuretics for heart failure, dialysis and related medications for kidney failure, supportive medications for liver failure, and blood transfusions for leukemia are a few other examples of medicines that reduce suffering at the end of life but aren’t covered by Medicare’s hospice payment plan because they also extend life for a while longer.

Provisions within the CARE Act could expand access to what’s called “concurrent care”. This would allow Medicare to pay for the usual hospice treatments, and a few of the others listed above that help people live better and maybe a while longer.

Interestingly, while most adults on Medicare, like Richard, cannot receive concurrent care, veterans receiving care through the Veterans Health Administration and children can. So there is a precedent.

For Richard, Remodulin made breathing easier, but it wouldn’t change the fact that he was likely to live six months or less. The dilemma was clear: stop the medication and enter hospice, but likely face death within hours, or stay on Remodulin, forgo hospice support, and hopefully have a few more weeks to months of life.

It’s a tough decision, one that weighs heavily on those who rely on medications that both alleviate symptoms and extend life. 

Conversations about the end of life are never easy. But what makes them even harder is the unspoken truth: we’re forced to put a price on something we can never truly quantify—how much we’re willing to pay for someone to die well.

Healthcare costs soar as life comes to an end. Medicare data confirm that hospitalizations are the primary driver of these rising expenses. The cost of medicine is a smaller contributing factor.

Within these tough and nuanced discussions lurks a bigger question: How much are we willing to invest in giving people a good death? What’s considered a good death will be different for each of us, but we all share the same fears of dying in pain and discomfort. 

When people want to continue living, we’re quick to pay the bills for treatments that offer a few more months, a few more breaths, a little more hope. But when people are ready to die, we decline to pay for medications, even if they help people live a bit longer and better.

A good death and the memory of it for loved ones left behind should count, too.

Richard decided that he wasn’t ready to unplug his Remodulin. I couldn’t blame him. With a favorite Rolling Stones song and a twirl of his motorized wheelchair, he checked himself out of the ICU.

Next stop? Las Vegas. The casinos. The flashing lights. And maybe, just maybe, a triple 7 on a slot machine.

For Richard, it wasn’t about the odds; it was about the joy and the fleeting happiness we all crave, even as his time was running out. 

 

Dr. Ulin is a palliative care fellow at Massachusetts General Hospital and the Dana Farber Cancer Institute.

Dr. Liu is a hospitalist and biopharma investor in New York City.

26
Nov
2024

Meet the 2025 Timmerman Traverse for Damon Runyon Cancer Research Team

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26 Nov 2024

I’m excited to announce the 2025 Timmerman Traverse for Damon Runyon Cancer Research Foundation team.

This group of 17 men and women are on a mission to raise more than $700,000 for the next generation of outstanding cancer researchers. In April, we’ll come together on the world-famous trek to Everest Base Camp, elevation 17,600 feet.

We’re raising awareness for cancer research. Raising funds to propel the careers of young scientists. Forming friendships. Preparing to marvel at the world’s most spectacular mountain range.

Who’s on the Team?

These people are tasked with raising $50,000 apiece for Damon Runyon Cancer Research Foundation. You can see their personal statements on why they are doing this, and how you can help, at the team donation page. 

Thanks to our early bird corporate sponsors:

 

 

 

 

 

 

 

 

 

 

 

 

Corporate sponsors, please reach out to anyone you know on the list above to ask how you can show your support. Or reach out to Elyse Hoffmann at Damon Runyon Cancer Research Foundation. elyse.hoffmann@damonrunyon.org.

If you are interested in joining the trekking team, see me. I am looking to add a couple of alternates. Tell me about your physical fitness, health history at altitude, and why you are passionate about cancer research. luke@timmermanreport.com. 

Thanks for your support. For more on Damon Runyon Cancer Research Foundation, see this short video on the Feb. 2024 Timmerman Traverse team that summited Kilimanjaro. 

Timmerman Traverse

25
Nov
2024

How to Quit Smoking and Prevent Cancer: Jonathan Bricker on The Long Run

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25 Nov 2024

Today’s guest on The Long Run is Jonathan Bricker.

Jonathan is a professor in the cancer prevention program that’s part of the Public Health Sciences Division at Fred Hutchinson Cancer Center in Seattle.

Jonathan Bricker, Professor, Cancer Prevention Program
Public Health Sciences Division, Fred Hutch Cancer Center

This episode is a little different than most. Jonathan is a clinical psychologist by training. His research team focuses on how to use a combination of technology tools — chatbots, smartphone apps, websites, telehealth – to help people quit smoking and break other harmful health habits. Pharmaceuticals aren’t the end-all, be-all here. But they sometimes can play a role in combination with tech-enabled behavioral interventions.

Despite major progress in reducing tobacco consumption in recent decades, smoking is still the leading cause of cancer death in the US. Anything that could help millions of people quit smoking has potential to reduce a huge source of suffering and death from cancer. It could make a bigger difference than any single pharmaceutical product.

This is a fascinating conversation that spans the boundaries of disciplines that don’t often converge – tech, biotech, and psychology. 

Jonathan has a popular TED talk about “the secret to self-control” that you can find in the show notes on TimmermanReport.com. He has found broad audiences for this work, at one point capturing the imagination of comedian Trevor Noah. And FYI, I’ve known Jonathan a while, as he happens to be an alumnus of one of my past Kilimanjaro expeditions for Fred Hutch.

Please enjoy this fascinating conversation with Jonathan Bricker about alleviating a major source of cancer suffering and death. You might even discover some insights here that could translate into creative ways to break other sorts of addictions.

 

If you like listening to The Long Run, you’ll love a subscription to Timmerman Report. This is where you can read my coverage of the most interesting startups in biotech, my weekly Frontpoints column, and commentary from a rotating cast of contributing writers. Individual subscriptions are available on a monthly, quarterly, or annual basis. Group subscriptions are available at a discount. Go to TimmermanReport.com and click on ‘Subscribe’ for more.

 

24
Nov
2024

A Story of Hope For Kids With Rare Disease

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24 Nov 2024

David Shaywitz

As we head into Thanksgiving, I wanted to share a story that highlights the promise and possibility that can emerge from a devastating diagnosis, and emphasizes what can happen when industry, academia, and — especially — impassioned parents and advocates join forces.

Consider the devasting rare genetic disease, spinal muscular atrophy, or SMA.

According to Cure SMA, SMA is “a progressive neurodegenerative disease that affects the motor nerve cells in the spinal cord and impacts the muscles used for activities such as breathing, eating, crawling, and walking.”  It is caused by mutations affecting the SMN1 gene, which is critical for motor neuron survival and function.

Today, there are several treatments (not quite cures) available for SMA patients, approaches that can be highly impactful in some patients, particularly if administered early in the course of the disease. 

The parents of one child with SMA have been particularly involved in driving the development of treatments: Dinakar Singh and Loren Eng, both high-powered investors whose two-year-old daughter Arya was diagnosed with SMA in 2002. The couple helped established the SMA Foundation the following year. The foundation went to work finding the most promising science in the SMA field, and ultimately securing $150 million to support basic, translational, and clinical research.

Loren Eng, president, SMA Foundation

Their drive to help their daughter was relentless.

As Loren Eng described in this piece from the Stanford Graduate School of Business magazine:

Arya had a milder form of the disease, which meant she would probably survive early childhood. But with no treatment in sight, her life would be a hellish series of hospitalizations and painful, relentless physical attrition. “The doctor said she might live to finish high school,” Eng recalls.

Eng devoted herself to changing that outcome.

According to Dr. Wendy Chung, a pediatric geneticist involved in Arya’s care in New York and now the chief of pediatrics at Children’s Hospital in Boston, Arya’s parents “have been smart, strategic, and passionate about getting Arya a treatment and transforming the field.”  
 
She adds, “Early on they were strategic about what the key questions were to answer, what tools were necessary for the field, how to bring a critical mass of scientists together, and how to engage biotech/pharma and point them in the right direction.”

As her parents and the SMA foundation fought to accelerate the development of effective medicines, Arya’s condition worsened.

By the time she was five, Arya was in a wheelchair. Each succeeding year brought new challenges as her physical capacity diminished, and the effects of her condition led to serious, sometimes life-threatening problems. As the muscles in her chest weakened, Arya lost the ability to cough, which is critical for clearing the airway during a respiratory illness. As a result, common colds could turn into pneumonia, a leading cause of death among people with SMA. “I missed tons of school because every time I got a cold, it would turn into two weeks of respiratory therapy,” Arya says.

Gradually, her muscles weakened so much that they could no longer hold bones in place. Her hips dislocated. Scoliosis twisted her spine; orthopedic deformities developed throughout her body, requiring multiple corrective surgeries. Pain shadowed her constantly.

Over time, the science advocated by the SMA Foundation advanced, and by the time she was 11, Arya was the second subject in a clinical trial for a SMA medicine. The drug candidate, nusinersen (Spinraza) is an antisense oligonucleotide aimed at the SMN2 gene, a “backup” gene that usually produces only small amounts of SMN because of alternative splicing that results in a truncated non-functional form of the protein. 

Nusinersen (developed by Carlsbad, Calif.-based Ionis Pharmaceuticals and Biogen) works by modulating splicing of SMN2 pre-mRNA, increasing the amount of full-length SMN protein that’s produced. The drug required regular injections into the spinal cord, but miraculously, seemed to halt the inexorable progression of disease in Arya. Her condition stabilized. 

Occasionally, the grueling regimen of operating-room visits and spinal injections tested Arya’s resolve. She was a little girl, and this was not how little girls were supposed to live. When she was scheduled to receive a dose on her 12th birthday, Arya broke down in tears and declared to her mother, “This is the worst birthday ever!” Eng tried to console her. “This is the best birthday present you will ever get,” she told Arya. Recalling the moment, Arya acknowledges that her mom was right. “I didn’t agree with her then, but I do now.”

She later participated in a clinical trial for a different drug, risdiplam (Evrysdi), a small molecule that also aims to increase the expression of full-length SMN2 pre-mRNA, and boost SMN production.  The mechanism of action of risdiplam (developed in a collaboration between South Plainfield, NJ-based PTC Therapeutics, the SMA Foundation, and Roche) is somewhat different than that of nusinersen. 

Most importantly, the drug can be taken as an oral pill. Arya subsequently switched to that medicine, which obviated the need for spinal cord injections. She was not eligible for a third medicine, Zolgensma (a gene therapy approach developed by Chicago-based AveXis, later acquired by Novartis), as it was approved only for much younger patients.

“Thankfully, Arya’s mind and heart have not been touched by SMA,” Dr. Chung says, adding, “She has been a strong advocate for others with disabilities.”

Now, flash forward to this summer. 

Each week, the New York Times features a wedding in their Vows section, a detailed portrait of a particularly interesting couple getting hitched. On August 9, 2024, the featured bride, who had graduated from Yale in 2022, was Arya Singh.

21
Nov
2024

Speaking Up for Science and Health. Even When Inconvenient

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21 Nov 2024

Steve Holtzman

“By their fruits, ye shall know them”

–Matthew 7:16

Immediately following the election of Donald Trump, a number of publications asked leaders of the biopharmaceutical industry and investment community to share their thoughts on how his likely policies and appointees to key positions of leadership will affect our industry.

Many of these leaders counseled us to “wait and see” before making predictions or expressing concern.

Others looked on the bright side. They anticipated benefits by way of lower corporate tax rates, less regulation and bureaucracy (particularly in the FDA), and a more permissive attitude toward mergers and acquisitions.

When a few days later, Trump announced the appointment of Robert F. Kennedy, Jr., to become Secretary of Health and Human Services, there were more expressions of concern, some rising to condemnation.

Even then, many told us, “Don’t worry. He’ll never be able to implement his most problematic health policies. Moreover, he’ll never be approved by the Senate”. (Having watched both, “Impeachment I” and “Impeachment II, The Sequel”, I am less sanguine about finding five Republican Senators to vote against the President’s wishes.)

Dr. Mehmet Oz, nominee to lead the Centers for Medicare and Medicaid Services

More recently, Dr. Mehmet Oz, a former TV show host who promoted pseudoscience to millions of viewers, including the notion that hydroxychloroquine was an effective treatment for Covid, has now been nominated to run the Centers for Medicare and Medicaid Services – the federal agency that pays the healthcare bills of over 90 million of our elderly and poorest citizens.

We are counseled to sit quietly, tallying our scorecard. We assign each new appointee (and policy) a value and hope the sum will come out a net positive for our industry. And the “wisdom” of quiet waiting, while obvious, goes unstated: do not anger a President known to be vindictive and retributive.

What is lacking in this approach to what already confronts us and lies ahead? For me, it is the recognition and acknowledgement that all of the policies and all of these appointees are connected by a deep thread of fundamental beliefs and values, and intersecting ideologies.

These range across:

  • What constitutes a fact and how do we ascertain and justify the truth of our claims to know a fact?
  • Does our government and do those of us who have economically thrived in our society have any responsibility for the well-being of those who have been less fortunate?
  • Do we believe that certain voices—as a function of their race, ethnicity, country of origin, economic status, class, biological sex, gender identity or orientation, religious belief, loyalty to power—deserve a privileged place in our electoral system and public discourse?
  • Do we believe that experience as a television personality who opines on matters relating to health qualifies such an individual to run massive government organizations that are responsible for the health of the people of our country? Or do we believe that relevant training, knowledge, and experience should be a prerequisite for such roles?

Sitting back and waiting also fails to acknowledge the nature and values of the biopharmaceutical industry that distinguish it from many other industries such as,  social media, computer games, investment banking, fossil fuels, and private equity.

These include:

  • The overwhelming majority of the men and women in the biopharmaceutical industry, scientists and non-scientists alike, have been drawn to our profession by the noble mission of creating new medicines to address suffering from disease. We place human health and well-being for all as among the highest of goals to be sought, pursued, and supported by a democratic nation, a free market, and society.
  • The ability of the biopharmaceutical industry to create new medicines is grounded in the power of the scientific method, itself grounded in rational discourse. The power of an argument lies in its fidelity to the facts, its logic, and its ability to elucidate our world, not the status or power of the speaker. [See, In re Heliocentrism; Galileo Galilei vs. Urban VIII, Sacra Congregatio Romanae et Universalis Inquisitionis (1633 AD)]. Admitting or not admitting the views of another as a matter of the speaker’s loyalty to power and/or membership in a preferred status group is the moral equivalent of discounting data that contravenes one’s preferred result.

Industry leaders have the privilege, but also the responsibility, to further the fabric of a richer, more just, and equitable society. This responsibility is especially acute for the leadership of an industry whose raison d’être is the health and well-being of all. 

While biopharmaceutical industry leaders have the responsibility to represent the economic interests of their shareholders, they do so best by investing in and finding groundbreaking medicines to treat crippling disease and then working to ensure their availability to all who can benefit from them. They need to fearlessly advocate to safeguard the preconditions in our country that make this endeavor so much as possible.

In addition, biopharmaceutical leaders need to motivate and represent their dedicated colleagues — particularly their younger colleagues — who devote their lives to finding these medicines with the sole mission of improving the lives of patients. Their work is based on the scientific method and grounded in this fundamental moral value. Both must be defended vigorously.

Cassandra was blessed with the ability to see the future and cursed not to be believed. If you take solace in the thought that last time round it wasn’t so bad (after all, we got Scott Gottlieb at the FDA) and believe that plus ça change, plus c’est la même chose, (the more things change, the more they stay the same) I ask you to look again.

If tens to hundreds of thousands of excess deaths resulting from “false facts” about, and the politicization of, Covid will not convince our industry’s leadership of the danger of focusing exclusively on the next quarterly earnings-per-share implications of the coming administration’s appointees, policies and underlying ideologies, then I don’t know what will. The long-term future of the industry depends on using our voices to defend the scientific method, loyalty to rational discourse not power, and humane policies that benefit us all.

 

Steven Holtzman currently serves as a Board member of and/or strategic advisor to several biotech companies.

20
Nov
2024

US-China Partnership: Just Hitting its Stride, and Now Threatened

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20 Nov 2024

Alex Harding, MD, entrepreneur-in-residence, Atlas Venture

Curon, Chimagen, Hengrui, LaNova…the list goes on of Chinese biotech companies that have recently licensed potential blockbuster drug candidates for cancer,  autoimmunity and more to US pharma and biotech companies for further development.

Over the past year or so, there has been a dramatic increase in both the number of deals to obtain rights to assets discovered in China, and the prices paid for those assets.

China has become more than a source of low-cost, high-quality manufacturing and contract research services. It’s now an important source of new drug discovery. US and European companies are taking these new discovered-in-China assets forward into global development, in some cases sending back significant milestones and royalty payments to the company that did the original work.

A symbiotic relationship has been evolving between the US and China. But it all could come to a halt now if the incoming Trump Administration and new Congress deliver on the promise to crack down on trade with China.

The timing couldn’t be worse, just as US-China relations in biopharma have started to blossom.

While in the past many people in the West have traditionally been skeptical of the quality of drug discovery work performed in China, often assuming (wrongly, it seems) that Chinese assets either have liabilities due to cutting corners or amounted to nothing more than trivial ‘patent busts’ – uncreative modifications to molecules to work around a competitor’s published patents—there is today broad respect for the quality and in particular speed of work being performed by Chinese companies.

Companies on both sides are benefitting from this relationship. Here’s how:

  • Typically, Western companies are still discovering and validating novel targets, as well as novel modalities and mechanisms
  • Chinese companies, watching this novel discovery and early development work closely, quickly create new molecules that address the same target or imitate the novel molecular mechanism. In some cases, these are merely me-too ‘patent busts,’ but in other cases these are truly ‘me-better’ molecules that contain some meaningfully improved features over the original molecule (e.g., potency, half-life, etc.)
  • The Chinese companies get the work done at a remarkably fast pace. Sometimes they stop once they have a preclinical development candidate, and sometimes they advance through phase 1 or even phase 2 studies in China, frequently with the intent of commercializing the new drug in China
  • At any point along this continuum between late preclinical and mid-clinical stages, Western companies purchase or license these assets for development globally. In some cases, the Chinese company retains rights to develop and commercialize the drug in China. The Summit Therapeutics/Akeso collaboration for the PD-1/VEGF bispecific antibody ivonescimab is one example.

Thus, Chinese molecular discovery and early development work is bookended by Western mechanistic and target discovery work on one side, and Western late-stage development and commercialization on the other.

This situation is evolving rapidly. Over the past year or so, there has been a feeding frenzy among venture capitalists and scrappy entrepreneurs. US-based VCs have scoured Chinese patent literature in search of assets matching their interests, either by using Google Translate, or, for the lucky ones who can read Chinese, in the original language. More recently, some pharmas have strengthened relationships with Chinese companies and are gaining access to these assets directly.

Already, as Western companies become more familiar with this source of high-quality molecular assets and Chinese firms become more adept at marketing their products externally, prices have increased and now nearly match the price for similar assets that originated in the West.

It will be important to watch what comes next. Chinese companies will probably continue to adapt rapidly. Will they take on more of a Western presence, and begin to develop drugs for Western markets on their own?

There are already a handful of global biotech companies with Chinese roots. BeiGene recently rebranded to BeOne to distance itself from its Chinese origins. Zai Labs’s President & COO was previously a US pharma executive. Other Chinese pharmas and biotechs have already hired experienced US-based executives, particularly to execute on business development.

Perhaps Chinese companies will begin to take on more late-stage development and commercialization in Western markets. Some Chinese companies could establish US operations and begin to run clinical trials in Western countries and even build sales forces there.

However, for Chinese companies that are either state-owned or have close ties to the Chinese government, this may not be possible, necessitating continued dealmaking with Western companies to enable development of their assets in Western markets.

I do expect Chinese companies to encroach into the earlier-stage discovery work still dominated by Western companies. Now that they have established themselves as skilled drug hunters, Chinese companies will likely invest more into basic research to discover and validate novel targets and biological mechanisms.

Rather than just being fast followers, Chinese companies will soon emerge as true competitors with Western firms on the cutting edge.

Western companies should be concerned. I am less confident that Western companies will adapt to remain competitive with the agility and relentless pace of Chinese companies. The speed with which they can create new molecules is impressive and should be studied. It may be hard to replicate.

Western biotech and pharma companies at times focus too much on elegant and innovative science at the expense of speed. ‘Cool’ science doesn’t always translate to better drugs, and it usually takes more time and costs more money.

On the other hand, Paragon Therapeutics is a US company founded in 2021 that has adopted a model of developing me-better and me-too biologics that imitate approved or otherwise derisked drugs and bring them expeditiously through clinical trials. It’s not the most creative approach scientifically, but it has led to exceptional financial returns on the public markets (see: Apogee, Spyre, Oruka, Jade, and Crescent).

Not only are Chinese labs moving fast to discover assets, but they also move fast into the clinic. While similar data packages are often required for a Phase 1 IND in China as in the US, there are timeline efficiencies in drafting regulatory documents, review, and subsequent trialing that enable rapid readouts.

Especially for a complex product like antibody-drug conjugates and cell therapies, where iterations on multiple components of the overall construct are needed, China has proven to be an effective Phase 1 testing ground for identifying the optimal product, gathering data quickly and efficiently not just from animals, but from healthy volunteers and patients. 

Cell therapies developed in China often start with investigator-initiated studies, for example, to rapidly progress through multiple iterations of these cellular constructs. Will FDA and European regulators increase flexibility to enable Western companies to better match the speed of their Chinese counterparts?

All of this progress is threatened by the geopolitical tension between China and the US. The BIOSECURE Act has made it more difficult for US companies to work with certain Chinese companies, but it has not yet seemed to impede the in-licensing and purchase of discrete assets from China. Chinese government restrictions have made it difficult to transfer certain data and materials outside of China (e.g., genetic data and patient samples), but so far we have not seen major issues with acquiring molecular assets for development outside of China.

The decision to partner with a Chinese company for drug manufacturing is not without its risks. In recent years there have been several Form 483s and Warning Letters issued by FDA to Chinese manufacturing firms for issues ranging from lack of sterility to willful destruction of documents. Of course, plenty of US-based contract manufacturers have had challenges with regulatory compliance, but there is a question of increased scrutiny for foreign-based manufacturers.

Politicians and government officials in both countries may feel pressure to show that they are tough on trade between the two countries. The flow of Chinese assets into Western companies has only recently begun in earnest, yet it feels like the spigot could be turned off at any time. Until it is, however, biotech and pharma professionals from both countries will be busy negotiating deals to bring attractive drug candidates into Western countries.

 

Thanks to Aimee Raleigh for her excellent comments on a draft of this article.

18
Nov
2024

Coastal Culture Clash Around AI in Biotech

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18 Nov 2024

David Shaywitz

“Does the crowd understand?
Is it East versus West
Or man against man?
Can any nation stand alone?”

Burning Heart, by Survivor – Rocky IV

In national politics, the culture wars may pit the coasts against the rest of the country.  In biotech, however, the AI culture war seems to pit the coasts against each other.

Consider this recent LinkedIn post by Chandana Haque, Executive Director of Recursion Pharmaceutical’s startup incubator, Altitude Labs (because they’re based in the mountains of Utah – get it?)

After back-to-back trips to Boston and SF this month, I’m reflecting on some key impressions about the differences between the coasts—especially when it comes to trends in early-stage biotech investing. 🚀

Where did Boston investors get excited? Core biotech: engineering cell machinery, new modalities, and regenerative medicine. This is their strength, and they know it well. But bring up machine learning, and you’ll see a shift; ML just isn’t on the radar for most of New England’s investment crowd, which sticks closely to traditional biotech.

In SF, it’s a different story. AI/ML is seen as critical to understanding complex biology, and it’s almost assumed that every startup will evaluate how ML fits into their approach. SF investors are also intrigued by ADCs, structural biology, and even spatial biology—areas where I found Boston often disengages. The West Coast’s interdisciplinary approach, mixing tech and bio, seems ingrained.

Time will tell how each approach shapes the field, but if you’re traveling coast-to-coast, you’ll feel the difference. What are you seeing?

In short, in the Bay Area, AI is what’s captivating everyone. This passion inevitably finds expression in the contemplation of biotechnology.

Chandana Haque

The attitude in Boston is, for the most part, rather more reserved. While there are obviously high-profile exceptions, including investors like Flagship Pioneering (which has embraced AI as tightly as any Bay Area fund), and young companies like nference, on balance I think Haque is spot on.

At a recent Boston area healthcare conference (which I can only discuss in general terms because it was held under Chatham House rules), I was struck by the code-switching I observed on a venture panel where a prominent West Coast investor at a firm championing the transformative potential of AI offered a conspicuously understated perspective.

It was not so much the substance but rather the emphasis and affect that was different from what I’ve heard him say in other contexts. Here, speaking to somewhat buttoned-up East Coast business audience, his tone was more guarded, his perspective more grounded, and the changes he foresaw more gradual. 

It’s also possible that in California, even VCs focused on biotech are bankrolled by limited partners who are captivated by the promise of technology and are drawn to VCs and startups that speak the language of radical transformation.

In Boston, the focus tends to be different – not least because of the overwhelming presence in Boston of biologists, physicians and other life science experts whose lived experience has reminded them (as it has reminded me) of the staggering complexity of biology and messiness and inherent humanity of medicine. 

Exhibit A for East Coast early-stage biotech investors (as it has been for many previous years) is the latest iteration of Atlas Venture’s annual Year In Review (watch it here), presented by Bruce Booth, summarizing the state of the biotech industry and ecosystem.

Bruce Booth, partner, Atlas Venture

As in previous years, the entire presentation should be required viewing for everyone in the life sciences.

Booth doesn’t spend all that much time on AI, but he calls it out as an area with “great investor interest.” The challenge, he says, is that because of the “incredible amount of buzz and hype” in this space, it’s been “difficult to figure out where the reality is.”

He points out that in 2014, “Recursion said they wanted 100 clinical programs in 10 years” (an aim that I imagine struck tech investors as admirably bold and biotech investors as hopelessly naïve). 

“Unfortunately,” Booth deadpans, “they missed that,” achieving only five clinical programs.

Yet even that represents an important achievement, Booth says, pointing out that “any biotech company that’s just ten years old that has five drugs in the clinic is actually incredibly productive.”

Booth believes AI isn’t just wispy and aspirational. “The reality is there,” he asserts. “AI and machine learning will have targeted impacts up and down the R&D process.”

Preclinical examples he cites include the use of AlphaFold to predict protein structure, and the potential of AI “to predict toxicities or create better routes for manufacturing drugs.”

On the development side of R&D, he suggests AI may contribute to patient selection and enrollment in clinical trials. Moreover, given the established capabilities of large language models, he thinks AI is also likely to provide assistance with regulatory documentation and writing.

But he struggles to find what might be termed West Coast conviction:

“Fundamentally, in the drug R&D space, given the complexity of human biology and the lack of massive datasets, we think that the impact of AI machine learning on R&D is going to be more likely around evolution than revolution. We’re not going to see drug discovery go from a multiyear process to just weeks or days.”

Consequently, Atlas plans to continue its existing (and, Booth says, proven) strategy of leveraging cutting-edge biomedical science to generate promising novel therapeutics.  If AI contributes to the development of a promising target or approach, they’re all for it, but they’re not looking to AI itself as the basis for a platform, nor do they see AI as the transformative unlock for biomedical discovery.

The Atlas view, perhaps not surprisingly, also represents the mindset of most senior pharma R&D executives that I’ve met. 

Yet, this skepticism may start to evolve if — or I suspect, when — AI’s impact is more palpably felt.

As I discussed in my last column, young physicians have quickly embraced an AI clinical app to help guide them through care of individual patients. This should serve as a reminder that while steps enabling change may be imperceptibly small, when change finally comes, it can be rapid. Before long, even big pharmas may find themselves persuaded to embrace their inner Californian.

Meanwhile, leading AI-first biotech companies seem to have become more aware of the essential expertise and implicit knowledge across a range of capabilities that veteran drug developers have accrued and are increasingly recognizing the importance of tapping into this vital experience, found in such abundance along the Charles River.

Perhaps, the next great biotech successfully integrating the approaches represented by the coasts will arise somewhere in the middle.

“If I can change,

And you change,

Everybody can change.”

Rocky Balboa, Rocky IV

1 2 3 4 5 … 77

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