Luma Group

December 2025

The promise of precision medicine has been to deliver safer, more effective treatments by tailoring them to the unique biology of disease. Historically, most treatments have only provided patients with symptomatic relief and rarely corrected the underlying disease biology, leaving them inadequately addressed. In 2003, the mapping of the human genome gave scientists a foundation to understand the link between genetic mutations and disease. Researchers have since continued to map other advanced “omics” such as proteomics and metabolomics. These high-resolution biological maps provide the tools to understand biology in finer resolution and to identify which genes and pathways result in disease. These mapping techniques allow researchers to develop drugs directly targeted to treat diseases and, in some cases, correct the underlying disease pathways.

This shift has transformed drug development from the creation of blunt tools to the crafting of surgical ones, redefining what is possible in therapy design. Targets once considered too dangerous to drug have become standard of care and transformed outcomes for patients. While the most profound impact has been in oncology, precision medicine is now unlocking disease-modifying targets far beyond cancers. In monogenic diseases, it has brought the field closer to true cures, and in immunology it has enabled the selective elimination of disease-causing cell types.

By examining biology at high resolution, scientists can pinpoint vulnerabilities that are specific to disease-driving proteins and design drugs that function only in that context. The result is a new generation of targeted therapies for conditions that were once considered untreatable. Aeovian Pharmaceuticals, Inc. (“Aeovian”) has applied this approach to unlock a new treatment for one of the most common genetic causes of severe pediatric epilepsy.

Aeovian’s Focus

Aeovian has applied a precision approach to one of the body’s most important signaling cascades, the mammalian target of rapamycin (“mTOR”) pathway. The mTOR pathway is a key metabolic regulator for the cell and controls a wide range of biological functions, including cell growth, proliferation, protein synthesis, survival, and metabolism. Mutations in this pathway can lead to catastrophic diseases like Tuberous Sclerosis Complex (TSC), one of the most severe forms of genetic epilepsy.

Aeovian’s first program is focused on delivering a new disease-modifying treatment for TSC. The disease arises from a mutation in the TSC1 or TSC2 genes that leaves cell growth and proliferation unchecked, resulting in benign tumors throughout the body, including in the brain. Although these benign tumors can endanger organ function, the most debilitating symptom is chronic epilepsy. Because patients today mainly rely on broad non-disease modifying antiseizure medications, more than 60% eventually become refractory to treatment. For some patients, this can mean up to 50 to 60 seizures each month with little or no hope of achieving control.

The mTOR pathway signals through two distinct complexes: mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). While overlapping functions exist between the two complexes, mTORC1 is highly involved in protein synthesis and cellular growth whereas mTORC2 helps control survival, and metabolism including glucose homeostasis. TSC mutations drive a selective overactivation of mTORC1, which appears to be the primary driver of disease. Existing mTOR inhibitors approved in TSC (everolimus, sirolimus) non-selectively inhibit both complexes and have been associated with high rates of dose-limiting adverse events. This has limited their chronic use for seizure control in TSC, with patients often only able to tolerate low doses with suboptimal efficacy.

It took years for scientists unravel that mTORC1 inhibition is the driver of therapeutic efficacy in TSC, and off-target inhibition of mTORC2 primarily drives toxicity. If mTORC1 could be selectively inhibited while sparing mTORC2, it could provide a single therapy capable of reducing benign tumor burden and controlling seizures. This could dramatically improve safety for patients and fundamentally transform care.

Figure 1: Whole-body symptoms and effects of TSC.

Source: Cleveland Clinic.

Aeovian’s Approach

Selectively targeting mTORC1 has long been considered nearly impossible due to the similarity to mTORC2 and the complexity of rapamycin’s chemical structure. Large Pharma and biotechs have spent years and hundreds of millions in R&D with limited success. Aeovian took this challenge head on. The team was able to identify how distinct structural motifs in rapamycin contributed differently to its function by mapping how mTOR inhibitors interact uniquely with each of the mTOR complexes. It had been previously shown that rapamycin inhibits mTORC1 and MTORC2 by two distinct mechanisms.  Whereas rapamycin inhibits mTORC1’s kinase functionality directly, it inhibits that of mTORC2 by potently disrupting the formation of new mTORC2 complexes.  Aeovian discovered that altering the binding kinetics of rapalogs to the mTOR protein, mTORC2 inhibition can be avoided while still maintaining potent mTORC1 inhibition

In a Phase 1 study with healthy volunteers, Aeovian’s candidate delivered on the safety promise reaching doses significantly exceeding that of everolimus, the approved non-selective mTOR inhibitor for TSC. While patient trials are still ahead, Aeovian’s results demonstrate the promise of precision medicine: targeted, well-tolerated therapies that can fundamentally change how complex diseases such as TSC are treated.

Aeovian’s therapeutic may not only improve seizure control, but as a disease-modifying drug it will have an impact on the litany of complications that come with a TSC diagnosis. Access to a precise, disease-modifying medication will be a life-altering improvement for patients.

Finding Aeovian

We’ve been following Aeovian for several years, and when we saw their Phase 1 data, it was clear the team had achieved something exceptional. We knew immediately that we wanted to be part of their journey. While there are many reasons we’re excited about this partnership, a few qualities rose to the top:

• Unique Biological Insight: Aeovian’s work is grounded in a deep, nuanced understanding of rapamycin and mTOR biology in TSC. This insight enabled them to engineer around long-standing toxicities, solve a challenge that had been considered out of reach with traditional small molecules, and begin to deliver on it in the clinic. Their commitment to building a rigorous, biology-first foundation has been essential to their success.
• Operational Excellence: The company’s leadership team brings extensive experience across all stages of biotech discovery and development. Remarkably, even as they advance through Phase 2, Aeovian will remain a nine-person team. This is a testament to exceptional focus, discipline, and execution. Their ability to operate with such precision is second to none.
• Patient Impact: The unmet need in TSC is undeniable. Despite decades of biological understanding, patients still lack effective treatment options. Aeovian chose to take on a disease where better therapies are urgently needed, and their commitment to delivering for this community is deeply aligned with our mission.

Better Data, Better Outcomes

At Luma Group, we back world-class teams solving the hardest problems in drug development. Aeovian is doing exactly that, bringing scientific rigor and operational excellence together to pursue life-changing therapies for patients who have waited far too long. We’re proud to partner with them as they work to transform care for individuals living with TSC.

Themasap Khan, Co-Founder and Partner at Luma Group

Publicly funded basic research has long served as the foundation for breakthroughs. Yet proposed federal cuts, including reductions to the NSF and NIH, now threaten the early-stage research that the private sector traditionally does not fund. These changes have already stalled projects, eliminated jobs, and raised concerns about broader delays in scientific progress.

At the same time, the United States faces intensifying competition with China, whose sustained investment in early-stage biotech has accelerated its global share of drug development. Although the US retains leadership in total R&D value, the trend signals growing vulnerability, one seemingly at odds with the administration’s broader “America First” agenda. The launch of the Genesis Mission, with its emphasis on AI-enabled science and biotechnology as a domain of national importance, hints at a potential restructuring rather than an abandonment of federal scientific priorities.

In this environment, the private sector has emerged as an essential stabilizing force. As public funding recedes, venture capital and philanthropy must intervene earlier in the research lifecycle. This shift can bring rigorous private-sector diligence to basic science, likely focusing resources on fewer projects but with greater efficiency. To handle this complexity, biology-specific AI tools will shift from optional luxuries to indispensable necessities. Ultimately, this disruption offers a chance to modernize our infrastructure: building a resilient ecosystem capable of expanding, not just restoring, American scientific progress.

December 2025

Public Research as the Engine of Private Innovation

The symbiotic relationship between public and private funding has led to amazing feats in science. While many discoveries are labeled “accidental,” they usually stem from scientists investigating fundamental questions, like how a cell responds to stimuli or what happens when a gene is perturbed. These questions form the bedrock of innovative breakthroughs.

For example, Nobel Laureate Dr. William Kaelin recently spoke about his academic work on trying to better understand how cells sense oxygen. His work did not start in trying to cure kidney diseases, which is what it eventually led to, but rather trying to answer a basic question about cell biology.

From penicillin to vaccines, history’s greatest breakthroughs stem from scientists simply asking “why” and “how.” This basic research is vital to scientific advancement, yet it is arguably the most vulnerable to proposed federal funding cuts. The private sector often cannot justify betting its bottom line on open-ended questions with uncertain returns. Without this foundational inquiry, however, the entire scientific pipeline will dry up.

What Changes Have Been Made and Where Are We Now?

The US has fundamentally changed its stance on the value of public sector funding. The 2026 budget proposed a 57% reduction in funding for the National Science Foundation (NSF) and a 40% reduction in funding for the National Institute of Health (NIH). These cuts resulted in 3,800 frozen or cancelled grants, evaporated billions in funding, and stalled thousands of careers.

Figure 1: Current and estimated future economic loss by county due to federal health research cuts

Source: Science Impacts and University of Pennsylvania.

The proposal also attacks the backbone of research: infrastructure. By capping “indirect cost” reimbursements, including the costs of buildings, labs, and other infrastructure necessary to support research at 15% (down from the standard 50-60%). The budget could handicap universities’ ability to power their labs and maintain essential equipment and personnel.

Scientists are already warning of a total standstill in discovery. It is a confusing geopolitical strategy: the administration explicitly aims to restore American global dominance, yet it is starving the very science that fuels international competitiveness. Cutting billions from research saves little in the wider federal budget, but the cost to American innovation and the biotech sector may be incalculable.

The Genesis Mission

On November 24, 2025, the White House announced the launch of The Genesis Mission¹, which is largely focused on advancing AI development and research. The order addresses “the need to invest in AI-enabled science to accelerate scientific advancement” and the goal of developing and harnessing AI to address national challenges. The inclusion of biotechnology in the order may be a signal that funding cuts will not be long-term, but that the administration is working on restructuring funding as well as new requirements for funding eligibility, such as programs developing or utilizing AI. Perhaps this is what modern day reforms in science look like.  

The order lists biotechnology as one of the “challenges of national importance that the Secretary assesses to have potential to be addressed through the Mission,” and while there are many types of biotechnology, it is not a far cry to consider the healthcare industry “of national importance.” This administration, and this recent executive order, are heavily focused on national security. Healthcare is not the first indicator that comes to mind when hearing those words, but a healthy population certainly contributes to national security.

This order and mission are new and do not directly mention NIH, NSF, or healthcare research, but an open-minded, optimistic citizen can see how the cuts to funding may be connected to a restructuring and updated national goals.

The “America First” Dilemma: Funding AI while abandoning Biotech

The current administration has been clear in their “America First” agenda, with the goal of prioritizing US economic, militaristic, and diplomatic interests rather than multilateral goals. As competition with China continues to grow and we inch towards a more bi-polar system of international power it is crucial that the US focuses policy on restoring and maintaining our international hegemony. During The Cold War, throughout which the US and Soviet Union competed for unipolar power status, a large contributor to America’s ability to establish status as the leading world power was public funding poured into scientific discovery and the rapid advancement of US technologies and economy.

The Genesis Mission mirrors these winning policies and exemplifies the US’s focus on AI advancement and a desire for rapid, well-funded growth in AI and similar technologies. However, it fails to safeguard the biotechnology and healthcare industries, leaving these industries vulnerable to China.  

As the US creates a funding vacuum in the life sciences, China is filling it, forcing American industry to rely on our primary competitor for innovation. The impact of this neglect is already visible in the data trends:

If “America First” remains the objective, the current strategy is self-defeating. One cannot secure international dominance while depending on geopolitical rivals for healthcare solutions. To compete, the US must invest domestically to ensure we remain the first to discover, commercialize, and distribute the medicine of the future.  

Figure 2: US-China license deal volumes over the past 10 years.

Source: Reuters from Evaluate’s Biomedtracker.

God Bless The Private Sector

Biopharma venture activity has normalized after the 2021 mania, but it has not collapsed in response to public-funding drama. The resilience of the private sector is backed by strategic venture capital firms, well-funded philanthropies, and strong public-private partnerships, all of which can provide a safety net and maintain scientific and technological advancement in the US as federal funding reform settles into a new normal.

Figure 3: Biopharma venture activity was reset after the 2021 bubble and has stabilized since.

Source: Luma Group internal analysis sourced from DealForma.

The research most at risk of losing public funding is early-stage, high-risk work that often shows little commercial opportunity. This shift may create an opportunity the research industry has not previously had. Unlike the public sector, private investors apply rigorous diligence to protect their stakeholders and returns. As science enters private pipelines earlier, it will face this meticulous scrutiny, and investors will likely pass on projects that might otherwise have received public funding and ultimately failed. Although this could leave a significant portion of research unfunded, it may also drive innovation by pushing scientists toward projects with clearer potential rather than spending years on ideas unlikely to materialize. While applying these standards earlier will be taxing for private investors, it also presents a growth opportunity as they deepen their expertise. Biotech-focused investors, already equipped with relevant knowledge and specialization, stand to benefit more than generalists.

Another significant opportunity lies in the integration of AI into investment firms. As the US prioritizes AI capabilities and innovation, and private investors face an influx of opportunities requiring time-consuming, meticulous diligence, AI will become essential. Investors will increasingly need advanced tools capable of supporting diligence on complex biological questions, making the development of biology-specific AI indispensable in the coming years. Such advancements will enable a more efficient and empowered private sector to make strategic investments and drive scientific innovation in the US. Although it is surprising that no robust tool already exists, emerging policies and declining public funding will be the catalyst that shifts this technology from a “nice to have” to a necessity. It may even lead to a reevaluation and modernization of decades of standard practice, and prompt investment in tools that could fundamentally enhance the capabilities of both researchers and investors.

Choosing to approach these policy changes as an opportunity for reform will ideally lead to a more strategic approach to funding and research for both public and private sectors, rather than relying on a strategy of throwing money at the problem. This chance to reform and integrate new technologies into investment strategies presents an opportunity to build an advanced, efficient, and competitive infrastructure to conduct research in the future. Which means, hopefully, that any research that was stopped or delayed will be made up for tenfold with a reformed and improved system.

It is easy to imagine the system simply collapsing, yet history tells a more grounded story. Since the 1970s, every decade has brought waves of major public-sector science and healthcare cuts. Each time, the system bent but did not break, in large part because the private sector stepped in to fill critical gaps. In several cases, the disruption even paved the way for stronger, more resilient structures. We have good reason to expect that pattern to hold.

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1. “Launching the Genesis Mission,” The White House, The United States Government, November 24, 2025, https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/ ↩︎

Despite remarkable medical advances, real-time monitoring of organ health remains out of reach. Curve Biosciences is closing this gap with Whole-Body Intelligence™—a platform built on the world’s largest curated tissue atlas that maps how disease reshapes each organ at the molecular level. By tracing these precise signatures in blood, Curve delivers liquid biopsy tests that move beyond cancer to detect and monitor disease across the full spectrum of organ health. Its first product, a liver test, is already demonstrating superior accuracy and the potential to transform early detection and chronic disease management.

October 2025

Despite extraordinary advances in medical diagnostics, understanding and monitoring organ health in real time is still a significant challenge. While imaging tools like MRIs can offer precise visualization of tissue structure, they are too slow and costly to be a viable option for every patient. This leaves a fundamental gap in physicians’ ability to monitor organ health in a timely, accurate, and affordable manner.

Liquid biopsy emerged as a promising solution to this challenge. With a simple blood draw, scientists can identify molecular biomarkers that describe what is happening in a patient’s body at a moment in time. These biomarkers can be several biological entities, such as fragments of DNA released by dying cells, RNA transcripts, proteins, or other circulating molecules. By decoding these signals, researchers can help clinicians assess organ health, detect disease, and monitor disease progression without the need for invasive tissue biopsies or repeated imaging procedures.

While not yet mature, liquid biopsy has made notable progress in oncology. It has enabled earlier cancer detection, real time monitoring of treatment response, and deep insights into tumor evolution and resistance. However, its potential can reach far beyond cancer. The same molecular readouts that reveal tumor biology can in principle capture information about chronic diseases, inflammatory conditions, and beyond – areas that remain completely unexplored.

Unlocking this potential requires a fundamental shift in how we analyze the blood. Historically, biomarker discovery has relied on comparing blood from healthy individuals to patients with disease to search for patterns that differentiate the two. Because DNA in the blood can come from anywhere in the body, these biomarker discovery readouts are often noisy and limited by an inability to pinpoint where in the body the biomarkers originated, putting a ceiling on their potential accuracy. To realize the full promise of liquid biopsy, discovery must start from the clarity of the tissue itself. By first mapping how disease reshapes molecular profiles within organs, we can be certain that when we detect those same signatures in the blood, they truly reflect the underlying biology from the target organ.

This approach is Curve Biosciences’ core focus. With the world’s largest curated tissue atlas, a blueprint of the human body by organ and disease state, Curve can deliver on liquid biopsy’s long promised potential.

Curve’s Approach

Curve is the world’s first company pioneering Whole-Body Intelligence™, developing first-in-class, blood-based monitoring tests to characterize the continuum of organ health: from healthy organs to chronic disease to cancer. Co-Founder and CEO Ritish Patnaik, PhD built the foundation of the company during his graduate work at Stanford in Professor Shan Wang’s lab. Ritish and his team at Curve manually curated the world’s largest tissue atlas to study how tissues transition from healthy to chronic disease to cancerous.

To study this transition, Curve focuses on tissue-specific methylation patterns on DNA, which are chemical modifications that change with age and disease and can serve as precise biomarkers for disease identification and progression. To generate a complete picture, the company has analyzed more than 400,000 samples spanning over 100,000 studies. Their close review of each sample ensured the data were optimized for processing, and free of errors. When inconsistencies appeared, Curve has personally reached out to investigators to fill gaps and correct mistakes, ultimately creating the largest and highest quality methylation atlas in the world. The success of this work depended on a purpose-built approach that only Curve’s team could have executed with 8+ years of careful effort.

Figure 1: Curve’s platform and product.

Source: Curve Biosciences.

With this Whole-Body Atlas™, Curve has characterized the methylation fingerprint of each organ across disease states and silenced the noise from other healthy organs that confound traditional biomarker discovery approaches. When analyzing a patient’s blood, Curve searches for those same signatures in circulating DNA fragments released by dying cells. Through Whole-Body Intelligence™ models trained on the Whole-Body Atlas™, they have an unprecedented ability to isolate noise to less than 0.02% of the sample, enabling an unobstructed view of organ health.

Figure 2: Curve’s approach to silence biological noise to find the signal.

Source: Curve Biosciences.

Curve’s First Molecular Map: The Liver

One of the body’s most essential, yet overlooked, organs is the liver. It quietly filters toxins and maintains systemic health, but when it becomes distressed, it often remains silent until damage is irreversible. Despite its critical role, the liver has long been underserved by medicine, only recently seeing renewed attention with the traction of FGF21 analogues and GLP-1 based therapies in metabolic associated steatohepatitis. Curve views the liver as the ideal starting point for its platform, to create a suite of tests designed to monitor early signs of fibrosis, assess treatment response, and enable the earliest possible detection of progressive chronic disease and cancer.

Figure 3: Methylation signatures across liver states.

Source: Curve Biosciences.

Curve’s early data in their first liver test already outperforms the benchmarks set by the standard-of-care, demonstrating the potential to reduce avoidable MRIs, lower healthcare costs, and save lives through accurate patient monitoring.

Curve is uniquely positioned to enable a new frontier in chronic disease monitoring, using the same molecular framework to guide treatment decisions in conditions such as liver cirrhosis, metabolic associated steatohepatitis (MASH), and obesity. With this foundation, Curve aims to redefine how medicine detects, tracks, and ultimately prevents disease at the organ level.

Finding Curve

We have known Curve’s Co-founder and CEO, Ritish Patnaik, for many years – since his earliest days considering taking the leap into entrepreneurship. Once we saw his prospective pilot data, we knew the company was on the brink of something transformational. We could not be more excited to partner with him and his team. There are too many wonderful characteristics to list out here, so we’ll focus on just a few of them that led us to partnering with the company:

• Data-first approach: Creating Curve’s Whole-Body Atlas™ with this level of manual curation is an undertaking only an ambitious PhD student would start. Sorting through more than 400,000 samples, identifying inconsistencies, and coordinating with principal investigators to stitch together a consistent dataset is painstaking and slow work.

• World-class team: Transforming how organ health is monitored isn’t as simple as just getting the science right. Teams need to develop a true product, with excellence from the original science all the way through commercial launch. Ritish has assembled that expertise in a team of exceptional leaders:
  
  
  • Nathan Hunkapiller, CSO: Nathan is a recognized leader in blood-based testing, having helped shape the field across some of its most influential companies. Before joining Curve, he served as Vice President and Head of R&D at Natera, a company now valued at more than $26bn, and as Senior Vice President and Head of R&D at Grail, where he led scientific development through its $8bn acquisition.
  • Chuba Ayolu, CTO: Chuba brings a proven track record in building and scaling blood testing from inception to commercial success. He was the founding scientist at Counsyl Diagnostics, where he helped develop the company’s core technology and guided it through rapid growth and its $375m acquisition. His experience spans both deep technical innovation and the operational rigor required to deliver clinically reliable products at scale.
  • Alice Chen, COO: Alice brings extensive commercial and product leadership experience from several of the industry’s most prominent blood testing companies. Prior to joining Curve, she was Senior Vice President of Product at Grail and previously held product and R&D leadership roles at Natera, Progyny, and Sienna Biopharmaceuticals. Her expertise bridges scientific development, product strategy, and market execution.
  • Shan Wang, Chief Innovation Officer, Scientific Co-Founder: Shan is an Endowed Professor at Stanford, having authored over 350 publications and more than 80 issued or pending patents from 30+ years. His pioneering research bridging engineering and medicine inspired the creation of the Whole-Body Atlas™, providing the foundation for its unique approach to biological discovery. His deep scientific insight and conviction in Curve’s mission keep him closely involved with the company.

This depth of expertise from early science to commercial scale-up is essential in pioneering a new technology, shaping a new market, and is a testament to the quality of the foundational work at Curve.

• Large unmet medical need: The chronic disease monitoring space remains largely untapped, awaiting a breakthrough approach to unlock reimbursement from the insurers. Liver disease stands to be the initial proof points for Curve’s platform to set the stage to tackle chronic disease.

Better data, better outcomes

At Luma Group, we back teams redefining treatment paradigms. Curve’s data-first approach embodies that spirit. The process to create Curve’s Whole-Body Atlas™ to enable Whole-Body Intelligence™ was tedious, impossible for AI to recreate, and forms the foundation of what sets Curve apart. We are proud to support Ritish and his team as they build a world where disease can be detected and monitored through only the blood and unlock better care for millions.

Neurodegeneration is a gradual loss of brain function that unfolds over decades before symptoms appear. It isn’t quite a silent killer like heart disease or cancer; it’s an emotional long goodbye. While neurodegeneration manifests in many ways, severe memory loss from dementia is among the most common outcome of this irreparable brain damage.

Each year, millions of individuals with dementia experience progressive losses in memory, personality, and functional independence. Countless family members and caregivers bear witness to this devastating life deterioration. Despite its toll on society, innovations combatting neurodegenerative disease have lagged far behind advances in heart disease that produced statins and the rapidly expanding arsenal of cancer-fighting drugs. These shortcomings are not due to lack of funding or interest from society, but instead because drug development for the brain faces complex challenges that other therapeutic areas do not.

Alzheimer’s Disease (AD) is the most common form of neurodegeneration and is responsible for ~70% of dementia cases. Over the past two years, the AD drug development space has started to turn a corner, highlighted by the first two disease modifying drug approvals. For the first time, patients have drugs that modestly slow cognitive decline, giving hope to both the patient and drug development communities.At Luma Group, we believe that these innovations are just the tip of the iceberg. The clinical learnings from AD on optimal diagnosis, delivery, and patient selection will catalyze further innovation in neurodegenerative proteinopathies like Parkinson’s disease, Lewy body dementia, Huntington’s disease, and amyotrophic lateral sclerosis (ALS). This whitepaper uses AD as a case study to explore the future of care in neurodegenerative disease. We present the current understanding of AD disease biology and treatment landscape for a generalist audience and give our vision of the next era of care in AD: a world where AD diagnosis is as easy as blood test and physicians can choose from a broad armamentarium of therapies to tackle the multifaceted features of neurodegeneration.

October 2025

What is Alzheimer’s Disease?

The defining hallmark of AD pathology in the brain is the presence of insoluble amyloid β plaques found outside of neurons and tau neurofibrillary tangles inside of neurons. The abnormal accumulation of these proteins has been the focus of AD drug discovery since Alois Alzheimer first reported on the disease in 1907. Alzheimer described a patient who “developed a rapid loss of memory” and was “disoriented in their own home.”¹ This description matches our current understanding of AD clinical presentation, as patients become increasingly unaware of their surroundings, lose all ability to recognize family members, and eventually become prone to delusions and hallucinations.

At the molecular level, cognitive decline closely correlates with tau tangle pathology, but amyloid plaques appear in the earliest stages of AD. This suggests a sequential mechanism where amyloid initiate disease, but tau drives neurodegeneration. Beyond these two proteins, other factors, like chronic neuroinflammation in response to amyloid and tau, contribute to disease progression. Thus, while amyloid plaques and tau tangles are considered the hallmark pathology, AD is now recognized as a multifactorial disease in which amyloid, tau, and neuroinflammation interact to cause progressive neuronal damage and cognitive decline.²

Figure 1: The sequential formation of amyloid plaques and tau tangles.

Source: Gomez W, Morales R, Maracaja-Coutinho V, et al. Down syndrome and Alzheimer’s disease: common molecular traits beyond the amyloid precursor protein. Aging (Albany NY) (2020 PMID: 31918411.

How are plaques and tangles formed?

Studies on amyloid processing have found that not all forms of amyloid β are toxic. Amyloid β is formed when two enzymes called β-secretase and γ-secretase cleave amyloid precursor protein (APP). Under normal conditions, these enzymes cleave APP to form soluble monomers of amyloid β, which occasionally make small aggregates that do not have any disease-causing properties. However, under AD conditions, these monomers aggregate into larger, insoluble amyloid fibrils and plaques that are a hallmark of AD, especially in early or presymptomatic AD. Tau neurofibrillary tangles on the other hand, tend to appear in the later stages of the disease. These are formed when native tau is modified with excessive phosphate groups (hypophosphorylation), which also makes tau more prone to aggregation.

Figure 2: A closer look at the formation of insoluble amyloid plaques and fibrils

Source: Hampel, H, Hardy, J, Blennow, K et al. The Amyloid-β Pathway in Alzheimer’s Disease. Mol Psychiatry (2021). PMID: 34456336.

Why do plaques form?

Genetics research has provided some of the clearest clues on why these disease-initiating aggregates form. Mutations in the PSEN1 gene, a key component of the γ-secretase enzyme, alter the cleavage of APP and lead to production of different sizes of amyloid β monomers. Some of these monomers—particularly the amyloid β species that is 42 amino acids long—are more prone to aggregating into amyloid plaques.³ Importantly, PSEN1 mutations are the most common cause of early-onset AD, often leading to symptoms decades earlier than the typical age of onset. Nearly all patients with PSEN1 mutations have symptoms before the age of 60, with some case studies reporting symptoms as early as 32.⁴ While understanding PSEN1 disease biology offers important context on AD’s underlying mechanisms, autosomal dominant, inherited AD only accounts for ~1% of all AD cases. The majority of AD is classified as sporadic AD, caused by a mix of environmental and genetic risk factors that predispose you for disease. For example, aging is the strongest environmental risk factor for developing AD and certain forms of a gene called APOE can increase or decrease the chance of developing AD.⁵

Figure 3: Prevalence of late-onset and early-onset AD

Source: Adapted from Sirkis DW, Bonham LW, Johnson TP, et al. Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease. Mol Psychiatry (2022). PMID: 35393555.

Evidence of neuroinflammation in AD

In the age of deeper and cheaper DNA sequencing, new genetic discoveries have expanded our understanding of AD beyond the amyloid hypothesis. Notably, genome-wide association studies have implicated microglia, the brain’s resident immune cell, as a driver of AD symptoms. These studies found that mutations in a gene called “triggering receptor on myeloid cells 2” (TREM2) increases AD risk by 3-5x, fueling a new area of research to elucidate the role of microglia and neuroinflammation in AD disease progression.^{6, 7} The neuroinflammation hypothesis is relatively nascent, but our current understanding suggests that microglia are initially neuroprotective by eliminating plaques and tangles in the brain. However, as the production of amyloid and tau rapidly increases in later stages of disease, microglia fail to properly clear these proteins. Under this hypothesis, the microglia become chronically activated, creating a sustained pro-inflammatory environment in the brain that contributes to neuronal damage. Some emerging evidence from imaging studies in human patients has now indicated that the co-localization of amyloid, tau, and activated microglia in the brain are high risk-factors for cognitive impairment.⁸ Although the exact mechanisms linking neuroinflammation to AD pathology and neurodegeneration remain unclear, the involvement of microglia underscores that AD is a multifactorial disease, engaging many cell types and processes in the brain.

AD is now commonly grouped into a larger family of proteinopathies, where pathogenic proteins such as amyloid and tau are the hallmarks of the disease and drive symptom progression. As we discuss later, AD drug discovery efforts have primarily targeted amyloid β, either inhibiting its production or targeting it directly to stop the disease. Additionally, the emergence of the neuroinflammation hypothesis has also sparked new interest in microglia targeting therapies. Despite our growing understanding of AD biology, drug discovery has been long and challenging, and the first approvals of disease-modifying drugs that slow the progression of AD have only happened in the past two years despite decades of research efforts and failed trials.

Why is Alzheimer’s Disease drug discovery so challenging?

The central nervous system (CNS), a dense, intertwined network of neurons connected by innumerable synapses, is by far the most complex organ system in the human body. It is fundamental to our capacity to process information, experience emotion, and control movement. This intricate web is central to our survival, and the human brain is unique compared to other species, which complicates our ability to develop and test innovative therapies.  

Figure 4: Challenges in CNS Drug Development

Source: Adapted from biorender.com

Biology differences limit predictivity of preclinical models

The human brain is uniquely complex compared to animal models (e.g., rodents, non-human primates, and others), resulting in low-fidelity preclinical studies that do not translate when we bring therapies into the clinic. While the human brain is notably larger compared to model species (even when you account for body size), this alone does not explain the lack of translatability. Equally as important, humans have developed a more structured cortex than other animals, with more cortical folding that increases our cortical density and surface area. At the cellular level, human brain cells are also more structurally complex and diverse: our neurons are longer and more interconnected (increased dendritic tree branching), and some human brain cells (e.g., interneurons, astrocytes, and glia) have much broader molecular diversity compared to animal models. Taken together, these differences help explain why current model systems fail to accurately recapitulate human brain function. Drug discovery is an iterative process that relies on rigorous preclinical studies conducted in animal models. However, because the brain lacks representative models, the path to developing AD therapies is especially difficult and high-risk.

Delivery challenges to the brain

CNS-targeted therapies must also overcome the restrictive blood-brain-barrier (BBB) to achieve relevant drug exposure levels. In organs like the liver, kidney, and heart, drugs transport freely from the blood into tissue. In the brain, however, drug penetrance is limited by the BBB, a tightly joined cellular layer that lines blood vessels in the brain and protects it from harmful pathogens. However, the BBB is also a formidable barrier for drug discovery, as over 98% of small molecules and nearly all antibodies fail to penetrate the BBB at therapeutically relevant levels. While all vertebrates have an intact BBB, many drugs tested in animal models fail to have comparable drug exposure levels when used in humans, adding a layer of additional complexity to CNS drug discovery. The BBB makes the CNS the most delivery-sensitive organ, and, combined with low-fidelity preclinical models, it is especially challenging to predict translatability of CNS therapies before entering clinical trials.

Clinical trial challenges

AD drug discovery has also been limited by the inability to only enroll patients with amyloid or tau pathology, confounding trial results. Until recently, AD patients were included in clinical trials based on questionnaires that assessed cognitive impairment. However, cognitive impairment is caused by many underlying conditions, and patients were often misdiagnosed with AD despite no evidence of amyloid or tau pathology. In early clinical trials, approximately 25% of patients were later found to have no amyloid pathology, even though these trials tested drugs that targeted the amyloid pathway.⁹

In recent years, the field has made many advancements using positron emission tomography (PET) imaging to positively confirm presence of amyloid and/or tau in living patients.¹⁰ Previously, amyloid and tau pathology diagnosis was only possible in post-mortem histological samples, limiting our ability to enrich clinical trials with patients who were positive for amyloid or tau. Using PET, patients are injected with a radiopharmaceutical tracer that makes amyloid or tau protein quantifiable using non-invasive imaging. With this well-validated method, we can now enroll patients who are positive for amyloid/tau pathology and can also stratify them based on the severity of their plaque burden. Given that modern AD drugs act directly on the amyloid pathway, PET has been a pivotal innovation that has pushed AD drug development forward.

The widespread adoption of PET imaging is reflective of the new age AD drug development, driven by novel diagnostics, new therapeutic innovations, and a deeper understanding of disease biology. As we will discuss in the next section, for the first time in history, patients have access to disease-modifying AD therapies. We are hopeful that this trend of novel diagnostic methods and transformative medicines will usher in a new era of neurodegenerative disease drug development, allowing us to precisely diagnose and treat the wide range of neurodegenerative proteinopathies, including AD, Parkinson’s Disease, dementia, ALS, and Huntington’s Disease.

History of Alzheimer’s Drug Development

The first AD therapies from the 1990s and early 2000s were approved for their ability to manage AD symptoms, not because they were disease-modifying. While they helped mask disease progression, the efficacy of these drugs wanes over time and they become less effective as neurodegeneration progresses. These early symptomatic therapies are key to delaying the onset of symptoms, but there was—and continues to be—great unmet need for a drug that slows or reverses disease progression.

Category	Approved drugs	Target Neurotransmitter	Mechanism & Rationale
Cholinesterase Inhibitors	tacrine, donepezil, rivastigmine, galantamine	Acetylcholine	Increases acetylcholine signaling as patients show loss of cholinergic neurons11
NMDA receptor antagonist	Memantine	Glutamate	Reduces neuron excitability by blocking glutamate signaling, as overactive excitability damages neurons11

Early drug development failures

The wealth of evidence implicating the amyloid β pathway has driven recent AD drug development efforts.¹² The first attempts of disease-modifying drugs were inhibitors of γ-secretase and β-secretase (BACE1) activity, which act by decreasing total amyloid monomer production. However, these efforts largely failed. Semagacestat, for example, was the only γ-secretase inhibitor to progress to Phase 3 trials, where it failed to meet its primary endpoints, only modestly reduced amyloid levels, and cognitive decline slightly increased in the treatment group versus placebo.¹³ Similarly, the BACE1 inhibitors also failed to show improvements in cognitive decline relative to placebo, despite significant decreases in amyloid monomer production and moderate decreases in plaque burden.¹⁴ This is notable as many of the BACE1 inhibitor trials recruited patients with only very early or prodromal AD, suggesting that even the earliest symptomatic stages of AD may be too late to intervene with the secretase inhibitor class of drugs.

Turning a corner: The first clinical trial successes

The failure of the secretase inhibitors has narrowed the spotlight on monoclonal antibodies that directly bind to amyloid β.  The first breakthrough came in 2021 with the accelerated approval of aducanumab (Aduhelm developed by Biogen). Lecanemab (Leqembi, developed jointly by Biogen, Eisai, and BioArctic) and donanemab (Kisunla developed by Eli Lilly) followed soon after. While aducanumab was eventually withdrawn from the market, lecanemab and donanemab were the first disease modifying AD drugs to receive full approval from the FDA. PET imaging played a key role in achieving these milestones, as it enabled rapid, non-invasive monitoring of disease modifying behavior, and was the biomarker that enabled use of the accelerated approval pathway. While these drugs have provided some validation of the amyloid hypothesis, they only show modest slowing of disease progression ^15,16 and there is ample opportunity to build better and more efficacious AD therapies. Additionally, a new, amyloid-targeted treatment risk has emerged in the form of amyloid-related imaging abnormalities (ARIA) caused by vasogenic edema/effusions (ARIA-E) or microhemorrhages (ARIA-H). The exact cause of ARIA is still not clear, but these are dose-limiting side effects that exclude carriers of the ε4 allele of APOE from receiving lecanemab and donanemab, who are at increased risk of developing these side effects.

What does the future hold?

The progress of the AD drug development space in the last 25 years is undeniable, and the momentum to generate better disease modifying therapies will only continue. Moving forward, we foresee a world where AD and other neurodegenerative diseases are diagnosed using criteria beyond their symptomatic presentation. Instead, Parkinson’s Disease, ALS, Huntington’s Disease, and dementia will be defined as proteinopathies with distinct molecular signatures. These protein-level diagnoses will guide clinical recommendations of protein-specific treatment options, attacking the underlying pathology of neurodegenerative disease. With this framework in mind and driven by the current era of technological innovation in the life sciences, we envision multiple trends that are shaping the neurodegenerative treatment landscape for better patient outcomes:

1. Diagnostics you can order from your primary care physician: Much like the implementation of PET tracers, the approval and broad adoption of blood-based diagnostics will be a meaningful step forward that benefits everyone a part of the AD patient journey. While PET is costly and requires recommendation of a specialist physician, blood-based diagnostics can be ordered by a primary care or community physician with a routine blood draw at a tenth of the cost. In the past year, both the Lumipulse (Fujibre) and Elecsys (Roche and Eli Lilly) blood-based diagnostics were approved by the FDA, with the Elecsys test being the first test approved for use in the primary care setting. Much like a routine lipid panel as part of a yearly checkup, we envision a future where patients can routinely access a “neurodegeneration panel” to screen the type of neurodegenerative disease or a multi-omics diagnostic to detect evidence of neuroinflammation or presymptomatic disease. Combined with new advances in AI/ML to deconvolute multifactorial signals, we look forward to seeing a future of early diagnosis of neurodegenerative disease and neuroinflammation, opening the door for prophylactic treatment of patients.
2. Moving beyond amyloid and passive immunotherapy: While lecanemab and donanemab are passive immunotherapies (i.e., antibodies that bind to amyloid without an apparent mechanism that clears plaques), we are looking forward to innovations that actively eliminate pathogenic proteins and strategies that move to targets beyond amyloid, such as tau. The first targeted protein degrader (vepdegestrant developed by Arvinas and Pfizer) is scheduled to be approved in 1H 2026 for breast cancer. On the heels of this historic FDA decision, the time for novel mechanisms of action in neurodegenerative disease beyond passive amyloid clearing is on the horizon. While the current iteration of protein degraders have unfavorable drug-like properties that make brain penetrance challenging, we are closely following development of novel degrader approaches, including protein-based methods and modulators of autophagy, mitophagy, and other protein homeostasis pathways. As we move into this new era of viewing neurodegenerative disease as proteinopathies, we believe in innovative therapies that go beyond amyloid, using mechanisms of action that actively eliminate pathogenic proteins.
3. Patient stratification that powers faster, smaller trials: Patients with neurodegenerative diseases face difficult outcomes with few disease modifying options. In the AD space, patients with PSEN and APP mutations (e.g., autosomal dominant AD) can see symptoms as early as their 30s. Estimates on the prevalence of autosomal dominant AD vary, but even conservative estimates suggest 10s of thousands of patients in the US.While these patients may be at highest risk of an aggressive disease course, they also have the clearest understanding of their underlying causes. The strong genetic nature of early-onset AD makes them especially addressable by gene therapies, and positive results from uniQure’s AMT-130 in Huntington’s Disease should serve as a beacon of innovation to follow suit in other neurodegenerative diseases.¹⁷ In a large patient population like AD, being able to enrich the treatment group with more homogenous biological underpinnings should attract innovation, not deter it, as these strategies open the door for faster, smaller trials that still have outsized impact in the patient community.
4. Delivery that opens the gates of the BBB: Building off the foundational antibody approvals, there remains much work to improve their efficacy. The BBB typically restricts biologics to 0.1% or less of the injected dose into the brain and several efforts are leveraging receptor mediated transcytosis to shuttle biologics across the BBB, increasing brain penetrance by 10-100x. Roche’s trontinemab will be the first clinical validation of an active transport approach by targeting the transferrin receptor.¹⁸ Many other strategies have followed suit in this area, targeting receptors such as human CD98 and ALPL.^19,20 Given that the CNS is the most delivery-sensitive therapeutic area, we are following how shuttle technologies can improve drug penetrance in the human brain, widening the therapeutic index of CNS-targeted therapies.
5. Drug discovery using human brains in a dish: Due to the low predictive power of animal studies for CNS drug discovery, the field has turned to alternative models of human biology. Brain spheroid models are 3D in vitro models that utilize neuronal and non-neuronal cells from humans to create miniaturized human brains in a dish. While these models are far from perfect and can only recreate small networks of cells without an intact BBB, they offer an alternative, human-based model that can be combined with results from animal studies. Importantly, the FDA has published guidance that it plans to phase out animal studies in favor of alternative models like brain organoids and understanding how to best implement these models is a necessity for the field moving forward.²¹
6. Resetting microglia to take back control of inflammation: With the neuroinflammation hypothesis taking hold in our understanding of AD, microglia targeted therapies are likely to play a major role in the AD treatment paradigm by synergizing with the amyloid therapies. Our current understanding of neurodegeneration has not definitively clarified the role of neuroinflammation in neuronal loss, but the preclinical evidence of an immune component in neurodegeneration grows daily. Moving forward, treatments that modulate microglia to control of neuroinflammation are likely to be necessary therapies in the armamentarium that can be used in combination to treat neurodegeneration.
7. Next-generation therapies progressing into the clinic: The therapeutic landscape for CNS diseases is nascent and new technological innovations are continuing to make their way into the brains of patients. In this next era of care for neurodegenerative diseases, we will see these therapies enter the clinic at scale, pushing the boundaries of how we think about patient treatment options. In Parkinson’s disease, we have already seen promising clinical data using pluripotent stem cell therapy.²² We are excited to see the continuation of these technological discoveries, including alternative cell therapies and strategies for cellular reprogramming, enter the patient treatment paradigm.

The next era of caring for neurodegenerative disease

Given these areas of core innovation that we are likely to see in the near term, we envision a multi-pronged approach in the future that gives patients with AD and other neurodegenerative diseases a multitude of options to address disease head on. Diagnostics will be the foundation of this new age of medicine, giving us predictive insight to identify the disease before symptom onset. In a perfect world, patients as young as their 40s will be empowered to take a panel of blood-based and genetic diagnostics, without the fear that their diagnosis will mean a slow-moving death sentence. Abolishing that fear will require disease modifying drugs that actively address the pathogenic underpinnings of disease, that are safe to use chronically and prophylactically. While this is a far cry from where we stand today, we believe that the innovations on the horizon will be the first steps to make this vision a reality.

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1. Andrade-Guerrero J, Santiago-Balmaseda A, Jeronimo-Aguilar P, et al. Alzheimer’s Disease: An Updated Overview of Its Genetics. Int ↩︎
2. Heneka, MT, van der Flier, WM, Jessen, F et al. Neuroinflammation in Alzheimer disease. Nat Rev Immunol (2025). PMID: 39653749. ↩︎
3. Andrade-Guerrero J, Santiago-Balmaseda A, Jeronimo-Aguilar P, et al. Alzheimer’s Disease: An Updated Overview of Its Genetics. Int J Mol Sci (2023). PMID: 36835161. ↩︎
4. Andrade-Guerrero J, Santiago-Balmaseda A, Jeronimo-Aguilar P, et al. Alzheimer’s Disease: An Updated Overview of Its Genetics. Int J Mol Sci (2023). PMID: 36835161. ↩︎
5. Eid A, Mhatre I, Richardson JR. Gene-environment interactions in Alzheimer’s disease: A potential path to precision medicine. Pharmacol Ther (2019. PMID: 30877021. ↩︎
6. Jonsson T, Stefansson H, Steinberg S, et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med (2013). PMID: 23150908. ↩︎
7. Guerreiro R, Wojtas A, Bras J, et al. TREM2 variants in Alzheimer’s disease. N Engl J Med (2013). PMID: 23150934; PMCID: PMC3631573. ↩︎
8. Pascoal TA, Benedet AL, Ashton NJ, et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med (2021). PMID: 34446931. ↩︎
9. Karran E, Hardy J. Antiamyloid therapy for Alzheimer’s disease–are we on the right road? N Engl J Med (2014). PMID: 24450897. ↩︎
10. Chapleau M, Iaccarino L, Soleimani-Meigooni D, et al. The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review. J Nucl Med (2022). PMID: 35649652. ↩︎
11. Raina P, Santaguida P, Ismaila A, et al. Effectiveness of cholinesterase inhibitors and memantine for treating dementia: evidence review for a clinical practice guideline. Ann Intern Med (2008). PMID: 18316756. ↩︎
12. Karran E, De Strooper B. The amyloid hypothesis in Alzheimer disease: new insights from new therapeutics. Nat Rev Drug Discov (2022). PMID: 35177833. ↩︎
13. Doody RS, Raman R, Farlow M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer’s disease. N Engl J Med (2013). PMID: 23883379. ↩︎
14. Egan MF, Kost J, Voss T, et al. Randomized Trial of Verubecestat for Prodromal Alzheimer’s Disease. N Engl J Med (2019. PMID: 30970186. ↩︎
15. Mintun MA, Lo AC, Duggan Evans C, et al. Donanemab in Early Alzheimer’s Disease. N Engl J Med (2021). PMID: 33720637. ↩︎
16. van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in Early Alzheimer’s Disease. N Engl J Med (2023). PMID: 36449413. ↩︎
17. https://www.uniqure.com/investors-media/press-releases ↩︎
18. Grimm HP, Schumacher V, Schäfer M, et al. Delivery of the Brainshuttle™ amyloid-beta antibody fusion trontinemab to non-human primate brain and projected efficacious dose regimens in humans. Mabs (2023). PMID: 37823690. ↩︎
19. Chew KS, Wells RC, Moshkforoush A, et al. CD98hc is a target for brain delivery of biotherapeutics. Nat Commun (2023). PMID: 37598178. ↩︎
20. Voyager Therapeutics, Corporate Presentation (October 2025) https://ir.voyagertherapeutics.com/static-files/329b71f5-f944-496c-b757-372a92e82b55 ↩︎
21. https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs ↩︎
22. Tabar V, Sarva H, Lozano AM, et al. Phase I trial of hES cell-derived dopaminergic neurons for Parkinson’s disease. Nature (2025). PMID: 40240592. ↩︎

September 2025

Healthcare is one of the most data-rich and capital-intensive sectors yet remains decades behind in analytics. High-stakes decisions rely on incomplete data and systems that are slow, manual, error-prone, and expensive. The result is billion-dollar missteps, extended timelines in an industry already operating on decade-long horizons, and delays in bringing life-saving medicines to patients.

Despite rapid advances in LLMs, these breakthroughs have yet to meaningfully change how strategic decisions are made in biotech. Today’s LLMs are fluent language tools. They can summarize dense papers, extract entities, or polish prose, but language is not the same as evidence-backed strategy.

Biotech demands reasoning across fragmented, multi-modal data: trials, patient outcomes, regulatory precedent, and competitive context, all shifting in real time.

Addressing this call for platforms that are purpose-built for biotech, fluent in the networked language of biology, and capable of turning vast, messy evidence into actionable, trusted insights. Only with tools like these will strategic decisions in biotech and healthcare match the rigor the field demands.

In this whitepaper, we (1) examine how strategic decisions in biotech are made today, (2) show where today’s LLMs help and where they fail, (3) propose a bio-native architecture built on a shared data foundation, multi-hop reasoning, and UX-driven validation loops, and (4) introduce LABI, Luma Group’s AI for Biotech Intelligence, and how we aim to apply these principles in practice.

How Are Strategic Decisions Made in Biotech Today?

Consider a typical diligence scenario to evaluate a new therapeutic candidate. You ask an AI system: “What is the current standard of care in this disease, and how does this candidate compare on endpoints, patient selection, safety, and competitive position? Pull the underlying trials, relevant patient datasets, regulatory precedents, and commercial context, and return it as a citation-backed memo with tables.”

It is a compelling vision: a single click yielding evidence-backed answers that can be trusted. But, when pushed with inquiries like this, today’s LLMs fall short. Results are often incomplete and inaccurate, with hallucinations that make them unfit for high-stakes strategic decisions.

Beyond these limits, the diligence process in biotech is uniquely complex and fundamentally different from generalist investing. It requires deep, cross-functional expertise across domains such as foundational biology, discovery and development, clinical data, regulatory precedent, reimbursement strategy, and commercial dynamics. Each domain is a fragmented and hard-to-reach pool of millions of data points that are still largely hunted down and tracked manually. Connecting them into a coherent view requires months of labor-intensive and error-prone work. Moreover, the landscape is constantly shifting. As the right information surfaces, connections emerge both within and across domains, which then need to be tested against multiple possible outcomes. A failure in a novel class, for example, can cascade across development, commercial potential, and competitor pipelines, inducing a rippling effect across domains.

To address these complexities, the industry still takes a piecemeal approach: scaling by headcount, subscribing to multiple data platforms, augmenting existing workflows with generic AI, and tracking and pulling data into manual workflows. As a result, high-stakes biotech decisions are made with fragmented, incomplete data, through manual, error-prone processes that risk costly missteps.

Figure 1: Biotech decision-making today: High-stakes biotech decisions are made with fragmented, incomplete data, through manual, error-prone processes that risk costly missteps.

AI’s Impact and Limitations in Biotech Decision Making

The real constraints on AI in biotech are not computational power or algorithms but rather data and complexity.

Existing LLM successes hit complexity walls: Even where LLMs work well, they struggle with complexity. Code writing AI has immediate feedback when code compiles or breaks but fails with large codebases. Travel planning LLMs can pull from standardized databases but fail when preferences conflict or constraints shift. These examples show that AI breaks down when reasoning must span interconnected systems, even with built-in advantages like immediate feedback and standardized data.

Biology uniquely amplifies these challenges: Biotech faces the same complexity scaling problems without any structural advantages. The heterogeneity of biological data creates fundamental problems. Clinical trials for identical indications might report results in completely different formats, representing disagreements about what constitutes meaningful measurement in biological systems. Reasoning becomes exponentially more complex when drug interactions, competitive landscapes, protein networks, and patient populations are layered on.

Figure 2: Complexity and network effects in biotech: Biotech complexity emerges from connections: foundational biology to clinical data, regulatory strategy to reimbursement, market dynamics to competitive intelligence. Each layer interlocks, creating numerous reasoning challenges.

User experience solutions remain unexplored: Other domains experiment across the full spectrum from autocomplete to autonomous agents, yet even well-resourced teams are pivoting back to human-in-the-loop systems for complex tasks. Biotech has barely explored beyond basic autocomplete. This represents an opportunity: biotech can learn from experimentation in other domains and design purpose-built solutions for its unique data structures and reasoning requirements.

Building a Bio-Native Intelligence Platform

Building AI systems that can reliably reason across interconnected biotech datasets requires three fundamental design principles:

Get the data in one place, and one language: Before clever prompts, we need a common evidence layer of disparate data pulled into a standard format with clear labels. Today, the facts live in a hundred sources, from registries to PDFs to supplements to figure panels, and none of the sources describe their findings the same way. While industries like travel planning have structured data and flight or hotel aggregators, biology lacks both standardization and aggregation.

LLMs should read messy sources, suggest mappings, line up fields that mean the same thing, and flag conflicts for a human to review, but the shared format and hub must come first. As data lands in this structure, simple checks keep things comparable: units and scales match, time frames line up, patient groups are matched, and endpoints mean the same thing. Every fact keeps its source, date, and version, so teams can trace provenance and monitor changes. And should be built in a format that is graph-native so platforms can follow and justify links and support the networked reasoning biology requires.

Build models that reason over networks: Networks underpin biology, so our systems must reason over links, not just lines of text. Defensible answers require walking through several connected steps, so-called “multi-hop reasoning”. Modern LLMs are strong at single lookups and fluent summaries, but when multi-hop reasoning is required to respond to a user, they often lose track of constraints, mix cohorts, or skip checks.

At the system layer, perhaps that means moving from “chain-of-thought” to “web-of-thought”: approaches that can traverse multiple paths in a graph at once, check constraints, and reconcile conflicts before proposing an answer.

At the model layer, we might give the model a map of these connections and make those links matter. Instead of treating everything as words in a row, the model should score and select hops (e.g., from a target to its pathways, then to assays and cohorts) and weigh how strong each link is. Along the way, we can encode simple and transparent requirements, so relationships shape the result, not just nearby text. Adopting these principles should enable us to get answers that are traceable, comparable, and reproducible, the qualities high-stakes biotech decisions require.

Figure 3: Chain-of-thought vs. Web-of-thought: Left: a linear “chain-of-thought” compresses evidence into one sentence, ignoring dependencies and hidden constraints. Right: a web-of-thought maps multi-hop links and conditions for action. Biology is a network, not a list, so credible answers require multi-hop reasoning with checks and citations, not a single pass-through text.

Create a user experience that provides validation and feedback loops: Unlike coding LLMs, where feedback is immediate, in biotech the impact of a single choice may not be clear until years later in the clinic or market. Reinforcement learning solved similar challenges through simulation, as in AlphaGo and AlphaZero, but full simulations of biotech investment decisions are not feasible. Adversarial AI, such as digital twin investors that stress-test recommendations, is a possible long-term path but remains speculative. A more immediate approach is to use interaction patterns as training signals. Analysts already perform checks and cross-validations that could be standardized. Systems can learn from explicit corrections as well as implicit behaviors: which trial comparisons are explored or ignored, which analyses are saved or discarded, and how experts navigate between drug mechanisms. In this way, the interface itself becomes validation infrastructure, creating feedback loops that biotech has lacked, offering a new path to make AI trustworthy for guiding high-stakes decisions. Taken together, these principles shift biotech analysis from scattered, manual workflows to something closer to the “single-click diligence” vision. Modernized data pipelines provide standardized, queryable inputs. Network-native models generate multi-hop insights that reflect the interconnected structure of biology, and validation built into the user experience ensures systems continuously refine their domain expertise. The result is not just fluent AI, but trusted decision infrastructure capable of analyzing and supporting the high-stakes choices that define biotech investing and strategic decision making. 

Why we built LABI

As scientists, operators, and investors, we make decisions that demand comprehensive coverage of the data landscape. We need foundational biology, clinical results, regulatory shifts, and more, distilled into clear signals and continuously updated to reflect the dynamic nature of the data. Over the past two years, we have tested existing analytical and AI tools in our workflows and observed firsthand what many across the industry also recognize: generic systems fall short in biotech. 

This gap is why we built LABI, Luma Group’s AI for Biotech Intelligence. LABI is an end-to-end platform purpose-built for biotech, designed to deliver comprehensive coverage of all critical data sources, translate that information into validated, traceable, decision-ready insights, and ensure those insights are maintained in real-time as new evidence becomes available.

Our team has been addressing the foundational limitations of applying generic AI to biology by building a biology-native platform. Rather than augmenting existing workflows with off-the-shelf tools, we are designing LABI from the ground up to capture the full complexity of the field.

LABI aggregates, curates, and harmonizes critical data pools, including peer-reviewed manuscripts, patient-level datasets, biorepositories, clinical trial records, regulatory filings, and commercial and financial datasets, spanning millions of sources. This enables LABI to speak the language of biology (graphs, tables, statistical analyses, and more) and to reason in a highly networked, real-time manner, so insights are both comprehensive and accurate.

At its core, LABI makes biology navigable. It organizes genes, proteins, pathways, samples, and outcomes into a connected map, so comparisons are valid. Every answer is anchored in evidence and linked directly back to sources to eliminate the frustration and dangers of hallucinated outputs. Agentic AI workflows continuously scan and cross-check new information, surfacing what has changed, when it changed, and why it matters. The platform incorporates feedback loops so that every interaction strengthens evidence prioritization, sharpens comparisons, and hardens checks, with improvements compounding over time.

 LABI is the platform we long wished we had as biotech investors, a platform that helps uncover the most meaningful data and insights in a field defined by complexity and constant change. Making the wrong choice or missing the right one has enormous consequences: missed signals can cost billions, delay decade-long timelines, and ultimately slow the delivery of life-saving medicines. The impact of this challenge extends far beyond capital allocation, touching everyone in healthcare who makes high-stakes strategic choices, from clinical development to corporate strategy. This is a mission we are deeply committed to, and we look forward to sharing more as it evolves.

Acknowledgements

Luma Group would like to acknowledge Manav Kumar for his thoughtful discussion and contributions to this white paper.

Jamie Kasuboski, Partner at Luma Group

There are many perceptions of what a venture capitalist (VC) is. In the biotech space, VCs are enigmatic because many of us never imagined becoming VCs, and many future VCs might have no idea they’re headed down that path either.

My story, like many in biotech, started with a passion to help sick people. I was fortunate to discover my passion at the age of six, when I told my parents that I wanted to be a genetic engineer. I didn’t fully understand what that entailed, but the film Jurassic Park sparked my curiosity. I was fascinated by the idea that nature had invented biological Legos called DNA that could be assembled to create humans, sea slugs, bananas, bacteria, mold, and, most intriguingly, entirely new life forms.

Less than two decades later, I received my PhD in Molecular and Cellular Biology. I loved deciphering nature’s clues and genetic codes to figure out the “how” and “why” in nature’s playbook. However, I didn’t yet know how to translate this knowledge from the lab into life-saving therapies. I continued my research journey post-PhD and eventually landed an industry postdoc position at Pfizer. At Pfizer, I soaked up every fact, lesson, piece of jargon, and process required to take an initial discovery and turn it into a drug. At this moment, something clicked for me: my true passion wasn’t just making discoveries; it was figuring out how to transform those discoveries into medicines capable of helping patients. My time at Pfizer also made it clear that I wasn’t ready to join an organization as large as Pfizer long-term. Thankfully, after years of trying different career trajectories and with the help of some great mentors, it became clear that biotech venture capital uniquely aligned both my personal goals: contributing to life-changing therapies, and professional goals: being able to earn a living while pursuing my personal passion.

As they say, “Do what you love, and you’ll never work a day in your life.”

July 2025

Introduction: Venture Capital as the Lifeline of Biotechnology

Translating laboratory discoveries into lifesaving treatments for patients is lengthy, requires massive amounts of capital, and is an extraordinarily challenging journey that is mired in constant failures and setbacks. It requires collaboration across multiple stakeholders, from academic researchers, biotechnology companies, and contract research organizations to regulators and pharmaceutical companies. Unlike other sectors, such as technology or manufacturing, biotech innovation rarely progresses within a single organization. Instead, technologies pass through specialized ecosystems and subsectors, undergoing numerous collaborations and handoffs along the way. Additionally, unlike the tech and manufacturing sectors, which scale by repeatedly iterating the same products, biotech and pharmaceutical companies must continuously innovate or acquire innovation due to IP lifecycle constraints. With all these challenges and transitions, venture capital has emerged as a critical niche for shaping and driving this complex process by uniquely tolerating the industry’s substantial financial demands, prolonged timelines, inherent uncertainties, and high failure rates.  

Investing in biotechnology is not for the faint-hearted. The investment characteristics are poorly matched to traditional financial institutions like banks, later-stage private equity, and public equity markets, which favor shorter investment horizons and lower-risk ventures. Without substantial funding on the order of hundreds of millions of dollars, these discoveries and innovations will never translate into life-saving treatments.  Venture capital firms provide the lion’s share of this critical funding, uniquely equipped with the expertise, alignment, capital, and passion, defined here as the endurance to persist over time, not just enthusiasm, to bridge the risky gap between discovery and clinical development.

A Brief History of Drug Development and the Relationship with Biotech VC

Drug development historically involved large pharmaceutical companies undertaking extensive internal research and development, characterized by decades-long timelines, massive investment requirements, and high failure rates. In the 1970s and 1980s, the emergence of biotechnology, particularly the groundbreaking ability to genetically engineer proteins, revolutionized drug discovery. Biotech startups leveraged academic science and nimble innovation to drive new therapeutic breakthroughs, shifting the paradigm from traditional pharma-dominated R&D to a more collaborative and innovative ecosystem. The rapid rise and success of these startups hinged significantly on venture capital contributions, which provided essential early-stage, patient capital to overcome high-risk barriers and bridge the gap between academic research and commercialization.

Together, VCs, researchers, and entrepreneurs brought transformative innovations, such as monoclonal antibodies, gene therapies, RNA-based technologies, and others to market. This laid the groundwork for modern pharmaceuticals and reshaped the slow-moving, conservative market into the innovation-driven, dynamic ecosystem it is today, saving billions of lives in the process.

Figure 1: Notable Examples of VC-Driven Biotechs.

The Innovation Funding Gap in Biotech

Typically, it takes over a decade and billions of dollars to guide a promising discovery from initial research through clinical trials and ultimately into the market.^1,2 Complicating matters further is the phenomenon known as “Eroom’s Law” (Figure 2), highlighting the troubling paradox that, despite technological advances, drug discovery productivity has steadily declined, resulting in escalating costs and diminishing returns.^3,4 Over recent decades, the average cost of successfully bringing a new drug to market has surged dramatically, surpassing $2 billion per approved drug (depending on who you ask).^5,6

These interrelated complexities have widened the innovation and funding gap: while groundbreaking academic research continues to flourish, translating these discoveries into viable commercial products has become increasingly expensive. This rising cost has significantly squeezed traditional pharmaceutical companies, resulting in an intensification of their dependency on external innovation and reducing internal R&D spend. Over the past two decades pharmaceutical companies have started to play an essential role in the clinical and commercial success of emerging therapies, serving as licensors that guide innovative technologies through the final stages of development and onto the market.

Biotechnology companies, whether private or public, often lack the substantial capital, specialized expertise for late-stage clinical trials, and the robust infrastructure required for successful commercialization. As a result, biotech firms commonly out-license or sell their assets to pharmaceutical companies, leveraging pharma’s financial resources and established commercial pathways to bring new therapies to patients’ bedsides. Influenced by Eroom’s Law and economic pressures, pharma companies have grown more conservative, preferring to wait longer before adopting new technologies, effectively becoming gatekeepers for the commercialization of potentially life-saving innovations.

Figure 2: Eroom’s Law depicted by illustrative historical regression of significant increase of drug discovery and development.

Source: Benchling.

It is precisely at this juncture of financial and scientific uncertainty that biotech venture capital plays an indispensable role. Passionate biotech venture capitalists actively embrace these early-stage challenges, investing precisely when others shy away to provide the bridge between groundbreaking scientific research and commercial viability. The importance of this funding model cannot be overstated; venture capital-backed companies have consistently delivered transformative therapies that shape modern medicine, and without them and their portfolio companies, the industry would quickly decline to a fraction of its size.

Venture capital support is not merely financial; it’s a strategic enabler of groundbreaking medical advances that significantly enhance human health outcomes. Through careful, informed, and visionary investments, biotech venture capital fuels innovation pipelines, enables scientific risks, and ensures that transformative ideas are not trapped in laboratories but rather brought effectively to the patients who urgently need them.

Pulse on the Current Market: When Enthusiasm Outpaces Passion

The rapid exit of generalist funds left hundreds of biotech companies facing financial strain, with low cash reserves and short runways, forcing many to scale back or shut down. This large capital void either needs to be filled by investors, which is unlikely given the sheer scale of capital needed, or there will be another market correction, which will entail more shutdowns, trade sales, and M&A opportunities. The mismatch between high burn rates and the long timelines needed for meaningful value inflection is pushing many promising companies into survival mode. Despite short-term pains, this correction has created compelling opportunities, as high-quality companies trade at steep discounts, offering attractive entry points for experienced investors with fresh capital and limited exposure to prior overvaluations.

Biotech capital markets are cyclical and highly sensitive to broader economic shifts, as seen during the COVID-19 era when the XBI, the S&P’s biotech index, nearly doubled in under a year, attracting trillions in capital. This influx, driven largely by generalist investors that lacked the deep understanding required for biotech investing, fueled innovation, accelerated drug development, and inflated valuations. But it also tipped the balance away from fundamentals. When immediate returns failed to materialize, many of these investors quickly exited, redirecting capital to areas like technology, triggering an abrupt market correction that disrupted the biotech ecosystem and led to the recent downturn.

Biotech VCs: The Sherpas of Innovation

Biotech venture capitalists provide more than financial resources. They serve as skilled sherpas, guiding startups through the challenging journey from early discovery, through clinical development, and ultimately to commercialization. Like Tenzing Norgay, the legendary Nepalese-Indian sherpa who helped Sir Edmund Hillary summit Mount Everest in 1953, the best VCs help startups navigate difficult terrain, carry critical burdens, and stay oriented toward the summit. They bring not only capital but also specialized experience, expansive networks, and strategic insight to each stage of a company’s evolution. So, pick your investors wisely.

VCs also fulfill an essential role as aligners. Biotech startups move through distinct phases, each with different personnel, structures, and missions. VCs ensure continuity. Moreover, effective VCs act as amplifiers, extending a company’s reach and influence within the broader ecosystem. This involves connecting startups with capital sources, facilitating interactions with pharmaceutical stakeholders, advocating externally to enhance the company’s visibility and reputation, and other critical processes.

Above all, the most distinguishing trait of the best biotech VCs is their deep-rooted passion. Many have years or decades of firsthand experience in research laboratories, biotech startups, or pharmaceutical companies, giving them an enduring resilience and ability to maintain unwavering enthusiasm despite the setbacks endemic to biotech innovation. This passion transcends mere enthusiasm; it embodies the capacity to persist through great adversity, remaining committed to the ambitious goal of bringing groundbreaking science to patients in need.

This mission requires VCs to be hands-on, guiding their portfolio through diverse challenges. With dozens of companies under management, VCs must tailor their guidance to each one. This demands not just expertise but time, attention, and strategic dexterity. As VC firms scale, managing their portfolios becomes more complex. Biotech investments often span decades, meaning a single VC firm may find itself managing multiple funds (often two to five at once), each with its own portfolio, LP base, and strategic objectives. The burden of guiding multiple companies across different stages while maintaining internal coherence stretches firms’ bandwidth and heightens the risk of misalignment.

It can be easy to overlook that venture capitalists operate their own business too. In addition to providing tailored support and resources to numerous portfolio companies, VCs must fundraise, manage investors, and oversee firm-level operations. Balancing these dual roles, shepherding others while running a firm, is an often-overlooked challenge. Additionally, VCs are still groups of people that can be prone to errors in decision making, risk calculation, or several other human shortcomings. The best biotech VCs understand this and try their best to stay aligned to fundamentals and remain disciplined even when others are not.  In short, they try to over-index on passion to help patients versus chasing short-term returns with enthusiasm.

Built for Biotech, Equipped for Impact

The world of biotech investing is as diverse as the companies and people who make up the industry. Historically, the most successful biotech investment firms have not been generalists, but specialists whose passion and focus mirror those of the scientists and entrepreneurs driving innovation. Most of these biotech funds take specialization even further, focusing their skills and strategies on specific niches within the broader biotech ecosystem.

We founded Luma Group with these same guiding principles, tailoring our strategy explicitly for the biotech industry and our portfolio. One of our initial decisions was to align our fund’s capital cycle with biotech product development timelines. Specifically, we launched a 15-year fund rather than the traditional 10-year structure. This choice reflects the reality that biotech development cycles often require more time than technology or other sectors and forcing a 10-year investment horizon onto biotech would be fundamentally mismatched. At the heart of our philosophy is a simple, unwavering belief: if we do our job, fewer patients will suffer tomorrow. Guided by this North Star, we set out to build a firm uniquely positioned to achieve such an ambitious goal.

Given the highly regulated and empirically driven nature of the biotech sector, it should come as no surprise that the greatest predictor of success is rigorous, accurate science. Amid all the uncertainties, getting the science right is predominantly about running the correct experiments informed by historical data. This principle has shaped a key mantra within our group: “Better data leads to better decisions, which leads to better outcomes for patients.”

This mantra is what guided Luma Group to establish a dedicated research division within our funds, supported by dozens of KOLs and advisors, from former pharmaceutical CEOs and top regulatory officials to PhDs and postdoctoral researchers. The breadth and depth offered by this network ensures coverage of every critical vertical in our portfolio and fund exposure:

• Academic Experts provide visibility into groundbreaking innovations and technologies
• Discovery and Development Specialists focus on execution and translating early-stage technologies into next-generation therapeutics, medical devices, or diagnostics
• Clinical and Regulatory Advisors assist in navigating the rapidly evolving clinical and regulatory landscape
• Commercial Experts help us understand pharmaceutical decision-making processes and broader market dynamics affecting our portfolio companies

To complement this network, we have developed a next-generation research platform built around proprietary analytical software known as LABI (Luma AI Brain Initiative). This platform provides both our fund and our portfolio access to trillions of curated data points through an intuitive interface that significantly reduces diligence times while leveraging advanced meta-analysis and analytics, empowering us to uncover insights others typically miss.

With these foundational elements in place, Luma Group has strategically aligned its passion and fund lifecycle with the asset class and companies that are driving innovation. With a lot of passion, hard work, and some luck, Luma Group can drive meaningful improvements in patient outcomes within our lifetime.

1. Pisano, G. P. (2006). Science Business: The Promise, the Reality, and the Future of Biotech. Harvard Business School Press. ↩︎
2. Booth, B. L., & Zemmel, R. W. (2004). Prospects for productivity. Nature Reviews Drug Discovery, 3(5), 451–456. ↩︎
3. Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012). Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery, 11(3), 191–200. ↩︎
4. Deloitte Centre for Health Solutions. (2022). Measuring the Return from Pharmaceutical Innovation 2022: Balancing the R&D equation. Deloitte Insights. ↩︎
5. DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33. ↩︎
6. Deloitte Centre for Health Solutions. (2022). Measuring the Return from Pharmaceutical Innovation 2022: Balancing the R&D equation. Deloitte Insights. ↩︎