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The convergence of various factors, including advancements in OMIC data, repurposing of precision oncology technologies and high demand from patients and pharmaceutical companies, has positioned inflammation and immunology sectors for explosive growth, driving the development of safer and more effective precision therapies.

March 2024

Inflammation and Immunology Disease Landscape

Inflammatory and Immunology (I&I) diseases affect approximately 35 million Americans or over 10% of the population. Despite the prevalence, current treatments are ineffective in managing these conditions.

Unsurprisingly, this lack of effective treatments has led to significant demand for new and innovative therapies that are safe and efficient. However, biotech and pharma R&D focus to date has largely been on more severe indications (e.g., oncology), which despite a smaller size, have seen rapid market growth. Precision therapy methods, harnessing established and de-risked technologies derived from precision oncology, hold the promise of catalyzing a comparable revolution in the treatment of I&I diseases. This presents an opportunity for enhanced disease management and ultimately, superior patient outcomes.

The lack of innovation in I&I drug development has been a critical factor contributing to the slower growth of the I&I market. Interestingly, despite the two times larger patient population than oncology, I&I diseases only generate 60% of the market size (Figure 1).

Figure 1: Comparison of I&I disease and oncology markets.

Source: SEER, NIH, IQVIA, Global Data, Evaluate Pharma.

This discrepancy cannot be attributed to differences in treatable disease subtypes or drug treatment costs, but instead to the limited treatment options available for I&I diseases. Limited options have resulted in redundant use cases and lower overall market penetration.

In comparison, oncology has more than twice the number of marketed drugs, which are more diverse in their mechanisms of action and use cases. For example, the top 16 selling oncology drugs generated over $50 billion in sales in 2022 alone according to Global Data, accounting for approximately 50% of the total sales, and 13 out of 16 of these therapies have novel biological mechanisms of action. On the other hand, the I&I market is dominated by only five drugs that comprise around 50% of the market according to Global Data and have only three unique biological mechanisms of action. This dynamic highlights the need for innovation and the potential for market expansion in the space.

The historical safety issues in I&I drug development have been a primary reason for the lag in the market’s growth. Many promising drugs that have demonstrated efficacy in preclinical and clinical trials have failed to enter the market due to safety concerns associated with chronic use. A prime example is CD20 targeting antibodies like Rituxan (rituximab) have shown promising efficacy in the clinic for multiple I&I indications, the class of drugs harbors severe safety issues/risks (opportunistic infection, anaphylaxis, acute coronary syndrome and even death) with chronic use. This “safety Achilles heel” results from target redundancy in disease and healthy cells/tissues, where many tractable drug targets overlap with those expressed in healthy states.

The need for more innovation in addressing these safety concerns has hampered the progress of I&I drug development, with only Stelara (an anti IL-12/IL23 antibodies used for the treatment of Crohn’s disease, ulcerative clotisese, plaque psoriasis and psoriatic arthritis) out of the top five selling I&I drugs entering the market in the past 15 years. This lack of innovation has significantly impacted the growth and potential of the I&I market. However, it highlights a potential opportunity to drive market growth with novel approaches.

Looking forward, I&I drug development can learn from the similar challenges experienced in oncology drug development in the 1990s and early 2000s, which were overcome by shifting to a personalized medicine approach.

Chemotherapy drugs (i.e., antineoplastics) were the legacy standard of care in oncology and have broad mechanisms of action, systemically killing rapidly dividing cells. While this can successfully target cancer, these cells also exist in several non-cancerous tissue types like hair and the lining of the stomach, resulting in the severe side effect profile that is ubiquitous in the class. The non-specific targeting of cells created an immense treatment burden on patients and spurred the improvements in specificity, efficacy and safety seen in the last decade. New scientific insights from collecting and analyzing massive patient datasets enabled this shift, paired with innovative technologies that allowed therapies to target tumor cells while selectively sparing healthy cells. This shift to precision approaches ushered in the era of precision oncology, unlocking enormous market growth that quadrupled in less than 15 years (Figure 2) and vastly improved patient outcomes (Figure 3). I&I drug development can leverage similar strategies to address safety concerns and drive innovation, paving the way for its growth and success in the market.

Figure 2: U.S. oncology sales have increased dramatically over the past 10 years with approvals of immunotherapies (e.g., Keytruda) and other targeted agents (e.g., Tagrisso) driving new sales volume.

Source: Global Data, Evaluate Pharma.

Figure 3: The growth in the U.S. oncology market was enabled by the dramatic improvements in patient care outcomes supporting their clinical success. This is particularly evident in front line EGFR NSCLC.

Note: Front line standard of care prior to TKI approvals assumed to be platinum-based chemotherapy. PFS: Progression-free survival, ORR: Overall response rate. Source: FDA label, SEER, clinical trials.

To overcome the innovation bottleneck and thrive in the market, I&I drug development can take cues from the success of oncology by making two fundamental shifts:

1. Embracing OMICs Large Data Sets: One crucial missing component in developing I&I precision therapies has been the need for novel biological insights from analyzing massive patient datasets. Until now, researchers have lacked the proper tools, such as single-cell sequencing, advanced fluorescence-activated cell sorting and advanced mass spectrometry, to accurately decipher the immune system’s complexities. By leveraging these technologies and analyzing extensive patient data, I&I drug developers can gain valuable insights into disease mechanisms and identify new targets for precision therapies.

2. Repurposing Precision Oncology Technologies: The technologies that revolutionized precision oncology, such as heterobifunctional molecules, bispecific antibodies and cell therapy, can be repurposed for developing precision I&I drugs. In oncology, these innovative technologies have already shown success in targeting tumor cells while sparing healthy cells, and they can also be applied to I&I diseases. By repurposing these proven technologies, I&I drug developers can accelerate the development of safe and effective therapies, overcoming the stagnation in the I&I market.

Investors and companies who focus on analyzing patient data and repurposing precision oncology technologies early on will likely meet the demand for new therapies during the transition to the era of precision therapies in I&I drug development. This success may result in significant therapeutics creation for the large unmet medical need, which are positioned well to gain strong market traction.

I&I Opportunity

Multiple converging factors have positioned the I&I field for rapid growth, potentially surpassing the oncology market. This historically underserved market now has the necessary pieces to overcome historical bottlenecks in I&I drug development and usher in a new era of precision therapies.

One essential advancement is the availability of massive OMIC data, which can be used to address and advance therapeutic development in I&I diseases across several axes. New decoding technologies, like single-cell sequencing, provide novel biological insights for developing precision I&I treatments. This wealth of data has enabled better patient stratification, allowing researchers to characterize patients more accurately and identify responders while excluding non-responders who may pose safety risks. Additionally, this data has revealed new target proteins and cell types that could overcome historical safety issues by increasing drug specificity and expanding the repertoire of potential targets for I&I drug development.

Moreover, I&I drug development can leverage cutting-edge precision oncology technologies that have emerged from the advancements made in the last two decades. These clinically validated technologies, such as adoptive cell therapy and bispecific antibodies, can be repurposed to address safety concerns in I&I drug development. In addition, these modular technologies can be more easily adapted for cost-effective and rapid I&I drug development. For example, companies have repurposed oncology bispecific antibody technology to target immune cells involved in atopic dermatitis while sparing healthy immune cells.

The demand for I&I therapies in the market remains enormous, with many diseases still lacking effective treatments. Many large pharmaceutical and biotechnology players have identified this gap in the market and have been exploring opportunities to access the value. Given their lean and waning I&I drug pipelines, they have looked outwards, lending to several lucrative biotech acquisitions. For instance, companies like Arena Pharmaceutical ($6.7B in 2021), Pandion Therapeutics ($1.85B in 2021) and Momenta Pharmaceuticals ($6.5 billion in 2020) have been acquired by pharmaceutical giants Pfizer, Merck and J&J, respectively, showcasing the appetite for new precision I&I therapies and the potential for continued acquisitions in the future (Figure 4).

Figure 4: Select M&A and licensing I&I deal activity from large cap pharma/biotech since 2020.

Target	Acquirer	Deal Type	Upfront	Total
Televant	Roche	M&A	$7.1 B	$7.1 B
Arena Pharmaceuticals	Pfizer	M&A	$6.7 B	$6.7 B
Momenta Pharmaceuticals	J&J	M&A	$6.5 B	$6.5 B
ChemoCentryx	Amgen	M&A	$3.7 B	$3.7 B
Chinook	Novartis	M&A	$3.5 B	$3.5 B
DICE Therapeutics	Eli Lilly	M&A	$2.4 B	$2.4 B
Pandion Therapeutics	Merck	M&A	$1.85 B	$1.85 B
Garcell Biotechnology	AstraZeneca	M&A	$1 B	$1.2 B
Nimbus Therapeutics	Takeda	License	$4 B	$6 B
EVQQ Therapeutics	Gilead	License	Not disclosed	$658.5 M

Source: Company Filings, Pitchbook.

Overall, the convergence of various factors, including advancements in OMIC data, repurposing of precision oncology technologies and high demand from patients and pharmaceutical companies, has positioned I&I for explosive growth, driving the development of safer and more effective precision therapies.

Luma is strategically positioned as a pioneering investor, ready to capitalize on the burgeoning I&I market. We have extensive expertise and active engagement in this market’s two primary value drivers. Additionally, we have an established presence in the emerging field of precision I&I -focused biotech, with active investments in this rapidly growing sector.

Value Driver #1: Luma’s expertise in identifying essential OMICs technology is central to our investment approach and ingrained in our DNA. Our fund has strategically invested in core technologies fueling the new wave of precision I&I therapies. As a result, we possess a keen understanding of the power of OMICs data sets and how to leverage them for the best drug discovery. This knowledge places Luma in a prime position to identify companies and technologies that are highly differentiated and poised for success.

Value Driver #2: Luma’s proficiency in repurposable precision oncology technologies is supported by a deep understanding and proven track record of funding and founding and investing in multiple precision medicine oncology companies. As a result, we are well-versed in cutting-edge technologies like bivalent small molecules, heterobifunctional molecules, bispecific antibodies and others that can be rapidly repurposed to develop precision I&I therapies (Figure 5). This expertise positions us at the forefront of the intersection of precision oncology and I&I, unlocking new possibilities for innovative treatments.

Figure 5: Precision therapeutics modalities with potential for translation into I&I disease.

Source: Nature, Cell.

Committed to catalyzing the new precision I&I market, our fund is already actively engaged and primed to capitalize on the new wave of I&I disease market growth. For example, in 2023, we invested in ROME Therapeutics – a company pioneering a new class of precision I&I therapies called LINE-1 reverse transcriptase inhibitors (NRTIs) – demonstrating our proactive approach to identifying and seizing first-mover opportunities.

ROME’s groundbreaking approach targets the uncharted territories of the human genome, often referred to as the ‘dark’ or noncoding regions. Utilizing cutting-edge sequencing and analytical methods, they have made remarkable discoveries. Specifically, they’ve identified certain genomic regions intricately linked to the progression of multiple types 1 interferon-driven I&I diseases, such as Lupus, also referred to as interferonopathies.

Their research has led to the identification of LINE-1 reverse transcriptase (RT) as a new therapeutic target. This target operates upstream of already validated targets, offering a unique approach to treatment (Figure 6). Significantly, it minimizes the risk of infection from opportunistic pathogens, given it is not an immunosuppressant. This promising development is rapidly progressing, and ROME is on track to introduce its first drug to clinical trials in 2024. ROME is applying its approach to advance earlier-stage programs across a spectrum of autoimmune diseases as well as neurodegenerative diseases and cancer.

Figure 6: Rome Therapeutics’ mechanism of action versus approved SLE treatments.

Source: Company materials.

Additionally, we recognize the value in and actively seek investments leveraging precision oncology technologies for I&I therapies, further showcasing our commitment to driving innovation and capturing market opportunities.

Luma is well-prepared to lead the I&I investment landscape, leveraging our expertise in OMICs technology and data, repurposable precision oncology technologies and active participation in pioneering I&I companies.

Conclusion

The I&I market is a rapidly expanding investment sector, but a lack of groundbreaking therapies has hindered its growth. Despite this challenge, the foundational elements are now coming together to propel the development of innovative precision therapies and unlock significant market growth. Luma possesses the expertise, experience and enthusiasm to be a pioneering investor in this promising market. Therefore, we are the ideal investment partner to capitalize on emerging opportunities in the precision I&I market, offering the best growth potential.

The AI revolution’s impact on society, particularly healthcare, is set to surpass the transformative effects of the Industrial and Information Revolutions. Real-world evidence of this can be seen in the application of AI-driven diagnostics and therapeutics, which have significantly enhanced the accuracy and speed of disease detection and rate of targeting and curing disease. The forthcoming role of AI in healthcare is ambitious yet achievable but requires a level of collaboration, innovation, and dedication to a brighter and healthier future.

April 2024

To read the scientific addendum to this whitepaper, click here.

The journey from a research lab to a patient for a single drug spans an average of 12 years. The accompanying cost is by some estimates over $2 billion. Even still, the odds of success remain minuscule, standing at one in 5,000.^1,2 The biotechnology industry, in its current state, appears inefficient; however, the integration of artificial intelligence (“AI”) with exemplary scientific practices presents a promising avenue for significant improvement.

The advent of AI models and applications has sparked a wave of breakthroughs, culminating in the creation of $6 trillion in market capitalization for the “Magnificent 7” stocks alone since the launch of ChatGPT (Figure 1).

Figure 1: The “Magnificent 7” stocks – Microsoft, Amazon, Meta, Apple, Google parent Alphabet, Nvidia and Tesla – drastically outperformed the major U.S. indexes in 2023 as the market recognized them as AI winners. Outperformance YTD has continued with more innovation in the AI space expected in 2024.

Source: Public market data as of April 10, 2024.

Unlike tech stocks, AI’s impact is not yet baked into healthcare valuations. AI has already begun generating new ideas, discoveries and potential opportunities to accelerate the healthcare sector to become more efficient. We can observe this trend by looking at the proliferation of AI mentions in scientific publications and healthcare AI expenditures (Figure 2). The healthcare industry spent approximately $13 billion on AI hardware and software in 2023, with projections to rise to $47 billion by 2028.³ CB Insights reports that between 2019 and 2022, investors injected $31.5 billion in equity funding into AI healthcare ventures.

Figure 2: The number of Pubmed abstracts with the keywords “Machine Learning” or “Artificial Intelligence” has increased dramatically over the past 15 years.

Source: Pubmed.

Broadly on AI: what is it, and why should I care?

AI, in its essence, encompasses the development of computer systems that can perform tasks that typically require human intelligence. These tasks include learning, decision-making, language understanding, and visual perception. The dawn of AI marks a significant leap forward in our ability to process information, automate complex processes and solve intricate problems across almost all sectors and industries. The potential societal impact of AI cannot be overstated. These technologies promise to revolutionize every sector of the economy, from manufacturing to healthcare, education to finance, by enhancing efficiency, unlocking new insights and opening avenues for innovation that were previously unimaginable.

At the core of recent advancements in AI are neural networks inspired by the human brain’s method of processing data inputs and generating predictive outputs. These networks, essential to AI’s evolution, frequently derive their architecture from biological structures, particularly the brain’s complex neural pathways. This biomimetic approach is far from being merely symbolic; it’s fundamentally practical, equipping AI systems to analyze and understand complex information similarly to how biological entities do. Moreover, the deployment of AI in biological contexts provides crucial insights that feedback into AI development, refining algorithms to better emulate natural neural processes. This continuous feedback loop not only drives technological advancements in healthcare but also enhances our comprehension of the biological inspirations behind these AI innovations.

AI’s impact on society and healthcare: shaving billions of dollars and years off the quest to save lives

The AI revolution’s impact on society, particularly healthcare, is set to surpass the transformative effects of the Industrial and Information Revolutions. Real-world evidence of this can be seen in the application of AI-driven models, e.g. Google DeepMind’s AlphaFold, to significantly enhance the speed of drug development.

As with many scientific breakthroughs, Google’s foray into AI within healthcare seems almost serendipitous. Initially, after Google’s DeepMind demonstrated remarkable proficiency in video games, the company decided to leverage its AI capabilities for more academic applications, such as healthcare. This backbone model was used to develop AlphaFold, an AI that predicts a protein’s 3D structure from its amino acid sequences (Figure 3). This is important because there are over 200 million known proteins, each with a unique 3D shape that determines how they work and what they do. Before AlphaFold, scientists could not accurately determine a protein’s unique 3D shape without millions of dollars and years of trial and error. Google taught AlphaFold to predict structures by showing it the sequences and structures of ~100,000 known proteins. Now, AlphaFold has predicted structures for an estimated 98.5% of human proteins with over 92.4% accuracy in positioning atoms, matching experimental methods like X-ray crystallography.² Better yet, AlphaFold’s predictions are freely available to the entire scientific community. This represents a massive leap forward in the quest to generate research tackling disease and finding new medicines. For one, AlphaFold can accurately detect over 50 eye diseases from 3D scans.⁴ This offers a glimpse into a world where early detection and treatment of diseases become the norm rather than the exception.

DeepMind is just one example underscoring AI’s ability to navigate the complexities of biology and healthcare data, translating into life-saving applications and fundamentally changing medical care approaches. Importantly, AI work is just starting; over half of the current 1,500 health AI vendors were established in the last seven years.

Figure 3: Determining protein structure from amino acid sequence.

Source: Baker Lab.

The inherent complexity of biology: the AI revolution will likely play out differently across different areas of healthcare, and data is king

Though Google could (relatively) easily apply its video game AI to biology in just four years, the application of AI in healthcare presents a unique set of challenges, starkly contrasting the relatively straightforward applications seen in other sectors. These challenges primarily stem from the intricate complexity of biological systems and the formidable task of obtaining robust, accurate, and comprehensive data in biology. Unlike the datasets used in finance and marketing, where variables are often well-defined and quantifiable, biological data encompasses many variables that interact in complex, usually unpredictable ways. This intrinsic complexity makes modeling biological systems a daunting task for AI.

One of the foundational hurdles in applying AI to healthcare is the heterogeneity and multidimensionality of biological data. Genetic, environmental and lifestyle factors influence human health and disease, each contributing to the overall complexity of biological systems. For instance, a single genetic mutation can have vastly different effects in different individuals (known as genetic penetrance), depending on the context of thousands of other genes and a myriad of environmental exposures and lifestyle choices. This level of complexity requires AI models to process vast amounts of data and understand and predict the interactions between these variables. This task remains at the frontier of current capabilities.

Moreover, obtaining high-quality, comprehensive biological data poses another significant challenge. Much of the data in healthcare is fragmented across different systems, encoded in unstructured formats or locked within the proprietary databases of research institutions and private companies. Privacy concerns and ethical considerations further complicate data collection, limiting access to the broad datasets necessary for training robust AI models. Even when data is available, it often needs more diversity and representativeness to ensure that AI-driven interventions are effective and equitable across different populations. Even with these limitations, groups like the Chan Zuckerberg Initiative and the Allen Institute are working on collecting robust and well-curved data to help train new models for applications in multiple diseases.

The dynamic nature of biological systems adds another layer of complexity. Unlike static datasets, biological data can change over time, with disease progression, treatment interventions, and lifestyle changes introducing continuous variability. Therefore, AI systems in healthcare must be capable of analyzing static snapshots of data and understanding and predicting how these data points evolve over time, a challenge that requires sophisticated modeling techniques and continuous learning capabilities.

Despite or, better yet, because of these challenges, the potential rewards of applying AI in healthcare are immense. AI has the potential to revolutionize diagnostics, personalized medicine and treatment planning, making healthcare more predictive, personalized and preventive. Overcoming the unique challenges posed by the complexity of biological systems and the difficulty of obtaining robust and accurate data will require innovative approaches to AI model development, data collection and interdisciplinary collaboration between technologists, biologists and clinicians. The journey towards fully realizing AI’s potential in healthcare is complex. Still, the great strides in this direction are promising, signaling a future where AI-driven insights lead to better health outcomes for all.

Great science alone isn’t enough: AI is necessary to continue advancing healthcare

In the dynamic landscape of modern business, adopting AI has transcended from a mere competitive edge to an indispensable necessity. This assertion holds particularly true for the healthcare sector, where AI’s integration promises to refine existing processes and workflows and redefine the paradigms of patient care and medical research. The economic benefits of AI in healthcare are significant; as noted, the healthcare industry is projected to spend $47 billion on AI hardware and software by 2028. This represents a lucrative opportunity for investors and entrepreneurs looking to expand on the AI revolution in healthcare.

AI’s ability to process and analyze data at a scale and speed unattainable by human capabilities alone has led to significant advancements in diagnostics, patient care, drug development and operational efficiencies. For instance, Philips developed the eICU program, which uses AI to aggregate and analyze patient data from ICUs to provide insights into medical decision-making, resulting in a 23% reduction in mortality rates across all four hospitals studied. Furthermore, the average length of stay for a patient in the ICU dropped by 49%, while lengths of stay throughout the health system decreased by 35%.⁵ In drug discovery and development, AI expedites the identification of potential therapeutic targets and predicts the efficacy of drug candidates, dramatically reducing the time and cost associated with bringing new drugs to market.

Beyond these immediate applications, AI facilitates a more personalized approach to healthcare, tailoring treatments and health plans to the individual’s unique genetic makeup and lifestyle factors. This shift towards customized medicine enhances the effectiveness of treatments. It represents a significant leap towards preventive healthcare, potentially saving costs and lives by addressing conditions before they escalate into more severe problems. However, the necessity of AI in healthcare extends beyond improving patient outcomes and operational efficiencies; it is also crucial for the sustainability and growth of businesses within this sector. In an environment where regulatory pressures, rising costs and increasing demands for high-quality patient care converge, AI provides the tools to navigate these challenges effectively. By automating routine tasks, AI allows healthcare professionals to focus on more complex and nuanced aspects of patient care, thereby enhancing patient outcomes. Furthermore, AI’s predictive capabilities enable healthcare organizations to make more informed strategic decisions, from resource allocation to market expansion strategies.

In essence, embracing AI in healthcare is not merely an option for forward-thinking organizations but a strategic imperative for all. Businesses that employ the power of AI will not only lead to innovation and efficiency but will also set the standard for patient care in the future. As AI continues to evolve and its applications within healthcare expand, the gap between adopters and non-adopters will widen, underscoring the urgency for all businesses in this sector to integrate AI into their operations. The go-forward success of healthcare businesses will increasingly depend on their ability to leverage AI technologies to meet the demands of a rapidly changing world, making the case for AI’s necessity not just compelling but categorical.

“Garbage in, Garbage out”: the quality and availability of omics data are critical factors to advancing AI in healthcare

The accuracy and power of AI model predictions, patterns, structures or generative outputs hinge critically on the quality of the input data, bringing the old modeling adage “Garbage in, Garbage out” into sharp relief. While the speed and breadth of applications that AI models have achieved is undeniably impressive, it’s crucial to recognize that the value of data and access to datasets is poised to soon surpass the value of the models. This insight is particularly relevant in the revolution currently underway in healthcare, driven by omics data – genomics, proteomics, metabolomics and more. This revolution is reshaping our understanding of disease, treatment, and wellness, as integrating these omics data types offers a comprehensive view of the biological underpinnings of health, thus paving the way for personalized medicine and targeted therapies.

However, the availability of these omics datasets and methodologies will significantly influence the landscape of healthcare innovation and access. The potential scenarios of a) widespread public availability, b) high restriction or c) a balanced approach to data access each illustrate varying impacts on healthcare progress, underscoring the intertwined future of AI models and omics data in defining the next frontier in healthcare.

• Scenario A: omics data is democratized, likely leading to an unprecedented acceleration in healthcare innovation – With scientists, researchers and clinicians worldwide having open access to vast datasets, the collaborative potential for discovering new disease mechanisms, therapeutic targets and treatment modalities would expand exponentially. This openness could also foster a more inclusive research environment, where even institutions with limited resources could contribute to and benefit from global scientific advancements. This is the type of open collaboration fostered by AlphaFold. It was built on publicly available datasets, and the AI model itself was made publicly accessible for anyone to use. Moreover, initiatives have been undertaken to standardize and curate massive genomic datasets, such as those by The Cancer Genome Atlas (“TCGA”), and the UK Biobank Proteomics and other datasets. All of these efforts have taken decades – and the dedication of tens of thousands of researchers – and still require massive curation and maintenance to generate effective AI model outputs.
• Scenario B: omics data access is tightly controlled by select entities, potentially leading the deployment of AI to become a double-edged sword – On one hand, AI could streamline research and development within these privileged circles, pushing the boundaries of proprietary medical breakthroughs. On the other hand, this restriction could limit AI’s learning potential by confining it to siloed datasets, potentially slowing the pace of innovation and widening the gap in healthcare access. Given the value of these datasets, we have already seen private entities begin to hoard data for commercial purposes or continue to restrict access to proprietary datasets to maintain a competitive edge. Companies like Flatiron Health and Tempus  have been gathering massive datasets for commercialization purposes, while almost all therapeutic companies restrict the publication of their internal datasets to maintain trade secrets and competitive advantage.
• Scenario C: a middle-ground, balanced approach to omics data availability has the highest likelihood of unfolding in the near future – There will likely be significant academic and government-funded generation and curation of large omic datasets, with private companies restricting access to proprietary datasets. Over time, these large public datasets will mature and expand to include new omics datasets, and standardization will likely play a crucial role. Large initiatives like the Chan Zuckerberg Initiative are already building databases with the scale and infrastructure necessary to generate high-quality datasets. Private companies will likely release data after it has been depleted of value or when there is greater value in sharing the data than in hoarding it. We have already seen small biotechs like Leash Bio adopt an open-source approach and share data with the strategy that more integration and data will only enhance their efforts to develop novel therapeutics.

Venture capital’s role in driving Healthcare x AI forward

From the vantage point of investment in the rapidly evolving landscape of healthcare, the integration of AI stands out not just as an opportunity but as a critical imperative. Investors increasingly recognize that the future healthcare leaders will be those organizations that adeptly harness AI to drive innovations, improve patient outcomes, and streamline operations. In contrast, companies that need to be faster to adopt AI technologies risk falling behind and potentially facing obsolescence. The stark reality is that the healthcare sector is at a pivotal inflection point where the adoption of AI can significantly differentiate between market leaders and laggards. These dynamic underscores the critical importance of investment strategies that prioritize and support the integration of AI in healthcare.

The argument for prioritizing investments in AI-enhanced healthcare companies is multifaceted. Firstly, AI’s ability to process and analyze vast datasets at speeds and accuracies unattainable by human capability transforms diagnostics, treatment personalization, and patient monitoring. This technological prowess is pivotal in decoding complex health data, leading to breakthroughs in understanding disease mechanisms and developing targeted therapies. For investors, backing companies that leverage AI in these areas offers the promise of substantial financial returns and the opportunity to contribute to significant societal impacts in terms of improved health outcomes and longevity.

Furthermore, the synergy between great science and AI utilization forms a cornerstone for the next generation of healthcare innovation. AI does not operate in a vacuum; its most significant potential is unlocked when combined with cutting-edge scientific discoveries and insights. Companies that cultivate a culture of innovation, where AI tools are used to accelerate and expand the impact of scientific research, are poised for success. This fusion of AI with scientific inquiry creates new frontiers in personalized medicine, making treatments more effective and reducing adverse outcomes. From an investment perspective, this represents fertile ground for growth, as companies at the forefront of this convergence are likely to be in charge of introducing novel healthcare solutions and capture significant market share.

Investors, including us at Luma Group, are encouraged to scrutinize the current AI capabilities of healthcare companies and their potential to innovate and adapt. The criteria for investment decisions should include an evaluation of a company’s commitment to AI integration, the robustness of its data infrastructure, the caliber of its scientific research, and its capacity for leveraging AI to translate scientific discoveries into clinical applications. Companies that demonstrate a clear vision for integrating AI with scientific exploration are the ones that will navigate the complexities of modern healthcare, delivering solutions that address unmet medical needs and driving the future of medicine.

One of our key focus areas here at Luma Group is investing in AI-driven enterprises pioneering drug discovery and diagnostics advancements. Among our investments is Rome Therapeutics, which delves into large human genetics datasets to explore the “dark genome”—a segment historically dismissed as evolutionary junk, with no coding for proteins or known biological function. Rome Therapeutics has discovered novel therapeutic targets for autoimmune diseases hidden within these previously overlooked genomic territories through its innovative AI models. Their AI approach examined the vast dark genome region to identify regions highly expressed in patients with specific autoimmune diseases. Within these regions, they discovered nonconical protein expression patterns and found ancient retroviral proteins that are driving the disease progression. This breakthrough could not have been achieved without the sophisticated analysis enabled by their AI technology.

Our recent investment Curve Biosciences is a trailblazer in diagnostics focusing on developing cutting-edge early-stage liver diagnostics that will leverage AI. Curve’s data engine analyzes methylation patterns in cell-free circulating DNA in the blood. While it is understood that total methylation levels escalate over time, the exact tissue methylation patterns distinguishable in cell-free DNA that strongly relate with disease progression have remained elusive. Through the application of AI to analyze vast patient tissue DNA methylation datasets across various stages of chronic disease progression, Curve Biosciences has pinpointed predictive methylation signatures with subtle, previously undetectable patterns of methylation that strongly correlate with the stage of and progression of the disease (Figure 4). This innovative approach exemplifies the power of AI in transforming diagnostics and how we understand disease mechanisms with an immediate application in diagnostics.

Figure 4: Curve leverage AI/ML to process large PCR, microarray, and sequencing data sets to inform their screening technology.

Source: Company Materials.

The investment perspective on AI in healthcare is clear: leveraging AI is not merely an option but a strategic imperative for success. As the sector continues to evolve, the combination of innovative science and AI utilization will distinguish the leaders from the followers. For investors, the message is equally clear: support and invest in companies that understand and act on this imperative, for they are the ones that will shape the future of healthcare, delivering both substantial financial returns and transformative improvements in human health outcomes.

Call to action

In the vast and ever-evolving landscape of healthcare, where the promise of groundbreaking innovation stands on the horizon, a profound opportunity exists to reshape healthcare and make meaningful improvements in human health. The forthcoming role of AI in healthcare is ambitious yet achievable but requires a level of collaboration, innovation, and dedication to a brighter and healthier future.

Looking ahead, AI’s influence on healthcare will only broaden as the technology itself advances. At some point in the not-so-distant future, we anticipate that the advent of Advanced General Intelligence (“AGI”) could lead to a whole new era in healthcare by surpassing human intelligence to predict diseases, tailor treatments to individual genetics and automate complex medical procedures. AGI promises to enhance diagnostic accuracy, optimize healthcare management and improve resource allocation, fundamentally increasing the quality and accessibility of care. AI’s path forward in this space will be complex and filled with challenges, yet it is paved with the potential for extraordinary advancements. This will require a collective effort by researchers, clinicians, technologists, entrepreneurs and investors, sharing knowledge, resources and visions to catalyze the development of solutions that can address the most pressing health challenges of our time. Moreover, this collective endeavor demands a commitment to relentless ideation and innovation. We are at the cusp of a new era in healthcare that leverages AI, genomics and emerging technologies to make new insights into human health and leverage them for new therapeutics, clinical trials, diagnostics and other healthcare applications. We believe AI will dramatically improve patient outcomes and redefine healthcare in the

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1. https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming-healthcare.html ↩︎
2. https://www.biopharmadive.com/news/new-drug-cost-research-development-market-jama-study/573381/ ↩︎
3. https://www.economist.com/technology-quarterly/2024/03/27/ais-will-make-health-care-safer-and-better ↩︎
4. https://deepmind.google/discover/blog/a-major-milestone-for-the-treatment-of-eye-disease/ ↩︎
5. https://www.fiercebiotech.com/medtech/philips-illustrates-gains-over-15-years-a-telehealth-powered-icu ↩︎

Advances in drug development and discovery have revolutionized the delivery of life-saving treatments to patients. Despite the immense impact of these innovations, the journey from concept to bedside remains largely misunderstood. Through this primer series, we explore the complex processes behind drug discovery, highlighting the challenges the industry still faces and the critical need for investment of time and resources to deliver the next generation of medicines.

September 2024

Introduction

Luma Group’s mission is to invest in the most innovative companies poised to make meaningful impacts on human health outcomes. We focus on therapeutics, diagnostics, and medical devices because they provide a unique opportunity for venture capital to thrive, while fueling innovation that will benefit humanity. Despite the enormous impact that medical improvements have had on nearly all lives, the process to develop these drugs (”drug discovery”) remains poorly understood by most due to its long, arduous, expensive and highly complex nature.

We seek to shed light on this topic, and several others in our primer series. Through these primers, we hope to provide additional clarity into several integral processes underlying the biotech industry to help a broader understanding of the challenges the industry faces and the necessity for continued investing here. We hope readers walk away with a deeper appreciation and a clearer understanding of the immense and collaborative effort involved in the biotechnology community’s journey to advance drug discovery and development. It truly takes a village to create a drug.

In part one of this primer series, we will explore the various steps of the drug discovery process, delving into the key elements of each step and the challenges and opportunities for future growth (Figure 1). Despite the inherent complexities and obstacles, we are in the golden age of innovation within our sector. Our goal is to help demystify the journey of how drugs progress from the laboratory bench to the patient’s bedside.

In this document, we break down the drug discovery process into distinct steps and summarize each with a table that reviews key aspects. These aspects include the step’s “significance,” which refers to its importance in the overall process of creating a drug; its “purpose,” highlighting the key data generation objectives; its “probability of in-stage success (PoS),” indicating the likelihood that a drug candidate will successfully advance to the next stage; its “timelines/costs,” which detail the expected duration and associated expenses; and its “areas for optimization,” where we identify opportunities for companies to enhance their risk-adjusted outcomes in drug discovery.

To supplement this series and other Luma content, we have compiled a target glossary of key technical terminology to support readers. This glossary can be accessed here.

For every 100 candidates entering the drug discovery process, it is estimated that only between 1 to 3 will ultimately become FDA-approved drugs that reach commercialization. However, it is important to note that in the earliest stages of development, such as compound screening, depending on the methodology used, a group may need to screen tens of thousands of compounds just to find a single hit. This reality underscores the challenge of quantifying asset attrition, and the graphic below serves to illustrate the monumental undertaking required to get a new drug to patients.

Figure 1:  Overview of critical endpoints, cost, and risk profile of each discovery and development stages.

Asset counts beings after developmental candidate nomination as after this point a drug format has been finalized for development and failure becomes a binary process. Source: Deal Room, Speedinvest.

Importance of Drug Discovery

Modern medicine has revolutionized billions of lives, from using antibiotics to combat once-deadly bacterial infections to developing cures for life-threatening genetic diseases. Although only a few drugs ultimately reach the hands of physicians, countless hours and numerous resources are poured into these efforts of discovering and developing the next generation of life-saving drugs. The entire journey – from taking a new biological discovery in a university lab to eventually saving a patient’s life – is what we think of as “drug discovery”.

Figure 2:  Key steps in the drug development process.

Source: Luma proprietary.

Early pioneers in drug discovery laid the foundation for our modern drug discovery process. Visionaries like Louis Pasteur, who pioneered the germ theory of disease, Alexander Fleming, who discovered penicillin, Jonas Salk, who developed the polio vaccine, and Dorothy Hodgkin, who unlocked the structures of important biochemical substances, dedicated their lives to transforming their scientific breakthroughs into what we now recognize as life-saving drugs.

Their work has inspired countless researchers to convert their own insights into new therapies that continue to save lives. In today’s landscape, regulators play a crucial role in drug discovery, and understanding their influence is essential to grasping the complexities of the process.

Over the past 120 years, the FDA in the US and the EMA in the EU have standardized the drug approval process, ensuring that only the safest drugs and most effective drugs reach the market. The rigorous standards set by these regulatory bodies have established best practices across the industry, even influencing other jurisdictions, thereby accelerating the timeline from discovery to approval and significantly increasing the efficiency of approvals. However, while this robust regulatory framework has positive outcomes, it is lengthy and costly, typically requiring $350 million and 10-15 years to bring a promising discovery to the point of commercial (i.e., patient care).¹  Navigating this complex process demands both experience and substantial capital, critical resources provided by venture capital investors to the innovative scientists driving discovery.

The global drug market has seen remarkable and sustained growth over the last 50 years, expanding from around $12 billion in the 1970s to over $1.56 trillion by 2020.² This tremendous growth, driven by the development of new therapies and the expansion of global markets, is expected to continue at approximate 6% compound annual growth rates, potentially reaching nearly $3 trillion within the next decade.³

This growth is driven by advancements in biotechnology, increased investment in research and development and the introduction of innovative therapies for chronic and complex diseases. For instance – the development of biologics, such as monoclonal antibodies and gene therapies, has revolutionized treatment options for conditions like cancer, rheumatoid arthritis, and genetic disorders.

The success of drugs such as Humira, one of the world’s best-selling drug (over ~$20B in sales/year), illustrates the potential for growth and impact in the pharmaceutical industry. Additionally, the rapid development and deployment of COVID-19 vaccines demonstrated the industry’s and regulator’s capacity for swift innovation and large-scale production in response to global health crises.

The Long Journey of Drug Discovery and Development

Step 1. Target Identification and Validation in Drug Discovery

Target identification and validation is the first step in the extensive drug discovery and development process. This crucial phase involves selecting a specific node within a disease pathway to target with a new drug. Typically, this process originates from foundational research conducted in academic labs, where scientists strive to understand the underlying biology of specific diseases. For example, researchers may find a protein or molecule crucial to disease progression (the “target”).

This foundational knowledge is the springboard for drug development – the earliest stage. Successfully transitioning this early-stage academic research into a plausible biotech venture often hinges on validating the target – expanding on the academic research that’s already been completed. See “Validating Drug Targets” below for a further explanation.

Identifying Drug Targets

Identification can take a top-down (“clinical sample”) or bottom-up (“research sample”) approach for identification. The top-down approach typically looks for causal relationships of a target from a patient sample and requires basic research to validate the causal nature. For instance, in breast cancer research, scientists took a clinical top-down approach to identify the HER2 protein, which is overexpressed in a subset of breast cancer tumors. This discovery was pivotal as it led to the development of targeted therapies like trastuzumab (Herceptin), which specifically inhibits HER2, thereby slowing tumor growth and improving patient outcomes.

The bottom-up approach aims to perturb specific target biology and look for outcomes reflecting patient disease pathology (Figure 3). Paclitaxel, a pioneering chemotherapeutic agent, was discovered through natural product research. Scientists at the US National Cancer Institute identified a compound in the Pacific yew tree (Taxus brevifolia) that inhibited cancer cell growth in cellular models. Later synthesized in laboratories to enhance production efficiency, this discovery has profoundly advanced the treatment of several cancers, including ovarian and breast cancer.⁴

The clinical sample approach has progressively become the first choice in drug development. By starting with patient samples from the target disease, it can help researchers identify mutations or aberrations only present in the diseased patients and provide a narrowed starting universe for testing. In the bottoms-up approach, discovery relies more heavily on trial and error as there isn’t as robust of an underlying hypothesis around potential disease drivers. However, once the target has been identified and validated, both paths converge on the same output to be taken forward into further stages of development.

Figure 3: Target identification and characterization workflow.

Source: Nature.

Validating Drug Targets

Once a potential target is identified, its role in the disease must be validated to ensure it is a viable candidate for drug development. This is where advanced techniques like “knockout mice” and CRISPR technology come into play.

Knockout mice are mice that are genetically engineered to lack a specific gene – generally the gene associated with the “target,” allowing researchers to observe the effects of its absence on disease progression. This method both offers more data to evaluate the target and helps to establish whether targeting the relevant gene can alter the disease’s course.

CRISPR technology has revolutionized target validation by enabling precise gene editing. This pioneering work has made it significantly easier and cheaper to manipulate multiple genes in cellular and animal models. Researchers can selectively disrupt or modify genes in cell lines or animal models to study their function and contribution to disease. This precise control can provide compelling evidence of a target’s relevance and potential as a therapeutic focus.

Real-World Example: Bridging Academia and Industry

To progress in the drug discovery process, an academic team generally needs to be brought into a commercial environment for capital formation. Spinning out academic research into the pharmaceutical industry requires strategic partnerships, and universities and research institutions often collaborate with biotech companies beginning at this stage. The large academic centers maintain specialized staff who are experienced in creating these collaborations in a way that rewards the university (or other institution) to the extent the commercialization effort is successful. A large part of our opportunities at Luma Group come from this nexus.

Key Takeaways for Step I

Step 2. Hit Identification and Screening in Drug Discovery

The next pivotal phase in drug discovery after target identification and validation is hit identification and screening. This stage involves finding compounds that can interact with the identified target, which may lead to a potential therapeutic effect. This step typically involves screening thousands to billions of compounds to identify the roughly <0.5% that interact with the target. The goal is to identify those that show some level of activity – these are referred to as “hits” and require further refinement and optimization. This is an iterative cycle, where researchers use the early results to increase the chances of finding more hits. Some of these hits will be taken forward as candidates and some may serve as backups or future “next generation” assets against the same target. Different strategies can be employed in hit identification and screening, which can provide unique advantages or differentiation in the drug discovery process. We will only cover the most common strategies in this primer.

High-Throughput Screening (HTS)

High-Throughput Screening (HTS) is a crucial and powerful tool in drug discovery that leverages automation to rapidly screen large libraries of compounds – ranging from thousands to millions – against a specific biological target. By utilizing advanced robotic systems and automated processes, HTS allows for the simultaneous execution of numerous biochemical, genetic or pharmacological assays, significantly accelerating the identification of active compounds, antibodies or genes that influence particular biomolecular pathways. One of the key advantages of HTS is its effectiveness in identifying inhibitors of kinases – a class of enzymatic proteins that play pivotal roles in regulating various cellular processes. Given their importance in disease pathways, kinases are prime targets for HTS, which can efficiently sift through extensive compound libraries to pinpoint molecules that inhibit these critical proteins. These libraries, which may be commercially available, proprietary, or a combination of both, typically contain thousands to billions of compounds.

Despite its broad utility, HTS has limitations.  It is less effective when applied to smaller compound libraries, or when the target has limited biological reagents, such as cell-free recombinant proteins, or when structural information about the target is minimal. Despite these challenges, HTS remains a cornerstone of the drug discovery process by identifying promising hits for further development.

Alternative Approaches to Hit Identification

While HTS remains a fundamental method in modern drug discovery, alternative approaches are often necessary, especially when HTS is either impractical or inefficient. For target proteins with poorly understood biology or those from challenging non-kinase classes (such as transcription factors and cell membrane proteins), these alternative strategies may be more successful in generating early-stage drug-like compounds.

1. Fragment-Based Drug Discovery (FBDD): Fragment-Based Drug Design involves identifying small chemical fragments that bind to a target protein using a hybrid HTS approach that leverages protein structural information. These fragments are typically simpler than traditional drug molecules, making analyzing their interactions with the target easier. Once a fragment is identified, medicinal chemists build on it, adding functional groups to enhance its binding affinity and specificity. While FBDD can address some of the limitations of traditional HTS, developing fragments into drug-like scaffolds requires significantly more effort. This process demands a substantial amount of structural information to guide the design and optimization. Despite these challenges, FBDD enables the creation of highly optimized drug candidates from small, initially weak-binding fragments.
2. Virtual Screening: Virtual screening leverages advanced computer models to predict which compounds are more likely to bind to a target, thereby eliminating the need for labor-intensive wet-lab based HTS methods. This approach simulates interactions between the target and potential drug molecules in silico, enabling researchers to prioritize compounds before any experimental testing. By exploring much larger compound libraries and focusing on the most promising candidates, virtual screening significantly reduces the time and cost associated with hit identification, streamlining the process for subsequent wet lab testing.

Although this approach can save much time and effort compared to manual HTS, it generally yields less reliable results. This is mainly due to limitations inherent in in silico approaches, such as a lack of atomic-level detail, unpredictable outcomes and challenges associated with modeling complex biological systems. While virtual screening is a powerful tool, it is regarded more as a supplementary method than a primary one. It will require further advancements before transitioning to a reliable and widely used primary screening method.

Real-World Example: Discovery of Gleevec (Imatinib)

A notable success story in hit identification through HTS is the discovery of Gleevec (imatinib), a groundbreaking treatment for chronic myeloid leukemia (CML). Researchers identified the BCR-ABL fusion protein as a key driver of CML. Using HTS, they screened hundreds of thousands of compounds to find inhibitors of this protein.⁵ Imatinib emerged as a potent and specific inhibitor of BCR-ABL, leading to its development and subsequent approval by the FDA. Gleevec has transformed CML from a fatal disease into a manageable condition for many patients, showcasing the power of HTS in drug discovery.

Key Takeaways for Stage II

Step 3. Lead Discovery and Optimization in Drug Development

The next critical phase is lead discovery and optimization after identifying potential hits in the drug discovery process. This stage focuses on picking one or more candidate compounds and then refining and improving those compounds to meet specific criteria, ensuring they possess the necessary properties to become effective drugs. Lead compounds are characterized by their potency, selectivity, and overall drug-like properties (see “Criteria for Lead Compounds,” below). This critical step can be one of the longer steps in the process, as development teams make minor modifications to the compound or compounds and repeatedly test those changes to develop stronger candidates for further development (figure 4). It would not be unusual for a team to test hundreds – or even thousands – of potential modifications before settling on a lead candidate.

Criteria for Lead Compounds

Optimization Strategies

Once lead compounds are identified, they undergo extensive optimization to enhance their properties and maximize their therapeutic potential. This crucial step in drug development involves refining the compounds’ efficacy, safety, and pharmacokinetic properties. While minor optimizations may occur later steps, this initial optimization phase is essential for defining the core characteristics and bulk properties of the drug.

Structure-Activity Relationship (SAR) Studies: SAR studies involve systematically modifying the chemical structure of lead compounds to understand the relationship between their structure and biological activity. By making small changes to the molecule, researchers can determine which modifications improve potency, selectivity, or other desirable characteristics. This iterative process helps fine-tune the compound to achieve optimal performance.

Medicinal Chemistry: Medicinal chemists play a crucial role in optimizing lead compounds. They use their expertise to design and synthesize new analogs, aiming to improve bioavailability, reduce toxicity, and enhance overall drug-like properties. This often involves addressing issues such as poor solubility, metabolic stability, or undesirable pharmacokinetics. Through medicinal chemistry, compounds can be modified to ensure they are suitable for further development.

Figure 4: Workflow for the iterative lead optimization process.

Source: Nature.

Real-World Example: Optimization of HIV Protease Inhibitors

A prime example of successful lead optimization is the development of HIV protease inhibitors. Initially, researchers identified compounds that could inhibit the HIV protease enzyme, which is essential for the maturation and replication of the virus. However, early inhibitors had limitations in terms of potency, bioavailability, and side effects. Through extensive SAR studies and medicinal chemistry efforts, scientists were able to optimize these inhibitors. They made structural modifications in the compound’s p2 and P2” region that binds in the enzymatic domain of the HIV proteases, enhancing potency against drug-resistant variants​.⁶ Additionally, they addressed issues related to bioavailability and metabolic stability, resulting in compounds that were more effective and had fewer side effects. For instance, the optimization of saquinavir, the first HIV protease inhibitor approved by the FDA, involved modifying its chemical structure to improve its pharmacokinetic profile. Researchers at Roche achieved these enhancements using computer-aided SAR and structure-based drug design.⁷ Subsequent protease inhibitors, such as ritonavir and lopinavir, were further refined to enhance efficacy and reduce adverse effects, significantly improving the treatment outcomes for patients with HIV/AIDS​.

Key Takeaways for Step III

Step 4. IND Enabling Studies

After the lead optimization phase, promising drug candidates enter preclinical development. This critical stage involves extensive testing to evaluate the drug candidate’s safety, efficacy, and pharmacokinetic properties before it progresses to clinical trials. IND-enabling work includes both in vitro and in vivo studies, providing a comprehensive understanding of the drug’s potential effects on biological systems. Though many of the approaches are similar to those used in lead optimization, they are typically much larger and longer studies done in cells and animals to understand the behavior of the drug holistically. These studies form the foundation for a data package submission to the FDA. FDA approval is required before a candidate can begin human testing in the US. Other countries have similar standards (Figure 5).

In Vitro Studies

1. Testing on Cell Cultures: In vitro studies involve testing drug candidates on cell cultures to assess their efficacy and toxicity. Researchers use various types of cells, including human cells, to observe how the drug interacts with its target and to identify any cytotoxic effects. This step is crucial for determining the drug’s initial efficacy and for screening out compounds with high toxicity levels early in the process.
2. ADME Studies: Absorption, Distribution, Metabolism, and Excretion (“ADME”) studies are essential in understanding how the drug behaves in the body. These studies help predict the drug’s bioavailability, distribution throughout the body, metabolic pathways, and excretion. ADME profiling ensures that the drug reaches its target in sufficient concentrations and has an appropriate half-life, minimizing the risk of adverse effects.

In Vivo Studies

In vivo, studies involve testing drug candidates in animal models to evaluate their safety and efficacy in living organism live mammals spanning from mice to non-human primates (new world monkeys). These studies provide critical insights that cannot be obtained from in vitro experiments alone. For example, dog and rat models are commonly used to understand the safety profile and how the drug is metabolized in the body. These models help researchers understand how the drug affects the entire organism and identify potential side effects.

Real-World Example: IND Enabling Testing of Monoclonal Antibodies

A notable real-world example of IND-enabling development is the testing of infliximab, a monoclonal antibody for autoimmune diseases. Engineered to target specific antigens involved in disease processes, infliximab underwent rigorous preclinical testing to ensure its safety and efficacy before reaching clinical trials. Cell-based in vitro studies confirmed its ability to neutralize tumor necrosis factor-alpha (TNF-α), a cytokine involved in inflammatory responses. Researchers developed a predictive mouse model of arthritis and tested whether these arthritic mice would show slowed or halted disease progression when treated with infliximab​.⁸ These in vivo studies demonstrated infliximab’s efficacy in reducing inflammation and preventing disease progression without significant toxicity​.⁹ These animal studies showed that infliximab could effectively reduce symptoms and inflammation markers, providing clear evidence of its therapeutic potential. The robust preclinical results supported advancing infliximab into clinical trials. This eventually led to its approval and widespread use in treating various autoimmune diseases.

Figure 5: Overview of the components for an IND Data Package.

Source: Adgy Life Sciences.

Key Takeaways for Step IV

Clinical Development will be covered in Part 2 of the primer series

Current Challenges in Drug Discovery

High Attrition Rates: One of the most significant challenges in drug discovery is the high attrition rate. The journey from initial discovery to an approved drug is fraught with obstacles, and many promising candidates fail during development. This failure can occur at any stage, from preclinical testing to late-stage clinical trials, often due to unforeseen toxicity, lack of efficacy, or poor pharmacokinetics. Even though many of the individual stages have high percentages, the communicative attrition rates result in>30% of drug candidates that enter clinical, and only 5-10% of these drug trials ultimately receive FDA approval. Still, depending on the stage at which clinical challenges emerge, the relevant company could have spent hundreds of millions of dollars.¹⁰ It may require further investment to remediate the challenges, or the endeavor could fail altogether. This means that any successful product’s commercial value must be high enough to offset the cost of any failures. 

High Cost of Research: Over the past 75 years, drug development costs have continuously escalated. This phenomenon, humorously dubbed Eroom’s Law, reflects an inverse trend to Moore’s Law for microchip development.¹¹ Eroom’s Law observes that the cost of drug development roughly doubles every year, in contrast to Moore’s Law, which states [that the number of computations a semiconductor can complete, per unit, etc etc.]. As a result, the barriers companies face in bringing a drug to market has continued to escalate. All else equal, we view this as one of the factors that continues to support high average returns on capital to the biotech venture capital industry; demand for capital continues to increase at a time in which this specialized form of capital remains scarce.

Figure 6: Eroom’s Law depicted by illustrative historical regression of significant increase of drug discovery and development.

Source: Benchling.

Valley of Death:  Significant requirements for capital and time asymmetries exist between value generation and risk reduction in drug development. Combined with the high costs and binary nature of outcomes, this necessitates that companies raise or spend substantial capital to mitigate risks during the development process. These gaps contribute to the “valley of death,” a critical phase where companies may lack the necessary funds to navigate lengthy and expensive stages of development, potentially forcing them to halt their programs (Figure 7). For instance, the biotech company Epizyme faced considerable financial challenges in advancing its cancer drug, Tazemetostat, through clinical trials but overcame these issues to be approved and is now commercially available. Despite promising early results, the high costs and extended timelines required significant capital, leading to delays and funding gaps that exemplify the difficulties of the “valley of death” in drug development.

Figure 7: Illustration of the drug discovery valley of death.

Source: Research gate.

Future Directions in Drug Discovery

Artificial Intelligence (AI): AI is revolutionizing drug discovery by enhancing the ability to predict, analyze and understand complex biological data. The integration of AI in drug discovery has the potential to significantly reduce the time and cost involved in bringing new drugs to market. Here are several ways AI is transforming this field:

1. Predicting Protein Structures: One of the most notable applications of AI in drug discovery is predicting protein structures. Accurate prediction of protein folding is crucial for understanding how drugs interact with their targets. DeepMind’s AlphaFold has made significant breakthroughs in this area. AlphaFold’s ability to predict the three-dimensional structures of proteins with high accuracy has provided researchers with vital insights into the fundamental mechanisms of diseases. This advancement accelerates the identification of new drug targets and designing molecules that can interact precisely with these targets.
2. Drug Design and Optimization: AI algorithms are being used to design and optimize drug candidates. Machine learning models can analyze large datasets of chemical compounds and their biological activities to predict which compounds will have the desired effects on specific targets. This process can save millions of dollars and accelerate the process in the lead optimization step by year. For example, Insilico Medicine utilized AI to identify new drug candidates for fibrosis, significantly speeding up the discovery process. AI-driven platforms can generate novel compounds, optimize existing ones, and predict their pharmacokinetic and toxicological properties, streamlining the drug design phase.
3. Virtual Screening: AI enhances initial screening by virtually predicting which compounds are most likely to bind to a target based in the hit to lead stage. Traditional virtual screening methods can be time-consuming and computationally intensive, but AI can process vast libraries of compounds more efficiently. Companies like Atomwise use AI to conduct initial virtual screenings, identifying potential drug candidates in a fraction of the time required by conventional methods. This approach increases the likelihood of finding effective drugs and reduces the reliance on high-throughput physical screening.
4. Biomarker Discovery: Identifying biomarkers is essential for developing targeted therapies and companion diagnostics. These biomarkers are samples from the patient that can help understand the disease progression and how well the patient is reacting to therapy. AI can analyze complex biological data, such as genomics, proteomics, and metabolomics, to identify potential biomarkers that indicate how a patient will respond to a particular treatment. IBM Watson for Drug Discovery, for example, uses AI to uncover hidden connections in biomedical data, aiding in the discovery of new biomarkers and therapeutic targets.
5. Predicting Clinical Trial Outcomes: AI models can predict the outcomes of clinical trials by analyzing historical trial data and identifying factors that influence success rates. This predictive capability helps in designing more effective trials, selecting the right patient populations, and optimizing protocols. AI-driven predictive analytics can reduce the risk of trial failure, saving time and resources.

We recently published a whitepaper on the impact of AI on healthcare, which you can read here.

Novel Therapeutic Modalities: The development of novel therapeutic modalities, including gene therapy, CRISPR and biologics, is opening new frontiers in medicine. Gene therapy aims to treat or prevent diseases by introducing, removing, or altering genetic material within a patient’s cells. An example is the FDA-approved gene therapy Luxturna, which treats a rare inherited vision loss. CRISPR technology, which allows precise genome editing, holds promise for treating genetic disorders by correcting mutations at their source. Biologics, such as monoclonal antibodies and cell-based therapies, offer targeted treatment options for a wide range of diseases, including autoimmune disorders and cancers.

Real-World Examples

AI in Drug Discovery: Exscientia was an AI-driven drug discovery company. In 2020, Exscientia collaborated with Sumitomo Dainippon Pharma to develop DSP-1181, a drug candidate for treating obsessive-compulsive disorder (OCD). Using its AI platform, Exscientia was able to design the drug in just 12 months, a process that typically takes 3 to 5 years using traditional methods. The AI system efficiently analyzed and processed vast amounts of data to identify and optimize the drug candidate, demonstrating a significant acceleration in the drug discovery process. Specifically, they used AI to rapidly predict new chemical structures during lead optimization, significantly faster than the traditional method DSP-1181 advanced to clinical trials, highlighting the potential of AI to speed up the development of new medications.¹² Exscientia entered an agreement to be acquired by Recursion Pharmaceuticals in August 2024.

CRISPR and Gene Therapy: The application of CRISPR technology in clinical settings is exemplified by ongoing trials for sickle cell disease and beta-thalassemia. Companies like CRISPR Therapeutics and Vertex Pharmaceuticals are leading the way with CRISPR-based treatments that aim to edit the faulty genes responsible for these conditions. Early results have shown promise, with some patients achieving significant improvements, highlighting the potential of gene editing as a therapeutic modality.

Precision Autoimmune Diseases: The development of biological therapies, such as adalimumab (Humira) and infliximab (Remicade), has pioneered the treatment of autoimmune diseases like rheumatoid arthritis and Crohn’s disease. These drugs specifically target inflammatory pathways, offering relief to patients who do not respond to traditional therapies. A significant challenge in treating autoimmune conditions is the ability to precisely target disease cells or tissues while avoiding toxicity in healthy ones. Recent biological insights have enabled the adaptation of clinically derisked technology from precision oncology for autoimmune applications. Luma recently published a white paper highlighting advancements and opportunities in this innovative field. The success of these biologics underscores the importance of targeted treatments in managing complex chronic conditions.

Concluding Thoughts

Drug discovery is not for the faint of heart. It requires unwavering conviction, passion, perseverance, a vast team of dedicated individuals, and substantial financial resources to bring a therapy to market. Despite these challenges, countless people in our sector commit decades, even entire lifetimes, to the hope of developing a drug that can offer patients a better life than their disease allows today. Although the journey can be daunting, it is profoundly rewarding to know that all this effort, time, and investment can significantly impact patients’ lives and improve global health within our lifetimes.

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1. https://www.forbes.com/sites/matthewherper/2013/08/11/how-the-staggering-cost-of-inventing-new-drugs-is-shaping-the-future-of-medicine/#7753cc096bfc ↩︎
2. https://www.biospace.com/article/releases/pharmaceutical-market-size-to-hit-around-usd-2-832-66-bn-by-2033/ ↩︎
3. https://www.biospace.com/article/releases/pharmaceutical-market-size-to-hit-around-usd-2-832-66-bn-by-2033/ ↩︎
4. https://web.archive.org/web/20150905144824/https://dtp.nci.nih.gov/timeline/flash/success_stories/S2_Taxol.htm ↩︎
5. https://www.nature.com/scitable/topicpage/gleevec-the-breakthrough-in-cancer-treatment-565/ ↩︎
6. https://www.mdpi.com/1422-0067/23/22/14178 ↩︎
7. https://link.springer.com/chapter/10.1007/978-981-16-5180-9_16 ↩︎
8. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC453150/ ↩︎
9. https://www.sciencedirect.com/science/article/abs/pii/016158909390106L ↩︎
10. https://link.springer.com/article/10.1007/s43441-023-00509-1 ↩︎
11. https://www.nature.com/articles/nrd3681 ↩︎
12. https://www.sumitomo-pharma.com/news/20200130.html ↩︎

For centuries, humans have sought to understand—and potentially slow, halt or reverse—the aging process. Recent advances in molecular and cellular biology have begun to illuminate the core hallmarks and drivers of aging, providing a foundational framework for new therapeutic approaches. While some of these therapies have not fulfilled their initial promise, others have achieved remarkable progress, and ongoing clinical trials may pave the way for a new class of longevity drugs. Still, despite growing optimism, the journey toward regulatory approval and widespread adoption of these treatments presents a unique set of challenges that must be navigated carefully.

February 2025

As a science-driven venture fund, we often have the pleasure of meeting and working with dedicated researchers who tirelessly strive to unlock new insights within human health, and no other field is as fascinating, complex and provocative as the field of aging. This emerging therapeutic space will affect all humans and is woven into the fabric of the human experience.

In this white paper, our team takes a deep dive into aging, longevity research and drug development. We delve into our evolving understanding of the mechanisms behind aging, examine the challenges that hinder the development of effective therapeutics and highlight the promising opportunities emerging in this rapidly advancing field, including the cutting-edge research being performed at Altos Labs, one of the Luma Group’s portfolio companies.

What is aging: unraveling a whole-body disease

We all have or will experience symptoms of aging, from stiffening joints to moments of forgetfulness, but what is the biology driving this seemingly inevitable process? Humans have studied aging for thousands of years but have just begun to understand the underlying causes of the aging process. The most significant advances have come with the help of modern medicine and science and have begun to shed light on core drivers in the aging process.  Although we have begun to make headway in understanding aging, there is still a vast amount that we don’t understand but are hopeful of uncovering within our lifetimes.

Thanks to groundbreaking advances in molecular biology techniques, our understanding of aging has moved from speculation to a science rooted in cellular mechanisms. These technologies have allowed researchers to dissect the intricate molecular pathways that drive aging, unlocking insights into the cellular damage, DNA repair deficits and protein misfolding that contribute to the process. What was once seen as a vague, inevitable decline is now recognized as a complex, multi-system condition with identifiable triggers. As we continue to decode these molecular signals, we edge closer to the possibility of interventions that could delay or even reverse the effects of aging.

What do we know: the molecular hallmarks of aging

Aging is a complex, multifaceted process that affects every cell and tissue in the human body. Over the past few decades, significant advances in molecular biology have allowed researchers to identify and characterize the core mechanisms driving this inevitable decline.

These mechanisms, known as the molecular hallmarks of aging, provide critical insights into how our bodies change over time. By understanding these hallmarks—ranging from genomic instability to mitochondrial dysfunction—scientists are uncovering potential targets for interventions aimed at slowing, or even reversing, the aging process. In this section, we explore the key molecular drivers of aging and how they impact human health and longevity and what promise they hold for drug development.

Graphic 1: The molecular hallmarks of aging.

Source: https://www.sciencedirect.com/science/article/pii/S0092867422013770.

Genomic Instability

Aging is closely linked to increased genomic instability, characterized by the accumulation of mutations, chromosomal abnormalities and DNA damage. These genetic insults arise from multiple sources, including replication errors, environmental factors, such as stress, radiation and toxins, as well as endogenous factors like reactive oxygen species (ROS) produced during normal cellular metabolism. Over time, the continual onslaught of genomic damage compromises the integrity of both nuclear and mitochondrial DNA, driving cellular malfunction, senescence and an elevated risk of cancer.

Graphic 2: Drivers of genomic instability and their effect on aging.

Source: https://www.sciencedirect.com/science/article/abs/pii/B9780128237618000203?via%3Dihub

Data from animal models underscore the profound impact of genomic integrity on longevity. For instance, transgenic mice overexpressing BubR1—a mitotic checkpoint regulator—demonstrated increased lifespans and a reduced incidence of age-related diseases.¹ Similarly, enhancing DNA repair and genomic stability through genes like SIRT6 has been shown to improve lifespan in mice by mitigating the accumulation of DNA damage.² Conversely, increasing genomic mutations in mice by altering DNA proofreading proteins leads to phenotypes resembling accelerated aging, including alopecia, kyphosis, osteoporosis, anemia, reduced fertility and cardiac enlargement. Related insights from human cells and progeroid syndromes (e.g., Werner’s syndrome) further confirm that defects in DNA repair mechanisms hasten aging.³

Despite these findings, pinpointing the root causes and precise mechanisms linking genomic instability to aging remains exceptionally complex. Many of the underlying pathways are shared by both healthy and aged cells, making it difficult to identify interventions that selectively improve genomic integrity without adversely affecting normal functions. Determining the optimal timing, method and location for DNA repair interventions is another formidable challenge; correcting one issue may inadvertently trigger new ones. Although numerous studies have reinforced the connection between genetic instability and age-related decline, they often fail to yield clear therapeutic targets or actionable pathways. As a result, developing safe and targeted strategies to address genomic instability—without introducing additional risks—remains a major hurdle in translating these insights into effective anti-aging therapies.

Telomere Attrition

Telomeres, repetitive DNA sequences that cap the ends of chromosomes, progressively shorten with each cell division due to the “end-replication problem.” Once telomeres reach a critically short length, cells are driven into senescence or apoptosis—an important safeguard against unchecked cell proliferation and cancer. Yet, this protective mechanism also contributes to tissue dysfunction as we age, linking telomere shortening to various age-related diseases such as cardiovascular disease, osteoporosis and immunosenescence.

While it is clear that telomeres play a role in aging, the precise nature of their influence remains uncertain. On one hand, shortening telomeres can push cells toward senescence, potentially driving age-related pathologies. On the other hand studies in animal models show that restoring telomerase activity can prevent telomere shortening and even reverse certain signs of aging, effectively extending lifespan in telomerase-deficient mice.⁴ Research on telomere length in human populations similarly links short telomeres with an increased risk of age-related diseases such as pulmonary fibrosis.⁵ Additionally, studies involving “hyperlong” telomeres in mice have revealed improved metabolic health and longer lifespans.⁶ Together, these findings underscore telomeres as regulators of the aging process.

Graphic 3: Cellular cascade of telomere shorting.

Source: https://sciencellonline.com/blog/aging-and-telomere-length-quantification-by-qpcr/

However, the relationship between telomere length and aging is far from straightforward. Some evidence suggests that individuals with relatively short telomeres may still live longer than those with longer telomeres, indicating that telomere length alone may not reliably predict healthspan (the healthy part of a lifespan) or lifespan (the total span of life).⁷ Rather than functioning as a simple “molecular clock,” telomeres likely interact with diverse cellular pathways, contributing to aging through multiple and sometimes contradictory mechanisms.

Complicating matters further, the dual role of telomeres in both cancer prevention and cancer promotion creates significant challenges for translating these insights into clinical therapies. Telomerase activation might slow or even reverse certain aging phenotypes, yet it could also enable cells to proliferate beyond their natural limits, increasing cancer risk. In essence, telomeres present a double-edged sword: although they offer potential avenues for intervention, such approaches must navigate the intricate balance between mitigating age-related decline and avoiding oncogenic transformation. Although the basic science surrounding telomeres is intriguing, these complexities highlight the need for a deeper biological understanding before safe and effective therapeutic strategies can be developed.

Epigenetic Alterations

Epigenetic changes—including DNA methylation, histone modifications and chromatin remodeling—adjust gene expression without altering the underlying DNA sequence. As we age, these epigenetic landscapes become increasingly disrupted, influencing cell identity, function and overall health. Such alterations have been implicated in a variety of age-related diseases, including cancer, cardiovascular conditions and neurodegenerative disorders. Using “epigenetic clocks” based on DNA methylation patterns can predict biological age, and administration of α-ketoglutarate has been shown to reduce the epigenetic age of cells by eight years.⁸ In animal models, inactivating histone acetyltransferase Kat7 delays cellular aging and extends lifespan, while overexpressing SIRT1 improves genomic stability and promotes healthier, longer life.⁹,¹⁰

Graphic 4: Age-related alterations and underlying mechanisms.

Source: https://www.science.org/doi/10.1126/sciadv.1600584

Despite these promising findings, the field of epigenetics in aging is still in its early stages. Establishing whether epigenetic modifications are genuinely causal—and not merely correlational—requires larger and more comprehensive datasets. Complicating this effort, epigenetic changes are influenced by a combination of genetic and environmental factors, making it challenging to isolate individual contributors. Although epigenetic markers currently serve as intriguing potential biomarkers for aging, significant maturation and validation of this research are needed before these discoveries can be translated into safe, effective therapeutic interventions.

Loss of Proteostasis

Proteostasis involves maintaining the delicate balance of protein synthesis, folding and degradation required for proper cellular function. As organisms age, this balance deteriorates, allowing misfolded or damaged proteins to accumulate and form toxic aggregates, such as the amyloid plaques seen in Alzheimer’s disease. The breakdown of proteostasis has also been implicated in various age-related conditions, including cataracts and sarcopenia.

Graphic 5:  Protein aggregation driven mechanisms of aging

Source: https://www.mdpi.com/1422-0067/24/10/8593

Several approved and clinical-stage therapies aim to modify proteostasis machinery directly. For instance, the proteasome inhibitor bortezomib, originally approved for oncology, effectively treats certain lymphomas and multiple myeloma by halting protein degradation and inducing cancer cell death. More recently, a growing focus has turned toward enhancing, rather than inhibiting, proteasome activity. By boosting the cell’s capacity to clear harmful proteins, these activator therapies may offer new strategies for combatting age-associated diseases. Real-world data reinforce the therapeutic potential of improving proteostasis. Overexpressing LAMP2A, a regulator of chaperone-mediated autophagy, improved proteostasis and extended lifespan in mice.¹¹ Similarly, administration of recombinant HSP70 enhanced proteasome activity, reduced brain lipofuscin—a hallmark of aging—and increased lifespan in mouse models.¹² These findings suggest that therapies targeting protein quality control mechanisms could significantly delay the onset and progression of age-related diseases.

However, there are still numerous challenges in developing proteostasis therapeutics for aging. While current hypotheses link declining proteasome activity to aging and disease states, it remains uncertain whether restoring proteostasis in humans will yield the same benefits observed in animal models. Developing effective proteasome activators has proven more complex than creating inhibitors, given the intricate biology of these pathways and the difficulty of achieving selective targeting. Nonetheless, several proteasome activators are moving through clinical development, and their outcomes will help determine whether proteostasis-enhancing interventions can overcome these therapeutic hurdles—and ultimately improve health span and longevity.

Disabled Autophagy

Autophagy, a cellular quality control mechanism, degrades and recycles damaged organelles and other cellular components to maintain overall cellular health. By preventing the accumulation of waste and facilitating the turnover of older or malfunctioning organelles to preserve cellular function. However, as we age, this process becomes less efficient, contributing to the onset and progression of age-related conditions, including neurodegenerative diseases, cancer and metabolic disorders.

Graphic 6: the role of autophagy in cellular health.

Source: https://www.preprints.org/manuscript/202303.0500/v1.

In mice, overexpressing the autophagy-related gene Atg5 extends lifespan and improves metabolic health.¹³ Similarly, spermidine, a naturally occurring compound that induces autophagy, has been shown to lengthen lifespan and mitigate age-related cardiac dysfunction.¹⁴ Further research has demonstrated that pharmacological agents like salicylates, which promote autophagy by inhibiting EP300, can enhance autophagic activity and increase longevity in mice.¹⁵

Despite these promising findings, the therapeutic potential of enhancing or restoring autophagy remains challenging to harness. Much like the complexities faced in targeting proteostasis, interventions aimed at modulating autophagy must navigate intricate biological networks and ensure selectivity and safety. Although GWAS data from diseases like ALS and Parkinson’s support the rationale for restoring proper autophagy levels, the difficulty in identifying suitable intervention points—and the need to avoid unintended consequences—continues to pose significant hurdles. As research advances, overcoming these challenges will be critical for translating the science of autophagy into effective therapies against age-related diseases.

Mitochondrial Dysfunction

Mitochondria are central to cellular function, not only as the primary generators of ATP but also as regulators of metabolism, reactive oxygen species (ROS) production and cell death. As organisms age, mitochondrial function declines, resulting in diminished energy production and increased ROS, both of which contribute to cellular damage and aging. This decline in mitochondrial health is associated with a broad spectrum of age-related pathologies, including neurodegenerative, cardiovascular and metabolic diseases. Interventions have emerged that target mitochondrial function to extend healthspan. For instance, supplementation with NAD+ precursors such as nicotinamide mononucleotide (NMN) has improved mitochondrial function, boosted energy levels and extended lifespan in mice.¹⁶ Similarly, the drug elamipretide, developed to protect mitochondrial membranes, has shown improvements in heart and muscle function in both aged mice and humans.¹⁷ These examples suggest that strategies aimed at sustaining or restoring mitochondrial health could delay aging and mitigate age-related diseases.

Graphic 7: The mechanisms of action of mitochondrial dysfunction in aging and disease.

Source: https://www.nature.com/articles/s43587-022-00191-2#Sec2

However, effective therapies must also consider the maintenance and removal of damaged mitochondria through mitophagy—an autophagy subtype dedicated to mitochondrial quality control. Mitophagy-targeted interventions face challenges akin to those encountered in general autophagy-based therapies, necessitating enhanced or activated protein degradation pathways capable of efficiently clearing dysfunctional organelles. Mitochondria contain their DNA, thus opening additional avenues for intervention through the modulation of mitochondrial polymerases and other enzymes. While numerous drug candidates focusing on mitochondrial health have progressed into clinical trials, none have yet secured FDA approval. Nevertheless, the results of these ongoing trials—whether successful or not—will likely illuminate the most promising mechanisms of action for improving mitophagy and mitochondrial function.¹⁸

Deregulated Nutrient-Sensing

Nutrient-sensing pathways—including insulin/IGF-1, mTOR and AMPK—are central regulators of metabolism and growth, constantly adjusting cellular processes in response to nutrient availability. During aging, however, deregulation of these pathways often leads to excessive anabolism and reduced autophagy, accelerating the aging process and increasing susceptibility to metabolic diseases like diabetes. Interventions that reduce nutrient signaling, such as caloric restriction or pharmacological inhibitors of nutrient-sensing pathways, have consistently extended lifespan across multiple species. For example, in human studies, two years of caloric restriction improved markers of inflammation and enhanced immune function, underscoring its potential to slow aging.¹⁹ In mice, pharmacological inhibition of mTOR with rapamycin has been shown to prolong lifespan and improve age-related outcomes, while metformin—a common diabetes medication that modulates nutrient-sensing pathways—is currently under investigation for its anti-aging potential in human trials.^20,21

Collectively, these findings suggest that interventions targeting nutrient-sensing pathways hold promise for extending lifespan and mitigating age-related diseases. The prevailing hypothesis is that these strategies induce beneficial stress responses, such as enhanced protein and mitochondrial turnover, thereby promoting cellular resilience and longevity. Still, determining the true magnitude of these benefits presents challenges, as it entails altering diets or administering drugs in highly heterogeneous aging populations with diverse health conditions over a very long time. Large-scale, extended-duration clinical trials are necessary to gain a more definitive understanding of their long-term effects. Some of these investigations are already in progress, but it may be years before their outcomes are known. Only then will we have a clearer picture of whether interventions like caloric restriction or low-dose metformin can reliably increase lifespan or significantly improve outcomes in age-related conditions.

Cellular Senescence

Cellular senescence occurs when cells cease to divide but remain metabolically active, secreting inflammatory and damaging factors that negatively affect surrounding cells and tissues. This accumulation of senescent cells is strongly implicated in age-related diseases, including osteoarthritis, atherosclerosis and neurodegeneration. Removing senescent cells, therefore, represents a promising therapeutic approach to mitigating the adverse effects of aging and age-related pathologies. Multiple lines of evidence indicate that these cells build up as we get older, and their targeted elimination can improve the overall health and function of tissues and organs. For example, senolytic drugs—therapies designed to selectively clear senescent cells—have shown success in animal models; the combination of dasatinib and quercetin, for instance, reduced the number of senescent cells in aged mice, improving markers of aging.²² Early human trials of senolytics have also reported encouraging results, demonstrating reduced senescence markers and improved health outcomes in age-related conditions.²³

Graphic 8: Mechanism of action for senoltyics in aging.

Source: https://computationalaginglab.github.io/computational_aging_course/bio/intro_aging_biology.html

Although the field of senolytics is still in its infancy, it holds significant promise. Like oncology, it benefits from the presence of a distinct pathological cell type as a target. Still, a major challenge lies in pinpointing the precise therapeutic nodes or targets that can effectively and selectively eliminate senescent cells, allowing tissues and organs to renew themselves with healthy, functional counterparts. Our current understanding of senescent cell biology remains limited, and the identification of specific, compelling targets is an ongoing endeavor. As research advances, filling these gaps in knowledge may pave the way for the development of safe and effective senolytic therapies.

Stem Cell Differentiation and Reprograming

Stem cells possess a remarkable capacity for self-renewal and differentiation into virtually any cell type, with each organ containing a specialized stem cell niche that generates new cells to replace those that are damaged or aged. This regenerative capacity is central to tissue repair and maintenance; however, it diminishes over time. As people grow older, stem cell activity declines, resulting in compromised tissue integrity and an elevated risk of diseases such as cancer and cardiovascular disorders. This phenomenon, termed stem cell exhaustion, arises from both intrinsic factors (e.g., DNA damage) and extrinsic factors (e.g., altered stem cell niches and systemic inflammation). Despite these challenges, stem cells remain highly promising therapeutic tools because they offer the potential to restore damaged or aging tissues using the body’s own healthy cells. Over the past two decades, researchers have made substantial progress in deriving stem cells from adult cells and guiding them to form a wide range of specialized cell types, thereby laying the groundwork for new treatments that may mitigate many aspects of age-related decline.

Graphic 9: Yamanaka factor stem cell reprograming and terminal differentiation.

Source: adapted from https://www.nature.com/articles/cdd201014.

A groundbreaking leap in this field came in 2006, when Dr. Shinya Yamanaka identified four key transcription factors—Oct4, Sox2, Klf4 and c-Myc—that could reprogram ordinary adult cells into induced pluripotent stem cells (iPSCs), resetting them to a stem cell-like state.²⁴ This discovery revolutionized the scientific landscape by offering an ethically sound and widely accessible alternative to embryonic stem cells, upending the long-standing belief in fixed cellular destinies. With iPSCs, scientists now have unprecedented opportunities to regenerate damaged tissues or organs from a patient’s own cells, sharply reducing immune rejection risks. This advance also underpins personalized medicine strategies aimed at treating conditions ranging from neurodegenerative diseases to heart disorders. Beyond direct therapeutic applications, iPSCs enable researchers to more accurately model human development and disease, as well as to test drugs on human-like tissues. Yamanaka’s work, which earned him a Nobel Prize, remains at the forefront of regenerative medicine. The impact of his discovery is evident not only in academic research but also in its translation toward clinical applications, exemplified by its central role in the strategy of one of Luma Group’s portfolio companies, Altos Lab, which aims to harness the power of cell reprogramming for therapeutic purposes.

Stem cell technologies thus hold immense potential for addressing many core challenges of aging by replacing or reprogramming pathological cells across a spectrum of age-related diseases. However, several critical obstacles remain. Scaling up cell production and ensuring efficient, precise delivery methods pose significant hurdles for cell replacement therapies (as discussed in the opportunity section). In vivo reprogramming may streamline this process by circumventing the need for large-scale cell manufacturing and intricate delivery protocols. Yet, this approach faces its own difficulties, especially the safe and reliable delivery of multiple reprogramming factors. Notably, this challenge is not unique to longevity research; the gene therapy sector is actively developing solutions that can be used for longevity. As such, continued investment in understanding stem cell biology, refining cell production and improving delivery mechanisms will be essential to fully realize the promise of stem cell–based and reprogramming-based interventions for aging.

Longevity Therapeutics: Challenges from Benchtop to Bedside

Developing therapeutics and conducting clinical trials for longevity presents unique challenges due to the complexity of aging as a biological process. As highlighted in the previous section, aging is influenced by numerous interconnected factors, including genetics, lifestyle and environmental influences, making it difficult to isolate specific targets for intervention. Additionally, the long timescales required to observe meaningful outcomes in human aging, along with the diversity in how individuals age, further complicate the design and execution of clinical trials. Even with these complexities, driven researchers and biotechs have been working on developing longevity therapeutics.

Running clinical trials for longevity therapies poses unique challenges (outlined below) that set them apart from traditional trials. Designing a study to demonstrate a direct extension of lifespan is inherently difficult, so many trials instead focus on specific age-related diseases as more measurable endpoints. Despite these obstacles, several clinical trials targeting age-associated conditions and interventions that may influence lifespan have been conducted, as summarized in the table below.

Length and Complexity of Trials

One of the primary challenges of longevity trials is their extended duration. Since these therapies aim to delay or prevent age-related decline, it may take years, if not decades, to see meaningful results. This long timeframe complicates the trial design, making it difficult to maintain patient adherence and gather consistent data over extended periods. Additionally, many aging-related processes unfold over a person’s lifetime, requiring researchers to use surrogate biomarkers rather than waiting for hard outcomes like mortality or disease onset.

Recruitment and Patient Population

Recruiting participants for longevity trials can be challenging due to the need for a healthy population rather than individuals already diagnosed with a specific condition. Identifying the right demographic—those who are at risk of age-related diseases but have not yet developed them—requires extensive screening and a clear understanding of the aging process. Moreover, convincing healthy individuals to participate in long-term trials, especially with the uncertainty of benefits, can be difficult.

Regulatory Considerations

Longevity research exists at the intersection of prevention and treatment, often challenging traditional regulatory frameworks. Aging is not classified as a disease, which complicates the approval process for therapies aimed at treating it. Regulatory agencies like the FDA do not yet have clear guidelines for evaluating interventions designed to slow aging. This creates significant uncertainty for companies attempting to bring longevity therapies to market.

Biomarkers and Measuring Success

Unlike traditional clinical trials, which measure success based on clear clinical outcomes like symptom improvement or disease remission, longevity trials require novel endpoints. Researchers must rely on biomarkers of aging—such as telomere length, epigenetic changes, or cellular senescence markers—to measure the efficacy of a therapy. Furthermore, these biomarkers also require dedicated development and regulatory approval, often involving extensive and time-consuming human testing. These biomarkers are still under development and debate, leading to potential variability in results and slower progress in validating new treatments.

Logistical Challenges

The logistics of running a longevity trial introduce another layer of complexity. Maintaining patient engagement over the span of years is difficult, especially when participants are often healthy at the start of the study and may not experience immediate benefits. Patient retention is crucial, yet drop-off rates can be high due to the lengthy commitment required. Furthermore, managing data collection across diverse populations over time presents significant challenges, particularly when studies are conducted across multiple geographic locations. Ensuring consistent quality control, adhering to standardized protocols, and synchronizing data from various sites are all critical to the integrity of these studies but are operationally demanding.

Examples of Real World Clinical Trials

Table 1: Overview of past age-related clinical trials.

	Objective	Status	Why it’s Significant
TAME (Targeting Aging with Metformin) Trial	Tests whether metformin can slow aging and delay age-related diseases such as heart disease, cancer and cognitive decline.	Ongoing, with efforts to get FDA approval to classify aging as an indication.	This trial aims to create a new regulatory path for anti-aging therapies by targeting aging rather than specific diseases.
UNITY Biotechnology Senolytics Trials	Evaluating the effectiveness of the mechanism of action for senolytics	UBX0101 was tested in patients with osteoarthritis, however it did not show significant improvements.	This was a prominent trial testing the senolytic hypothesis
CALERIE (Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy)	A long-term trial investigating the effects of caloric restriction on markers of aging, metabolic health and lifespan in humans.	The trial showed calorie restriction caused a significant reduction in all measured conventional cardiometabolic risk factors	First long-term study of caloric restriction in non-obese humans and has contributed valuable data on dietary interventions influence aging.
ResTORbio RTB101 Trial	Evaluate RTB101 (an mTOR modulator) effects to enhance immune function in the elderly and reduce the incidence of respiratory infections.	Despite promising early results, the Phase 3 trial failed to meet its primary endpoint and development was discontinued.	The mTOR pathway is one of the most well-studied mechanisms of aging and the study aimed to translate animal research into human longevity therapies.
GRF6019 and GRF6021 (Plasma Dilution Therapy)	These studies aim to evaluate the effectiveness of young plasma treatments on the effects of cognitive decline and motor activity in Alzheimer’s and Parkinson’s disease.	Both trials showed good tolerability but mixed efficacy. The Alzheimer’s trial revealed no significant improvement, while the Parkinson’s trial hinted at early efficacy. Larger studies are needed to determine overall effectiveness.	This trial taps into the intriguing concept of “parabiosis” and seeks to understand whether modifying blood plasma can rejuvenate aging tissues.

Source: https://www.afar.org/tame-trial, https://pmc.ncbi.nlm.nih.gov/articles/PMC10168460/, https://pmc.ncbi.nlm.nih.gov/articles/PMC3525758/, https://www.sciencedirect.com/science/article/pii/S246850112030002X, https://pubmed.ncbi.nlm.nih.gov/33967047/ and https://clinicaltrials.gov/search?intr=GRF6021

Even with these hurdles, numerous academic and industry-led clinical trials targeting longevity have been initiated. To navigate the long timelines typically required for such research, many trials focus on age-related diseases, such as cardiovascular disease, neurodegeneration, or metabolic disorders, rather than on general life extension. These diseases have more well-defined clinical endpoints, such as the incidence of heart attacks, fibrosis of tissues, or cognitive decline, that can be measured within a reasonable time frame—usually years rather than decades. By targeting diseases closely associated with aging, researchers aim to demonstrate the effectiveness of therapies in extending healthy lifespan, which can then serve as a stepping stone toward broader life extension goals.

Opportunities within Longevity Therapeutics

Despite the significant hurdles, uncertainties and unproven outcomes that characterize the current landscape of longevity therapeutics, our fund believes this area still warrants careful attention. Although many remain skeptical, Luma is an active investor in this space because of the pace at which researchers are unraveling the biological mechanisms of aging—and the potential for these insights to translate into therapies that extend healthspan, creating a breakthrough moment in the near future. Our focus is on identifying companies and technologies that demonstrate not only strong scientific foundations but also clear, realistic pathways from bench to bedside. While we recognize that many questions remain unanswered, we see a measured but meaningful opportunity to invest in advancing this promising frontier of medicine.

One of our earliest investments was in Altos Labs, which we hope will pioneer a new class of drugs for numerous age-related disorders. Their core technology builds on two groundbreaking scientific discoveries. The first is the identification of the Yamanaka factors, which allow any cell type to be reprogrammed into an induced pluripotent stem cell (iPSC) state. This Nobel Prize–winning discovery triggered a wave of stem cell research, providing researchers—including my partner Themasap and me—with the tools to create and study stem cells in the lab. Today, thousands of researchers are capable of generating stem cells and are developing methods to differentiate them into virtually any cell type. We have begun to understand the factors and pathways that enable the conversion of stem cells into every specialized cell in the human body, all in a dish.

Although the discovery of Yamanaka factors and iPSCs revolutionized our understanding of stem cells, this insight, on its own, does not constitute a therapeutic solution. However, it enables the generation of specific cell types ex vivo that could be reintroduced into the body to replace damaged or lost tissues. While many companies are pursuing ex vivo therapies—in which cells are grown outside the body and then injected into diseased or aged tissues to restore function—this approach comes with significant challenges. One major hurdle is scaling up cell production. Producing the billions of specialized cells required to meet clinical and commercial demands is currently very difficult. The process still occurs on a relatively small scale and can suffer from considerable batch-to-batch variability. Additionally, the field has yet to solve the delivery problem. Even if we can generate the necessary cells at scale, we must determine how to precisely deliver them to the affected tissues. These cells often require extremely accurate placement to function properly, making their targeted delivery a complex, and often overlooked, challenge. For instance, some companies are developing ex vivo cell translatplate approaches for Parkinsons’ Disease. This requires developing iPSC-derived dopaminergic neurons to replace those lost in the substantia nigra—a region located deep in the interior of the brain—where delivery of therapeutic cells poses a significant challenge.

The second key discovery for Altos originated in the laboratory of Dr. Juan Carlos Izpisua Belmonte at the Salk Institute for Biological Studies. While Yamanaka factors were originally known for their ability to revert cells all the way back to a stem cell state, the Belmonte lab found a more nuanced approach. Rather than fully resetting a cell’s identity, these factors could be applied in a way that only “partially” reprograms the cell, making it more resistant to stress while preserving its original characteristics. The result is a cell that retains youth’s functional benefits without losing its specialized role in the body. They have seen this work across various tissue types and organs, from skin to kidneys and others.²⁵ Luma remains excited about the progress the Altos has made and believes they will be one of the next generation companies that help redefine longevity.

What does the future of aging look like?

The future of aging therapeutics is poised to revolutionize healthcare, as advances in biotechnology, genomics and drug development converge to target the root causes of aging. Rather than simply managing age-related diseases, emerging therapies aim to intervene at the cellular and molecular levels, promoting longevity and healthier aging. These advancements will take time and will initially focus on areas where we have the deepest scientific knowledge, gradually expanding to other aspects of aging. Progress is already being made in addressing individual age-related diseases, such as fibrosis and cardiometabolic conditions, while researchers work to uncover the science that will unlock solutions for the next wave of age-related conditions. With breakthroughs in areas like senolytics, epigenetic reprogramming and personalized medicine, the next generation of therapeutics could extend healthspan—allowing individuals to live longer, more vibrant lives. As the global population ages, the development of these therapies will play a critical role in reshaping both healthcare and society.

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Age-related macular degeneration (AMD) affects millions worldwide, leading to progressive vision loss and limited treatment options. Character Bio is changing that paradigm by combining an AI-driven multi-omics platform with the world’s richest AMD dataset to develop new precision therapies for these patients. At Luma, we invest in transformative companies, and Character Bio’s groundbreaking work promises to redefine the future of eye health.

March 2025

Over the past two decades, the biotech and pharmaceutical industries have advanced significantly, driven by discoveries from first-generation “-OMICs” data, like genomics. Early successes such as HER2-targeted breast cancer therapies demonstrated that data-centric approaches yield transformative treatments, ushering in the era of precision medicine and numerous therapies. Yet, one truth is increasingly clear as healthcare evolves: we need more high-quality multidimensional data to propel the next wave of precision therapies.

At Luma, our investment philosophy is simple yet powerful: better data leads to better scientific decision-making and, ultimately, better patient outcomes. This principle shapes our investment strategy, guiding us to partner with companies leveraging robust, multi-dimensional data to drive drug discovery and accelerate clinical development. Researchers now have powerful tools—e.g., single-cell sequencing, metabolomics and proteomics—to generate large datasets. But more data doesn’t automatically mean better data. The key is to derive meaningful insights. A capability that must grow with data generation. For complex diseases, a holistic understanding of interrelated datasets is vital in helping to unravel disease biology.

We view multi-OMIC data as foundational—and our recent investment in Character Biosciences (Character Bio) reflects this holistic, data-centric approach. Forward-thinking companies like Character Bio are illuminating the complexity behind numerous unmet medical needs and unlocking new discoveries by increasingly leveraging AI and machine learning to analyze these enormous datasets.

Graphic 1: Concatenation and Analysis of Multi-OMIC Dataset

Source: adapted from https://levelup.gitconnected.com/multi-omics-analysis-3857956a7a3d

Below, we share why we invested in what we believe to be a paradigm-shifting ophthalmology-focused company – one leveraging AI-driven, multi-OMICs data and insights to pioneer new drugs and clinical trial designs

Off the Beaten Path: A Move into Ophthalmology

In early 2024, we turned our focus to ophthalmology—specifically, the urgent unmet needs in age-related macular degeneration (AMD). Globally, 1 in 8 people over 50 show signs of AMD, and roughly 10% will progress to geographic atrophy (GA), an advanced form of dry AMD. Both intermediate dry AMD and GA are progressive diseases of blindness and carry a high clinical burden yet currently lack disease-modifying treatments.

The impact is deeply personal: vision loss from AMD and GA can make reading, driving, or recognizing loved ones increasingly difficult—turning daily life into a source of anxiety and isolation. Patients are left with either no available options or therapies with limited efficacy and concerning safety profiles. However, breakthroughs in gene therapy and retinal regeneration point to a coming inflection point – offering new pathways to preserve vision and restore hope for millions.

Graphic 2: Disease Pathology and Progression of Dry AMD

Source: Image provided by Character Biosciences

What struck us most about the AMD/GA landscape is the notable innovation gap in therapeutic development and clinical trial design. Despite a large patient population and extensive genomics research, progress toward effective treatments has been limited. Fields such as oncology and cardiometabolic diseases have leveraged genetic insights to significantly advance drug development, and therapies based on causal genetics are 2.6x more likely to succeed in clinical trials.¹ Additionally, two-thirds of all drugs approved by the FDA in 2021 were supported by robust genetic correlation.² These observations reinforce our belief that the AMD/GA field is primed for breakthroughs driven by leveraging large OMIC datasets to improve therapies and trial outcomes.

We believe that leveraging large multi-OMICs data to pinpoint the complex disease drivers behind AMD/GA pathologies—specifically complement dysregulation, lipid deposition and ischemia—can lead to targeted therapies and smarter clinical trials.

Finding Character Bio

Through our exploration in ophthalmology, we were fortunate enough to meet with CEO and Co-Founder, Cheng Zhang and the Character Bio team back in 2024. From the outset, Character Bio stood out to us for many important reasons:

• Origin in Rich Data: Character Bio has dedicated resources to building out the largest,
  richest database developed for AMD drug discovery and development via self-sponsored
  observational trials since day one.
• Integrated Approach: Rather than focusing on a single biological pathway, Character Bio employs AI to analyze genetic and longitudinal clinical data simultaneously offering an unparalleled view into disease mechanisms, especially those that drive progression.
• Patient Stratification & Clinical Trial Design: Character Bio’s platform not only identifies high-value targets but also stratifies patients most likely to progress and respond to therapy, drastically improving the odds of clinical success.

An exciting motif that frequently emerged in our conversations with industry leaders was the recurring sentiment: “I wish I had their multi-OMICs datasets.” This feedback further bolstered our conviction that Character Bio had developed something truly distinct. That validation was further reinforced by the recent signing of a multi-target drug discovery collaboration agreement with Bausch + Lomb earlier this year.

Big Data, Big Impact

Our investment in Character Bio underscores the transformative power of “better data, better decisions,” a principle that guides much of our work at Luma. We’re excited to see Character Bio continue to reshape treatment for intractable diseases like dry AMD and show the impact that AI-driven analytics have on developing effective therapies. The company’s focus on ophthalmological research has the potential to give people back the freedom to navigate the world with confidence.

1. https://www.nature.com/articles/s41586-024-07316-0 ↩︎
2. https://www.nature.com/articles/d41573-022-00120-3 ↩︎