A new clinical risk model may transform how obesity is managed, by identifying which individuals are most likely to develop serious complications, regardless of their body mass index (BMI).
Developed by researchers at Queen Mary University of London and the Berlin Institute of Health, the tool, called OBSCORE, uses just 20 routinely collected clinical variables to predict the future risk of 18 obesity-related conditions, ranging from type 2 diabetes to cardiovascular disease.
Published in Nature Medicine, the study challenges the long-standing reliance on BMI as the primary metric for assessing obesity-related health risk.
Moving beyond BMI
BMI has long served as a simple proxy for obesity, but it fails to capture the biological heterogeneity of patients. Two individuals with similar BMI can have vastly different risks of developing complications.
The new model addresses this limitation directly. As described in the study, it “provides information beyond BMI” by integrating multiple dimensions of health into a unified risk score.
These include demographic data, clinical biomarkers, disease history, and lifestyle factors, variables already commonly available in healthcare settings.
The findings show that BMI alone is a poor discriminator of risk. The model consistently outperformed BMI-based approaches across all tested outcomes.
Large-scale data enables precision risk prediction
To build the model, researchers analyzed health data from nearly 200,000 individuals with overweight or obesity from the UK Biobank.
Using an interpretable machine learning framework, they screened more than 2,000 potential predictors and distilled them into a core set of 20 features that best predicted long-term health outcomes.
The resulting OBSCORE model estimates the 10-year risk of developing 18 conditions, including cardiovascular disease, kidney disease, sleep apnea, and metabolic disorders.
The model demonstrated strong predictive performance, with median concordance indices around 0.75 across outcomes, indicating robust discrimination between high- and low-risk individuals.
Hidden high-risk individuals
One of the most striking findings is that high-risk individuals are not always those with the highest BMI.
A substantial proportion of individuals classified as high risk fell into the “overweight” category (BMI 27–30 kg/m²), rather than obesity. In some outcomes, up to ~40% of those in the highest risk group had BMI below the obesity threshold.
This reveals a critical gap in current clinical practice: individuals who may benefit from intervention could be overlooked simply because they do not meet BMI-based criteria.
On the other hand, some individuals with obesity may have relatively low risk and may not require intensive intervention.
Strong risk stratification across diseases
Beyond prediction, the scientists believe that OBSCORE enables meaningful risk stratification. Individuals in the highest risk group showed dramatically higher rates of disease compared to those in the lowest group.
For example, the study reports:
Up to 89-fold higher risk for chronic kidney disease
42-fold higher risk for type 2 diabetes
47-fold higher risk for cardiovascular mortality
These differences exceed those observed when comparing individuals based solely on BMI categories, underscoring the added value of multidimensional risk assessment.
Clinical and healthcare implications
The implications of these findings are significant, particularly in the context of emerging obesity therapies.
Highly effective drugs such as GLP-1 receptor agonists and dual incretin therapies have transformed treatment options, but their high cost and limited availability make patient prioritization essential.
As the authors note, current systems lack robust frameworks to identify which patients should receive treatment.
OBSCORE offers a potential solution by enabling risk-based allocation of interventions, ensuring that treatment is directed toward those most likely to benefit.
This could improve clinical outcomes while optimizing healthcare resource use.
Toward implementation in clinical practice
One of the key strengths of OBSCORE is its practicality. Unlike many predictive models, it relies on a small number of variables that are already routinely collected, making it suitable for integration into electronic health records.
The researchers envision the model being used as a decision-support tool in clinical settings, complementing rather than replacing existing frameworks.
External validation in independent cohorts—including populations of different ancestry, demonstrated strong generalizability, further supporting its potential for real-world deployment.
Limitations and next steps
Despite its promise, the model requires further validation in broader populations, including younger individuals and more diverse healthcare settings.
Additionally, while OBSCORE effectively stratifies risk, translating these predictions into actionable treatment thresholds will require clinical consensus and cost-effectiveness analyses.
The authors also emphasize that the model identifies predictive, not necessarily causal, factors, and should be interpreted accordingly.
Taken together, the findings mark a shift toward precision medicine in obesity, moving from simplistic metrics like BMI to data-driven, individualized risk assessment.
By capturing the complex interplay of metabolic, clinical, and behavioral factors, OBSCORE could enable earlier intervention, better targeting of therapies, and improved long-term outcomes for patients living with overweight and obesity.
A machine learning model can improve genetic prediction of type 1 diabetes by as much as 10%, show results from a University of California, San Diego study.
The researchers used the machine‑learning model T1GRS to improve on a gold standard polygenic genetic risk score used to predict who is likely to develop the condition called GRS2.
Type 1 diabetes is an autoimmune condition that impacts around 2 million people in the U.S. While it is a multifactorial condition, genetics plays a big role and around 50% of a person’s susceptibility comes from genetics.
“The natural history of type 1 diabetes suggests that the disease occurs in genetically susceptible individuals exposed to environmental triggers, leading to the development of islet-specific autoantibodies and autoreactive T cells and progressive loss of insulin secretory function, although the underlying etiology is not fully understood,” write lead author Kyle Gaulton, PhD, associate professor of pediatrics at UC San Diego School of Medicine, and colleagues in Nature Genetics.
The GRS2 polygenic risk score has been widely tested and can be used to predict newborns who are at high risk of developing type 1 diabetes. While early prediction can’t necessarily stop the disease it can help to prevent emergencies like diabetic ketoacidosis at diagnosis, allow families time to prepare and could allow use of therapies to delay onset of the condition.
In this study, Gaulton and colleagues carried out a genome‑wide association study in 20,355 people with type 1 diabetes and 797,363 non‑diabetic Europeans, as well as a further analysis around the MHC region in 10,107 diabetic and 19,639 nondiabetic individuals.
“The MHC has ‘blocks’ of co-inherited genetic information that are very highly enriched in individuals with type 1 diabetes,” said co-first author Emily Griffin, PhD, a postdoctoral fellow in Gaulton’s lab. “If you have them, it doesn’t mean that you’re going to get diabetes, but if you don’t have them, it means you have a very low chance of getting diabetes.”
Overall 160 risk signals were identified, and the team trained their T1GRS model to predict who was likely to develop type 1 diabetes based on their genetics. The model was able to improve on the GRS2 model predictions by up to 10% in both populations of European and African American ancestry.
Overall the new score correctly flagged about 89 of 100 people with type 1 diabetes while correctly reassuring about 84 of 100 people without the disease.
“Our results highlight the value of combining the results of genetic association studies with machine learning methods to improve the prediction of complex diseases,” conclude the authors.
According to the American Brain Foundation, over one in three people around the world are affected by neurological conditions, the leading cause of illness and disability worldwide. This silent epidemic is not country-specific. Neurological conditions such as lysosomal storage disorders, rare enzyme deficiencies, and Alzheimer’s and Parkinson’s disease take their victims, regardless of age, race, or location.
For decades, scientists have struggled to deliver therapeutics to the brain, only to be thwarted by the highly protective blood-brain barrier (BBB). First-generation approaches demonstrated proof of principle but still require advancements to improve the ability to reach specific areas of the brain, or specific cell types, safely, and with sufficient dosage to enable meaningful therapeutic effects.
Although much remains unknown generally about brain biology and its defensive mechanisms, novel therapies for devastating neurological diseases are progressing into clinical trials. There is no magic bullet—no promises, no cures—but a gleaming light can be seen in this particular long and dark tunnel.
Dedicated scientists continue to work on gene therapies for the indications that most benefit from a once-and-done approach, in addition to neurological shuttles to address those disorders that require therapeutic tempering and dosage control.
Expanding platform technologies
In 2021, JCR Pharmaceuticals received regulatory approval for the first biotherapeutic, IZCARGO (pabinafusp alfa), designed to cross the BBB to deliver a therapeutic enzyme for the treatment of a lysosomal storage disorder called mucopolysaccharidosis type II (MPS II) or Hunter syndrome.
The platform technology has been expanded to exploit receptor-mediated transcytosis (RMT) to address other lysosomal storage and neurodegenerative diseases. Still, delivery to specific cells or parts of the brain remains challenging, along with efficient delivery of antisense oligonucleotides or siRNA.
“The issue is not delivery across the BBB, but the endosomal escape to efficiently suppress the target RNA,” said Hiroyuki Sonoda, PhD, representative director, president, and CSO, at JCR Pharmaceuticals. “Small molecule CNS delivery is related to physicochemical properties. The structural design needs to make them lipophilic, yet also able to evade typical transporter clearing mechanisms.”
The first approved blood-brain barrier penetration technology was developed into the J-Brain Cargo platform that can help drugs cross the blood-brain barrier. [JCR Pharmaceuticals]
J‑Brain Cargo® uses RMT, mainly focusing on the transferrin receptor (TfR). Other promising candidates target different receptors. “We have successfully transported enzymes, antibodies, peptides, decoy receptors, antisense oligos, and siRNA into the CNS,” commented Sonoda. J‑Brain Cargo is particularly suited for enzyme replacement therapies in lysosomal storage disorders and conditions where dose control, reversibility, and titration are important.
For gene therapies, JCR developed the JUST-AAV platform technology. Novel changes in the capsid almost completely eliminate liver tropism. The modified capsids express miniaturized antibodies on the capsid surface against receptors on selected tissues, organs, or the BBB, enhancing targeted delivery. JUST‑AAV is for diseases where continuous transgene expression is desired to achieve the optimal effect.
Several candidates are in global clinical trials, including JR-141 (pabinafusp alfa) for individuals with MPS II (also known as Hunter syndrome), JR-171 to treat MPS I (also known as Hurler, Hurler Scheie, or Scheie syndromes), and JR-441 for individuals with MPS IIIA (also known as Sanfilippo syndrome A).
Programs in collaboration with MEDIPAL HOLDINGS CORPORATION are in different stages of clinical and pre-clinical development for individuals with MPS IIIB (also known as Sanfilippo syndrome B), Fucosidosis, and GM2 gangliosidosis (including Tay-Sachs and Sandhoff disease).
Collaborating with leading pharmaceutical companies is core to JCR’s strategy to bring these platform technologies to broader application. “We enable our partner by turning their biologics into CNS-penetrating versions of their original molecule,” said Sonoda.
JCR manufactures most of its drug products in-house. Last year, they were selected for the Ministry of Economy, Trade and Industry’s “Regenerative CDMO Subsidy” to expand biomanufacturing capacity for regenerative, cell, and gene therapies.
Optimizing BBB transport
“Protein engineering architecture differentiates our delivery technology along with its optimization for efficacy, safety, and tolerability,” said Ryan Watts, PhD, co-founder and CEO of Denali Therapeutics.
The TransportVehicle (TV) technology has the RMT binding site integrated directly into the constant domain (Fc) of an antibody for optimal properties and modularity. This allows the same TV sequences to transport a range of large molecule biotherapeutics such as enzymes, oligonucleotides, and antibodies for systemic administration. The engineered Fc domains bind to specific natural transport receptors expressed at the BBB, such as TfR.
The integration of the receptor-mediated transcytosis binding site into the TransportVehicle (TV) technology allows the same TV sequences to transport a range of large molecule biotherapeutics, such as enzymes, oligonucleotides, and antibodies, for systemic administration. [Denali Therapeutics]
“Our research recently demonstrated that a TV platform-enabled anti-Ab antibody improved distribution in the brain and significantly reduced risk of Amyloid-Related Imaging Abnormalities (ARIA) in a mouse model of Alzheimer’s disease, when compared with a conventional anti-Ab antibody.1 The study provides the first mechanistic insight for mitigating the risk of ARIA,” detailed Watts.
The Enzyme TransportVehicle (ETV) contains a fusion of a therapeutic enzyme. The Fc portion of the fusion molecule binds the apical surface of the TfR to avoid interference with normal iron transport.
In March 2026, Denali’s lead ETV program, Avlayah (tividenofusp alfa-eknm), received FDA accelerated approval for the pediatric treatment of the lysosomal storage disorder MPS II. Avlayah is the foundation for their broader ETV franchise, addressing other lysosomal storage disorders such as MPS IIIA. Results from the open-label Phase I/II clinical trial are available.2
Their Oligonucleotide TransportVehicle (OTV) platform is an engineered TV conjugated to an oligonucleotide for the systemic delivery of genetic medicines to the brain. Extensive characterization and research demonstrate the ability of OTV to elicit broad biodistribution of oligonucleotide therapies throughout the CNS following systemic exposure.
“For example, our investigational therapy DNL628 for the treatment of Alzheimer’s disease is designed to cross the BBB and reduce the tau protein by targeting the MAPT gene that encodes for tau,” explained Watts.
Lastly, the Antibody TransportVehicle (ATV) platform is designed to enable brain delivery of antibodies capable of selective immune activation and a targeted therapeutic approach after intravenous administration. The investigational anti-Ab antibody therapy DNL921, for example, is designed to reduce amyloid plaques and avoid ARIA.
The TV-enabled clinical development portfolio also includes candidates for frontotemporal dementia-granulin and Pompe disease.
Advancing clinical options
“It is exciting to begin to see that delivery through the BBB is possible using gene therapy or shuttle approaches,” said Todd Carter, PhD, CSO at Voyager Therapeutics. Although first-generation therapeutics are demonstrating meaningful levels of delivery, optimization, and improvement of the functionality, exposure duration, and therapeutic effects are still needed.
“For some diseases, gene therapy is the preferred treatment modality, as both the capsid and the payload can be modified to perform a specific job,” said Carter. But viral vector delivery for gene therapy has had problems with liver-based toxicity.
For the best human translation opportunities, Voyager developed a model in non-human primates (NHPs) requiring cross-species activity across multiple NHP species. This strategy resulted in the company’s TRACER (Tropism Redirection of AAV by Cell-type-specific Expression of RNA) technology, used to screen tens of millions of vector variants using barcoded libraries in which capsids were modified with slight insertions of seven to nine amino acids.
TRACER, Voyager’s unbiased capsid and receptor discovery engine, identified ALPL as a broadly enabling brain delivery receptor. [Voyager Therapeutics]
Successful expression in neurons demonstrated that the capsids crossing the BBB worked. Directed evolution improved them. “Next, we needed to determine the mechanism—the receptors they were targeting,” said Carter. This led to the identification of the receptor, alkaline phosphatase (ALPL), tissue nonspecific.
Now, Voyager has multiple families of capsids that mediate delivery into the brain, are detargeted from the liver, and, for the most advanced, have improved the capsid’s ability to target the brain using ALPL. “Using the ALPL receptor elevates delivery to the brain and allows us to substantially reduce dosage,” said Carter.
“I would not have picked ALPL just on face value,” added Mihalis Kariolis, PhD, vice president of non-viral therapeutics at Voyager Therapeutics. “It highlights the power of the unbiased TRACER approach. Expanding the number of brain delivery receptors provides highly differentiated options to reduce side effects and expand the diversity of treatment modalities.”
Both gene therapy and shuttle approaches have opportunities in different indications. Once-and-done gene therapy is not tweakable, whereas shuttle-based dosing is. “In our APOE gene therapy program, we want to reduce existing APOE4 and replace it with APOE2 permanently,” said Carter. “The shuttle has advantages in situations where permanent ongoing delivery is not required.”
Voyager’s most advanced program (VY7523) is a tau monoclonal antibody that is exquisitely specific for pathological tau. Data will be available in the second half of the year. A gene therapy (VY1706) moving into the clinic this year is designed to knock down tau mRNA and protein intracellularly. A collaboration with Neurocrine Biosciences focuses on Friedreich’s ataxia (FA) and is also expected to enter the clinic this year.
Combining transport receptors
The protective BBB is crucial for maintaining homeostasis and ensuring proper neurological function. Comprised of both cellular and acellular components, this sophisticated structure tightly regulates information flow between the periphery and the brain. According to Tanya Wallace, PhD, vice president of neuroscience discovery research at AbbVie, despite the BBB’s importance, many seemingly basic biological questions remain unanswered, fueling additional global research.
The complexity of the BBB also represents a significant bottleneck for advancing therapeutics targeting brain-related disorders. Historically, achieving therapeutically relevant levels of drugs in the brain has been a major challenge in treating serious diseases such as Alzheimer’s and Parkinson’s diseases. “A notable success story is the development of L-DOPA, a prodrug that leverages existing transport mechanisms to cross the BBB,” said Wallace. Once in the brain, L-DOPA is metabolized into dopamine, offering a key symptomatic treatment for Parkinson’s disease.
Breakthroughs in delivery now allow scientists to leverage more technologies that can bring not only small molecules but also complex biologics into the brain. The Modular Delivery (MODELTM) platform exemplifies this progress. The platform enables engineering of bispecific antibodies, capable of targeting naturally expressed BBB receptors such as TfR and CD98. TfR and CD98 are well-characterized at the BBB, and, together, they offer distinct advantages for increasing brain exposure to therapeutics.
“By engaging these transport pathways, the platform can enhance the uptake of a variety of therapeutics, including antibodies and oligonucleotides,” highlighted Wallace. “This multi-receptor strategy provides flexibility to optimize the balance of uptake, release, and distribution in the brain, paving the way for potentially more effective treatments across neurological disease areas.”
This platform technology facilitated the development of ABBV-1758, which is progressing in clinical development. ABBV-1758 utilizes TfR to transport a 3pE-Ab antibody across the BBB to enable the removal of amyloid beta plaques, a pathological hallmark of Alzheimer’s disease.
As scientists aspire to further refine delivery strategies, ongoing research is exploring additional receptors and innovative approaches, including insulin-like growth factor 1 receptor (IGF-1R) and brain cell-type-specific targeting. The field is rapidly evolving to advance more precise, personalized interventions for challenging neuroscience conditions.
“Successful brain delivery requires more than just advances in transport technology; it demands interdisciplinary collaboration, novel preclinical models, and thoughtful clinical translation,” Wallace pointed out. Continued biological research and investment into innovative discovery platforms will be crucial for bringing transformative therapies to patients with the greatest unmet needs.
References
Pizzo ME, Plowey ED, Khoury N et al. Transferrin receptor-targeted anti-amyloid antibody enhances brain delivery and mitigates ARIA. Science. 2025 Aug 7;389(6760):eads3204. doi: 10.1126/science.ads3204.
Muenzer J, Burton BK, Harmatz P et al. An intravenous brain-penetrant enzyme therapy for mucopolysaccharidosis II. N Engl J Med. 2026 Jan 1;394(1):39-50. doi: 10.1056/NEJMoa2508681.
A strategic imperative in mood disorders is to identify innovative mechanisms that translate into improved therapeutics when compared to the extant options. More specifically, there is a need for treatments with greater efficacy, shorter time-to-peak efficacy, greater durability of effect as well as improved tolerability profiles. Moreover, priority has also shifted towards identifying mood disorder therapeutics capable of targeting domains of psychopathology that are most pervasive, debilitating and inadequately treated by conventional pharmacology (e.g., anhedonia, cognitive impairment).
To inform precision management of obesity, this study developed and externally validated a parsimonious model (OBSCORE) that accurately predicts the risk of 18 obesity-related complications. This was achieved by integrating thousands of clinical, molecular and other health-related characteristics assessed in 200,000 individuals with overweight or obesity within a machine-learning framework.
OBSCORE is a machine learning-based risk prediction tool that uses a set of clinical features to stratify individuals with a body mass index (BMI) of ≥ 27 kg m−2 by their 10-year risk of obesity-related complications, outperforming existing models. OBSCORE is generalizable across diverse populations, supporting risk-based prioritization of obesity interventions that goes beyond simple BMI thresholds.
Body mass index has its limitations, but for now it’s the metric medicine often defaults to when predicting weight-related health problems. A new tool promises to better define who’s at risk for obesity complications, based on measures that include BMI but also family history, diet, current illness, and socioeconomic factors culled from medical records.
One aim of the research is to better understand who’s a candidate for an obesity drug, often prescribed based on BMI alone or BMI in combination with another disease. Over time, GLP-1 medications, whose initial target was type 2 diabetes, have revealed their power to ease cardiovascular disease, kidney disease, liver disease, sleep apnea, and osteoarthritis, in addition to promoting significant weight loss. But discerning who’s the best fit for the costly, lifelong treatment has been uncertain.
“We really wanted to have an integrated model that enables us to look at not one, but 18 different obesity-relevant complications,” Claudia Langenberg, co-author of a study about the new model published Thursday in Nature Medicine, said in a media briefing Tuesday. She is director and professor of medicine and population health at Precision Healthcare University Research Institute of Queen Mary University of London.
Conditions: Preterm Birth; Preeclampsia; Gestational Diabetes Mellitus (GDM); Small for Gestational Age (SGA); Large for Gestational Age (LGA); Asthma Childhood; Atopic Dermatitis; Autism; Otitis Media Recurrent; Obesity
Cases of bowel and ovarian cancer are rising, but only among people under 50, according to research published in the British Medical Journal Oncology today, April 28, 2026. While other types of cancer are also rising in older adults, this particular trend among younger adults is striking.
A key factor, the researchers’ work suggests, is excess weight. But that does not fully explain the trends they saw.
In particular, there was a significant rise in 11 cancers among the younger adults with known behavioral risk factors. These cancers were: thyroid, multiple myeloma, liver, kidney, gallbladder, bowel, pancreatic, womb lining (endometrial), mouth, breast, and ovarian cancers.
Rates of all these cancers also rose significantly among the older adults, with the notable exceptions of bowel and ovarian cancers.
Besides mouth cancer, all 11 cancers associated with known behavioral risks were linked to obesity. And six (liver, bowel, mouth, pancreas, kidney, and ovary) were also linked to smoking; four (liver, bowel, mouth, and breast) were associated with alcohol intake; three (bowel, breast, and endometrial) were linked to physical inactivity; and one (bowel) was associated with dietary factors.
“Of the 11 cancers we identified which were increasing and linked to known lifestyle factors—the most common by far in younger adults was breast cancer,” the study’s lead author, professor Montserrat Garcia-Closas, MD, DrPH, told Inside Precision Medicine. Garcia-Closas is in Integrative Cancer Epidemiology, Division of Genetics and Epidemiology, and The Cancer Epidemiology and Prevention Research Unit, The Institute of Cancer Research, London.
The rising incidence of certain cancers among people under 50 isn’t unique to England, and one major question is whether changes in behavioral risk factors might be to blame.
This research group analyzed cancer incidence trends in England from the National Disease Registry Service for the period 2001 to 2019, comparing patterns by sex in two age groups: 20–49 year olds and those aged 50+ for more than 20 different cancer types.
This database, “Captures virtually every cancer diagnosis in England going back decades—one of the most complete registries in the world. That scale is what allows us to track trends reliably across the whole population, not just a sample,” said Garcia-Closas.
The team used national health surveys to look at trends in established risk factors: smoking, alcohol intake; diet (high red/processed meat, low fiber intake), excess weight (BMI), and physical inactivity to quantify any changes by age and sex and estimate the proportion of cancers attributable to specific risk factors.
Their analysis showed that new cases of 16 out of 22 cancers in younger women, and 11 out of 21 cancers in younger men, increased significantly in England between 2001 and 2019.
And five cancers—endometrial, kidney, pancreatic, multiple myeloma, and thyroid cancer— increased significantly faster in younger than in older women, while multiple myeloma increased faster in younger than in older men.
But with the exception of excess weight, trends in these risk factors over the past one to two decades have been stable or improving for younger adults, with the largest reductions of around 7% in red meat consumption.
The average daily amount of red meat eaten, they report, fell from 38 grams in 2008 to 17 grams in 2018 among younger men, and from 22 grams to 10 grams in younger women. And average processed meat intake in younger women was half that of younger men: 10 grams versus around 20 grams. And while more than 90% of younger adults weren’t eating enough fiber in 2018, their intake remained stable or slightly improved in both sexes between 2009 and 2019. And these trends were similar in older adults.
Established behavioral risk factors accounted for a substantial share of cancer cases. In 2019 these contributed 68%–65% of mouth cancers for younger and older men, respectively; 42%–48% of liver cancers; 49%–53% of bowel cancers, 29%–33% of kidney cancers, and 36%–34% of pancreatic cancers.
Among women they accounted for 52%–45% of mouth cancers; 35%–42% of endometrial cancers; 44%–46% of liver cancers; 38%–42% of bowel cancers; 33%–37% of kidney cancers; 31%–28% of pancreatic cancers; and 19% to 24% of gallbladder cancers.
Excess weight was the risk factor associated with most cancers in 2019, ranging from 5% for ovarian cancer to 37% for endometrial cancers.
“These patterns suggest that while similar risk factors across ages are likely, some cancers may have age-specific exposures, susceptibilities, or differences in screening and detection practices,” write the researchers.
“Prevention takes a long time and we must act now with what we know, with better and more effective public health policy and programs to address the overweight and obesity epidemic,” said Garcia-Closas.
Eli Lilly struck a deal Tuesday to develop new forms of gene editors potentially capable of inserting entire genes into patients.
The collaboration, with artificial intelligence-focused biotech Profluent, is sparse on details, including the number of programs the two companies would work on, the types of diseases they’ll pursue, or how much Lilly was paying upfront. But if every one of its efforts works out, Lilly would pay Profluent $2.25 billion in milestones payments.
The deal is part of a larger push by Lilly into gene editing. The big pharma, flush with record revenues from its obesity and diabetes drugs, has opened a new genetic medicine center in Boston and bought up a series of gene editing or gene therapy companies over the last few years.
(pabinafusp alfa), designed to cross the BBB to deliver a therapeutic enzyme for the treatment of a lysosomal storage disorder called mucopolysaccharidosis type II (MPS II) or Hunter syndrome.
