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Moss Powering the Next Drug Frontier

For decades, Chinese hamster ovary (CHO) cells have been the gold standard for producing biologic drugs, from monoclonal antibodies to enzyme therapies. But for some of the most complex and fragile proteins, even CHO systems can fall short. Now, an unlikely contender—moss—is offering a new path forward.

At Germany-based Eleva, researchers are using Physcomitrium patens, a simple moss species, as a suspension cell culture system for producing recombinant human proteins that are difficult and sometimes impossible to manufacture in conventional platforms. According to Andreas Schaaf, PhD, Eleva’s CSO, these include “glycoproteins with complex or sensitive glycosylation requirements,” as well as cytokines, immune-cytokines, complement regulators, enzymes for rare metabolic disorders, and advanced antibody formats such as antibody-toxin conjugates.

The technology has deep academic roots. Plant biotechnologist Ralf Reski, PhD, at the University of Freiburg, helped develop P. patens into a model species for synthetic and systems biology and co-invented the moss bioreactor. His research led to the founding of Greenovation, now known as Eleva, which has since advanced the platform toward clinical-stage drug development.

The company’s first moss-produced drug candidate to enter clinical studies was a recombinant alpha-galactosidase enzyme replacement therapy (ERT) for Fabry disease, a rare lysosomal storage disorder. Current ERT options for Fabry patients are limited by short circulating half-life, inefficient uptake into key affected cell types, and immunogenicity. Eleva believes the more uniform glycosylation achieved through moss production could help overcome these limitations.

A particularly significant demonstration of the platform is Eleva’s recombinant complement Factor H candidate, currently in Phase Ib. Factor H is a large complement-regulatory glycoprotein used to target complement-related renal diseases such as C3 glomerulopathy (C3G), lupus nephritis (LN), and potentially dry age-related macular degeneration (AMD).

Schaaf notes that Factor H “had long resisted reliable expression in conventional systems such as yeast or CHO cells.” For patients with C3G—many of whom are young and face a 50% rate of kidney failure within ten years—the ability to restore natural Factor H activity could represent a major therapeutic shift. Current treatments often rely on broader complement suppression and carry boxed warnings for serious infections.

So why moss?

Unlike mammalian cells, which often generate heterogeneous glycan mixtures, moss produces more uniform glycosylation profiles due to its simplified glycan-processing pathway. This matters because glycosylation can directly affect a drug’s stability, efficacy, and immunogenicity.

Moss cells are also largely unaffected by toxic cytokine feedback, which in mammalian systems can inhibit growth or trigger apoptosis, limiting secretion efficiency and yields. Plant-specific chaperones and folding assistants, including protein disulfide isomerases, also help prevent protein aggregation and support the correct assembly of complex multimeric proteins.

“Moss offers clear advantages over other expression systems for certain protein classes that are difficult or impossible to manufacture otherwise,” Schaaf says, adding that such therapies might otherwise be “deprioritized or abandoned.”

There are practical manufacturing advantages, too. Moss requires no animal-derived media supplements, eliminating mammalian virus risk and removing the need for costly viral filtration in downstream processing. It is also less sensitive to fluctuations in temperature and pH, giving manufacturers greater process flexibility and potentially lowering production costs.

Still, Eleva is careful not to position moss as a replacement for CHO. Björn Cochlovius, PhD, CEO of Eleva, says standard proteins will continue to be best served by established systems. “The goal is not to replace CHO or other systems with moss when those other systems deliver well,” he explains.

Instead, the aim is to ensure that the range of therapeutic candidates in development is not defined by the limits of existing manufacturing platforms. Yields for large-scale GMP production and improving predictability remain ongoing challenges, but commercially viable titers are already being achieved through continuous optimization.

Cochlovius believes regulatory precedent and growing CDMO partnerships will further strengthen adoption. “A moss-based platform capable of reliably producing this category of proteins at scale would open a pipeline of programs that are currently inaccessible,” he says.

For biotech developers—and for patients with limited treatment options—that could make all the difference. Sometimes, the future of medicine grows in the smallest places.

The post Moss Powering the Next Drug Frontier appeared first on GEN – Genetic Engineering and Biotechnology News.

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Regulators Should Rely on Peers’ GMP Audits to Cut Inspection Burden

Biopharma is a global industry with drug firms routinely supplying medicines to multiple markets from the same manufacturing plant. But while globalization has helped expand revenues, it has also increased the number of GMP inspections developers undergo.

The average biopharmaceutical production facility has 2.68 good manufacturing practices (GMP) inspections a year, with auditors spending up to nine days on site per visit, according to recent analysis.

Preparing for an inspection typically involves GAP analysis to determine how current practices measure up to regulations, followed by corrective actions.

Companies also need to ensure they have the correct documentation for all operations. How long these preparatory steps take varies for each company. However, according to the U.S. Center for Professional Innovation and Education, getting set up for an audit can take anywhere from six months to a year.

Down with duplication

But drug companies should not have to undergo multiple GMP visits, according to the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA), which says regulators can cut the number they carry out through collaboration.

Sérgio Cavalheiro Filho, IFPMA’s regulatory affairs manager, tells GEN, “The most pressing compliance challenge relating to good manufacturing practice today is the inefficiency created by duplicative inspections.

“In an increasingly complex and globalized manufacturing landscape, it is critical that we look to reduce unnecessary duplication through greater inspection reliance amongst those national regulatory agencies that belong to the Pharmaceutical Inspection Co-operation Scheme.”

For the uninitiated, the Pharmaceutical Inspection Co-operation Scheme is an informal arrangement between regulators focused on GMP. Its key aims are to harmonize inspections and promote information sharing between regulators.

It also aims to foster trust between regulatory agencies, with the idea being to encourage them to rely on GMP inspections carried out by fellow regulators rather than re-auditing sites themselves each time certification is sought.

“Greater inspection reliance would allow both regulators and companies to focus resources where they matter most: patient safety and product development,” Filho says.

IFPMA made the case for greater inspection reliance in a position paper, arguing that while pilot mutual recognition efforts have shown promise, regulators have yet to fully embrace the approach.

Filho tells GEN, “Regulators have made meaningful progress on GMP harmonization through frameworks such as PIC/S and ICH, but more consistent use of inspection reliance is needed to translate alignment on paper into real efficiency.”

Part of the problem is that advanced modalities, such as mAbs and cell and gene therapies, are often perceived as being higher risk, which means, despite the various mutual recognition agreements, regulators still tend to carry out their own inspections.

However, in such cases, trusting others’ audits is a more efficient option, according to Filho, who says, “Relying on trusted regulatory partners where appropriate is a well‑tested and effective strategy that enables regulators to focus on higher‑risk activities. And, any steps to reduce the incidence of the GMP audits they face would be welcomed by biopharma, Filho adds.

“Industry supports moving from pilots to routine reliance, underpinned by sound legal and data‑sharing frameworks. GMP challenges are also increasingly addressed through collaboration between manufacturers and technology suppliers, and through digitalization, automation, and AI‑enabled tools that strengthen monitoring and quality oversight within robust quality systems,” he says.

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STAT+: Color Health moving deeper into cancer services, complete with virtual ‘tumor boards’

The way Color Health’s CEO Othman Laraki sees it, cancer has a scaling problem. New science regularly sets new standards of care, increasing the intricacy of managing an already complex illness. Cancer patients are multiplying faster than oncologists, Laraki said, and costs, too, are exploding. All this makes it difficult for everyone to receive the best possible therapy. The solution that the Silicon Valley executive sees is inevitable.

“In our mind, the only way this is going to be addressed and solved is in a virtual first, AI-driven manner,” Laraki said. “In the coming years, the biggest cancer centers will be virtual first.”

Virtual care for cancer may sound like an oxymoron. After all, the pillars of cancer treatment are almost all hands-on: surgery, radiation, infusions, and the like. But Color Health has been building out a virtual cancer clinic — including a virtual “tumor board” of multidisciplinary experts —  that the company says can deliver and manage care at a high quality. The company just received a certification from the American Society of Clinical Oncology to back it up.

Continue to STAT+ to read the full story…

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CoCoGraph AI Model Generates Molecules that Comply with Rules of Chemistry

Developing new molecular compounds is crucial to address pressing challenges, from drug discovery to sustainable materials. However, discovering viable new molecules is challenging due to the vastness of the search space. 

In a new paper published in Nature Machine Intelligence titled, “A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules,” researchers from Universitat Rovira i Virgili (URV) have developed an AI tool capable of generating molecules that are guaranteed to comply with the rules of chemistry. The model, named CoCoGraph, operates similarly to generative AI tools for text or images, such as ChatGPT or Dall-E.  

“These models create new content that looks very much like the real thing. Our algorithm does the same, but with molecules,” said Roger Guimerà, PhD, ICREA research professor in the department of chemical engineering at URV and co-corresponding author of the study. He explains that the number of possible chemical molecules could be up to 10⁶⁰ variants, which is more than the number of water molecules in the ocean.  

CoCoGraph uses a diffusion model, a technique common in image generation, which progressively “disorders” a real molecule and trains the system to learn how to reconstruct it. 

Marta Sales-Pardo, PhD, professor in the department of chemical engineering at URV and co-corresponding author of the study, explains that the model begins with a real molecule, breaks the bonds, and then creates new bonds at random. The model then learns to reverse this process to reconstruct coherent structures. 

Notably, CoCoGraph directly incorporates the basic rules of chemistry, such as maintaining the correct number of bonds, to guarantee that generated molecules are chemically valid. The system is also more efficient, uses fewer parameters, and requires less computing power to generate molecules more quickly. 

The research team has compared the performance of CoCoGraph with other state-of-the-art models and analyzed 36 physicochemical properties, such as solubility and structural complexity. For two-thirds of these properties, the CoCoGraph generated molecules are chemically more realistic than those from other models. 

Although the model cannot yet design molecules with a specific function, researchers have identified molecules with properties similar to the drug, paracetamol. They have also explored techniques to partially modify an existing molecule to create new variants with similar characteristics, which are useful for optimizing drugs or developing new materials. 

The next step is to design molecules with specific properties, such as solubility and low toxicity. If successful, the technology could accelerate the discovery of new solutions across pharmacology and materials science in a chemical universe that is still practically unexplored. 

The post CoCoGraph AI Model Generates Molecules that Comply with Rules of Chemistry appeared first on GEN – Genetic Engineering and Biotechnology News.

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<![CDATA[Learn how dual antipsychotics can mask the true cause of high prolactin, and how stepwise switches to aripiprazole or clozapine restore cycles.]]>
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Matthew Rabinowitz: Engineering a New Era of Diagnosis

Jonathan D. Grinstein, PhD, North American Editor of Inside Precision Medicine, hosts a new series called Behind the Breakthroughs that features the people shaping the future of medicine. With each episode, Jonathan gives listeners access to his guests’ motivational tales and visions for this emerging, game-changing field.

Matthew Rabinowitz switched from engineering and computational research to medicine after a breakthrough on the Human Genome Project. He realized that telecommunications, aerospace, and machine learning technologies could help him understand human biology. His shift in focus was influenced not only by scientific interest but also by personal loss, including the deaths of family members affected by genetic conditions. These experiences convinced him that current diagnostic methods were inadequate, especially for patients and families in critical situations.

After founding Natera, Rabinowitz and his team developed Panorama Prenatal Test, a noninvasive prenatal test. This technology uses DNA variant analysis, Bayesian statistical methods, and machine learning to detect genetic conditions in cell-free fetal DNA in maternal blood samples. It increased accuracy, accessibility, and reduced invasive procedures. Myome, his new project, uses whole genome sequencing to find rare diseases. Myome uses AI models to assess cancer and cardiovascular disease risks using genomic and clinical data to improve early detection and prevention.

In this episode, Rabinowitz discusses regulatory constraints, fragmented data systems, and difficulties translating complex genetic information into clinical decisions. In the long run, he wants to create blood test diagnostics that can predict health and allow proactive medical intervention.

Rabinowitz uses several technical engineering and computational concepts, including:

  • Packet Switching: a method of dividing data into smaller units that are transmitted independently and reassembled at their destination
  • Transformer Model: a type of artificial intelligence system that processes entire datasets simultaneously to identify relationships between elements, widely used in modern AI systems such as GPT
  • Gradient Descent: an iterative method used in machine learning to minimize error by adjusting model parameters

This interview has been edited for length and clarity.

 

IPM: What originally drew you into applying engineering and machine learning to genetics and clinical diagnostics?

Rabinowitz: There was all this incredible work happening in the early 2000s around the Human Genome Project, along with applications of signal processing and machine learning, which is what I focused on during my electrical engineering training.

There were really three catalysts for me.

One was in 2003, when my sister gave birth to a child with Down syndrome at one of the top hospitals in the country, and they didn’t know until he was born. I spent six days flying around trying to help. They went through one procedure after another and after six days, the baby died from complications. It was absolutely horrific.

Second, I couldn’t believe that we had all these advanced technologies in our phones, laptops, and spacecraft, but they hadn’t made their way into clinical diagnostics. At that point, I felt I had to apply my background in signal processing and early machine learning to these problems.

The third reason was about 15 years ago, I lost a child due to a genetic condition, an absolutely devastating experience. After going through that, I felt there was a path I needed to follow.

The engineer in me took over. It felt like a problem I had to solve. It was like being struck twice: unrelated events, but the same kind of tragedy.

That’s when we used a pregnancy sample to apply for NIH funding to improve prenatal testing. We got that grant, then several others, and ultimately built Panorama, which has transformed pregnancy care globally.

From there, one thing led to another. Now, through Myome and companies like Natera, we’re working on projects that could save the U.S. healthcare system around $200 billion per year. It’s been a very personal mission. 

 

IPM: What are you seeing today with whole genome analysis that feels fundamentally new or different?

Rabinowitz: We’re now diagnosing conditions with whole genome analysis that simply weren’t detectable before. Myome has largely led the charge.

When I look at these case studies today, I get the same feeling I had 20 years ago. How were we not able to see this before?

We’ve spent a lot of time extracting signal from noise so you don’t need multiple sequential tests. You can start with the whole genome and layer analyses. This includes SNPs, CNVs, difficult deletions, tandem repeats, mitochondrial DNA, and methylation.

One example: an eight-year-old with developmental delay, autism, and hypotonia had already undergone exome sequencing with no findings. We identified a subtle deletion about one kilobase involving a single exon too large for short-read breakpoints and too small for coverage changes. That finding completely changed the child’s life.

Another example: a man in his mid-20s with dystonia, convulsions, and vomiting had undergone standard neuromuscular panels. They missed a tandem repeat very difficult to detect with short-read or exome sequencing. We developed new statistical methods and identified the breakpoints, which changed his life.

More broadly, rare disease costs in the U.S. are about $1 trillion annually with ~47 physicians involved over a 4–7 year diagnostic journey and massive lost productivity. The fact that we can now catch these cases is remarkable.

On the pregnancy side the belief was that the issue was solvable, but the technologies were limited. People at the time used shotgun sequencing and looked at DNA quantity. We instead analyzed SNPs between individuals.

We built a massively multiplexed PCR system ~20,000 primers in a single reaction. The challenge is noise, cross reactions, and primer dimers. We developed a machine learning optimization so every primer is tuned relative to every other, standardizing thermodynamics across the system.

From there, we built a statistical framework integrating across trillions of hypotheses, crossover events, noise, and fetal fraction. When it converges, you see a clear maximum likelihood peak that tells you what’s happening. If not, you know something is wrong.

This allowed us to detect things others couldn’t: very high sensitivity for aneuploidy and structural variants like microdeletions.

We could detect triploidy and vanishing twins, determine zygosity, and even de novo mutations, which are more than five times as common as Down syndrome. It was a completely new approach combining passion, engineering, and statistics.

 

IPM: Why can’t we have one universal test that does everything?

Rabinowitz: The short answer is that there’s so much more we can extract from each sample, especially with AI.

These transformer models trained to predict the next word require learning enormous context. With gradient descent, backpropagation, and large datasets, the performance is extraordinary. We didn’t fully appreciate the significance early on but today the possibilities are enormous.

That said, you can’t have one universal test. First, sample context matters. In pregnancy, you’re analyzing fetal cell-free DNA very different from adult disease testing. Second, it’s not just blood. There are many analytes. Beyond DNA, you need methylation, RNA, proteins. Most diseases require a multi-analyte approach. Third, regulation. You need to validate each test rigorously. You can’t validate everything at once across the genome. We also have variants of unknown significance. If you look for everything, interpretation becomes a problem.

So you have to focus your inquiry and ensure results are validated and actionable. That said, from a single blood draw, we can already do an incredible amount.

 

IPM: How has cfDNA and noninvasive testing evolved with AI?

Rabinowitz: Around 2017–2018, Natera began applying deep learning to diagnostics. [It was] one of the first large-scale uses in genetic testing. We had used neural networks earlier (e.g., in HIV mutation analysis) but this was different.

We applied convolutional neural networks to detect microdeletions in low fractions of cell-free DNA. A key example is 22q11.2 deletion syndrome. This occurs in about one in every 1,500 to 2,000 pregnancies. It’s more common than many screened conditions. Early detection allows intervention at birth. We initially used classical statistics, but after generating millions of samples, we trained deep learning models. The AI learned noise patterns and edge cases better than we could model, like Kasparov versus Deep Blue.

In a study of ~20,000 patients, we saw 100% sensitivity for larger deletions and ~83% for smaller ones, with a specificity of 99.95%. That translated to a positive predictive value (PPV) of ~53%, compared to 3–5% clinicians are used to.

Despite this, adoption has been slow due to reimbursement and guidelines, which is frustrating, because many children still miss early diagnosis.

 

IPM: Looking forward, how is AI transforming broader healthcare and genomics?

Rabinowitz: Today, we’re combining whole genome sequencing with AI and clinical data to predict disease risk far more accurately than even five years ago.

Across ~30 major diseases we can now predict susceptibility at a transformative level. If applied broadly, for example to people over 45, we could save over $200 billion annually by catching diseases earlier. Many interventions are simple like diet and lifestyle. Even small improvements matter. Every 1% increase in sensitivity can mean ~$7 billion in savings.

We’re also predicting neoantigens for personalized cancer vaccines, training on real patient outcomes—something that wasn’t possible before. And we’re building foundational genomic models, like language models, that learn the structure of the genome itself.

So across diagnostics, treatment, and prevention, AI is fundamentally transforming the field.

 

IPM: You mentioned earlier that you underestimated neural networks. What changed your perspective?

Rabinowitz: Around 2005, we were applying machine learning to genetics. We weren’t wrong, but we underestimated neural networks. We worked on HIV drug resistance. predicting which mutations respond to which therapies.

We used lasso regression, support vector machines, [and] carefully constrained models. Neural networks didn’t perform as well, which is what we expected. Our mindset was to control complexity to avoid overfitting. What we didn’t anticipate was massive data, stochastic training, and compute power, which allowed neural networks to escape local minima and scale. 

In 2010, I had a patent on training neural networks with memory. It lapsed because Stanford didn’t maintain it. That was right before Google Brain scaled these approaches. The lesson is to stay open-minded. Technology can open possibilities you don’t see coming.

 

IPM: How do you see the future of diagnostics evolving from a single blood draw?

Rabinowitz: We’re moving toward a world where a single blood draw can tell us an enormous amount. Historically, progress was slow: blood cell counting in the 1800s, automation in the mid-1900s, cell-free DNA in the 1990s. Since then, progress has been explosive. From one sample, we can identify incidental findings (e.g., rare diseases, pharmacogenomics, and predictive risk) across many conditions. We can also detect cancer noninvasively through circulating DNA.

The capabilities are remarkable, but the genome is complex—three billion bases, with interactions that require enormous data to model.

 

IPM: What challenges remain in making these technologies widely usable?

Rabinowitz: Two main challenges. First, data, standardizing and aggregating clinical data across institutions, is historically very fragmented. AI is helping, but more coordination is needed.

Second, education. As we generate more information, explaining it to doctors and patients becomes harder. What’s known, what’s uncertain, and what action to take. At Myome and Natera, we invest heavily in genetic counseling. But across the field, there’s a tendency to simplify by withholding information. That won’t scale. We need better ways to communicate complexity responsibly.

 

IPM: How important is diversity and multi-ethnic data in building accurate models?

Rabinowitz: It’s absolutely critical and still underserved. Many models were trained on homogeneous populations, limiting accuracy. We’ve focused on building multi-ethnic models using diverse datasets and functional genomics, but we still need more data from underrepresented populations.

For example, in cardiovascular disease, we built a multi-ethnic model using large datasets.

We were able to reclassify ~50% of patients in the intermediate-risk category, identifying who is truly high risk (>20%) versus low risk (<5%). That improved decision-making significantly with over 10% improvement in classification. When followed over time, outcomes matched predictions closely.

This has huge implications for individuals and for healthcare systems. By identifying risk earlier and intervening, often with simple lifestyle changes, we can reduce costs and improve outcomes at scale. That’s why building diverse, high-quality datasets is so important. It’s one of the most powerful ways to improve healthcare globally.

 

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Next Generation CRISPR Gene Editing Could Help Target Cancer Cells

A CRISPR gene editing protein called Cas12a2 can be turned into a kind of programmable self‑destruct switch for cells, which researchers think could be a new way to treat conditions like cancer if the technique is validated.

Cas12a2 eliminates eukaryotic cells based purely on which RNA transcripts they express, and the investigators showed this can be used to selectively destroy virus‑infected cells, unedited cells, and cancer cells bearing a single‑nucleotide mutation.

“Common molecular and cell-based interventions, such as small-molecule inhibitors, toxins, antibodies, lytic viruses or programmed immune cells, eliminate cells through specific proteins or survival pathways; however, these methods cannot be tailored to arbitrary genetic or transcriptional states as well as difficult-to-drug scenarios such as mutations in non-coding sequences or complex etiologies,” write co-lead author Yang Liu, PhD, assistant professor in biochemistry at University of Utah Health, and colleagues in Nature.

“A cell-killing approach triggered directly by the specific recognition of prescribed DNA or RNA sequences could greatly broaden the range of targetable conditions, creating new means to counter select against specific cells in a variety of situations and applications.”

In this study, the researchers first tested the technology in yeast and human cell lines against a harmless target. They found that the guided Cas12a2 destroyed the cells carrying the marker by effectively shredding their DNA. When they checked for off-target effects they were rare and weak.

They then tested the technology on cancer cells carrying the HPV virus by targeting viral RNA. The method killed cells containing the virus, but not cells negative for HPV. The team also used the Cas12a2 method to “clean up” after gene editing by killing unedited cells and enriching edited ones. Finally, they tested if Cas12a2 could recognize a single mutation in the cancer gene KRAS and showed it could destroy cells with this mutation while leaving cells with a non-mutant version of KRAS alone. This worked even when those cells were resistant to an approved KRAS drug.

“The enzyme that we’re working with is extremely specific,” Liu says. “It does not touch the healthy cells. So if we’re thinking about a cancer therapy, you’re treating cancer with no side effects. That was striking to us. We did not know that was possible.”

This research is early stage, and it will take some time to enter the clinic, as testing in animal models is needed first, but the research team say the results are promising. The technology is being developed commercially by German biotech Akribion Therapeutics, a biotech spin-off from BRAIN Biotech launched in 2024.

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“Failure to Launch” Syndrome: How to Stop Enabling Your Grown Child

When Zeke was in high school, he struggled with anxiety and substance use problems, and he left college after the first semester. Now 25, he is living at home, and his mom Carol is frustrated. While she’s pushed him to go back to school or work, he has only held one part-time job at a local smoothie shop and quit after a few months, embarrassed that high school classmates would see him working there. Another attempt at trade school to become an electrician also didn’t take — it didn’t feel like the right fit. Now he rarely leaves the house, stays up all night playing video games or scrolling online, and sleeps most of the day.

Failure to launch syndrome, highly dependent adult children, boomerang kids — there’s no standard term or definition, but if you’re a parent in this situation you recognize it. You are worried and frustrated about your adult child’s difficulty in leaving the nest, and you don’t know what to do because everything you’ve tried so far hasn’t worked. 

“These aren’t kids who come back home because they finished school, and the first job they get doesn’t pay enough for them to afford rent on an apartment,” says Theresa Welles, the Shapiro Family Director of the Bubrick Center for Pediatric OCD at the Child Mind Institute. “We’re talking about young adults who functionally have hit a wall, so to speak. They’re caught in a loop of dependency.”

What is failure to launch syndrome?

It’s not uncommon for adult children to live with their parents: According to Pew Research Center, 18 percent of adults ages 25 to 34 lived in their parents’ home in 2023, with young men more likely than young women to do so (20 percent vs. 15 percent). Young adults might leave home for a period of time and then move back in with their parents because they can’t find a job. Or for religious or cultural reasons, some adult children expect to live in the family home until they get married. Living at home is not the main criterion for determining a “failure to launch.”

While there is no official clinical definition, researchers who study this group of young adults generally categorize someone as a highly dependent adult child if they are:

  • Not in school, working, or actively looking for work (though physically capable of doing so)
  • Financially dependent on their parents for housing and other necessities
  • Emotionally reliant on parents (i.e., needing constant reassurance that they are okay)  

They usually have very limited social interactions other than online. Often, they have mental health challenges such as anxiety, depression, or OCD, which is a contributing factor, Dr. Welles says.

“They’re at the developmental stage of early adulthood, they’re figuring out who they are,” Dr. Welles says. “The fancy term in psychology is ‘individuation,’ but it’s essentially who you are, both as part of your family and separate from your family.” Highly dependent adult children haven’t made much progress in this stage for several years. Many of them want to change their life path and become more independent, but they struggle with anxiety or fear of failure and don’t follow through on the necessary steps. “Reliance on parents reduces opportunities to build autonomy, which in turn maintains that reliance,” she says. So, they remain stuck.  

Dependent behaviors and parental accommodations

Young adults who are highly dependent often fall into certain patterns of behavior. They don’t do their own laundry, cook, clean, or help out around the house. They rarely leave the home and often shut themselves in their bedroom or live in the basement, avoiding talking to others in person. As a result, they rely on their parents to act as an intermediary with the outside world, such as making doctor’s appointments. They might blame their parents for their difficulties in life.

While parents may not like the situation, they struggle to get their adult child to change. So instead, they accommodate them — especially when they are concerned about their child’s mental health challenges.

“In the world of neurodiversity, accommodations are a good thing — we want accommodations for testing and sensory environments,” says Natalia Aíza, LPC, the author of the forthcoming Anxious to Launch: Parenting Strategies to Help Your Adult Child Move On. “But in the anxious-to-launch world, accommodations are actually interfering with your child becoming independent.”

Aíza gives some examples of unhelpful family accommodations: You make sure there’s food in the fridge, don’t ask them to contribute to paying bills, and may give them spending money. When they get angry or upset, you accept the behavior and feel guilty, thinking you are to blame for the situation. If they are anxious when you aren’t nearby, you don’t travel because it causes them stress. Instead of expecting them to take steps to find a therapist, you do the legwork.

“The number one behavior of the highly dependent adult child is avoidance. I cannot emphasize this enough,” Aíza says. “If your child has a full-on virtual life, that’s their social outlet. They are avoiding real-life challenges. They are avoiding working at jobs that are unpleasant. They are probably avoiding adulting tasks that should fall on them at this point. So, we swoop in and take care of those tasks for them.”

A modern version of an old problem

While adult children have lived with their parents in past generations, researchers argue that phenomenon of highly dependent adult children is on the rise, and young people today seem particularly susceptible. Adolescence is more prolonged now in many cultures, and there’s an emphasis on finding a fulfilling career, not just a job that pays the bills.

Technology contributes to the problem. Playing video games, watching videos, scrolling through social media — “these activities don’t help matters because they can do things that feel like they’re accomplishing something,” Dr. Welles says.  

How to stop enabling your grown child

In Dr. Welles’s practice, she has worked with families where she initially treated the teen for anxiety or OCD, then involved the parents more deeply when the young adult had trouble launching. In one case, the son was in the habit of playing video games late at night and would sleep through class the next day. He had anxiety and depression, and his parents didn’t want to take away video games because it was the one thing he enjoyed doing. But they started turning off the Wi-Fi in the house at a certain time at night.

“It sounds so extreme, like he’s being punished,” Dr. Welles says. “But it’s about saying to him, ‘We’re going to pull back on ways we’ve accommodated that may have unintentionally made your anxiety worse.’” It was important that the parents validated his feelings, saying things like, “You feel like you’re in danger, as if you’re standing in front of a bear, and that’s really hard. But that’s the anxiety lying to you, and it won’t go away if we keep accommodating things that allow you to avoid what you need to do in order to overcome this anxiety.”

And tactics like these made a difference over time. The son is now attending college part-time and working as a server at restaurant. He has a girlfriend and has plans to save enough to move into an apartment with a friend.

Setting boundaries with your adult child

If the adult child doesn’t seem motivated to find a job, Aíza has recommended that parents take them off the family cellphone plan, giving them warning that this will happen by the next month’s bill. “This is not necessarily the most strategic financial choice” because it’s often much cheaper per person on a family plan, she acknowledges. “But it is a perfect first accommodation to remove because it is telling your adult child, ‘This is something you can handle. You can be responsible for it financially and logistically. It is something that I control, and I want to stop controlling parts of your life.’” And it’s often the motivation they need to find a job — something that can earn them $100 for the monthly cell phone bill is small enough that it feels doable.

When families take steps like these, the adult child will likely get angry or upset. “That’s hard. But think about when your kids were toddlers, and they wanted to touch a hot stove,” Dr. Welles says. “They were mad when you said, ‘No, you can’t touch that stove,’ but that didn’t mean you let them do it.”

“The good news is, generally speaking, even if there’s unhappiness in the beginning,” she continues, “pretty quickly, once they start to feel better and are doing the things that they actually care about, it can really help.”

Supporting without enabling adult children

Highly dependent adult children might accuse parents of not being supportive when they pull back on accommodations. Dr. Welles suggests communicating that you hear them and validate their feelings: “You can say things like, ‘Hey, I know this is tough or ‘I know that this makes you really nervous.’ But you combine it with the confidence that they can do it, like ‘I also know you can do it, as hard as it is.’”

Sometimes, you might think you are being supportive when you are actually enabling — like filling out a job application on behalf of the child. “Even if it works and they get an interview, you’re accommodating their anxiety,” Dr. Welles says. “But also, there’s going to be a point when you can’t do something for the child — the interview or the job itself — so the earlier that you can pull back the better.”

If your adult child has both ADHD and anxiety, you can support their executive functioning skills without accommodating the anxiety. “Maybe you sit down with them on Mondays and look at their schedule to help them determine if there’s a way you can help them organize, as opposed to you stepping in and letting them avoid things they need to do because they’re anxious about it,” Dr. Welles says.

Aíza encourages giving the adult child the minimum amount of help needed, to avoid creating another form of dependency. “It’s about noticing, ‘Am I working harder at this than they are?’” she says. “A lot of times the answer is ‘yes,’ and that’s a signal to back off and put more expectations on the child.”

Treatment for highly dependent adult children

While there is no standard treatment for highly dependent adult children, early evidence has shown a form of therapy called SPACE-FTL (Supportive Parenting for Anxious Childhood Emotions – Failure to Launch) to be promising. A variation on an effective treatment for anxiety and OCD, SPACE-FTL involves only the parents, since the adult child is often resistant to seeking help. The program helps parents reduce accommodations step by step and engage extended family and friends to help de-escalate conflict. 

One tactic is to make a plan to deliver a change in accommodation in writing — for instance, explaining that you will stop paying the cellphone bill at the end of the month and why. Doing it in writing (on paper or in a text) makes the message clear and helps you remain calm and non-reactive. If you are expecting an angry or violent response, they can ask a grandparent, uncle, or family friend be in the house when you deliver the letter, since that might make the response less extreme. The relative or friend may even spend the night if the adult child is more likely to cool off when others are present.

Asking for others’ help also helps you stop blaming yourself for the situation. “A lot of parents of highly dependent adults feel shame, but this is not something happening to only one family,” Aíza says. “We need to build on our social supports and get other people on our team so that we don’t feel so isolated in this process. Your adult child may be resisting change, but you don’t have to. It might sound cruel, but our central mandate as parents is making sure our child is okay after we’re gone. We brought them on earth to survive us — that is the design.”

Frequently Asked Questions

What is “failure to launch syndrome”?

“Failure to launch” isn’t a formal diagnosis but describes young adults who are stuck in a pattern of dependence. They’re typically not working or in school, rely on parents financially and emotionally, and struggle to move forward with adult responsibilities.

How can I motivate my adult child to become independent?

Change often starts with parents gradually pulling back on accommodations while staying supportive and calm. Set clear expectations, validate their feelings, and shift responsibility back to them in manageable steps so they can build confidence and autonomy.

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