Bispecific antibodies are moving rapidly from late-line rescue therapy into earlier phases of multiple myeloma care. The latest signal comes from MonumenTAL-6, a Phase III study evaluating Johnson & Johnson’s TECVAYLI® (teclistamab) plus TALVEY® (talquetamab) in patients with relapsed or refractory multiple myeloma who had received one to four prior lines of therapy, including an anti-CD38 antibody and lenalidomide.
According to topline results released by the company, the dual bispecific regimen reduced the risk of disease progression or death by 89% compared with investigator’s choice standard of care, corresponding to a hazard ratio of 0.11. The combination also reduced the risk of death by 62%, with a hazard ratio of 0.38. A second investigational arm, TALVEY plus pomalidomide, also met the primary endpoint, reducing the risk of progression or death by 73%.
A dual-antigen strategy
The clinical rationale is clear. TECVAYLI targets BCMA, a validated plasma cell antigen, while TALVEY targets GPRC5D, another antigen highly expressed on myeloma cells. Combining the two agents could deepen tumor control by engaging T cells against two distinct myeloma-associated targets, potentially reducing the likelihood that disease escapes through antigen loss or heterogeneous target expression.
This dual-antigen approach is particularly relevant in earlier-line relapse, where patients may still have better marrow reserve, less heavily treated immune systems, and more opportunity to achieve durable disease control. It also reflects a broader shift in myeloma treatment: immunotherapy is no longer being reserved only for patients who have exhausted conventional options.
“These findings add to a growing body of Phase III evidence evaluating the survival outcomes associated with the early use of immunotherapy doublets in the treatment journey,” said Ajay K. Nooka, MD, MPH, director of the Myeloma Program at Emory University School of Medicine.
Topline data, not yet full clinical detail
MonumenTAL-6 compared TECVAYLI plus TALVEY and TALVEY plus pomalidomide with investigator’s choice of elotuzumab, pomalidomide and dexamethasone or pomalidomide, bortezomib and dexamethasone. The Independent Data Monitoring Committee recommended unblinding the study at the first interim analysis, and Johnson & Johnson said full data will be presented at a future medical meeting and submitted to global regulatory authorities.
That timing matters. The reported hazard ratios are striking, particularly for progression-free survival with the TECVAYLI-TALVEY arm. However, clinicians will need the full dataset to assess response depth, duration, subgroup consistency, treatment discontinuation, infection burden, cytopenias, quality-of-life effects, and practical feasibility in community settings.
Safety will be central to interpretation. Both drugs are T-cell redirecting bispecific antibodies and carry risks including cytokine release syndrome, neurologic toxicity including ICANS, infections, cytopenias, and other target-specific adverse events. The company reported that overall safety profiles in MonumenTAL-6 were consistent with the known profiles of the individual monotherapies, but detailed rates from the combination study have not yet been released.
Implications for myeloma sequencing
If confirmed, these data could strengthen the case for earlier use of off-the-shelf bispecific combinations in relapsed myeloma. TECVAYLI is already approved in combination with daratumumab and hyaluronidase in certain patients after at least one prior line of therapy, while TALVEY is currently approved as monotherapy for more heavily pretreated relapsed or refractory disease.
The key question is no longer only whether bispecific antibodies work, but how they should be sequenced and combined alongside CD38 antibodies, proteasome inhibitors, immunomodulatory drugs, CAR T therapies, and emerging antibody-drug conjugates. A BCMA/GPRC5D combination may offer a way to intensify immune pressure without the manufacturing delays associated with autologous cell therapy, but it will also require careful infection prevention, monitoring infrastructure, and patient selection.
For now, MonumenTAL-6 adds an important signal to the changing treatment landscape. Earlier-line myeloma therapy is becoming increasingly immunotherapy-based, and dual-antigen targeting may become one of the strategies used to push responses deeper and make remission more durable.
Retina4IRD, an AI-based clinician decision support system, enhances inherited retinal disease diagnosis through multimodal imaging and clinical data integration, achieving 88.5% accuracy in a randomized clinical trial.
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Yesterday, my friend who lives in the Bay Area told me that I should be asking people about their peptide “stack,” which is where someone takes multiple peptides for presumably greater health benefit.
Amid rising concern over efforts to contain HIV, Merck has reached voluntary licensing deals with seven companies to make generic versions of an experimental, once-a-month prevention pill in 129 low- and middle-income countries.
The agreements will allow the manufacturers — three of which are based in Africa and four in India — to sell generic versions to both public and private buyers, which is designed to ensure the medicine becomes available to the widest possible population. Moreover, the deals are royalty free, which makes it easier for the generic companies to predict costs.
The timing of the announcement is notable because Merck is striking these deals while its medicine, known as alimatravir, is currently undergoing a pair of late-stage clinical trials. By doing so now, the company believes it will help the generic makers plan for wide-scale production and the necessary steps to receive regulatory approvals around the world.
Researchers in Japan have identified a compound that could help muscles stay strong as we age. Preclinical results published today in Scientific Reports uncover a promising new approach to treat conditions causing muscle loss and may help people preserve independence and quality of life in their later years.
Skeletal muscle is essential for movement, accounting for approximately 40% of an adult’s total body weight. Yet it is also one of the first tissues to decline with age, progressively leading to weakness, scarring, fat accumulation, and loss of fast-twitch muscle fibers.
The study focused on hepatocyte growth factor (HGF), a molecule that plays a crucial role in activating the repair of muscle fibers. In healthy muscle, this molecule is present in the tissue surrounding muscle fibers. When the muscle is injured or stimulated through exercise, HGF is released and binds to c-met receptors on stem cells within the skeletal muscle, known as satellite cells. This activates the satellite cells and enables them to proliferate and differentiate to repair muscle fibers.
Aging can interfere with this process, making it a key driver of age-related muscle wasting. In an earlier study, the same team found that aging causes HGF to undergo nitration, a chemical modification that prevents it from binding to c-met receptors.
“HGF is not necessarily missing as we age,” said Ryuichi Tatsumi, PhD, professor at Kyushu University. “Rather, it can be chemically altered after it is made. That led us to wonder whether a compound with strong antioxidant capacity might protect HGF, either by preventing nitration or by compensating for the functional loss it causes.”
Tatsumi’s team then identified two compounds with strong antioxidant activity that could potentially interfere with HGF nitration: glutathione trisulfide (GSSSG) and lipoic acid trisulfide (LASSS). Both belong to a drug class known as trisulfides that has been gaining attention in preclinical research for their protective and anti-inflammatory properties across a wide range of indications.
While both drugs were able to suppress HGF nitration, only LASSS restored its ability to bind to c-met receptors. In fact, the drug candidate more than doubled HGF’s binding affinity while simultaneously preventing nitration.
“This exceeded our expectations,” said Tatsumi. “We knew trisulfides had diverse biological functions, but we never expected that simply mixing HGF with LASSS would produce such a striking effect. What this tells us is that LASSS does more than simply neutralize reactive molecules. It may interact directly with HGF and induce a subtle structural change, creating an enhanced ‘Super HGF’ form that binds c-met more strongly and resists nitration.”
The researchers then tested these effects in a mouse model of muscle atrophy. Compared to untreated animals, mice receiving LASSS showed a significant reduction in HGF nitration. Although further preclinical studies are needed before this approach can be tested in humans, these early findings point toward a promising strategy to preserve the muscle’s natural regenerative capacity and support healthy aging.
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to treat conditions across most major acute and chronic diseases), the complexity is even greater.
Scientists explore vast quantities of possible molecules, looking for the rare few that will bind to the right target, remain stable in the human body, and be manufacturable at scale. Today, AI is speeding up these processes and has quickly become a core part of the infrastructure in pharmaceutical R&D.
AI-assisted design is a growing part of how biologic drug candidates are developed, and companies like AstraZeneca are actively building its engineering teams to push this further. “Everything we do, whether it’s design, make, test, or analyze, is now computationally enhanced,” says Puja Sapra, senior vice president and head of R&D biologics engineering and oncology targeted discovery at AstraZeneca. “The cycle times are getting shorter while productivity and innovation increase.”
Sapra explains that AstraZeneca’s approach follows a build-measure-learn loop. AI generates or prioritizes candidate molecules computationally, predicting which designs are most likely to succeed. Scientists then focus lab resources only on the top-ranked candidates. This leads to a tighter feedback cycle with fewer dead ends, faster iteration, and the ability to go after disease targets that were previously considered untreatable by medicine. Because the number of possible molecular combinations far exceeds what any human team can systematically explore, using AI to narrow and refine the options for testing has become a major focus in biologics drug design.
Navigating complex drug design problems
Beyond accelerating timelines, AI is also being applied to the discovery of entirely new classes of medicines. Traditional biologics typically target one disease pathway. The next generation of drugs can hit multiple targets simultaneously or precisely deliver therapeutic payloads to specific cells. Achieving this requires optimization across many variables at once. Looking ahead AI-driven models could help design these increasingly complex, multi-specific biologics, explains Puja Sapra. “For example,” she continues, “such models could help identify which two or three targets to prioritize based on the underlying biology, then optimize across multiple parameters to balance a molecule’s potency, stability, manufacturability, and safety.” “Drugging the undruggable is becoming a reality,” Sapra says. “These technologies will eventually enable us to develop medicines against targets once thought impossible to reach. The potential for benefit to patients is remarkable.”
The data moat
McKinsey estimates that generative AI, combined with other computational tools, could cut drug discovery timelines by as much as 50%. But every AI model is only as good as its training data. In drug discovery, that means ample quantities of high-quality biological data. Experiments can provide a rich source of such data. Whether they succeed or fail, each experiment generates a signal about what does and does not work.
“Data is our differentiator,” says Sapra, explaining how the company’s datasets are proprietary and multimodal and include molecular structures, binding measurements, safety profiles, and manufacturing outcomes. “We’ve built an intentionally diverse portfolio across multiple disease areas and drug types. All of that data empowers us to fine-tune frontier AI models with richer, more representative training sets.” She continues, “Further, we have invested in deep screening technologies to generate additional datasets required in volume to constantly refine and validate our models.”
Building an autonomous discovery engine
To bring all of that data together in one place, AstraZeneca is building what it calls a “lab of the future” facility in Kendall Square, Cambridge, Massachusetts where AI and robotic automation will be able to form a continuous, closed-loop discovery system. “Where a self-driving car uses sensors and models to navigate its environment, this system uses AI to make predictions, robotic systems to execute experiments, and instruments to generate data,” explains Sapra. That data feeds directly back into the models, accelerating each subsequent cycle.
“Throughout, scientists will remain central to the process, providing the oversight, judgement, and strategic direction that ensure outputs are explainable, tolerable, and directed toward potential patient benefit,” she adds.
Eventually, automated high-throughput systems will be able to make and evaluate thousands of molecular interactions on a weekly basis. “This will generate AI-ready data at a scale that traditional workflows cannot match,” Sapra says. “Robotic sample handling, automated quality checks, and integrated data pipelines also have the potential to help accelerate early drug development timelines significantly.”
The next frontier: Generating medicines from scratch
Ultimately, Sapra says, the end-state vision for AI in biologic drug discovery is what the field calls “de novo” design. For this, the goal is for AI to generate entirely new protein sequences that precisely fit the desired drug properties. This includes designing the structure, predicting safety, how it will behave in the body and how to make it manufacturable.
“The field is making great progress toward a completely AI-generated biologic, designed from scratch all the way to a clinical candidate,” Sapra says. “As we continue to leverage frontier models and fine-tune them with the right datasets, we bring ourselves closer to this reality. I believe it will come. It’s a matter of time.”
Several key elements are needed to reach this point, however. First is richer and more standardized training data across the industry. Second, robust evaluation benchmarks for AI-generated candidates. And third, teams that know how to work at the intersection of machine learning and biology. Of all the prerequisites, however, safety prediction may be the most consequential, and perhaps the least discussed, Sapra says.
“One of the hardest problems in de novo design is predicting whether a computationally generated molecule will be safe in the human body,” Sapra explains. AstraZeneca is tackling this with what amounts to virtual clinical trials. These are advanced cell systems and micro-scale organ models that function as physical testbeds, paired with AI that learns from their outputs.
“These systems have the potential to generate enhanced biological signals without traditional testing bottlenecks, and they’re a critical missing piece in closing the loop between AI-generated designs and clinical-ready candidates,” Sapra adds.
A shift currently underway is the move toward agentic AI systems that can simultaneously generate molecule candidates and predict how efficacious and safe they are likely to be. These autonomous workflows can connect disease-level insights directly to molecule design, bridging what were previously separate data silos. “The complexity of the biology goes hand-in-hand with the design of the molecule,” summarizes Sapra.
Human talent unlocks AI potential
The transformation underway in biologics is not just about technology. “With more autonomous systems, human oversight remains at the heart of this approach—ensuring explainable and ethical AI for the benefit of patients,” says Sapra.
For scientists, working with AI is a collaborative process. “Scientists will work hand-in-hand with these model systems,” she says. “There will be a world where models will design molecules, then scientists will work with the systems to test those molecules and put all that data together.” Through this process of human checks, balances, and judgement calls, the models will evolve and constantly improve, ultimately with potential to benefit patients.
For engineers, designing and building effective systems ready for human-AI collaboration will mean ensuring high levels of model transparency and explainability. According to Sapra, AstraZeneca’s engineering teams include data scientists, automation specialists, and AI engineers, who are developing systems that act as “thinking partners” rather than black boxes. “Engineers are designing systems that generate, validate, and learn at speed. And the problems are genuinely hard: Multimodal data fusion, closed-loop optimization, uncertainty quantification, and interpretability at the point of clinical decision-making,” she adds.
In taking on such technically demanding challenges, engineers and scientists have the opportunity to contribute to the research and development of potentially life-changing treatments for many diseases, says Sapra. “The biologic medicines we can develop today, and those we’ll design tomorrow, depend on combining world-class AI and engineering talent with deep scientific expertise.”
This article has been initiated and funded by AstraZeneca. Z4-85058, July 2026.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Early diagnosis of Alzheimer’s disease is important to ensure timely and accurate treatment. We show that a blood-based circular RNA signature can accurately detect Alzheimer’s disease as well as, or better than, current biomarkers and even predict who will develop symptoms before they appear. This less invasive approach could offer earlier diagnosis and improved monitoring of Alzheimer’s disease.
Those living with type 1 diabetes not only face the challenges of living with a chronic illness, but they also contend with the potential of developing secondary conditions, including kidney disease. Nearly a third of patients with type 1 diabetes develop kidney disease, resulting in diabetes as the leading cause of kidney disease worldwide. With such a broad impact, and few approved therapies, research into therapeutic strategies to help patients prevent or recover from diabetes-induced chronic kidney disease is much needed.
Research on type 2 diabetes suggests that inhibitors against the SGLT2 protein improve function and reduce damage to kidneys. Thought the mechanism behind this protective effect remains unclear, an SGLT2 inhibitor, dapagliflozin, has shown success in clinical trials led by Petter Bjornstad, MD, physician-scientist and executive director of the University of Washington Medicine Diabetes Institute.
Bjornstad & colleagues have developed their clinical trial to assess the effects of dapagliflozin in adolescents. Their new paper published in Science Translational Medicine shows the results of a section of their phase III data from the Adolescent Type 1 Diabetes Treatment With SGLT2i for hyperglycEMia & hyPerfilTration (ATTEMPT) trial.
The authors described this analysis of the broader trial as “a multimodal view of how the kidney of a person with T1D responds to SGLT2 inhibition.”
Data from 98 children aged 12 to 21, was analyzed to detail the effects of dapagliflozin in combination with insulin therapy on kidney function and physiology. Participants in both a placebo-control group and the treatment group were treated for 16 weeks, then underwent “sequential kidney biopsies, multiparametric kidney MRI, and plasma/urine proteomics,” though biopsies were only carried out in patients over 18 years of age.
Molecular profiles of the serialized biopsies showed a reversal of molecular hallmarks including transcriptional shifts using single-cell RNA seq, downregulation of glycolysis, gluconeogenesis, and oxidative stress markers, and reduced inflammatory gene expression.
“These molecular changes paralleled clinical improvements, including attenuation of hyperfiltration, improved glycemic control, and normalization of medullary oxygenation,” the authors wrote.
Analysis of proteomics in urine samples showed similar results. The tissue changes included decreased injury markers and increased numbers of protective proteins, suggesting that dapagliflozin has a reversing effect on diabetes-induced kidney damage.
“Cross-cohort comparison against healthy controls showed that over 55% of dapagliflozin-responsive genes shifted toward healthy control expression patterns,” indicated the authors.
Not only were kidney markers improved in treated patients, but these patients also showed overall improvement including improved blood sugar control and more normal kidney function.
“Because adjunctive SGLT2 inhibition is considered for people with T1D in the future, our results offer mechanistic reassurance that the drug engages kidney-protective pathways similar to those in type 2 diabetes,” they wrote.
In addition to determining the mechanistic impacts of dapagliflozin in kidney disease using a subset of data from a clinical trial, the authors point out that this work has a broader impact for other clinical trials with multiple data streams.
“Our study showcases the power of deep phenotyping in clinical trials to elucidate drug mechanisms in vivo,” they point out.
“Ultimately, combining rigorous clinical trials with translational science approaches will accelerate the development of therapies to reduce the burden of DKD in T1D,” concluded the authors.
Background: The global adoption of English-medium instruction (EMI) in higher education has introduced complex implementation challenges, the severity of which often depends on the resources available within specific educational contexts. Evidence remains limited in public medical schools in under-resourced, non-Anglophone countries, which serve a socioeconomically and educationally diverse student population. In such settings, the direct transfer of existing EMI integration models presents significant practical challenges. Objective: This exploratory, observational, monocentric study aimed to investigate medical students’ perceptions and attitudes toward EMI implementation at the Faculty of Medicine and Pharmacy of Rabat (FMPR) in Morocco and to evaluate their readiness for EMI adoption. Methods: A cross-sectional survey was administered to 102 second-cycle medical students at FMPR. The 23-item questionnaire included Likert-type scales and multiple-choice questions across 4 domains: demographic data, self-reported English language proficiency, language perceptions and attitudes, and EMI needs. Bivariate, univariate, and multivariate logistic regression analyses were conducted to determine the effects of the explanatory variables on EMI choice. Results: The sample comprised 65 female and 37 male participants. Most reported using English speaking and listening skills “often” on a daily basis, while writing skills were reported as being used “rarely.” Participants rated their general English proficiency and their academic listening and reading skills as “good.” Overall, 82.4% (84/102) of students were strongly in favor of implementing EMI at FMPR. Bivariate analysis showed significant positive associations between EMI choice and age (=.04), course year (<.001), interest in learning English (=.006), and the belief that English should be the language of higher education (=.02). Preference for EMI increased alongside course year, peaking at 96.4% (27/28) among students in the third and fourth years. Multivariate logistic regression confirmed that being in the fourth vs third course year (adjusted odds ratio 22.12, 95% CI 2.56‐190.84; =.005) and having an interest in learning English (adjusted odds ratio 5.12, 95% CI 1.05‐25.03; =.04) were significant positive predictors of EMI choice. Conclusions: Medical students at FMPR exhibit highly positive perceptions and attitudes toward the potential implementation of EMI, despite variations in their self-reported English readiness. These findings provide actionable insights for the successful integration of EMI that extend beyond the Moroccan context, offering a valuable framework for medical institutions in other under-resourced, low- and middle-income, non-Anglophone settings across the Global South.
