24 June 2025

Swedish healthtech pioneer Mavatar has announced two major milestones in its journey to reshape global healthcare: the launch of its first U.S. office and the upcoming commercial rollout of its flagship AI-powered research platform, Mavatar Discovery. These developments signal not just international expansion, but a bold move to redefine how we understand, diagnose, and treat disease.

“At Mavatar, we’re helping shift healthcare from reactive to predictive, from generic to deeply personal.” said Johan Juhlin, Mavatar CEO in an exclusive interview with MoveTheNeedle.news.

 

Mavatar began with a bold question: What if we could test treatments before they ever reach a patient—on that patient’s digital twin?

As an AI company, Mavatar builds such high-resolution digital twins of both diseases and patients. These twins simulate how a disease behaves inside the body and how different treatments may work for each individual.

“It allows us to match the right drug to the right person—or even to help develop entirely new drugs—before a single dose is given in real life,” Juhlin explained. “Why this matters? Because 38–75% of current drug treatments are ineffective. That translates into enormous patient suffering, unnecessary side effects, and billions in wasted healthcare spending. Mavatar exists to help solve that problem.”

Mavatar was born when Juhlin was approached by a research group that had spent over 20 years decoding the molecular mechanisms of disease. They had built a remarkable scientific foundation—but didn’t yet know how to translate it into real-world impact. Together, they spent over a year designing the core concept for what became Mavatar.

 “This was long before AI became mainstream,” Juhlin pointed out. “We started building our algorithms and data engine years before the rise of ChatGPT or the current hype cycle. Our approach is rooted in genomic medicine—analysing changes in gene expression across individual patients to understand disease at the molecular level. Because even two people with the same diagnosis may have very different biological profiles—and may respond very differently to the same treatment. Today’s “one-size-fits-most” model simply isn’t good enough.”

What Is Mavatar Discovery?

The company’s next-generation, AI-powered research platform, Mavatar Discovery, is designed to accelerate disease understanding and drug development. Built for pharmaceutical companies, biotech innovators, and academic researchers, it’s more than a tool—it’s a paradigm shift in how we explore biology and develop new therapies, according to Juhlin.

“What sets Mavatar Discovery apart is our rigorous, fully data-driven foundation. While many platforms rely on predefined assumptions or curated knowledge bases, Mavatar Discovery analyses disease mechanisms in real time using massive-scale transcriptomic data. This allows researchers to uncover novel biological insights, identify new therapeutic targets, and generate actionable hypotheses faster—and with greater precision. “

The result? Faster discoveries, lower development costs, and a deeper understanding of disease complexity across tissues, conditions, and patient populations. It's not just about efficiency—it's about unlocking the next era of precision medicine.

“At Mavatar, AI isn’t an add-on—it’s foundational,” Juhlin elaborated. “Our proprietary framework, DINA (Deep Integrated Network Analysis), is powered by advanced machine learning models that have been trained to recognize complex gene interactions across tissues and diseases. We combine both classical statistical methods and modern machine learning to analyse vast transcriptomic datasets and uncover hidden biological mechanisms.”

Use Cases That Matter

As a use case, Juhlin highlights biomarker selection—where machine learning identifies the most relevant markers for a given disease or patient subgroup. “This allows us to build high-resolution digital twins that simulate disease progression and treatment response, supporting faster and more precise decision-making in both research and clinical settings. In short, we use AI to move from assumptions to evidence—and from raw data to actionable insights.”

Other impactful use cases include  Biomarker and Target Discovery, where researchers can identify novel biomarkers and therapeutic targets by exploring gene interactions across tissues and diseases—without needing coding skills or internal bioinformatics support; Drug Repurposing, where the platform enables rapid identification of new uses for existing drugs (for example, during the COVID-19 pandemic, Mavatar helped uncover two compounds with clinical symptom-reducing effects—validated by real-world outcomes); Disease Stratification, where Mavatar Discovery helps researchers subgroup patients based on molecular signatures rather than clinical labels, leading to more precise therapy targeting and smarter trial design, particularly in complex diseases like cancer or autoimmune conditions;  and Cross-Disease Learning and Rare Disease Research, where the platform’s ability to detect shared biology across diseases comes into play, creating powerful opportunities in areas like rare diseases—where traditional patient datasets are limited.

“For example, in Epidermolysis Bullosa, a rare genetic skin condition, we conducted a case study using Mavatar Discovery. Our system identified a highly significant pathway linking genes associated with this condition to other diseases within our large-scale data framework,” Juhlin added. “This kind of cross-learning opens doors for therapy repurposing—potentially applying existing treatments to rare diseases where options are currently limited.”

Mavatar’s platform also supports Faster Hypothesis Testing: what might take months of data wrangling and lab work can now happen in hours. Users can test hypotheses, visualize gene networks, and work with structured, actionable insights directly in the platform—accelerating discovery and reducing costs.

In short, Mavatar Discovery provides a smarter, scalable way for scientists and pharma teams to explore biology, prioritize leads, and innovate with confidence—even in areas where data is traditionally scarce.

Commercial Launch in September

The commercial launch of Mavatar Discovery is planned for September this year. “Our initial target users include pharmaceutical companies, biotech researchers, and academic institutions—essentially anyone looking to better understand disease biology, therapeutic targets, and cross-disease mechanisms,” said Juhlin. “We’re already in a closed beta phase with selected users around the world, including researchers from both industry and academia. These early testers are helping us fine-tune the platform’s usability, functionality, and scientific output ahead of launch.”

At launch, users will be able to:

·     Explore interactive, tissue-specific gene networks

·     Identify novel biomarkers and disease drivers

·     Perform cross-disease comparisons and functional enrichment

·     Access curated insights from large-scale transcriptomic datasets—without the need for coding or internal bioinformatics resources.

U.S. Expansion: Why Now?

Meanwhile. Mavatar has just taken the first step towards establishing a U.S. entity. “While we haven’t finalized the specific location yet, we’re currently evaluating both New York and Boston—two dynamic life science hubs with strong ecosystems for innovation, investment, and talent,” said Juhlin. “The timing aligns with our upcoming commercial launch of Mavatar Discovery, and our growing engagement with U.S.-based partners across pharma, academia, and research. We’re also in parallel processes to build strategic collaborations and recruit top-tier talent, particularly for our commercial, regulatory, and quality teams.”

The U.S. is a key market for Mavatar—not only because of its size and influence, but because it’s at the forefront of precision medicine adoption. “We want to be where the momentum is—and make sure we have the right people on the ground to scale effectively.”

 The U.S. expansion is a strategic move that positions Mavatar at the centre of the global life science ecosystem. Over the next 3–5 years, the goal is to establish Mavatar as a leading force in AI-driven precision medicine, with a strong presence in both research and clinical markets.

“Ultimately, we want to make data-driven, individualized healthcare accessible at scale—and the U.S. will be a critical launchpad for that global mission,” Juhlin concluded.