Request ConsultationArtificial intelligence could help researchers identify disease mechanisms, improve drug target selection, and accelerate early stage drug discovery for liver and heart diseases. The AstraZeneca and Insitro partnership highlights how AI and experimental biology could support the development of new treatments.
The pharmaceutical industry is increasingly turning to artificial intelligence (AI) to address some of the most difficult challenges in modern medicine. Although scientific research has delivered major advances in disease diagnosis and treatment, developing medicines for complex conditions remains a lengthy, expensive, and uncertain process. Researchers must identify the biological mechanisms responsible for disease, discover suitable drug targets, evaluate potential compounds, and demonstrate safety and effectiveness through clinical trials.
Liver and heart diseases present particularly important opportunities for innovation. Conditions such as metabolic dysfunction-associated steatohepatitis (MASH) and cardiomyopathies involve complicated biological processes that can differ significantly between patients. Identifying the underlying causes and developing treatments that address these mechanisms remain important priorities for pharmaceutical researchers.
A recent partnership between AstraZeneca and artificial intelligence biotechnology company Insitro highlights the growing role of AI in this field. According to a Reuters report published on October 15, 2026, AstraZeneca agreed to pay $40 million upfront, with Insitro eligible for approximately $1.46 billion in potential development and commercial milestone payments, along with future sales royalties.
The collaboration will focus on discovering treatments for MASH and cardiomyopathies using Insitro’s AI-powered Virtual Human Platforms. The agreement raises an important question: Can AI help researchers discover better treatments for difficult liver and heart diseases, and what challenges must pharmaceutical companies overcome before these technologies deliver benefits to patients?
Understanding AI-Powered Drug Discovery
Artificial intelligence is changing how pharmaceutical researchers investigate diseases and develop potential medicines. Traditional drug discovery often involves extensive laboratory experiments, analysis of biological samples, and repeated testing to determine which compounds interact with specific disease-related targets.
AI can support this process by analysing large datasets, identifying patterns, and generating predictions that help researchers prioritize promising scientific opportunities.
Machine learning models can examine information from genetic studies, molecular experiments, clinical research, and laboratory-generated biological data. By combining these sources, researchers may identify relationships between particular genes, proteins, cellular processes, and disease outcomes that are difficult to recognize using conventional analytical methods alone.
However, AI does not independently establish that a particular biological mechanism causes disease. Its predictions must be evaluated through laboratory experiments and subsequent clinical research.
The integration of computational models with biological experiments is particularly important.
Laboratory-generated data can provide information about how cells respond to different conditions, while machine learning can help interpret those observations and identify patterns that warrant further investigation.
This combination may allow researchers to move beyond identifying statistical associations and investigate biological mechanisms that could form the basis of further treatments.For pharmaceutical companies, the objective is not simply to generate more drug candidates. It is to identify better targets, reduce unnecessary experiments, and improve the quality of decisions made during the earliest stage of drug development.
Why AstraZeneca is Investing in AI for Liver and Heart Diseases
The collaboration between AstraZeneca and insitro reflects the growing interest in using advanced computational technologies to investigate diseases with complex underlying biology.
Under the agreement, insitro will use its Virtual Human platforms, which combine laboratory-grown cell models and machine learning, to investigate disease mechanisms and advanced drug targets nominated by AstraZeneca toward the development-candidate stage. AstraZeneca will finance and conduct subsequent clinical trials for selected programmes.
The financial structure includes a $40 million upfront payment and the possibility of approximately $1.46 billion in additional development and commercial milestones payments, together with potential single-digit royalties on future sales.
These figures represent potential payments rather than guaranteed revenue or evidence of successful drug development.
The partnership is significant because it combines Insitro’s AI and biological research capabilities with AstraZeneca toward the development-candidate stage. AstraZeneca toward the development-candidate stage. AstraZeneca will finance and conduct subsequent clinical trials for selected programmes.
The financial structure includes a $40 million upfront payment and the possibility of approximately $1.46 billion in additional development and commercial milestone payments, together with potential single-digit royalties on future sales.
The partnership is significant because it combines Insitro’s AI and biological research capabilities with AstraZeneca’s pharmaceutical development infrastructure. By bringing computational analysis and experimental biology together, the companies aim to identify disease mechanisms that could lead to new treatment approaches.
For the pharmaceutical industry, this model also illustrates how partnerships between biotechnology companies and established drug developers can support innovation without requiring every organization to build all the necessary technologies internally.
How AI Could Accelerate Liver Disease Treatments
Identifying the biological drivers of MASH
Metabolic dysfunction-associated steatohepatitis, commonly known as MASH, is a progressive liver disease associated with metabolic dysfunctions. It involves the accumulation of fat in the liver alongside inflammation and cellular injury. Some patients develop fibrosis, which can progress to cirrhosis and other serious complications.
Developing treatments for MASH has been challenging because the disease does not follow an identical biological pathway in every patient. Metabolic abnormalities, inflammation, genetic factors, and other biological processes can contribute differently to disease progression.
AI could help researchers analyse these interacting mechanisms and identify biological factors that play a causal role in liver injury.
By examining genetic information, laboratory experiments, and cellular responses, machine learning systems may help scientists identify potential drug targets that have previously received limited attention. Researchers can then investigate whether modifying these targets improves disease-related outcomes.
These approaches could support the development of treatments aimed at underlying disease mechanisms rather than focusing exclusively on individual benefits without causing unacceptable side effects.
Improving Drug Candidate Selection
Another potential application involves identifying compounds that interact with relevant biological targets.
Traditional drug discovery can require extensive testing of different molecules to determine their activity, selectivity, stability, and other properties. AI models may help prioritize candidates by predicting their molecular interactions and likely biological behaviour.
This could reduce the number of compounds requiring detailed laboratory evaluation and allow researchers to concentrate resources on candidates with stronger preliminary evidence.
For liver diseases, this process may help identify compounds capable of influencing relevant metabolic and inflammatory pathways. Scientists must still verify their predictions experimentally and establish whether a candidate can reach the liver at an appropriate concentration.
Supporting Research into Disease Progression
AI may also help researchers understand why some patients experience relatively slow disease progression while others develop advanced fibrosis or serious liver complications.
By analysing biological and clinical datasets, researchers, can investigate pattern associates with different stages of disease. These findings could support the identification of biomarkers that help classify patients and guide future clinical research.
Such information may eventually contribute to more individualized treatment strategies. However, any proposed biomarker must be validated in an appropriate patient population before it can reliably guide clinical decisions.
How AI Could Improve Heart Disease Treatments
Investigating the Cause of Cardiomyopathies
Cardiomyopathies are disorders affecting the heart muscle that can interfere with its ability to pump blood effectively. Depending on the specific conditions, patients may experience abnormal heart muscle structure, reduced pumping function, heart rhythm disturbances, or heart failure.
These disorders can have genetic, metabolic, inflammatory, or other underlying causes. Their complexity makes it difficult to develop treatments that work consistently across all patients.
AI could help researchers examine genetic data, molecular pathways, and experimental findings to identify mechanisms responsible for particular forms of cardiomyopathy.By connecting genetic variations with cellular abnormalities and disease characteristics, computational models may help scientists identify targets that influence heart muscle function. Laboratory experiments can then determine whether these targets are suitable for therapeutic intervention.
The AstraZeneca-in sitero collaboration specifically includes cardiomyopathies, reflecting the need for new approaches to investigate the biological mechanism underlying heart muscle disease.
Developing More Targeted Medicines
AI-Supported research could help identify treatments designed for particular disease mechanisms rather than relying on approaches that affect broader biological processes.
For example, a drug candidate developed around a specific molecular pathway may be most useful for patients whose disease depends on that pathway. Understanding these differences could help researchers design clinical trials around clearly defined patient groups.
More targeted approaches may improve the chances of detecting a treatment effect and help scientists understand which patients are most likely to benefit.
However, predictions about patient responses must be confirmed through clinical studies. Heart diseases can involve multiple interacting mechanisms, meaning that modifying one target may not be sufficient to produce substantial clinical improvement.
Supporting Biomarker-Based Research
Biomarkers can help researchers measure biological changes, identify disease subtypes, and evaluate whether a treatment is affecting its intended target.
AI model could support the discovery of biomarkers by analysing patterns across genetic information, laboratory measurements, imaging findings, and other clinical data.
In validated settings, such biomarkers could improve patient selection and support more informative clinical trials. They could also help researchers determine whether an experimental medicine is producing the expected biological response.
Importantly, a change in a biomarker does not necessarily mean that a patient will live longer or experience fewer complications. Clinical outcomes remain essential when establishing the actual value of a treatment.
How AI Could Transform the Drug Development Process
Moving From Data Analysis to Biological Discovery
AI can identify patterns in biological datasets that may be difficult to recognize through conventional research methods. By analysing genetic information, cellular behaviour, and experimental findings, AI models can help researchers prioritize disease-related targets and investigate promising biological mechanisms.
In the AstraZeneca-insitro partnership, this approach aims to uncover disease biology and advance selected targets toward drug development. Combining computational predictions with laboratory validation could help researchers make more infomed decision during early-stage discovery.
Reducing Unnecessary Laboratory Experiments
Drug discovery involves testing numerous compounds to determine their biological activity, safety, and suitability for further development. AI can help prioritize candidates by predicting molecular interactions and identifying potentially useful chemical properties.
This may reduce unnecessary experiments and allow researchers to allocate resources more efficiently. However, predictions must still be confirmed through laboratory studies, safety assessments, and clinical trials.
Validated biomarkers could also help researchers could help researchers select suitable participants for clinical trials and evaluate treatments responses. However, these approaches require clinical evidence before they can guide routine treatment decisions.
The Role of Pharmaceutical Partnerships and Investment
The AstraZeneca-insitro agreement demonstrates how pharmaceutical companies are combining AI technologies with established drug development capabilities. Biotechnology companies contribute specialized computational platforms and biological research expertise, while pharmaceutical companies provide clinical development, regulatory, manufacturing, and commercialization capabilities.
The agreements include a $40 million upfront payment and approximately $1.46 billion in potential milestone payments, which depend on development and commercial achievements and are not guaranteed.
Such partnerships can support innovation and distribute development risks. However, their long-term value depends on whether research programmes produce safe, effective, and commercially viable medicines.
Key Challenges Facing AI-Driven Drug Discovery
- Data Quality and Reliability: AI predictions depend on reliable datasets. Incomplete information, inconsistent measurements, and limited patient representation can affect model accuracy. Researchers must validate findings using independent datasets and laboratory experiments.
- Translating Predictions Into Effective Medicines: Identifying a promising target does not guarantee successful treatment. Researchers must establish whether a candidate reaches the intended tissue, affects the relevant disease mechanism, and provides meaningful benefits without unacceptable side effects.
- Clinical and Regulatory Requirements: AI-supported medicines must undergo appropriate preclinical assessment and clinical trials. Researchers must demonstrate safety, effectiveness, and suitable dosing before regulatory approval can be considered.
- Cost and Commercial Viability: AI platforms require investments in computing infrastructure, specialized expertise, and laboratory systems. Companies must also fund clinical trials, manufacturing, and regulatory activities. Commercial success will depend on demonstrating clinical value and patient accessibility.
What Pharmaceutical Companies Should Focus on Next
Pharmaceutical companies should prioritize biologically validated targets, combine AI predictions with experimental evidence, and design clinical trials around clearly defined patient populations. They should also assess data quality, model reliability, potential bias, manufacturing feasibility, and long-term safety.
Partnerships between technology-focused biotechnology firms and established pharmaceutical companies can help connect scientific discoveries with clinical development. Nevertheless, realistic drug candidates may not succeed.
Artificial intelligence could accelerate liver and heart disease research by helping scientists identify disease mechanisms, prioritize drug targets, and develop more targeted treatment strategies.
The AstraZeneca-insitro collaboration highlights growing pharmaceutical interest in combining machine learning with experimental biology to investigate MASG and cardiomyopathies. AI may improve early-stage research efficiency and support biomarker discovery, but it cannot replace laboratory validation or clinical trials.
Data reliability, biological complexity, regulatory requirements, manufacturing costs, and accessibility will determine whether these discoveries become successful medicines.
Ultimately, AI should be viewed as a tool that strengthens scientific research rather than a replacement for it. Its long-term value will depend on whether computational insights translate into safe, effective, and accessible treatments that improve outcomes for patients with liver and heart diseases.
About the Authors
Aditi Shivarkar
Aditi, Vice President at Precedence Research, brings over 15 years of expertise at the intersection of technology, innovation, and strategic market intelligence. A visionary leader, she excels in transforming complex data into actionable insights that empower businesses to thrive in dynamic markets. Her leadership combines analytical precision with forward-thinking strategy, driving measurable growth, competitive advantage, and lasting impact across industries.
Aman Singh
Aman Singh with over 13 years of progressive expertise at the intersection of technology, innovation, and strategic market intelligence, Aman Singh stands as a leading authority in global research and consulting. Renowned for his ability to decode complex technological transformations, he provides forward-looking insights that drive strategic decision-making. At Precedence Research, Aman leads a global team of analysts, fostering a culture of research excellence, analytical precision, and visionary thinking.
Piyush Pawar
Piyush Pawar brings over a decade of experience as Senior Manager, Sales & Business Growth, acting as the essential liaison between clients and our research authors. He translates sophisticated insights into practical strategies, ensuring client objectives are met with precision. Piyush’s expertise in market dynamics, relationship management, and strategic execution enables organizations to leverage intelligence effectively, achieving operational excellence, innovation, and sustained growth.