AI and computational biology transforming chronic liver disease research 

AI and computational biology transforming chronic liver disease research 

Chronic liver disease poses a significant global health challenge, with millions of lives lost each year. A lack of available treatments and a shortage of viable livers for transplantation make this a pressing issue that demands attention. Fortunately, the convergence of Artificial Intelligence (AI) and computational biology has accelerated research into innovative therapies for liver disease, bringing hope to patients and transforming the medical landscape. Dimitris Polychronopoulos, Director of In Silico Biology at Ochre Bio, a biotech R&D company, explores how AI and computational biology-led innovations are accelerating research into chronic liver disease therapies. 

The challenge of liver disease 

In the United Kingdom alone, liver disease claims the lives of approximately 10,000 people every year, highlighting the urgent need for improved testing and treatment. And this is a growing problem: the number of people hospitalised due to liver disease was 22% higher in 2022 than in 2021, ​​according to the recent UK government report, Liver disease profiles, July 2023 update – half of this increase (11.7%) is due to alcohol-related liver disease.  

The medical community and pharmaceutical industry are investing considerable efforts into developing novel therapies to address unmet medical needs and enhance patient outcomes, paving the way for promising advancements in liver disease treatment. However, liver disease research comes with its own set of challenges. Identifying specific genetic markers associated with different liver characteristics is vital for understanding the disease and developing targeted treatments, but analysing vast amounts of genetic information and extracting meaningful insights is a complex computational task. To overcome this analytical hurdle, scientists are harnessing the power of AI and computational biology to redefine liver disease research and discover innovative therapies. 

Novel therapies and the role of AI 

Leading the way in this transformative approach is Ochre Bio, a biotech R&D company using AI and computational biology techniques to develop ground breaking treatments for chronic liver disease. Traditionally, the preclinical phase of drug development can take six to 10 years, however, Ochre Bio has accelerated this process, reducing it to less than 60 days, by combining two cutting-edge approaches: deep phenotyping and AI.  

As more and more biopharma companies rely on external biotech innovation – just 28% of new drugs filed by the top 20 biopharma companies in 2015-2021 originated internally – companies that can increase the rate and success of therapeutic discovery and development will be vital. 

To the best of our knowledge, Ochre Bio has generated more human liver deep phenotyping data than anyone else. Deep phenotyping involves studying how perturbation of genes affects the liver, by juxtaposing tissue, blood and clinical phenotypes with functional genomics data to produce a map, or knowledge graph, of the relationships between genes and phenotype.By delving deep into this multimodal data, scientists gain valuable insights into disease development and therapeutic response, which guides them in identifying possible targets for novel medicines. 

The pivotal role of AI in the field of liver disease therapy development cannot be overstated. We have a greater understanding of the complexities of liver disease progression than ever before, due to the wealth of biological data (including genomic and clinical data) at our fingertips. However, the sheer volume of this data makes it incredibly challenging to pull out meaningful insights. AI algorithms allow scientists to analyse and interpret vast amounts of data more efficiently – helping uncover patterns, correlations and predictions that might otherwise remain hidden and significantly reducing the time and cost associated with drug discovery. 

Accelerating discovery and validation 

In the pursuit of target discovery, scientists can leverage the power of computational biology and AI by combining systematic cellular models, Machine Learning techniques, knowledge graphs and recommendation systems (RecSys). This comprehensive, multi-faceted approach provides a deep understanding of how various factors, including medications, interact with the human body. Knowledge graphs and especially heterogeneous ones, which showcase interrelationships among different elements, represent a convenient and inherent way to organise and comprehend vast amounts of data, significantly contributing towards making the data more Findable, Accessible, Interoperable and Reusable (FAIR).  

For scientists seeking to analyse complex data and make ground breaking discoveries, computational biology and AI have become invaluable tools. With these technologies, researchers can identify patterns within deep phenotyping data – enabling the identification of possible genes for further clinical study and the classification of patients into distinct groups based on their unique characteristics. Combined, these techniques not only predict drug targets but can also help predict disease progression and therapeutic response – leading to the development of more tailored and effective therapies, including targeted therapies for specific liver disease subtypes. 

The future of chronic liver disease research 

Looking forward, the impact of AI and computational biology in chronic liver disease research will be transformative, helping researchers gain a holistic understanding of disease development and progression. This knowledge is already enabling early detection, personalised treatment plans and tailored lifestyle interventions, but will continue to play a huge role in therapy development too.  

By harnessing the power of these technologies, scientists are driving innovative discoveries, accelerating drug development, increasing the supply of donor livers and providing hope for millions of individuals affected by chronic liver disease. As AI and computational biology continue to advance, the future holds tremendous potential for advancements in early diagnosis, personalised treatments, and ultimately, the eradication of this devastating disease. 

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