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Big data and Google BigQuery improve cancer drug development by detecting bacteria

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Big data and Google BigQuery improve cancer drug development by detecting bacteria

Developing new drugs is a risky and expensive process. The cost of creating and bringing a new drug to clinical trials can reach billions of pounds, with no guarantee of success. Sometimes a drug may perform well in one part of the world during a clinical trial but fail to meet expectations in another region.

One factor that can influence the effectiveness of medicines is the bacteria present in the human body. Each individual has a unique mix of bacteria that can impact how well a medication works or if it works at all. This relationship is particularly crucial in cancer treatment, where bacteria in tumors can hinder potentially life-saving therapies.

Understanding the complex interaction between drugs and bacteria is essential for pharmaceutical researchers and clinicians. With the high cost of developing a new drug, which can be as much as $2.6 billion, being able to model this relationship is crucial.

BioCorteX is a research company specializing in using advanced data science techniques to analyze the connection between bacteria and drug candidates, focusing initially on oncology and antibody-drug conjugates. By improving the understanding of how bacteria influence medications, BioCorteX aims to enhance the success rate of drugs in clinical trials, leading to shorter development cycles and more effective treatments for patients.

Co-founder Nik Sharma explains, “We founded the company out of frustration with the variability in individual responses to treatments. Bacteria, which are integral to human health, can interact with pharmaceuticals and affect their efficacy. This interaction could be a key reason why drugs may work for some individuals but fail on a larger scale.”

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Clinical trials

In clinical trials, a drug may succeed in one population group but fail in another due to variations in the body’s bacteria.

Understanding this complex relationship, with numerous variables involving human bacteria and the drugs being tested, poses a significant mathematical challenge. Sharma highlights the vastness of bacterial and pharmaceutical diversity, making the task daunting.

Sharma and Mo Alomari, a former Rolls-Royce engineer, collaborated to address this issue. Alomari’s expertise in modeling systems with numerous variables led to the founding of BioCorteX. The company aimed to simulate bacteria-drug interactions using computer-based methods.

Building one of the largest knowledge graphs in biology, BioCorteX analyzes the interaction between bacteria and drug candidates, involving billions of connections. To handle this immense data, the company developed its own database using Google’s BigQuery, enabling frequent updates and efficient processing.

Sharma emphasizes the significance of this knowledge graph, stating, “No existing software could manage our graph’s size. Building it on BigQuery allowed us to scale economically and update our data multiple times a day.”

Knowledge graph

The knowledge graph contains billions of nodes and edges stored on BigQuery.

Alomari explains, “Existing graph databases couldn’t handle the scale of our data, so we developed a custom solution on top of BigQuery, treating it as a graph database.” BioCorteX can process new data through the system multiple times daily at minimal cost.

By utilizing data from pharmaceutical companies, BioCorteX identifies bacterial interference in drugs, predicting their impact on patient outcomes. This analysis helps determine drug compatibility at scale, accelerating the assessment of drug assets.

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The modeling conducted by BioCorteX is quicker and more cost-effective than traditional clinical trials. The company’s approach extends beyond new drugs to evaluating unsuccessful assets, shedding light on hidden interactions that may have led to failure.

BioCorteX’s technology is versatile, applicable not only to cancer treatment but also to studying viruses and fungi. Sharma notes, “Our technology can provide valuable insights across various sectors, including consumer health.”

The ability to model different scenarios in drug development, such as comparing studies in various regions, demonstrates the potential of BioCorteX’s data-driven approach in advancing medical research and improving patient outcomes.

A pharmaceutical company or drug company cannot control the occurrence of this interaction – it is inevitable. Therefore, they have the option to either comprehend it or continue with the current 96% failure rate. Moving forward, the goal is to provide the correct drug on the first attempt for all individuals.

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