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Computer-Based Modelling to Optimise Cancer Treatment

Computer Based Modelling to Optimise Cancer Treatment

University of South Australia researcher, Dr Stephani Reuter Lange, received a grant that will bring personalised cancer treatment one step closer to becoming a reality for more patients.

This will reportedly help in the researcher’s work as she explores how computer-based modelling can optimise cancer treatment and remove the need for expensive clinical trials.

About the Initiative

The $300,000 project will involve using the science of mathematical and statistical models to characterise drug behaviour, helping clinicians make an educated decision on the most appropriate treatment regimes.

The work will provide an evidence-basis for dose individualisation of cancer therapies, offsetting the need for the conduct of costly, large-scale clinical trials.

Ultimately, it will lead to improved patient outcomes and provide a framework on which treatment guidelines can be based on the optimal use of new and existing cancer therapies.

Background

While cancer treatments are most successful when personalised to an individual, most cancer medicines are still administered with a “one size fits all” approach.

Despite substantial improvements in the treatment of cancer, three out of 10 patients will still not survive longer than five years.

This can be attributed to cancer progression or death from severe treatment-related side effects.

There is no field of medicine in which individualisation of medicines is more important than in the area of oncology.

There is large variability in how patients respond to many cancer medicines, which can result in either under-treatment that leads to cancer progression, or overtreatment that can have significant toxic side effects.

The concept of dose individualisation means that the amount of a drug administered to an individual patient can be tailored in order to maximise tumour response and minimise side effects.

The project will focus on the use of computer-based modelling methods to identify dose individualisation strategies for best treatment practice.

While the merits of individualised drug dosages are clear, conducting the large-scale clinical trials required to implement the treatment in practice is a complex and costly process which means cancer treatments currently on offer remain standardised rather than personalised.

Another Cancer-Related Research

Dr Reuter Lange’s project is one of two from the University that were successful in the Cancer Council Beat Cancer Project funding announcement with Professor Peter Hoffmann also securing $100,000 to further critical work in identifying the extent of endometrial cancer and where it has spread.

The research project will focus on developing less intrusive diagnosis and treatment techniques for endometrial cancer, the most diagnosed gynaecological cancer in Australia.

According to the professor, radiological imaging is unreliable in determining the stage of endometrial cancer.

This means that the majority of patients have to undergo surgical staging and removal of lymph nodes, even though the minority will actually have cancer that has spread to the lymph nodes or metastatic disease.

The funding will go towards identifying molecular tissue markers that indicate the presence of metastatic disease so that future patients may not need to undergo unnecessary radical surgery or Lymphadenectomy in order to get an accurate diagnosis.

Utilising Technology for Healthcare

OpenGov Asia earlier reported on an initiative of CSIRO’s Data61, the data science arm of Australia’s national science agency, and UNSW Sydney’s Kirby Institute.

They have developed a new tool that harnesses artificial intelligence (AI) and Twitter for the earlier detection of acute disease events.

The researchers have developed a technique, which was able to detect the disease outbreak up to nine hours before it was officially reported and before the first news story broke.

They can draw upon informal sources such as social media data to understand how acute disease events occur.

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