Treatment :
Cancer treatment varies from surgery and various therapies to destroy any malignant cells in the body, targeted therapies and stem cell transplants. Biomarkers can help determine what kind of therapy would work best. Precision oncology helps find the available targeted therapies. The price of new drugs is going up steeply, and personalised drugs cost even more – but they bear the promise of being more effective than generic medications. As medical and technological innovations expand cancer treatment, the possibility for super-focused drugs is getting nearer in oncology treatments.
Next-generation targeted therapies :
There are drugs that can block cancer growth or spread by interfering with specific molecules involved in the development, progression, and spread of cancer. Such targeted therapies are designed only to stop cancerous cells, using information about a person’s genes and proteins. Such precision drugs can contribute to the prevention, diagnosis and treatment of the disease, resulting in unbounded patient benefits. Next-generation targeted therapies have already gained momentum in the past couple of years. I expect the field to take off rapidly as the costs of the technology will inevitably go down as the technology will be more available, therefore more affordable.
Molecular cancer diagnostics :
Matching the right targeted therapy to the right patient based on the individual molecular genetic alterations in each cancer patient’s tumour is a promising and attractive precision oncology approach. Oncompass Medicine uses A.I.-based algorithms to match genetic mutations found in patients’ tumour samples with effective targeted cancer therapies. This way, patients can receive precisely targeted treatments specific to the kind of cancerous tissue they have. Their RealTime Oncology Treatment Calculator can significantly improve the selection of the right targeted therapy of each cancer patient based on the individual molecular genetic profile of their cancer.
Artificial intelligence-based therapy design
Even if we can extract tumour cells from blood and sequence their DNA as fast as possible, deciding on the treatment is still a struggle. No oncologist can see through the millions of studies and thousands of clinical trials by keeping all of the patient’s parameters and mutations in mind.
However, artificial intelligence algorithms can. In the U.K., Addenbrooke’s Hospital in Cambridge uses Project InnerEye, an A.I. deep-learning tool supporting the treatment of cancer patients. The A.I. analyses hospital data to identify tumours on patient scans. And it’s amazingly reliable, cutting CT processing times and treatment planning by up to 90%. Dutch researchers developed an evolutionary algorithm with intelligent search behaviour to generate better solutions at an ever-increasing speed. The A.I. created “better plans and more insight than the doctors thought possible.”
In silico trials :
In principle, clinical trials take years and cost more than 2 billion dollars for every approved treatment. The number of failed drug candidates is enormous, so spending years and millions on a clinical trial is no guarantee it will lead to an approved treatment. However, the pandemic has changed the world of clinical trials forever. Trials should unquestionably use more data and A.I. models. It is inevitable to build the right skill sets to implement new technologies. Novel approaches like in silico trials with advanced biological networks, organs-on-a-chip or even network medicine will then help choose the right drug candidates within seconds.
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