How agricultural theory could change care against cancer

In 2018, I waited after Christmas to inform my children 10 and 12 years old that I had metastatic male breast cancer and was to die in 32 to 42 months. Because the disease is rare in men, no clinical trial has been available to me. The emotions were overwhelming, but in these dark moments, my oncologist, will be fucked Buys from the University of Utah, offered critical information. She said that the field had to focus on “using the drugs we have in better ways”.

This key advice launched me on a new path. I worked with a group of pioneering scientists – Bob Gatenby, Joel Brown, Sandy Anderson, Dawn Lemanne and Carlo Maley – to adapt my care against cancer to incorporate principles that farmers use to avoid pests. Six and a half later, I am still alive. The disease has grown up in me, but not in the way you might imagine.

Despite decades of billions of dollars research and investment, a diagnosis of advanced metastatic cancer remains almost universally deadly. The reason why this disease continues to kill is summarized in a word: evolution. A treatment initially works, but cancer cells almost always evolve resistance until the drug ceases to work.

This problem is similar to the one that is commonly found in farms. When agricultural pests are bombed with high doses of pesticides over long periods, invasive insects evolve and become resistant. To overcome this obstacle, farmers have developed resistance management plans and many have proven to be effective. These include minimizing the use of pesticides, rotary pesticides of different chemical classes and the application of chemicals only if necessary depending on the continuous monitoring of pest populations.

My colleagues had taken note of parallels between cancer and farm pests. Then they incorporated some of the same ideas into math models of cancer. From now on, these algorithms can shed light on the procedures step by step to use medicines at the right time, order and right dose to reduce the risk of evolution of cancer changing treatment, while safe controlling the disease.

The use of algorithms bypassing defects in the approach of traditional oncology where therapies end up rewarding resistant cancer cells. Standard practices also eliminate cancer cells sensitive to treatment which interfere with the growth of those who are resistant to treatment. Ironically, for similar reasons, the continuous application of pesticides at maximum doses is prohibited by American law.

As a scientist, I worked with my colleagues and my oncologist to develop my own management plan. Instead of waiting for my cancer to become resistant to special treatment before moving on to the next medication, we use cancer drugs approved in a much more intelligent manner. My plan is to run 15 drugs from different chemical classes, using non -drug -based approaches to minimize the consumption of drugs and constantly monitor the disease to adapt the treatment. So far it works. I survived my original prognosis, but we do not know if this is due to my treatment strategy. It could have happened without that. Our approach must be properly tested in a clinical trial, which costs millions.

The approach and costs of these trials create a problem. You cannot patent empirical rules for resistance management, so there is no financial incentive to finance such tests. To encourage clinical trials, the biomedical field could draw inspiration from the technological industry. Instead of medication patents, the pharmacy could take advantage of the proprietary data and commercial-secret algorithms. The Food and Drug Administration of the United States should also adapt its framework to adopt AI agents who interact independently with patients and service providers, and the decision-making tools directed by AI which dynamically adapt the processing to the needs of the patient according to the inputs of continuous data. Before the new Republican administration, I had several meetings with FDA officials, and they were delighted with this future. They worked with Congress to adapt and clarify regulatory frameworks for rapidly evolving AI technologies. The possibilities of optimizing personalized care are extraordinary.

From now on, FDA layoffs and subsidy reductions by the National Institutes of Health have put the system in a terrifying descending spiral. This happens at a time when we have these new profitable solutions to transform certain fatal cancers into chronically manageable diseases – as we have done for diabetes and HIV / AIDS – using already available medicines.

Developing “algorithms as drugs” technologies that use the drugs we already have is a new essential complement to the existing pharmaceutical system. It offers the potential of a cancer care revolution. We need improved algorithms to optimize the selection, sequencing and the treatment calendar and depth learning algorithms that analyze data varieties to tell doctors when and how to change the processing to prevent drug resistance in a specific patient. The initial algorithms are ready and pending. The first small step is to support clinical trials. I took a chance on them and I am not yet dead. Thanks to startups (Storyline Health Inc. and Primordial AI Inc.), awareness (the Masterclass of Cancer Uncharted) and with basic research efforts, I do everything I can to help my pioneering colleagues bring these solutions to other patients quickly. They need more help.

One in seven will die from cancer. One in five will develop cancer of our life. The new drugs are too expensive and many do not prolong life significantly. It’s time to adopt a new approach.

Christopher Gregg, Ph.D., is a professor of neurobiology and human genetics at the University of Utah. He has conflicts of financial interest in Storyline Health Inc., Primordial Ai Inc., Depoiq Inc. and Rubicon Ai Inc. and directs the free online Cancer patient masterclass Uncharted To share the knowledge of this field.