Precision Medicine Tool May Personalize Pediatric Cancer Treatment

Researchers from University of Utah Health have developed a precision medicine tool to provide insights into how medications can best be used in pediatric cancer populations.

Drug dynamics, or pharmacodynamics, characterize the physiological, molecular, and biochemical effects of a drug within the human body. These factors can be impacted by a patient's age, medical conditions, and other medications that they may be taking. Understanding these dynamics is key to informing patients’ treatment plans, but for pediatric cancer patients, data on drug movement is often lacking.

“There are many critical knowledge gaps related to how drugs move or act,” explained Jonathan Constance, PhD, associate member at Huntsman Cancer Institute in the Cell Response & Regulation Program and assistant professor of clinical pharmacology at University of Utah Health, in a news release. “The majority of drugs prescribed to children with cancer lack dosing information specific to these patients. This urgent, unmet public health need results in off-label prescribing and puts these patients at risk.”

To address this, the research team developed a pharmacology platform to help gain insights into how these drugs can be used effectively and safely in pediatric populations. Specifically, the tool focuses on investigating factors that influence drug movement, flagging early signs of drug toxicity, and exploring drug-chemotherapy interactions.

The platform is connected to the health system’s electronic health record (EHR) system to identify when a blood draw is taken from a pediatric cancer patient. Using data from these blood draws, the research team can conduct analyses to gather information about potential drug interactions, the impact of certain drugs on patients with other medical conditions alongside their cancer, and how long medications remain in a patient’s bloodstream.

“In this way, we can better understand the underlying issues concerning drug safety and efficacy, and importantly, have the data that can support optimization of drug use,” Constance noted.

The combination of biomarker and EHR data allows the researchers to conduct various studies of childhood cancer and drug efficacy at both the individual and population levels, which is key to improving treatment.

The research team further emphasized that the tool may have additional applications outside of looking at drug treatment in pediatric populations, like informing strategies for reproductive health preservation in cancer patients.

“Being able to create a unique plan for each patient is the future of cancer care,” Constance stated. “We are just getting started. We hope this platform will be able to aid clinicians in creating personalized treatment plans for their patients.”

Other cancer researchers are also looking to advanced analytics tools to bolster precision medicine efforts.

Researchers from the University of Texas at Arlington (UTA) recently revealed that they have created an artificial intelligence (AI) algorithm to provide personalized treatment recommendations for esophageal cancer.

Esophageal cancer often goes undetected early on because its symptoms – like indigestion – are common. However, as the condition progresses, it becomes increasingly more difficult to treat.

To address these challenges, the pharmacokinetic model is designed to determine the ideal pharmaceutical dose for each esophageal cancer patient, identify contributing factors to the cancer’s progression, and select optimal drug combinations and dosage profiles.