Virtual Clinical Trial Model Seen to Predict Real Responses of Melanoma Patients to Therapies

Virtual Clinical Trial Model Seen to Predict Real Responses of Melanoma Patients to Therapies

Researchers with Moffitt Cancer Center developed a computational framework that, they say, can accurately predict patient responses  to cancer treatments in a virtual clinical trial, called phase i.

This new methodology integrates actual patient responses and preclinical studies results, to capture the diverse clinical outcome of cancer patients.

The study, “Phase i trials in melanoma: A framework to translate preclinical findings to the clinic,” led by Alexander Anderson, PhD, chair of the Integrated Mathematical Oncology (IMO) Department at Moffitt, and Eunjung Kim, PhD, an applied research scientist, was recently published in the European Journal of Cancer.

Significant improvements in cancer research in recent years have led to more efficient therapies, but many drugs that reach clinical trials still end up failing, despite positive preclinical results. One possible reason is the inability of preclinical studies to predict how effective a drug will be in the long term or in different patients.

Cancer is also known to be a complex system that evolves and adapts, shaping its responses according to its surroundings and to specific cancer treatments. Preclinical studies with tumor cells models are often unable to mimic theses aspects, failing to accurately reflect what happens in cancer patients.

“Purely experimental approaches are unpractical given the complexity of interactions and timescales involved in cancer. Mathematical modeling can capture the fine mechanistic details of a process and integrate these components to extract fundamental behaviors of cells and between cells and their environment,” Anderson said in a press release.

The research team tested a mathematical-based computational model that could predict long-term responses of virtual melanoma cancer patient to different treatments: no treatment, chemotherapy alone, AKT inhibitors, and AKT inhibitors plus chemotherapy in sequence and in combination. They confirmed the model’s accuracy through laboratory experiments.

A virtual experiment testing different combinations of an AKT inhibitor and chemotherapy in virtual patients was then developed, using the same treatment combinations and doses of an actual clinical trial. It produced virtual patient tumor volume responses that statistically matched those observed in a heterogeneous group of melanoma patients in that clinical trial, the team reported. [The Phase 1 study (NCT00848718) tested an AKT inhibitor, MK-2206, in combination with different chemotherapy regimes in patients with solid tumors.]

Importantly, the researchers wrote, “our approach predicts optimal AKT inhibitor scheduling suggesting more effective but less toxic treatment strategies.”

“By using a range of mathematical modeling approaches targeted at specific types of cancer, Moffitt’s IMO Department is aiding in the development and testing of new treatment strategies, as well as facilitating a deeper understanding of why they fail,” Anderson said.

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