Mathematical model identifies effective drug combinations for non-small cell lung cancer

Researchers at Houston Methodist have developed an advanced mathematical model that predicts how novel treatment combinations could significantly extend progression-free survival in patients with non-small cell lung cancer (NSCLC), the most common type of lung cancer.

Using advanced mathematical modeling, a team led by Prashant Dogra, Ph.D., and Zhihui “Bill” Wang, Ph.D., of the Mathematics in Medicine Program at the Houston Methodist Research Institute, expanded the MD Anderson Cancer Center's initial research into the Anti-miR-155 molecule in mice. Dogra and Wang examined the clinical potential of anti-miR-155 – a small RNA molecule – in simulated patients and identified novel drug combinations that could significantly improve treatment effectiveness and progression-free survival.

MicroRNA-155 (miR-155) is known to play a critical role in worsening treatment outcomes in NSCLC by contributing to drug resistance and immunosuppression. In particular, increased levels of miR-155 may contribute to tumors escaping immune recognition and reduce the effectiveness of standard therapies such as chemotherapy and immunotherapy. To combat this, researchers have attempted to use a synthetic therapeutic molecule called anti-miR-155 to neutralize the negative effects of miR-155.

“In this way, we increase the effectiveness of current standard treatments such as cisplatin and immune checkpoint inhibitors, ultimately leading to improved survival rates as shown in our model,” said Wang. “By neutralizing the overactivity of miR-155, we can restore balance in the immune system and improve the effectiveness of cancer treatments.”

Chemotherapy, immunotherapy and anti-miR-155 therapy can be considered different yet complementary approaches to treating non-small cell lung cancer, Wang added.

The researchers calibrated their computational model with preclinical data from the MD Anderson Cancer Center lab of George Calin, MD, Ph.D., using information from mouse studies that provided real biological data on how anti-miR-155 works in the body including effects on tumor growth and drug resistance.

This allowed them to refine their mathematical model to ensure that it accurately represented the relevant biological processes. They modified the model for use in humans by taking into account differences between species, such as body size and metabolism, to simulate and predict how the treatment might work in humans.

Due to significant biological differences, there is typically uncertainty when transitioning from animal studies to clinical trials. However, Wang, Dogra and their team's mathematical model helps address this problem by providing insights through extensive computer simulations into how the treatment might work in different human patients, predicting outcomes such as progression-free survival and identifying the best drug combinations.

“By using a combination of in vivo data from animal studies and advanced mathematical modeling to predict how the therapy would work in humans, this work closes the critical gap between the preclinical development and clinical translation of anti-miR-155 and “We are testing this treatment in humans,” Dogra said. “This approach provides a solid foundation for designing more effective clinical trials and helps accelerate the process, making the transition from preclinical to clinical testing more efficient and targeted.”

Their next steps will focus on further preclinical testing to confirm the safety and effectiveness of anti-miR-155 therapy in combination with standard drugs before moving on to human trials.

“Our approach to combining mathematical modeling with therapeutic development could revolutionize the way we deliver new cancer treatments to patients,” said Wang. “This goes beyond non-small cell lung cancer. It could accelerate treatment development for many types of cancer.”

The method and results of this study are described in an article titled “Translational Modeling-Based Evidence for Improved Efficacy of Standard Drugs in Combination with Anti-microRNA-155 in Non-Small Cell Lung Cancer,” which appeared in the Journal last month Molecular cancer.

Dogra and Wang are the corresponding authors of the study, and their collaborators were Vrushaly Shinglot, Javier Ruiz-Ramírez, Joseph Cave, Joseph D. Butner, Carmine Schiavone, Dan G. Duda, Ahmed O. Kaseb, Caroline Chung, Eugene J. Koay , Vittorio Cristini, Bulent Ozpolat and George A. Calin.