PhD student honored with PhRMA Foundation Trainee Challenge Award

The PhRMA Foundation’s Challenge Awards are competitions that invite researchers to submit papers or research proposals addressing pressing issues in research and health care.

Bhat received a 2025 Trainee Challenge Award on Artificial Intelligence (AI) and Machine Learning in Clinical and Translational Science. The award was one of six presented jointly by the PhRMA Foundation and the American Society for Clinical Pharmacology and Therapeutics as part of a Clinical and Translational Science special call exploring how AI and machine learning are transforming drug development. As the first author on one of the recognized papers, Bhat earned a $5,000 prize and coverage of the article’s publication fee.

“Receiving this award is an honor and motivates me to continue investigating how AI and data science can enhance drug development and improve human health,” says Bhat.

The paper, ‘Network Modeling of Biomarker Systems in Liver Steatosis and Fibrosis,’ was authored by Bhat and corresponding author Murali Ramanathan, PhD, professor of pharmaceutical sciences. It examines metabolic dysfunction-associated fatty liver disease, which results in the accumulation of fat (steatosis), inflammation and scarring (fibrosis) of liver tissue, a condition affecting roughly one in four people in the United States. This condition is often asymptomatic but can progress to more serious conditions such as cirrhosis and cancer.

Bhat and Ramanathan studied the effects of liver fat accumulation and scarring, which can be measured non-invasively using an ultrasound elastography, on drug metabolism. Since the liver is critical for breaking down drugs so they can be cleared from the body, they reasoned that elastography measures of liver fat and scarring could help guide personalized drug dosing. Using a combination of AI and statistical methods, the team identified several key physiological factors associated with greater liver steatosis and fibrosis, including age, obesity, liver enzyme levels and proteins linked to chronic inflammation. They developed a model showing how different physiological processes interact and contribute to fat buildup and scarring in the liver, which may enable clinicians to utilize elastography measures to optimize treatments for patients with liver disease.

The research was conducted in the new Laboratory for AI and Clinical Pharmacology in the Department of Pharmaceutical Sciences, directed by Ramananthan, with Bhat as a member of the research team. Affiliated with the UB Institute for AI and Data Science, the lab applies AI to analyze complex biomedical data with the goal of improving drug development for diseases.

Bhat intends to continue her dissertation research exploring the application of AI methods to advance precision medicine and expressed her sincere appreciation to Ramananthan for his mentorship and guidance.