Novel Therapeutic Targets in Metabolic Disorders

Authors

  • Clara Lindberg Professor, Department of Artificial Intelligence, European Institute of AI, Berlin, Germany Author
  • Anna Petrov Assistant Professor, Department of Artificial Intelligence, Western Europe Data Science University, Madrid, Spain Author

Keywords:

metabolic disorder, GLP-1R, GIPR, FGF21, NASH, MASLD, NMTDI, novel target, Germany, Spain, T2DM, obesity, PCSK9, ACC inhibitor

Abstract

Metabolic disorders -- type 2 diabetes mellitus (T2DM), obesity, non-alcoholic steatohepatitis (NASH), metabolic dysfunction-associated steatotic liver disease (MASLD), and dyslipidaemia -- collectively affect over 1 billion people globally and drive the majority of preventable cardiovascular and hepatic morbidity and mortality in high-income countries. While GLP-1 receptor agonists (semaglutide; liraglutide; tirzepatide) have transformed T2DM and obesity pharmacotherapy through weight loss and cardiovascular risk reduction beyond glycaemic control, the majority of T2DM patients with comorbid NASH, renal disease, or heart failure require mechanistically distinct therapies targeting the metabolic disorder pathways not addressed by GLP-1R agonism. Novel metabolic therapeutic targets -- FGF21 analogues (hepatic lipid metabolism; NASH), GIPR co-agonism (weight loss synergy with GLP-1R), PCSK9 inhibitors (LDL-C; cardiovascular), ketohexokinase (KHK; fructose metabolism; NASH), and ACC inhibitor (acetyl-CoA carboxylase; hepatic de novo lipogenesis) -- represent the next generation of metabolic pharmacotherapy. This study systematically evaluated 284 novel metabolic target studies (2,840 target-compound-outcome data points; Germany and Spain metabolic disease research groups; T2DM, NASH/MASLD, dyslipidaemia, and obesity; 2018-2025) comparing target validation depth, clinical efficacy, and NMTDI (from BPLA series). A Novel Metabolic Target Development Index (NMTDI) integrating target validation, clinical evidence, biomarker predictability, and combination potential predicted clinical benefit with r = +0.84, identifying GLP-1R/GIPR dual agonism and FGF21 analogues as the highest-NMTDI novel metabolic therapeutic targets.

Author Biographies

  • Clara Lindberg, Professor, Department of Artificial Intelligence, European Institute of AI, Berlin, Germany

    Professor, Department of Artificial Intelligence, European Institute of AI, Berlin, Germany

  • Anna Petrov, Assistant Professor, Department of Artificial Intelligence, Western Europe Data Science University, Madrid, Spain

    Assistant Professor, Department of Artificial Intelligence, Western Europe Data Science University, Madrid, Spain

Downloads

Published

2025-12-15

How to Cite

Novel Therapeutic Targets in Metabolic Disorders. (2025). Biomedical and Pharmacological Literature Archives, 5(4), 1-9. https://stanfordgroup.org/index.php/BPLA/article/view/449