In-silico Design and Validation of Novel Lactate Dehydrogenase Inhibitors for Cancer Therapy

Abstract

Lactate is an important metabolite in both cancer and non-cancer cells. A class of enzymes known as lactate dehydrogenases, LDHs, allows cancer cells to transform excess pyruvate into lactate in anaerobic respiration, and then reconvert lactate into pyruvate for ATP production. Lactate has been shown to mediate immunosuppression and metabolic rewiring in tumors, accelerating tumor progression. Thus, developing new effective therapeutics against the action of LDH is critical to choking the Warburg effect in various types of cancer. We use the DepMap portal to identify LDH isoforms LDHA and LDHB as potential targets for cancer therapy and show that the LDHA target has a high predictive power for prognosis across many different cancers. We then use in silico techniques, such as virtual screening, pharmacophore modeling, protein structure analysis, and inhibitor selectivity analysis to design novel and effective small molecules that can inhibit both LDHA and LDHB, potentially addressing lactate-mediated immunosuppression and tumorigenesis. This study pushes forward an in-silico-centered pre-clinical ligand-based drug discovery pipeline to create optimal results in short amounts of time obviating the need for traditional expensive trial-and-error methods.