Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs

Molecules

Russell A. Norris¹  Jordan E. Morningstar¹  Andrew Stoddard¹  Tyler C. Beck¹  Thomas A. Dix²  Patrick Woster²  Catherine Mills²  Yuri Peterson²  Kelsey Moore³  Le Mai³  Diana Fulmer³  Kristina Stayer³  Cortney Gensemer³  Rachel Biggs³  Natalie Koren³  Sarah Dooley³  Ayesha Vohra³  Taylor Petrucci³  Lilong Guo³  Jaclyn Dunne³  Julianna Weninger³  Kendra Springs³  Jennie Kwon³ 

[1] College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA;Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA;Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC 29425, USA;

关键词: MEK1;    machine learning;    toxicity;    cancer;    drug discovery;    drug development;   

DOI  :  10.3390/molecules27123678

来源: DOAJ

【 摘 要 】

Cancer is the second most common cause of death in the United States, accounting for 602,350 deaths in 2020. Cancer-related death rates have declined by 27% over the past two decades, partially due to the identification of novel anti-cancer drugs. Despite improvements in cancer treatment, newly approved oncology drugs are associated with increased toxicity risk. These toxicities may be mitigated by pharmacokinetic optimization and reductions in off-target interactions. As such, there is a need for early-stage implementation of pharmacokinetic (PK) prediction tools. Several PK prediction platforms exist, including pkCSM, SuperCypsPred, Pred-hERG, Similarity Ensemble Approach (SEA), and SwissADME. These tools can be used in screening hits, allowing for the selection of compounds were reduced toxicity and/or risk of attrition. In this short commentary, we used PK prediction tools in the optimization of mitogen activated extracellular signal-related kinase kinase 1 (MEK1) inhibitors. In doing so, we identified MEK1 inhibitors with retained activity and optimized predictive PK properties, devoid of hERG inhibition. These data support the use of publicly available PK prediction platforms in early-stage drug discovery to design safer drugs.

【 授权许可】

Unknown