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

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 appr...

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Autores principales: Beck, Tyler C., Springs, Kendra, Morningstar, Jordan E., Mills, Catherine, Stoddard, Andrew, Guo, Lilong, Moore, Kelsey, Gensemer, Cortney, Biggs, Rachel, Petrucci, Taylor, Kwon, Jennie, Stayer, Kristina, Koren, Natalie, Dunne, Jaclyn, Fulmer, Diana, Vohra, Ayesha, Mai, Le, Dooley, Sarah, Weninger, Julianna, Peterson, Yuri, Woster, Patrick, Dix, Thomas A., Norris, Russell A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227314/
https://www.ncbi.nlm.nih.gov/pubmed/35744803
http://dx.doi.org/10.3390/molecules27123678
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author Beck, Tyler C.
Springs, Kendra
Morningstar, Jordan E.
Mills, Catherine
Stoddard, Andrew
Guo, Lilong
Moore, Kelsey
Gensemer, Cortney
Biggs, Rachel
Petrucci, Taylor
Kwon, Jennie
Stayer, Kristina
Koren, Natalie
Dunne, Jaclyn
Fulmer, Diana
Vohra, Ayesha
Mai, Le
Dooley, Sarah
Weninger, Julianna
Peterson, Yuri
Woster, Patrick
Dix, Thomas A.
Norris, Russell A.
author_facet Beck, Tyler C.
Springs, Kendra
Morningstar, Jordan E.
Mills, Catherine
Stoddard, Andrew
Guo, Lilong
Moore, Kelsey
Gensemer, Cortney
Biggs, Rachel
Petrucci, Taylor
Kwon, Jennie
Stayer, Kristina
Koren, Natalie
Dunne, Jaclyn
Fulmer, Diana
Vohra, Ayesha
Mai, Le
Dooley, Sarah
Weninger, Julianna
Peterson, Yuri
Woster, Patrick
Dix, Thomas A.
Norris, Russell A.
author_sort Beck, Tyler C.
collection PubMed
description 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.
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spelling pubmed-92273142022-06-25 Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs Beck, Tyler C. Springs, Kendra Morningstar, Jordan E. Mills, Catherine Stoddard, Andrew Guo, Lilong Moore, Kelsey Gensemer, Cortney Biggs, Rachel Petrucci, Taylor Kwon, Jennie Stayer, Kristina Koren, Natalie Dunne, Jaclyn Fulmer, Diana Vohra, Ayesha Mai, Le Dooley, Sarah Weninger, Julianna Peterson, Yuri Woster, Patrick Dix, Thomas A. Norris, Russell A. Molecules Communication 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. MDPI 2022-06-08 /pmc/articles/PMC9227314/ /pubmed/35744803 http://dx.doi.org/10.3390/molecules27123678 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Beck, Tyler C.
Springs, Kendra
Morningstar, Jordan E.
Mills, Catherine
Stoddard, Andrew
Guo, Lilong
Moore, Kelsey
Gensemer, Cortney
Biggs, Rachel
Petrucci, Taylor
Kwon, Jennie
Stayer, Kristina
Koren, Natalie
Dunne, Jaclyn
Fulmer, Diana
Vohra, Ayesha
Mai, Le
Dooley, Sarah
Weninger, Julianna
Peterson, Yuri
Woster, Patrick
Dix, Thomas A.
Norris, Russell A.
Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title_full Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title_fullStr Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title_full_unstemmed Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title_short Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs
title_sort application of pharmacokinetic prediction platforms in the design of optimized anti-cancer drugs
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227314/
https://www.ncbi.nlm.nih.gov/pubmed/35744803
http://dx.doi.org/10.3390/molecules27123678
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