Nav: Home

Newly developed mathematical model could be used to predict cancer drug side effects

December 20, 2019

A research team at Kobe University Hospital have further illuminated the likelihood of cancer drug side effects that can occur due to genetic mutations in the drug-metabolizing enzyme. The team led by Dr. TAKAOKA Yutaka also developed a mathematical model by using the results of molecular simulation analyses to predict the possibility of side effects.

It is hoped that this research will pave the way for effective predictions of cancer drug side effects and treatment results.

These research findings were first published in the American Scientific Journal 'PLOS ONE' on November 15 2019.

Research Background

Predictions regarding cancer treatment effectiveness and side effects can be made relating to 1. Drug metabolism and 2. Drug effectiveness on administration. However, how well drugs will be metabolized, their effectiveness and the likelihood of side-effects depends on individual differences. For example, before a patient with colon cancer is treated with the anti-cancer drug Irinotecan, a genetic analysis of their UGT1A1 must be performed. UGT1A1 is an enzyme found mainly in the liver which is responsible for processing many chemical substances, including Irinotecan. It is known that the patient with mutations in the UGT1A1 gene (in particular the mutations UGT1A1*6 and UGT1A1*28) have difficulty metabolizing this cancer drug, making severe side effects.

In recent years, genetic analysis technology has been advancing and new mutations in UGT1A1 are being discovered. To date, around 70 different mutations have been found. The ability of each of these newly discovered mutations to metabolize drugs is unknown, therefore it is difficult to accurately determine the likelihood of adverse reactions to anti-cancer agents.

Research Methodology

Professor Takaoka et al. used the results from molecular computer simulation analyses and wet laboratory experiments (using cells) to develop the following mathematical model for drug metabolism by the UGT1A1 (Figure 1).

They succeeded in using this mathematical model to predict the ability of UGT1A1 mutants to metabolize the anti-cancer agent with high accuracy- as shown in the bar graph (Figure 2). The predictions using the mathematical equation (gray bars) are very similar to the actual results (black bars).

Based on these results, this method was able to predict the drug metabolizing ability of UGT1A1 mutations. It is hoped that this methodology could be used to predict the possibility of cancer drug side-effects before they are prescribed- even for newly discovered mutations of UGT1A1.

Further Research

It is expected that further research using a similar methodology could be utilized to predict cancer drug effectiveness. Professor Takaoka et al. have already used RIKEN's K-computer to perform a basic analysis and they are currently working towards being able to predict the effectiveness of drugs utilized in lung cancer treatment.
-end-


Kobe University

Related Cancer Drug Articles:

Major trial shows breast cancer drug can hit prostate cancer Achilles heel
A drug already licensed for the treatment of breast and ovarian cancers is more effective than targeted hormone therapy at keeping cancer in check in some men with advanced prostate cancer, a major clinical trial reports.
New opportunity for cancer drug development
After years of research on cell surface receptors called Frizzleds, researchers at Karolinska Institutet in Sweden provide the proof-of-principle that these receptors are druggable by small molecules.
Choosing the right drug to fight cancer
Biochemists at Université de Montreal discover a new mechanism to better predict whether an anti-cancer therapy will work.
Yale study identifies how cancer drug inhibits DNA repair in cancer cells
According to researchers at Yale Cancer Center, a cancer drug thought to be of limited use possesses a superpower of sorts: It is able to stop certain cancer cells from repairing their DNA in order to survive.
Testing cells for cancer drug resistance
Biophysicists at Ruhr-Universität Bochum (RUB) have demonstrated that Raman microscopy can be used to detect the resistance of tumour cells to cancer drugs.
ALS drug may help treat prostate cancer
Researchers have discovered a new use for an old drug as a potential treatment for prostate cancer.
Researchers find prostate cancer drug byproduct can fuel cancer cells
A genetic anomaly in certain men with prostate cancer may impact their response to common drugs used to treat the disease, according to new research at Cleveland Clinic.
Ovarian cancer drug shows promise in pancreatic cancer patients with BRCA mutation
A targeted therapy that has shown its power in fighting ovarian cancer in women including those with BRCA1 and BRCA2 mutations may also help patients with aggressive pancreatic cancer who harbor these mutations and have few or no other treatment options.
New lab study reveals how breast cancer drug can accelerate cancer cell growth
The breast cancer drug lapatinib which is designed to shrink tumors can sometimes cause them to grow in the lab, according to a new study.
Deep learning predicts drug-drug and drug-food interactions
A Korean research team from KAIST developed a computational framework, DeepDDI, that accurately predicts and generates 86 types of drug-drug and drug-food interactions as outputs of human-readable sentences, which allows in-depth understanding of the drug-drug and drug-food interactions.
More Cancer Drug News and Cancer Drug Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Processing The Pandemic
Between the pandemic and America's reckoning with racism and police brutality, many of us are anxious, angry, and depressed. This hour, TED Fellow and writer Laurel Braitman helps us process it all.
Now Playing: Science for the People

#568 Poker Face Psychology
Anyone who's seen pop culture depictions of poker might think statistics and math is the only way to get ahead. But no, there's psychology too. Author Maria Konnikova took her Ph.D. in psychology to the poker table, and turned out to be good. So good, she went pro in poker, and learned all about her own biases on the way. We're talking about her new book "The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win".
Now Playing: Radiolab

Invisible Allies
As scientists have been scrambling to find new and better ways to treat covid-19, they've come across some unexpected allies. Invisible and primordial, these protectors have been with us all along. And they just might help us to better weather this viral storm. To kick things off, we travel through time from a homeless shelter to a military hospital, pondering the pandemic-fighting power of the sun. And then, we dive deep into the periodic table to look at how a simple element might actually be a microbe's biggest foe. This episode was reported by Simon Adler and Molly Webster, and produced by Annie McEwen and Pat Walters. Support Radiolab today at Radiolab.org/donate.