Thursday, January 02, 2020

Dengue virus becomes resistant to certain vaccines


While searching for a new approach in vaccine development for dengue, researchers discovered that the dengue virus changes its shape through mutations in envelope protein to evade vaccines and therapeutics.

DENV2 (a dengue virus) exists as smooth spherical surface particles while growing at the mosquito's physiological temperature (29 degrees Celsius). It then changes to bumpy surfaced particles at human physiological temperature (37 degrees Celsius).

This ability to morph helps the virus to evade the immune system of the human host. Hence, understanding the mechanism behind this is important for therapeutics and vaccine development, reported the study.

"Together with Professor Pei-Yong Shi from UTMB, we found that in laboratory-developed DENV2 strains, mutations in the virus' E protein causes its transformation into bumpy particles. These structural changes can cause vaccines and therapeutics to be ineffective against the virus," said Ms Xin-Ni Lim, the study's lead author who is from Duke-NUS' Emerging Infectious Diseases (EID) Programme.

The team also tested four DENV2 strains obtained from patients. They observed that in contrast to the laboratory-adapted viruses, the majority of these clinical strains maintained smooth surface structure at 37 degrees Celsius.

However, at 40 degrees Celsius, the temperature of a fever, all virus strains took on a bumpy surface.

"Our study gives a new direction to vaccine development and treatment for dengue disease. For prevention of disease through vaccines that are administered to the patient before dengue infection, we should use those that are effective against the smooth surface virus," said Dr Sheemei Lok, Professor, Duke-NUS' EID and corresponding author of this study.

"When it comes to patients displaying fever symptoms, treatment strategies effective against the bumpy surface particles should be implemented," added Dr Lok.

"This study is a first step towards gaining more insight into how DENV2 reacts and adapts to the host's immunological defences. We were also able to use computational modelling approaches to predict why particles from different DENV2 strains are more or less adept at morphing from the smooth to bumpy structures. By better understanding the interactions between the virus and the host, we will be able to develop better therapies and vaccines to treat or prevent infections, and contribute to public health outcomes," said Dr Peter Bond, Principal Investigator from A*STAR's BII.

The study's findings also show that the lab adapted DENV2 may not be a good model for research, as its structure is different from the clinical strains isolated from patients. The team is planning to study the other DENV serotypes to find out if there are any other possible structural changes.


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Friday, April 05, 2019

New method better predicts tumour growth rate in 12 cancers

In a new research, scientists have come up with a new formula that predicts tumour growth rate more accurately, a crucial statistic used to schedule screenings and set dosing regimens in cancer treatment.

The mathematical method successfully estimated the doubling time, the amount of time for a tumour to double in size, for 12 types of cancer, ranging from breast and prostate cancers to melanoma.

"This novel method allows clinicians and drug development scientists to use routinely-generated clinical data to infer doubling times of solid tumours. This parameter can be used to design individualised dosing regimens and develop reliable models for anticancer therapeutics," said the Dr.


Tumour doubling time can significantly affect the outcome of anticancer therapy, but the rate is challenging to determine. Current methods calculate doubling time by measuring the size of a tumour at two points in time and assuming cancer will grow at an exponential rate.

However, most doubling times calculated using this method are overestimated, and tiny changes in tumour size can make determining growth rates difficult.

The error impacts the ability of clinicians to schedule optimal follow-up screenings, set effective dosing regimens, and determine whether surgery, chemotherapy or radiation therapy  is the best form of treatment.
 

The researchers instead base their method on data extracted from progression-free survival  plots - the length of time during and after treatment that cancer does not grow or spread.

Progression-free survival plots explained the Dr., inherently contain information that could help identify tumour growth rates.

The investigators examined data from 47 clinical trials that reported plots for any of 12 cancer types: melanoma; pancreatic, lung, prostate, gastric, colorectal and three forms of breast cancer; hepatocellular (liver) and renal cell (kidney) carcinoma; and glioblastoma multiforme (brain).

The cancer growth rates predicted by the researchers using progression-free survival plots were within close range to the reported actual tumour doubling times. 


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