New Computer Model May Be Used to Find Medicines for Sickle Cell Disease Patients
A new computer model is able to simulate how red blood cells become misshapen in patients with sickle cell disease. The model may be useful in identifying which medicines could be beneficial for preventing the development of sickled cells.
The study about that research, “Quantitative prediction of erythrocyte sickling for the development of advanced sickle cell therapies,” was published recently in the journal Science Advances.
While normally round, red blood cells in patients with sickle cell disease become stiff and sickle-shaped (similar to a crescent moon).
As the red blood cells become misshapen, hemoglobin — the protein responsible for carrying oxygen throughout the body — gets clumped inside the cell as it becomes deprived of oxygen.
Sickled red blood cells tend to stick together and the clumps that form become stuck in blood vessels, leading patients with sickle cell disease to suffer from pain (vaso-occlusive crisis), swelling, and strokes.
Furthermore, while current therapies are designed to target the downstream effects of the sickling process, there are no medicines that try to stop the process itself. “Few drug candidates aim at inhibiting HbS polymerization and sickling of RBCs [red blood cells], the root cause of SCD,” the authors wrote.
A group of inter-disciplinary researchers from Brown University in Providence, Rhode Island, set out to develop a computer program that can simulate how the cell sickling process occurs, and can be used to screen new therapy candidates.
“We wanted to build a model that considers the entire sickling process and could be used to quickly and inexpensively pre-screen new drug candidates,” Lu Lu, a doctoral student at Brown and the study’s co-lead author, said in a press release.
Using previously established models, researchers were able to combine and simplify them to create a single model that contains all the important information regarding the blood sickling process.
The researchers also took into account how blood sickles in different organs. For example, red blood sickles differently in organs that have high amounts of oxygen (such as the lungs) and low amounts of oxygen (such as the kidneys).
People who use the new computer model will be able to specify the organ they are trying to simulate, as well as the degree of severity of the disease for a specific patient.
The model then can be used to predict whether a drug will be effective by allowing users to include its mechanism of action. The model will take this information into account and determine the effect of the drug on a large population of red blood cells.
“Sometimes a drug can be designed to work on one parameter, but ends up having a different effects on other parameters,” said George Karniadakis, PhD, a professor at Brown and senior author of the study. “The model can tell if those effects are synergistic or whether they may negate each other. So the model can give us an idea of the overall effect of the drug.”
The model also can be used to provide direction for the drug dosage based on patient-specific data, which can help reduce the side effects of these drugs.
In order to validate the model, researchers used it to see if it could predict the outcomes that were achieved previously from laboratory and animal experiments conducted by lab members. The model returned results that were consistent with those obtained from recent screening experiments.
The authors of the study concluded that “Together, the proposed kinetic model is capable of predicting RBC sickling based on patient- and organ-specific data and thus can be used to guide the prognosis for SCD [sickle cell disease] patients with various degrees of severity.”
Researchers hope the model will be beneficial in finding potential therapy candidates.
“Clinical drug trials are very expensive and the vast majority of them are unsuccessful,” said Karniadakis. “The hope here is that we can do in silico [computerized] trials to screen potential medications before proceeding to a clinical trial.”