As a part of our aim to implement business in science, synthetic biology is a promising way to do so. As for our project’s journey to treat Alzheimer’s, we went through some ways to achieve a business plan related to our work. Alzheimer’s Disease causes 60% to 70% of cases of dementia, usually starting after age 65. However, other cases of Alzheimer’s Disease can be at younger ages. At the same time, The risk factors and some symptoms can hint at it. Hence, it is preferred to do a periodic medical examination. However, if they discover a case, there is no cure to treat AD. Current medications can reduce or prevent the symptoms without a certain solution to the main disorder. So, in Zekra-ذكرى project, we tried to eliminate the problem as we described in the wiki.
Looking from a business aspect, we assumed a long-term and short-term business model. It’s a prolonged process to achieve our idea to be a commercial medicine that can be used for every patient. And this is because of many regulations, as mentioned on the
Almost all Alzheimer’s patients get diagnosed in late stages. Accordingly, treatment protocol becomes harder. So we thought about a diagnostic tool (Smartwatch software) as an option that can be added to smartwatches to detect disease markers. Also, regarding our dry lab work, we made a software tool based on our pipeline of protein modeling.
Short-term plan:
Regarding our project, we tried to define a tool to approach the prognosis of Alzheimer’s disease,
whereas Alzheimer’s severity escalates if not detected at its early stages. In addition, symptoms only appear
at their mid or even late stages, at which various neural and perceptional functions have already been diminished.
Thus, the prognosis would be very beneficial to help protect patients at risk of developing the disease. Many approaches
are currently used on the molecular level. Molecular tests focus on detecting genetic risk factors that directly
correlate to Alzheimer’s, but these approaches mainly rely on advanced molecular techniques.
Meanwhile, tests for diagnosis and stage identification include brain imaging using MRI and PET scans or
lab tests that require patient cerebrospinal fluid (CSF) samples. However, the previously mentioned
approaches are invasive and not cost/time effective, so patients would not go for any of them unless
they start developing actual symptoms and health issues.
Therefore, our idea was to develop an easy,
portable device that could be available for everyone to use for at-home diagnosis. The availability
and small cost would spread awareness and encourage people to keep track of Alzheimer’s risk factors
and protect themselves from the consequences of developing such a fatal disease. Smartwatches are
currently used to track various health-related information, like heart rates and oxygen levels.
It mainly depends on a type of signal (light, sound, heat) detected by the watch and transformed
into a digital reading for the user to see. In our case, many blood biomarkers for Alzheimer’s were discovered,
and the concentration of those peptides circulating in the blood should indicate whether the user is at risk or not.
Labeling those peptides by a light-emitting probe would provide us with the signal needed for detection; the intensity
of the light signal would be directly proportional to the peptide levels in the blood.
Over our dry lab work journey, we went through a systematic pipeline to reach an optimization
for each part of our system. We optimized a protocol for our work to model proteins, rank
these models according to structure assessment, dock them, calculate the energy of the structures,
and run MD simulations. However, this pipeline was divided into a lot of tools and software.
So firstly,
we tried to write our codes for each tool to do more than one job at once. Then, we decided to
connect all these codes to make multi-protein jobs simultaneously. We present ‘software name’ to you
as software can do a long pipeline of protein tools at once, starting with a structural assessment of
your models and ranking them, then docking chosen models and ranking them after calculating binding
energy in addition to the MD simulation feature. From just a PDB file, you can reach most of the needed
results in your dry lab work.
The Short-term plan has been made to fund our long term-plan to reach the cure of Alzheimer’s as an achievement of our theory.
Long-term plan:
As we explained our project on the wiki, we are aiming to treat Alzheimer’s, not just reduction of the symptoms.
It will take a long time of research and trials to implement our treatment system. However, we made a canvas model
for the cure as a commercial plan for our project.
