▲ ▼ Personalised medicine for depression
It is not just that depression is hard, treating depression itself is very hard. Due to number of variables for the occurrence of depression like environment, genetics and numerous other factors in the brain; most common treatments available for depression don't work for everyone.
Patients with depression often have to undergo, long cycles of medication to see if the particular treatment has any positive feedback. Current treatment for depression like SRI/SNRI medications do work for many after several weeks but if it doesn't then the long course of medications, usually with side-effects are useless.
There is a need gap for personalised medicine for depression, where the patient can be diagnosed fast, easily and accurate medication be prescribed immediately instead of current trial & error type of depression treatments.
A drug that increases dopamine can reverse the effects of inflammation on the brain in depression, Emory study shows - https://news.emory.edu/stories/2023/01/som_bhc_inflammation_felger/story.html
Stanford team has successfully demonstrated the use of AI to predict effective depression treatment based on brainwave patterns, if this approach gets adopted in mainstream medicine then it can reduce the need for trial & error type approach for depression treatments.
Researchers are working to develop personalised medicine to treat depression, first step being the biomarkers(something which doctors can reliably measure which tells them about illness e.g. pulse, hormone etc.) to identify depression.
One leading group in this area is CAN-BIND (CANADIAN BIOMARKER INTEGRATION NETWORK IN DEPRESSION), they are trying to find biomarker (gene, size of certain brain area) for depression. If a biomarker for particular depression is found, then doctors can do a blood test and suggest effective anti-depressant treatment i.e. tying a specific drug to that depression. This is still very nascent area of research.
There is need gap for personalised medicine for depression, startups working on biomarkers could potentially solve this need gap. It would be interesting to hear from any biotech startups working in this area.