Pharmacogenetics of Major Depressive Disorder (MDD): Progress in Two Serotonergic Targets
Bibliographic record
Abstract
Major depressive disorder (MDD) is a multifaceted, debilitating condition affecting over 300 million people worldwide. It contributes significantly to social, psychological and economic burdens on individuals and on society at large. Currently, the most widely prescribed antidepressant medications are selective serotonin reuptake inhibitors (SSRIs), which act by blocking serotonin (5-HT) reuptake into presynaptic neurons, thereby increasing the extracellular 5-HT concentration in the brain. However, response to SSRIs and other psychotropic medications used to treat depression is highly variable, with only about one third of patients responding to treatment with an SSRI. This may reflect, at least in part, the genetic heterogeneity of depressed individuals. Studies investigating the genetic components of depression aim to improve treatment outcomes and possibly pave the way for personalized medicine in which the first medication prescribed is the one most likely to result in remission. This review presents the results of several studies on two 5-HT related genes: SLC6A4 and HTR2A, which encode for the serotonin transporter and the serotonin-2A receptor, respectively. Extensive studies have demonstrated that possessing two copies of the long allele (L/L) of the SLC6A4 gene can predict better responses to the SSRI Escitalopram. However, this finding was significant only in the Caucasian population. In addition to this, several single nucleotide polymorphisms in the HTR2A gene also predict clinical outcome, although molecular mechanisms remain unclear. Hence, the results indicate that while there is significant potential for predicting treatment response associated with these and other genetic targets, there is much work left to be done to establish conclusive evidence for and feasibility of pharmacogenetic testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".