UNVEILING MISSED OPPORTUNITIES: EXPLORING THE INTERPLAY OF CLINICAL OBSERVATIONS AND PHARMACOGENOMIC INSIGHTS IN A NON- VERBAL, NEURODEVELOPMENTALLY COMPLEX AUTISTIC ADULT
Bibliographic record
Abstract
Abstract Background Neurodevelopmental disorders (NDD) are chronic, heterogeneous conditions often coexisting with various psychiatric disorders. It is thought that many individuals in a general outpatient psychiatric department may have undiagnosed NDD.[1] The complexity of NDD arises from intricate interactions between genetic and environmental factors, contributing to a diverse and medically complex population.[2] Managing individuals with NDD presents challenges for medical professionals due to the varied neurophysiological sensitivities to medications, leading physicians to rely on clinical experience and symptom observation. This case report illustrates the condition of a 25-year-old non-verbal male diagnosed with severe Autism Spectrum Disorder (ASD), residing in a specialized group home with 24- hour care. Notably, the patient, identified with a rare GABA-B receptor mutation through whole exome sequence analysis, exhibited daily escalating self-directed and aggressive behaviors, including head- banging, grabbing, hitting, and self-induced vomiting. Aims and Objectives The purpose of the case report is to examine the pharmacogenomic test results in the context of NDD, specifically addressing the barriers that exist in interpreting the test results in this patient population. The second objective of this study is to comment on potentially missed interventions stemming from barriers,[3]and examine whether pharmacogenomic testing should be considered an early investigation in the context of NDD. Methods The patient described in the case report was hospitalized and the Genecept 2.0 assay from Dynacare in Canada was used for the pharmacogenomic testing. Results Polymorphisms in the following genes were identified: SLC6A4 (5-HTTLPR), DRD2 (rs1799732), MTHFR (C677T), CYP1A2 (1F/1F genotype), CYP2B6 (*6/*6 genotype). The patient gradually improved with resolution of infectious processes. A poor response to dopamine antagonists was also observed. Discussion and Conclusion This case report demonstrates that pharmacogenomic testing accurately predicted the lack of response to D2 antagonists observed clinically. Genetic results concerning the serotonin reuptake transporter polymorphism align with current ASD literature. Additionally, it suggests that potential interventions such as supplementing with tryptophan and addressing deficient folate metabolism cascade in childhood or prospectively may have been overlooked, considering their links with ASD. The comprehensive case management underscores the under-utilization of pharmacogenomic testing despite its potential to guide early treatment for neurodevelopmentally complex patients and prevent injurious outcomes. In summary, pharmacogenomics should be considered an early intervention in cases of neurodevelopmental disorders. References [1]Francé s et al. (2022) Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: a systematic review in accordance with the PRISMA criteria. Child Adolesc Psychiatry Ment Health 16:27. [2]Parenti et al. (2022) Neurodevelopmental Disorders: From Genetics to Functional Pathways. Trends in Neurosciences 43:608-621. [3]Jameson et al. (2021). What Are the Barriers and Enablers to the Implementation of Pharmacogenetic Testing in Mental Health Care Settings? Frontiers in Genetics 12.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".