BACKGROUND: Autistic spectrum disorders (ASD) are severe neurodevelopmental alterations characterised by deficits in social communication and repetitive and restricted behaviours. About a third of patients receive pharmacological treatment for comorbid symptoms. However, 30–50% do not respond adequately and/or present severe and long-lasting side effects. METHODS: Genetic variants in CYP1A2, CYP2C19, CYP2D6 and SLC6A4 were investigated in N = 42 ASD sufferers resistant to pharmacological treatment. Clinical recommendations based on their pharmacogenetic profiles were provided within 24–48 h of receiving a biological sample. RESULTS: A total of 39 participants (93%) improved after the pharmacogenetic intervention according to their CGI scores (difference in basal-final scores: 2.26, SD 1.55) and 37 participants (88%) according to their CGAS scores (average improvement of 20.29, SD 11.85). Twenty-three of them (55%) achieved symptom stability (CGI ≤ 3 and CGAS improvement ≥ 20 points), requiring less frequent visits to their clinicians and hospital stays. Furthermore, the clinical improvement was higher than that observed in a control group (N = 62) with no pharmacogenetic interventions, in which 66% responded to treatment (difference in CGI scores: −0.87, SD 9.4, p = 1 × 10−5; difference in CGAS scores: 6.59, SD 7.76, p = 5 × 10−8). CONCLUSIONS: The implementation of pharmacogenetic interventions has the potential to significantly improve the clinical outcomes in severe comorbid ASD populations with drug treatment resistance and poor prognosis.

We present g-Nomic, a pharmacogenetics interpretation software that analyzes globally a prescribed medication taking into account the personal background genetics, drug–drug interactions, lifestyle, nutritional supplements, inhibitors, inducers, and other risks to analyze primary or secondary metabolism pathways. G-Nomic provides a set of recommendations describing the suitability of a given combination of drugs for each patient according to their genes and polymedication. G-Nomic is updated monthly including data from the new drugs to be included, their known interactions, and the relevant pharmacokinetic biomarkers. For the interactions, the list is curated manually, only keeping those with clinical relevance. For each drug, their FDA and EMA drug labels are accessed, to check for relevant enzymes and transport proteins that influence its pharmacokinetics, and for their ability to induce or inhibit other enzymes, particularly the CYP-450 system. When this information is not available, a PubMed search is made to look for these characteristics. In addition, a distinction is made between drugs and prodrugs. A query on the g-Nomic software begins with entering the medication by either their common or commercial name. Non-pharmacological substances can be also added or selected under “lifestyle habits”. The lifestyle list is dynamic, showing only the substances known to interact with the drugs that are currently selected, and includes herb compounds, such as St. John’s wort, as well as proper lifestyle substances such as grapefruit or cigarette smoking. The software provides a list of the genes classified as primary biomarkers as candidates for genetic testing, and a list of the interactions that have been detected. If genetic information is available then, or is made available at a later point, these results can also be entered and the software returns pharmacogenetics recommendations regarding specific genotypes. g-Nomic takes all the above-mentioned parameters in an easy and user-friendly tool making prescription safer.

Aim: Genetic variants on metabolic and transport enzymes are good candidates to explain inter-individual differences in response to antipsychotics. The aim of this study is to evaluate and compare the influence of the CYP2D6 , CYPC19, CYP1A2 and ABCB1 variants on plasma levels, treatment response and side effects of antipsychotics. Methods: Twenty polymorphisms in selected genes were genotyped in 318 patients diagnosed with schizophrenia, schizoaffective or delusional disorder treated with antipsychotics (clozapine, olanzapine, paliperidone, risperidone, aripiprazole and quetiapine). Plasma drug levels were determined after 6 weeks of treatment. The Positive and Negative Symptoms Scale (PANSS) and UKU scale of side effects were recorded at baseline and after 12 weeks of treatment. The effect of gene variants on plasma drug levels, treatment response and adverse effects were examined by multinomial regression. Results: CYP1A2 was found to be associated with psychic side effects (P = 0.02), with variants predicting higher enzyme activity associated with lower adverse effects, and was the strongest predictor for this adverse effect of all the studied factors. Functional variants in CYP genes were associated with plasma level differences, with higher activity variants associated with lower plasma levels. No association with improvement of the condition, as measured by the PANSS score, was found in this study. Conclusion: The results suggest that increased CYP1A2 activity protects against psychic side effects. Few studies have evaluated the impact of genetic factors on treatment response or side effects, and only in relation to a selection of adverse reactions. These results are a step towards better understanding of the factors behind the different aspects of clinical outcomes, such as various adverse effects.

Autistic spectrum disorders (ASD) children and adolescents usually present comorbidities, with 40-70% of them affected by attention deficit hyperactivity disorders (ADHD). The first option of pharmacological treatment for these patients is methylphenidate (MPH). ASD children present more side effects and poorer responses to MPH than ADHD children. The objective of our study is to identify genetic biomarkers of response to MPH in ASD children and adolescents to improve its efficacy and safety. Patients and Methods: A retrospective study with a total of 140 ASD children and adolescents on MPH treatment was included. Fifteen polymorphisms within genes coding for the MPH target NET1 (SLC6A2) and for its primary metabolic pathway (CES1) were genotyped. Multivariate analyses including response phenotypes (efficacy, side-effects, presence of somnolence, irritability, mood alterations, aggressivity, shutdown, other side-effects) were performed for every polymorphism and haplotype. Results: Single marker analyses considering gender, age, and dose as covariates showed association between CES1 variants and MPH-induced side effects (rs2244613-G (p=0.04), rs2302722-C (p=0.02), rs2307235-A (p=0.03), and rs8192950-T alleles (p=0.03)), and marginal association between the CES1 rs2302722-C allele and presence of somnolence (p=0.05) and the SLC6A2 rs36029-G allele and shutdown (p=0.05). A CES1 haplotype combination was associated with efficacy and side effects (p=0.02 and 0.03 respectively). SLC6A2 haplotype combination was associated with somnolence (p=0.05). Conclusion: CES1 genetic variants may influence the clinical outcome of MPH treatment in ASD comorbid with ADHD children and adolescents.