Clinical Utility and Implementation of Pharmacogenomics for the Personalisation of Antipsychotic Treatments.

Decades of pharmacogenetic research have revealed genetic biomarkers of clinical response to antipsychotics. Genetic variants in antipsychotic targets, dopamine and serotonin receptors in particular, and in metabolic enzymes have been associated with the efficacy and toxicity of antipsychotic treatments. However, genetic prediction of antipsychotic response based on these biomarkers is far from accurate. Despite the clinical validity of these findings, the clinical utility remains unclear. Nevertheless, genetic information on CYP metabolic enzymes responsible for the biotransformation of most commercially available antipsychotics has proven to be effective for the personalisation of clinical dosing, resulting in a reduction of induced side effects and in an increase in efficacy. However, pharmacogenetic information is rarely used in psychiatric settings as a prescription aid. Lack of studies on cost-effectiveness, absence of clinical guidelines based on pharmacogenetic biomarkers for several commonly used antipsychotics, the cost of genetic testing and the delay in results delivery hamper the implementation of pharmacogenetic interventions in clinical settings. This narrative review will comment on the existing pharmacogenetic information, the clinical utility of pharmacogenetic findings, and their current and future implementations.

Comprehensive Analysis of Drug Utilization Patterns, Gender Disparities, Lifestyle Influences, and Genetic Factors: Insights from Elderly Cohort Using g-Nomic® Software.

Polypharmacy is a global healthcare concern, especially among the elderly, leading to drug interactions and adverse reactions, which are significant causes of death in developed nations. However, the integration of pharmacogenetics can help mitigate these risks. In this study, the data from 483 patients, primarily elderly and polymedicated, were analyzed using Eugenomic®'s personalized prescription software, g-Nomic®. The most prescribed drug classes included antihypertensives, platelet aggregation inhibitors, cholesterol-lowering drugs, and gastroprotective medications. Drug-lifestyle interactions primarily involved inhibitions but also included inductions. Interactions were analyzed considering gender. Significant genetic variants identified in the study encompassed ABCB1, SLCO1B1, CYP2C19, CYP2C9, CYP2D6, CYP3A4, ABCG2, NAT2, SLC22A1, and G6PD. To prevent adverse reactions and enhance medication effectiveness, it is strongly recommended to consider pharmacogenetics testing. This approach shows great promise in optimizing medication regimens and ultimately improving patient outcomes.

Pharmacogenetic Interventions Improve the Clinical Outcome of Treatment-Resistant Autistic Spectrum Disorder Sufferers.

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.

CES1 and SLC6A2 Genetic Variants As Predictors of Response To Methylphenidate in Autism Spectrum Disorders.

Purpose: 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.

Case Report: Fatigue and Bleeding in a Polymedicated Patient Using Several Herbal Supplementations, Detected with g-Nomic® Software.

This was a case report of severe fatigue and bleeding in a 65-year-old man with ischemic heart disease who was wearing a stent and taking multiple medications for hypertension and diabetes. The use of a drug interaction and personalized prescription software (g-Nomic®) revealed potential interactions, involving acetylsalicylic acid and several non-pharmaceutical products including ginger, blueberry extracts, pineapple juice, docosahexaenoic acid and liquorice. Correction of these interactions resulted in complete remission of the reported side effects. This supports the idea that non-pharmaceuticals potentiated the effects of acetylsalicylic acid on haemostasis, producing the bleeding that would have caused fatigue. It is important to use appropriate tools to detect drug interactions that also take into account commonly used non-pharmaceutical products. Drug interactions can be considered illnesses by themselves.

g-Nomic: a new pharmacogenetics interpretation software.

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.