Clinically relevant genetic variants of drug-metabolizing enzyme and transporter genes detected in Thai children and adolescents with autism spectrum disorder

Exploratory focused pharmacogenetic testing reveals novel markers associated with risperidone pharmacokinetics in Saudi children with autism

Background: Autism spectrum disorders (ASDs) encompass a broad range of phenotypes characterized by diverse neurological alterations. Genomic studies have revealed considerable overlap between the molecular mechanisms implicated in the etiology of ASD and genes involved in the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of antipsychotic drugs employed in ASD management. Given the conflicting data originating from candidate PK or PD gene association studies in diverse ethnogeographic ASD populations, dosage individualization based on "actionable" pharmacogenetic (PGx) markers has limited application in clinical practice. Additionally, off-label use of different antipsychotics is an ongoing practice, which is justified given the shortage of approved cures, despite the lack of satisfactory evidence for its safety according to precision medicine. This exploratory study aimed to identify PGx markers predictive of risperidone (RIS) exposure in autistic Saudi children. Methods: This prospective cohort study enrolled 89 Saudi children with ASD treated with RIS-based antipsychotic therapy. Plasma levels of RIS and 9-OH-RIS were measured using a liquid chromatography-tandem mass spectrometry system. To enable focused exploratory testing, genotyping was performed with the Axiom PharmacoFocus Array, which included a collection of probe sets targeting PK/PD genes. A total of 720 PGx markers were included in the association analysis. Results: A total of 27 PGx variants were found to have a prominent impact on various RIS PK parameters; most were not located within the genes involved in the classical RIS PK pathway. Specifically, 8 markers in 7 genes were identified as the PGx markers with the strongest impact on RIS levels (p < 0.01). Four PGx variants in 3 genes were strongly associated with 9-OH-RIS levels, while 5 markers in 5 different genes explained the interindividual variability in the total active moiety. Notably, 6 CYP2D6 variants exhibited strong linkage disequilibrium; however, they significantly influenced only the metabolic ratio and had no considerable effects on the individual estimates of RIS, 9-OH-RIS, or the total active moiety. After correction for multiple testing, rs78998153 in UGT2B17 (which is highly expressed in the brain) remained the most significant PGx marker positively adjusting the metabolic ratio. For the first time, certain human leukocyte antigen (HLA) markers were found to enhance various RIS exposure parameters, which reinforces the gut-brain axis theory of ASD etiology and its suggested inflammatory impacts on drug bioavailability through modulation of the brain, gastrointestinal tract and/or hepatic expression of metabolizing enzymes and transporters. Conclusion: Our hypothesis-generating approach identified a broad spectrum of PGx markers that interactively influence RIS exposure in ASD children, which indicated the need for further validation in population PK modeling studies to define polygenic scores for antipsychotic efficacy and safety, which could facilitate personalized therapeutic decision-making in this complex neurodevelopmental condition.

Pharmacogenomics and non-genetic factors affecting drug response in autism spectrum disorder in Thai and other populations: current evidence and future implications

Autism spectrum disorder (ASD) may affect family and social life profoundly. Although there is no selective pharmacotherapy for ASD, the Food and Drug Administration (FDA) has recommended risperidone/aripiprazole to treat the associated symptoms of ASD, such as agitation/irritability. Strong associations of some pharmacokinetic/pharmacodynamic gene variants, e.g., CYP2D6 and DRD2, with risperidone-induced hyperprolactinemia have been found in children with ASD, but such strong genetic associations have not been found directly for aripiprazole in ASD. In addition to pharmacogenomic (PGx) factors, drug-drug interactions (DDIs) and possibly cumulative effects of DDIs and PGx may affect the safety or effectiveness of risperidone/aripiprazole, which should be assessed in future clinical studies in children with ASD. Reimbursement, knowledge, and education of healthcare professionals are the key obstacles preventing the successful implementation of ASD pharmacogenomics into routine clinical practice. The preparation of national and international PGx-based dosing guidelines for risperidone/aripiprazole based on robust evidence may advance precision medicine for ASD.

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform

Among the most prevalent neurodevelopmental disorders, Autism Spectrum Disorder (ASD) is highly diverse showing a broad phenotypic spectrum. ASD also couples with a broad range of mutations, both de novo and inherited. In this study, we used a proprietary SNP genotyping chip to analyze the genomic DNA of 250 Vietnamese children diagnosed with ASD. Our Single Nucleotide Polymorphism (SNP) genotyping chip directly targets more than 800 thousand SNPs in the genome. Our primary focus was to identify pathogenic/likely pathogenic mutations that are potentially linked to more severe symptoms of autism. We identified and validated 23 pathogenic/likely pathogenic mutations in this initial study. The data shows that these mutations were detected in several cases spanning multiple biological pathways. Among the confirmed SNPs, mutations were identified in genes previously known to be strongly associated with ASD such as SLCO1B1, ACADSB, TCF4, HCP5, MOCOS, SRD5A2, MCCC2, DCC, and PRKN while several other mutations are known to associate with autistic traits or other neurodevelopmental disorders. Some mutations were found in multiple patients and some patients carried multiple pathogenic/likely pathogenic mutations. These findings contribute to the identification of potential targets for therapeutic solutions in what is considered a genetically heterogeneous neurodevelopmental disorder.

Post-treatment symptomatic improvement of the eastern Indian ADHD probands is influenced by CYP2D6 genetic variations

OBJECTIVES

Symptomatic remediation from attention deficit hyperactivity disorder (ADHD)-associated traits is achieved by treatment with methylphenidate (MPH)/atomoxetine (ATX). We have analyzed the association of functional CYP2D6 variations, rs1065852, rs3892097, rs1135840, and rs1058164, with ADHD in the Indian subjects.

METHODS

Subjects were recruited following the Diagnostic and Statistical Manual for Mental Disorders. Trait scores were obtained from the Conner's Parents Rating Scale-Revised. After obtaining informed consent, blood was collected for DNA isolation, and genotyping was performed by PCR or TaqMan-based methods. Probands were treated with MPH or ATX based on age, symptoms, and drug availability. Treatment outcome was assessed using a structured questionnaire. Data obtained was analyzed to identify the association of CYP2D6 variations and the SLC6A3 rs28363170 with the treatment outcome.

RESULTS

The frequency of rs1135840 "G" and rs1065852 "G" was higher in the male ADHD probands. Bias in parental transmission (p=0.007) and association with higher trait scores were observed for rs1065852 "A". Independent influence of rs1065852 on ADHD was also observed. Probands carrying rs1065852 'GG', rs1135840 'CG', and rs28363170 10R exhibited significant symptomatic improvement with MPH, while probands with rs1135840 'CC' and rs28363170 9R showed improvement after ATX treatment.

CONCLUSIONS

ADHD probands having specific CYP2D6 genetic variations respond differentially to pharmaceutical intervention.

Pharmacogenetics and Precision Medicine Approaches for the Improvement of COVID-19 Therapies

Many drugs are being administered to tackle coronavirus disease 2019 (COVID-19) pandemic situations without establishing clinical effectiveness or tailoring safety. A repurposing strategy might be more effective and successful if pharmacogenetic interventions are being considered in future clinical studies/trials. Although it is very unlikely that there are almost no pharmacogenetic data for COVID-19 drugs, however, from inferring the pharmacokinetic (PK)/pharmacodynamic(PD) properties and some pharmacogenetic evidence in other diseases/clinical conditions, it is highly likely that pharmacogenetic associations are also feasible in at least some COVID-19 drugs. We strongly mandate to undertake a pharmacogenetic assessment for at least these drug–gene pairs (atazanavir–UGT1A1, ABCB1, SLCO1B1, APOA5; efavirenz–CYP2B6; nevirapine–HLA, CYP2B6, ABCB1; lopinavir–SLCO1B3, ABCC2; ribavirin–SLC28A2; tocilizumab–FCGR3A; ivermectin–ABCB1; oseltamivir–CES1, ABCB1; clopidogrel–CYP2C19, ABCB1, warfarin–CYP2C9, VKORC1; non-steroidal anti-inflammatory drugs (NSAIDs)–CYP2C9) in COVID-19 patients for advancing precision medicine. Molecular docking and computational studies are promising to achieve new therapeutics against SARS-CoV-2 infection. The current situation in the discovery of anti-SARS-CoV-2 agents at four important targets from in silico studies has been described and summarized in this review. Although natural occurring compounds from different herbs against SARS-CoV-2 infection are favorable, however, accurate experimental investigation of these compounds is warranted to provide insightful information. Moreover, clinical considerations of drug–drug interactions (DDIs) and drug–herb interactions (DHIs) of the existing repurposed drugs along with pharmacogenetic (e.g., efavirenz and CYP2B6) and herbogenetic (e.g., andrographolide and CYP2C9) interventions, collectively called multifactorial drug–gene interactions (DGIs), may further accelerate the development of precision COVID-19 therapies in the real-world clinical settings.

Influence of the Aryl Hydrocarbon Receptor Activating Environmental Pollutants on Autism Spectrum Disorder

Autism spectrum disorder (ASD) is an umbrella term that includes many different disorders that affect the development, communication, and behavior of an individual. Prevalence of ASD has risen exponentially in the past couple of decades. ASD has a complex etiology and traditionally recognized risk factors only account for a small percentage of incidence of the disorder. Recent studies have examined factors beyond the conventional risk factors (e.g., environmental pollution). There has been an increase in air pollution since the beginning of industrialization. Most environmental pollutants cause toxicities through activation of several cellular receptors, such as the aryl hydrocarbon receptor (AhR)/cytochrome P450 (CYPs) pathway. There is little research on the involvement of AhR in contributing to ASD. Although a few reviews have discussed and addressed the link between increased prevalence of ASD and exposure to environmental pollutants, the mechanism governing this effect, specifically the role of AhR in ASD development and the molecular mechanisms involved, have not been discussed or reviewed before. This article reviews the state of knowledge regarding the impact of the AhR/CYP pathway modulation upon exposure to environmental pollutants on ASD risk, incidence, and development. It also explores the molecular mechanisms involved, such as epigenesis and polymorphism. In addition, the review explores possible new AhR-mediated mechanisms of several drugs used for treatment of ASD, such as sulforaphane, resveratrol, haloperidol, and metformin.

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R ² (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.

Multi-factorial pharmacokinetic interactions: unraveling complexities in precision drug therapy

Introduction: Precision drug therapy requires accounting for pertinent factors in pharmacokinetic (PK) inter-individual variability (i.e., pharmacogenetics, diseases, polypharmacy, and natural product use) that can cause sub-therapeutic or adverse effects. Although each of these individual factors can alter victim drug PK, multi-factorial interactions can cause additive, synergistic, or opposing effects. Determining the magnitude and direction of these complex multi-factorial effects requires understanding the rate-limiting redundant and/or sequential PK processes for each drug.Areas covered: Perturbations in drug-metabolizing enzymes and/or transporters are integral to single- and multi-factorial PK interactions. Examples of single factor PK interactions presented include gene-drug (pharmacogenetic), disease-drug, drug-drug, and natural product-drug interactions. Examples of multi-factorial PK interactions presented include drug-gene-drug, natural product-gene-drug, gene-gene-drug, disease-natural product-drug, and disease-gene-drug interactions. Clear interpretation of multi-factorial interactions can be complicated by study design, complexity in victim drug PK, and incomplete mechanistic understanding of victim drug PK.Expert opinion: Incorporation of complex multi-factorial PK interactions into precision drug therapy requires advances in clinical decision tools, intentional PK study designs, drug-metabolizing enzyme and transporter fractional contribution determinations, systems and computational approaches (e.g., physiologically-based pharmacokinetic modeling), and PK phenotyping of progressive diseases.

SLCO1B1 and ABCG2 Gene Polymorphisms in a Thai Population

Introduction

Genetic polymorphisms of drug transporters influence drug transporter activity and alter pharmacokinetic profiles of the drugs. Organic anion transporting polypeptide 1B1 (OATP1B1) and breast cancer resistance protein (BCRP) are important transporters encoded by solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene and ATP-binding cassette subfamily G member 2 (ABCG2) gene, respectively. Polymorphisms in these genes are associated with increased plasma statins concentrations, statin-induced myopathy and poor response to allopurinol treatment.

Purpose

We explored allele and genotype frequencies of SLCO1B1 and ABCG2 genes including their predicted phenotypes in 53 Thai participants. Of these, 17 had chronic kidney disease and were on statins.

Materials and Methods

Genotyping analysis for SLCO1B1 c.521T>C (rs4149056), c.388A>G (rs2306283), g.-11187G>A (rs4149015), and ABCG2 c.421C>A (rs2231142) was done by using TaqMan^® Real time PCR. All were tested for Hardy–Weinberg Equilibrium.

Results

Most of the participants (80%) had normal function haplotypes SLCO1B1 (*1A and *1B) while decreased (*5, *15, and *17) and unknown (*21) function haplotypes were less observed. Four phenotypes of SLCO1B1 were observed: 69.81% had normal function (*1A/*1A,*1A/*1B, and *1B/*1B), 13.21% had intermediate function (*1A/*17, *1B/*15 and *1B/*17), 9.43% had indeterminate function (*1A/*21 and *1B/*21) and 7.55% had low function (*5/*15, *15/*15, and *15/*17). ABCG2 c.421A allele frequency was 25%. The frequency of ABCG2 c.421CA and AA phenotypes were 37.7% and 5.7%, respectively. The allele and genotype frequencies observed are consistent with reports in Asians. However, there were differences in major allele distributions between Asians and Caucasians for SLCO1B1 c.388A>G; SLCO1B1 c.388G were highly found in Asians, but c.388A were more observed in Caucasians.

Conclusion

This study showed that in the Thai population, there were 4 SNPs of SLCO1B1 and ABCG2 genes. This finding may be clinically applied to minimize inter-individual variability of drugs such as statins and allopurinol. Further study with a larger sample size is needed to assess the drug profiles and responses to treatment.

Development of a physiologically based pharmacokinetic model for mefloquine and its application alongside a clinical effectiveness model to select an optimal dose for prevention of malaria in young Caucasian children

AIMS

To predict the optimal chemoprophylactic dose of mefloquine in infants of 5-10 kg using physiologically based pharmacokinetic (PBPK) and clinical effectiveness models.

METHODS

The PBPK model was developed in Simcyp version 14.1 and verified against clinical pharmacokinetic data in adults; the final model, accounting for developmental physiology and enzyme ontogeny was then applied in the paediatric population. The clinical effectiveness model utilized real-world chemoprophylaxis data with stratification of output by age and including infant data from the UK population.

RESULTS

PBPK simulations in infant populations depend on the assumed fraction of mefloquine metabolized by CYP3A4 (0.47, 0.95) and on the associated CYP3A4 ontogeny (Salem, Upreti). However, all scenarios suggest that a dose of 62.5 mg weekly achieves or exceeds the exposure in adults following a 250 mg weekly dose and results in a minimum plasma concentration of 620 ng ml^-1 , which is considered necessary to achieve 95% prophylactic efficacy. The clinical effectiveness model predicts a 96% protective efficacy from mefloquine chemoprophylaxis at 62.5 mg weekly.

CONCLUSIONS

The PBPK and clinical effectiveness models are mutually supportive and suggest a prophylactic dose of 62.5 mg weekly in the Caucasian 5-10 kg infant population travelling to endemic countries. This dual approach offers a novel route to dose selection in a vulnerable population, where clinical trials would be difficult to conduct.

Pharmacogenomic Study Reveals New Variants of Drug Metabolizing Enzyme and Transporter Genes Associated with Steady-State Plasma Concentrations of Risperidone and 9-Hydroxyrisperidone in Thai Autism Spectrum Disorder Patients

The present study sought to investigate the genetic variants in drug metabolizing enzyme and transporter (DMET) genes associated with steady-state plasma concentrations of risperidone among Thai autism spectrum disorder (ASD) patients. ASD patients taking risperidone for at least 1 month were enrolled for this pharmacogenomic study. Genotyping profile was obtained using Affymetrix DMET Plus array interrogating 1931 variants in 231 genes. Steady-state plasma risperidone and 9-hydroxyrisperidone were measured using liquid chromatography/tandem mass spectrometry assay. The final analysis included 483 markers for 167 genes. Six variants, ABCB11 (c.3084A > G, c.^∗420A > G, c.^∗368G > A, and c.^∗236G > A) and ADH7 (c.690G > A and c.-5360G > A), were found to be associated with plasma concentrations of risperidone. 9-Hydroxyrisperidone and the total active-moiety levels were associated with six gene variants, SCLO1B1 (c.-11187G > A and c.521T > C), SLCO1B3 (c.334G > T, c.699A > G, and c.1557G > A), and SLC7A5 c.^∗438C > G. Polymorphisms in UGT2B4 c.^∗448A > G and CYP2D6 (c.1661G > C, c.4180G > C, and c.-2178G > A) showed considerable but not significant associations with metabolic ratio. This pharmacogenomic study identifies new genetic variants of DMET genes in monitoring risperidone therapy.