Meta-analysis of the Influence of UGT Genetic Polymorphisms on Lamotrigine Concentration

Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance

Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.

Dosage Optimization of Lamotrigine in Pregnancy: A Pharmacometric Approach using Modeling and Simulation

Lamotrigine is the most widely used anti-epileptic drug in pregnancy due to its low teratogenicity. However, there is an increased metabolism & clearance of Lamotrigine in pregnancy contributing to suboptimal drug therapy and poor disease control, prompting the need for pro-active dosage adjustments. The present study aimed to develop a pharmacometric model-based framework for recommending optimal dosage regimen for Lamotrigine in pregnancy. A systematic review was performed to obtain the literature aggregate data on clearance of Lamotrigine in pregnancy. The data was incorporated into simulations using PUMAS software for estimating the plasma concentrations at preconception stage and three trimesters. Simulated drug-exposures for different doses were investigated to ascertain plasma concentrations similar to the pre-conception and above minimum effective concentration. The simulated mean steady state trough plasma concentrations (mg/L) of Lamotrigine in non-pregnant and pregnant women at 3 trimesters decreased significantly (p<0.001) viz. 4.31±1.14, 3.17± 0.93, 2.14±0.86, 1.51±0.65 respectively. The simulation studies revealed that 150mg, 175mg, 225mg and 250mg twice daily doses, in pre-conception stage and three trimesters respectively achieve the target concentrations. Thus, the model-informed dosage regimen of Lamotrigine proposed in this study shall be considered to initiate the dosing in pregnant women, however the safety and efficacy of the drug have to be assured through therapeutic drug monitoring, in order to avoid therapeutic failure of Lamotrigine in pregnancy. This article is protected by copyright. All rights reserved.

Potential pharmacogenomic targets in bipolar disorder: considerations for current testing and the development of decision support tools to individualize treatment selection

Background

Treatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations.

Main body

We searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information.

Conclusions

The complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.

Influence of genetic variants and antiepileptic drug co-treatment on lamotrigine plasma concentration in Mexican Mestizo patients with epilepsy

Genetic and nongenetic factors may contribute to lamotrigine (LTG) plasma concentration variability among patients. We simultaneously investigated the association of UGT1A1, UGT1A4, UGT2B7, ABCB1, ABCG2, and SLC22A1 variants, as well as antiepileptic drug co-treatment, on LTG plasma concentration in 97 Mexican Mestizo (MM) patients with epilepsy. UGT1A4*1b was associated with lower LTG dose-corrected concentrations. Patients with the UGT2B7-161T allele were treated with 21.22% higher LTG daily dose than those with CC genotype. Two novel UGT1A4 variants (c.526A>T; p.Thr185= and c.496T>C; p.Ser166Leu) were identified in one patient. Patients treated with LTG + valproic acid (VPA) showed higher LTG plasma concentration than patients did on LTG monotherapy or LTG + inducer. Despite the numerous drug-metabolizing enzymes and transporter genetic variants analyzed, our results revealed that co-treatment with VPA was the most significant factor influencing LTG plasma concentration, followed by UGT1A4*1b, and that patients carrying UGT2B7 c.-161T required higher LTG daily doses. These data provide valuable information for the clinical use of LTG in MM patients with epilepsy.

A repository of protein abundance data of drug metabolizing enzymes and transporters for applications in physiologically based pharmacokinetic (PBPK) modelling and simulation

Population factors such as age, gender, ethnicity, genotype and disease state can cause inter-individual variability in pharmacokinetic (PK) profile of drugs. Primarily, this variability arises from differences in abundance of drug metabolizing enzymes and transporters (DMET) among individuals and/or groups. Hence, availability of compiled data on abundance of DMET proteins in different populations can be useful for developing physiologically based pharmacokinetic (PBPK) models. The latter are routinely employed for prediction of PK profiles and drug interactions during drug development and in case of special populations, where clinical studies either are not feasible or have ethical concerns. Therefore, the main aim of this work was to develop a repository of literature-reported DMET abundance data in various human tissues, which included compilation of information on sample size, technique(s) involved, and the demographic factors. The collation of literature reported data revealed high inter-laboratory variability in abundance of DMET proteins. We carried out unbiased meta-analysis to obtain weighted mean and percent coefficient of variation (%CV) values. The obtained %CV values were then integrated into a PBPK model to highlight the variability in drug PK in healthy adults, taking lamotrigine as a model drug. The validated PBPK model was extrapolated to predict PK of lamotrigine in paediatric and hepatic impaired populations. This study thus exemplifies importance of the DMET protein abundance database, and use of determined values of weighted mean and %CV after meta-analysis in PBPK modelling for the prediction of PK of drugs in healthy and special populations.