SLC transporters as therapeutic targets: Emerging opportunities

Solute carrier (SLC) transporters-a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes-have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

Organic cation transporter 1 (OCT1) modulates multiple cardiometabolic traits through effects on hepatic thiamine content.

A constellation of metabolic disorders, including obesity, dysregulated lipids, and elevations in blood glucose levels, has been associated with cardiovascular disease and diabetes. Analysis of data from recently published genome-wide association studies (GWAS) demonstrated that reduced-function polymorphisms in the organic cation transporter, OCT1 (SLC22A1), are significantly associated with higher total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) levels and an increased risk for type 2 diabetes mellitus, yet the mechanism linking OCT1 to these metabolic traits remains puzzling. Here, we show that OCT1, widely characterized as a drug transporter, plays a key role in modulating hepatic glucose and lipid metabolism, potentially by mediating thiamine (vitamin B1) uptake and hence its levels in the liver. Deletion of Oct1 in mice resulted in reduced activity of thiamine-dependent enzymes, including pyruvate dehydrogenase (PDH), which disrupted the hepatic glucose-fatty acid cycle and shifted the source of energy production from glucose to fatty acids, leading to a reduction in glucose utilization, increased gluconeogenesis, and altered lipid metabolism. In turn, these effects resulted in increased total body adiposity and systemic levels of glucose and lipids. Importantly, wild-type mice on thiamine deficient diets (TDs) exhibited impaired glucose metabolism that phenocopied Oct1 deficient mice. Collectively, our study reveals a critical role of hepatic thiamine deficiency through OCT1 deficiency in promoting the metabolic inflexibility that leads to the pathogenesis of cardiometabolic disease

Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies.

Variation in steroid hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels

Membrane transporters in drug development

Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling. © 2010 Macmillan Publishers Limited

Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children