The genetic basis of variability in drug responses

Pharmacogenomics of GPCR Drug Targets

Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs and account for a global sales volume of over 180 billion US dollars annually, the prevalence of genetic variation among GPCRs targeted by drugs is unknown. By analyzing data from 68,496 individuals, we find that GPCRs targeted by drugs show genetic variation within functional regions such as drug- and effector-binding sites in the human population. We experimentally show that certain variants of μ-opioid and Cholecystokinin-A receptors could lead to altered or adverse drug response. By analyzing UK National Health Service drug prescription and sales data, we suggest that characterizing GPCR variants could increase prescription precision, improving patients’ quality of life, and relieve the economic and societal burden due to variable drug responsiveness.

Video Abstract

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GPCRs targeted by FDA-approved drugs show genetic variation in the human population

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Genetic variation occurs in functional sites and may result in altered drug response

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We present an online resource of GPCR genetic variants for pharmacogenomics research

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Understanding variation in drug targets may help alleviate economic healthcare burden

Identification of cancer genes that are independent of dominant proliferation and lineage programs

Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells.

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

Integrating cardiomyocytes from human pluripotent stem cells in safety pharmacology: has the time come?

Cardiotoxicity is a severe side effect of drugs that induce structural or electrophysiological changes in heart muscle cells. As a result, the heart undergoes failure and potentially lethal arrhythmias. It is still a major reason for drug failure in preclinical and clinical phases of drug discovery. Current methods for predicting cardiotoxicity are based on guidelines that combine electrophysiological analysis of cell lines expressing ion channels ectopically in vitro with animal models and clinical trials. Although no new cases of drugs linked to lethal arrhythmias have been reported since the introduction of these guidelines in 2005, their limited predictive power likely means that potentially valuable drugs may not reach clinical practice. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are now emerging as potentially more predictive alternatives, particularly for the early phases of preclinical research. However, these cells are phenotypically immature and culture and assay methods not standardized, which could be a hurdle to the development of predictive computational models and their implementation into the drug discovery pipeline, in contrast to the ambitions of the comprehensive pro-arrhythmia in vitro assay (CiPA) initiative. Here, we review present and future preclinical cardiotoxicity screening and suggest possible hPSC-CM-based strategies that may help to move the field forward. Coordinated efforts by basic scientists, companies and hPSC banks to standardize experimental conditions for generating reliable and reproducible safety indices will be helpful not only for cardiotoxicity prediction but also for precision medicine.

LINKED ARTICLES

This article is part of a themed section on New Insights into Cardiotoxicity Caused by Chemotherapeutic Agents. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.21/issuetoc.

Circulating Tumor Cells as Cancer Biomarkers in the Clinic

It is believed that the development of metastatic cancer requires the presence of circulating tumor cells (CTCs) , which are found in a patient's circulation as rare abnormal cells comingled with billions of the normal red and white blood cells. The systems developed for detection of CTCs have brought progress to cancer treatment. The molecular characterization of CTCs can aid in the development of new drugs, and their presence during treatment can help clinicians determine the prognosis of the patient. Studies have been carried out in patients early in the disease course, with only primary tumors, and the role of CTCs in prognosis seems to be as important as it is in patients with metastatic disease. The published studies on CTCs have focused on their prognostic significance, their utility in real-time monitoring of therapies, the identification of therapeutic and resistance targets, and understanding the process of metastasis . The analysis of CTCs during the early stages, as a "liquid biopsy," helps to monitor patients at different points in the disease course, including minimal residual disease, providing valuable information about the very early assessment of treatment effectiveness. Finally, CTCs can be used to screen patients with family histories of cancer or with diseases that can lead to the development of cancer. With standard protocols, this easily obtained and practical tool can be used to prevent the growth and spread of cancer. In this chapter, we review some important aspects of CTCs , surveying the disease aspects where these cells have been investigated.

HEROD: a human ethnic and regional specific omics database

Motivation

Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data.

Results

Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface.

Availability and implementation

The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported.

Contact

phacyz@nus.edu.sg.

Study of Statin- and Loratadine-Induced Muscle Pain Mechanisms Using Human Skeletal Muscle Cells

Many drugs can cause unexpected muscle disorders, often necessitating the cessation of an effective medication. Inhibition of monocarboxylate transporters (MCTs) may potentially lead to perturbation of l-lactic acid homeostasis and muscular toxicity. Previous studies have shown that statins and loratadine have the potential to inhibit l-lactic acid efflux by MCTs (MCT1 and 4). The main objective of this study was to confirm the inhibitory potentials of atorvastatin, simvastatin (acid and lactone forms), rosuvastatin, and loratadine on l-lactic acid transport using primary human skeletal muscle cells (SkMC). Loratadine (IC₅₀ 31 and 15 µM) and atorvastatin (IC₅₀ ~130 and 210 µM) demonstrated the greatest potency for inhibition of l-lactic acid efflux at pH 7.0 and 7.4, respectively (~2.5-fold l-lactic acid intracellular accumulation). Simvastatin acid exhibited weak inhibitory potency on l-lactic acid efflux with an intracellular lactic acid increase of 25–35%. No l-lactic acid efflux inhibition was observed for simvastatin lactone or rosuvastatin. Pretreatment studies showed no change in inhibitory potential and did not affect lactic acid transport for all tested drugs. In conclusion, we have demonstrated that loratadine and atorvastatin can inhibit the efflux transport of l-lactic acid in SkMC. Inhibition of l-lactic acid efflux may cause an accumulation of intracellular l-lactic acid leading to the reported drug-induced myotoxicity.

Advancing psychiatric pharmacogenomics using drug development paradigms

Drugs used to treat psychiatric disorders, even when taken as directed, fail to provide adequate relief for a sizeable proportion of patients. Despite our advancements in understanding human genetics and development of high-throughput tools to probe variation, pharmacogenomics has yielded marginal ability to predict drug response for psychiatric disorders. Here, I review the current pharmacogenomics paradigm, identifying opportunities to incorporate drug development strategies designed to increase the probability of delivering a successful molecule to the clinic. This includes using in-depth pharmacokinetic profiles, clear measures of target engagement and target-specific pharmacodynamic responses orthogonal to clinical response. The complex pharmacological profiles psychiatric drugs require re-examination of simplified clinical response-oriented pharmacogenetic hypotheses, in favor of a more complete patient profile.

A link prediction approach to cancer drug sensitivity prediction

BACKGROUND

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine.

RESULTS

In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant.

CONCLUSIONS

We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Learning approaches to improve prediction of drug sensitivity in breast cancer patients

Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment. In this paper, we present three learning approaches to improve the prediction of breast cancer patients' response to chemotherapy drug: the instance selection approach, the oversampling approach, and the hybrid approach. We evaluate the performance of our approaches and compare them against the baseline approach using the Area Under the ROC Curve (AUC) on clinical trial data, in addition to testing the stability of the approaches. Our experimental results show the stability of our approaches giving the highest AUC with statistical significance.

Methylphenidate and Atomoxetine-Responsive Prefrontal Cortical Genetic Overlaps in "Impulsive" SHR/NCrl and Wistar Rats

Impulsivity, the predisposition to act prematurely without foresight, is associated with a number of neuropsychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD). Identifying genetic underpinnings of impulsive behavior may help decipher the complex etiology and neurobiological factors of disorders marked by impulsivity. To identify potential genetic factors of impulsivity, we examined common differentially expressed genes (DEGs) in the prefrontal cortex (PFC) of adolescent SHR/NCrl and Wistar rats, which showed marked decrease in preference for the large but delayed reward, compared with WKY/NCrl rats, in the delay discounting task. Of these DEGs, we examined drug-responsive transcripts whose mRNA levels were altered following treatment (in SHR/NCrl and Wistar rats) with drugs that alleviate impulsivity, namely, the ADHD medications methylphenidate and atomoxetine. Prefrontal cortical genetic overlaps between SHR/NCrl and Wistar rats in comparison with WKY/NCrl included genes associated with transcription (e.g., Btg2, Fos, Nr4a2), synaptic plasticity (e.g., Arc, Homer2), and neuron apoptosis (Grik2, Nmnat1). Treatment with methylphenidate and/or atomoxetine increased choice of the large, delayed reward in SHR/NCrl and Wistar rats and changed, in varying degrees, mRNA levels of Nr4a2, Btg2, and Homer2, genes with previously described roles in neuropsychiatric disorders characterized by impulsivity. While further studies are required, we dissected potential genetic factors that may influence impulsivity by identifying genetic overlaps in the PFC of "impulsive" SHR/NCrl and Wistar rats. Notably, these are also drug-responsive transcripts which may be studied further as biomarkers to predict response to ADHD drugs, and as potential targets for the development of treatments to improve impulsivity.

Aspirin as a COX inhibitor and anti-inflammatory drug in human skeletal muscle

Although aspirin is one of the most common anti-inflammatory drugs in the world, the effect of aspirin on human skeletal muscle inflammation is almost completely unknown. This study examined the potential effects and related time course of an orally consumed aspirin dose on the inflammatory prostaglandin E₂ (PGE₂)/cyclooxygenase (COX) pathway in human skeletal muscle. Skeletal muscle biopsies were taken from the vastus lateralis of 10 healthy adults (5 male and 5 female, 25 ± 2 yr old) before (Pre) and 2, 4, and 24 h after (Post) a standard dose (975mg) of aspirin and partitioned for analysis of 1) in vivo PGE₂ levels in resting skeletal muscle and 2) ex vivo skeletal muscle PGE₂ production when stimulated with the COX substrate arachidonic acid (5 μM). PGE₂ levels in vivo and PGE₂ production ex vivo were generally unchanged at each time point after aspirin consumption. However, most individuals clearly showed suppression of PGE₂, but at varying time points after aspirin consumption. When the maximum suppression after aspirin consumption was examined for each individual, independent of time, PGE₂ levels in vivo (184 ± 17 and 104 ± 23pg/g wet wt at Pre and Post, respectively) and PGE₂ production ex vivo (2.74 ± 0.17 and 2.09 ± 0.11pg·mg wet wt^-1·min^-1 at Pre and Post, respectively) were reduced ( P < 0.05) by 44% and 24%, respectively. These results provide evidence that orally consumed aspirin can inhibit the COX pathway and reduce the inflammatory mediator PGE₂ in human skeletal muscle. Findings from this study highlight the need to expand our knowledge regarding the potential role for aspirin regulation of the deleterious influence of inflammation on skeletal muscle health in aging and exercising individuals. NEW & NOTEWORTHY This study demonstrated that orally consumed aspirin can target the prostaglandin/cyclooxygenase pathway in human skeletal muscle. This pathway has been shown to regulate skeletal muscle metabolism and inflammation in aging and exercising individuals. Given the prevalence of aspirin consumption, these findings may have implications for skeletal muscle health in a large segment of the population.

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions

Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies.

Adverse Drug Reaction Monitoring in a Tertiary Care Psychiatry Setting: A Comparative Study between Inpatients and Outpatients

BACKGROUND

Psychotropic medications are the mainstay of treatment in psychiatric disorders and are associated with ADRs which affect the compliance and treatment course. Previous studies have looked at the frequency, profile of ADRs and their management aspects. However, the systematic comparison between IP and OP was lacking even though there is a prescription pattern difference. Hence this study was aimed to compare the proportion, pattern, severity and resolution of ADRs once detected.

METHODS

This is a hospital based, prospective follow up study done in the psychiatry ward and outpatient setting for a period of 6 months. A total of 491 patients (200 IP, 291 OP) who received psychotropics were monitored in the study. UKU side effect rating scale was used to detect ADRs, WHO - UMC scale for causality, Modified Hartwig and Siegel Scale to assess severity of ADR and CDSCO suspected ADR form for reporting it.

RESULTS

Out of 491 patients who were recruited for the study, 83 patients developed ADRs (34 IP, 49 OP, P = 0.963). The mean number of ADRs per patient was found to be higher in IP (IP-2.17±1.14, OP-1.65±1.12, P-0.01). Severe ADRs were observed to be higher IP (IP-67.64%, OP-38.7%, P-0.014) which was statistically significant. There is no statistically significant difference in distribution of ADRs across all age groups (P-0.475).

CONCLUSION

The study results emphasises the need for active pharmacovigilance so that ADRs are detected and managed at the earliest, hence reducing the morbidity and improving compliance. There is also need for systematic long term, multicentric study to further examine and correlatethe observations of our study.

A tool for discovering drug sensitivity and gene expression associations in cancer cells

The sensitivity of cancer cells to anticancer drugs is a crucial factor for developing effective treatments. However, it is still challenging to precisely predict the effectiveness of therapeutics in humans within a complex genomic and molecular context. We developed an interface which allows the user to rapidly explore drug sensitivity and gene expression associations. Predictions for how expression of various genes affect anticancer drug activity are available for all genes for a set of therapeutics based on data from various cell lines of different origin in the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer projects. Our application makes discovery or validation of drug sensitivity and gene expression associations efficient. Effectiveness of this tool is demonstrated by multiple known and novel examples.

Genetic and epigenetic changes in host ABCB1 influences malaria susceptibility to Plasmodium falciparum

Multiple mechanisms such as genetic and epigenetic variations within a key gene may play a role in malarial susceptibility and response to anti-malarial drugs in the population. ABCB1 is one of the well-studied membrane transporter genes that code for the P-glycoprotein (an efflux protein) and whose effect on malaria disease predisposition and susceptibility to drugs remains to be understood. We studied the association of single nucleotide variations in human ABCB1 that influences its function in subjects with uncomplicated and complicated malaria caused by Plasmodium falciparum (Pf). Global DNA methylation and ABCB1 DNA promoter methylation levels were performed along with transcriptional response and protein expression in subjects with malaria and healthy controls. The rs2032582 locus was significantly associated with complicated and combined malaria groups when compared to controls (p < 0.05). Significant DNA methylation difference was noticed between case and control (p < 0.05). In addition, global DNA methylation levels of the host DNA were inversely proportional to parasitemia in individuals with Pf infection. Our study also revealed the correlation between ABCB1 DNA promoter methylation with rs1128503 and rs2032582 polymorphisms in malaria and was related to increased expression of ABCB1 protein levels in complicated malaria group (p < 0.05) when compared to uncomplicated malaria and control groups. The study provides evidence for multiple mechanisms that may regulate the role of host ABCB1 function to mediate aetiology of malaria susceptibility, prognosis and drug response. These may have clinical implications and therapeutic application for various malarial conditions.

Rare ER protein misfolding-mistrafficking disorders: Therapeutic developments

The presence of a functional protein at the appropriate location in the cell is the result of the processes of transcription, translation, folding and trafficking to the correct destination. There are numerous diseases that are caused by protein misfolding, mainly due to mutations in the respective gene. The consequences of this misfolding may be that proteins effectively lose their function, either by being removed by the cellular quality control machinery or by accumulating at the incorrect intracellular or extracellular location. A number of mutations that lead to protein misfolding and affect trafficking to the final destination, e.g. Cystic fibrosis, Wilson's disease, and Progressive Familial Intrahepatic 1 cholestasis, result in proteins that retain partial function if their folding and trafficking is restored either by molecular or pharmacological means. In this review, we discuss several mutant proteins within this class of misfolding diseases and provide an update on the status of molecular and therapeutic developments and potential therapeutic strategies being developed to counter these diseases.

DIFFERENTIAL PATHWAY DEPENDENCY DISCOVERY ASSOCIATED WITH DRUG RESPONSE ACROSS CANCER CELL LINES

The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known genegene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells' drug response as well as in designing experiments for the purpose of personalized treatment regimens.

Translational Safety Genetics

The emerging field of translational safety genetics is providing new opportunities to enhance drug discovery and development. Genetic variation in therapeutic drug targets, off-target interactors and relevant drug metabolism/disposition pathways can contribute to diverse drug pharmacologic and toxicologic responses between different animal species, strains and geographic origins. Recent advances in the sequencing of rodent, canine, nonhuman primate, and minipig genomes have dramatically improved the ability to select the most appropriate animal species for preclinical drug toxicity studies based on genotypic characterization of drug targets/pathways and drug metabolism and/or disposition, thus avoiding inconclusive or misleading animal studies, consistent with the principles of the 3Rs (replacement, reduction and refinement). The genetic background of individual animals should also be taken into consideration when interpreting phenotypic outcomes from toxicity studies and susceptibilities to spontaneous safety-relevant background findings.

GM-CSF-Producing Th Cells in Rats Sensitive and Resistant to Experimental Autoimmune Encephalomyelitis

Given that granulocyte macrophage colony-stimulating factor (GM-CSF) is identified as the key factor to endow auto-reactive Th cells with the potential to induce neuroinflammation in experimental autoimmune encephalomyelitis (EAE) models, the frequency and phenotype of GM-CSF-producing (GM-CSF+) Th cells in draining lymph nodes (dLNs) and spinal cord (SC) of Albino Oxford (AO) and Dark Agouti (DA) rats immunized for EAE were examined. The generation of neuroantigen-specific GM-CSF+ Th lymphocytes was impaired in dLNs of AO rats (relatively resistant to EAE induction) compared with their DA counterparts (susceptible to EAE) reflecting impaired CD4+ lymphocyte proliferation and less supportive of GM-CSF+ Th cell differentiation dLN cytokine microenvironment. Immunophenotyping of GM-CSF+ Th cells showed their phenotypic heterogeneity in both strains and revealed lower frequency of IL-17+IFN-γ+, IL-17+IFN-γ-, and IL-17-IFN-γ+ cells accompanied by higher frequency of IL-17-IFN-γ- cells among them in AO than in DA rats. Compared with DA, in AO rats was also found (i) slightly lower surface density of CCR2 (drives accumulation of highly pathogenic GM-CSF+IFN-γ+ Th17 cells in SC) on GM-CSF+IFN-γ+ Th17 lymphocytes from dLNs, and (ii) diminished CCL2 mRNA expression in SC tissue, suggesting their impaired migration into the SC. Moreover, dLN and SC cytokine environments in AO rats were shown to be less supportive of GM-CSF+IFN-γ+ Th17 cell differentiation (judging by lower expression of mRNAs for IL-1β, IL-6 and IL-23/p19). In accordance with the (i) lower frequency of GM-CSF+ Th cells in dLNs and SC of AO rats and their lower GM-CSF production, and (ii) impaired CCL2 expression in the SC tissue, the proportion of proinflammatory monocytes among peripheral blood cells and their progeny (CD45hi cells) among the SC CD11b+ cells were reduced in AO compared with DA rats. Collectively, the results indicate that the strain specificities in efficacy of several mechanisms controlling (auto)reactive CD4+ lymphocyte expansion/differentiation into the cells with pathogenic phenotype and migration of the latter to the SC contribute to AO rat resistance to EAE.

Pharmacogenomics and pharmacogenetics for the intensive care unit: a narrative review

PURPOSE

Knowledge of how alterations in pharmacogenomics and pharmacogenetics may affect drug therapy in the intensive care unit (ICU) has received little study. We review the clinically relevant application of pharmacogenetics and pharmacogenomics to drugs and conditions encountered in the ICU.

SOURCE

We selected relevant literature to illustrate the important concepts contained within.

PRINCIPAL FINDINGS

Two main approaches have been used to identify genetic abnormalities - the candidate gene approach and the genome-wide approach. Genetic variability in response to drugs may occur as a result of alterations of drug-metabolizing (cytochrome P [CYP]) enzymes, receptors, and transport proteins leading to enhancement or delay in the therapeutic response. Of relevance to the ICU, genetic variation in CYP-450 isoenzymes results in altered effects of midazolam, fentanyl, morphine, codeine, phenytoin, clopidogrel, warfarin, carvedilol, metoprolol, HMG-CoA reductase inhibitors, calcineurin inhibitors, non-steroidal anti-inflammatory agents, proton pump inhibitors, and ondansetron. Changes in cholinesterase enzyme function may affect the disposition of succinylcholine, benzylisoquinoline muscle relaxants, remifentanil, and hydralazine. Genetic variation in transport proteins leads to differences in the response to opioids and clopidogrel. Polymorphisms in drug receptors result in altered effects of β-blockers, catecholamines, antipsychotic agents, and opioids. Genetic variation also contributes to the diversity and incidence of diseases and conditions such as sepsis, malignant hyperthermia, drug-induced hypersensitivity reactions, cardiac channelopathies, thromboembolic disease, and congestive heart failure.

CONCLUSION

Application of pharmacogenetics and pharmacogenomics has seen improvements in drug therapy. Ongoing study and incorporation of these concepts into clinical decision making in the ICU has the potential to affect patient outcomes.

Pharmacogenomics of Drug Metabolizing Enzymes and Transporters: Relevance to Precision Medicine

The interindividual genetic variations in drug metabolizing enzymes and transporters influence the efficacy and toxicity of numerous drugs. As a fundamental element in precision medicine, pharmacogenomics, the study of responses of individuals to medication based on their genomic information, enables the evaluation of some specific genetic variants responsible for an individual’s particular drug response. In this article, we review the contributions of genetic polymorphisms to major individual variations in drug pharmacotherapy, focusing specifically on the pharmacogenomics of phase-I drug metabolizing enzymes and transporters. Substantial frequency differences in key variants of drug metabolizing enzymes and transporters, as well as their possible functional consequences, have also been discussed across geographic regions. The current effort illustrates the common presence of variability in drug responses among individuals and across all geographic regions. This information will aid health-care professionals in prescribing the most appropriate treatment aimed at achieving the best possible beneficial outcomes while avoiding unwanted effects for a particular patient.

Genome Sequence Variability Predicts Drug Precautions and Withdrawals from the Market

Despite substantial premarket efforts, a significant portion of approved drugs has been withdrawn from the market for safety reasons. The deleterious impact of nonsynonymous substitutions predicted by the SIFT algorithm on structure and function of drug-related proteins was evaluated for 2504 personal genomes. Both withdrawn (n = 154) and precautionary (Beers criteria (n = 90), and US FDA pharmacogenomic biomarkers (n = 96)) drugs showed significantly lower genomic deleteriousness scores (P < 0.001) compared to others (n = 752). Furthermore, the rates of drug withdrawals and precautions correlated significantly with the deleteriousness scores of the drugs (P < 0.01); this trend was confirmed for all drugs included in the withdrawal and precaution lists by the United Nations, European Medicines Agency, DrugBank, Beers criteria, and US FDA. Our findings suggest that the person-to-person genome sequence variability is a strong independent predictor of drug withdrawals and precautions. We propose novel measures of drug safety based on personal genome sequence analysis.

The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes

A surrogate model is a black box model that reproduces the output of another more complex model at a single time point. This is to be distinguished from the method of surrogate data, used in time series. The purpose of a surrogate is to reduce the time necessary for a computation at the cost of rigor and generality. We describe a method of constructing surrogates in the form of support vector machine (SVM) regressions for the purpose of exploring the parameter space of physiological models. Our focus is on the methodology of surrogate creation and accuracy assessment in comparison to the original model. This is done in the context of a simulation of hemorrhage in one model, “Small”, and renal denervation in another, HumMod. In both cases, the surrogate predicts the drop in mean arterial pressure following the intervention. We asked three questions concerning surrogate models: (1) how many training examples are necessary to obtain an accurate surrogate, (2) is surrogate accuracy homogeneous, and (3) how much can computation time be reduced when using a surrogate. We found the minimum training set size that would guarantee maximal accuracy was widely variable, but could be algorithmically generated. The average error for the pressure response to the protocols was -0.05±2.47 in Small, and -0.3 +/- 3.94 mmHg in HumMod. In the Small model, error grew with actual pressure drop, and in HumMod, larger pressure drops were overestimated by the surrogates. Surrogate use resulted in a 6 order of magnitude decrease in computation time. These results suggest surrogate modeling is a valuable tool for generating predictions of an integrative model’s behavior on densely sampled subsets of its parameter space.

Next-Generation Sequencing: The Translational Medicine Approach from "Bench to Bedside to Population"

Humans have predicted the relationship between heredity and diseases for a long time. Only in the beginning of the last century, scientists begin to discover the connotations between different genes and disease phenotypes. Recent trends in next-generation sequencing (NGS) technologies have brought a great momentum in biomedical research that in turn has remarkably augmented our basic understanding of human biology and its associated diseases. State-of-the-art next generation biotechnologies have started making huge strides in our current understanding of mechanisms of various chronic illnesses like cancers, metabolic disorders, neurodegenerative anomalies, etc. We are experiencing a renaissance in biomedical research primarily driven by next generation biotechnologies like genomics, transcriptomics, proteomics, metabolomics, lipidomics etc. Although genomic discoveries are at the forefront of next generation omics technologies, however, their implementation into clinical arena had been painstakingly slow mainly because of high reaction costs and unavailability of requisite computational tools for large-scale data analysis. However rapid innovations and steadily lowering cost of sequence-based chemistries along with the development of advanced bioinformatics tools have lately prompted launching and implementation of large-scale massively parallel genome sequencing programs in different fields ranging from medical genetics, infectious biology, agriculture sciences etc. Recent advances in large-scale omics-technologies is bringing healthcare research beyond the traditional “bench to bedside” approach to more of a continuum that will include improvements, in public healthcare and will be primarily based on predictive, preventive, personalized, and participatory medicine approach (P4). Recent large-scale research projects in genetic and infectious disease biology have indicated that massively parallel whole-genome/whole-exome sequencing, transcriptome analysis, and other functional genomic tools can reveal large number of unique functional elements and/or markers that otherwise would be undetected by traditional sequencing methodologies. Therefore, latest trends in the biomedical research is giving birth to the new branch in medicine commonly referred to as personalized and/or precision medicine. Developments in the post-genomic era are believed to completely restructure the present clinical pattern of disease prevention and treatment as well as methods of diagnosis and prognosis. The next important step in the direction of the precision/personalized medicine approach should be its early adoption in clinics for future medical interventions. Consequently, in coming year’s next generation biotechnologies will reorient medical practice more towards disease prediction and prevention approaches rather than curing them at later stages of their development and progression, even at wider population level(s) for general public healthcare system.

Impact of Pharmacogenetic Markers of CYP2D6 and DRD2 on Prolactin Response in Risperidone-Treated Thai Children and Adolescents With Autism Spectrum Disorders

OBJECTIVE

The aim of the study was to identify the impact of pharmacogenetic markers associated with prolactin concentration in risperidone-treated children and adolescents with autism spectrum disorders.

METHODS

One hundred forty-seven children and adolescents with autism, aged 3 to 19 years, received risperidone. The clinical data of patients were recorded from medical records. Prolactin levels were measured by chemiluminescence immunoassay. Three CYP2D6 single nucleotide polymorphisms, CYP2D6*4 (1846G>A), *10 (100C>T), and *41 (2988G>A), 1 gene deletion (*5), and DRD2 Taq1A (rs1800497) polymorphism were genotyped by TaqMan real-time polymerase chain reaction.

RESULTS

The 3 common allelic frequencies were CYP2D6*10 (55.10%), *1 (32.65%), and *5 (6.12%), respectively. Patients were grouped according to their CYP2D6 genotypes. There was no significant correlation between the concentrations of prolactin among the CYP2D6 genotypes. In addition, there were no statistical differences in the prolactin response among the CYP2D6-predicted phenotypes of extensive metabolizer and intermediate metabolizer. The DRD2 genotype frequencies were Taq1A A2A2 (38.77%), A1A2 (41.50%), and A1A1 (19.73%), respectively. There were statistically significant differences in prolactin level of patients among the 3 groups (P = 0.033). The median prolactin level in patients with DRD2 Taq1A A2A2 (17.80 ng/mL) was significantly higher than A1A2 (17.10 ng/mL) and A1A1 (12.70 ng/mL).

CONCLUSIONS

DRD2 Taq1A A2A2 polymorphisms may play a significant role in the hyperprolactinemia- associated with risperidone treatment in children and adolescent with autism spectrum disorder. Many drugs used chronically in psychiatric diseases exert their effects mainly through the dopamine D2 receptor. It is therefore possible that these drugs could alter the expression of any dopamine receptor, thus affecting the pharmacodynamics characteristics and toxicity of drug substrates during pharmacotherapy.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

Prediction of disease-gene-drug relationships following a differential network analysis

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.

Neuroimaging genetic risk for Alzheimer's disease in preclinical individuals: From candidate genes to polygenic approaches

Better characterization of the preclinical phase of Alzheimer's disease (AD) is needed in order to develop effective interventions. Neuropathological changes in AD, including neuronal loss and the formation of proteinaceous deposits, begin up to 20 years before the onset of clinical symptoms. As such, the emergence of cognitive impairment should not be the sole basis used to diagnose AD nor to evaluate individuals for enrollment in clinical trials for preventative AD treatments. Instead, early preclinical biomarkers of disease and genetic risk should be used to determine most likely prognosis and enroll individuals in appropriate clinical trials. Neuroimaging-based biomarkers and genetic analysis together present a powerful system for classifying preclinical pathology in patients. Disease modifying interventions are more likely to produce positive outcomes when administered early in the course of AD. In this review, we examine the utility of the neuroimaging genetics field as it applies to AD and early detection during the preclinical phase. Neuroimaging studies focused on single genetic risk factors are summarized. However, we particularly focus on the recent increased interest in polygenic methods and discuss the benefits and disadvantages of these approaches. We discuss challenges in the neuroimaging genetics field, including limitations of statistical power arising from small effect sizes and the over-use of cross-sectional designs. Despite the limitations, neuroimaging genetics has already begun to influence clinical trial design and will play a major role in the prevention of AD.

From Prescription to Transcription: Genome Sequence as Drug Target

Personalizing treatments to account for genetically mediated differences in drug responses is an exciting opportunity to improve patient outcomes. In this issue, Soccio et al. reveal new mechanisms by which non-coding variants alter the activity of the anti-diabetic drug rosiglitazone.

Pharmacogenetics of adverse reactions to antiepileptic drugs

INTRODUCTION

Adverse drug reactions (ADRs) are a major public health concern and a leading cause of morbidity and mortality in the world. In the case of antiepileptic drugs (AEDs), ADRs constitute a barrier to successful treatment since they decrease treatment adherence and impact patients' quality of life of patients. Pharmacogenetics aims to identify genetic polymorphisms associated with drug safety. This article presents a review of genes coding for drug metabolising enzymes and drug transporters, and HLA system genes that have been linked to AED-induced ADRs.

DEVELOPMENT

To date, several genetic variations associated with drug safety have been reported: CYP2C9*2 and *3 alleles, which code for enzymes with decreased activity, have been linked to phenytoin (PHT)-induced neurotoxicity; GSTM1 null alleles with hepatotoxicity induced by carbamazepine (CBZ) and valproic acid (VPA); EPHX1 polymorphisms with teratogenesis; ABCC2 genetic variations with CBZ- and VPA-induced neurological ADRs; and HLA alleles (e.g. HLA-B*15:02, -A*31:01, -B*15:11, -C*08:01) with cutaneous ADRs.

CONCLUSIONS

Published findings show that there are ADRs with a pharmacogenetic basis and a high interethnic variability, which indicates a need for future studies in different populations to gather more useful results for larger number of patients. The search for biomarkers that would allow predicting ADRs to AEDs could improve pharmacotherapy for epilepsy.

Neuropsychological mechanism underlying antidepressant effect: a systematic meta-analysis

Antidepressants are widely used in clinical practice for the treatment of depression and other mood disorders. Numerous neuroimaging studies have recently examined how antidepressants influence emotional processes. However, both clinical trials and neuroimaging studies have reported inconsistent responses to antidepressants. Moreover, the neuropsychological mechanisms by which antidepressants act to improve depressive features remain underspecified. This systematic meta-analysis summarizes pharmacological neuroimaging studies (before February 2013) and the antidepressant effects on human brain activity underlying emotional processes. Sixty fMRI studies (involving 1569 subjects) applying antidepressants vs control were included in the current quantitative Activation Likelihood Estimation (ALE) meta-analysis. Pooling of results by ALE meta-analyses was stratified for population (mood disorder patients/healthy volunteers), emotional valence (positive/negative emotions) and treatment effects (increased/decreased brain activity). For both patients and healthy volunteers, the medial prefrontal and core limbic parts of the emotional network (for example, anterior cingulate, amygdala and thalamus) were increased in response to positive emotions but decreased to negative emotions by repeated antidepressant administration. Moreover, selective antidepressant effects were uncovered in patients and healthy volunteers, respectively. Antidepressants increased activity in the dorsolateral prefrontal (dlPFC), a key region mediating emotion regulation, during both negative and positive emotions in patients. Repeated antidepressant administration decreased brain responses to positive emotions in the nucleus accumbens, putamen, medial prefrontal and midbrain in healthy volunteers. Antidepressants act to normalize abnormal neural responses in depressed patients by increasing brain activity to positive stimuli and decreasing activity to negative stimuli in the emotional network, and increasing engagement of the regulatory mechanism in dlPFC.

Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Genetic variations of PIP4K2A confer vulnerability to poor antipsychotic response in severely ill schizophrenia patients

Literature suggests that disease severity and neurotransmitter signaling pathway genes can accurately identify antipsychotic response in schizophrenia patients. However, putative role of signaling molecules has not been tested in schizophrenia patients based on severity of illness, despite its biological plausibility. In the present study we investigated the possible association of polymorphisms from five candidate genes RGS4, SLC6A3, PIP4K2A, BDNF, PI4KA with response to antipsychotic in variably ill schizophrenia patients. Thus in present study, a total 53 SNPs on the basis of previous reports and functional grounds were examined for their association with antipsychotic response in 423 schizophrenia patients segregated into low and high severity groups. Additionally, haplotype, diplotype, multivariate logistic regression and multifactor-dimensionality reduction (MDR) analyses were performed. Furthermore, observed associations were investigated in atypical monotherapy (n = 355) and risperidone (n = 260) treated subgroups. All associations were estimated as odds ratio (OR) and 95% confidence interval (CI) and test for multiple corrections was applied. Single locus analysis showed significant association of nine variants from SLC6A3, PIP4K2A and BDNF genes with incomplete antipsychotic response in schizophrenia patients with high severity. We identified significant association of six marker diplotype ATTGCT/ATTGCT (rs746203-rs10828317-rs7094131-rs2296624-rs11013052-rs1409396) of PIP4K2A gene in incomplete responders (corrected p-value = 0.001; adjusted-OR = 3.19, 95%-CI = 1.46–6.98) with high severity. These associations were further observed in atypical monotherapy and risperidone sub-groups. MDR approach identified gene-gene interaction among BDNF_rs7103411-BDNF_rs1491851-SLC6A3_rs40184 in severely ill incomplete responders (OR = 7.91, 95%-CI = 4.08–15.36). While RGS4_rs2842026-SLC6A3_rs2975226 interacted synergistically in incomplete responders with low severity (OR = 4.09, 95%-CI = 2.09–8.02). Our findings provide strong evidence that diplotype ATTGCT/ATTGCT of PIP4K2A gene conferred approximately three-times higher incomplete responsiveness towards antipsychotics in severely ill patients. These results are consistent with the known role of phosphatidyl-inositol-signaling elements in antipsychotic action and outcome. Findings have implication for future molecular genetic studies as well as personalized medicine. However more work is warranted to elucidate underlying causal biological pathway.

Inconsistency in large pharmacogenomic studies

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

Association between cholesterol synthesis/absorption markers and effects of cholesterol lowering by atorvastatin among patients with high risk of coronary heart disease

No indices are currently available to facilitate clinicians to identify patients who need either statin monotherapy or statin-ezetimibe combined treatment. We aimed to investigate whether cholesterol synthesis and absorption markers can predict the cholesterol-lowering response to statin. Total 306 statin-naïve patients with high risk of coronary heart disease (CHD) were treated with atorvastatin 20 mg/day for 1 month. Cholesterol synthesis and absorption markers and LDL cholesterol (LDL-C) levels were measured before and after treatment. Atorvastatin decreased LDL-C by 36.8% (range: decrease of 74.5% to increase of 31.9%). Baseline cholesterol synthesis marker lathosterol and cholesterol absorption marker campesterol codetermined the effect of atorvastatin treatment. The effect of cholesterol lowering by atorvastatin was significantly associated with baseline lathosterol levels but modified bidirectionally by baseline campesterol levels. In patients with the highest baseline campesterol levels, atorvastatin treatment decreased cholesterol absorption by 46.1%, which enhanced the effect of LDL-C lowering. Atorvastatin treatment increased cholesterol absorption by 52.3% in those with the lowest baseline campesterol levels, which attenuated the effect of LDL-C reduction. Especially those with the highest lathosterol but the lowest campesterol levels at baseline had significantly less LDL-C reduction than those with the same baseline lathosterol levels but the highest campesterol levels (27.3% versus 42.4%, P = 0.002). These results suggest that combined patterns of cholesterol synthesis/absorption markers, rather than each single marker, are potential predictors of the LDL-C-lowering effects of atorvastatin in high-risk CHD patients.

Genetic Polymorphisms of Cytochrome P450 Enzymes and the Effect on Interindividual, Pharmacokinetic Variability in Extensive Metabolizers

Genetic polymorphisms of cytochrome P450 (CYP) enzymes are one of the factors that contribute to the pharmacokinetic (PK) variability of drugs. PK variability is observed in the bimodal distribution between extensive metabolizers (EMs) and poor metabolizers (PMs). PK variability may also exist between individuals genotyped as homozygous EMs and heterozygous EMs. This may carry implications for drug dosing and drug response (e.g., risk of therapeutic failure or drug toxicity). Studies have reported significant PK differences between homozygous and heterozygous EMs. Some literature suggests that this distinction may be of clinical relevance. Due to study design limitations and data that are either sparse or conflicting, generalizations regarding the potential impact of the CYP genotype, within EMs, are difficult. Optimally designed clinical trials are needed. This review evaluates the potential impact of CYP genetic polymorphisms on interindividual PK variability of drugs within an EM population.

Imaging genetics: implications for research on variable antidepressant drug response

Genetic variation of SLC6A4, HTR1A, MAOA, COMT and BDNF has been associated with depression, variable antidepressant drug responses as well as impacts on brain regions of emotion processing that are modulated by antidepressants. Pharmacogenetic studies are using psychometric outcome measures of drug response and are hampered by small effect sizes that might be overcome by the use of intermediate endophenotypes of drug response, which are suggested by imaging studies. Such an approach will not only tighten the relationship between genes and drug response, but also yield new insights into the neurobiology of depression and individual drug responses. This article provides a comprehensive overview of pharmacogenetic, imaging genetics and drug response studies, utilizing imaging techniques within the context of antidepressive drug therapy.

The Protective Effect of Minocycline in a Paraquat-Induced Parkinson's Disease Model in Drosophila is Modified in Altered Genetic Backgrounds

Epidemiological studies link the herbicide paraquat to increased incidence of Parkinson's disease (PD). We previously reported that Drosophila exposed to paraquat recapitulate PD symptoms, including region-specific degeneration of dopaminergic neurons. Minocycline, a tetracycline derivative, exerts ameliorative effects in neurodegenerative disease models, including Drosophila. We investigated whether our environmental toxin-based PD model could contribute to an understanding of cellular and genetic mechanisms of minocycline action and whether we could assess potential interference with these drug effects in altered genetic backgrounds. Cofeeding of minocycline with paraquat prolonged survival, rescued mobility defects, blocked generation of reactive oxygen species, and extended dopaminergic neuron survival, as has been reported previously for a genetic model of PD in Drosophila. We then extended this study to identify potential interactions of minocycline with genes regulating dopamine homeostasis that might modify protection against paraquat and found that deficits in GTP cyclohydrolase adversely affect minocycline rescue. We further performed genetic studies to identify signaling pathways that are necessary for minocycline protection against paraquat toxicity and found that mutations in the Drosophila genes that encode c-Jun N-terminal kinase (JNK) and Akt/Protein kinase B block minocycline rescue.

The role of pharmacogenetics in nonmalignant gastrointestinal diseases

Genetic variation influences the absorption and efflux of drugs in the intestine, the metabolism of drugs in the liver and the effects of these drugs on their target proteins. Indeed, variations in genes whose products have a role in the pathophysiology of nonmalignant gastrointestinal diseases, such as IBD, have been shown to affect the response of patients to therapy. This Review provides an overview of pharmacogenetics in the management of nonmalignant gastrointestinal diseases on the basis of data from clinical trials. Genetic variants that have the greatest effect on the management of patients with IBD involve the metabolism of thiopurines. Variation in drug metabolism by cytochrome P450 enzymes also requires attention so as to avoid drug interactions in patients receiving tricyclic antidepressants and PPIs. Few genotyping tests are currently used in the clinical management of patients with nonmalignant gastrointestinal diseases, owing to a lack of data from clinical trials showing their effectiveness in predicting nonresponse or adverse outcomes. However, pharmacogenetics could have a beneficial role in enabling pharmacotherapy for nonmalignant gastrointestinal diseases to be targeted to the individual patient.

Integration of pharmacogenetics and pharmacogenomics in drug development: implications for regulatory and medical decision making in pediatric diseases

This article aims to provide an overview of the current situation regarding pharmacogenetic and pharmacogenomic (PG) studies in pediatrics, with a special focus on the role of PG data in the regulatory decision-making process. Despite the gap in pharmacogenetic research due to the lack of translational studies in adults and children, several technologies exist in drug development and biomarkers validation, which could supply valuable information concerning labeling and dosing recommendations. If performed under strict good clinical practice quality criteria, such findings could be included in the submission package of new chemical entities and used as additional information for prescribers, supporting further evaluation and understanding of the efficacy and safety profile of new medicines. Even though regulatory authorities may be aware of the potential role of PG in medical practice and guidances are available about the integration of PG in drug development, most data obtained from PG studies are not used by prescribers. The challenge is to better understand whether PG markers can be used to assess potential differences in drug response during the clinical program, so PG data can be integrated into the regulatory decision-making process, enabling the introduction of labeling information that promotes optimal dosing in the pediatric population.