From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions

Implementation of pharmacogenomics testing for precision medicine

Great strides have been made in the past decade to lower barriers to clinical pharmacogenomics implementation. Nevertheless, PGx consultation prior to prescribing therapeutics is not yet mainstream. This review addresses the current climate surrounding PGx implementation, focusing primarily on strategies for implementation at academic institutions, particularly at The University of Chicago, and provides an up-to-date guide of resources supporting the development of PGx programs. Remaining challenges and recent strategies for overcoming these challenges to implementation are discussed.

Gene-Environment Interactions: My Unique Journey

I am deeply honored to be invited to write this scientific autobiography. As a physician-scientist, pediatrician, molecular biologist, and geneticist, I have authored/coauthored more than 600 publications in the fields of clinical medicine, biochemistry, biophysics, pharmacology, drug metabolism, toxicology, molecular biology, cancer, standardized gene nomenclature, developmental toxicology and teratogenesis, mouse genetics, human genetics, and evolutionary genomics. Looking back, I think my career can be divided into four distinct research areas, which I summarize mostly chronologically in this article: (a) discovery and characterization of the AHR/CYP1 axis, (b) pharmacogenomics and genetic prediction of response to drugs and other environmental toxicants, (c) standardized drug-metabolizing gene nomenclature based on evolutionary divergence, and (d) discovery and characterization of the SLC39A8 gene encoding the ZIP8 metal cation influx transporter. Collectively, all four topics embrace gene-environment interactions, hence the title of my autobiography.

Review - The impact of pharmacogenetics on the outcome of immune checkpoint inhibitors

The development of immune checkpoint inhibitors (ICIs) has a tremendous effect on the treatment options for multiple types of cancer. Nonetheless, there is a large interpatient variability in response, survival, and the development of immune-related adverse events (irAEs). Pharmacogenetics is the general term for germline genetic variations, which may cause the observed interindividual differences in response or toxicity to treatment. These genetic variations can either be single-nucleotide polymorphisms (SNPs) or structural variants, such as gene deletions, amplifications or rearrangements. For ICIs, pharmacogenetic variation in the human leukocyte antigen molecules has also been studied with regard to treatment outcome. This review presents a summary of the literature regarding the pharmacogenetics of ICI treatment, discusses the most important known genetic variations and offers recommendations on the application of pharmacogenetics for ICI treatment.

Clinical utilities and end-user experience of pharmacogenomics: 39 mo of clinical implementation experience in an Australian hospital setting

BACKGROUND

Pharmacogenomics (PG) testing is under-utilised in Australia. Our research provides Australia-specific data on the perspectives of patients who have had PG testing and those of the clinicians involved in their care, with the aim to inform wider adoption of PG into routine clinical practice.

AIM

To investigate the frequency of actionable drug gene interactions and assess the perceived utility of PG among patients and clinicians.

METHODS

We conducted a retrospective audit of PG undertaken by 100 patients at an Australian public hospital genetics service from 2018 to 2021. Via electronic surveys we compared and contrasted the experience, understanding and usage of results between these patients and their clinicians.

RESULTS

Of 100 patients who had PG, 84% were taking prescription medications, of which 67% were taking medications with actionable drug-gene interactions. Twenty-five out of 81 invited patients and 17 out of 89 invited clinicians completed the surveys. Sixty-eight percent of patients understood their PG results and 48% had medications changed following testing. Paired patient-clinician surveys showed patient-perceived utility and experience was positive, contrasting their clinicians’ hesitancy on PG adoption who identified insufficient education/training, lack of clinical support, test turnaround time and cost as barriers to adoption.

CONCLUSION

Our dichotomous findings between the perspectives of our patient and clinician cohorts suggest the uptake of PG is likely to be driven by patients and clinicians need to be prepared to provide information and guidance to their patients.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children

Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.

Sources of Interindividual Variability

The efficacy, safety, and tolerability of drugs are dependent on numerous factors that influence their disposition. A dose that is efficacious and safe for one individual may result in sub-therapeutic or toxic blood concentrations in others. A significant source of this variability in drug response is drug metabolism, where differences in presystemic and systemic biotransformation efficiency result in variable degrees of systemic exposure (e.g., AUC, C_max, and/or C_min) following administration of a fixed dose.Interindividual differences in drug biotransformation have been studied extensively. It is recognized that both intrinsic factors (e.g., genetics, age, sex, and disease states) and extrinsic factors (e.g., diet , chemical exposures from the environment, and the microbiome) play a significant role. For drug-metabolizing enzymes, genetic variation can result in the complete absence or enhanced expression of a functional enzyme. In addition, upregulation and downregulation of gene expression, in response to an altered cellular environment, can achieve the same range of metabolic function (phenotype), but often in a less predictable and time-dependent manner. Understanding the mechanistic basis for variability in drug disposition and response is essential if we are to move beyond the era of empirical, trial-and-error dose selection and into an age of personalized medicine that will improve outcomes in maintaining health and treating disease.

A Review of the Important Role of CYP2D6 in Pharmacogenomics

Cytochrome P450 2D6 (CYP2D6) is a critical pharmacogene involved in the metabolism of ~20% of commonly used drugs across a broad spectrum of medical disciplines including psychiatry, pain management, oncology and cardiology. Nevertheless, CYP2D6 is highly polymorphic with single-nucleotide polymorphisms, small insertions/deletions and larger structural variants including multiplications, deletions, tandem arrangements, and hybridisations with non-functional CYP2D7 pseudogenes. The frequency of these variants differs across populations, and they significantly influence the drug-metabolising enzymatic function of CYP2D6. Importantly, altered CYP2D6 function has been associated with both adverse drug reactions and reduced drug efficacy, and there is growing recognition of the clinical and economic burdens associated with suboptimal drug utilisation. To date, pharmacogenomic clinical guidelines for at least 48 CYP2D6-substrate drugs have been developed by prominent pharmacogenomics societies, which contain therapeutic recommendations based on CYP2D6-predicted categories of metaboliser phenotype. Novel algorithms to interpret CYP2D6 function from sequencing data that consider structural variants, and machine learning approaches to characterise the functional impact of novel variants, are being developed. However, CYP2D6 genotyping is yet to be implemented broadly into clinical practice, and so further effort and initiatives are required to overcome the implementation challenges and deliver the potential benefits to the bedside.

Effect of probiotics on obesity-related markers per enterotype: a double-blind, placebo-controlled, randomized clinical trial

Background

Prevention and improvement of disease symptoms are important issues, and probiotics are suggested as a good treatment for controlling the obesity. Human gut microbiota has different community structures. Because gut microbial composition is assumed to be linked to probiotic function, this study evaluated the efficacy of probiotics on obesity-related clinical markers according to gut microbial enterotype.

Methods

Fifty subjects with body mass index over 25 kg/m² were randomly assigned to either the probiotic or placebo group. Each group received either unlabeled placebo or probiotic capsules for 12 weeks. Body weight, waist circumference, and body composition were measured every 3 weeks. Using computed tomography, total abdominal fat area and visceral fat area were measured. Blood and fecal samples were collected before and after the intervention for biochemical parameters and gut microbial compositions analysis.

Results

Gut microbial compositions of all the subjects were classified into two enterotypes according to Prevotella/Bacteroides ratio. The fat percentage, blood glucose, and insulin significantly increased in the Prevotella-rich enterotype of the placebo group. The obesity-related markers, such as waist circumference, total fat area, visceral fat, and ratio of visceral to subcutaneous fat area, were significantly reduced in the probiotic group. The decrease of obesity-related markers was greater in the Prevotella-rich enterotype than in the Bacteroides-rich enterotype.

Conclusion

Administration of probiotics improved obesity-related markers in obese people, and the efficacy of probiotics differed per gut microbial enterotype and greater responses were observed in the Prevotella-dominant enterotype.

Big data in pharmacogenomics: current applications, perspectives and pitfalls

The efficiency of new generation sequencing methods and the reduction of their cost has led pharmacogenomics to gradually supplant pharmacogenetics, leading to new applications in personalized medicine along with new perspectives in drug design or identification of drug response factors. The amount of data generated in genomics fits the definition of big data, and need a specific bioinformatics processing following standard steps: data collection, processing, analysis and interpretation. Pitfalls of pharmacogenomics studies are directly related to these steps. This review aims to describe these steps from a pharmacogenomic point of view, focusing on bioinformatics aspects.

Quantitative systems pharmacology and the personalized drug-microbiota-diet axis

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible 'biomarkers', which are predictors of one's biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying 'endophenotypes' in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

[Porphyria cutanea tara]

Porphyria cutanea tara (PCT) has a prevelance of about 40 new diagnoses per 1 million people per year and is the most frequently occurring type of porphyria worldwide. Inhibition of the uroporphyrinogen decarboxylase (UROD) is the main cause of the disease, which can be the result of a heterozygous or homozygous mutation of the UROD gene; however, xenobiotics or other diseases may play an important role for the precipitation of the disease. Risk factors include alcohol, estrogen, iron overload, and hemochromatosis, hepatitis C or poisoning, e.g., with polyhalogenated aromatic compounds such as hexachlorobenzene. Signs and symptoms are blisters, skin fragility, erosions hyperpigmentation, sclerodermoid plaques. Therapy includes sun protection, prevention of risk factors, phlebotomy, and chloroquine.

Head-and-neck squamous cell carcinoma risk in smokers: no association detected between phenotype and AHR, CYP1A1, CYP1A2, or CYP1B1 genotype

BACKGROUND

Head-and-neck squamous cell carcinoma (HNSCC) differs between smokers and nonsmokers in etiology and clinical presentation. Because of demonstrated unequivocal involvement in smoking-induced cancer in laboratory animals, four candidate genes--AHR, CYP1A1, CYP1A2, and CYP1B1--were selected for a clinical genotype-phenotype association study of HNSCC risk in smokers. Thirty-six single-nucleotide variants (mostly tag-SNPs) within and near these four genes [16 (AHR), 4 (CYP1A1), 4 (CYP1A2), and 12 (CYP1B1)] were chosen.

METHODS

Extreme discordant phenotype (EDP) method of analysis was used to increase statistical power. HNSCC patients--having smoked 1-40 cigarette pack-years--represented the "highly-sensitive" (HS) population; heavy smokers having smoked ≥80 cigarette-pack-years without any type of cancer comprised the "highly-resistant" (HR) group. The vast majority of smokers were intermediate and discarded from consideration. Statistical tests were performed on N = 112 HS and N = 99 HR DNA samples from whole blood.

CONCLUSIONS

Among the four genes and flanking regions--one haploblock, ACTTGATC in the 5' portion of CYP1B1, retained statistical significance after 100,000 permutations (P = 0.0042); among our study population, this haploblock was found in 36.4% of African-American, but only 1.49% of Caucasian, HNSCC chromosomes. Interestingly, in the 1000 Genomes Project database, frequency of this haplotype (in 1322 African and 1006 Caucasian chromosomes) is 0.356 and 0.003, respectively. This study represents an excellent example of "spurious association by population stratification". Considering the cohort size, we therefore conclude that the variant alleles chosen for these four genes, alone or in combinations, are not statistically significantly associated with risk of cigarette-smoking-induced HNSCC.

Preparation of Thermosensitive Gel for Controlled Release of Levofloxacin and Their Application in the Treatment of Multidrug-Resistant Bacteria

Levofloxacin is a synthetic broad-spectrum antibacterial agent for oral or intravenous administration. Chemically, levofloxacin is the levorotatory isomer (L-isomer) of racemate ofloxacin, a fluoroquinolone antibacterial agent. Quinolone derivatives rapidly and specifically inhibit the synthesis of bacterial DNA. Levofloxacin has in vitro activity against a broad range of aerobic and anaerobic Gram-positive and Gram-negative bacteria. However, formulation of combined poloxamers thermoregulated (as Pluronic® F127) and levofloxacin for use in multiresistant bacterial treatment were poorly described in the current literature. Thus, the aim of the present work is to characterize poloxamers for levofloxacin controlled release and their use in the treatment of multidrug bacterial resistance. Micelles were produced in colloidal dispersions, with a diameter between 5 and 100 nm, which form spontaneously from amphiphilic molecules under certain conditions as concentration and temperature. Encapsulation of levofloxacin into nanospheres showed efficiency and enhancement of antimicrobial activity against Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae when compared with only levofloxacin. Furthermore, all formulations were not cytotoxic for NIH/3T3 cell lineage. In conclusion, poloxamers combined with levofloxacin have shown promising results, better than alone, decreasing the minimal inhibitory concentration of the studied bacterial multiresistance strains. In the future, this new formulation will be used after being tested in animal models in patients with resistant bacterial strains.

Current status of biomarker research in neurology

Neurology is one of the typical disciplines where personalized medicine has been recently becoming an important part of clinical practice. In this article, the brief overview and a number of examples of the use of biomarkers and personalized medicine in neurology are described. The various issues in neurology are described in relation to the personalized medicine and diagnostic, prognostic as well as predictive blood and cerebrospinal fluid biomarkers. Such neurological domains discussed in this work are neuro-oncology and primary brain tumors glioblastoma and oligodendroglioma, cerebrovascular diseases focusing on stroke, neurodegenerative disorders especially Alzheimer's and Parkinson's diseases and demyelinating diseases such as multiple sclerosis. Actual state of the art and future perspectives in diagnostics and personalized treatment in diverse domains of neurology are given.

Mirror, mirror on the wall: which microbiomes will help heal them all?

BACKGROUND

Clinicians have known for centuries that there is substantial variability between patients in their response to medications-some individuals exhibit a miraculous recovery while others fail to respond at all. Still others experience dangerous side effects. The hunt for the factors responsible for this variation has been aided by the ability to sequence the human genome, but this just provides part of the picture. Here, we discuss the emerging field of study focused on the human microbiome and how it may help to better predict drug response and improve the treatment of human disease.

DISCUSSION

Various clinical disciplines characterize drug response using either continuous or categorical descriptors that are then correlated to environmental and genetic risk factors. However, these approaches typically ignore the microbiome, which can directly metabolize drugs into downstream metabolites with altered activity, clearance, and/or toxicity. Variations in the ability of each individual's microbiome to metabolize drugs may be an underappreciated source of differences in clinical response. Complementary studies in humans and animal models are necessary to elucidate the mechanisms responsible and to test the feasibility of identifying microbiome-based biomarkers of treatment outcomes. We propose that the predictive power of genetic testing could be improved by taking a more comprehensive view of human genetics that encompasses our human and microbial genomes. Furthermore, unlike the human genome, the microbiome is rapidly altered by diet, pharmaceuticals, and other interventions, providing the potential to improve patient care by re-shaping our associated microbial communities.

Predose and Postdose Blood Gene Expression Profiles Identify the Individuals Susceptible to Acetaminophen-Induced Liver Injury in Rats

The extent of drug-induced liver injury (DILI) can vary greatly between different individuals. Thus, it is crucial to identify susceptible population to DILI. The aim of this study was to determine whether transcriptomics analysis of predose and postdose rat blood would allow prediction of susceptible individuals to DILI using the widely applied analgesic acetaminophen (APAP) as a model drug. Based on ranking in alanine aminotransferase levels, five most susceptible and five most resistant rats were identified as two sub-groups after APAP treatment. Predose and postdose gene expression profiles of blood samples from these rats were determined by microarray analysis. The expression of 158 genes innately differed in the susceptible rats from the resistant rats in predose data. In order to identify more reliable biomarkers related to drug responses for detecting individuals susceptibility to APAP-induced liver injury (AILI), the changes of these genes' expression posterior to APAP treatment were detected. Through the further screening method based on the trends of gene expression between the two sub-groups before and after drug treatment, 10 genes were identified as potential predose biomarkers to distinguish between the susceptible and resistant rats. Among them, four genes, Incenp, Rpgrip1, Sbf1, and Mmp12, were found to be reproducibly in real-time PCR with an independent set of animals. They were all innately higher expressed in resistant rats to AILI, which are closely related to cell proliferation and tissue repair functions. It indicated that rats with higher ability of cell proliferation and tissue repair prior to drug treatment might be more resistant to AILI. In this study, we demonstrated that combination of predose and postdose gene expression profiles in blood might identify the drug related inter-individual variation in DILI, which is a novel and important methodology for identifying susceptible population to DILI.

CYP2D6 genetic polymorphisms in Southern Mexican Mayan Lacandones and Mestizos from Chiapas

AIM

In previous CYP2D6 genotyping studies in Mexican-Amerindians a very low frequency of poor metabolizers (PMs) has been reported. Moreover, ultrarapid metabolizers (UMs) status has only been analyzed in some groups from Northern Mexico.

MATERIALS & METHODS

In the present study we evaluated the hypothesis of low frequency of PMs in Mexican-Amerindians in Southern Mexican populations from Chiapas (Lacandones [ML] vs Mestizos [MM]). The frequency of UMs is also reported. CYP2D6 alleles *2, *3, *4, *5, *6, *10, *17, *35 and *41 and copy number variations were analyzed in 154 ML and 100 MM healthy volunteers.

RESULTS

The PM frequency was 0% in MLs and 1% in MMs, and for UMs was 2.6% in MLs and 3% in MMs.

CONCLUSION

The present data support previous findings reporting a very low frequency of CYP2D6 PMs in Mexican-Amerindians. Furthermore, the predicted UM phenotype in both MMs and MLs was lower than those reported for most Mexican populations.

Neonatal medications

Neonatal abstinence syndrome (NAS) is reaching epidemic proportions related to perinatal use of opioids. There are many approaches to assess and manage NAS, including one we have outlined. A standardized approach is likely to reduce length of stay and variability in practice. Circumcision is a frequent, painful procedure performed in the neonatal period. The rationale for providing analgesia is presented as well as a review of methods. Pharmacogenomics and pharmacogenetics have expanded our understanding of diseases and their drug therapy. Some applications of pharmacogenomics to the neonatal period are presented, along with pediatric challenges of developmental expression of drug-metabolizing enzymes.

Pharmacogenomics and targeted therapy of cancer: Focusing on non-small cell lung cancer

Recent studies have been established high degree of genetic diversity in solid organ tumors among individuals and even between individual tumor cells. This intratumor and intertumor genetic diversity results in a heterogeneous tumor with unique characteristics which potentially allows effective drug therapy. The goal of pharmacogenomics is to elucidate the genetic network(s) that underlie drug efficacy and drug resistance. Advances in targeted and personalized therapy play an increasingly important role in many common cancers, notably lung cancer, due to the high incidence, prevalence, mortality and the greater tendency towards drug resistance seen in these patients. Non-small cell lung cancer (NSCLC) is characterized by mutations in the epidermal growth factor receptor (EGFR) and or downstream kinase pathways. This has led to the development of highly selective monoclonal antibodies and EGFR tyrosine kinase inhibitors (EGFR-TKIs) to prevent cancer initiation, proliferation, differentiation, angiogenesis, survival, and invasion. However, resistance to many of these new treatments is induced and further pharmacogenomic analysis has revealed mutations associated with increased or reduced drug efficacy. Combinations of kinase inhibitors or potentially the targeting of cancer stem cells may further increase the success of pharmacogenomics in treating patients with lung cancer.

Genome-wide association studies in pharmacogenomics of antidepressants

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.

Improved drug therapy: triangulating phenomics with genomics and metabolomics

Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.

A review of the role of electronic health record in genomic research

Electronic health record (EHR)-driven genomic research is a recent strategy used to answer research questions using EHR data linked to DNA samples. In models using EHR, after the subject's DNA is collected, a linkage between the DNA sample and the EHR data is maintained. This makes the EHR the paramount source of phenotypic information. The National Human Genome Research Institute sponsored Electronic Medical Records and Genomics (eMERGE) network began in five sites in 2007 and was expanded to nine sites in 2012. This network has developed the methods and best practices for utilizing EHR as a tool for genomic research. Therefore, it is vital to understand the configuration of EHR used to capture data in clinical practice and feasibility of integration with clinical genetic test results. We present a detailed review of the role and importance of EHR in the field of genomic research.

Genetic tests: clinical validity and clinical utility

When evaluating the appropriate use of new genetic tests, clinicians and health care policymakers must consider the accuracy with which a test identifies a patient's clinical status (clinical validity) and the risks and benefits resulting from test use (clinical utility). Genetic tests in current use vary in accuracy and potential to improve health outcomes, and these test properties may be influenced by testing technology and the clinical setting in which the test is used. This unit defines clinical validity and clinical utility, provides examples, and considers the implications of these test properties for clinical practice.

Wellness and health omics linked to the environment: the WHOLE approach to personalized medicine

The WHOLE approach to personalized medicine represents an effort to integrate clinical and genomic profiling jointly into preventative health care and the promotion of wellness. Our premise is that genotypes alone are insufficient to predict health outcomes, since they fail to account for individualized responses to the environment and life history. Instead, integrative genomic approaches incorporating whole genome sequences and transcriptome and epigenome profiles, all combined with extensive clinical data obtained at annual health evaluations, have the potential to provide more informative wellness classification. As with traditional medicine where the physician interprets subclinical signs in light of the person's health history, truly personalized medicine will be founded on algorithms that extract relevant information from genomes but will also require interpretation in light of the triggers, behaviors, and environment that are unique to each person. This chapter discusses some of the major obstacles to implementation, from development of risk scores through integration of diverse omic data types to presentation of results in a format that fosters development of personal health action plans.

Sources of interindividual variability

The efficacy, safety, and tolerability of drugs are dependent on numerous factors that influence their disposition. A dose that is efficacious and safe for one individual may result in sub-therapeutic or toxic blood concentrations in other individuals. A major source of this variability in drug response is drug metabolism, where differences in pre-systemic and systemic biotransformation efficiency result in variable degrees of systemic exposure (e.g., AUC, C max, and/or C min) following administration of a fixed dose.Interindividual differences in drug biotransformation have been studied extensively. It is well recognized that both intrinsic (such as genetics, age, sex, and disease states) and extrinsic (such as diet, chemical exposures from the environment, and even sunlight) factors play a significant role. For the family of cytochrome P450 enzymes, the most critical of the drug metabolizing enzymes, genetic variation can result in the complete absence or enhanced expression of a functional enzyme. In addition, up- and down-regulation of gene expression, in response to an altered cellular environment, can achieve the same range of metabolic function (phenotype), but often in a less reliably predictable and time-dependent manner. Understanding the mechanistic basis for drug disposition and response variability is essential if we are to move beyond the era of empirical, trial-and-error dose selection and into an age of personalized medicine that brings with it true improvements in health outcomes in the therapeutic treatment of disease.

Pharmacogenomics and pharmacoepigenomics in pediatric medicine

In the past several years, human genetics studies have progressed from monogenic to complex and common diseases because of the advancement in technologies. There is increased knowledge of the pharmacokinetics and pharmacogenomics of the drugs in adults as well as in children. These technological developments provided new diagnostic, prognostic, and therapeutic opportunities. We are now in a position to address many additional ambitious questions. For instance, in clinical medicine, interindividual variation in drug response is a major problem. Some of the heterogeneity of drug safety and efficacy among individuals can be explained by pharmacogenomics. It has also the potential to improve the treatment in both adults and children. In pediatrics however, there is ontogeny and metabolic capacity in children is different compared to adults. Several specific developmental changes may underlie some of the variability in drug response seen in children. They may also be responsible for adverse drug reactions (ADRs). Therefore, much of the diversity in drug effects cannot be explained by studying the genomic diversity alone. It is necessary to include the effect of growth (involves variations in gene expression) along with genetic differences when explaining the variability in treatment response. In this respect epigenomics may expand the scope of pharmacogenomics towards optimization of drug therapy. Future studies must focus on periods of maturation of the drug-metabolizing enzymes and polymorphisms in their genes by using candidate gene approach, gene expression analysis, genome-wide haplotype mapping, and proteomics. The integration of genetic data and clinical phenotypes along with the role of other factors is necessary to evaluate both efficacy and ADRs of any drug. It may require extensive genetic epidemiological studies spanning over many years.

Connecting the dots: applications of network medicine in pharmacology and disease

In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease.(1) Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting "big picture" allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.

Genetic risk prediction: individualized variability in susceptibility to toxicants

Genetic risk prediction uses genetic data to individualize prediction of outcome or effect from a known harmful toxicant. Several examples of toxicogenetics (usually binary traits) are discussed, reflecting largely Mendelian traits before the Human Genome Project began in 1990. Numerous complexities of the genome and what constitutes "a gene" have emerged during these past two decades. Examples of toxicogenomics (continuous outcomes, gradients) are examined. Most xenobiotic-induced environmental diseases resemble human complex diseases or other multifactorial traits such as height; these traits result from hundreds of low-effect genes. Consequently, uncovering an association between a trait and a genetic variant in a large cohort can provide important information about underlying biology; however, screening for a specific variant in an individual worker or patient has poor predictive value and little clinical utility. Individualized risk assessment for toxicants that cause environmental diseases, although a lofty goal, remains to be achieved.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Genetic variability of drug-metabolizing enzymes: the dual impact on psychiatric therapy and regulation of brain function

Polymorphic drug-metabolizing enzymes (DMEs) are responsible for the metabolism of the majority of psychotropic drugs. By explaining a large portion of variability in individual drug metabolism, pharmacogenetics offers a diagnostic tool in the burgeoning era of personalized medicine. This review updates existing evidence on the influence of pharmacogenetic variants on drug exposure and discusses the rationale for genetic testing in the clinical context. Dose adjustments based on pharmacogenetic knowledge are the first step to translate pharmacogenetics into clinical practice. However, also clinical factors, such as the consequences on toxicity and therapeutic failure, must be considered to provide clinical recommendations and assess the cost-effectiveness of pharmacogenetic treatment strategies. DME polymorphisms are relevant not only for clinical pharmacology and practice but also for research in psychiatry and neuroscience. Several DMEs, above all the cytochrome P (CYP) enzymes, are expressed in the brain, where they may contribute to the local biochemical homeostasis. Of particular interest is the possibility of DMEs playing a physiological role through their action on endogenous substrates, which may underlie the reported associations between genetic polymorphisms and cognitive function, personality and vulnerability to mental disorders. Neuroimaging studies have recently presented evidence of an effect of the CYP2D6 polymorphism on basic brain function. This review summarizes evidence on the effect of DME polymorphisms on brain function that adds to the well-known effects of DME polymorphisms on pharmacokinetics in explaining the range of phenotypes that are relevant to psychiatric practice.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Personalized medicine: is it a pharmacogenetic mirage?

The notion of personalized medicine has developed from the application of the discipline of pharmacogenetics to clinical medicine. Although the clinical relevance of genetically-determined inter-individual differences in pharmacokinetics is poorly understood, and the genotype-phenotype association data on clinical outcomes often inconsistent, officially approved drug labels frequently include pharmacogenetic information concerning the safety and/or efficacy of a number of drugs and refer to the availability of the pharmacogenetic test concerned. Regulatory authorities differ in their approach to these issues. Evidence emerging subsequently has generally revealed the pharmacogenetic information included in the label to be premature. Revised drugs labels, together with a flurry of other collateral activities, have raised public expectations of personalized medicine, promoted as 'the right drug at the right dose the first time.' These expectations place the prescribing physician in a dilemma and at risk of litigation, especially when evidence-based information on genotype-related dosing schedules is to all intent and purposes non-existent and guidelines, intended to improve the clinical utility of available pharmacogenetic information or tests, distance themselves from any responsibility. Lack of efficacy or an adverse drug reaction is frequently related to non-genetic factors. Phenoconversion, arising from drug interactions, poses another often neglected challenge to any potential success of personalized medicine by mimicking genetically-determined enzyme deficiency. A more realistic promotion of personalized medicine should acknowledge current limitations and emphasize that pharmacogenetic testing can only improve the likelihood of diminishing a specific toxic effect or increasing the likelihood of a beneficial effect and that application of pharmacogenetics to clinical medicine cannot adequately predict drug response in individual patients.

Pharmacogenomics: historical perspective and current status

Pharmacogenomics and its predecessor pharmacogenetics study the contribution of genetic factors to the interindividual variability in drug efficacy and safety. One of the major goals of pharmacogenomics is to tailor drugs to individuals based on their genetic makeup and molecular profile. From early findings in the 1950s uncovering inherited deficiencies in drug metabolism that explained drug-related adverse events, to nowadays genome-wide approaches assessing genetic variation in multiple genes, pharmacogenomics has come a long way. The evolution of pharmacogenomics has paralleled the evolution of genotyping technologies, the completion of the human genome sequencing and the HapMap project. Despite these advances, the implementation of pharmacogenomics in clinical practice has yet been limited. Here we present an overview of the history and current applications of pharmacogenomics in patient selection, dosing, and drug development with illustrative examples of these categories. Some of the challenges in the field and future perspectives are also presented.

Effects of genetic variation in the P2RX7 gene on pharmacodynamics of a P2X(7) receptor antagonist: a prospective genotyping approach

AIMS

To investigate the effects of two single nucleotide polymorphisms (SNPs) in the human P2X₇ receptor gene (P2RX7)--1068G>A (A348T) and 1513A>C (E496A)--on P2X₇ receptor function, using a specific receptor antagonist (GSK1370319A) and prospective genetic stratification.

METHODS

Lipopolysaccharide- and ATP-stimulated interleukin-1β production was determined in the presence or absence of GSK1370319A in blood culture from 32 prospectively genotyped subjects.

RESULTS

There was approximately 6.7-fold difference (P < 0.0001) in IC₅₀ for inhibition of ATP-stimulated interleukin-1β release by GSK1370319A between individuals with the homozygous gain--(1068A) and loss-of-function (1513C) genotypes (expressing the 348T, 496E and 348A, 496A alleles, respectively).

CONCLUSIONS

Leukocyte P2X₇ receptors had significantly altered pharmacodynamic responses to a specific antagonist (GSK1370319A), directly related to SNP genotype.

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

Insights on personalized medicine from Ayurveda

The "omics" era of research has provided vital information on the genetic and biochemical diversity of individuals. This has lead to the emergence of "personalized medicine," wherein one aims to design specific drugs for individual patients or subtypes of patients. Indeed, the ongoing patent wars on this matter, suggest that personalized medicine represents a major goal for today's pharmaceutical industries. Although the concept of personalized medicine is new to modern medicine, it is a well-established concept in Ayurveda, the traditional system of Indian medicine that is still being practiced. Therefore, this article discusses topics that are crucial for the advancement of modern personalized medicine. These topics include disease susceptibility, disease subtypes, and Ayurvedic therapeutics. First, we explain how Ayurveda, Traditional Chinese Medicine, and Traditional Korean medicine or Sasang Constitutional medicine; conceptualize disease susceptibility and disease subtypes. Next, we focus on conceptual similarities between molecular medicine and Ayurvedic concepts of disease susceptibility and disease subtypes. For each topic, we explain the relevant experimental evidence reported in the literature. We also propose new hypotheses and suggest experimental approaches for their testing and validation.

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.