Relating human genetic variation to variation in drug responses

Overnutrition and Lipotoxicity: Impaired Efferocytosis and Chronic Inflammation as Precursors to Multifaceted Disease Pathogenesis

Overnutrition, driven by the consumption of high-fat, high-sugar diets, has reached epidemic proportions and poses a significant global health challenge. Prolonged overnutrition leads to the deposition of excessive lipids in adipose and non-adipose tissues, a condition known as lipotoxicity. The intricate interplay between overnutrition-induced lipotoxicity and the immune system plays a pivotal role in the pathogenesis of various diseases. This review aims to elucidate the consequences of impaired efferocytosis, caused by lipotoxicity-poisoned macrophages, leading to chronic inflammation and the subsequent development of severe infectious diseases, autoimmunity, and cancer, as well as chronic pulmonary and cardiovascular diseases. Chronic overnutrition promotes adipose tissue expansion which induces cellular stress and inflammatory responses, contributing to insulin resistance, dyslipidemia, and metabolic syndrome. Moreover, sustained exposure to lipotoxicity impairs the efferocytic capacity of macrophages, compromising their ability to efficiently engulf and remove dead cells. The unresolved chronic inflammation perpetuates a pro-inflammatory microenvironment, exacerbating tissue damage and promoting the development of various diseases. The interaction between overnutrition, lipotoxicity, and impaired efferocytosis highlights a critical pathway through which chronic inflammation emerges, facilitating the development of severe infectious diseases, autoimmunity, cancer, and chronic pulmonary and cardiovascular diseases. Understanding these intricate connections sheds light on potential therapeutic avenues to mitigate the detrimental effects of overnutrition and lipotoxicity on immune function and tissue homeostasis, thereby paving the way for novel interventions aimed at reducing the burden of these multifaceted diseases on global health.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

PharmoCo: a graph-based visualization of pharmacogenomic plausibility check reports for clinical decision support systems

The first approaches in recent years for the integration of pharmacogenomic plausibility checks into clinical practice show both a promising improvement in the drug therapy safety, but also difficulties in application. One of the difficulties is the meaningful interpretation of the text-based results by the medical practitioner. We propose here as an appropriate and sensible solution to avoid misunderstandings and to include evidence-based, pharmacogenomic recommendations in prescriptions, which should be the graph-based visualization of the reports. This allows for a plausible interpretation and relate complex, even contradictory guidelines. The improved overview over the pharmacogenomics (PGx) guidelines using the graphical visualization makes the medical practitioner's choice of dose and medication more patient-specific, improves the treatment outcome and thus, increases the drug therapy safety.

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Central and Peripheral Nervous System Complications of Vasculitis Syndromes from Pathology to Bedside: Part 2-Peripheral Nervous System

PURPOSE OF REVIEW

Peripheral nervous system vasculitides (PNSV) are a heterogeneous group of disorders with a clinical subset that may differ in prognosis and therapy. We provide a comprehensive update on the clinical assessment, diagnosis, complications, treatment, and follow-up of PNSV.

RECENT FINDINGS

Progress in neuroimaging, molecular testing, and peripheral nerve biopsy has improved clinical assessment and decision-making of PNSV, also providing novel insights on how to prevent misdiagnosis and increase diagnostic certainty. Advances in imaging techniques, allowing to clearly display the vessel walls, have also enhanced the possibility to differentiate inflammatory from non-inflammatory vascular lesions, while recent histopathology data have identified the main morphological criteria for more accurate diagnosis and differential diagnoses. Overall, the identification of peculiar morphological findings tends to improve diagnostic accuracy by defining a clearer boundary between systemic and non-systemic neuropathies. Therefore, the definition of epineurium vessel wall damage, type of vascular lesion, characterization of lymphocyte populations, antibodies, and inflammatory factors, as well as the identification of direct nerve damage or degeneration, are the common goals for pathologists and clinicians, who will both benefit for data integration and findings translation. Nevertheless, to date, treatment is still largely empiric and, in some cases, unsatisfactory, thus often precluding precise prognostic prediction. In this context, new diagnostic techniques and multidisciplinary management will be essential in the proper diagnosis and prompt management of PNSV, as highlighted in the present review. Thirty to fifty percent of all patients with vasculitis have signs of polyneuropathy. Neuropathies associated with systemic vasculitis are best managed according to the guidelines of the underlying disease because appropriate workup and initiation of treatment can reduce morbidity. Steroids, or in severe or progressive cases, cyclophosphamide pulse therapy is the standard therapy in non-systemic vasculitic neuropathies. Some patients need long-term immunosuppression. The use of novel technologies for high-throughput genotyping will permit to determine the genetic influence of related phenotypes in patients with PNSV.

Synergism interaction between genetic polymorphisms in drug metabolizing enzymes and NSAIDs on upper gastrointestinal haemorrhage: a multicenter case-control study

BACKGROUND

Interindividual genetic variations contribute to differences in patients' response to drugs as well as to the development of certain disorders. Patients who use non-steroidal anti-inflammatory drugs (NSAIDs) may develop serious gastrointestinal disorders, mainly upper gastrointestinal haemorrhage (UGIH). Studies about the interaction between NSAIDs and genetic variations on the risk of UGIH are scarce. Therefore, we investigated the effect of 16 single nucleotide polymorphisms (SNPs) involved in drug metabolism on the risk of NSAIDs-induced UGIH.

MATERIALS AND METHODS

We conducted a multicenter case-control study of 326 cases and 748 controls. Participants were sub-grouped into four categories according to NSAID exposure and genetic profile. We estimated odds ratios (ORs) and their 95% confidence intervals (CI) using generalized linear mixed models for dependent binomial variables and then calculated the measures of interaction, synergism index (S), and relative excess risk due to interaction (RERI). We undertook stratified analyses by the type of NSAID (aspirin, non-aspirin).

RESULTS

We observed an excess risk of UGIH due to an interaction between any NSAID, non-aspirin NSAIDs or aspirin and carrying certain SNPs. The greatest excess risk was observed for carriers of: rs2180314:C>G [any NSAID: S = 3.30 (95%CI: 1.24-8.80), RERI = 4.39 (95%CI: 0.70-8.07); non-aspirin NSAIDs: S = 3.42 (95%CI: 1.12-10.47), RERI = 3.97 (95%CI: 0.44-7.50)], and rs4809957:A>G [any NSAID: S = 2.11 (95%CI: 0.90-4.97), RERI = 3.46 (95%CI: -0.40-7.31)]. Aspirin use by carriers of rs6664:C>T is also associated with increased risk of UGIH [OR_{aspirin(+),wild-type}: 2.22 (95%CI: 0.69-7.17) vs. OR_{aspirin(+),genetic-variation}: 7.72 (95%CI: 2.75-21.68)], yet larger sample size is needed to confirm this observation.

CONCLUSIONS

The joint effect of the SNPs s2180314:C>G and rs4809957:A>G and NSAIDs are more than three times higher than the sum of their individual effects. Personalized prescriptions based on genotyping would permit a better weighing of risks and benefits from NSAID consumption.KEY MESSAGESMulticenter case-control study of the effect of genetic variations involved in drug metabolism on upper gastrointestinal haemorrhage (UGIH) induced by NSAIDs (aspirin and non-aspirin).There is a statistically significant additive synergism interaction between certain genetic polymorphisms and NSAIDs on UGIH: rs2180314:C>G and rs4809957:A>G. The joint effect of each of these single nucleotide polymorphisms and NSAIDs on UGIH is more than three times higher than the sum of their individual effects.Genetic profiling and personalized prescriptions would be useful in managing the risks and benefits associated with NSAIDs.

Pharmacists as Personalized Medicine Experts (PRIME): Experiences Implementing Pharmacist-Led Pharmacogenomic Testing in Primary Care Practices

Research exploring the integration of pharmacogenomics (PGx) testing by pharmacists into their primary care practices (including community pharmacies) has focused on the “external” factors that impact practice implementation. In this study, additional “internal” factors, related to the capabilities, opportunities, and motivations of pharmacists that influence their ability to implement PGx testing, were analyzed. Semi-structured interview data from the Pharmacists as Personalized Medicine Experts (PRIME) study, which examined the barriers and facilitators to implementing PGx testing by pharmacists into primary care practice, were analyzed. Through thematic analysis, using the theoretical domains framework (TDF) domains as deductive codes, the authors identified the most relevant TDF domains and applied the behavioural change wheel (BCW) to generate intervention types to aid in the implementation of PGx testing. Pharmacists described how their professional identities, practice environments, self-confidence, and beliefs in the benefits of PGx impacted their ability to provide a PGx-testing service. Potential interventions to improve the implementation of the PGx service included preparing pharmacists for managing an increased patient load, helping pharmacists navigate the software and technology requirements associated with the PGx service, and streamlining workflows and documentation requirements. As interest in the wide-scale implementation of PGx testing through community pharmacies grows, additional strategies need to address the “internal” factors that influence the ability of pharmacists to integrate testing into their practices.

Developing nociceptor-selective treatments for acute and chronic pain

Despite substantial efforts dedicated to the development of new, nonaddictive analgesics, success in treating pain has been limited. Clinically available analgesic agents generally lack efficacy and may have undesirable side effects. Traditional target-based drug discovery efforts that generate compounds with selectivity for single targets have a high rate of attrition because of their poor clinical efficacy. Here, we examine the challenges associated with the current analgesic drug discovery model and review evidence in favor of stem cell–derived neuronal-based screening approaches for the identification of analgesic targets and compounds for treating diverse forms of acute and chronic pain.

Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice

Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.

Toward Interprofessional Education of Pharmacogenomics: An Interdisciplinary Assessment

INTRODUCTION

Pharmacogenomics, which emerged from disciplines such as pharmacology and genetics, is an increasingly important interdisciplinary field of health research, as indicated by the rapid growth of related literature. The aim of this study was to evaluate knowledge among genetics and pharmacology health-care students and to evaluate their exposure to and perceptions of pharmacogenomics.

METHODS

An anonymous, 28-item online survey was distributed to medical and pharmacy students enrolled at Yarmouk University, Jordan.

RESULTS

The respondents (n = 300) had an overall moderate level of knowledge regarding genetics and pharmacology. Most respondents recognized the benefits of pharmacogenomics for therapy optimization, but they had insufficient exposure to the topic. Most respondents supported providing pharmacogenetic testing in Jordan. The most preferred educational format in pharmacogenomics was integration in pharmacology courses.

DISCUSSION/CONCLUSION

Medical and pharmacy students are becoming increasingly aware of the importance of pharmacogenomics in therapy optimization. Challenges such as the complexity of the topic and low retention of previous knowledge should be addressed to promote pharmacogenomics education. More work is needed to increase students' exposure to pharmacogenomics information. A deeper integration of pharmacogenomics applications into pharmacology courses is proposed to emphasize applications of pharmacogenomics.

The influence of evolutionary history on human health and disease

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

Neuroinflammation and Precision Medicine in Pediatric Neurocritical Care: Multi-Modal Monitoring of Immunometabolic Dysfunction

The understanding of molecular biology in neurocritical care (NCC) is expanding rapidly and recognizing the important contribution of neuroinflammation, specifically changes in immunometabolism, towards pathological disease processes encountered across all illnesses in the NCC. Additionally, the importance of individualized inflammatory responses has been emphasized, acknowledging that not all individuals have the same mechanisms contributing towards their presentation. By understanding cellular processes that drive disease, we can make better personalized therapy decisions to improve patient outcomes. While the understanding of these cellular processes is evolving, the ability to measure such cellular responses at bedside to make acute care decisions is lacking. In this overview, we review cellular mechanisms involved in pathological neuroinflammation with a focus on immunometabolic dysfunction and review non-invasive bedside tools that have the potential to measure indirect and direct markers of shifts in cellular metabolism related to neuroinflammation. These tools include near-infrared spectroscopy, transcranial doppler, elastography, electroencephalography, magnetic resonance imaging and spectroscopy, and cytokine analysis. Additionally, we review the importance of genetic testing in providing information about unique metabolic profiles to guide individualized interpretation of bedside data. Together in tandem, these modalities have the potential to provide real time information and guide more informed treatment decisions.

Clinical Genetic Screening in Adult Patients with Kidney Disease

Expanded accessibility of genetic sequencing technologies, such as chromosomal microarray and massively parallel sequencing approaches, is changing the management of hereditary kidney diseases. Genetic causes account for a substantial proportion of pediatric kidney disease cases, and with increased utilization of diagnostic genetic testing in nephrology, they are now also detected at appreciable frequencies in adult populations. Establishing a molecular diagnosis can have many potential benefits for patient care, such as guiding treatment, familial testing, and providing deeper insights on the molecular pathogenesis of kidney diseases. Today, with wider clinical use of genetic testing as part of the diagnostic evaluation, nephrologists have the challenging task of selecting the most suitable genetic test for each patient, and then applying the results into the appropriate clinical contexts. This review is intended to familiarize nephrologists with the various technical, logistical, and ethical considerations accompanying the increasing utilization of genetic testing in nephrology care.

Drug Development and the Use of Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Disease Modeling and Drug Toxicity Screening

: Over the years, numerous groups have employed human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a superb human-compatible model for investigating the function and dysfunction of cardiomyocytes, drug screening and toxicity, disease modeling and for the development of novel drugs for heart diseases. In this review, we discuss the broad use of iPSC-CMs for drug development and disease modeling, in two related themes. In the first theme-drug development, adverse drug reactions, mechanisms of cardiotoxicity and the need for efficient drug screening protocols-we discuss the critical need to screen old and new drugs, the process of drug development, marketing and Adverse Drug reactions (ADRs), drug-induced cardiotoxicity, safety screening during drug development, drug development and patient-specific effect and different mechanisms of ADRs. In the second theme-using iPSC-CMs for disease modeling and developing novel drugs for heart diseases-we discuss the rationale for using iPSC-CMs and modeling acquired and inherited heart diseases with iPSC-CMs.

A review on advances in graphene-derivative/polysaccharide bionanocomposites: Therapeutics, pharmacogenomics and toxicity

Graphene-based bionanocomposites are employed in several ailments, such as cancers and infectious diseases, due to their large surface area (to carry drugs), photothermal properties, and ease of their functionalization (owing to their active groups). Modification of graphene-derivatives with polysaccharides is a promising strategy to decrease their toxicity and improve target ability, which consequently enhances their biotherapeutic efficacy. Herein, functionalization of graphene-based materials with carbohydrate polymers (e.g., chitosan, starch, alginate, hyaluronic acid, and cellulose) are presented. Subsequently, recent advances in graphene nanomaterial/polysaccharide-based bionanocomposites in infection treatment and cancer therapy are comprehensively discussed. Pharmacogenomic and toxicity assessments for these bionanocomposites are also highlighted to provide insight for future optimized and smart investigations and researches.

Safety, Tolerability, and Dose Proportionality of a Novel Transdermal Fentanyl Matrix Patch and Bioequivalence With a Matrix Fentanyl Patch: Two Phase 1 Single-Center Open-Label, Randomized Crossover Studies in Healthy Japanese Volunteers

Two open‐label, single‐dose, randomized crossover studies were conducted in healthy Japanesemen to (1) assess dose proportionality of 5 doses (1.38, 2.75, 5.5, 8.25, and 11.0 mg) of Lafenta, a novel matrix‐type transdermal fentanyl patch with a rate‐controlling membrane; and (2) compare patch bioequivalence (11.0 mg) with a commercially available reference patch (Durotep MT Patch [16.8 mg]). Pharmacokinetics, adhesion performance, residual fentanyl, and safety parameters were assessed. Increases in mean AUC_0‐t and C_max after application of the test patch were dose proportional. The test patch (11.0 mg) was bioequivalent to the 16.8‐mg reference patch in terms of mean AUC_0‐inf, AUC_0‐t, and C_max. Residual fentanyl levels 72 hours postapplication were lower in the test than in the reference patch. Differences in adhesion performance between the test and the reference patch did not affect delivery efficacy and reliability of the novel matrix patch. Safety findings were in line with previous experiences with fentanyl. Both studies showed low variation in fentanyl exposure and delivery via the test patch. The test patch provided equivalent fentanyl exposure at a lower dose than the reference patch formulation with lower variability and the potential to lower medicinal waste.

Major CYP450 polymorphism Among Saudi Patients

BACKGROUND

Cytochrome P450 (CYP) contributes to a huge collection of medicinal products' Phase I metabolization. We aimed to summarize and investigate the current evidence regarding the frequency of CYP2D6, CYP2C9, CYP2C19, MDR1 in Saudi Arabia.

METHODS

A computerized search in four databases was done using the relevant keywords. Screening process was done in two steps; title and abstract screening and full-text screening. Data of demographic and characteristics of included studies and patients was extracted and tabulated.

RESULTS

Ten studies were eligible for our criteria and were included in this systematic review. Age of participants ranged between 17-65 years. Only two subjects showed PM phenotype of CYP2C19 in Saudi population. The most frequent alleles were CYP2C19*1 (62.9%), CYP2C19*2 (11.2%-32%), and CYP2C19*17 (25.7%). The CYP2C19m1 was observed in 97 cases of extensive metabolizing (EM) phenotype CYP2C19. Concerning the CYP2C9, the most frequent alleles were CYP2C9*1 and CYP2C9*2, and the most frequent genotype was CYP2C9*1*1. The CYP2D6*41 allele and C1236T MDR1 were the most frequent allele in this population.

CONCLUSION

The current evidence suggests that Saudi Arabians resembled European in the frequency of CYP2C19, Caucasians in both the incidence of CYP2C9 and CYP2C19m1 and absence of CYP2C19m2. The CYP2D6*41 allele frequency in Saudi Arabians is relatively high. We recommend a further research to evaluate the basic and clinical relevance of gene polymorphism in such ethnicity.

Influence of Polymorphisms Involved in Platelet Activation and Inflammatory Response on Aspirin-Related Upper Gastrointestinal Bleeding: A Case-Control Study

Background

Despite the wide benefits of aspirin and its cost-effectiveness, aspirin prescriptions have been reduced due to idiosyncratic responses in susceptible individuals. Low-dose aspirin and single-nucleotide polymorphisms (SNPs) are independently associated with increased risk of gastrointestinal hemorrhage; however, to-date, no studies investigated the SNP-aspirin interaction effect on upper gastrointestinal hemorrhage (UGIH). Therefore, we aimed to evaluate the role of 25 SNPs in multiple genes involved in platelet activation, angiogenesis and inflammatory response in aspirin-related UGIH.

Methods

A multicenter, full case–control study was conducted in patients exposed and unexposed to aspirin. Three hundred twenty-six cases diagnosed with UGIH were matched with 748 controls (1:3) by age, gender, health center, and recruitment date. Only adults of European origin were included. Participants were stratified by aspirin exposure and genotype [(Aspirin₍₋₎, wild-type), (Aspirin₍₊₎, wild-type), (Aspirin₍₊₎, genetic variation), (Aspirin₍₋₎, genetic variation)]. For each SNP, the Odds Ratio of UGIH and their 95% confidence intervals were estimated in each subgroup by using the generalized linear mixed models for dependent binomial variables. SNP-aspirin interaction effect was estimated through Relative Excess Risk due to Interaction (RERI) measures.

Results

We observed two categories of SNPs that might modify the risk magnitude of UGIH in aspirin consumers. Seven SNPs (rs1387180 A > G, rs2238631 T > C, rs1799964 T > C, rs5050 T > C/T > G, rs689466 T > C, rs1799983 T > A/T > G, and rs7756935 C > A) were “positive modifiers” associated with an excess of risk from aspirin exposure and carrying that genetic variation (1.75 ≤ RERI ≤ 4.95). On the contrary, the following nine SNPs (rs2243086 G > T, rs1131882 G > A, rs4311994 C > T, rs10120688 G > A, rs4251961 T > C, rs3778355 G > C, rs1330344 C > T, rs5275 A > G/A > T, and rs3779647 C > T) were “negative modifiers” and associated with a reduced risk in aspirin users (−2.74 ≤ RERI ≤ −0.95).

Conclusion

This preliminary study suggests that polymorphisms in genes involved in platelets activity, angiogenesis and inflammatory response might modify the risk of aspirin-related UGIH. Further studies with larger sample size and in different populations are needed to confirm our findings. If confirmed, this might have great impact on public health, thanks to aspirin’s prophylactic properties in diseases of high incidence and severity.

Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms

Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify ‘population-differentiated’ SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug–gene databases to identify ‘population-differentiated’ drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify ‘population differentiated’ drugs could help to facilitate data-driven decision-making for a more personalized medicine.

Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases

Adverse drug reactions (ADRs) constitute key factors in determining successful medication therapy in clinical situations. Integrative analysis of electronic medical record (EMR) data and use of proper analytical tools are requisite to conduct retrospective surveillance of clinical decisions on medications. Thus, we suggest that electronic medical recording and human genetic databases are considered together in future directions of pharmacovigilance. We analyzed EMR-based ADR studies indexed on PubMed during the period from 2005 to 2017 and retrospectively acquired 1161 (29.6%) articles describing drug-induced adverse reactions (e.g., liver, kidney, nervous system, immune system, and inflammatory responses). Of them, only 102 (8.79%) articles contained useful information to detect or predict ADRs in the context of clinical medication alerts. Since insufficiency of EMR datasets and their improper analyses may provide false warnings on clinical decision, efforts should be made to overcome possible problems on data-mining, analysis, statistics, and standardization. Thus, we address the characteristics and limitations on retrospective EMR database studies in hospital settings. Since gene expression and genetic variations among individuals impact ADRs, pharmacokinetics, and pharmacodynamics, appropriate paths for pharmacovigilance may be optimized using suitable databases available in public domain (e.g., genome-wide association studies (GWAS), non-coding RNAs, microRNAs, proteomics, and genetic variations), novel targets, and biomarkers. These efforts with new validated biomarker analyses would be of help to repurpose clinical and translational research infrastructure and ultimately future personalized therapy considering ADRs.

Genetic Signatures of Drug Response Variability in Drosophila melanogaster

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.

Genomics-Guided Immunotherapy for Precision Medicine in Cancer

Next-generation sequencing (NGS) data have been central to the development of targeted therapy and immunotherapy for precision oncology. In targeted therapy, drugs directly attack cancer, by altering the expression of critical cancer genes identified with cancer genome profiling. Immunotherapy drugs indirectly attack cancer, by inducing the immune system to attack and treat cancer. Harnessing genomic data for deployment and development of immunotherapy comprises the field of immunogenomics. The discovery of a link between cancer cells escaping immune destruction and cancer progression, led to extensive research into this mechanism and drug development. In the past few years, FDA has granted accelerated approval to several immunotherapy cancer treatment drugs, pembrolizumab, nivolumab, and atezolizumab, belonging to the class of checkpoint inhibitors. Utilization of pretreatment genomic cancer screening to identify patients most likely to respond to immunotherapy and to customize immunotherapy for a given patient, promises to improve cancer treatment outcomes. Recent advances in molecular profiling, high-throughput sequencing, and computational efficiency has made immunogenomics the major tenet of precision medicine in cancer treatment. This review provides a brief overview on the state of art of immunogenomics in precision cancer medicine.

Pharmacogenomics in the Nigerian population: the past, the present and the future

The Nigerian population exhibits huge ethnic and genetic diversity, typical of African populations, which can be harnessed for improved drug-response and disease management. Existing data on genes relevant to drug response, so far generated for the population, indeed confirm the prevalence of some clinically significant pharmacogenes. These reports detail prevailing genetic alleles and metabolic phenotypes of vital drug metabolizing monooxygenases, transferases and drug transporters. While the utilization of existing pharmacogenomic data for healthcare delivery remains unpopular, several past and on-going studies suggest that a future shift toward genotype-stratified dosing of drugs and disease management in the population is imminent. This review discusses the present state of pharmacogenomics in Nigeria and the potential benefits of sustained research in this field for the population.

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays

MOTIVATION

High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.

RESULTS

We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters.

AVAILABILITY AND IMPLEMENTATION

The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mast Cell/Proteinase Activated Receptor 2 (PAR2) Mediated Interactions in the Pathogenesis of Discogenic Back Pain

Mast cells (MCs) are present in the painful degenerate human intervertebral disc (IVD) and are associated with disease pathogenesis. MCs release granules containing enzymatic and inflammatory factors in response to stimulants or allergens. The serine protease, tryptase, is unique to MCs and its activation of the G-protein coupled receptor, Protease Activated Receptor 2 (PAR2), induces inflammation and degradation in osteoarthritic cartilage. Our previously published work has demonstrated increased levels of MC marker tryptase in IVD samples from discogenic back pain patients compared to healthy control IVD samples including expression of chemotactic agents that may facilitate MC migration into the IVD. To further elucidate MCs’ role in the IVD and mechanisms underlying its effects, we investigated whether (1) human IVD cells can promote MC migration, (2) MC tryptase can mediate up-regulation of inflammatory/catabolic process in human IVD cells and tissue, and (3) the potential of PAR2 antagonist to function as a therapeutic drug in in vitro human and ex vivo bovine pilot models of disease. MC migration was quantitatively assessed using conditioned media from primary human IVD cells and MC migration examined through Matrigel. Exposure to soluble IVD factors significantly enhanced MC migration, suggesting IVD cells can recruit MCs. We also demonstrated significant upregulation of MC chemokine SCF and angiogenic factor VEGFA gene expression in human IVD cells in vitro in response to recombinant human tryptase, suggesting tryptase can enhance recruitment of MCs and promotion of angiogenesis into the usually avascular IVD. Furthermore, tryptase can degrade proteoglycans in IVD tissue as demonstrated by significant increases in glycosaminoglycans released into surrounding media. This can create a catabolic microenvironment compromising structural integrity and facilitating vascular migration usually inhibited by the anti-angiogenic IVD matrix. Finally, as a “proof of concept” study, we examined the therapeutic potential of PAR2 antagonist (PAR2A) on human IVD cells and bovine organ culture IVD model. While preliminary data shows promise and points toward structural restoration of the bovine IVD including down-regulation of VEGFA, effects of PAR2 antagonist on human IVD cells differ between gender and donors suggesting that further validation is required with larger cohorts of human specimens.

Pharmacogenomics-based practice in North Cyprus: its adoption by pharmacists and their attitudes and knowledge

Background Pharmacogenomics is a branch of biotechnological science integrating medicine, pharmacology, and genomics techniques. Moreover, it focuses on creating drug therapies in order to analyze genetic differences in patients causing various responses to a single therapeutic regimen. Objective This cross sectional study aimed to examine the attitude, knowledge and adoption among pharmacists in North Cyprus and the most appropriate method to improve education among them. Setting Community pharmacy setting. Method A total of 103 out of 140 pharmacists responded to a pre-tested and validated questionnaire consisting of 25 items during July through September 2016. Main outcome measure Pharmacists attitude, knowledge and adoption towards pharmacogenomic tests. Result Data showed that most of the pharmacists in North Cyprus had positive attitude and knowledge scores with mean value of 28.3 ± 5.3 (out of 40) and 6.9 ± 0.8 (out of 10) respectively, further findings showed that there is a significant difference among age groups in their total attitude score (p < 0.05). Conclusion Even though pharmacogenomics is a field promising a variety of benefits, it is vital to implement it in clinical settings in order to improve outcomes. Our findings highlight the necessity for more education on the availability and interpretation of pharmacogenomics tests.

Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions

A key issue in drug design is how population variation affects drug efficacy by altering binding affinity (BA) in different individuals, an essential consideration for government regulators. Ideally, we would like to evaluate the BA perturbations of millions of single-nucleotide variants (SNVs). However, only hundreds of protein-drug complexes with SNVs have experimentally characterized BAs, constituting too small a gold standard for straightforward statistical model training. Thus, we take a hybrid approach: using physically based calculations to bootstrap the parameterization of a full model. In particular, we do 3D structure-based docking on ∼10,000 SNVs modifying known protein-drug complexes to construct a pseudo gold standard. Then we use this augmented set of BAs to train a statistical model combining structure, ligand and sequence features and illustrate how it can be applied to millions of SNVs. Finally, we show that our model has good cross-validated performance (97% AUROC) and can also be validated by orthogonal ligand-binding data.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics

Inherited genetic variations in pharmacogenetic loci are widely acknowledged as important determinants of phenotypic differences in drug response, and may be actionable in the clinic. However, recent studies suggest that a considerable number of novel rare variants in pharmacogenes likely contribute to a still unexplained fraction of the observed interindividual variability. Next-generation sequencing (NGS) represents a rapid, relatively inexpensive, large-scale DNA sequencing technology with potential relevance as a comprehensive pharmacogenetic genotyping platform to identify genetic variation related to drug therapy. However, many obstacles remain before the clinical use of NGS-based test results, including technical challenges, functional interpretation, and strict requirements for diagnostic tests. Advanced computational analyses, high-throughput screening methodologies, and generation of shared resources with cell-based and clinical information will facilitate the integration of NGS data into candidate genotyping approaches, likely enhancing future drug phenotype predictions in patients.

Comparative study of the effect of atorvastatin and fenofibrate on high-density lipoprotein cholesterol levels in patients with type 2 diabetes

Diabetes is the most common metabolic disease. Type 2 diabetes is a variable combination of insulin resistance and disorder in insulin secretion, leading to disorder of lipids and plasma lipoproteins. The most common pattern of dyslipidemia in diabetic is high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C). This study was conducted to find a more effective drug to increase HDL-C. In this study, 80 patients (26 males and 54 females) with type 2 diabetes received fenofibrate in cross-sectional way for 2 months, and they did not take antilipid drugs for 2 month. Then, they underwent atorvastatin for 2 months and HDL-C was measured before and after taking drugs. Patients did not change their diet during this study. Effect of atorvastatin and fenofibrate on HDL-C levels in patients with type 2 diabetes was evaluated. The mean HDL-C and total cholesterol (TC) before and after taking drugs were 36.5 mg/dL and 174.56 mg/dL, respectively. After atorvastatin, the mean HDL-C and TC were 43.30 and 150.144 mg/dL, respectively, and after fenofibrate, 43.40 were mg/dL and 146.36 mg/dL, respectively. Atorvastatin caused increase in HDL-C by 18.44% and reduction in TC by 13.82% and fenofibrate increase in HDL-C by18.62% and reduction in TC by 16.05%. No difference was seen between atorvastatin and fenofibrate in terms of effect on the HDL-C excess (P = 0.449). In addition, no difference was seen between atorvastatin and fenofibrate in terms of effect on TC reduction (P = 0.992). In conclusion various factors are involved in increasing the HDL, such as race, sex, nutrition, physical activity and, of course, medications. The effect of medications is also different on races and genetics. The value of increase in HDL-C after Fenofibrate and Atorvastatin was associated with gender so that it caused more increase of HDL-C in females.

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype—that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.

Pharmacomicrobiomics: a novel route towards personalized medicine?

Inter-individual heterogeneity in drug response is a serious problem that affects the patient's wellbeing and poses enormous clinical and financial burdens on a societal level. Pharmacogenomics has been at the forefront of research into the impact of individual genetic background on drug response variability or drug toxicity, and recently the gut microbiome, which has also been called the second genome, has been recognized as an important player in this respect. Moreover, the microbiome is a very attractive target for improving drug efficacy and safety due to the opportunities to manipulate its composition. Pharmacomicrobiomics is an emerging field that investigates the interplay of microbiome variation and drugs response and disposition (absorption, distribution, metabolism and excretion). In this review, we provide a historical overview and examine current state-of-the-art knowledge on the complex interactions between gut microbiome, host and drugs. We argue that combining pharmacogenomics and pharmacomicrobiomics will provide an important foundation for making major advances in personalized medicine.

Genetic variation in human drug-related genes

Background

Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied.

Methods

Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations.

Results

We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents.

Conclusions

This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users.

Cell Cycle Model System for Advancing Cancer Biomarker Research

Progress in understanding the complexity of a devastating disease such as cancer has underscored the need for developing comprehensive panels of molecular markers for early disease detection and precision medicine applications. The present study was conducted to assess whether a cohesive biological context can be assigned to protein markers derived from public data mining, and whether mass spectrometry can be utilized to screen for the co-expression of functionally related biomarkers to be recommended for further exploration in clinical context. Cell cycle arrest/release experiments of MCF7/SKBR3 breast cancer and MCF10 non-tumorigenic cells were used as a surrogate to support the production of proteins relevant to aberrant cell proliferation. Information downloaded from the scientific public domain was queried with bioinformatics tools to generate an initial list of 1038 cancer-associated proteins. Mass spectrometric analysis of cell extracts identified 352 proteins that could be matched to the public list. Differential expression, enrichment, and protein-protein interaction analysis of the proteomic data revealed several functionally-related clusters of relevance to cancer. The results demonstrate that public data derived from independent experiments can be used to inform biological research and support the development of molecular assays for probing the characteristics of a disease.

Disease modeling studies using induced pluripotent stem cells: are we using enough controls?

The comparison of differentiated induced pluripotent stem cells (iPSCs) derived from patients with disease to differentiated iPSCs derived from healthy patients enables powerful disease modeling. By performing an informal retrospective survey of disease modeling studies published in high impact journals, we found that the median and average number of controls used in these studies were 1 and 1.6, respectively. The bulk of these studies did not control for age, gender and ethnicity. Since a large proportion of phenotypic differences observed between iPSC lines are due to genetic variation or variation between lines, this is an insufficient number of controls to confidently rule out standard variation. Future studies need to include more controls and ensure that these controls are appropriately matched for gender, age and ethnicity.

Physicians' pharmacogenomics information needs and seeking behavior: a study with case vignettes

Background

Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. We investigated physicians’ pharmacogenomics information needs and information-seeking behavior, in order to guide the design of pharmacogenomics information resources that effectively meet clinical information needs.

Methods

We performed a formative, mixed-method assessment of physicians’ information-seeking process in three pharmacogenomics case vignettes. Interactions of 6 physicians’ with online pharmacogenomics resources were recorded, transcribed, and analyzed for prominent themes. Quantitative data included information-seeking duration, page navigations, and number of searches entered.

Results

We found that participants searched an average of 8 min per case vignette, spent less than 30 s reviewing specific content, and rarely refined search terms. Participants’ information needs included a need for clinically meaningful descriptions of test interpretations, a molecular basis for the clinical effect of drug variation, information on the logistics of carrying out a genetic test (including questions related to cost, availability, test turn-around time, insurance coverage, and accessibility of expert support).Also, participants sought alternative therapies that would not require genetic testing.

Conclusion

This study of pharmacogenomics information-seeking behavior indicates that content to support their information needs is dispersed and hard to find. Our results reveal a set of themes that information resources can use to help physicians find and apply pharmacogenomics information to the care of their patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0510-9) contains supplementary material, which is available to authorized users.

Comprehensive strategy for the design of precision drugs and identification of genetic signature behind proneness of the disease-a pharmacogenomic approach

The proneness of diseases and susceptibility towards drugs vary from person to person. At present, there is a strong demand for the personalization of drugs. The genetic signature behind proneness of the disease has been studied through a comprehensive 'octopodial approach'. All the genetic variants included in the approach have been introduced. The breast cancer associated with BRCA1 mutation has been taken as the illustrative example to introduce all these factors. The genetic variants associated with the drug action of tamoxifen have been fully illustrated in the manuscript. The design of a new personalized anti-breast cancer drug has been explained in the third phase. For the design of new personalized drugs, a metabolite of anti-cancer drug chlorambucil has been taken as the template. The design of drug has been made with respect to the protein 1T15 of BRCA1 gene corresponding to the genetic signature of rs28897696.

Genetic advances in sporadic inclusion body myositis

PURPOSE OF REVIEW

To describe recent developments in the genetics of sporadic inclusion body myositis (sIBM).

RECENT FINDINGS

Genes located within major histocompatibility complex regions remain the strongest genetic association with sIBM. The rs10527454 polymorphism in the TOMM40 gene seems to have a disease modifying effect on sIBM by delaying the onset of symptoms, and this effect may be enhanced by the APOE ε3/ε3 genotype. Rare variants in the VCP and SQSTM1 genes have been identified in sIBM patients in two studies using targeted next-generation sequencing and whole-exome sequencing. Two studies have confirmed the correlation between the amount of cytochrome c oxidase -deficient fibres and the proportion of mitochondrial DNA (mtDNA) deletions in sIBM. Some rare variants in mtDNA-related nuclear genes have also been reported.

SUMMARY

There have been advances in the genetics of sIBM over the past 2 years facilitated by the use of next-generation sequencing. Genes that cause hereditary IBM, which has clinical or pathological features resembling sIBM, have provided clues to the genetic basis of sIBM. To date, genes located in major histocompatibility complex regions and genes involved in protein homeostasis or mtDNA maintenance have been implicated in sIBM. Whole-exome sequencing-association studies, RNA sequencing, and whole-genome sequencing in large sIBM cohorts will be key tools to unravel the genetics of sIBM and its contribution to disease aetiopathogenesis.

Dose of Phenobarbital and Age of Treatment at Early Life are Two Key Factors for the Persistent Induction of Cytochrome P450 Enzymes in Adult Mouse Liver

Drug treatment of neonates and infants and its long-term consequences on drug responses have emerged in recent years as a major challenge for health care professionals. In the current study, we use phenobarbital as a model drug and mouse as an in vivo model to demonstrate that the dose of phenobarbital and age of treatment are two key factors for the persistent induction of gene expression and consequential increases of enzyme activities of Cyp2b, Cyp2c, and Cyp3a in adult livers. We show that phenobarbital treatment at early life of day 5 after birth with a low dose (<100 mg/kg) does not change expression and enzyme activities of Cyp2b, Cyp2c, and Cyp3a in adult mouse liver, whereas phenobarbital treatment with a high dose (>200 mg/kg) significantly increases expression and enzyme activities of these P450s in adult liver. We also demonstrate that phenobarbital treatment before day 10 after birth, but not at later ages, significantly increases mRNAs, proteins, and enzyme activities of the tested P450s. Such persistent induction of P450 gene expression and enzyme activities in adult livers by phenobarbital treatment only occurs within a sensitive age window early in life. The persistent induction in gene expression and enzyme activities is higher in female mice than in male mice for Cyp2b10 but not for Cyp2c29 and Cyp3a11. These results will stimulate studies to evaluate the long-term impacts of drug treatment with different doses at neonatal and infant ages on drug metabolism, therapeutic efficacy, and drug-induced toxicity throughout the rest of life.

Coupled positive and negative feedbacks produce diverse gene expression patterns in colonies

Formation of patterns is a common feature in the development of multicellular organism as well as of microbial communities. To investigate the formation of gene expression patterns in colonies, we build a mathematical model of two-dimensional colony growth, where cells carry a coupled positive-and-negative-feedback circuit. We demonstrate that the model can produce sectored, target (concentric), uniform, and scattered expression patterns of regulators, depending on gene expression dynamics and nutrient diffusion. We reconstructed the same regulatory structure in Escherichia coli cells and found gene expression patterns on the surface of colonies similar to the ones produced by the computer simulations. By comparing computer simulations and experimental results, we observed that very simple rules of gene expression can yield a spectrum of well-defined patterns in a growing colony. Our results suggest that variations of the protein content among cells lead to a high level of heterogeneity in colonies.

Importance Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Relevance of G-quadruplex structures to pharmacogenetics

G-quadruplexes are non-canonical secondary structures formed within nucleic acids that are involved in modulating cellular processes such as replication, gene regulation, recombination and epigenetics. Within genes, there is mounting evidence of G-quadruplex involvement in transcriptional and post transcriptional regulation. We report the presence of potential G-quadruplex motifs within relevant sites of some important pharmacogenes and discuss the possible implications of this on the function and expression of these genes. Appreciating the location and potential functions of these motifs may be of value when considering the impacts of some pharmacogenetic variants. G-quadruplexes are also the focus of drug development efforts in oncology and we highlight the broader pharmacological implications of treatment strategies that may target G-quadruplexes.

Personalized pharmacogenomics: predicting efficacy and adverse drug reactions

Drug response varies between individuals owing to disease heterogeneity, environmental factors, and genetic factors. Genetic factors can affect both the pharmacokinetics and pharmacodynamics of a drug, leading to changes in local and systemic drug exposure and/or changes in the function of the drug target, altering drug response. Several pharmacogenetic biomarkers are already utilized in clinical practice and have been shown to improve clinical outcomes. However, a large number of other biomarkers have never made it beyond the discovery stage. Concerted effort is needed to improve the translation of pharmacogenetic biomarkers into clinical practice, and this will involve the use of standardized phenotyping and genotyping strategies, collaborative work, multidisciplinary approaches to identifying and replicating associations, and cooperation with industry to facilitate translation and commercialization. Acceptance of these approaches by clinicians, regulators, patients, and the public will be important in determining future success.

The interaction of a single-nucleotide polymorphism with age on response to interferon-α and ribavirin therapy in female patients with hepatitis C infection

Older female patients exhibit a poor response to the current standard treatment for hepatitis C, interferon-α, and ribavirin (PEG-IFN-α/RBV). In this study, we reported that the combination of age and the genotype of a novel SNP can predict response to standard treatment (P = 7.31 × 10(-8)). The model incorporating genotype of the novel SNP, rs1287948, predicts response more accurately (AUC = 0.934; 95% CI = 0.881-0.988) in women as compared with the model using age and the previously identified SNP, rs8099917.

Considerations for rare variants in drug metabolism genes and the clinical implications

INTRODUCTION

Large-scale whole genome and exome resequencing studies have revealed that humans have a high level of deleterious rare variation, which has important implications for the design of future pharmacogenetics studies.

AREAS COVERED

Current pharmacogenetic guidelines focus on the implementation of common variation into dosing guidelines. However, it is becoming apparent that rare variation may also play an important role in differential drug response. Current sequencing technologies offer the opportunity to examine rare variation, but there are many challenges associated with such analyses. Nonetheless, if a comprehensive picture of the role that genetic variants play in treatment outcomes is to be obtained, it will be necessary to include the entire spectrum of variation, including rare variants, into pharmacogenetic research.

EXPERT OPINION

In order to implement pharmacogenetics in the clinic, patients should be genotyped for clinically actionable pharmacogenetic variants and patients responding unfavourably to treatment after pharmacogenetics-based dosing should be identified and resequenced to identify additional functionally relevant variants, including rare variants. All derived information should be added to a central database to allow for the updating of existing dosing guidelines. By routinely implementing such strategies, pharmacogenetics-based treatment guidelines will continue to improve.

A one-step imaging assay to monitor cell cycle state and apoptosis in mammalian cells

High-content screening (HCS; fluorescence microscopy with multiple markers followed by automated image analysis) is gaining popularity in drug discovery due to the rich information it reveals about drug responses. It is particularly useful in studying anti-mitotic drug responses since mitotic arrest provides an activity biomarker. One conventional way to probe mitotic arrest and downstream apoptosis response is to use mitosis- and apoptosis-specific antibodies in cell-based imaging assays. However, weakly attached cells, especially dead cells, are mostly washed out during antibody labeling steps. Here, we report a rapid and convenient one-step cell-imaging assay that accurately measures cell-cycle state and apoptosis in mammalian cells. The assay uses three fluorescent dyes to stain living cells, involves no wash, and is fixable after live-cell labeling. Compared to the antibody-based method, this assay is quicker, more cost-effective, and yields more accurate dose-response results.

Chemotherapy-induced peripheral neurotoxicity and ototoxicity: new paradigms for translational genomics

In view of advances in early detection and treatment, the 5-year relative survival rate for all cancer patients combined is now approximately 66%. As a result, there are more than 13.7 million cancer survivors in the United States, with this number increasing by 2% annually. For many patients, improvements in survival have been countered by therapy-associated adverse effects that may seriously impair long-term functional status, workplace productivity, and quality of life. Approximately 20% to 40% of cancer patients given neurotoxic chemotherapy develop chemotherapy-induced peripheral neurotoxicity (CIPN), which represents one of the most common and potentially permanent nonhematologic side effects of chemotherapy. Permanent bilateral hearing loss and/or tinnitus can result from several ototoxic therapies, including cisplatin- or carboplatin-based chemotherapy. CIPN and ototoxicity represent important challenges because of the lack of means for effective prevention, mitigation, or a priori identification of high-risk patients, and few studies have applied modern genomic approaches to understand underlying mechanisms/pathways. Translational genomics, including cell-based models, now offer opportunities to make inroads for the first time to develop preventive and interventional strategies for CIPN, ototoxicity, and other treatment-related complications. This commentary provides current perspective on a successful research strategy, with a focus on cisplatin, developed by an experienced, transdisciplinary group of researchers and clinicians, representing pharmacogenomics, statistical genetics, neurology, hearing science, medical oncology, epidemiology, and cancer survivorship. Principles outlined herein are applicable to the construction of research programs in translational genomics with strong clinical relevance and highlight unprecedented opportunities to understand, prevent, and treat long-term treatment-related morbidities.

The role of pharmacogenomic biomarkers in predicting and improving drug response: part 2: challenges impeding clinical implementation

Many obstacles currently limit the ability to apply our knowledge of gene-drug associations in pharmacy practice. However, steps are gradually being taken toward the goal of incorporating genetic testing into routine patient care.