The genetics of drug efficacy: opportunities and challenges

Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications

Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies.

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

Analysis of the Relationship between Rheumatoid Arthritis and Osteoporosis Based on Mendelian Randomization

BACKGROUND

As the global population ages, the World Health Organization has found a yearly increase in the incidence of rheumatoid arthritis and osteoporosis. This trend poses a challenge to public health and healthcare and calls for the implementation of more preventive and treatment measures to address these health issues.

OBJECTIVE

This study aims to investigate the causal relationship between rheumatoid arthritis (RA) and osteoporosis (OP) using the Mendelian randomization (MR) method.

METHODS

OP diagnosis was based on the gold standard of bone mineral density (BMD). Single nucleotide polymorphisms (SNPs) were identified from the genome-wide association research database formed by RA and BMD, with a parameter setting of P < 5×10-8, chain imbalance r2<0.01, and kb = 10,000. Five complementary MR methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode estimation based on mode, and weighted estimation based on mode, were used to evaluate the causal relationship between RA and OP/BMD using odds ratio (OR) values and 95% confidence intervals (CI). Sensitivity analyses were performed using heterogeneity tests, horizontal pleiotropy, and individual rejection tests.

RESULTS

A total of 78 instrumental variables were identified that were closely related to both RA and BMD in mixed populations, while 14 instrumental variables were identified in the European population and 38 instrumental variables were identified in the Asian population. Using IVW as the main analysis method, the MR analysis results of RA and BMD showed the following: mixed population OR = 0.96, 95%CI: 0.93-1.00; European population OR = 0.55, 95%CI: 0.27-1.12; and Asian population OR = 0.95, 95%CI: 0.90-1.01. Sensitivity analyses showed that the MR results were robust.

CONCLUSION

The study found insufficient evidence of a causal relationship between RA and OP/BMD, suggesting that RA may not have a direct effect on OP/BMD.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Statistical assessment of biomarker replicability using MAJAR method

In the era of precision medicine, many biomarkers have been discovered to be associated with drug efficacy and safety responses, which can be used for patient stratification and drug response prediction. Due to the small sample size and limited power of randomized clinical studies, meta-analysis is usually conducted to aggregate all available studies to maximize the power for identifying prognostic and predictive biomarkers. However, it is often challenging to find an independent study to replicate the discoveries from the meta-analysis (e.g. meta-analysis of pharmacogenomics genome-wide association studies (PGx GWAS)), which seriously limits the potential impacts of the discovered biomarkers. To overcome this challenge, we develop a novel statistical framework, MAJAR (meta-analysis of joint effect associations for biomarker replicability assessment), to jointly test prognostic and predictive effects and assess the replicability of identified biomarkers by implementing an enhanced expectation-maximization algorithm and calculating their posterior-probability-of-replicabilities and Bayesian false discovery rates (Fdr). Extensive simulation studies were conducted to compare the performance of MAJAR and existing methods in terms of Fdr, power, and computational efficiency. The simulation results showed improved statistical power with well-controlled Fdr of MAJAR over existing methods and robustness to outliers under different data generation processes. We further demonstrated the advantages of MAJAR over existing methods by applying MAJAR to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial. Compared to testing main effects only, MAJAR identified 12 novel variants associated with the treatment-related low-density lipoprotein cholesterol reduction from baseline.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS

MOTIVATION

Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect.

RESULTS

In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials.

AVAILABILITY AND IMPLEMENTATION

The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell-led drug repurposing for Alzheimer's disease

Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.

Diabetes precision medicine: plenty of potential, pitfalls and perils but not yet ready for prime time

Rapid advances in technology and data science have the potential to improve the precision of preventive and therapeutic interventions, and enable the right treatment to be recommended, at the right time, to the right person. There are well-described examples of successful precision medicine approaches for monogenic conditions such as specific diets for phenylketonuria, and sulfonylurea treatments for certain types of MODY. However, the majority of chronic diseases are polygenic, and it is unlikely that the research strategies used for monogenic diseases will deliver similar changes to practice for polygenic traits. Type 2 diabetes, for example, is a multifactorial, heterogeneous, polygenic palette of metabolic disorders. In this non-systematic review I highlight limitations of the evidence, and the challenges that need to be overcome prior to implementation of precision medicine in the prevention and management of type 2 diabetes. Most precision medicine approaches are spuriously precise, overly complex and too narrowly focused on predicting blood glucose levels with a limited set of characteristics of individuals rather than the whole person and their context. Overall, the evidence to date is insufficient to justify widespread implementation of precision medicine approaches into routine clinical practice for type 2 diabetes. We need to retain a degree of humility and healthy scepticism when evaluating novel strategies, and to demand that existing evidence thresholds are exceeded prior to implementation.

Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods

Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches.

To try to predict an individual’s drug response using genetic data, most studies have used traditional polygenic risk score (PRS) methods. Here, the authors develop a pharmacogenomics-specific PRS method, which can improve drug response prediction and patient stratification in pharmacogenomics studies.

Pharmacoepitranscriptomic landscape revealing m6A modification could be a drug-effect biomarker for cancer treatment

RNA chemical modifications are a new but rapidly developing field. They can directly affect RNA splicing, transport, stability, and translation. Consequently, they are involved in the occurrence and development of diseases that have been studied extensively in recent years. However, few studies have focused on the correlation between chemical modifications and drug effects. Here, we provide a landscape of six RNA modifications in pharmacogene RNA (pharmacoepitranscriptomics) to fully clarify the correlation between chemical modifications and drugs. We performed systematic and comprehensive analyses on pharmacoepitranscriptomics, including basic characteristics of RNA modification and modification-associated mutations and drugs affected by them. Our results show that chemical modifications are common in pharmacogenes, especially N6-methyladenosine (m6A) modification. In addition, we found a very close relationship between chemical modifications and anti-tumor drugs. More interestingly, the results demonstrate the importance of m6A modification for anti-tumor drugs, especially for drugs in triple-negative breast cancer (TNBC), ovarian cancer, and acute myelocytic leukemia (AML). These results indicate that pharmacoepitranscriptomics could be a new source of drug-effect biomarkers, especially for m6A and anti-tumor drugs.

From pharmacogenetics to pharmaco-omics: Milestones and future directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

With high drug attrition, protein-protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches-statistical and distance-based-and our novel per-phenotype approach, 'context-specific interaction' (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76-95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

The genomics of heart failure: design and rationale of the HERMES consortium

Aims

The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure.

Methods and results

The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P < 5 × 10⁻⁸ under an additive genetic model.

Conclusions

HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

Genetics plays a limited role in predicting chronic obstructive pulmonary disease treatment response and exacerbation

BACKGROUND

Combination treatments, targeting multiple disease processes, benefit subjects with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, predicting treatment response and exacerbation risk remain challenging.

OBJECTIVE

To identify genetic associations with AECOPD risk and response to combination therapy (fluticasone furoate, umeclidinium bromide and vilanterol).

METHODS

The genetic basis of AECOPD disease was investigated in 19,841 subjects from 23 clinical studies and 2 disease cohorts to identify exacerbation disease targets. AECOPD pharmacogenetic effects were examined in 8439 moderate to severe COPD patients with exacerbation rate, lung function and quality of life endpoints; results were followed up in an additional 2201 subjects.

RESULTS

We did not identify significant associations in the AECOPD disease analysis. In the AECOPD pharmacogenetics analysis, rs56195836 (MAPK8) was significantly associated with moderate to severe exacerbation rate in subjects on fluticasone furoate with baseline blood eosinophils ≥150 cells/μl (P = 1.8 × 10^-8). Post-hoc, one variant was associated with on-treatment moderate to severe exacerbation rate stratifying by exacerbation history. AZU1 rs1962343 was significantly associated in subjects with frequent moderate exacerbation history when treated with fluticasone furoate/vilanterol (P = 1.1 × 10^-8). Neither of these signals was supported in independent follow-up.

CONCLUSION

Common genetic variants do not play major roles in AECOPD disease nor predict response to triple therapy or its components in moderate to very severe COPD.

The Role of Mitochondrial DNA Variation in Drug Response: A Systematic Review

Background: The triad of drug efficacy, toxicity and resistance underpins the risk-benefit balance of all therapeutics. The application of pharmacogenomics has the potential to improve the risk-benefit balance of a given therapeutic via the stratification of patient populations based on DNA variants. A growth in the understanding of the particulars of the mitochondrial genome, alongside the availability of techniques for its interrogation has resulted in a growing body of literature examining the impact of mitochondrial DNA (mtDNA) variation upon drug response. Objective: To critically evaluate and summarize the available literature, across a defined period, in a systematic fashion in order to map out the current landscape of the subject area and identify how the field may continue to advance. Methods: A systematic review of the literature published between January 2009 and December 2020 was conducted using the PubMed database with the following key inclusion criteria: reference to specific mtDNA polymorphisms or haplogroups, a core objective to examine associations between mtDNA variants and drug response, and research performed using human subjects or human in vitro models. Results: Review of the literature identified 24 articles reporting an investigation of the association between mtDNA variant(s) and drug efficacy, toxicity or resistance that met the key inclusion criteria. This included 10 articles examining mtDNA variations associated with antiretroviral therapy response, 4 articles examining mtDNA variants associated with anticancer agent response and 4 articles examining mtDNA variants associated with antimicrobial agent response. The remaining articles covered a wide breadth of medications and were therefore grouped together and referred to as "other." Conclusions: Investigation of the impact of mtDNA variation upon drug response has been sporadic to-date. Collective assessment of the associations identified in the articles was inconclusive due to heterogeneous methods and outcomes, limited racial/ethnic groups, lack of replication and inadequate statistical power. There remains a high degree of idiosyncrasy in drug response and this area has the potential to explain variation in drug response in a clinical setting, therefore further research is likely to be of clinical benefit.

Current challenges and opportunities for pharmacogenomics: perspective of the Industry Pharmacogenomics Working Group (I-PWG)

Pharmaceutical companies have increasingly utilized genomic data for the selection of drug targets and the development of precision medicine approaches. Most major pharmaceutical companies routinely collect DNA from clinical trial participants and conduct pharmacogenomic (PGx) studies. However, the implementation of PGx studies during clinical development presents a number of challenges. These challenges include adapting to a constantly changing global regulatory environment, challenges in study design and clinical implementation, and the increasing concerns over patient privacy. Advances in the field of genomics are also providing new opportunities for pharmaceutical companies, including the availability of large genomic databases linked to patient health information, the growing use of polygenic risk scores, and the direct sequencing of clinical trial participants. The Industry Pharmacogenomics Working Group (I-PWG) is an association of pharmaceutical companies actively working in the field of pharmacogenomics. This I-PWG perspective will provide an overview of the steps pharmaceutical companies are taking to address each of these challenges, and the approaches being taken to capitalize on emerging scientific opportunities.

Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.

Impact of study design and statistical model in pharmacogenetic studies with gene-treatment interaction

Gene-treatment interactions, just like drug-drug interactions, can have dramatic effects on a patient response and therefore influence the clinician decision at the patient's bedside. Crossover designs, although they are known to decrease the number of subjects in drug-interaction studies, are seldom used in pharmacogenetic studies. We propose to evaluate, via realistic clinical trial simulations, to what extent crossover designs can help quantifying the gene-treatment interaction effect. We explored different scenarios of crossover and parallel design studies comparing two symptom-modifying treatments in a chronic and stable disease accounting for the impact of a one gene and one gene-treatment interaction. We varied the number of subjects, the between and within subject variabilities, the gene polymorphism frequency and the effect sizes of the treatment, gene, and gene-treatment interaction. Each simulated dataset was analyzed using three models: (i) estimating only the treatment effect, (ii) estimating the treatment and the gene effects, and (iii) estimating the treatment, the gene, and the gene-treatment interaction effects. We showed how ignoring the gene-treatment interaction results in the wrong treatment effect estimates. We also highlighted how crossover studies are more powerful to detect a treatment effect in the presence of a gene-treatment interaction and more often lead to correct treatment attribution.

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

Pharmacogenetic assessment of tafenoquine efficacy in patients with Plasmodium vivax malaria

Supplemental Digital Content is available in the text.

Plasmodium vivax has the largest geographic range of human malaria species and is challenging to manage and eradicate due to its ability to establish a dormant liver stage, the hypnozoite, which can reactivate leading to relapse. Until recently, the only treatment approved to kill hypnozoites was the 8-aminoquinoline, primaquine, requiring daily treatment for 14 days. Tafenoquine, an 8-aminoquinoline single-dose treatment with activity against P. vivax hypnozoites, has recently been approved by the US Food and Drug Administration and Australian Therapeutic Goods Administration for the radical cure of P. vivax malaria in patients 16 years and older. We conducted an exploratory pharmacogenetic analysis (GSK Study 208099) to assess the role of host genome-wide variation on tafenoquine efficacy in patients with P. vivax malaria using data from three GSK clinical trials, GATHER and DETECTIVE Part 1 and Part 2. Recurrence-free efficacy at 6 and 4 months and time to recurrence up to 6 months postdosing were analyzed in 438 P. vivax malaria patients treated with tafenoquine. Among the approximately 10.6 million host genetic variants analyzed, two signals reached genome-wide significance (P value ≤ 5 × 10⁻⁸). rs62103056, and variants in a chromosome 12 intergenic region, were associated with recurrence-free efficacy at 6 and 4 months, respectively. Neither of the signals has an obvious biological rationale and would need replication in an independent population. This is the first genome-wide association study to evaluate genetic influence on response to tafenoquine in P. vivax malaria.

Apremilast Pharmacogenomics in Russian Patients with Moderate-to-Severe and Severe Psoriasis

One of the target drugs for plaque psoriasis treatment is apremilast, which is a selective phosphodiesterase 4 (PDE4) inhibitor. In this study, 34 moderate-to-severe and severe plaque psoriasis patients from Russia were treated with apremilast for 26 weeks. This allowed us to observe the effectiveness of splitting patient cohorts based on clinical outcomes, which were assessed using the Psoriasis Area Severity Index (PASI). In total, 14 patients (41%) indicated having an advanced outcome with delta PASI 75 after treatment; 20 patients indicated having moderate or no effects. Genome variability was investigated using the Illumina Infinium Global Screening Array. Genome-wide analysis revealed apremilast therapy clinical outcome associations at three compact genome regions with undefined functions situated on chromosomes 2, 4, and 5, as well as on a single single-nucleotide polymorphism (SNP) on chromosome 23. Pre-selected SNP sets were associated with psoriasis vulgaris analysis, which was used to identify four SNP-associated targeted therapy efficiencies: IL1β (rs1143633), IL4 (IL13) (rs20541), IL23R (rs2201841), and TNFα (rs1800629) genes. Moreover, we showed that the use of the global polygenic risk score allowed for the prediction of onset psoriasis in Russians. Therefore, these results can serve as a starting point for creating a predictive model of apremilast therapy response in the targeted therapy of patients with psoriasis vulgaris.

Secure large-scale genome-wide association studies using homomorphic encryption

Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.

miR-1262 Transcriptionally Modulated by an Enhancer Genetic Variant Improves Efficiency of Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors in Advanced Lung Adenocarcinoma

Advanced nonsmall-cell lung cancer (NSCLC) patients with mutated epidermal growth factor receptor (EGFR) can remarkably benefit from target therapy of EGFR-tyrosine kinase inhibitors (TKIs). However, increasing drug sensitivity and improving outcomes of NSCLC patients to EGFR-TKI therapy remains a challenge. Several studies have shown a link between microRNAs and drug resistance in cancer. In this study, we hypothesized that the rs12740674 single nucleotide polymorphism in the enhancer of miR-1262 may affect its expression, which may impact the outcome of NSCLC patients treated with EGFR-TKIs. The rs12740674 polymorphism was genotyped in two independent cohorts, including 319 EGFR-TKI treated stage IIIB/IV NSCLC patients. The allele-specific regulation on miR-1262 transcription by rs12740674 and impacts of miR-1262 on gefitinib sensitivity were evaluated in vitro and in vivo. Cox regression analyses indicated that the rs12740674 T allele was significantly associated with short survival time in both cohorts (p < 0.05). Luciferase assays demonstrated that the rs12740674 T allelic enhancer showed weaker capability to promote miR-1262 transcription compared with the C allelic enhancer, which may be due to reduced transcription factor binding according to electrophoretic mobility shift assays. Furthermore, significantly decreased miR-1262 expression in NSCLC and nontumor lung tissues of T allele carriers was observed compared with levels in C allele carriers. Moreover, miR-1262 expression enhanced the anticancer effects of gefitinib on NSCLC cells. Our data indicate that miR-1262 may be a potential therapeutic target for NSCLC.

Pharmacogenetic investigation of efficacy response to mepolizumab in eosinophilic granulomatosis with polyangiitis

Treatment of patients with the rare disease eosinophilic granulomatosis with polyangiitis (EGPA) with mepolizumab, a monoclonal antibody to interleukin-5 (IL-5) that reduces blood eosinophil counts, as an add-on therapy to glucocorticoid treatment, results in more accrued weeks in remission, reductions in glucocorticoid use and reductions in relapse rate. However, treatment response varies across a continuum. Therefore, to investigate if large genetic effects could identify responders, the impact of genetic variants on efficacy in EGPA subjects taking mepolizumab and glucocorticoids was assessed in this post hoc study. Using linear regression and a negative binomial model, genetic variant association with three endpoints (accrued duration of remission, average oral glucocorticoid dose, and frequency of relapse) was tested in 61 EGPA subjects dosed with mepolizumab from MIRRA, a phase 3 trial. Candidate gene and genome-wide approaches were used. The candidate gene analysis was designed to investigate drug target effects with eight gene regions selected that were focused on the intersection of the glucocorticoid response (steroidal response) and IL-5 response mechanisms and recognizing potential overlap between EGPA and severe eosinophilic asthma diseases for which mepolizumab is used. The sample size was insufficient to enable testing of rare variants for effects. No genetic variant from either the candidate gene analysis or the GWAS associated with any endpoint. Thresholds to declare significance were p < 0.0008 (candidate variant) and p < 2.5 × 10^–8 (genome-wide) analyses. Large genetic effects on mepolizumab-treatment response were not identified which could help differentiate responders from non-responders.

Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology

Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Regulostat Inferelator: a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes

With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary ‘on’ or ‘off’ response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify ‘regulostat’ constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of fine-tuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug–regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.

A systems-level framework for anti-epilepsy drug discovery

Modern anti-seizure drug development yielded benefits in terms of improved pharmacokinetics, safety and tolerability profiles, but offered no advances in efficacy compared to previous older generations of anti-seizure drugs. Despite significant advances in our understanding of the genetic bases to epilepsy, and a welcome renewed interest on the severe monogenic epilepsies, modern genetics has yet to directly inform more effective or disease-modifying anti-seizure drugs. Here, we describe a new approach to the identification of novel disease modifying anti-epilepsy drugs. The systems genetics approach aims to first identify pathophysiological mechanisms by integrating polygenic risk with cellular gene expression profiles and then to relate these molecular mechanisms to druggable targets using a gene regulatory (regulome) framework. The approach offers an exciting and flexible framework for future drug discovery in epilepsy, and is applicable to any disease for which appropriate cell-type and disease-context specific data exist. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

An isogenic hiPSC-derived BBB-on-a-chip

The blood-brain barrier (BBB) is composed of brain microvascular endothelial cells (BMECs) that regulate brain homeostasis, and astrocytes within the brain are involved in the maintenance of the BBB or modulation of its integrity in disease states via secreted factors. A major challenge in modeling the normal or diseased BBB is that conventional in vitro models lack either the physiological complexity of the BBB or key functional features such as formation of a sufficiently tight barrier. In this study, we utilized human induced pluripotent stem cell (hiPSC)-derived BMECs in a BBB-on-a-chip device that supports flow and coculture with an astrocyte-laden 3D hydrogel. The BMECs are separated from the hydrogel by a porous membrane with either 0.4 or 8.0 μm pore size, making the device suitable for studying the transport of molecules or cells, respectively, across the BBB. In addition, all cells seeded in the device are differentiated from the same hiPSC line, which could enable genetic and rare disease modeling. Formation of a confluent BMEC barrier was confirmed by immunocytochemistry of tight junction proteins and measurement of fluorescein permeability. Integrity of the barrier was further assessed by performing impedance spectroscopy in the device. Finally, the ability of this device to recapitulate a disease model of BBB disruption was demonstrated, with apical addition of TGF-β1 leading to transendothelial electrical resistance reduction and indicators of astrocyte activation. These results demonstrate the utility of the fabricated device for a broad range of applications such as drug screening and mechanistic studies of BBB disruption.

PathFXweb: a web application for identifying drug safety and efficacy phenotypes

Summary

Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug’s pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb.

Availability and implementation

https://www.pathfxweb.net/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Novel analytical methods to interpret large sequencing data from small sample sizes

Background

Targeted therapies have greatly improved cancer patient prognosis. For instance, chronic myeloid leukemia is now well treated with imatinib, a tyrosine kinase inhibitor. Around 80% of the patients reach complete remission. However, despite its great efficiency, some patients are resistant to the drug. This heterogeneity in the response might be associated with pharmacokinetic parameters, varying between individuals because of genetic variants. To assess this issue, next-generation sequencing of large panels of genes can be performed from patient samples. However, the common problem in pharmacogenetic studies is the availability of samples, often limited. In the end, large sequencing data are obtained from small sample sizes; therefore, classical statistical analyses cannot be applied to identify interesting targets. To overcome this concern, here, we described original and underused statistical methods to analyze large sequencing data from a restricted number of samples.

Results

To evaluate the relevance of our method, 48 genes involved in pharmacokinetics were sequenced by next-generation sequencing from 24 chronic myeloid leukemia patients, either sensitive or resistant to imatinib treatment. Using a graphical representation, from 708 identified polymorphisms, a reduced list of 115 candidates was obtained. Then, by analyzing each gene and the distribution of variant alleles, several candidates were highlighted such as UGT1A9, PTPN22, and ERCC5. These genes were already associated with the transport, the metabolism, and even the sensitivity to imatinib in previous studies.

Conclusions

These relevant tests are great alternatives to inferential statistics not applicable to next-generation sequencing experiments performed on small sample sizes. These approaches permit to reduce the number of targets and find good candidates for further treatment sensitivity studies.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0235-1) contains supplementary material, which is available to authorized users.

Application of Cancer Organoid Model for Drug Screening and Personalized Therapy

Drug screening—i.e., testing the effects of a number of drugs in multiple cell lines—is used for drug discovery and development, and can also be performed to evaluate the heterogeneity of a disease entity. Notably, intertumoral heterogeneity is a large hurdle to overcome for establishing standard cancer treatment, necessitating disease models better than conventional established 2D cell lines for screening novel treatment candidates. In the present review, we outline recent progress regarding experimental cancer models having more physiological and clinical relevance for drug screening, which are important for the successful evaluation of cellular response to drugs. The review is particularly focused on drug screening using the cancer organoid model, which is emerging as a better physiological disease model than conventional established 2D cell lines. We also review the use of cancer organoids to examine intertumor and intratumor heterogeneity, and introduce the perspective of the clinical use of cancer organoids to enable precision medicine.

Polymorphisms of ADME-related genes and their implications for drug safety and efficacy in Amazonian Amerindians

The variation in the allelic frequencies of polymorphic pharmacogenes among different ethnic groups may be responsible for severe adverse reactions to or altered efficacy of a wide variety of drugs. Amazonian Amerindian populations have a unique genetic profile that may have a fundamental on the efficacy and safety of certain drugs. The genetic characteristics of these populations are poorly known, which can negatively impact the systematic application of treatments guided by pharmacogenomic guidelines. We investigated the diversity of 32 polymorphisms in genes responsible for drug Absorption, Distribution, Metabolism and Excretion (ADME) in Amazonian Amerindians, and compared the findings with populations from other continents available in the 1000 Genomes database. We found significantly different (P ≤ 1.56E-03) allelic frequencies and genotype distributions in many study markers in comparison with African, European, American and Asian populations. Based on FST values, the Amerindian population was also the most distinct (mean FST = 0.09917). These data highlight the unique genetic profile of the indigenous population from the Brazilian Amazon region, which is potentially important from a pharmacogenetic viewpoint. Understanding the diversity of ADME- related genetic markers is crucial to the implementation of individualized pharmacogenomic treatment protocols in Amerindian populations, as well as populations with a high degree of admixture with this ethnic group, such as the general Brazilian population.

Pharmacology and pharmacogenetics of prednisone and prednisolone in patients with nephrotic syndrome

Nephrotic syndrome is one of the most common glomerular disorders in childhood. Glucocorticoids have been the cornerstone of the treatment of childhood nephrotic syndrome for several decades, as the majority of children achieves complete remission after prednisone or prednisolone treatment. Currently, treatment guidelines for the first manifestation and relapse of nephrotic syndrome are mostly standardized, while large inter-individual variation is present in the clinical course of disease and side effects of glucocorticoid treatment. This review describes the mechanisms of glucocorticoid action and clinical pharmacokinetics and pharmacodynamics of prednisone and prednisolone in nephrotic syndrome patients. However, these mechanisms do not account for the large inter-individual variability in the response to glucocorticoid treatment. Previous research has shown that genetic factors can have a major influence on the pharmacokinetic and dynamic profile of the individual patient. Therefore, pharmacogenetics may have a promising role in personalized medicine for patients with nephrotic syndrome. Currently, little is known about the impact of genetic polymorphisms on glucocorticoid response and steroid-related toxicities in children with nephrotic syndrome. Although the evidence is limited, the data summarized in this study do suggest a role for pharmacogenetics to improve individualization of glucocorticoid therapy. Therefore, studies in larger cohorts with nephrotic syndrome patients are necessary to draw final conclusions about the influence of genetic polymorphisms on the glucocorticoid response and steroid-related toxicities to ultimately implement pharmacogenetics in clinical practice.

No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study

BACKGROUND

Studies have shown strong positive associations between serum urate (SU) levels and chronic kidney disease (CKD) risk; however, whether the relation is causal remains uncertain. We evaluate whether genetic data are consistent with a causal impact of SU level on the risk of CKD and estimated glomerular filtration rate (eGFR).

METHODS AND FINDINGS

We used Mendelian randomization (MR) methods to evaluate the presence of a causal effect. We used aggregated genome-wide association data (N = 110,347 for SU, N = 69,374 for gout, N = 133,413 for eGFR, N = 117,165 for CKD), electronic-medical-record-linked UK Biobank data (N = 335,212), and population-based cohorts (N = 13,425), all in individuals of European ancestry, for SU levels and CKD. Our MR analysis showed that SU has a causal effect on neither eGFR level nor CKD risk across all MR analyses (all P > 0.05). These null associations contrasted with our epidemiological association findings from the 4 population-based cohorts (change in eGFR level per 1-mg/dl [59.48 μmol/l] increase in SU: -1.99 ml/min/1.73 m2; 95% CI -2.86 to -1.11; P = 8.08 × 10(-6); odds ratio [OR] for CKD: 1.48; 95% CI 1.32 to 1.65; P = 1.52 × 10(-11)). In contrast, the same MR approaches showed that SU has a causal effect on the risk of gout (OR estimates ranging from 3.41 to 6.04 per 1-mg/dl increase in SU, all P < 10-3), which served as a positive control of our approach. Overall, our MR analysis had >99% power to detect a causal effect of SU level on the risk of CKD of the same magnitude as the observed epidemiological association between SU and CKD. Limitations of this study include the lifelong effect of a genetic perturbation not being the same as an acute perturbation, the inability to study non-European populations, and some sample overlap between the datasets used in the study.

CONCLUSIONS

Evidence from our series of causal inference approaches using genetics does not support a causal effect of SU level on eGFR level or CKD risk. Reducing SU levels is unlikely to reduce the risk of CKD development.

A resource of variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure-based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

Pharmacogenomics Education Improves Pharmacy Student Perceptions of Their Abilities and Roles in Its Use

Objective. To assess whether a required first-year course, Principles in Genetics and Pharmacogenomics, and integration into subsequent courses affected pharmacy students' perceptions of pharmacogenomics. Methods. A survey was distributed to Professional Year (PY) 1 students during the first and last weeks of the course from 2014 to 2016. A follow-up survey was distributed to PY2, PY3, and PY4 students. Results. Respondents consistently agreed that pharmacogenomics is clinically relevant. After the course, PY1 students are more comfortable in their knowledge and role in the application of pharmacogenomics. Although their comfort reverts to some degree, PY2-PY4 students believe that they should be able to apply pharmacogenomics clinically. Most PY2-PY4 students indicate that later courses review pharmacogenomics. Conclusion. A required course in genetics and pharmacogenomics can promote a perception that pharmacists should have knowledge of, and be involved in the use of genetic information clinically. Inclusion of pharmacogenomic concepts in subsequent curricular components may help in maintaining these perceptions.

A scientist engineer's contribution to therapeutic discovery and development

An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging.

The germline genetic component of drug sensitivity in cancer cell lines

Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is at-most anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in cancer cell lines. We develop a joint analysis approach that leverages both germline and somatic variants, before applying it to screening data from 993 cell lines and 265 drugs. Surprisingly, we find that the germline contribution to variation in drug susceptibility can be as large or larger than effects due to somatic mutations. Several of the associations identified have a direct relationship to the drug target. Finally, using 17-AAG response as an example, we show how germline effects in combination with transcriptomic data can be leveraged for improved patient stratification and to identify new markers for drug sensitivity.

Little is known about the contribution of germline genetic variants to cancer drug sensitivity. Here, the authors devise an approach for joint analysis of germline variants and somatic mutations, identifying substantial germline contributions to variation in drug sensitivity.

Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR addresses some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either before or after approval of the drug, and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized, and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Influence of NOD2 Variants on Trichuris suis ova Treatment Outcome in Crohn’s Disease

A recent randomized study of whipworm Trichuris suis ova (TSO) in ileal Crohn’s disease failed to demonstrate a clinical benefit compared to placebo after 12 weeks. Nonetheless, it has recently been shown that the spontaneous small intestinal inflammatory changes in Nod2^-/- (Nucleotide-binding oligomerization domain 2) mice could be substantially ameliorated when these mice were colonized by Trichuris muris. Those and complementary epidemiologic findings in humans lead to the hypothesis that helminths may be advantageous only in patients carrying defective NOD2 variants. Thus, 207 participants of the TSO trial were retrospectively genotyped for six functional NOD2 genetic variants to evaluate whether the treatment outcome differed in patients carrying NOD2 variants. We observed no significant association of the NOD2 variants or their haplotypes with clinical outcome after TSO treatment.

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.