Pharmacogenetics in drug discovery and development: a translational perspective

A Multibranch Neural Network for Drug-Target Affinity Prediction Using Similarity Information

Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. In recent years, graph structure-based deep learning models have garnered significant attention in this field. However, these models typically handle drug or target protein in isolation and only extract the molecular structure information on the drug or protein itself. To address this limitation, existing network-based models represent drug-target interactions or affinities as a knowledge graph to capture the interaction information. In this study, we propose a novel solution. Specifically, we introduce drug similarity information and protein similarity information into the field of DTA prediction. Moreover, we propose a network framework that autonomously extracts similarity information, avoiding reliance on knowledge graphs. Based on this framework, we design a multibranch neural network called GASI-DTA. This network integrates similarity information, sequence information, and molecular structure information. Comprehensive experimental results conducted on two benchmark data sets and three cold-start scenarios demonstrate that our model outperforms state-of-the-art graph structure-based methods in nearly all metrics. Furthermore, it exhibits significant advantages over existing network-based models, outperforming the best of them in the majority of metrics. Our study's code and data are openly accessible at http://github.com/XiaoLin-Yang-S/GASI-DTA.

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.

Drug-target affinity prediction with extended graph learning-convolutional networks

BACKGROUND

High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery.

RESULTS

To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy.

CONCLUSIONS

The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Multimodal contrastive representation learning for drug-target binding affinity prediction

In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era of big data. Recent years we have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. However, these models only considered a single modality of drug and target information, and some valuable information was not fully utilized. In fact, the information of different modalities of drug and target can complement each other, and more valuable information can be obtained by fusing the information of different modalities. In this paper, we introduce a multimodal information fusion model for DTA prediction that is called FMDTA, which fully considers drug/target information in both string and graph modalities and balances the feature representations of different modalities by a contrastive learning approach. In addition, we exploited the alignment information of drug atoms and target residues to capture the positional information of string patterns, which can extract more useful feature information in SMILES and target sequences. Experimental results on two benchmark datasets show that FMDTA outperforms the state-of-the-art model, demonstrating the feasibility and excellent feature capture capability of FMDTA. The code of FMDTA and the data are available at: https://github.com/bestdoubleLin/FMDTA.

Drug-microbiota interactions: an emerging priority for precision medicine

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

A survey of drug-target interaction and affinity prediction methods via graph neural networks

The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play important roles in the field of drug discovery. However, biological experiment-based methods are time-consuming and expensive. Recently, computational-based approaches have accelerated the process of drug-target relationship prediction. Drug and target features are represented in structure-based, sequence-based, and graph-based ways. Although some achievements have been made regarding structure-based representations and sequence-based representations, the acquired feature information is not sufficiently rich. Molecular graph-based representations are some of the more popular approaches, and they have also generated a great deal of interest. In this article, we provide an overview of the DTI prediction and DTA prediction tasks based on graph neural networks (GNNs). We briefly discuss the molecular graphs of drugs, the primary sequences of target proteins, and the graph reSLBpresentations of target proteins. Meanwhile, we conducted experiments on various fundamental datasets to substantiate the plausibility of DTI and DTA utilizing graph neural networks.

Profiling of Circulating Tumor Cells for Screening of Selective Inhibitors of Tumor-Initiating Stem-Like Cells

A critical barrier to effective cancer therapy is the improvement of drug selectivity, toxicity, and reduced recurrence of tumors expanded from tumor-initiating stem-like cells (TICs). The aim is to identify circulating tumor cell (CTC)-biomarkers and to identify an effective combination of TIC-specific, repurposed federal drug administration (FDA)-approved drugs. Three different types of high-throughput screens targeting the TIC population are employed: these include a CD133 (+) cell viability screen, a NANOG expression screen, and a drug combination screen. When combined in a refined secondary screening approach that targets Nanog expression with the same FDA-approved drug library, histone deacetylase (HDAC) inhibitor(s) combined with all-trans retinoic acid (ATRA) demonstrate the highest efficacy for inhibition of TIC growth in vitro and in vivo. Addition of immune checkpoint inhibitor further decreases recurrence and extends PDX mouse survival. RNA-seq analysis of TICs reveals that combined drug treatment reduces many Toll-like receptors (TLR) and stemness genes through repression of the lncRNA MIR22HG. This downregulation induces PTEN and TET2, leading to loss of the self-renewal property of TICs. Thus, CTC biomarker analysis would predict the prognosis and therapy response to this drug combination. In general, biomarker-guided stratification of HCC patients and TIC-targeted therapy should eradicate TICs to extend HCC patient survival.

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs

Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.

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.

Recycled Translation: Repurposing Drugs for Stroke

Stroke, which continues to be a leading cause of death and long-term disability worldwide, has often been described as a clinical graveyard. While multiple small molecule therapeutics have undergone clinical trials in stroke, currently only one Food and Drug Administration (FDA)-approved medication exists for the treatment of stroke, the biological, recombinant tissue plasminogen activator (rt-PA). Repurposing of therapeutics which have previously gained FDA approval for alternative indications serves as a prospective option for stroke therapeutic translation. In contrast to de novo drug development, repurposing strategies have patient-centered and economic advantages. These include increased safety, increased chance of approval, decreased time to approval, and decreased capital investment. Presently, 37 active stroke clinical trials utilize repurposed therapeutics with various initial indications and dosing paradigms. The currently studied repurposed therapeutics fall into six mechanistic categories: (1) anticoagulation; (2) vasculature integrity, response, or red blood cell (RBC) alterations; (3) immune system regulation; (4) neurotransmission; and (5) neuroprotection. Directed hypothesis-driven computational investigation utilizing drug databases, in silico drug-protein interaction modeling, genomic data, and consensus methodology can determine if the current mechanistic repurposing categories have the highest chance of translational success or if other mechanistic avenues should be explored. With this increased focus on repurposed therapeutic strategies over de novo strategies, evolution and optimization of regulatory protections are needed to incentivize innovators and investigators.

Graph-sequence attention and transformer for predicting drug-target affinity

Drug-target binding affinity (DTA) prediction has drawn increasing interest due to its substantial position in the drug discovery process. The development of new drugs is costly, time-consuming, and often accompanied by safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. Therefore, it is of great significance to develop effective computational methods to predict DTAs. The attention mechanisms allow the computational method to focus on the most relevant parts of the input and have been proven to be useful for various tasks. In this study, we proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. For the representation of drugs, we use Bi-directional Gated Recurrent Units (BiGRU) to extract the SMILES representation from SMILES sequences, and graph neural networks to extract the graph representation of the molecular graphs. Then we utilize an attention mechanism to fuse the two representations of the drug. For the target/protein, we utilized an efficient transformer to learn the representation of the protein, which can capture the long-distance relationships in the sequence of amino acids. We conduct extensive experiments to compare our model with state-of-the-art models. Experimental results show that our model outperforms the current state-of-the-art methods on two independent datasets.

Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction

Identifying new therapeutic indications for existing drugs is a major challenge in drug repositioning. Most computational drug repositioning methods focus on known targets. Analyzing multiple aspects of various protein associations provides an opportunity to discover underlying drug-associated proteins that can be used to improve the performance of the drug repositioning approaches. In this study, machine learning models were developed based on the similarities of diversified biological features, including protein interaction, topological network, sequence alignment, and biological function to predict protein pairs associating with the same drugs. The crucial set of features was identified, and the high performances of protein pair predictions were achieved with an area under the curve (AUC) value of more than 93%. Based on drug chemical structures, the drug similarity levels of the promising protein pairs were used to quantify the inferred drug-associated proteins. Furthermore, these proteins were employed to establish an augmented drug-protein matrix to enhance the efficiency of three existing drug repositioning techniques: a similarity constrained matrix factorization for the drug-disease associations (SCMFDD), an ensemble meta-paths and singular value decomposition (EMP-SVD) model, and a topology similarity and singular value decomposition (TS-SVD) technique. The results showed that the augmented matrix helped to improve the performance up to 4% more in comparison to the original matrix for SCMFDD and EMP-SVD, and about 1% more for TS-SVD. In summary, inferring new protein pairs related to the same drugs increase the opportunity to reveal missing drug-associated proteins that are important for drug development via the drug repositioning technique.

Can artificial intelligence accelerate preclinical drug discovery and precision medicine?

Precision medicine leverages molecular biomarkers for selecting optimal treatment strategies. Accordingly, stratifying patients into responder and non-responder has strongly accelerated drug discovery and drug approvals in the last two decades. Recently, the applications of artificial intelligence (AI) in healthcare have been promoting these processes, and continue to improve patient care through systematically analysing large-scale molecular data and electronic health records. In particular, preclinical pharmacogenomics data empowered AI to unfold its full potential.Areas covered: Here, we discuss the opportunities of AI in pharmacogenomics, drug discovery and precision medicine. In particular, we shed some light on the advancements in computational biomedicine from statistical, machine learning (ML) to complex deep learning (DL) models.Expert opinion: AI has already strongly impacted drug discovery, and will continue to revolutionise academic research and the pharmaceutical industry. Its algorithms aid the identification of novel treatment options through molecular signatures and thus pave the way for the next generation of precision medicine.

Translational advances of melanocortin drugs: Integrating biology, chemistry and genetics

Melanocortin receptors have emerged as important targets with a very unusual versatility, as their widespread distribution on multiple tissues (e.g. skin, adrenal glands, brain, immune cells, exocrine glands) together with the variety of physiological processes they control (pigmentation, cortisol release, satiety mechanism, inflammation, secretions), place this family of receptors as genuine therapeutic targets for many disorders. This review focuses in the journey of the development of melanocortin receptors as therapeutic targets from the discovery of their existence in the early 1990 s to the approval of the first few drugs of this class. Two major areas of development characterise the current state of melanocortin drug development: their role in obesity, recently culminated with the approval of setmelanotide, and their potential for the treatment of chronic inflammatory and autoimmune diseases like rheumatoid arthritis, multiple sclerosis or fibrosis. The pro-resolving nature of these drugs offers the advantage of acting by mimicking the way our body naturally resolves inflammation, expecting fewer side effects and a more balanced (i.e. non-immunosuppressive) response from them. Here we also review the approaches followed for the design and development of novel compounds, the importance of the GPCR nature of these receptors in the process of drug development, therapeutic value, current challenges and successes, and the potential for the implementation of precision medicine approaches through the incorporation of genetics advances.

Synthetic lethality theory approaches to effective substance discovery and functional mechanisms elucidation of anti-cancer phytomedicine

BACKGROUND

Longstanding, successful use of combinations of phytopharmaceuticals in traditional Chinese medicine (TCM) has caught the attention of several pharmacologists to natural medicines. However, the development and popularisation of TCM is mainly limited because of the unavailability of reports clarifying the mechanisms of action and pharmacologically active ingredients in such formulations. Previous studies on natural medicines have mostly focused on their dominant components using forward pharmacology which often neglects trace components. It is necessary to assess the pharmacological and therapeutic superiority of many such trace components in comparison with single constituents.

PURPOSE

In this study, we aimed to propose a new pharmacological research strategy for TCM. In particular, we presented the possibility that the effective mechanism of action of trace components of TCM is based on synthetic lethality. We sincerely hope to explore this theory further.

METHOD

We obtained retrieve published research information related to synthetic lethality, phytochemicals and Chinese medicine from PubMed and Google scholar. Based on the inclusion criteria, 71 studies were selected and discussed in this review.

RESULTS

As an interaction among genes, synthetic lethality can amplify co-regulatory biological effects exponentially. Synthetic strategies have been successfully applied for research and development of anti-tumour agents, including poly ADP-ribose polymerase inhibitors and clinical combination of chemotherapeutic agents for efficacy enhancement and toxicity reduction. TCM drugs contain several secondary metabolites to combat environmental stresses, providing a multi-component basis for corresponding synergistic targets. Therefore, we aimed to study whether this method could be used to identify active components present in trace amounts in TCM drugs. Based on a reverse concept of target-component-effect and identified synergistic targets, we explored the mechanisms of action of weakly active components present in trace amounts in TCM drugs to assess combinations of potential synergistic components.

CONCLUSION

This pattern of synthetic lethality not only elucidated the mechanisms of action of TCM drugs from a new perspective but also inspired future studies on discovering naturally occurring active components.

How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction. We summarize and examine state-of-the-art deep learning methods that have been published recently. Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear in the training dataset. In particular, all the five evaluated deep learning methods performed worst than the similarity-regularized matrix factorization (SRMF) method in our drug blind test. We outline the challenges in applying deep learning approach to drug response prediction and suggest unique opportunities for deep learning integrated with established bioinformatics analyses to overcome some of these challenges.

Novel test strategies for in vitro seizure liability assessment

INTRODUCTION

The increasing incidence of mental illnesses and neurodegenerative diseases results in a high demand for drugs targeting the central nervous system (CNS). These drugs easily reach the CNS, have a high affinity for CNS targets, and are prone to cause seizures as an adverse drug reaction. Current seizure liability assessment heavily depends on in vivo or ex vivo animal models and is therefore ethically debated, labor intensive, expensive, and not always predictive for human risk.

AREAS COVERED

The demand for CNS drugs urges the development of alternative safety assessment strategies. Yet, the complexity of the CNS hampers reliable detection of compound-induced seizures. This review provides an overview of the requirements of in vitro seizure liability assays and highlights recent advances, including micro-electrode array (MEA) recordings using rodent and human cell models.

EXPERT OPINION

Successful and cost-effective replacement of in vivo and ex vivo models for seizure liability screening can reduce animal use for drug development, while increasing the predictive value of the assays, particularly if human cell models are used. However, these novel test strategies require further validation and standardization as well as additional refinements to better mimic the human in vivo situation and increase their predictive value.

Serotonergic receptor gene polymorphism and response to selective serotonin reuptake inhibitors in ethnic Malay patients with first episode of major depressive disorder

The polymorphisms of the 5HTR1A and 5HTR2A receptor genes (rs6295C/G and rs6311G/A) have been evaluated for association with SSRI treatment outcome in various populations with different results. The present study was carried out to determine the association between genotypes of HTR1A-rs6295 and HTR2A-rs6311 with SSRI treatment outcome among the ethnic Malay patients diagnosed with first-episode major depressive disorder (MDD). The patients were recruited from four tertiary hospitals in the Klang Valley region of Malaysia. Predefined efficacy phenotypes based on 25% (partial early response) and 50% (clinical efficacy response) reduction in Montgomery Asberg Depression Rating Scale-self Rated score (MADRS-S) were adopted for assessment of treatment efficacy in this study. Self-reporting for adverse effects (AE) was documented using the Patient Rated Inventory of Side Effect (PRISE) after treatment with SSRI for up to 6 weeks. Adjusted binary logistic regression between genotypes of the polymorphism obtained using sequencing technique with the treatment outcome phenotypes was performed. The 142 patients recruited were made up of 96 females (67.6%) and 46 males (32.4%). Clinical efficacy and Partial early response phenotypes were not significantly associated with genotypes of HTR1A and HTR2A polymorphism. The GG genotype of HTR2A polymorphism has decreased odds for dizziness (CNS) and increased odds for poor concentration. The GA genotype increases the odd for excessive sweating, diarrhoea, constipation and blurred vision. The CC genotype of HTR1A-rs6295 decreases the odd for nausea/vomiting and increases the odd for anxiety. Thus, some genotypes of HTR1A and HTR2A polymorphism were associated with SSRI treatment outcomes in ethnic Malay MDD patients.

Reassessment of Pioglitazone for Alzheimer's Disease

Alzheimer's disease is a quintessential 'unmet medical need', accounting for ∼65% of progressive cognitive impairment among the elderly, and 700,000 deaths in the United States in 2020. In 2019, the cost of caring for Alzheimer's sufferers was $244B, not including the emotional and physical toll on caregivers. In spite of this dismal reality, no treatments are available that reduce the risk of developing AD or that offer prolonged mitiagation of its most devestating symptoms. This review summarizes key aspects of the biology and genetics of Alzheimer's disease, and we describe how pioglitazone improves many of the patholophysiological determinants of AD. We also summarize the results of pre-clinical experiments, longitudinal observational studies, and clinical trials. The results of animal testing suggest that pioglitazone can be corrective as well as protective, and that its efficacy is enhanced in a time- and dose-dependent manner, but the dose-effect relations are not monotonic or sigmoid. Longitudinal cohort studies suggests that it delays the onset of dementia in individuals with pre-existing type 2 diabetes mellitus, which small scale, unblinded pilot studies seem to confirm. However, the results of placebo-controlled, blinded clinical trials have not borne this out, and we discuss possible explanations for these discrepancies.

GraphDTA: predicting drug-target binding affinity with graph neural networks

SUMMARY

The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug-target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug-target affinity. We show that graph neural networks not only predict drug-target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug-target binding affinity prediction, and that representing drugs as graphs can lead to further improvements.

AVAILABILITY OF IMPLEMENTATION

The proposed models are implemented in Python. Related data, pre-trained models and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations

Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.

Molecular mechanics approaches for rational drug design: forcefields and solvation models

The use of molecular mechanics (MM) in understanding the energy and target of a drug, its structures, and properties has increased recently. This is achieved by the formulation of a simple MM energy equation, which represents the sum of the different energy interactions, often referred to as “forcefields” (FFs). The concept of FFs is now widely used as one of the fundamental tools for the in silico prediction of drug-target interactions. To generate more accurate predictions in the in silico drug discovery projects, the solvent effects are often taken into account. This review seeks to present an introductory guide for the reader on the fundamentals of MM with special emphasis on the role of FFs and the solvation models.

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.

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer’s disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

A survey and systematic assessment of computational methods for drug response prediction

Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancers. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to drug response prediction. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assess 17 representative methods for drug response prediction, which have been developed in the past 5 years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

Integrating Organs-on-Chips: Multiplexing, Scaling, Vascularization, and Innervation

Organs-on-chips (OoCs) have attracted significant attention because they can be designed to mimic in vivo environments. Beyond constructing a single OoC, recent efforts have tried to integrate multiple OoCs to broaden potential applications such as disease modeling and drug discoveries. However, various challenges remain for integrating OoCs towards in vivo-like operation, such as incorporating various connections for integrating multiple OoCs. We review multiplexed OoCs and challenges they face: scaling, vascularization, and innervation. In our opinion, future OoCs will be constructed to have increased predictive power for in vivo phenomena and will ultimately become a mainstream tool for high quality biomedical and pharmaceutical research.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Sirtuins in Alzheimer's Disease: SIRT2-Related GenoPhenotypes and Implications for PharmacoEpiGenetics

Sirtuins (SIRT1-7) are NAD⁺-dependent protein deacetylases/ADP ribosyltransferases with important roles in chromatin silencing, cell cycle regulation, cellular differentiation, cellular stress response, metabolism and aging. Sirtuins are components of the epigenetic machinery, which is disturbed in Alzheimer’s disease (AD), contributing to AD pathogenesis. There is an association between the SIRT2-C/T genotype (rs10410544) (50.92%) and AD susceptibility in the APOEε4-negative population (SIRT2-C/C, 34.72%; SIRT2-T/T 14.36%). The integration of SIRT2 and APOE variants in bigenic clusters yields 18 haplotypes. The 5 most frequent bigenic genotypes in AD are 33CT (27.81%), 33CC (21.36%), 34CT (15.29%), 34CC (9.76%) and 33TT (7.18%). There is an accumulation of APOE-3/4 and APOE-4/4 carriers in SIRT2-T/T > SIRT2-C/T > SIRT2-C/C carriers, and also of SIRT2-T/T and SIRT2-C/T carriers in patients who harbor the APOE-4/4 genotype. SIRT2 variants influence biochemical, hematological, metabolic and cardiovascular phenotypes, and modestly affect the pharmacoepigenetic outcome in AD. SIRT2-C/T carriers are the best responders, SIRT2-T/T carriers show an intermediate pattern, and SIRT2-C/C carriers are the worst responders to a multifactorial treatment. In APOE-SIRT2 bigenic clusters, 33CC carriers respond better than 33TT and 34CT carriers, whereas 24CC and 44CC carriers behave as the worst responders. CYP2D6 extensive metabolizers (EM) are the best responders, poor metabolizers (PM) are the worst responders, and ultra-rapid metabolizers (UM) tend to be better responders that intermediate metabolizers (IM). In association with CYP2D6 genophenotypes, SIRT2-C/T-EMs are the best responders. Some Sirtuin modulators might be potential candidates for AD treatment.

Induction of high tolerance to artemisinin by sub-lethal administration: A new in vitro model of P. falciparum

Artemisinin resistance is a major threat to malaria control efforts. Resistance is characterized by an increase in the Plasmodium falciparum parasite clearance half-life following treatment with artemisinin-based combination therapies (ACTs) and an increase in the percentage of surviving parasites. The remarkably short blood half-life of artemisinin derivatives may contribute to drug-resistance, possibly through factors including sub-lethal plasma concentrations and inadequate exposure. Here we selected for a new strain of artemisinin resistant parasites, termed the artemisinin resistant strain 1 (ARS1), by treating P. falciparum Palo Alto (PA) cultures with sub-lethal concentrations of dihydroartemisinin (DHA). The resistance phenotype was maintained for over 1 year through monthly maintenance treatments with low doses of 2.5 nM DHA. There was a moderate increase in the DHA IC50 in ARS1 when compared with parental strain PA after 72 h of drug exposure (from 0.68 nM to 2 nM DHA). In addition, ARS1 survived treatment physiologically relevant DHA concentrations (700 nM) observed in patients. Furthermore, we confirmed a lack of cross-resistance against a panel of antimalarials commonly used as partner drugs in ACTs. Finally, ARS1 did not contain Pfk13 propeller domain mutations associated with ART resistance in the Greater Mekong Region. With a stable growth rate, ARS1 represents a valuable tool for the development of new antimalarial compounds and studies to further elucidate the mechanisms of ART resistance.

Development of a zebrafish sepsis model for high-throughput drug discovery

Sepsis is a leading cause of death worldwide. Current treatment modalities remain largely supportive. Intervention strategies focused on inhibiting specific mediators of the inflammatory host response have been largely unsuccessful, a consequence of an inadequate understanding of the complexity and heterogeneity of the innate immune response. Moreover, the conventional drug development pipeline is time consuming and expensive and the low success rates associated with cell-based screens underline the need for whole organism screening strategies, especially for complex pathological processes. Here, we established an LPS-induced zebrafish endotoxemia model, which exhibits the major hallmarks of human sepsis including, edema and tissue/organ damage, increased vascular permeability and vascular leakage accompanied by an altered expression of cellular junction proteins, increased cytokine expression, immune cell activation and ROS production, reduced circulation and increased platelet aggregation. We tested the suitability of the model for phenotype-based drug screening using three primary readouts: mortality, vascular leakage, and ROS production. Preliminary screening identified fasudil, a drug known to protect against vascular leakage in murine models, as a lead hit thereby validating the utility of our model for sepsis drug screens. This zebrafish sepsis model has the potential to rapidly analyze sepsis associated pathologies and cellular processes in the whole organism, as well as to screen and validate large numbers of compounds that can modify sepsis pathology in vivo.

Pharmacogenetic considerations in the treatment of Alzheimer's disease

The practical pharmacogenetics of Alzheimer's disease (AD) is circumscribed to acetylcholinesterase inhibitors (AChEIs) and memantine. However, pharmacogenetic procedures should be applied to novel strategies in AD therapeutics including: novel AChEIs and neurotransmitter regulators, anti-Aβ treatments, anti-tau treatments, pleiotropic products, epigenetic drugs and combination therapies. Genes involved in the pharmacogenetic network are under the influence of the epigenetic machinery which regulates gene expression transcriptionally and post-transcriptionally, configuring the fundamentals of pharmacoepigenomics. Over 60% of AD patients present concomitant pathologies demanding additional treatments which increase the likelihood of drug-drug interactions. Lipid metabolism dysfunction is a pathogenic mechanism inherent to AD neurodegeneration. The therapeutic response to hypolipidemic compounds is influenced by the APOE and CYP genotypes. The development of novel compounds and the use of combination/multifactorial treatments require the implantation of pharmacogenomic procedures for the avoidance of ADRs and the optimization of therapeutics.

Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting

This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.

Opportunities in pharmacogenomics for the treatment of Alzheimer's disease

ABSTRACT 

In Alzheimer's disease (AD), approximately 10–20% of direct costs are associated with pharmacological treatment. Pharmacogenomics account for 30–90% variability in pharmacokinetics and pharmacodynamics. Genes potentially involved in the pharmacogenomics outcome include pathogenic, mechanistic, metabolic, transporter and pleiotropic genes. Over 75% of the Caucasian population is defective for the CYP2D6+2C9+2C19 cluster. Polymorphic variants in the APOE-TOMM40 region influence AD pharmacogenomics. APOE-4 carriers are the worst responders and APOE-3 carriers are the best responders to conventional treatments. TOMM40 poly T-S/S carriers are the best responders, VL/VL and S/VL carriers are intermediate responders and L/L carriers are the worst responders. The haplotype 4/4-L/L is probably responsible for early onset of the disease, a faster cognitive decline and a poor response to different treatments.

Unwarranted optimism in media portrayals of genetic research on addiction overshadows critical ethical and social concerns

The cost of addiction in the United States, in combination with a host of new tools and techniques, has fueled an explosion of genetic research on addiction. Because the media has the capacity to reflect and influence public perception, there is a need to examine how treatments and preventive approaches projected to emerge from addiction genetic research are presented to the public. The authors conducted a textual analysis of 145 news articles reporting on genetic research on addiction from popular print media in the United States and from popular news and medical internet sites. In articles that report on prevention, the media emphasize vaccine development and identifying individuals at genetic risk through population screening. Articles that emphasize treatment often promote current pharmaceutical solutions and highlight the possibility of tailoring treatments to specific genetic variants. The authors raise concerns about the tendency of this coverage to focus on the benefits of pharmaceutical treatments and genetic-based approaches to prevention while neglecting or downplaying potential risks and ethical issues. This analysis suggests a need for more balanced, evidence-based media reporting on the potential outcomes of genetic research.

Identification of a variant in KDR associated with serum VEGFR2 and pharmacodynamics of Pazopanib

PURPOSE

VEGF receptor (VEGFR) kinases are important drug targets in oncology that affect function of systemic endothelial cells. To discover genetic markers that affect VEGFR inhibitor pharmacodynamics, we performed a genome-wide association study of serum soluble vascular VEGFR2 concentrations [sVEGFR2], a pharmacodynamic biomarker for VEGFR2 inhibitors.

EXPERIMENTAL DESIGN

We conducted a genome-wide association study (GWAS) of [sVEGFR2] in 736 healthy Old Order Amish volunteers. Gene variants identified from the GWAS were genotyped serially in a cohort of 128 patients with advanced solid tumor with baseline [sVEGFR2] measurements, and in 121 patients with renal carcinoma with [sVEGFR2] measured before and during pazopanib therapy.

RESULTS

rs34231037 (C482R) in KDR, the gene encoding sVEGFR2 was found to be highly associated with [sVEGFR2], explaining 23% of the variance (P = 2.7 × 10(-37)). Association of rs34231037 with [sVEGFR2] was replicated in 128 patients with cancer with comparable effect size (P = 0.025). Furthermore, rs34231037 was a significant predictor of changes in [sVEGFR2] in response to pazopanib (P = 0.01).

CONCLUSION

Our findings suggest that genome-wide analysis of phenotypes in healthy populations can expedite identification of candidate pharmacogenetic markers. Genotyping for germline variants in KDR may have clinical utility in identifying patients with cancer with unusual sensitivity to effects of VEGFR2 kinase inhibitors.

Skeletal muscle tissue engineering: methods to form skeletal myotubes and their applications

Skeletal muscle tissue engineering (SMTE) aims to repair or regenerate defective skeletal muscle tissue lost by traumatic injury, tumor ablation, or muscular disease. However, two decades after the introduction of SMTE, the engineering of functional skeletal muscle in the laboratory still remains a great challenge, and numerous techniques for growing functional muscle tissues are constantly being developed. This article reviews the recent findings regarding the methodology and various technical aspects of SMTE, including cell alignment and differentiation. We describe the structure and organization of muscle and discuss the methods for myoblast alignment cultured in vitro. To better understand muscle formation and to enhance the engineering of skeletal muscle, we also address the molecular basics of myogenesis and discuss different methods to induce myoblast differentiation into myotubes. We then provide an overview of different coculture systems involving skeletal muscle cells, and highlight major applications of engineered skeletal muscle tissues. Finally, potential challenges and future research directions for SMTE are outlined.

Effects of non-pharmacological or pharmacological interventions on cognition and brain plasticity of aging individuals

Brain aging and aging-related neurodegenerative disorders are major health challenges faced by modern societies. Brain aging is associated with cognitive and functional decline and represents the favourable background for the onset and development of dementia. Brain aging is associated with early and subtle anatomo-functional physiological changes that often precede the appearance of clinical signs of cognitive decline. Neuroimaging approaches unveiled the functional correlates of these alterations and helped in the identification of therapeutic targets that can be potentially useful in counteracting age-dependent cognitive decline. A growing body of evidence supports the notion that cognitive stimulation and aerobic training can preserve and enhance operational skills in elderly individuals as well as reduce the incidence of dementia. This review aims at providing an extensive and critical overview of the most recent data that support the efficacy of non-pharmacological and pharmacological interventions aimed at enhancing cognition and brain plasticity in healthy elderly individuals as well as delaying the cognitive decline associated with dementia.

How good is "evidence" from clinical studies of drug effects and why might such evidence fail in the prediction of the clinical utility of drugs?

Promising evidence from clinical studies of drug effects does not always translate to improvements in patient outcomes. In this review, we discuss why early evidence is often ill suited to the task of predicting the clinical utility of drugs. The current gap between initially described drug effects and their subsequent clinical utility results from deficits in the design, conduct, analysis, reporting, and synthesis of clinical studies-often creating conditions that generate favorable, but ultimately incorrect, conclusions regarding drug effects. There are potential solutions that could improve the relevance of clinical evidence in predicting the real-world effectiveness of drugs. What is needed is a new emphasis on clinical utility, with nonconflicted entities playing a greater role in the generation, synthesis, and interpretation of clinical evidence. Clinical studies should adopt strong design features, reflect clinical practice, and evaluate outcomes and comparisons that are meaningful to patients. Transformative changes to the research agenda may generate more meaningful and accurate evidence on drug effects to guide clinical decision making.

Development of a broad-based ADME panel for use in pharmacogenomic studies

Aim: To optimally address the interindividual variability observed in pharmacokinetic drug response, we have created a custom genotyping panel that interrogates most of the key genetic variations present in a set of 181 prioritized genes responsible for the absorption, distribution, metabolism and excretion (ADME) of many therapeutic agents. This consensus list of genes and variants was based on the ADME core and extended gene lists compiled by a group of pharmaceutical companies as having relevance. Although these pharmacokinetic genes and pathways are well known, tools that can interrogate a large number of these genes simultaneously within a single experiment are not currently available. Methods: Using novel design strategies, we have developed an optimized and validated ADME genotyping panel, encompassing approximately 3000 variants, that has broad applicability to any study or clinical trial that would benefit from the evaluation of an extensive list of ADME genes. Results & conclusion: Over the course of three design iterations, overall assay conversion rates were improved from 83 to 97% resulting in a panel that fills in many of the gaps in coverage present on currently available commercial genotyping assays. The utility of the assay has been demonstrated by the screening of more than 1000 samples resulting in the discovery of novel pharmacogenomic associations. The assay, and the underlying methods, will continue to be a valuable tool for use in future pharmacogenomic studies.

Original submitted 28 November 2013; Revision submitted 13 May 2014

Genome-wide association studies in pharmacogenomics: successes and lessons

OBJECTIVE

As genotyping technology has progressed, genome-wide association studies (GWAS) have matured into efficient and effective tools for mapping genes underlying human phenotypes.

METHODS

Recent studies have shown the utility of the GWAS approach for examining pharmacogenomic traits, including drug metabolism, efficacy, and toxicity.

RESULTS

Application of GWAS to pharmacogenomic outcomes presents unique challenges and opportunities.

CONCLUSION

In the current review, we discuss the potential promises and potential caveats of this approach specifically as it relates to pharmacogenomic studies. Concerns with study design, power and sample size, and analysis are reviewed. We further examine the features of successful pharmacogenomic GWAS, and describe consortia efforts that are likely to expand the reach of pharmacogenomic GWAS in the future.

New applications of disease genetics and pharmacogenetics to drug development

TOMMORROW is a Phase III delay of onset clinical trial to determine whether low doses of pioglitazone, a molecule that induces mitochondrial doubling, delays the onset of MCI-AD in normal subjects treated with low dose compared to placebo. BOLD imaging studies in rodents and man were used to find the dose that increases oxygen consumption at central regions of the brain in higher proportion than activation of large corticol regions. The trial is made practical by the use of a pharmacogenetic algorithm based on TOMM40 and APOE genotypes and age to identify normal subjects at high risk of MCI-AD between the ages of 65-83 years within a five year follow-up period.

The Coriell personalized medicine collaborative pharmacogenomics appraisal, evidence scoring and interpretation system

Implementation of pharmacogenomics (PGx) in clinical care can lead to improved drug efficacy and reduced adverse drug reactions. However, there has been a lag in adoption of PGx tests in clinical practice. This is due in part to a paucity of rigorous systems for translating published clinical and scientific data into standardized diagnostic tests with clear therapeutic recommendations. Here we describe the Pharmacogenomics Appraisal, Evidence Scoring and Interpretation System (PhAESIS), developed as part of the Coriell Personalized Medicine Collaborative research study, and its application to seven commonly prescribed drugs.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Gene-environment interactions of selected pharmacogenes in arterial hypertension

Hypertension affects approximately 1 billion people worldwide. Owing to population aging, hypertension-related cardiovascular burden is expected to rise in the near future. In addition to genetic variants influencing the blood pressure response to antihypertensive drugs, several genes encoding for drug-metabolizing or -transporting enzymes have been associated with blood pressure and/or hypertension in humans (e.g., ACE, CYP1A2, CYP3A5, ABCB1 and MTHFR) regardless of drug treatment. These genes are also involved in the metabolism and transport of endogenous substances and their effects may be modified by selected environmental factors, such as diet or lifestyle. However, little is currently known on the complex interplay between environmental factors, endogenous factors, genetic variants and drugs on blood pressure control. This review will discuss the respective role of population-based primary prevention and personalized medicine for arterial hypertension, taking a pharmacogenomics' perspective focusing on selected pharmacogenes.

Frequency of MDR1 single nucleotide polymorphisms in a Jordanian population, including a novel variant

The multidrug resistance gene (MDR1 or ABCB1) codes for P-glycoprotein, which plays an important role in regulating absorption, distribution, and elimination of drugs. We examined MDR1 gene variants in 100 unrelated subjects from various regions of Jordan. The MDR1 gene was scanned using direct sequencing. Six rare variants in MDR1 were detected, including a new variant, T3075A. This variant did not affect the protein sequence (synonym for threonine). Among the common SNPs, the frequencies of rs1128503 (C1236T) genotypes were: 0.23 (CC), 0.41 (CT) and 0.36 (TT). For the rs2032582 (G2677T) SNP, genotype frequencies were 0.38 for GG, 0.45 for GT, 0.13 for TT, 0.03 for GA, and 0.01 for TA, whereas for rs1045642 (C3435T), genotype frequencies were 0.17 for CC, 0.5 for CT and 0.33 for TT. The observed distribution of the common variants in the Jordanian population was within the range detected in other populations. These data on MDR1 gene variants in the Jordanian population will be useful for investigations on response to P-glycoprotein substrate drugs.