Pharmacogenomics: the promise of personalized medicine

Proteogenomic Approaches for Diseasome Studies

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Preliminary Investigation of a Rapid and Feasible Therapeutic Drug Monitoring Method for the Real-Time Estimation of Blood Pazopanib Concentrations

Pazopanib is a multi-kinase inhibitor used to treat advanced/metastatic renal cell carcinoma and advanced soft tissue tumors; however, side effects such as diarrhea and hypertension have been reported, and dosage adjustment based on drug concentration in the blood is necessary. However, measuring pazopanib concentrations in blood using the existing methods is time-consuming; and current dosage adjustments are made using the results of blood samples taken at the patient's previous hospital visit (approximately a month prior). If the concentration of pazopanib could be measured during the waiting period for a doctor's examination at the hospital (in approximately 30 min), the dosage could be adjusted according to the patient's condition on that day. Therefore, we aimed to develop a method for rapidly measuring blood pazopanib concentrations (in approximately 25 min) using common analytical devices (a tabletop centrifuge and a spectrometer). This method allowed for pazopanib quantification in the therapeutic concentration range (25-50 μg/mL). Additionally, eight popular concomitant medications taken simultaneously with pazopanib did not interfere with the measurements. We used the developed method to measure blood concentration in two patients and obtained similar results to those measured using the previously reported HPLC method. By integrating it with the point of care and sample collection by finger pick, this method can be used for measurements in pharmacies and patients' homes. This method can maximize the therapeutic effects of pazopanib by dose adjustment to control adverse events.

Challenges and opportunities for SERS in the infrared: materials and methods

In the wake of a global, heightened interest towards biomarker and disease detection prompted by the SARS-CoV-2 pandemic, surface enhanced Raman spectroscopy (SERS) positions itself again at the forefront of biosensing innovation. But is it ready to move from the laboratory to the clinic? This review presents the challenges associated with the application of SERS to the biomedical field, and thus, to the use of excitation sources in the near infrared, where biological windows allow for cell and through-tissue measurements. Two main tackling strategies will be discussed: (1) acting on the design of the enhancing substrate, which includes manipulation of nanoparticle shape, material, and supramolecular architecture, and (2) acting on the spectral collection set-up. A final perspective highlights the upcoming scientific and technological bets that need to be won in order for SERS to stably transition from benchtop to bedside.

Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine

As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.

Experience with comprehensive pharmacogenomic multi-gene panel in clinical practice: a retrospective single-center study

AIM

To assess the prevalence of actionable pharmacogenetic interventions in patients who underwent pharmacogenetic testing with a multi-gene panel.

METHODS

We retrospectively reviewed single-center electronic health records. A total of 319 patients were enrolled who underwent pharmacogenomic testing with the RightMed test panel using TaqMan quantitative real-time PCR method and copy number variation analysis to determine the SNPs in the 27 target genes.

RESULTS

Actionable drug-gene pairs were found in 235 (73.7%) patients. Relevant guidelines on genotype-based prescribing were available for 133 (56.7%) patients at the time of testing. Based on the patients' genotype, 139 (43.6%) patients were using at least one drug with significant pharmacogenetic interactions.

CONCLUSION

Two out of three patients had at least one drug-gene pair in their therapy. Further studies should assess the clinical effectiveness of integrating pharmacogenomic data into patients' electronic health records.

A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis

Background

Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.

Methods

We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting.

Results

We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%.

Conclusion

Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models

Genotype-to-phenotype prediction is a central problem of human genetics. In recent years, it has become possible to construct complex predictive models for phenotypes, thanks to the availability of large genome data sets as well as efficient and scalable machine learning tools. In this paper, we make a threefold contribution to this problem. First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. We find that this is indeed the case if model features include a sufficiently rich set of covariates, but probably not otherwise. Second, we ask if the conventional selection of single nucleotide polymorphisms (SNPs) by genome wide association studies (GWAS) can be replaced by a more efficient procedure, taking into account information in previously selected SNPs. We propose such a procedure, based on a sequential feature importance estimation with decision trees, and show that this approach indeed produced informative SNP sets that are much more compact than when selected with GWAS. Finally, we show that the highest prediction accuracy can ultimately be achieved by ensembling individual linear and nonlinear models. To the best of our knowledge, for some of the phenotypes that we consider (asthma, hypothyroidism), our results are a new state-of-the-art.

The prospects of precision psychiatry

Since the turn of the twenty-first century, biomedical psychiatry around the globe has embraced the so-called precision medicine paradigm, a model for medical research that uses innovative techniques for data collection and analysis to reevaluate traditional theories of disease. The goal of precision medicine is to improve diagnostics by restratifying the patient population on the basis of a deeper understanding of disease processes. This paper argues that precision is ill-fitting for psychiatry for two reasons. First, in psychiatry, unlike in fields like oncology, precision medicine has been understood as an attempt to improve medicine by casting out, rather than merely revising, traditional taxonomic tools. Second, in psychiatry the term "biomarker" is often used in reference to signs or symptoms that allow patients to be classified and then matched with treatments; however, in oncology "biomarker" usually refers to a disease mechanism that is useful not only for diagnostics, but also for discovering causal pathways that drug therapies can target. Given these differences between how the precision medicine paradigm operates in psychiatry and in other medical fields like oncology, while precision psychiatry may offer successful rhetoric, it is not a promising paradigm.

Pharmacogenetics of Drug-Resistant Epilepsy (Review of Literature)

Pharmacogenomic studies in epilepsy are justified by the high prevalence rate of this disease and the high cost of its treatment, frequent drug resistance, different response to the drug, the possibility of using reliable methods to assess the control of seizures and side effects of antiepileptic drugs. Candidate genes encode proteins involved in pharmacokinetic processes (drug transporters, metabolizing enzymes), pharmacodynamic processes (receptors, ion channels, enzymes, regulatory proteins, secondary messengers) and drug hypersensitivity (immune factors). This article provides an overview of the literature on the influence of genetic factors on treatment in epilepsy.

Precision and personalized assessment, diagnosis and treatment in psychiatry

"Precision medicine" and "personalized medicine" constitute goals of research since antiquity and this was intensified with the arrival of the "evidence-based medicine." precision and personalized psychiatry (3P) when achieved will constitute a radical shift in our paradigm and it will be even more transformative than in other fields of medicine. The biggest problems so far are the problematic definition of mental disorder, available treatments seem to concern broad categories rather than specific disorders and finally clinical predictors of treatment response or side effects and biological markers do not exist. Precision and personalized psychiatry like all precision medicine will be a laborious and costly task; thus the partnership of scientists with industry and the commercialization of new methods and technologies will be an important element for success. The development of an appropriate legal framework which will both support development and progress but also will protect the rights and the privacy of patients and their families is essential.

Practicality of 3D Printed Personalized Medicines in Therapeutics

Technological advances in science over the past century have paved the way for remedial treatment outcomes in various diseases. Pharmacogenomic predispositions, the emergence of multidrug resistance, medication and formulation errors contribute significantly to patient mortality. The concept of "personalized" or "precision" medicines provides a window to addressing these issues and hence reducing mortality. The emergence of three-dimensional printing of medicines over the past decades has generated interests in therapeutics and dispensing, whereby the provisions of personalized medicines can be built within the framework of producing medicines at dispensaries or pharmacies. This plan is a good replacement of the fit-for-all modality in conventional therapeutics, where clinicians are constrained to prescribe pre-formulated dose units available on the market. However, three-dimension printing of personalized medicines faces several hurdles, but these are not insurmountable. In this review, we explore the relevance of personalized medicines in therapeutics and how three-dimensional printing makes a good fit in current gaps within conventional therapeutics in order to secure an effective implementation of personalized medicines. We also explore the deployment of three-dimensional printing of personalized medicines based on practical, legal and regulatory provisions.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Implementation of a Renal Precision Medicine Program: Clinician Attitudes and Acceptance

A precision health initiative was implemented across a multi-hospital health system, wherein a panel of genetic variants was tested and utilized in the clinical care of chronic kidney disease (CKD) patients. Pharmacogenomic predictors of antihypertensive response and genomic predictors of CKD were provided to clinicians caring for nephrology patients. To assess clinician knowledge, attitudes, and willingness to act on genetic testing results, a Likert-scale survey was sent to and self-administered by these nephrology providers (N = 76). Most respondents agreed that utilizing pharmacogenomic-guided antihypertensive prescribing is valuable (4.0 ± 0.7 on a scale of 1 to 5, where 5 indicates strong agreement). However, the respondents also expressed reluctance to use genetic testing for CKD risk stratification due to a perceived lack of supporting evidence (3.2 ± 0.9). Exploratory sub-group analyses associated this reluctance with negative responses to both knowledge and attitude discipline questions, thus suggesting reduced exposure to and comfort with genetic information. Given the evolving nature of genomic implementation in clinical care, further education is warranted to help overcome these perception barriers.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.

OBJECTIVE

This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.

METHODS

The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities.

RESULTS

The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.

CONCLUSIONS

The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

Robust outcome weighted learning for optimal individualized treatment rules

Personalized medicine has received increasing attentions among scientific communities in recent years. Because patients often have heterogenous responses to treatments, discovering individualized treatment rules (ITR) is an important component of precision medicine. To that end, one needs to develop a proper decision rule using patient-specific characteristics to maximize the expected clinical outcome, i.e. the optimal ITR. Recently, outcome weighted learning (OWL) has been proposed to estimate optimal ITR under a weighted classification framework. Since most of commonly used loss functions are unbounded, the resulting ITR may suffer similar effects of outliers as the corresponding classifiers. In this paper, we propose robust OWL (ROWL) to build more stable ITRs using a new family of bounded and non-convex loss functions. Moreover, we extend the proposed ROWL method to the multiple treatment setting under the angle-based classification structure. Our theoretical results show that ROWL is Fisher consistent, and can provide the estimation of rewards' ratios for the resulting ITRs. We develop an efficient difference of convex functions algorithm (DCA) to solve the corresponding nonconvex optimization problem. Through analysis of simulated examples and a real medical dataset, we demonstrate that the proposed ROWL method yields more competitive performance in terms of the empirical value function and the misclassification error than several existing methods.

Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes

More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which 8207 variants are in strong LD (r2>0.8) with known pharmacogenomic variants. Of the latter, eight are coding or structural variants predicted to have high impact, with 19 additional missense variants that are predicted to have moderate impact. In conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the association signals and represent the missing causative variants underlying drug response phenotypes.

Association Study Among Candidate Genetic Polymorphisms and Chemotherapy-Related Severe Toxicity in Testicular Cancer Patients

Testicular cancer is one of the most commonly occurring malignant tumors in young men with fourfold higher rate of incidence and threefold higher mortality rates in Chile than the average global rates. Surgery is the initial line of treatment for testicular cancers, and is generally followed by chemotherapy, usually with combinations of bleomycin, etoposide, and cisplatin (BEP). However, the adverse effects of chemotherapy vary significantly among individuals; therefore, the present study explored the association of functionally significant allelic variations in genes related to the pharmacokinetics/pharmacodynamics of BEP and DNA repair enzymes with chemotherapy-induced toxicity in BEP-treated testicular cancer patients. We prospectively recruited 119 patients diagnosed with testicular cancer from 2010 to 2017. Genetic polymorphisms were analyzed using PCR and/or qPCR with TaqMan^®probes. Toxicity was evaluated based on the Common Terminology Criteria for Adverse Events, v4.03. After univariate analyses to define more relevant genetic variants (p < 0.2) and clinical conditions in relation to severe (III–IV) adverse drug reactions (ADRs), stepwise forward multivariate logistic regression analyses were performed. As expected, the main severe ADRs associated with the non-genetic variables were hematological (neutropenia and leukopenia). Univariate statistical analyses revealed that patients with ERCC2 rs13181 T/G and/or CYP3A4 rs2740574 A/G genotypes are more likely to develop alopecia; patients with ERCC2 rs238406 C/C genotype may develop leukopenia, and patients with GSTT1-null genotype could develop lymphocytopenia (III–IV). Patients with ERCC2 rs1799793 A/A were at risk of developing severe anemia. The BLMH rs1050565 G/G genotype was found to be associated with pain, and the GSTP1 G/G genotype was linked infection (p < 0.05). Multivariate analysis showed an association between specific ERCC1/2 genotypes and cumulative dose of BEP drugs with the appearance of severe leukopenia and/or febrile neutropenia. Grades III–IV vomiting, nausea, and alopecia could be partly explained by the presence of specific ERCC1/2, MDR1, GSTP1, and BLMH genotypes (p < 0.05). Hence, we provide evidence for the usefulness of pharmacogenetics as a tool for predicting severe ADRs in testicular cancer patients treated with BEP chemotherapy.

Mechanical characterization and ex vivo evaluation of anticancer and antiviral drug printed bioadhesive film for the treatment of cervical cancer

As research progresses on personalized medicines, it is clear that personalized and flexible formulations can provide effective treatment with reduced side effects especially for diseases like cancer, characteristic of high patient variability. 2D and 3D printers are frequently reported in the literature for the preparation of pharmaceutical products with adjusted dose and selected drug combinations. However, in-depth characterization studies of these formulations are rather limited. In this paper, ex vivo and mechanical characterization studies of antiviral and anticancer drug printed film formulations designed for personalized application were performed. Effects of the printing process with pharmaceutical formulations such as paclitaxel (PCX):cyclodextrin (CD) complex or cidofovir (CDV) encapsulated into poly(ethylene glycol)-polycaprolactone (PEG-PCL) nanoparticles on the films were evaluated through a series of mechanical characterization studies. Inkjet printing process was found to cause no significant change in the thicknesses of the film formulations, while mechanical strength and surface free energy increased and nano-sized voids in the film structure decreased. According to the mechanical characterization data, the unprinted film had maximum force (F_max) value of 15.6 MPa whereas F_max increased to 43.8 MPa for PCX:CD complex printed film and to 37.7 MPa for the antiviral CDV-PEG-PCL nanoparticle printed film. In the light of ex vivo findings of sheep cervix-uterine tissue, bioadhesive properties of film formulations significantly improved after inkjet printing with different drug formulations. It has also been shown that the anticancer formulation printed on the film was maintained at the cervix tissue surface for >12 h. This study has shown for the first time that inkjet printing process does not adversely affect the mechanical properties of the bioadhesive film formulations. It has also been shown that durable bioadhesive film formulations for personalized dosing can be prepared by combining nanotechnology and inkjet printing.

HUME: large-scale detection of causal genetic factors of adverse drug reactions

Motivation

Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously.

Results

To address this challenge, we propose HUME, a multi-phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co-prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic-Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records.

Supplementary information

Supplementary data are available at Bioinformatics online.

Estimating individualized treatment rules for ordinal treatments

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.

System pharmacogenomics application in infectious diseases

The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.

Personalised pulmonary rehabilitation in COPD

This review summarises ongoing developments in personalised medicine and individualised medicine in chronic obstructive pulmonary disease (COPD). Currently applied classification systems largely ignore the complexity and heterogeneity of the COPD syndrome. Personalised medicine has to consider the influence of unique circumstances of the person, which contribute to this heterogeneity and complexity. Pulmonary rehabilitation is described as a comprehensive, individualised intervention based on thorough assessment of identifiable treatable traits. Partnership in care will become a crucial factor to improve and maintain health. Tolerating uncertainty and unpredictability will enrich future doctor-patient relationships.

Inkjet printing of antiviral PCL nanoparticles and anticancer cyclodextrin inclusion complexes on bioadhesive film for cervical administration

Personalized medicine is an important treatment approach for diseases like cancer with high intrasubject variability. In this framework, printing is one of the most promising methods since it permits dose and geometry adjustment of the final product. With this study, a combination product consisting of anticancer (paclitaxel) and antiviral (cidofovir) drugs was manufactured by inkjet printing onto adhesive film for local treatment of cervical cancers as a result of HPV infection. Furthermore, solubility problem of paclitaxel was overcome by maintaining this poorly soluble drug in a cyclodextrin inclusion complex and release of cidofovir was controlled by encapsulation in polycaprolactone nanoparticles. In vitro characterization studies of printed film formulations were performed and cell culture studies showed that drug loaded film formulation was effective on human cervical adenocarcinoma cells. Our study suggests that inkjet printing technology can be utilized in the development of antiviral/anticancer combination dosage forms for mucosal application. The drug amount in the delivery system can be accurately controlled and modified. Moreover, prolonged drug release time can be obtained. Printing of anticancer and antiviral drugs on film seem to be a potential approach for HPV-related cervical cancer treatment and a good candidate for further studies.

A novel 4-arm DNA/RNA Nanoconstruct triggering Rapid Apoptosis of Triple Negative Breast Cancer Cells within 24 hours

Measuring at ~30 nm, a fully customizable holliday junction DNA nanoconstruct, was designed to simultaneously carry three unmodified SiRNA strands for apoptosis gene knockout in cancer cells without any assistance from commercial transfection kits. In brief, a holliday junction structure was intelligently designed to present one arm with a cell targeting aptamer (AS1411) while the remaining three arms to carry different SiRNA strands by means of DNA/RNA duplex for inducing apoptosis in cancer cells. By carrying the three SiRNA strands (AKT, MDM2 and Survivin) into triple negative breast MDA-MB-231 cancer cells, cell number had reduced by up to ~82% within 24 hours solely from one single administration of 32 picomoles. In the immunoblotting studies, up-elevation of phosphorylated p53 was observed for more than 8 hours while the three genes of interest were suppressed by nearly half by the 4-hour mark upon administration. Furthermore, we were able to demonstrate high cell selectivity of the nanoconstruct and did not exhibit usual morphological stress induced from liposomal-based transfection agents. To the best of the authors' knowledge, this system represents the first of its kind in current literature utilizing a short and highly customizable holliday DNA junction to carry SiRNA for apoptosis studies.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Future methods in pharmacy practice research

This article describes the current and future practice of pharmacy scenario underpinning and guiding this research and then suggests future directions and strategies for such research. First, it sets the scene by discussing the key drivers which could influence the change in pharmacy practice research. These are demographics, technology and professional standards. Second, deriving from this, it seeks to predict and forecast the future shifts in use of methodologies. Third, new research areas and availability of data impacting on future methods are discussed. These include the impact of aging information technology users on healthcare, understanding and responding to cultural and social disparities, implementing multidisciplinary initiatives to improve health care, medicines optimization and predictive risk analysis, and pharmacy as business and health care institution. Finally, implications of the trends for pharmacy practice research methods are discussed.

Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies

With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study. Copyright © 2016 John Wiley & Sons, Ltd.

The 3-I framework: a framework for developing public policies regarding pharmacogenomics (PGx) testing in Canada

The 3-I framework of analyzing the ideas, interests, and institutions around a topic has been used by political scientists to guide public policy development. In Canada, there is a lack of policy governing pharmacogenomics (PGx) testing compared to other developed nations. The goal of this study was to use the 3-I framework, a policy development tool, and apply it to PGx testing to identify and analyze areas where current policy is limited and challenges exist in bringing PGx testing into wide-spread clinical practice in Canada. A scoping review of the literature was conducted to determine the extent and challenges of PGx policy implementation at federal and provincial levels. Based on the 3-I analysis, contentious ideas related to PGx are (i) genetic discrimination, (ii) informed consent, (iii) the lack of knowledge about PGx in health care, (iv) the value of PGx testing, (v) the roles of health care workers in the coordination of PGx services, and (vi) confidentiality and privacy. The 3-I framework is a useful tool for policy makers, and applying it to PGx policy development is a new approach in Canadian genomics. Policy makers at every organizational level can use this analysis to help develop targeted PGx policies.

Personalized medicine and economic evaluation in oncology: all theory and no practice?

The clinical definition of personalized medicine (PM) is closely related to that of pharmacogenomics. Ideally, PM could lead the pharmaceutical industry to differentiate products by subgroups of patients with the same pathology and find new gene targets for drug discovery. Here, we focus on the potential impact of PM on the design of clinical trials and economic evaluations limited to oncology (its first and main field of application). Then, we assess the European economic evaluations focused on trastuzumab and cetuximab, the two drugs usually mentioned as emblematic examples of targeted therapies. Clinical results of PM in oncology have not been as encouraging as hoped so far. Of course, economic evaluations on targeted therapies cannot help overcome the lack of clinical evidence for most of them. The two paradigmatic examples of cetuximab and trastuzumab indicate that the methodological implications on economic evaluations debated in the literature are more theoretical than practical.

Investigation of Host-Gut Microbiota Modulation of Therapeutic Outcome

A broader understanding of factors underlying interindividual variation in pharmacotherapy is important for our pursuit of "personalized medicine." Based on knowledge gleaned from the investigation of human genetics, drug-metabolizing enzymes, and transporters, clinicians and pharmacists are able to tailor pharmacotherapies according to the genotype of patients. However, human host factors only form part of the equation that accounts for heterogeneity in therapeutic outcome. Notably, the gut microbiota possesses wide-ranging metabolic activities that expand the metabolic functions of the human host beyond that encoded by the human genome. In this review, we first illustrate the mechanisms in which gut microbes modulate pharmacokinetics and therapeutic outcome. Second, we discuss the application of metabonomics in deciphering the complex host-gut microbiota interaction in pharmacotherapy. Third, we highlight an integrative approach with particular mention of the investigation of gut microbiota using culture-based and culture-independent techniques to complement the investigation of the host-gut microbiota axes in pharmaceutical research.

SNP genetic polymorphisms of MDR-1, CYP1A2 and CYPB11 genes in four canine breeds upon toxicological evaluation

The fields of pharmacogenetics and pharmacogenomics have become increasingly promising regarding the clinical application of genetic data to aid in prevention of adverse reactions. Specific screening tests can predict which animals express modified proteins or genetic sequences responsible for adverse effects associated with a drug. Among the genetic variations that have been investigated in dogs, the multidrug resistance gene (MDR) is the best studied. However, other genes such as CYP1A2 and CYP2B11 control the protein syntheses involved in the metabolism of many drugs. In the present study, the MDR-1, CYP1A2 and CYP2B11 genes were examined to identify SNP polymorphisms associated with these genes in the following four canine breeds: Uruguayan Cimarron, Border Collie, Labrador Retriever and German Shepherd. The results revealed that several SNPs of the CYP1A2 and CYP2B11 genes are potential targets for drug sensitivity investigations.

Words matter: distinguishing "personalized medicine" and "biologically personalized therapeutics"

"Personalized medicine" has become a generic term referring to techniques that evaluate either the host or the disease to enhance the likelihood of beneficial patient outcomes from treatment interventions. There is, however, much more to personalization of care than just identifying the biotherapeutic strategy with the highest likelihood of benefit. In its new meaning, "personalized medicine" could overshadow the individually tailored, whole-person care that is at the bedrock of what people need and want when they are ill. Since names and definitional terms set the scope of the discourse, they have the power to define what personalized medicine includes or does not include, thus influencing the scope of the professional purview regarding the delivery of personalized care. Taxonomic accuracy is important in understanding the differences between therapeutic interventions that are distinguishable in their aims, indications, scope, benefits, and risks. In order to restore the due emphasis to the patient and his or her needs, we assert that it is necessary, albeit belated, to deconflate the contemporary term "personalized medicine" by taxonomizing this therapeutic strategy more accurately as "biologically personalized therapeutics" (BPT). The scope of truly personalized medicine and its relationship to biologically personalized therapeutics is described, emphasizing that the best of care must give due recognition and emphasis to both BPT and truly personalized medicine.

Preappointment testing for BRAF/KIT mutation in advanced melanoma: a model in molecular data delivery for individualized medicine

The emergence of individualized medicine is driven by developments in precision diagnostics, epitomized by molecular testing. Because treatment decisions are being made based on such molecular data, data management is gaining major importance. Among data management challenges, creating workflow solutions for timely delivery of molecular data has become pivotal. This study aims to design and implement a scalable process that permits preappointment BRAF/KIT mutation analysis in melanoma patients, allowing molecular results necessary for treatment plans to be available before the patient's appointment. Process implementation aims to provide a model for efficient molecular data delivery for individualized medicine. We examined the existing process of BRAF/KIT testing in melanoma patients visiting our institution for oncology consultation. We created 5 working groups, each designing a specific segment of an alternative process that would allow preappointment BRAF/KIT testing and delivery of results. Data were captured and analyzed to evaluate the success of the alternative process. For 1 year, 35 (59%) of 55 patients had prior BRAF/KIT testing. The remaining 20 patients went through the new process of preappointment testing; results were available at the time of appointment for 12 patients (overall preappointment results availability, 85.5%). The overall process averaged 13.4 ± 4.7 days. In conclusion, we describe the successful implementation of a scalable workflow solution that permits preappointment BRAF/KIT mutation analysis and result delivery in melanoma patients. This sets the stage for further applications of this model to other conditions, answering an increasing demand for robust delivery of molecular data for individualized medicine.

Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval

Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the 'missing heritability' of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 × 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.

Biomedical data integration: using XML to link clinical and research data sets

Data integration occurs when a query proceeds through multiple data sets, thereby relating diverse data extracted from different data sources. Data integration is particularly important to biomedical researchers since data obtained from experiments on human tissue specimens have little applied value unless they can be combined with medical data (i.e., pathologic and clinical information). In the past, research data were correlated with medical data by manually retrieving, reading, assembling and abstracting patient charts, pathology reports, radiology reports and the results of special tests and procedures. Manual annotation of research data is impractical when experiments involve hundreds or thousands of tissue specimens resulting in large, complex data collections. The purpose of this paper is to review how XML (eXtensible Markup Language) provides the fundamental tools that support biomedical data integration. The article also discusses some of the most important challenges that block the widespread availability of annotated biomedical data sets.

Sulforaphane: translational research from laboratory bench to clinic

Cruciferous vegetables are widely acknowledged to provide chemopreventive benefits in humans, but they are not generally consumed at levels that effect significant change in biomarkers of health. Because consumers have embraced the notion that dietary supplements may prevent disease, this review considers whether an appropriately validated sulforaphane-yielding broccoli sprout supplement may deliver clinical benefit. The crucifer-derived bioactive phytochemical sulforaphane is a significant inducer of nuclear factor erythroid 2-related factor 2 (Nrf2), the transcription factor that activates the cell's endogenous defenses via a battery of cytoprotective genes. For a broccoli sprout supplement to demonstrate bioactivity in vivo, it must retain both the sulforaphane-yielding precursor compound, glucoraphanin, and the activity of glucoraphanin's intrinsic myrosinase enzyme. Many broccoli sprout supplements are myrosinase inactive, but current labeling does not reflect this. For the benefit of clinicians and consumers, this review summarizes the findings of in vitro studies and clinical trials, interpreting them in the context of clinical relevance. Standardization of sulforaphane nomenclature and assay protocols will be necessary to remove inconsistency and ambiguity in the labeling of currently available broccoli sprout products.

The Learning-Adapting-Leveling model: from theory to hypothesis of steps for implementation of basic genome-based evidence in personalized medicine

We see a backlog in the effective and efficient integration of personalized medicine applications such as genome-based information and technologies into healthcare systems. This article aims to expand on the steps of a published innovative model, which addresses the bottleneck of real-time integration into healthcare. We present a deconstruction of the Learning-Adapting-Leveling model to simplify the steps. We found out that throughout the technology transfer pipeline, contacts, assessments and adaptations/feedback loops are made with health needs assessment, health technology assessment and health impact assessment professionals in the same order by the academic-industrial complex, resulting in early-on involvement of all stakeholders. We conclude that the model steps can be used to resolve the bottleneck of implementation of personalized medicine application into healthcare systems.

The faces of personalized medicine: a framework for understanding its meaning and scope

The objective of this article was to provide a framework for understanding the different definitions of the term "personalized medicine." The term personalized medicine is used regularly but interpreted in different ways. This article approaches the term by starting with a broad view of clinical medicine, where three components can be distinguished: the questions (e.g., what is the diagnosis?), the methods used to answer them (e.g., a test), and the available actions (e.g., to give or not give a particular drug). Existing definitions of personalized medicine disagree about which questions, methods, and actions fall within its domain. Some define the term narrowly, referring to the use of a diagnostic test to predict drug response, thereby clarifying whether or not a patient will benefit from that drug. An example of this combination is the HER2/neu test to predict the effectiveness of trastuzumab in breast cancer. Many who adopt this definition associate the concept of personalized medicine with fields such as genetics, genomics, and other types of "-omics." In contrast, others view personalized medicine as a concept that has always existed, because medicine has always considered the needs of the individual. One definition of personalized medicine that accommodates both interpretations is "the use of combined knowledge (genetic or otherwise) about a person to predict disease susceptibility, disease prognosis, or treatment response and thereby improve that person's health." This predictive ability can increase over time through innovations in various technologies, resulting in further improvements in health outcomes. Moreover, these developments can lead to a better understanding of the underlying causes of disease, which can eventually lead to breakthroughs in the treatment of individual patients. In that sense, a truly personalized form of medicine can also be seen as an ideal, a goal that will be achieved only after multiple advances in science. Although the term personalized medicine was rechristened somewhat recently, our ability to personalize medicine will continue to advance in unimaginable ways as we come to learn more about the heterogeneity that exists among individuals and diseases.

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.

Promoting an Inclusive Approach to Benefit Sharing: Expanding the Scope of the CBD?

The Convention on Biological Diversity (CBD) is a major international agreement to ensure the conservation of biological diversity, the sustainable use of various components of biological diversity, and fair and equitable access and benefit sharing of advances arising from the use of related genetic resources. The CBD excludes human genetic resources. In light of the rapid advances in biotechnology, genetic resources are increasingly being utilised by different types of users and in different industries. This usage is not confined to plants, animals or micro-organisms but includes human genetic resources and sometimes a mix of such resources. In the absence of any international agreement, various national governments are framing their own rules and guidelines. This patchwork of regulation may eventually impede global research efforts. This chapter argues that the CBD is qualified to be the central agency at the global level for the advance of broader benefit sharing frameworks. By implication, the scope of the CBD should be expanded to include human genetic resources.