Challenges for biomedical informatics and pharmacogenomics

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.

Accurate filtering of privacy-sensitive information in raw genomic data

Sequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption.

Genomics and pharmacogenomics of pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukaemia (ALL) is a prevalent form of pediatric cancer that accounts for 70-80% of all leukemias. Genome-based analysis, exome sequencing, transcriptomics and proteomics have provided insight into genetic classification of ALL and helped identify novel subtypes of the disease. B and T cell-based ALL are two well-characterized genomic subtypes, significantly marked by bone marrow disorders, along with mutations in trisomy 21 and T53. The other ALLs include Early T-cell precursor ALL, Philadelphia chromosome-like ALL, Down syndrome-associated ALL and Relapsed ALL. Chromosomal number forms a basis of classification, such as, hypodiploid ALL, near-haploid, low-hypodiploid, high-hypodiploid and hypodiploid-ALL. Advances in therapies targeting ALL have been noteworthy, with significant pre-clinical and clinical studies on drug pharmacokinetics and pharmacodynamics. Methotrexate and 6-mercaptopurine are leading drugs with best demonstrated efficacies against childhood ALL. The drugs in combination, following dose titration, have also been used for maintenance therapy. Methotrexate-polyglutamate is a key metabolite that specifically targets the disease pathogenesis, and 6-thioguanine nucleotides, derived from 6-mercaptopurine, impede replication and transcription processes, inducing cytotoxicity. Additionally, glucocorticoids, asparaginase, anthracycline, vincristine and cytarabine that trans-repress gene expression, deprives cells of asparagine, triggers cell cycle arrest, influences cytochrome-P450 polymorphism and inhibits DNA polymerase, respectively, have been used in chemotherapy in ALL patients. Overall, this review covers the progress in genome technology related to different sub-types of ALL and pharmacokinetics and pharmacodynamics of its medications. It also enlightens adverse effects of current drugs, and emphasizes the necessity of genome-wide association studies for restricting childhood ALL.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system

BACKGROUND

A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS).

METHODS

Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm.

RESULTS

As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%.

CONCLUSIONS

Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

High-Content Screening with siRNA Optimizes a Cell Biological Approach to Drug Discovery: Defining the Role of P53 Activation in the Cellular Response to Anticancer Drugs

Deciphering the effects of compounds on molecular events within living cells is becoming an increasingly important component of drug discovery. In a model application of the industrial drug discovery process, the authors profiled a panel of 22 compounds using hierarchical cluster analysis of multiparameter high-content screening measurements from nearly 500,000 cells per microplate. RNAi protein knockdown methodology was used with high-content screening to dissect the effects of 2 anticancer drugs on multiple target activities. Camptothecin activated p53 in A549 lung carcinoma cells pretreated with scrambled siRNA, exhibited concentration-dependent cell cycle blocks, and induced moderate microtubule stabilization. Knockdown of camptothecin-induced p53 protein expression with p53 siRNA inhibited the G1/S blocking activity of the drug and diminished its microtubule-stabilizing activity. Paclitaxel activated p53 protein at low concentrations but exhibited G2/M cell cycle blocking activity at higher concentrations where microtubules were stabilized. In cells treated with p53 siRNA, paclitaxel failed to activate p53 protein, but the knockdown did not have a significant effect on the ability of paclitaxel to stabilize microtubules or induce a G2/M cell cycle block. Thus, this model application of the use of RNAi technology within the context of high-content screening shows the potential to provide massive amounts of combinatorial cell biological information on the temporal and spatial responses that cells mount to treatment by promising therapeutic candidates.

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

PharmGKB: the Pharmacogenomics Knowledge Base

The Pharmacogenomics Knowledge Base, PharmGKB, is an interactive tool for researchers investigating how genetic variation affects drug response. The PharmGKB Web site, http://www.pharmgkb.org , displays genotype, molecular, and clinical knowledge integrated into pathway representations and Very Important Pharmacogene (VIP) summaries with links to additional external resources. Users can search and browse the knowledgebase by genes, variants, drugs, diseases, and pathways. Registration is free to the entire research community, but subject to agreement to use for research purposes only and not to redistribute. Registered users can access and download data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

Electronic health record design and implementation for pharmacogenomics: a local perspective

PURPOSE

The design of electronic health records to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation.

METHODS

The design, implemented features, and evolution of a locally developed electronic health record that supports a large pharmacogenomics program at a tertiary-care academic medical center was tracked over a 4-year development period.

RESULTS

Developers and program staff created electronic health record mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the electronic health record until drug-gene interactions met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug-response phenotype populated a summary of drug-gene interactions, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records.

CONCLUSION

The design of a locally developed electronic health record supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.

Improvements on a privacy-protection algorithm for DNA sequences with generalization lattices

When developing personal DNA databases, there must be an appropriate guarantee of anonymity, which means that the data cannot be related back to individuals. DNA lattice anonymization (DNALA) is a successful method for making personal DNA sequences anonymous. However, it uses time-consuming multiple sequence alignment and a low-accuracy greedy clustering algorithm. Furthermore, DNALA is not an online algorithm, and so it cannot quickly return results when the database is updated. This study improves the DNALA method. Specifically, we replaced the multiple sequence alignment in DNALA with global pairwise sequence alignment to save time, and we designed a hybrid clustering algorithm comprised of a maximum weight matching (MWM)-based algorithm and an online algorithm. The MWM-based algorithm is more accurate than the greedy algorithm in DNALA and has the same time complexity. The online algorithm can process data quickly when the database is updated.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions

MOTIVATION

Genetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the "assumed average". Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity. Here, we seek to aid researchers, database curators, and clinicians in their search for relevant information by automatically extracting these data from literature.

APPROACH

We automatically populate a repository of information on genetic variants, relations to drugs, occurrence in sub-populations, and associations with disease. We mine textual data from PubMed abstracts to discover such genotype-phenotype associations, focusing on SNPs that can be associated with variations in drug response. The overall repository covers relations found between genes, variants, alleles, drugs, diseases, adverse drug reactions, populations, and allele frequencies. We cross-reference these data to EntrezGene, PharmGKB, PubChem, and others.

RESULTS

The performance regarding entity recognition and relation extraction yields a precision of 90-92% for the major entity types (gene, drug, disease), and 76-84% for relations involving these types. Comparison of our repository to PharmGKB reveals a coverage of 93% of gene-drug associations in PharmGKB and 97% of the gene-variant mappings based on 180,000 PubMed abstracts.

AVAILABILITY

http://bioai4core.fulton.asu.edu/snpshot.

Technical and policy approaches to balancing patient privacy and data sharing in clinical and translational research

INTRODUCTION

Clinical researchers need to share data to support scientific validation and information reuse and to comply with a host of regulations and directives from funders. Various organizations are constructing informatics resources in the form of centralized databases to ensure reuse of data derived from sponsored research. The widespread use of such open databases is contingent on the protection of patient privacy.

METHODS

We review privacy-related problems associated with data sharing for clinical research from technical and policy perspectives. We investigate existing policies for secondary data sharing and privacy requirements in the context of data derived from research and clinical settings. In particular, we focus on policies specified by the US National Institutes of Health and the Health Insurance Portability and Accountability Act and touch on how these policies are related to current and future use of data stored in public database archives. We address aspects of data privacy and identifiability from a technical, although approachable, perspective and summarize how biomedical databanks can be exploited and seemingly anonymous records can be reidentified using various resources without hacking into secure computer systems.

RESULTS

We highlight which clinical and translational data features, specified in emerging research models, are potentially vulnerable or exploitable. In the process, we recount a recent privacy-related concern associated with the publication of aggregate statistics from pooled genome-wide association studies that have had a significant impact on the data sharing policies of National Institutes of Health-sponsored databanks.

CONCLUSION

Based on our analysis and observations we provide a list of recommendations that cover various technical, legal, and policy mechanisms that open clinical databases can adopt to strengthen data privacy protection as they move toward wider deployment and adoption.

Pharmacogenetics in acute lymphoblastic leukemia

Progress in the treatment of acute lymphoblastic leukemia (ALL) in children has been remarkable, from a disease being lethal four decades ago to current cure rates exceeding 80%. This exemplary progress is largely due to the optimization of existing treatment modalities rather than the discovery of new antileukemic agents. However, despite these high cure rates, the annual number of children whose leukemia relapses after their initial therapy remains greater than that of new cases of most types of childhood cancers. The aim of pharmacogenetics is to develop strategies to personalize treatment and tailor therapy to individual patients, with the goal of optimizing efficacy and safety through better understanding of human genome variability and its influence on drug response. In this review, we summarize recent pharmacogenomic studies related to the treatment of pediatric ALL. These studies illustrate the promise of pharmacogenomics to further advance the treatment of human cancers, with childhood leukemia serving as a paradigm.

A computational model to protect patient data from location-based re-identification

OBJECTIVE

Health care organizations must preserve a patient's anonymity when disclosing personal data. Traditionally, patient identity has been protected by stripping identifiers from sensitive data such as DNA. However, simple automated methods can re-identify patient data using public information. In this paper, we present a solution to prevent a threat to patient anonymity that arises when multiple health care organizations disclose data. In this setting, a patient's location visit pattern, or "trail", can re-identify seemingly anonymous DNA to patient identity. This threat exists because health care organizations (1) cannot prevent the disclosure of certain types of patient information and (2) do not know how to systematically avoid trail re-identification. In this paper, we develop and evaluate computational methods that health care organizations can apply to disclose patient-specific DNA records that are impregnable to trail re-identification.

METHODS AND MATERIALS

To prevent trail re-identification, we introduce a formal model called k-unlinkability, which enables health care administrators to specify different degrees of patient anonymity. Specifically, k-unlinkability is satisfied when the trail of each DNA record is linkable to no less than k identified records. We present several algorithms that enable health care organizations to coordinate their data disclosure, so that they can determine which DNA records can be shared without violating k-unlinkability. We evaluate the algorithms with the trails of patient populations derived from publicly available hospital discharge databases. Algorithm efficacy is evaluated using metrics based on real world applications, including the number of suppressed records and the number of organizations that disclose records.

RESULTS

Our experiments indicate that it is unnecessary to suppress all patient records that initially violate k-unlinkability. Rather, only portions of the trails need to be suppressed. For example, if each hospital discloses 100% of its data on patients diagnosed with cystic fibrosis, then 48% of the DNA records are 5-unlinkable. A naïve solution would suppress the 52% of the DNA records that violate 5-unlinkability. However, by applying our protection algorithms, the hospitals can disclose 95% of the DNA records, all of which are 5-unlinkable. Similar findings hold for all populations studied.

CONCLUSION

This research demonstrates that patient anonymity can be formally protected in shared databases. Our findings illustrate that significant quantities of patient-specific data can be disclosed with provable protection from trail re-identification. The configurability of our methods allows health care administrators to quantify the effects of different levels of privacy protection and formulate policy accordingly.

Proteome informatics II: bioinformatics for comparative proteomics

The present review attempts to cover the most recent initiatives directed towards representing, storing, displaying and processing protein-related data suited to undertake "comparative proteomics" studies. Data interpretation is brought into focus. Efforts invested into analysing and interpreting experimental data increasingly express the need for adding meaning. This trend is perceptible in work dedicated to determining ontologies, modelling interaction networks, etc. In parallel, technical advances in computer science are spurred by the development of the Web and the growing need to channel and understand massive volumes of data. Biology benefits from these advances as an application of choice for many generic solutions. Some examples of bioinformatics solutions are discussed and directions for on-going and future work conclude the review.

Potential impact of advanced clinical information technology on cancer care in 2015

New clinical information technologies now sporadically available will soon be in routine clinical use, bringing many changes to all phases of the cancer care continuum. For example, new technologies such as: (1) The next generation Internet; (2) Real-time clinical decision support systems; (3) Off-line, population-based systems; (4) Large, integrated, individual patient-level phenotypic and genotypic databases with intelligent data mining capabilities; (5) Wireless, invasive and non-invasive physiologic monitoring devices; (6) Natural Language Processing (NLP) systems; and (7) Mathematical models of complex biological systems all have the potential to impact significantly the provision of cancer care throughout its continuum. While new information management and communication techniques and technologies will reduce many of the inefficiencies and inaccuracies of our present systems, there will be an equal, and potentially far more dangerous, set of unintended consequences. Informatics investigators, cancer specialists, and health system administrators must focus on the study of what is working and what is not, as well as, on development and testing of the new clinical information management and communication technologies, if we are to be ready for the future.

PharmGED: Pharmacogenetic Effect Database

Prediction and elucidation of pharmacogenetic effects is important for facilitating the development of personalized medicines. Knowledge of polymorphism-induced and other types of drug-response variations is needed for facilitating such studies. Although databases of pharmacogenetic knowledge, polymorphism and toxicogenomic information have appeared, some of the relevant data are provided in separate web-pages and in terms of relatively long descriptions quoted from literatures. To facilitate easy and quick assessment of the relevant information, it is helpful to develop databases that provide all of the information related to a pharmacogenetic effect in the same web-page and in brief descriptions. We developed a database, Pharmacogenetic Effect Database (PharmGED), for providing sequence, function, polymorphism, affected drugs and pharmacogenetic effects. PharmGED can be accessed at free of charge for academic use. It currently contains 1825 entries covering 108 disease conditions, 266 distinct proteins, 693 polymorphisms, 414 drugs/ligands cited from 856 references.

Data integration and genomic medicine

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

PHARMACOGENOMICS OF ACUTE LEUKEMIA

Over the past four decades, treatment of acute leukemia in children has made remarkable progress, from this disease being lethal to now achieving cure rates of 80% for acute lymphoblastic leukemia and 45% for acute myeloid leukemia. This progress is largely owed to the optimization of existing treatment modalities rather than the discovery of new agents. However, the annual number of patients with leukemia who experience relapse after initial therapy remains greater than that of new cases of most childhood cancers. The aim of pharmacogenetics is to develop strategies to personalize medications and tailor treatment regimens to individual patients, with the goal of enhancing efficacy and safety through better understanding of the person's genetic makeup. In this review, we summarize recent pharmacogenomic studies related to the treatment of pediatric acute leukemia. These include work using candidate-gene approaches, as well as genome-wide studies using haplotype mapping and gene expression profiling. These strategies illustrate the promise of pharmacogenomics to further advance the treatment of human cancers, with childhood leukemia serving as a paradigm.

BIOMARKERS IN PSYCHOTROPIC DRUG DEVELOPMENT: Integration of Data across Multiple Domains

This review focuses on the current status of biomarkers and/or approaches critical to assessing novel neuroscience targets with an emphasis on new paradigms and challenges in this field of research. The importance of biomarker data integration for psychotropic drug development is illustrated with examples for clinically used medications and investigational drugs. The question remains how to verify access to the brain. Early imaging studies including micro-PET can help to overcome this. However, in case of delayed tracer development or because of no feasible application of brain imaging effects of the molecule, using CSF as a matrix could fill this gap. Proteomic research using CSF will hopefully have a major impact on the development of treatments for psychiatric disorders.

Computer-assisted phenotype characterization for genetic research in psychiatry

Psychiatric disorders differ from other complex phenotypes in their lack of objectively assessable biological markers that contribute to the establishment of a research diagnosis for genetic studies. To nevertheless allow for the delineation of genetically meaningful diagnostic entities for psychiatric genetic research, comprehensive phenotype characterization procedures are required. It is widely agreed that these should include the standardized assessment of life-time clinical symptomatology, sociodemographic, and environmental factors. Data should be based on several sources, i.e. diagnostic interviews with probands and their relatives as well as a thorough review of medical records, and final assignment of diagnosis should follow robust algorithms (i.e. best-estimate procedures, consensus diagnosis). Here, we outline a practical implementation of such a phenotype characterization strategy, including patient recruitment, study enrolment procedures, comprehensive diagnostic assessment, and data management. We argue that successful psychiatric phenotype characterization requires flexible tools. For this purpose, we have developed a computer-assisted phenotype characterization inventory, built around the backbone of a relational database. It allows for the straightforward assessment of symptoms, automated error checks and diagnostic assignment, easily manageable data storage and handling, and flexible data transfer between various research centers even across language barriers, while at the same time keeping up with the highest standards for the protection of sensitive patient data.

Environmental health research in the post-genome era: new fields, new challenges, and new opportunities

The human genome sequence provides researchers with a genetic framework to eventually understand the relationships of gene-environment interactions. This wealth of information has led to the birth of several related areas of research, including proteomics, functional genomics, pharmacogenomics, and toxicogenomics. Developing techniques such as DNA/protein microarrays, small-interfering RNA (siRNA) applications, two-dimensional gel electrophoresis, and mass spectrometry in conjunction with advanced analysis software and the availability of Internet databases offers a powerful set of tools to investigate an individual's response to specific stimuli. This review summarizes these emerging scientific fields and techniques focusing specifically on their applications to the complexities of gene-environment interactions and their potential role in environ-mental biosecurity.

How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems

The increasing integration of patient-specific genomic data into clinical practice and research raises serious privacy concerns. Various systems have been proposed that protect privacy by removing or encrypting explicitly identifying information, such as name or social security number, into pseudonyms. Though these systems claim to protect identity from being disclosed, they lack formal proofs. In this paper, we study the erosion of privacy when genomic data, either pseudonymous or data believed to be anonymous, are released into a distributed healthcare environment. Several algorithms are introduced, collectively called RE-Identification of Data In Trails (REIDIT), which link genomic data to named individuals in publicly available records by leveraging unique features in patient-location visit patterns. Algorithmic proofs of re-identification are developed and we demonstrate, with experiments on real-world data, that susceptibility to re-identification is neither trivial nor the result of bizarre isolated occurrences. We propose that such techniques can be applied as system tests of privacy protection capabilities.

An evaluation of the current state of genomic data privacy protection technology and a roadmap for the future

The incorporation of genomic data into personal medical records poses many challenges to patient privacy. In response, various systems for preserving patient privacy in shared genomic data have been developed and deployed. Although these systems de-identify the data by removing explicit identifiers (e.g., name, address, or Social Security number) and incorporate sound security design principles, they suffer from a lack of formal modeling of inferences learnable from shared data. This report evaluates the extent to which current protection systems are capable of withstanding a range of re-identification methods, including genotype-phenotype inferences, location-visit patterns, family structures, and dictionary attacks. For a comparative re-identification analysis, the systems are mapped to a common formalism. Although there is variation in susceptibility, each system is deficient in its protection capacity. The author discovers patterns of protection failure and discusses several of the reasons why these systems are susceptible. The analyses and discussion within provide guideposts for the development of next-generation protection methods amenable to formal proofs.

Using literature and data to learn Bayesian networks as clinical models of ovarian tumors

Thanks to its increasing availability, electronic literature has become a potential source of information for the development of complex Bayesian networks (BN), when human expertise is missing or data is scarce or contains much noise. This opportunity raises the question of how to integrate information from free-text resources with statistical data in learning Bayesian networks. Firstly, we report on the collection of prior information resources in the ovarian cancer domain, which includes "kernel" annotations of the domain variables. We introduce methods based on the annotations and literature to derive informative pairwise dependency measures, which are derived from the statistical cooccurrence of the names of the variables, from the similarity of the "kernel" descriptions of the variables and from a combined method. We perform wide-scale evaluation of these text-based dependency scores against an expert reference and against data scores (the mutual information (MI) and a Bayesian score). Next, we transform the text-based dependency measures into informative text-based priors for Bayesian network structures. Finally, we report the benefit of such informative text-based priors on the performance of a Bayesian network for the classification of ovarian tumors from clinical data.

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.

Leukotriene antagonists

Hepatotoxicity is the most common cause of fulminant hepatic failure in the United States and the main indication for market withdrawal of drugs. This condition has been increasingly recognized as a problem of enormous medical, financial legal, and regulatory importance. It is in context of this heightened awareness of hepatotoxicity, particularly associated with new high profile drugs, that the authors reviews the published data regarding liver injury related to a novel group of asthma drugs.

Molecular diagnostics: an FDA perspective

The potential medical applications of microarrays and in vitro diagnostic devices for global assessments of DNA sequence variations, relative RNA abundance and measurements of proteins have generated much excitement, and some skepticism, within the biomedical community. It has been suggested that within the next decade these microarrays and diagnostic devices will be routinely used in the selection, assessment and quality control of the best drugs for pharmaceutical development, at the bedside for diagnostics and for clinical monitoring of both desired and adverse outcomes of therapeutic interventions. Realizing such potential will be a challenge to the entire scientific community as often breakthroughs which show great promise at the bench fail to meet the requirements of clinicians and regulatory scientists, and to make the transition into common clinical and regulatory practice. The development of a co-operative framework between regulators, product sponsors and technology experts will be essential for realizing the revolutionary promise these platforms could have on the evolution of drug development, regulatory science, the practice of medicine and public health.

Medical applications of microarray technologies: a regulatory science perspective

The potential medical applications of microarrays have generated much excitement, and some skepticism, within the biomedical community. Some researchers have suggested that within the decade microarrays will be routinely used in the selection, assessment, and quality control of the best drugs for pharmaceutical development, as well as for disease diagnosis and for monitoring desired and adverse outcomes of therapeutic interventions. Realizing this potential will be a challenge for the whole scientific community, as breakthroughs that show great promise at the bench often fail to meet the requirements of clinicians and regulatory scientists. The development of a cooperative framework among regulators, product sponsors, and technology experts will be essential for realizing the revolutionary promise that microarrays hold for drug development, regulatory science, medical practice and public health.

Biomedical informatics: the future for drug development

The problems that exist in drug development are well documented: the limited number of new chemical entities, increased cost of drug development, problems in clinical trials (Phase III), product launches that result in withdrawal, and pressure to reduce the cost of pharmaceuticals from the government. It appears that the promise of genomics has not yet reached its full potential to impact the process. This review identifies the need to develop and implement the area of biomedical informatics for increased success in drug development and healthcare in general.

Primer on medical genomics part II: Background principles and methods in molecular genetics

The nucleus of every human cell contains the full complement of the human genome, which consists of approximately 30,000 to 70,000 named and unnamed genes and many intergenic DNA sequences. The double-helical DNA molecule in a human cell, associated with special proteins, is highly compacted into 22 pairs of autosomal chromosomes and an additional pair of sex chromosomes. The entire cellular DNA consists of approximately 3 billion base pairs, of which only 1% is thought to encode a functional protein or a polypeptide. Genetic information is expressed and regulated through a complex system of DNA transcription, RNA processing, RNA translation, and posttranslational and cotranslational modification of proteins. Advances in molecular biology techniques have allowed accurate and rapid characterization of DNA sequences as well as identification and quantification of cellular RNA and protein. Global analytic methods and human genetic mapping are expected to accelerate the process of identification and localization of disease genes. In this second part of an educational series in medical genomics, selected principles and methods in molecular biology are recapped, with the intent to prepare the reader for forthcoming articles with a more direct focus on aspects of the subject matter.