Pharmacogenomics and the drug discovery pipeline: when should it be implemented?

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

Precision medicine: from pharmacogenomics to pharmacoproteomics

Disease progression and drug response may vary significantly from patient to patient. Fortunately, the rapid development of high-throughput ‘omics’ technologies has allowed for the identification of potential biomarkers that may aid in the understanding of the heterogeneities in disease development and treatment outcomes. However, mechanistic gaps remain when the genome or the proteome are investigated independently in response to drug treatment. In this article, we discuss the current status of pharmacogenomics in precision medicine and highlight the needs for concordant analysis at the proteome and metabolome levels via the more recently-evolved fields of pharmacoproteomics, toxicoproteomics, and pharmacometabolomics. Integrated ‘omics’ investigations will be critical in piecing together targetable mechanisms of action for both drug development and monitoring of therapy in order to fully apply precision medicine to the clinic.

Systematic identification of pharmacogenomics information from clinical trials

Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as 'what drug should be prescribed for a patient with mutant alleles?' Typically, answers to such questions can be found in publications mentioning the relationships of the gene-drug-disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5 years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene-drug-disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.

The institutional review board is an impediment to human research: the result is more animal-based research

Biomedical research today can be generally classified as human-based or nonhuman animal-based, each with separate and distinct review boards that must approve research protocols. Researchers wishing to work with humans or human tissues have become frustrated by the required burdensome approval panel, the Institutional Review Board. However, scientists have found it is much easier to work with the animal-based research review board, the Institutional Animal Care and Use Committee. Consequently, animals are used for investigations even when scientists believe these studies should be performed with humans or human tissue. This situation deserves attention from society and more specifically the animal protection and patient advocate communities, as neither patients nor animals are well served by the present situation.

Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication

BACKGROUND

Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naïve strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication.

METHODS

Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (τ²).

RESULTS

Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to τ² whenever the susceptibility allele is common (MAF ≥ 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model.

CONCLUSION

Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.

Is the use of sentient animals in basic research justifiable?

Animals can be used in many ways in science and scientific research. Given that society values sentient animals and that basic research is not goal oriented, the question is raised: "Is the use of sentient animals in basic research justifiable?" We explore this in the context of funding issues, outcomes from basic research, and the position of society as a whole on using sentient animals in research that is not goal oriented. We conclude that the use of sentient animals in basic research cannot be justified in light of society's priorities.

The International Consortium on Lithium Genetics (ConLiGen): an initiative by the NIMH and IGSLI to study the genetic basis of response to lithium treatment

For more than half a decade, lithium has been successfully used to treat bipolar disorder. Worldwide, it is considered the first-line mood stabilizer. Apart from its proven antimanic and prophylactic effects, considerable evidence also suggests an antisuicidal effect in affective disorders. Lithium is also effectively used to augment antidepressant drugs in the treatment of refractory major depressive episodes and prevent relapses in recurrent unipolar depression. In contrast to many psychiatric drugs, lithium has outlasted various pharmacotherapeutic 'fashions', and remains an indispensable element in contemporary psychopharmacology. Nevertheless, data from pharmacogenetic studies of lithium are comparatively sparse, and these studies are generally characterized by small sample sizes and varying definitions of response. Here, we present an international effort to elucidate the genetic underpinnings of lithium response in bipolar disorder. Following an initiative by the International Group for the Study of Lithium-Treated Patients (www.IGSLI.org) and the Unit on the Genetic Basis of Mood and Anxiety Disorders at the National Institute of Mental Health,lithium researchers from around the world have formed the Consortium on Lithium Genetics (www.ConLiGen.org) to establish the largest sample to date for genome-wide studies of lithium response in bipolar disorder, currently comprising more than 1,200 patients characterized for response to lithium treatment. A stringent phenotype definition of response is one of the hallmarks of this collaboration. ConLiGen invites all lithium researchers to join its efforts.

GPCR signaling: understanding the pathway to successful drug discovery

Modulators of G protein-coupled receptors (GPCRs) form a key area for the pharmaceutical industry, representing approximately 27% of all Food and Drug Administration (FDA)-approved drugs. Consequently, there are a wide variety of in vitro plate-based screening technologies that enable the measurement of compound affinity, potency, and efficacy for almost every type of GPCR. However, to maximize success it is prudent to ensure that (i) the most suitable assay formats are identified, (ii) they are configured optimally to detect the desired compound activity, and (iii) that they form a basis for predicting clinical effects. To achieve this, an understanding of the pathways and mechanisms of receptor activation relevant to the disease mechanism, as well as the benefits and/or limitations of the specific techniques, is key.

Sequentially testing for a gene-drug interaction in a genomewide analysis

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs.

Routine pharmacogenetic testing in clinical practice: dream or reality?

Pharmacogenetics (PGx) has become progressively popular in recent years, thanks to growing anticipation among scientists, healthcare providers and the general public for the incorporation of genetic tests into the diagnostic arsenal at the physician’s disposal. Indeed, much research has been dedicated to elucidation of genetic determinants underlying interindividual variability in pharmacokinetic parameters, as well as drug safety and efficacy. However, few PGx applications have thus far been realized in healthcare management. This review uses examples from PGx research of psychiatric drugs to illustrate why the current published findings are inadequate and insufficient for utilization as routine clinical predictors of treatment safety, efficacy or dosing. I therefore suggest the necessary steps to demonstrate the validity, utility and cost–effectiveness of PGx. These recommendations include a whole range of aspects, starting from standardization of criteria and assessment of the technical quality of genotyping assays, up to design of prospective PGx studies, providing the basis for reimbursement programs to be recognized in routine clinical practice.

Foundations, promises and uncertainties of personalized medicine

Personalized medicine introduces the promise to use molecular markers that signal the risk of disease or its presence before clinical signs and symptoms appear. This information underlies a new healthcare strategy focused on prevention and early intervention, rather than reaction to advanced stages of disease. Such a strategy can delay disease onset or minimize symptom severity. The molecular foundations that enable personalized medicine include detection of variation in nucleotide sequence of genes and in characteristic patterns of gene expression, proteins and metabolites. Genetic and molecular patterns are correlated with disease manifestations, drug responses, treatment prognosis, or prediction of predisposition to future disease states. However, the uncertainties for personalized medicine are considerable, including economic, ethical, legal, and societal questions. Although much of its promise remains unproven to date, the foundations of personalized medicine appear solid and evidence is accumulating rapidly pointing to its growing importance in healthcare (Fig. 1).

Assessing Theoretical Risk and Benefit suggested by Genetic Association Studies of CCR5: Experience in a Drug Development Programme for Maraviroc

The proliferation of published gene association studies of the CCR5A32 mutation is of relevance to drug development of a CCR5 antagonist for HIV, in highlighting potential safety concerns. We conducted an initial review of all non-HIV gene association studies of CCR5-Δ32, followed by detailed meta-analyses in the three disease areas most commonly reported. Our review indicated no consistent evidence of increased risk of susceptibility to hepatitis C virus infection or multiple sclerosis among individuals with CCR5-Δ32 mutation, and suggested treatment with a CCR5 inhibitor is unlikely to have related adverse effects. There was, however, evidence to suggest rheumatoid arthritis as a potential therapeutic target for a CCR5 antagonist. Clinical evidence would be required to confirm these findings.

Strategies to optimize the validity of disease models in the drug discovery process

Models of human diseases are necessary for experimental research into the biological basis of disease and for the development of treatments. They have an enormous impact upon the success of biomedical research. However, in spite of this, a consistent system for evaluating, expressing and comparing the clinical validity of disease models is not available. The purpose of this paper is, therefore, to provide a theoretical discussion of the concepts behind disease models and to develop a terminology and a framework to analyze and express the clinical validity of disease models.

Enantiomeric separation and determination of absolute stereochemistry of asymmetric molecules in drug discovery—Building chiral technology toolboxes

The application of Chiral Technology, or the (extensive) use of techniques or tools for the determination of absolute stereochemistry and the enantiomeric or chiral separation of racemic small molecule potential lead compounds, has been critical to successfully discovering and developing chiral drugs in the pharmaceutical industry. This has been due to the rapid increase over the past 10-15 years in potential drug candidates containing one or more asymmetric centers. Based on the experiences of one pharmaceutical company, a summary of the establishment of a Chiral Technology toolbox, including the implementation of known tools as well as the design, development, and implementation of new Chiral Technology tools, is provided.

Representation of target-bound drugs by computed conformers: implications for conformational libraries

BACKGROUND

The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.

RESULTS

Integration of two public databases allowed superposition of conformers for 193 approved drugs with 5507 crystallised target-bound counterparts. The generation of 9600 drug conformers using an atomic force field was carried out to obtain an optimal coverage of the conformational space. Bioactive conformations are best described by a conformational ensemble: half of all drugs exhibit multiple active states, distributed over the entire range of the reachable energy and conformational space.A number of up to 100 conformers per drug enabled us to reproduce the bound states within a similarity threshold of 1.0 angstroms in 70% of all cases. This fraction rises to about 90% for smaller or average sized drugs.

CONCLUSION

Single drugs adopt multiple bioactive conformations if they interact with different target proteins. Due to the structural diversity of binding sites they adopt conformations that are distributed over a broad conformational space and wide energy range. Since the majority of drugs is well represented by a predefined low number of conformers (up to 100) this procedure is a valuable method to compare compounds by three-dimensional features or for fast similarity searches starting with pharmacophores. The underlying 9600 generated drug conformers are downloadable from the Super Drug Web site 1. All superpositions are visualised at the same source. Additional conformers (110,000) of 2400 classified WHO-drugs are also available.

The future may be closer than you think: a response from the Personalized Medicine Coalition to the Royal Society's report on personalized medicine

A recent report from the British Royal Society on the prospects for personalized medicine provides a sobering assessment of the field and its prospects. The report contends that pharmacogenetics has little clinical relevance at the moment and will only progress with the completion of large, cumbersome clinical trials. The report goes on to note that the regulatory infrastructure, medical education initiatives and public deliberation necessary to make personalized medicine a reality are essentially nonexistent, at least so far. In our view, personalized medicine is much more than a hypothetical protocol designed to correlate genotypes with prescriptions. We argue that the development of personalized medicine is a broader phenomenon that is already being practiced in one form or another in many contexts. Both academic medicine and the pharmaceutical industry have a huge stake in bringing pharmacogenetic-based personalized medicine to fruition; we expect both entities to act as drivers of what will be a long-term, iterative process.

The impact of molecular biology on drug discovery

The discipline of molecular biology has become increasingly important in recent times for the process of drug discovery. We describe the impact of molecular biology across the whole process of drug discovery and development, including (i) the identification and validation of new drug targets, (ii) the development of molecular screens to find new candidate drugs, and (iii) the generation of safety data and competences leading to enhanced clinical efficacy. We also speculate on emerging developments in drug discovery where it seems likely that molecular biology will play an even more vital role in the generation of future therapies.

Gene expression profiling in the discovery, optimization and development of novel drugs: one universal screening platform

With recent advances in robotics and high-content screening and analysis methods, transcriptional profiling can now be utilized as a comprehensive forward chemical genomics platform for drug discovery, lead selection and lead optimization. It can be used to define the state of a cell on the basis of gene networks, and to search for drugs that can shift cellular states in a manner predicted at the genome level to be therapeutically beneficial. The treatment of cells with compounds produces transcriptional 'fingerprints' that reveal mechanism-of-action, and enable discrimination between individual compounds based on drug behaviors important to all phases of drug discovery and development.

'The 39 steps' in gene expression profiling: critical issues and proposed best practices for microarray experiments

Gene expression microarrays have been used widely to address increasingly complex biological questions and to produce an unprecedented amount of data, but have yet to realize their full potential. The interpretation of microarray data remains a major challenge because of the complexity of the underlying biological networks. To gather meaningful expression data, it is crucial to develop standardized approaches for vigilant study design, controlled annotation of resources, careful quality control of experiments, robust statistics, and data registration and storage. This article reviews the steps needed in the design and execution of valid microarray experiments so that global gene expression data can play a major role in the pursuit of future biological discoveries that will impact drug development.

Anticipating change in drug development: the emerging era of translational medicine and therapeutics

Despite the large investments made in drug discovery in the past decade, there is still a dearth of new drugs. This highlights the persistence of a model of drug development that has not adapted to changes in science, public perception of drug companies or the marketplace. A high profit margin in the United States has shielded drug development from the usual economic pressures that would ordinarily drive reform. The strategy of merger, pursued by many companies to compensate for the failure to develop new drugs, has, in most cases, compounded the problem, imposing geographic and cultural segregation on an already inefficient process.