Data visualization techniques for the pharmaceutical sciences have not been extensively investigated. The purpose of this study was to evaluate the usefulness of VizStruct, a multidimensional visualization tool, for applications in pharmacokinetics, pharmacodynamics, and pharmacogenomics.
The VizStruct tool uses the first harmonic of the discrete Fourier transform to map multidimensional data to two dimensions for visualization. The mapping was used to visualize several published pharmacokinetic, pharmacodynamic, and pharmacogenomic data sets. The VizStruct approach was evaluated using simulated population pharmacokinetics data sets, the data from Dalen and colleagues (Clin. Pharmacol. Ther. 63:444−452, 1998) on the kinetics of nortriptyline and its 10-hydroxy-nortriptyline metabolite in subjects with differing number of copies of the CYP2D6, and the gene expression profiling data of Bohen and colleagues (Proc. Natl. Acad. Sci. USA 100:1926−1930, 2003) on follicular lymphoma patients responsive and nonresponsive to rituximab.
The VizStruct mapping preserves the key characteristics of multidimensional data in two dimensions in a manner that facilitates visualization. The mapping is computationally efficient and can be used for cluster detection and class prediction in pharmaceutical data sets. The VizStruct visualization succinctly summarized the salient similarities and differences in the nortriptyline and 10-hydroxynortriptyline pharmacokinetic profiles in subjects with increasing number of CYP2D6 gene copies. In the simulated population pharmacokinetic data sets, it was capable of discriminating the subtle differences between pharmacokinetic profiles derived from 1- and 2-compartment models with the same area under the curve. The two-dimensional VizStruct mapping computed from a subset of 102 informative genes from the Bohen and colleagues data set effectively separated the rituximab responder, rituximab nonresponder, and control subject groups.
The VizStruct approach is a computationally efficient and effective approach for visualizing complex, multidimensional data sets. It could have many useful applications in the pharmaceutical sciences.
microarray; pharmacodynamics; pharmacogenomic modeling; pharmacokinetics; visualization algorithms
Genome-wide association studies (GWAS) have emerged as a powerful tool to identify loci that affect drug response or susceptibility to adverse drug reactions. However, current GWAS based on a simple analysis of associations between genotype and phenotype ignores the biochemical reactions of drug response, thus limiting the scope of inference about its genetic architecture. To facilitate the inference of GWAS in pharmacogenomics, we sought to undertake the mathematical integration of the pharmacodynamic process of drug reactions through computational models. By estimating and testing the genetic control of pharmacodynamic and pharmacokinetic parameters, this mechanistic approach does not only enhance the biological and clinical relevance of significant genetic associations, but also improve the statistical power and robustness of gene detection. This report discusses the general principle and development of pharmacodynamics-based GWAS, highlights the practical use of this approach in addressing various pharmacogenomic problems, and suggests that this approach will be an important method to study the genetic architecture of drug responses or reactions.
It is generally anticipated that pharmacogenomic information will have a large impact on drug development and will facilitate individualized drug treatment. However, there has been relatively little quantitative modeling to assess how pharmacogenomic information could best be utilized in clinical practice. Using a quantitative model, we demonstrate that efficacy is increased and toxicity is reduced when a genetically-guided dose adjustment strategy is utilized in a clinical trial. However, there is limited information available about the genetic variables affecting the disposition or mechanism of action of most commonly used medications. These genetic factors must be identified to enable pharmacogenomic testing to be routinely used in the clinic. A recently described murine haplotype-based computational genetic analysis method provides one strategy for identifying genetic factors regulating the pharmacokinetics and pharmacodynamics of commonly used medications.
In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic ‘pharmacogenomics assistant’. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.
whole-genome sequencing; personalized pharmacogenomics profile; informatics solutions; microattribution; drug metabolism; gene variants
Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions.
A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model.
There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models.
Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.
Deep learning; Genomewide association study; Pharmacogenomics; Single nucleotide polymorphisms; Adverse drug reaction
Microarrays have been utilized in many biological, physiological and pharmacological studies as a high-throughput genomic technique. Several generations of Affymetrix GeneChip® microarrays are widely used in gene expression studies. However, differences in intensities of signals for different probe sets that represent the same gene on various types of Affymetrix chips make comparison of datasets complicated.
Materials and Methods
A power coefficient scaling factor was applied in the pharmacokinetic/ pharmacodynamic (PK/PD) modeling to account for differences in probe set sensitivities (i.e., signal intensities). Microarray data from muscle and liver following methylprednisolone 50 mg/kg i.v. bolus and 0.3 mg/kg/h infusion regimens were taken as an exemplar.
The scaling factor applied to the pharmacodynamic output function was used to solve the problem of intensity differences between probe sets. This approach yielded consistent pharmacodynamic parameters for the applied models.
Modeling of pharmacodynamic/pharmacogenomic (PD/PG) data from diverse chips should be performed with caution due to differential probe set intensities. In such circumstances, a power scaling factor can be applied in the modeling.
bioinformatics; computational biology; pharmacodynamics; pharmacogenomics; pharmacokinetics
Maxwell's great paper of 1865 established his dynamical theory of the electromagnetic field. The origins of the paper lay in his earlier papers of 1856, in which he began the mathematical elaboration of Faraday's researches into electromagnetism, and of 1861–1862, in which the displacement current was introduced. These earlier works were based upon mechanical analogies. In the paper of 1865, the focus shifts to the role of the fields themselves as a description of electromagnetic phenomena. The somewhat artificial mechanical models by which he had arrived at his field equations a few years earlier were stripped away. Maxwell's introduction of the concept of fields to explain physical phenomena provided the essential link between the mechanical world of Newtonian physics and the theory of fields, as elaborated by Einstein and others, which lies at the heart of twentieth and twenty-first century physics. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.
Maxwell; electromagnetism; equations of the electromagnetic field; discovery of Maxwell's equations
This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.
Computational pain model; Noxious processing; Gate control theory; Artificial neural network; Neuron models
The pharmacogenomic effects of a corticosteroid (CS) were assessed in rat skeletal muscle using microarrays. Adrenalectomized (ADX) rats were treated with methylprednisolone (MPL) by either 50 mg/kg intravenous injection or 7-day 0.3 mg/kg/h infusion through subcutaneously implanted pumps. RNAs extracted from individual rat muscles were hybridized to Affymetrix Rat Genome Genechips. Data mining yielded 653 and 2316 CS-responsive probe sets following MPL bolus and infusion treatments. Of these, 196 genes were controlled by MPL under both dosing conditions. Cluster analysis revealed that 124 probe sets exhibited three typical expression dynamic profiles following acute dosing. Cluster A consisted of up-regulated probe sets which were grouped into five subclusters each exhibiting unique temporal patterns during the infusion. Cluster B comprised down-regulated probe sets which were divided into two subclusters with distinct dynamics during the infusion. Cluster C probe sets exhibited delayed down-regulation under both bolus and infusion conditions. Among those, 104 probe sets were further grouped into subclusters based on their profiles following chronic MPL dosing. Several mathematical models were proposed and adequately captured the temporal patterns for each subcluster. Multiple types of dosing regimens are needed to resolve common determinants of gene regulation as chronic exposure results in unexpected differences in gene expression compared to acute dosing. Pharmacokinetic/pharmacodynamic (PK/PD) modeling provides a quantitative tool for elucidating the complexities of CS pharmacogenomics in skeletal muscle.
Microarray studies; pharmacokinetics; pharmacodynamics; mathematical models; computational biology
Purpose of Review
Pharmacogenomics is the study of differences in drug response based on individual genetic background. With rapidly advancing genomic technologies and decreased costs of genotyping, the field of pharmacogenomics continues to develop. Application to patients with kidney disease provides growing opportunities for improving drug therapy.
Pharmacogenomics studies are lacking in patients with chronic kidney disease and dialysis but are abundant in the kidney transplant field. A clinically actionable genetic variant exists in the CYP3A5 gene, with the initial tacrolimus dose selection optimized based on CYP3A5 genotype. Though many pharmacogenomics studies have focused on transplant immunosuppression pharmacokinetics, an expanding literature on pharmacodynamic outcomes like calcineurin inhibitor toxicity and new onset diabetes is providing new information on patients at risk.
Appropriately powered pharmacogenomics studies with well-defined phenotypes are needed to validate existing studies and unearth new findings in patients with kidney disease, especially the chronic kidney disease and dialysis population.
pharmacogenetics; kidney; transplant; pharmacokinetics; pharmacodynamics
The PharmacoGenomic Mutation Database (PGMD) is a comprehensive manually curated pharmacogenomics database. Two major sources of PGMD data are peer-reviewed literature and Food and Drug Administration (FDA) and European Medicines Agency (EMA) drug labels. PGMD curators capture information on exact genomic location and sequence changes, on resulting phenotype, drugs administered, patient population, study design, disease context, statistical significance and other properties of reported pharmacogenomic variants. Variants are annotated into functional categories on the basis of their influence on pharmacokinetics, pharmacodynamics, efficacy or clinical outcome. The current release of PGMD includes over 117 000 unique pharmacogenomic observations, covering all 24 disease superclasses and nearly 1400 drugs. Over 2800 genes have associated pharmacogenomic variants, including genes in proximity to intergenic variants. PGMD is optimized for use in annotating next-generation sequencing data by providing genomic coordinates for all covered variants, including Single Nucleotide Polymorphisms (SNPs), insertions, deletions, haplotypes, diplotypes, Variable Number Tandem Repeats (VNTR), copy number variations and structural variations.
There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.
High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest.
corticosteroids; data mining; expression profiling; gene chips; methylprednisolone; microarrays; modeling; pharmacodynamics; skeletal muscle; time series
Purpose of review
The therapeutic index of many medications, especially in children, is very narrow with substantial risk for toxicity at doses required for therapeutic effects. This is particularly relevant to cancer chemotherapy, where the risk of toxicity must be balanced against potential suboptimal (low) systemic exposure that can be less effective in patients with the higher rates of drug clearance. The purpose of this review is to discuss genetic factors that lead to interpatient differences in the pharmacokinetics and pharmacodynamics of these medications.
Genome wide agonistic studies of pediatric patient populations are revealing genome variations that may affect susceptibility to specific diseases and that influence the pharmacokinetic and pharmacodynamic characteristics of medications. Several genetic factors with relatively small effect may be combined in the determination of a pharmacogenomic phenotype and considering these polygenic models may be mandatory in order to predict the related drug response phenotypes. These findings have potential to yield new insights into disease pathogenesis, and lead to molecular diagnostics that can be used to optimize the treatment of childhood cancers
Advances in genome technology and their comprehensive and systematic deployment to elucidate the genomic basis of inter-patient differences in drug response and disease risk, hold great promise to ultimately enhance the efficacy and reduce the toxicity of drug therapy in children.
pharmacogenomics; pharmacokinetics; pharmacodynamics; leukemia
Warfarin is the current standard of care in oral anticoagulation therapy. It is commonly prescribed to treat venous thromboembolism, pulmonary embolism, acute myocardial infarction, and to decrease the risk of stroke in atrial fibrillation. Warfarin therapy is challenging because of marked and often unpredictable inter-individual dosing variations that effectively reach and maintain adequate anticoagulation. Several researchers have developed pharmacogenetic-guided maintenance dose algorithms that incorporate genetics and individual patient characteristics. However, there is limited information available concerning dosing during warfarin initiation. This is considered the most clinically challenging therapeutic phase. In such, the risk of recurrent thromboembolism and hemorrhage are elevated. The objective of this retrospective study is to predict the individual initial doses for Puerto Rican patients (n=175) commencing anticoagulation therapy at Veterans Affairs Caribbean Healthcare System (VACHS) using pharmacogenetic/pharmacokinetic-driven model. A pharmacogenetic driven model (R2=0.4809) was developed in Puerto Rican patients and combined with pharmacokinetic formulas that enabled us to predict the individual initial doses for patients (n=121) commencing anticoagulation therapy. WinNonlin® pharmacokinetic-pharmacodynamic simulations were carried out to determine the predictability of this model. This model demonstrated promising results with few (n=10) simulations outside of their respective therapy range. A customized pharmacogenetic-based warfarin maintenance dose algorithm (R2=0.7659) was developed in a derivation cohort of 131 patients. The predictability of this developed pharmacogenetic algorithm was compared with the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm and it demonstrated superior predictability within our study population.
Hysteresis loops are phenomena that sometimes are encountered in the
analysis of pharmacokinetic and pharmacodynamic relationships spanning from
pre-clinical to clinical studies. When hysteresis occurs it provides insight
into the complexity of drug action and disposition that can be encountered.
Hysteresis loops suggest that the relationship between drug concentration and
the effect being measured is not a simple direct relationship, but may have an
inherent time delay and disequilibrium, which may be the result of metabolites,
the consequence of changes in pharmacodynamics or the use of a non-specific
assay or may involve an indirect relationship. Counter-clockwise hysteresis has
been generally defined as the process in which effect can increase with time for
a given drug concentration, while in the case of clockwise hysteresis the
measured effect decreases with time for a given drug concentration. Hysteresis
loops can occur as a consequence of a number of different pharmacokinetic and
pharmacodynamic mechanisms including tolerance, distributional delay, feedback
regulation, input and output rate changes, agonistic or antagonistic active
metabolites, uptake into active site, slow receptor kinetics, delayed or
modified activity, time-dependent protein binding and the use of racemic drugs
among other factors. In this review, each of these various causes of hysteresis
loops are discussed, with incorporation of relevant examples of drugs
demonstrating these relationships for illustrative purposes. Furthermore, the
effect that pharmaceutical formulation has on the occurrence and potential
change in direction of the hysteresis loop, and the major pharmacokinetic /
pharmacodynamic modeling approaches utilized to collapse and model hysteresis
This paper addresses the problem of estimating the depth of anesthesia in clinical practice where many drugs are used in combination. The aim of the project is to use pharmacokinetically-derived data to predict episodes of light anesthesia. The weighted linear combination of anesthetic drug concentrations was computed using a stochastic pharmacokinetic model. The clinical definition of light anesthesia was based on the hemodynamic consequences of autonomic nervous system responses to surgical stimuli. A rule-based expert system was used to review anesthesia records to determine instances of light anesthesia using hemodynamic criteria. It was assumed that light anesthesia was a direct consequence of the weighted linear combination of drug concentrations in the patient's body that decreased below a certain threshold. We augmented traditional two-compartment models with a stochastic component of anesthetics' concentrations to compensate for interpatient pharmacokinetic and pharmacodynamic variability. A cohort of 532 clinical anesthesia cases was examined and parameters of two compartment pharmacokinetic models for 6 intravenously administered anesthetic drugs (fentanyl, thiopenthal, morphine, propofol, midazolam, ketamine) were estimated, as well as the parameters for 2 inhalational anesthetics (N2O and isoflurane). These parameters were then prospectively applied to 22 cases that were not used for parameter estimation, and the predictive ability of the pharmacokinetic model was determined. The goal of the study is the development of a pharmacokinetic model that will be useful in predicting light anesthesia in the clinically relevant circumstance where many drugs are used concurrently.
Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.
Epistasis; genotyping; personalized medicine; pharmacogenomics; quality control; statistics; study design
Pharmacogenetics and pharmacogenomics involve the study of the role of inheritance in individual variation in drug response, a phenotype that varies from potentially life-threatening adverse drug reactions to equally serious lack of therapeutic efficacy. Pharmacogenetics-pharmacogenomics represents a major component of the movement to `individualized medicine'. Pharmacogenetic studies originally focused on monogenic traits, often involving genetic variation in drug metabolism. However, contemporary studies increasingly involve entire `pathways' that include both pharmacokinetics (PKs)—factors that influence the concentration of a drug reaching its target(s)—and pharmacodynamics (PDs), factors associated with the drug target(s), as well as genome-wide approaches. The convergence of advances in pharmacogenetics with rapid developments in human genomics has resulted in the evolution of pharmacogenetics into pharmacogenomics. At the same time, studies of drug response are expanding beyond genomics to encompass pharmacotranscriptomics and pharmacometabolomics to become a systems-based discipline. This discipline is also increasingly moving across the `translational interface' into the clinic and is being incorporated into the drug development process and governmental regulation of that process. The article will provide an overview of the development of pharmacogenetics-pharmacogenomics, the scientific advances that have contributed to the continuing evolution of this discipline, the incorporation of transcriptomic and metabolomic data into attempts to understand and predict variation in drug response phenotypes as well as challenges associated with the `translation' of this important aspect of biomedical science into the clinic.
Cancer pharmacogenomics is an evolving landscape and has the potential to significantly impact cancer care and precision medicine. Harnessing and understanding the genetic code of both the patient (germline) and the tumor (somatic) provides the opportunity for personalized dose and therapy selection for cancer patients. While germline DNA is useful in understanding the pharmacokinetic and pharmacodynamic disposition of a drug, somatic DNA is particularly useful in identifying drug targets and predicting drug response. Molecular profiling of somatic DNA has resulted in the current breadth of targeted therapies available, expanding the armamentarium to battle cancer. This review provides an update on cancer pharmacogenomics and genomics-based medicine, challenges in applying pharmacogenomics to the clinical setting, and patient perspectives on the use of pharmacogenomics to personalize cancer therapy.
oncology; personalized; pharmacogenetics; germline; somatic; DNA; biomarker
Pioglitazone is the most widely used thiazolidinedione and acts as an insulin-sensitizer through activation of the Peroxisome Proliferator-Activated Receptor-γ (PPARγ). Pioglitazone is approved for use in the management of type 2 diabetes mellitus (T2DM), but its use in other therapeutic areas is increasing due to pleiotropic effects. In this hypothesis article, the current clinical evidence on pioglitazone pharmacogenomics is summarized and related to variability in pioglitazone response. How genetic variation in the human genome affects the pharmacokinetics and pharmacodynamics of pioglitazone was examined. For pharmacodynamic effects, hypoglycemic and anti-atherosclerotic effects, risks of fracture or edema, and the increase in body mass index in response to pioglitazone based on genotype were examined. The genes CYP2C8 and PPARG are the most extensively studied to date and selected polymorphisms contribute to respective variability in pioglitazone pharmacokinetics and pharmacodynamics. We hypothesized that genetic variation in pioglitazone pathway genes contributes meaningfully to the clinically observed variability in drug response. To test the hypothesis that genetic variation in PPARG associates with variability in pioglitazone response, we conducted a meta-analysis to synthesize the currently available data on the PPARG p.Pro12Ala polymorphism. The results showed that PPARG 12Ala carriers had a more favorable change in fasting blood glucose from baseline as compared to patients with the wild-type Pro12Pro genotype (p = 0.018). Unfortunately, findings for many other genes lack replication in independent cohorts to confirm association; further studies are needed. Also, the biological functionality of these polymorphisms is unknown. Based on current evidence, we propose that pharmacogenomics may provide an important tool to individualize pioglitazone therapy and better optimize therapy in patients with T2DM or other conditions for which pioglitazone is being used.
pioglitazone; thiazolidinedione; CYP2C8; cytochrome P450; PPAR; pharmacokinetics; pharmacodynamics
The sulfonylureas stimulate insulin release from pancreatic β cells, and have been a cornerstone of Type 2 diabetes pharmacotherapy for over 50 years. Although sulfonylureas are effective antihyperglycemic agents, interindividual variability exists in drug response (i.e., pharmacodynamics), disposition (i.e., pharmacokinetics) and adverse effects. The field of pharmacogenomics has been applied to sulfonylurea clinical studies in order to elucidate the genetic underpinnings of this response variability. Historically, most studies have sought to determine the influence of polymorphisms in drug-metabolizing enzyme genes on sulfonylurea pharmacokinetics in humans. More recently, polymorphisms in sulfonylurea drug target genes and diabetes risk genes have been implicated as important determinants of sulfonylurea pharmacodynamics in patients with Type 2 diabetes. As such, the purpose of this review is to discuss sulfonylurea pharmacogenomics in the setting of Type 2 diabetes, specifically focusing on polymorphisms in drug target and diabetes risk genes, and their relationship with interindividual variability in sulfonylurea response and adverse effects.
drug target; KCNJ11; pharmacogenomic; sulfonylurea; SUR1; TCF7L2; Type 2 diabetes
Treatment-resistance in schizophrenia remains a public health problem: about 20% to 30% of patients do not respond to antipsychotic therapy. Clozapine has been shown to be effective in about one-third of patients, but the medical risks and weekly blood tests limit its broad application. While the heterogeneity of the disease and the duration of untreated psychosis are important, pharmacogenomic aspects must also be considered. Pharmacogenomic investigations offer the opportunity to individualize antipsychotic therapy according to the growing knowledge of the function and effect of the genetic polymorphisms that affect the pharmacokinetics and pharmacodynamics of antipsychotics. On the pharmacokinetic level, polymorphic phase I and II drug-metabolizing enzymes and transport proteins affect drug concentration at the target structure. The cytochrome P450 enzymes, N-acetyltransferase, and multidrug resistance protein (MDR1) particularly influence this parameter. Genetic alterations affecting drug pharmacodynamic properties have an impact on therapeutic outcome that is generally independent of the applied dosage regimen. A combined analysis of genetic polymorphisms in the dopaminergic and serotonergic receptors, neurotransmitter transporters, and other target structures involved in psychiatric disorders is already a powerful predictor of therapeutic outcome. An understanding of other factors influencing gene expression and protein production will facilitate individualized therapy in the future.
antipsychotic; pharmacogenomics; pharmacokinetics; nonresponse; schizophrenia
Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.
systems biology; PBPK; software; multiscale; modeling; simulation; oncology; signal transduction
Chlorpyrifos (CPF) is a commonly used organophosphorus pesticide. Several pharmacokinetic and pharmacodynamic studies have been conducted in rats in which CPF was administered as a single bolus dose. However, there is limited data regarding the pharmacokinetics and pharmacodynamics following daily exposure. Since occupational exposures often consist of repeated, daily exposures, there is a need to evaluate the pharmacokinetics and pharmacodynamics of CPF under exposure conditions which more accurately reflect real world human exposures. In this study, the pharmacokinetics and pharmacodynamics of CPF were assessed in male Long Evans rats exposed daily to CPF (0, 3 or 10mg/kg/day, s.c. in peanut oil) over a ten day study period. Throughout the study, multiple pharmacokinetic (urinary TCPy levels and tissue CPF and metabolite levels) and pharmacodynamic (blood and brain AChE activity) determinants were measured. Average blood AChE activity on day ten was 54 and 33 percent of baseline among animals in the 3 and 10mg/kg/day CPF treatment groups, respectively, while average brain AChE activity was 67 and 28 percent of baseline. Comparable dose-response relationships between brain AChE inhibition and blood AChE inhibition, suggests that blood AChE activity is a valid biomarker of brain AChE activity. The pharmacokinetic and pharmacodynamic measures collected in this study were also used to optimize a rat physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model for multiple s.c. exposures to CPF based on a previously published rat PBPK/PD model for CPF following a single bolus injection. This optimized model will be useful for determining pharmacokinetic and pharmacodynamic responses over a wide range of doses and durations of exposure, which will improve extrapolation of results between rats and humans.
Chlorpyrifos; Pesticide, Pharmacokinetics; Pharmacodynamics; Subcutaneous; Cholinesterase