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.
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
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.
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
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
There has been considerable promise and hope that pharmacogenomics will optimize existing treatments for major depression, as well as identify novel targets for drug discovery. Immediately after the sequencing of the human genome, there was much hope that tremendous progress in pharmacogenomics would rapidly be achieved. In the past 10 years this initial enthusiasm has been replaced by a more sober optimism, as we have gone a long way towards the goal of guiding therapeutics based on genomics. While the effort to translate discovery to clinical applications is ongoing, we now have a vast body of knowledge as well as a clear direction forward. This article will provide a critical appraisal of the state of the art in the pharmacogenomics of depression, both in terms of pharmacodynamics and pharmacokinetics.
depression; antidepressant; genetics; pharmacogenomics; serotonin transporter (SLC6A4); adverse reaction; drug target; therapeutics
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.
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.
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
Despite remarkable progress, pharmacotherapy in general, including that for the treatment of depressive conditions, has often ignored the magnitude and clinical significance of the huge interindividual variations in pharmacokinetics and pharmacodynamics, resulting in poor compliance, suboptimal therapeutic effects, and treatment resistance. Advances in pharmacogenomics and computer modeling technologies hold promise for achieving the goals of “individualized” (“personalized”) medicine. However, the challenges for realizing such goals remain substantial. These include the packaging and interpretation of genotyping results, changes in medical practice (innovation diffusion), and infrasiructural, financing, ethical, and organizational issues related to the use of new information.
pharmacogenomic; individualized medicine; antidepressant; treatment response; personalized medicine; pharmacogenetic; psychopharmacology
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 purpose of this study was to develop a stochastic version of corticosteriod fifth generation pharmacogenomic model. The Gillespie algorithm was used to generate the independent time courses of the receptor messenger RNA (mRNA). Initial parameters for the stochastic simulation were adapted from the study by Jin et al. The result obtained from the proposed stochastic model showed an overall agreement with the deterministic fifth generation model. This study suggested that because the stochastic model takes into account the “noise” nature of gene regulation, it would have potential application in pharmacogenomic modeling.
pharmacogenomic modeling; corticosteroid; stochastic; gene; Gillespie algorithm; Monte Carlo simulation
The new era of personalized medicine, which integrates the uniqueness of an individual with respect to the pharmacokinetics and pharmacodynamics of a drug, holds promise as a means to provide greater safety and efficacy in drug design and development. Personalized medicine is particularly important in oncology, whereby most clinically used anticancer drugs have a narrow therapeutic window and exhibit a large interindividual pharmacokinetic and pharmacodynamic variability. This variability can be explained, at least in part, by genetic variations in the genes encoding drug metabolizing enzymes, transporters, or drug targets. Understanding of how genetic variations influence drug disposition and action could help in tailoring cancer therapy based on individual’s genetic makeup. This review focuses on the pharmacogenomics of drug metabolizing enzymes and drug transporters, with a particular highlight of examples whereby genetic variations in the metabolizing enzymes and transporters influence the pharmacokinetics and/or response of chemotherapeutic agents.
polymorphisms; personalized medicine; oncology; enzymes; transporters; drug
Pharmacogenomics is defined as the study of the impacts of heritable traits on pharmacology and toxicology. Candidate genes with potential pharmacogenomic importance include drug transporters involved in absorption and excretion, phase I enzymes (e.g., cytochrome P450-dependent mixed-function oxidases) and phase II enzymes (e.g., glucuronosyltransferases) contributing to metabolism, and those molecules (e.g., albumin, A1-acid glycoprotein, and lipoproteins) involved in the distribution of antifungal compounds. By using the tools of population genetics to define interindividual differences in drug absorption, distribution, metabolism, and excretion, pharmacogenomic models for genetic variations in antifungal pharmacokinetics can be derived. Pharmacogenomic factors may become especially important in the treatment of immunocompromised patients or those with persistent or refractory mycoses that cannot be explained by elevated MICs and where rational dosage optimization of the antifungal agent may be particularly critical. Pharmacogenomics has the potential to shift the paradigm of therapy and to improve the selection of antifungal compounds and adjustment of dosage based upon individual variations in drug absorption, metabolism, and excretion.
Physicians continue to struggle with the clinical management of pain, in part because of the large interindividual variability in the efficacy, occurrence of side effects and undesired severe adverse drug reactions from the prescribed analgesics. Pharmacogenomics, the study of how an individual's genetic inheritance affects the body's response to medications, has an important role and can explain some of this interindividual variability. Genetic identification of known variant alleles that affect the pharmacokinetics or pharmacodynamics of medications used for pain management can enable physicians to select the appropriate analgesic drug and dosing regimen for an individual patient, instead of empirical selection and dosing escalation. In this article, clinically relevant pharmacogenomic targets for the management of opioid pain, including efflux transporters, proteins that metabolize drugs, enzymes that regulate the neurotransmitters that modulate pain, and opioid receptors, will be reviewed.
Pharmacogenomics is the science about how inherited factors influence the effects of drugs. Drug response is always a result of mutually interacting genes with important modifications from environmental and constitutional factors. Based on the genetic variability of pharmacokinetic and in some cases pharmacodynamic variability we mention possible implications for the acute and preventive treatment of migraine. Pharmacogenomics will most likely in the future be one part of our therapeutic armamentarium and will provide a stronger scientific basis for optimizing drug therapy on the basis of each patient’s genetic constitution.
Migraine; Pharmacogenomics; Pharmacokinetics; Pharmacodynamics
With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.
Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.
Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.
The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.
The substantial reduction in ischemic events provided by the dual antiplatelet regimen with aspirin and clopidogrel is well documented in patients with acute coronary syndrome and patients undergoing percutaneous coronary intervention. Recently the variable response to the antiplatelet agents has received considerable attention after several “boxed warnings” on clopidogrel. This led to intense controversy on pharmacokinetic, pharmacodynamic, and pharmacogenomic issues of antiplatelet drugs, especially clopidogrel. Research use of platelet function testing has been successfully validated in identifying new antiplatelet drugs like prasugrel and ticagrelor. These platelet function assays are no longer regarded just as a laboratory phenomenon but rather as tools that have been shown to predict mortality in several clinical trials. It is believed that suboptimal response to an antiplatelet regimen (pharmacodynamic effect) may be associated with cardiovascular, cerebrovascular, and peripheral arterial events. There has been intense controversy about this variable response of antiplatelet drugs and the role of platelet function testing to guide antiplatelet therapy. While the importance of routine platelet function testing may be uncertain, it may be useful in high-risk patients such as those with diabetes mellitus, diffuse three vessels coronary artery disease, left main stenosis, diffuse atherosclerotic disease, and those with chronic renal failure undergoing percutaneous coronary intervention. It could also be useful in patients with suspected pharmacodynamic interaction with other drugs to assure the adequacy of platelet inhibition. While we wait for definitive trials, a predictive prognostic algorithm is necessary to individualize antiplatelet therapy with P2Y12 inhibitors based on platelet function assays and genetic testing.
platelet function testing; platelet function assay; clopidogrel; coronary artery disease; acute coronary syndrome; coronary artery stenting
Dual antiplatelet therapy with aspirin and a P2Y12 receptor inhibitor represents the cornerstone therapy for patients with acute coronary syndromes or undergoing percutaneous interventions, leading to a reduction of subsequent ischemic events. Variable response to clopidogrel has received close attention, and pharmacokinetic, pharmacodynamic, and pharmacogenomic factors have been identified as culprits. This led to the introduction of newer, potentially safer, and more effective antiplatelet agents (prasugrel and ticagrelor). Additionally, several point-of-care assays of platelet function have been developed in recent years to rapidly screen individuals on antiplatelet therapy. While the routine use of platelet function testing is uncertain and not currently recommended, it may be useful in instances when the degree of platelet inhibition may be uncertain such as high-risk patients undergoing percutaneous coronary intervention or when there may be a suspected pharmacodynamic interaction with other drugs. The current paper focuses on the P2Y12 receptor inhibitors and their pharmacogenetics and indications in patients with acute coronary syndromes or receiving percutaneous coronary interventions as well as the applicability of platelet function testing in this clinical context.
The systemic pharmacokinetics and pharmacodynamics of small molecules are determined by subcellular transport phenomena. Although approaches used to study the subcellular distribution of small molecules have gradually evolved over the past several decades, experimental analysis and prediction of cellular pharmacokinetics remains a challenge. In this article, we surveyed the progress of subcellular distribution research since the 1960s, with a focus on the advantages, disadvantages and limitations of the various experimental techniques. Critical review of the existing body of knowledge pointed to many opportunities to advance the rational design of organelle-targeted chemical agents. These opportunities include: 1) development of quantitative, nonfluorescence-based, whole cell methods and techniques to measure the subcellular distribution of chemical agents in multiple compartments; 2) exploratory experimentation with nonspecific transport probes that have not been enriched with putative, organelle-targeting features; 3) elaboration of hypothesis-driven, mechanistic and modeling-based approaches to guide experiments aimed at elucidating subcellular distribution and transport; and 4) introduction of revolutionary conceptual approaches borrowed from the field of synthetic biology combined with cutting edge experimental strategies. In our laboratory, state-of-the-art subcellular transport studies are now being aimed at understanding the formation of new intracellular membrane structures in response to drug therapy, exploring the function of drug-membrane complexes as intracellular drug depots, and synthesizing new organelles with extraordinary physical and chemical properties.
drug transport; pharmacokinetics; biodistribution; drug targeting; databases; mathematical modeling; drug delivery; drug-membrane aggregates; unnatural organelles; synthetic organelles
The newly developed U.S. Common Medication Information Infrastructure was used as a basis to capture and formally express the properties of drugs relevant to research and the clinical application of pharmacogenomics. Two associated taxonomies within the model, Mechanism of Action and Physiologic Effect, were enriched to accommodate pharmacogenomic use-cases; the 4,000 active ingredients in the VA NDF-RT drug file were related to the enhanced taxonomies. Pharmacokinetics were independently modeled for pharmacogenomics and tested against thirty-one high-profile drugs to demonstrate our approach.
In this paper we view alcohol dependence and the response to treatment as a recurrent bio-behavioral process developing in time and propose formal models of this process combining behavior and biology in silico. The behavioral components of alcohol dependence and treatment are formally described by a stochastic process of human behavior, which serves as an event generator challenging the metabolic system. The biological component is driven by the biochemistry of alcohol intoxication described by deterministic models of ethanol pharmacodynamics and pharmacokinetics to enable simulation of drinking addiction in humans. Derived from the known physiology of ethanol and the literature of both ethanol intoxication and ethanol absorption, the different models are distilled into a minimal model (as simple as the complexity of the data allows) that can represent any specific patient. We use these modeling and simulation techniques to explain responses to placebo and ondansetron treatment observed in clinical studies. Specifically, the response to placebo was explained by a reduction of the probability of environmental reinforcement, while the effect of ondansetron was explained by a gradual decline in the degree of ethanol-induced neuromodulation. Further, we use in silico experiments to study critical transitions in blood alcohol levels after specific average number of drinks per day, and propose the existence of two critical thresholds in the human – one at 5 and another at 11 drinks/day – at which the system shifts from stable to critical and to super critical state indicating a state of alcohol addiction. The advantages of such a model-based investigation are that (1) the process of instigation of alcohol dependence and its treatment can be deconstructed into meaningful steps, which allow for individualized treatment tailoring, and (2) physiology and behavior can be quantified in different (animal or human) studies and then the results can be integrated in silico.
alcohol dependence; computer simulation; metabolic modeling; stochastic process
The frequency of resistance to β-lactams among nosocomial isolates has been increasing due to extended-spectrum β-lactamase (ESBL)-producing enteric bacilli. Although clinical outcome data are desirable, assessment of clinical efficacy has been limited due to the lack of a statistically meaningful number of well-documented cases. Since time above the MIC (T>MIC) is the pharmacokinetic-pharmacodynamic (PK-PD) measure that best correlates with in vivo activity of β-lactams, a stochastic model was used to predict the probability of PK-PD target attainment ranging from 30 (P30) to 70% (P70) T>MIC, for standard dosing regimens of both piperacillin-tazobactam and cefepime against Escherichia coli and Klebsiella pneumoniae ESBL phenotypes. The P70/30 T>MIC for cefepime at 2 g every 12 h against E. coli and K. pneumoniae was 0.99/1.0 and 0.96/1.0 and for a regimen of 1 g every 12 h was 0.96/1.0 and 0.93/0.99, respectively. For piperacillin-tazobactam at 3.375 g every 4 h against E. coli and K. pneumoniae, the P70/30 T>MIC was 0.77/0.96 and 0.48/0.77 and for a regimen of 3.375 g every 6 h was 0.28/0.91 and 0.16/0.69, respectively. These data suggest that the probability of achieving T>MIC target attainment rates is generally higher with cefepime than with piperacillin-tazobactam for present-day ESBL-producing strains when one uses contemporary dosing regimens.