Biomarkers, validation and pharmacokinetic-pharmacodynamic modelling

Use of a PK/PD Model to Select Cetagliptin Dosages for Patients with Type 2 Diabetes in Phase 3 Trials

BACKGROUND

Cetagliptin is a novel dipeptidyl peptidase-4 (DPP-4) inhibitor developed for the treatment of patients with type 2 diabetes (T2D). Several phase 1 studies have been conducted in China. Modelling and simulation were used to obtain cetagliptin dose for phase 3 trials in T2D patients.

METHODS

A pharmacokinetic (PK)/pharmacodynamic (PD) model and model-based analysis of the relationship between hemoglobin A1c (HbA1c) and dosage was explored to guide dose selection of cetagliptin for phase 3 trials. The PK/PD data were derived from four phase 1 clinical studies, and sitagliptin 100 mg was employed as a positive control in studies 1, 3, and 4.

RESULTS

The PK profiles of cetagliptin were well described by a two-compartment model with first-order absorption, saturated efflux, and first-order elimination. The final PD model was a sigmoid maximum inhibitory efficacy (Emax) model with the Hill coefficient. The final model accurately captured cetagliptin PK/PD, demonstrated by goodness-of-fit plots. Based on weighted average inhibition (WAI), the relationship between HbA1c and dose was well displayed. Cetagliptin 50 mg once daily or above as monotherapy or as add-on therapy appeared more effective in HbA1c reduction than sitagliptin 100 mg. Cetagliptin 50 mg or 100 mg once daily was selected as the dose for phase 3 trials of cetagliptin in T2D patients.

CONCLUSIONS

The PK/PD model supports dose selection of cetagliptin for phase 3 trials. A model‑informed approach can be used to replace a dose-finding trial and accelerate cetagliptin's development.

Drug Development for New Psychiatric Drug Therapies

Drug development is an expensive, high risk, and highly regulated process. Only about 6.2% of new molecules tested for mental disorders eventually achieve Food and Drug Administration (FDA) approval. New molecular entities are produced, and extensive in vitro animal testing is performed before they are evaluated in humans. The compound is used in animals to predict clinical effects in humans, and studies addressing pharmacodynamics, pharmacokinetics, toxicology, and mutagenicity are conducted. Human research proceeds in three stages with the ultimate goal of proving that a new agent is efficacious and safe for a treatment of a specific disease in humans. If efficacy and safety are demonstrated in two Phase III studies, then the sponsor can submit a new drug application (NDA) to the FDA. The FDA oversees each step of the process to assure that good research practices are followed, data integrity is assured, and human research subjects are protected.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Useful pharmacodynamic endpoints in children: selection, measurement, and next steps

Pharmacodynamic (PD) endpoints are essential for establishing the benefit-to-risk ratio for therapeutic interventions in children and neonates. This article discusses the selection of an appropriate measure of response, the PD endpoint, which is a critical methodological step in designing pediatric efficacy and safety studies. We provide an overview of existing guidance on the choice of PD endpoints in pediatric clinical research. We identified several considerations relevant to the selection and measurement of PD endpoints in pediatric clinical trials, including the use of biomarkers, modeling, compliance, scoring systems, and validated measurement tools. To be useful, PD endpoints in children need to be clinically relevant, responsive to both treatment and/or disease progression, reproducible, and reliable. In most pediatric disease areas, this requires significant validation efforts. We propose a minimal set of criteria for useful PD endpoint selection and measurement. We conclude that, given the current heterogeneity of pediatric PD endpoint definitions and measurements, both across and within defined disease areas, there is an acute need for internationally agreed, validated, and condition-specific pediatric PD endpoints that consider the needs of all stakeholders, including healthcare providers, policy makers, patients, and families.

Pharmacokinetic/Pharmacodynamic Modeling for Drug Development in Oncology

High drug attrition rates remain a critical issue in oncology drug development. A series of steps during drug development must be addressed to better understand the pharmacokinetic (PK) and pharmacodynamic (PD) properties of novel agents and, thus, increase their probability of success. As available data continues to expand in both volume and complexity, comprehensive integration of PK and PD information into a robust mathematical model represents a very useful tool throughout all stages of drug development. During the discovery phase, PK/PD models can be used to identify and select the best drug candidates, which helps characterize the mechanism of action and disease behavior of a given drug, to predict clinical response in humans, and to facilitate a better understanding about the potential clinical relevance of preclinical efficacy data. During early drug development, PK/PD modeling can optimize the design of clinical trials, guide the dose and regimen that should be tested further, help evaluate proof of mechanism in humans, anticipate the effect in certain subpopulations, and better predict drug-drug interactions; all of these effects could lead to a more efficient drug development process. Because of certain peculiarities of immunotherapies, such as PK and PD characteristics, PK/PD modeling could be particularly relevant and thus have an important impact on decision making during the development of these agents.

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.

Biomarker analysis for oncology

Cancer biomarkers are biological, chemical or biophysical entities that are present in tumor tissues or body fluids which give valuable information about current and future behavior of cancer. This review discusses the applicability of biomarkers in different stages of cancer from cancer risk assessment to recurrence. In medical practice, biomarkers can be helpful in finding out one's potential cancer risk, confirming whether or not one is already affected with a particular type of cancer, to which drug will the cancer respond best and in what doses should it be administered, the effectiveness of the treatment and whether the cancer will recur. Although biomarker discovery and validation is a very challenging process, when considering its applications and advantages, it is well worth the effort.

Current industrial practices and regulatory requirements to assess analyte and reagent stability using ligand-binding assays

Specific guidelines on bioanalytical method validation for drug development support are recommended by regulatory agencies. Regarding stability assessment, US FDA states that 'Stability procedures should evaluate the stability of the analytes during sample collection and handling, after long-term (frozen at the intended storage temperature) and short-term (bench-top, room temperature) storage, and after going through freeze and thaw cycles and the analytical process'. Additional regulatory considerations are discussed including topics such as analyte and reagent stability. This article reviews the regulatory requirements as issued by the USA (FDA), Europe (EMA) and Japan (MHLW), for stability studies where bioanalytical methods are used to support drug development programs and summarizes the current industry standard for conducting stability studies when utilizing ligand-binding assays.

MicroRNA signatures as biomarkers and therapeutic target for CNS embryonal tumors: the pros and the cons

Embryonal tumors of the central nervous system represent a heterogeneous group of childhood cancers with an unknown pathogenesis; diagnosis, on the basis of histological appearance alone, is controversial and patients’ response to therapy is difficult to predict. They encompass medulloblastoma, atypical teratoid/rhabdoid tumors and a group of primitive neuroectodermal tumors. All are aggressive tumors with the tendency to disseminate throughout the central nervous system. The large amount of genomic and molecular data generated over the last 5–10 years encourages optimism that new molecular targets will soon improve outcomes. Recent neurobiological studies have uncovered the key role of microRNAs (miRNAs) in embryonal tumors biology and their potential use as biomarkers is increasingly being recognized and investigated. However the successful use of microRNAs as reliable biomarkers for the detection and management of pediatric brain tumors represents a substantial challenge. This review debates the importance of miRNAs in the biology of central nervous systemembryonal tumors focusing on medulloblastoma and atypical teratoid/rhabdoid tumors and highlights the advantages as well as the limitations of their prospective application as biomarkers and candidates for molecular therapeutic targets.

Breath volatile organic compounds for the gut-fatty liver axis: Promise, peril, and path forward

The worldwide interest in the gut microbiome and its impact on the upstream liver highlight a critical upside to breath research: it can uniquely measure otherwise unmeasurable biology. Bacteria make gases [volatile organic compounds (VOCs)] that are directly relevant to pathophysiology of the fatty liver and associated conditions, including obesity. Measurement of these VOCs and their metabolites in the exhaled breath, therefore, present an opportunity to safely and easily evaluate, on both a personal and a population level, some of our most pressing public health threats. This is an opportunity that must be pursued. To date, however, breath analysis remains a slowly evolving field which only occasionally impacts clinical research or patient care. One major obstacle to progress is that breath analysis is inherently and emphatically mutli-disciplinary: it connects engineering, chemistry, breath mechanics, biology and medicine. Unbalanced or incomplete teams may produce inconsistent and often unsatisfactory results. A second impediment is the lack of a well-known stepwise structure for the development of non-invasive diagnostics. As a result, the breath research landscape is replete with orphaned single-center pilot studies. Often, important hypotheses and key observations have not been pursued to maturation. This paper reviews the rationale and requirements for breath VOC research applied to the gut-fatty liver axis and offers some suggestions for future development.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

Publication and reporting conduct for pharmacodynamic analyses of tumor tissue in early-phase oncology trials

PURPOSE

In principle, nondiagnostic biopsies for pharmacodynamic (PD) studies are carried out to inform decision-making in drug development. Because such procedures have no therapeutic value, their ethical justification requires that results be published. We aimed to assess the frequency of nonpublication of PD data in early phase cancer trials and to identify factors that prevent full publication of data.

METHODS

We identified a sample of early-phase cancer trials containing invasive nondiagnostic tissue procurement for PD analysis from American Society of Clinical Oncology and American Association for Cancer Research meeting abstracts published between 1995 and 2005. These trials were followed to publication to determine frequency of nonpublication of PD data. Corresponding authors on early-phase cancer trials using invasive nondiagnostic research procedures were also surveyed to identify factors preventing full publication of PD data.

RESULTS

In a sample of 90 trials, 22.2% (20 trials) resulted in no trial publication. Of published trials expected to contain PD reports, 16 (17.8%) did not include any PD data, and 21 (23.3%) reported incomplete PD data. We surveyed 92 authors; nonpublication was regarded as a frequent occurrence, and the most commonly cited barrier to full publication of PD data was strategic considerations in publication (58.8% of responding authors).

CONCLUSIONS

Our results suggest ways that investigators, study planners, and reviewers can improve the burden/knowledge value balance in PD studies.

Pharmacokinetic-pharmacodynamic modeling of the inhibitory effects of naproxen on the time-courses of inflammatory pain, fever, and the ex vivo synthesis of TXB2 and PGE2 in rats

PURPOSE

To quantify and compare the time-course and potency of the analgesic and antipyretic effects of naproxen in conjunction with the inhibition of PGE(2) and TXB(2).

METHODS

Analgesia was investigated in a rat model with carrageenan-induced arthritis using a gait analysis method. Antipyretics were studied in a yeast-induced fever model using telemetrically recorded body temperature. Inhibition of TXB(2) and PGE(2) synthesis was determined ex vivo. Pharmacokinetic profiles were obtained in satellite animals. Population PKPD modeling was used to analyze the data.

RESULTS

The IC(50) values (95% CI) of naproxen for analgesia (27 (0-130) μM), antipyretics (40 (30-65) μM) and inhibition of PGE(2) (13 (6-45) μM) were in similar range, whereas inhibition of TXB(2) (5 (4-8) μM) was observed at lower concentrations. Variability in the behavioral measurement of analgesia was larger than for the other endpoints. The inhibition of fever by naproxen was followed by an increased rebound body temperature.

CONCLUSION

Due to better sensitivity and similar drug-induced inhibition, the biomarker PGE(2) and the antipyretic effect would be suitable alternative endpoints to the analgesic effects for characterization and comparisons of potency and time-courses of drug candidates affecting the COX-2 pathway and to support human dose projections.

Literature mining for the discovery of hidden connections between drugs, genes and diseases

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.

The biomedical literature is an important source of knowledge on the function of genes and on the mechanisms by which these genes regulate cellular processes. Several text mining approaches have been developed to leverage this rich source of information by automatically extracting associations between concepts such as genes, diseases and drugs from a large body of text. Here, we describe a new method that extracts novel, not yet recognized associations between genes, diseases, drugs and cellular processes from the biomedical literature. Our method is built on the assumption that even if two concepts do not have a direct connection in literature, they may be functionally related if they are both connected to an overlapping set of concepts. Using this approach we predicted several novel connections between genes, diseases, drugs and pathways. Our results imply that our method is able to predict novel relationships from literature and, most importantly, that these newly identified relationships are biologically relevant. Our method can aid the drug discovery process where it can be used to find novel drug targets, increase insight in mode of action of a drug or find novel applications for known drugs.

"Fit-for-purpose" method validation and application of a biomarker (C-terminal telopeptides of type 1 collagen) in denosumab clinical studies

Biomarkers are used to study drug effects, exposure-response relationships, and facilitate early decision making during development. Denosumab, a fully human monoclonal antibody against receptor activator of nuclear factor-kappaB ligand, profoundly inhibits bone resorption. C-terminal telopeptides of type I collagen (CTx), a bone resorption biomarker, provides early indications of denosumab effectiveness and informs protracted clinical outcomes (e.g., bone mineral density). Because of the dynamic relationship between denosumab and CTx, a precise and robust assay was desired. Thus, we adopted a fit-for-purpose approach to modify and validate a commercial CTx diagnostic kit to meet the intended applications of a quantitative pharmacodynamic biomarker for denosumab development. Seven standards were prepared to replace five calibrators provided in the kit. Three quality controls (QC) and two sample controls were used to characterize and monitor assay performance. Robotic workstations were used for standard and QC preparation and assay execution. Method validation experiments were conducted with rigor and procedures similar to those used for drug bioanalysis. The method demonstrated a linear range of 0.0490-2.34 ng/mL with four-parameter logistic regression. Inter-assay total error of validation samples in serum was < or = 26.7%. Extensive tests were conducted on selectivity in sera from target populations, specificity, stability, parallelism, and dilutional linearity. Applications to samples from numerous clinical studies confirmed that the CTx method was reliable, robust, and fit for use as an early indicator of denosumab effectiveness. Refinement supported the confidence for use in pharmacokinetic/pharmacodynamic modeling, dose selections, correlation to clinical effects, and formulation bioequivalence work.

Development and validation of an LC-MS/MS method for quantification of cyclic guanosine 3',5'-monophosphate (cGMP) in clinical applications: a comparison with a EIA method

An LC-MS/MS method was developed and validated to quantify endogenous cyclic guanosine 3',5'-monophosphate (cGMP) in human plasma. The LC-MS/MS and competitive enzyme immunoassay (EIA) assays were compared. cGMP concentrations of 20 human plasma samples were measured by both methods. For the MS-based assay, plasma samples were subjected to a simple protein precipitation procedure by acetonitrile prior to analysis by electrospray ionization LC-MS/MS. De-protonated analytes generated in negative ionization mode were monitored through multiple reaction monitoring (MRM). A stable isotope-labeled internal standard, (13)C(10),(15)N(5)-cGMP, which was biosynthesized in-house, was used in the LC-MS/MS method. The competitive EIA was validated using a commercially available cGMP fluorescence assay kit. The intra-assay accuracy and precision for MS-based assay for cGMP were 6-10.1% CV and -3.6% to 7.3% relative error (RE), respectively, while inter-assay precision and accuracy were 5.6-8.1% CV and -2.1% to 6.3% RE, respectively. The intra-assay accuracy and precision for EIA were 17.9-27.1% CV and -4.9% to 24.5% RE, respectively, while inter-assay precision and accuracy were 15.1-39.5% CV and -30.8% to 4.37% RE, respectively. Near the lower limits of detection, there was little correlation between the cGMP concentration values in human plasma generated by these two methods (R(2)=0.197, P=0.05). Overall, the MS-based assay offered better selectivity, recovery, precision and accuracy over a linear range of 0.5-20ng/mL. The LC-MS/MS method provides an effective tool for the quantitation of cGMP to support clinical mechanistic studies of curative pharmaceuticals.

Validation of a flow cytometry based chemokine internalization assay for use in evaluating the pharmacodynamic response to a receptor antagonist

Pharmacodynamic assays are important in clinical trial design to investigate the relationship between drug concentration (pharmacokinetics) and drug "effect' or biological activity. Increasingly flow cytometry is being used to examine the pharmacodynamic effect of new drug entities. However, to date, the analytical validation of cytometry based assays is limited and there is no suitable guidance for method validation of flow cytometry-based pharmacodynamic assays. Here we report the validation of a flow cytometry-based chemokine internalization assay for use in evaluating the effect of a receptor antagonist in clinical trials. The assay method was validated by examining the stability of the reagent, assay robustness, sensitivity, repeatability and reproducibility precision. Experimental results show the assay reagent was stable over 26 weeks. The assay demonstrated a sensitivity to distinguish 0.005 μg/ml of a CCR2 antagonist with a %CV of 13.3%. The intra-assay repeatability was less than 15% with an inter-assay repeatability of less than 20%. In vivo study results demonstrated that the assay was consistent and a reliable measure of antagonist activity.

Sperm chromatin: fertile grounds for proteomic discovery of clinical tools

Sperm are remarkably complex cells with a singularly important mission: to deliver paternal DNA and its associated factors to the oocyte to start a new life. The integrity of sperm DNA is a keystone of reproductive success, which includes fertilization and embryonic development. In addition, the significance in these processes of proteins that associate with sperm DNA is increasingly being appreciated. In this review, we highlight proteomic studies that have identified sperm chromatin proteins with fertility roles that have been validated by molecular studies in model organisms or correlations in the clinic. Up to 50% of male-factor infertility cases in the clinic have no known cause and therefore no direct treatment. In-depth study of the molecular basis of infertility has great potential to inform the development of sensitive diagnostic tools and effective therapies that will address this incongruity. Because sperm rely on testis-specific protein isoforms and post-translational modifications for their development and function, sperm-specific processes are ideal for proteomic explorations that can bridge the research lab and fertility clinic.

Nanovehicular intracellular delivery systems

This article provides an overview of principles and barriers relevant to intracellular drug and gene transport, accumulation and retention (collectively called as drug delivery) by means of nanovehicles (NV). The aim is to deliver a cargo to a particular intracellular site, if possible, to exert a local action. Some of the principles discussed in this article apply to noncolloidal drugs that are not permeable to the plasma membrane or to the blood-brain barrier. NV are defined as a wide range of nanosized particles leading to colloidal objects which are capable of entering cells and tissues and delivering a cargo intracelullarly. Different localization and targeting means are discussed. Limited discussion on pharmacokinetics and pharmacodynamics is also presented. NVs are contrasted to micro-delivery and current nanotechnologies which are already in commercial use. Newer developments in NV technologies are outlined and future applications are stressed. We also briefly review the existing modeling tools and approaches to quantitatively describe the behavior of targeted NV within the vascular and tumor compartments, an area of particular importance. While we list "elementary" phenomena related to different level of complexity of delivery to cancer, we also stress importance of multi-scale modeling and bottom-up systems biology approach.

Pharmacokinetic-pharmacodynamic modelling of fluvoxamine 5-HT transporter occupancy in rat frontal cortex

BACKGROUND AND PURPOSE

The pharmacokinetic-pharmacodynamic (PK-PD) correlation of fluvoxamine 5-HT transporter (SERT) occupancy was determined in rat frontal cortex ex vivo.

EXPERIMENTAL APPROACH

Rats (n=47) with permanent arterial and venous cannulas received a 30 min intravenous infusion of fluvoxamine (1 or 7.3 mg kg(-1)). At various time points after dosing, brains were collected for determination of fluvoxamine concentration and SERT occupancy. In addition, the time course of fluvoxamine concentration in plasma was determined up to the time of brain collection. In a separate study (n=26), the time course of fluvoxamine concentration in brain extracellular fluid (ECF) and plasma was determined. The results of the investigations were interpreted by nonlinear mixed effects modeling.

KEY RESULTS

Highest SERT occupancy was reached at the first time point (10 or 15 min) and maintained for 1.5 and 7 h after 1 and 7.3 mg kg(-1), respectively. Thereafter, SERT occupancy decreased linearly at a rate of 8% h(-1). SERT occupancy could be directly related to plasma, brain ECF and brain tissue concentrations by a hyperbolic function (Bmax model). Maximal SERT occupancy (Bmax) was 95%. Estimated concentrations at half-maximal SERT occupancy (EC50) in plasma, ECF and brain tissue were 0.48, 0.22 and 14.8 ng mL(-1) respectively. The minimum value of the objective function decreased 12 points for ECF and brain tissue concentrations relative to plasma (P<0.01), presumably as a result of nonlinear brain distribution.

CONCLUSIONS AND IMPLICATIONS

The proposed PK-PD model constitutes a useful basis for prediction of the time course of ex vivo SERT occupancy in behavioural studies with selective serotonin reuptake inhibitors.

Pharmacokinetic modeling of non-linear brain distribution of fluvoxamine in the rat

INTRODUCTION

A pharmacokinetic (PK) model is proposed for estimation of total and free brain concentrations of fluvoxamine.

MATERIALS AND METHODS

Rats with arterial and venous cannulas and a microdialysis probe in the frontal cortex received intravenous infusions of 1, 3.7 or 7.3 mg.kg(-1) of fluvoxamine.

ANALYSIS

With increasing dose a disproportional increase in brain concentrations was observed. The kinetics of brain distribution was estimated by simultaneous analysis of plasma, free brain ECF and total brain tissue concentrations. The PK model consists of three compartments for fluvoxamine concentrations in plasma in combination with a catenary two compartment model for distribution into the brain. In this catenary model, the mass exchange between a shallow perfusion-limited and a deep brain compartment is described by a passive diffusion term and a saturable active efflux term.

RESULTS

The model resulted in precise estimates of the parameters describing passive influx into (k in) of 0.16 min(-1) and efflux from the shallow brain compartment (k out) of 0.019 min(-1) and the fluvoxamine concentration at which 50% of the maximum active efflux (C 50) is reached of 710 ng.ml(-1). The proposed brain distribution model constitutes a basis for precise characterization of the PK-PD correlation of fluvoxamine by taking into account the non-linearity in brain distribution.

Development of translational pharmacokinetic-pharmacodynamic models

Contemporary models in the field of pharmacokinetic-pharmacodynamic (PK-PD) modeling often incorporate the fundamental principles of capacity limitation and operation of turnover processes to describe the time course of pharmacological effects in mechanistic terms. This permits the identification of drug- and system-specific factors that govern drug responses. There is considerable interest in utilizing mechanism-based PK-PD models in translational pharmacology, whereby in silico, in vitro, and preclinical data may be effectively coupled with relevant models to streamline the discovery and development of new therapeutic agents. These translational PK-PD models form the subject of this review.

PK/PD modelling and simulations: utility in drug development

Pharmacokinetic/pharmacodynamic (PK/PD) modelling and simulation can be used as an 'applied science' tool to provide answers on efficacy and safety of new drugs faster and at a lower cost. PK/PD modelling can be used from the preclinical phase through all clinical phases of drug development. Optimal use of PK/PD modelling and simulation will lead to fewer failed compounds, fewer study failures and smaller numbers of studies needed for registration. For PK/PD modelling to fulfil its potential in drug development, it needs to be embraced across the industry and regulatory agencies, and more education on this topic is required.

Integrated pharmacokinetics and pharmacodynamics in drug development

Drug development is a complex, lengthy and expensive process. Pharmaceutical companies and regulatory authorities have recognised that the drug development process needs optimisation for efficiency in view of the return on investments. Pharmacokinetics and pharmacodynamics are the two main principles determining the relationship between dose and response. This article provides an update on integrated approaches towards drug development by linking pharmacokinetics, pharmacodynamics and disease aspects into mathematical models. Gradually, a transition is taking place from a rather empirical approach towards a modelling- and simulation-based approach to drug development. The main learning phases should be phases 0, I and II, whereas phase III studies should merely have a confirmatory purpose. In model-based drug development, mechanism-based mathematical models, which are iteratively refined along the path of development, incorporate the accumulating knowledge of the investigational drug, the disease and their mutual interference in different subsets of the target population. These models facilitate the design of the next study and improve the probability of achieving the projected efficacy and safety endpoints. In this article, several theoretical and practical aspects of an integrated approach towards drug development are discussed, together with some case studies from different therapeutic areas illustrating the application of pharmacokinetic/pharmacodynamic disease models at different stages of drug development.

Biomarkers in drug discovery and development

Biomarkers have shown promising utilities at various stages of the pharmaceutical R & D. With the recent technological advancements and the introduction of protein and gene arrays, high performance instrumentation (e.g., high-field nuclear magnetic resonance and high-resolution mass spectrometers), and bioinformatics, decisions on safety and efficacy criteria can be made with a higher degree of confidence. However, there is a scarcity of robust and valid biomarkers to accelerate the drug development process from pre-clinical through all stages of clinical studies. In this article, a brief overview of current definitions, biomarker categories, challenges in biological and analytical validation, along with several clinical examples will be presented.

The potential of proteomics and peptidomics for allergy and asthma research

Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricted window of the proteome that consists of peptides and small proteins not easily manageable by conventional gel electrophoresis prompted the development of separation methods based on liquid chromatography. This new research field termed peptidomics already contributed, together with proteomics to enlarge our knowledge about biological processes and supported by sophisticated bioinformatics tools, to the discovery of new diagnostic and therapeutic targets. The technological capabilities of biophysical separation, mass spectrometry and bioinformatics form the basis of discovery programs that aim at mining the proteome starting from microgram amounts of protein extracts derived from body fluids and tissues. Proteomics and peptidomics have a great potential to speed up allergy and asthma research, where disease- and tissue-specific samples are easy to obtain.

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

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

Disease System Analysis: Basic Disease Progression Models in Degenerative Disease

PURPOSE

To describe the disease status of degenerative diseases (i.e., type 2 diabetes mellitus, Parkinson's disease) as function of disease process and treatment effects, a family of disease progression models is introduced.

METHODS

Disease progression is described using a progression rate (Rdp) acting on the synthesis or elimination parameters of the indirect response model. Symptomatic effects act as disease-dependent or -independent effects on the synthesis or elimination parameters. Protective drug effects act as disease dependent or -independent effects on Rdp.

RESULTS

Simulations with the ten disease models show distinctly different signature profiles of treatment effects on disease status. Symptomatic effects result in improvement of disease status with a subsequent deterioration. Treatment cessation results in a disease status equal to the situation where treatment had not been applied. Protective effects result in a lasting reduction, or even reversal, of the disease progression rate and the resulting disease status during the treatment period. After cessation of treatment the natural disease course will continue from the disease status at that point.

CONCLUSION

Disease system analysis constitutes a scientific basis for the distinction between symptomatic versus protective drug effects in relation to specific disease processes as well as the identification of the exposure-response relationship during the time-course of disease.

Differential and quantitative molecular analysis of ischemia complexity reduction by isotopic labeling of proteins using a neural embryonic stem cell model

The analysis of rapid changes of protein expression in living systems in response to insults requires rigorous methods of complexity reduction. To control dynamic pattern of hundreds or even thousands of protein isoforms, we applied a novel method of differential molecular analysis to a cellular model which is suited to study ischemia. Neural derivatives of murine embryonic stem cells were exposed to chemical ischemia. The model was used to obtain starting material for a quantitative differential proteomics analysis. Fractionation of phosphoproteins from these samples and subsequent identification by mass spectrometry of differential proteins provide proof of principle of how novel molecular analytical tools provide new insight into the network of neuroprotective molecular events during specific situations of neuronal stress and related pharmaceutical intervention. Our results indicate a particular role of an isoform of the acidic calcium-independent phospholipase A2 in this type of insult.

Proteinase Activity in Human and Murine Saliva as a Biomarker for Proteinase Inhibitor Efficacy

As molecularly targeted agents reach the clinic, there is a need for assays to detect their presence and effectiveness against target molecules in vivo. Proteinase inhibitors are one example of a class of therapeutic agent for which satisfactory methods of identifying successful target modulation in vivo are lacking. This is of particular importance while these drugs are in clinical trials because standard maximum-tolerated dose-finding studies often are not suitable due to lack of toxicity. Saliva represents a readily accessible bodily fluid that can be repeatedly sampled and used for assaying in vivo effects of systemic drugs. Here we show the development of a simple assay that can be used to measure proteinase activity in saliva and proteinase inhibition after systemic treatment with three different proteinase inhibitors. A variety of gelatinolytic activities present in human and murine saliva have been assayed with a fluorescent dye-labeled substrate and assigned to different proteinase categories by inclusion of specific classes of inhibitors. Treatment of mice with either matrix metalloproteinase inhibitors or a urokinase inhibitor for a period as short as 48 hours results in levels of the drugs that can be detected in saliva by mass spectrometry and concomitant decreases in salivary proteinase activity, thus demonstrating that these inhibitors successfully modulate their targets in vivo.

Genomic biomarkers for cancer assessment: implementation challenges for laboratory practice

Genomic biomarkers are an emerging class of laboratory tests, which present special implementation challenges for clinical laboratory services, compared to conventional laboratory tests. These challenges, which include analytical, bioinformatics, bioethical, interpretation and commercialization issues, represent real obstacles to widespread implementation of these tests. Technical challenges include the capacity to detect and identify many different kinds of markers for different diseases in a short time period, capacity to identify simultaneously gene rearrangements, amplification, inhibition, deletions and replications. Bioinformatics challenges include rapid analysis of genomic data, as well as the cross reference to other genomic data, and to other laboratory tests. Bioethical issues relate to consent to retain and use genetic data, which may be obtained inadvertently during analysis for genomic markers. Interpretation challenges include observations that the particular genomic markers may not be independent variables, as other undetected genomic alterations could invalidate or alter genomic marker interpretation. Further, as early experience with predictive genetic markers for cancer has shown, proprietary commercial interests may conflict with public health values of identifying genomic markers in subject populations. Based on our 10 years of experience with genomic biomarkers, important implementation strategies for genomic markers include development of:Standard high throughput analyzers capable of detecting any alteration of any genomic variant at any time. Bioinformatics analysis online, coupled to stored patient data. Laboratory service framework that preserves confidentiality but integrates genomic data with other laboratory tests. Laboratory service framework, which links consents, genomic analysis, reports to both specimen and data repositories. Overall, the laboratory service challenges for genomic markers are to manage very large analytical sets and very large data sets in finite time with responsible interpretation, all within finite funding. To meet these challenges, implementation strategies beyond the one disease, one diagnosis, one genomic marker concept must begin now.