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Background: \ud TKM-130803 is a specific anti-EBOV therapeutic comprised of two small interfering RNAs (siRNA) siLpol-2 and siVP35-2. The pharmacokinetics (PK) of these siRNAs was defined in Ebola virus disease (EVD) patients, with reference to efficacy (ET) and toxicology thresholds (TT). The relationship between PK and patient survival was explored.\ud \ud Methods: \ud Pharmacokinetic (PK) and pharmacodynamic (PD) data were available for seven participants with EVD in Sierra Leone who received 0·3 mg/kg of TKM-130803 by intravenous infusion over 2 h daily for up to 7 days. Plasma concentration of siRNA was compared to survival at 14 days. PK data were fitted to two-compartment models then Monte Carlo simulated PK profiles were compared to ET (Cmax 0·04–0·57 ng/mL and mean concentration 1·43 ng/mL), and TT (3000 ng/mL).\ud \ud Findings: \ud Viral loads (VL) were not significantly different at treatment onset or during treatment (p = 0·1) in subjects who survived or died. siRNA was in quantitative excess of virus genomes throughout treatment, but the 95% percentile exceeded TT. The maximum AUC for which the 95% percentile remained under TT was a continuous infusion of 0·15 mg/kg/day. Plasma concentration of both siRNAs were higher in subjects who died compared to subjects who survived (p

The purpose of this study was to assess the pharmacokinetics of dexmedetomidine in the ICU settings during the prolonged infusion and to compare it with the existing literature data using the Bayesian population modeling with literature-based informative priors. Thirty-eight patients were included in the analysis with concentration measurements obtained at two occasions: first from 0 to 24 h after infusion initiation and second from 0 to 8 h after infusion end. Data analysis was conducted using WinBUGS software. The prior information on dexmedetomidine pharmacokinetics was elicited from the literature study pooling results from a relatively large group of 95 children. A two compartment PK model, with allometrically scaled parameters, maturation of clearance and t-student residual distribution on a log-scale was used to describe the data. The incorporation of time-dependent (different between two occasions) PK parameters improved the model. It was observed that volume of distribution is 1.5-fold higher during the second occasion. There was also an evidence of increased (1.3-fold) clearance for the second occasion with posterior probability equal to 62 %. This work demonstrated the usefulness of Bayesian modeling with informative priors in analyzing pharmacokinetic data and comparing it with existing literature knowledge. Electronic supplementary material The online version of this article (doi:10.1007/s10928-016-9474-0) contains supplementary material, which is available to authorized users.

Hepatocellular carcinoma (HCC) accompanied by severe liver dysfunction is a serious disease, which results in altered hepatic clearance. Generally, maintenance doses depend upon drug clearance, so individual dosage regimens should be customized for HCC patients based on the condition of patients. Based on clearance of CYP isoform-specific substrates at the microsomal level (CLM), microsomal protein per gram of liver (MPPGL), liver weight, hepatic blood flow, hepatic clearance values (CLH) for 10 CYPs in HCC patients (n=102) were extrapolated using a predictive bottom-up pharmacokinetic model. Compared with controls, the CLM values for CYP2C9, 2D6, 2E1 were significantly increased in HCC patients. Additionally, CYP1A2, 2C8, 2C19 CLM values decreased while the values for CYP2A6, 2B6, 3A4/5 were unchanged. The MPPGL values in HCC tissues were significantly reduced. CLH values of HCC patients for CYP1A2, 2A6, 2B6, 2C8, 2C19, and 3A4/5 were significantly reduced, while this for CYP2E1 were markedly increased and those for CYP2C9 and 2D6 did not change. Moreover, disease (fibrosis and cirrhosis) and polymorphisms of the CYP genes have influenced the CLH for some CYPs. Prediction of the effects of HCC on drug clearance may be helpful for the design of clinical studies and the clinical management of drugs in HCC patients.

Purpose Changes in drug absorption and first-pass metabolism have been reported throughout the pediatric age range. Our aim is to characterize both intestinal and hepatic CYP3A-mediated metabolism of midazolam in children in order to predict first-pass and systemic metabolism of CYP3A substrates. Methods Pharmacokinetic (PK) data of midazolam and 1-OH-midazolam from 264 post-operative children 1–18 years of age after oral administration were analyzed using a physiological population PK modelling approach. In the model, consisting of physiological compartments representing the gastro-intestinal tract and liver,intrinsic intestinal and hepatic clearances were estimated to derive values for bioavailability and plasma clearance. Results The whole-organ intrinsic clearance in the gut wall and liver were found to increase with body weight, with a 105 (95% confidence interval (CI): 5–405) times lower intrinsic gut wall clearance than the intrinsic hepatic clearance (i.e. 5.08 L/h (relative standard error (RSE) 10%) versus 527 L/h (RSE 7%) for a 16 kg individual, respectively). When expressed per gram of organ, intrinsic clearance increases with increasing body weight in the gut wall, but decreases in the liver, indicating that CYP3A-mediated intrinsic clearance and local bioavailability in the gut wall and liver do not change with age in parallel. The resulting total bioavailability was found to be age-independent with a median of 20.8% in children (95%CI: 3.8–50.0%). Conclusion In conclusion, the intrinsic CYP3A-mediated gut wall clearance is substantially lower than the intrinsic hepatic CYP3A-mediated clearance in children from 1 to 18 years of age, and contributes less to the overall first-pass metabolism compared to adults. Electronic supplementary material The online version of this article (10.1007/s11095-018-2458-6) contains, which is available to authorized users.

Objective Respiratory depression is a serious and potentially life-threatening side-effect of opioid therapy. The objective of this investigation was to characterize the relationship between buprenorphine or fentanyl exposure and the effectiveness and safety outcome in rats. Methods Data on the time course of the antinociceptive and respiratory depressant effect were analyzed on the basis of population logistic regression PK–PD models using non-linear mixed effects modeling software (NONMEM). The pharmacokinetics of buprenorphine and fentanyl were described by a three- and two-compartment model, respectively. A logistic regression model (linear logit model) was used to characterize the relationship between drug exposure and the binary effectiveness and safety outcome. Results For buprenorphine, the odds ratios (OR) were 28.5 (95% CI, 6.9–50.1) and 2.10 (95% CI, 0.71–3.49) for the antinociceptive and respiratory depressant effect, respectively. For fentanyl these odds ratios were 3.03 (95% CI, 1.87–4.21) and 2.54 (95% CI, 1.26–3.82), respectively. Conclusion The calculated safety index (ORantinociception/ORrespiratory depression) for fentanyl of 1.20 suggests that fentanyl has a low safety margin, implicating that fentanyl needs to be titrated with caution. For buprenorphine the safety index is 13.54 suggesting that buprenorphine is a relatively safe opioid.

Background and purpose: For development of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) models, continuous recording of drug effects is essential. We therefore explored the use of isoprenaline in the continuous measurement of the cardiovascular effects of antagonists of b-adrenoceptors (b-blockers). The aim was to validate heart rate as a pharmacodynamic endpoint under continuous isoprenaline-induced tachycardia by means of PK-PD modelling of S(� )-atenolol. Experimental approach: Groups of WKY rats received a 15 min iv infusion of 5 mg kg � 1 S(� )-atenolol, with or without iv infusion of 5 m gk g � 1 h � 1 isoprenaline. Heart rate was continuously monitored and blood samples were taken. Key results: A three-compartment model best described the pharmacokinetics of S(� )-atenolol. The PK–PD relationship was described by a sigmoid Emax model and an effect compartment was used to resolve the observed hysteresis. In the group without isoprenaline, the variability in heart rate (30 b.p.m.) approximated the maximal effect (Emax ¼ 43718 b.p.m.), leaving the parameter estimate of potency (EC50 ¼ 28727 ng ml � 1 ) unreliable. Both precise and reliable parameter estimates were obtained during isoprenaline-induced tachycardia: 517713 b.p.m. (E0), 168715 b.p.m. (Emax), 49714 ng ml � 1 (EC50), 0.04270.012 min � 1 (keo) and 0.9570.34 (n). Conclusions and implications: Reduction of heart rate during isoprenaline-induced tachycardia is a reliable pharmacodynamic endpoint for b-blockers in vivo in rats. Consequently this experimental approach will be used to investigate the relationship between drug characteristics and in vivo effects of different b-blockers. British Journal of Pharmacology (2007) 151, 356–366; doi:10.1038/sj.bjp.0707234; published online 10 April 2007

The predominant driver of bioanalysis in supporting drug development is the intended use of the data. Ligand-binding assays (LBA) are widely used for the analysis of protein biotherapeutics and target ligands (L) to support pharmacokinetics/pharmacodynamics (PK/PD) and safety assessments. For monoclonal antibody drugs (mAb), in particular, which non-covalently bind to L, multiple forms of mAb and L can exist in vivo, including free mAb, free L, and mono- and/or bivalent complexes of mAb and L. Given the complexity of the dynamic binding equilibrium occurring in the body after dosing and multiple sources of perturbation of the equilibrium during bioanalysis, it is clear that ex vivo quantification of the forms of interest (free, bound, or total mAb and L) may differ from the actual ones in vivo. LBA reagents and assay formats can be designed in principle to measure the total or free forms of mAb and L. However, confirmation of the forms being measured under the specified conditions can be technically challenging. The assay forms and issues must be clearly communicated and understood appropriately by all stakeholders as the program proceeds through the development process. This paper focuses on monoclonal antibody biotherapeutics and their circulatory L that are either secreted as soluble forms or shed from membrane receptors. It presents an investigation into the theoretical and practical considerations for total/free analyte assessment to increase awareness in the scientific community and offer bioanalytical approaches to provide appropriate PK/PD information required at specific phases of drug development.

The citalopram for Alzheimer’s disease trial evaluated citalopram for the management for agitation in Alzheimer’s disease patients. Sparse data was available from this elderly patient population. A nonlinear mixed effects population pharmacokinetic modeling approach was used to describe the pharmacokinetics of R- and S-citalopram and their primary metabolite (desmethylcitalopram). A structural model with 4 compartments (one compartment/compound) with linear oral absorption and elimination described the data adequately. Overall, the model showed that clearance of the R-enantiomer was slower than the clearance of the S-enantiomer. Without accounting for any patient-specific covariates, the population estimate of the metabolic clearance of citalopram was 8.6 (R-citalopram) and 14 L/h (S-citalopram). The population estimate of the clearance of desmethylcitalopram was 23.8 (R-Dcit) and 38.5 L/h (S-Dcit). Several patient-specific covariates were found to have a significant effect on the pharmacokinetics of R,S-citalopram and desmethylcitalopram. A significant difference in the metabolic clearance of R-citalopram between males and females (13 vs 9.05 L/h) was identified in this analysis. Both R- and S-citalopram metabolic clearance decreased with age. Additionally, consistent with literature reports S-citalopram metabolic clearance increased with increasing body weight and was significantly influenced by CYPC19 genotype, with a difference of 5.8 L/h between extensive/rapid and intermediate/poor metabolizers. R,S-desmethylcitalopram clearance increased with increasing body weight. This model may allow for the opportunity to delineate the effect of R- and S-citalopram on pharmacodynamics outcomes related to the management of agitation in Alzheimer’s disease. Electronic supplementary material The online version of this article (doi:10.1007/s10928-015-9457-6) contains supplementary material, which is available to authorized users.

This paper proposes a framework to study the drug effect at the molecular level in order to address the following question of current interest in the drug community: Given a fixed total delivered drug, which is better, frequent small or infrequent large drug dosages? A hybrid system model is proposed to link the drug's pharmacokinetic and pharmacodynamic information, and allows the drug effects for different dosages and treatment schedules to be compared. A hybrid model facilitates the modeling of continuous quantitative changes that leads to discrete transitions. An optimal dosage-frequency regimen and the necessary and sufficient conditions for the drug to be effective are obtained analytically when the drug is designed to control a target gene. Then, we extend the analysis to the case where the target gene is part of a genetic regulatory network. A crucial observation is that there exists a "sweet spot," defined as the "drug efficacy region (DER)" in this paper, for certain dosage and frequency arrangements given the total delivered drug. This paper quantifies the therapeutic benefits of dosage regimen lying within the DER. Simulations are performed using MATLAB/SIMULINK to validate the analytical results.

Three-dimensional (3-D) positron emission tomography (PET) imaging of inhaled radiolabeled aerosol in the nasal or pulmonary regions provides an in vivo measurement of drug distribution using the drug itself as the tracer. Repeated or dynamic PET scans over the time after inhalation provides us with further information about the fate of the deposited drug. These quantitative measurements are sufficient to describe the performance of a drug or device and they are obtained in a noninvasive fashion, which cannot be achieved by using any other methods. Using this PET-imaging paradigm, we conducted a sequence of drug studies to evaluate the performance of aerosol drugs and delivery devices; to compare the performance of similar drugs from different manufacturers; to assess the similarity between different formulations and propellants for the same drug; to appraise delivery devices such as spacers and nebulizers, etc. This paper reviews only the imaging and data analysis techniques developed for the above-mentioned studies that include multi-modality image registration, region definition and region-based data analysis, and nonregion-based data analysis. We separated the techniques into nasal and pulmonary studies because of the uniqueness of each group. Specific drugs or devices are not identified and no result about drug performance is given because the imaging and data analysis methodology, which is the focus of this paper, applies to all these studies regardless of the drugs or their delivery devices. The quantitative data are used as the scientific basis for evaluation although we also developed visualization techniques to enhance the results drawn from the data.

The present paper is an ode to a classic citation by Benet and Hoener (2002. Clin Pharm Ther 71(3):115-121). The now classic paper had a huge impact on drug development and the way the issue of protein binding is perceived and interpreted. Although the authors very clearly pointed out the limitations and underlying assumptions for their delineations, these are too often overlooked and the classic paper's message is misinterpreted by broadening to cases that were not intended. Some members of the scientific community concluded from the paper that protein binding is not important. This was clearly not intended by the authors, as they finished their paper with a paragraph entitled: "When is protein binding important?" Misinterpretation of the underlying assumptions in the classic work can result in major pitfalls in drug development. Therefore, we revisit the topic of protein binding with the intention of clarifying when clinically relevant changes should be considered during drug development.

Background and objective:Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug. Methods: In this paper, we present a new methodology for explicit optimization of dose regimens during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range. Results: We demonstrate that our algorithm is able to achieve the clinical objective of PK estimation while simultaneously individualizing the dose to every subject in the trial. Our algorithm computes dose regimens that, on average, have a relative efficiency of 97% with a standard deviation of less than 5%. The results show that the algorithm can be relied upon to ensure that the subjects in the trial are minimally over- and under-exposed to the test therapy. Conclusions: The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials.

In this paper we present a mathematical solution that allows the elimination rate-constant or half life of a drug to be estimated from a single blood drug measurement. This is of great utility in clinical areas involving care of criticallly ill or vulnerable patients, where providing more than one blood sample can involve significant risks. The calculations used in our approach, based solely on a single sample, do not require complex pharmacokinetic software, but instead can be simply performed at the patient's bedside using standard personal computing tools. The proposed method allows a personalised estimate of the drug's half life, which is preferable to using population averages, or using estimates based on proxy markers of lagging organ function, which are both indirect and generally inaccurate for a patient with confounding factors.

Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU.

Background and objective Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communicable diseases (NCDs), the non-transmissible and long-lasting diseases since they are the leading cause of death globally. Drugs that are used in NCDs increase the probability of DDIs as a result of long time usage. This work proposes an Integrated Action Crossing (IAC) method that is effective in predicting the NCDs DDIs based on pharmacokinetic (PK) mechanism. Methods Drug-Enzyme (CYP450) and Drug-Transporter actions including substrate, inhibitor and inducer affect the PK mechanism of other drugs. Hence, this paper proposes an enzyme and transporter protein integrated action crossing method for DDIs prediction in NCDs. The NCDs Drugs information was retrieved from the DrugBank database and the actions of enzymes and transporter proteins that were crossed and integrated. The datasets were generated for machine training. Results Three machine learning approaches: Support Vector Machine, k-Nearest Neighbors, and Neural Networks were used for the assessment of the method. Performance evaluation was performed through five-fold cross validation and the different datasets and learning methods were compared. Two layers NNs achieved the best performance at the accuracy of 83.15% (F-Measure 85.23% and AUC 0.901). Conclusions The IAC method delivers better performance compared to the conventional method for the identification of NCDs DDIs.

The objective of this paper is to apply a previously developed semi-physiologic pharmacokinetic model implemented in NONMEM to simulate bioequivalence trials (BE) of acetyl salicylic acid (ASA) in order to validate the model performance against ASA human experimental data. ASA is a drug with first-pass hepatic and intestinal metabolism following Michaelis–Menten kinetics that leads to the formation of two main metabolites in two generations (first and second generation metabolites). The first aim was to adapt the semi-physiological model for ASA in NOMMEN using ASA pharmacokinetic parameters from literature, showing its sequential metabolism. The second aim was to validate this model by comparing the results obtained in NONMEM simulations with published experimental data at a dose of 1000 mg. The validated model was used to simulate bioequivalence trials at 3 dose schemes (100, 1000 and 3000 mg) and with 6 test formulations with decreasing in vivo dissolution rate constants versus the reference formulation ( k D 8–0.25 h − 1 ). Finally, the third aim was to determine which analyte (parent drug, first generation or second generation metabolite) was more sensitive to changes in formulation performance. The validation results showed that the concentration–time curves obtained with the simulations reproduced closely the published experimental data, confirming model performance. The parent drug (ASA) was the analyte that showed to be more sensitive to the decrease in pharmaceutical quality, with the highest decrease in Cmax and AUC ratio between test and reference formulations.

The ocular pharmacokinetic (PK) of azithromycin in rabbit eye tissues was evaluated following single-dose and multiple-dose administrations of azithromycin. Because of destructive sampling, only the average ocular tissue concentration–time curve could be captured and used to estimate the PK parameters failing to obtain their standard deviations. In the present study, formulas were explained in details to estimate the major PK parameter errors in destructive sampling. The PK parameters were obtained by analyzing average ocular tissue concentration–time curve and their standard deviations were calculated using the formula proposed in this paper. A case study was included to elucidate the potential application of the formulas. F-test for equality of variances and independent-samples t-test were carried out between test formulation and reference formulation in PK parameters. Thus, a new and simple method was proposed to compare PK behavior for these studies using destructive sampling, which can be potentially applied into other cases in the future. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:2371–2379, 2013

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.

The aim of this paper is to assess the performance of a new mechanistic model for analysing 13C-octanoic acid breath test data using simulation studies. The 13C-octanoic acid breath test is widely used for indirect assessment of the rate of gastric emptying and it is yet to achieve universal acceptance due to inconsistencies when the results are compared with simultaneous and direct measurements using scintigraphy. The new semi-mechanistic model has five separate compartments; stomach, intestine, central and peripheral body and breath compartments. Stomach and breath profiles were simulated for 50 individuals under four conditions: variability on all parameters; no variability on the rate constant of gastric emptying and the rate constant of absorption; variability on the rate constant of gastric emptying and the rate constant of absorption only; and no variability on all parameters. A mono-exponential model was fitted to the stomach profile and the new semi-mechanistic model and three other widely used methods were fitted to the breath profiles. The gastric emptying half times from stomach profiles correlate better (R2 = 1, 1, 1, 1 for the four conditions) with the half emptying times from the semi-mechanistic model compared with half emptying times from the modified exponential model (R2 = 0.72, 0.53, 0.88, 1), Ghoos method (R2 = 0.72, 0.54, 0.88, 1) and Wagner–Nelson method (R2 = 0.79, 0.68, 0.89, 1) for the four simulation studies. The semi-mechanistic model is very effective for the assessment of GE using the 13C-octanoic acid breath test and could be applied in the development of drugs that influence GE.

A new approach has been developed to determine the factors that influence the balance of drug elimination from the body. This approach is based on 1. a six-compartment-model with compartments connected by different flow rates assuming kinetic processes of first order, 2. on solutions of geometric series and 3. on numerical solutions of a system of non-linear equations. In the model, different ways of drug elimination have been considered: renal, liver and fecal elimination of the drug and its metabolism in the liver. The organs have been characterized by their drug availabilities. Further, the metabolic activity of the liver, the efficiency of drug absorption and re-absorption from the gastrointestinal (GI) tract have been included. This paper identifies three events, characterized by their efficiencies--1. hepatic excretion, 2. elimination of drug from liver into the gall bladder in its non-metabolized form and 3. the re-absorption of the drug from GI tract--as necessary conditions of enterohepatic circulation of (EHC). The product of these efficiencies has been introduced as the efficiency of enterohepatic circulation. Further, the drug bioavailability as a function of the efficiency of EHC is presented. The study shows that--based on the total amounts of non-metabolized drug in urine after p.o. and i.v. administration to animals with and without cannulated bile duct and in the bile of cannulated animals--the efficiency of EHC, bioavailability of the drug, renal and hepatic availability of the drug, metabolic activity of the liver and efficiency of drug absorption and re-absorption from the gut can be determined. Additionally, it has been shown that, depending on the efficiency of enterohepatic circulation, small variabilities in drug pharmacokinetic properties can cause high variance of drug bioavailability. The publication points towards the efficiency of EHC as on a factor that plays a key role in establishing in vitro-in vivo correlation.

This paper systematically evaluates a pharmacokinetic compartmental model for identifying tumor hypoxia using dynamic positron emission tomography (PET) imaging with 18F-fluoromisonidazole (FMISO). A generic irreversible one-plasma two-tissue compartmental model was used. A dynamic PET image dataset was simulated with three tumor regions-normoxic, hypoxic and necrotic-embedded in a normal-tissue background, and with an image-based arterial input function. Each voxelized tissue's time activity curve (TAC) was simulated with typical values of kinetic parameters, as deduced from FMISO-PET data from nine head-and-neck cancer patients. The dynamic dataset was first produced without any statistical noise to ensure that correct kinetic parameters were reproducible. Next, to investigate the stability of kinetic parameter estimation in the presence of noise, 1000 noisy samples of the dynamic dataset were generated, from which 1000 noisy estimates of kinetic parameters were calculated and used to estimate the sample mean and covariance matrix. It is found that a more peaked input function gave less variation in various kinetic parameters, and the variation of kinetic parameters could also be reduced by two region-of-interest averaging techniques. To further investigate how bias in the arterial input function affected the kinetic parameter estimation, a shift error was introduced in the peak amplitude and peak location of the input TAC, and the bias of various kinetic parameters calculated. In summary, mathematical phantom studies have been used to determine the statistical accuracy and precision of model-based kinetic analysis, which helps to validate this analysis and provides guidance in planning clinical dynamic FMISO-PET studies.

The aim of this paper was to analyze the impact of anesthesia induced by urethane on pharmacokinetics (PK) parameters of fluconazole (FCZ), mostly eliminated via renal excretion and voriconazole (VRC), eliminated mainly by hepatic metabolism. FCZ and VRC PK were investigated after administration of 10 mg/kg i.v. and 5 mg/kg i.v. doses to awake and urethane anesthetized Wistar rats (n = 6 per group), respectively. After dosing, blood samples were collected up to 18 h (FCZ) or 12 h (VRC) and the plasma data analysis was performed using the software MONOLIX v. 4.2.2. The population PK parameters and microconstants were determined by fitting plasma concentration-time profiles to two-compartment model for FCZ and three-compartment model for VRC. Fitting of FCZ plasma profiles after dosing to awake and anaesthetized animals resulted in a volume of distribution (V) of 9.3 and 8.1 L/kg, and k10 values of 0.12 and 0.14 h(-1) , respectively. VRC plasma profiles in awake and anaesthetized showed V 8.7 of and 7.6 L/kg, and k10 of 0.15 and 0.16 h(-1) , respectively. No statistical differences between plasma PK parameters and microconstants for the same drug in both animal conditions studied were observed (α = 0.05).

In this paper, the two non-linear mixed-effects programs NONMEM and NLME were compared for their use in population pharmacokinetic/pharmacodynamic (PK/PD) modelling. We have described the first-order conditional estimation (FOCE) method as implemented in NONMEM and the alternating algorithm in NLME proposed by Lindstrom and Bates. The two programs were tested using clinical PK/PD data of a new gonadotropin-releasing hormone (GnRH) antagonist degarelix currently being developed for prostate cancer treatment. The pharmacokinetics of intravenous administered degarelix was analysed using a three compartment model while the pharmacodynamics was analysed using a turnover model with a pool compartment. The results indicated that the two algorithms produce consistent parameter estimates. The bias and precision of the two algorithms were further investigated using a parametric bootstrap procedure which showed that NONMEM produced more accurate results than NLME together with the nlmeODE package for this specific study.

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.

The liver is the central organ for detoxification of xenobiotics in the body. In pharmacokinetic modeling, hepatic metabolization capacity is typically quantified as hepatic clearance computed as degradation in well-stirred compartments. This is an accurate mechanistic description once a quasi-equilibrium between blood and surrounding tissue is established. However, this model structure cannot be used to simulate spatio-temporal distribution during the first instants after drug injection. In this paper, we introduce a new spatially resolved model to simulate first pass perfusion of compounds within the naive liver. The model is based on vascular structures obtained from computed tomography as well as physiologically based mass transfer descriptions obtained from pharmacokinetic modeling. The physiological architecture of hepatic tissue in our model is governed by both vascular geometry and the composition of the connecting hepatic tissue. In particular, we here consider locally distributed mass flow in liver tissue instead of considering well-stirred compartments. Experimentally, the model structure corresponds to an isolated perfused liver and provides an ideal platform to address first pass effects and questions of hepatic heterogeneity. The model was evaluated for three exemplary compounds covering key aspects of perfusion, distribution and metabolization within the liver. As pathophysiological states we considered the influence of steatosis and carbon tetrachloride-induced liver necrosis on total hepatic distribution and metabolic capacity. Notably, we found that our computational predictions are in qualitative agreement with previously published experimental data. The simulation results provide an unprecedented level of detail in compound concentration profiles during first pass perfusion, both spatio-temporally in liver tissue itself and temporally in the outflowing blood. We expect our model to be the foundation of further spatially resolved models of the liver in the future., Author Summary The liver continuously removes xenobiotic compounds from the blood in the mammalian body. Most computational models represent the liver as composed of few well-stirred subcompartments so that a spatially resolved simulation of hepatic perfusion and compound distribution right after drug administration is currently not available. To mechanistically describe the local distribution of compounds in liver tissue during first pass perfusion, we here present a computational model which combines micro-CT based vascular structures with mass transfer descriptions used in physiologically based pharmacokinetic modeling. In the resulting spatio-temporal model, hepatic mass transfer is governed by the physiological architecture and the composition of the connecting hepatic tissue, such that hepatic heterogeneity and spatial distribution can be described mechanistically. The performance of our model is shown for exemplary compounds addressing key aspects of distribution and metabolization of drugs within a mouse liver. We furthermore investigate the impact of steatosis and carbon tetrachloride-induced liver necrosis. Notably, we find that our computational predictions are in qualitative agreement with previous experimental results in animal models. In the future, our spatially resolved model will be extended by including additional physiological information and by taking into account recirculation through the body.

A diffusion limited physiologically based pharmacokinetic model for rats and mice was developed to characterize the absorption, distribution, metabolism, and elimination of naphthalene after inhalation exposure. This model includes compartments for arterial and venous blood, lung, liver, kidney, fat, and other organs. Primary sites for naphthalene metabolism to naphthalene oxide are the lung and the liver. The data used to create this model were generated from National Toxicology Program inhalation and iv studies on naphthalene and consisted of blood time-course data of the parent compound in both rats and mice. To examine the basis for possible interspecies differences in response to naphthalene, the model was extended to describe the distribution and metabolism of naphthalene oxide and the depletion and resynthesis of glutathione. After testing several alternative models, the one presented in this paper shows the best fit to the data with the fewest assumptions possible. The model indicates that tissue dosimetry of the parent compound alone does not explain why this chemical was carcinogenic to the female mouse lung but not to the rat lung. The species difference may be due to a combination of higher levels of naphthalene oxide in the mouse lung and a greater susceptibility of the mouse lung to epoxide-induced carcinogenesis. However, conclusions regarding which metabolite(s) may be responsible for the lung toxicity could not be reached.

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.

In this paper, we develop a spatio-temporal modeling approach to describe blood and drug flow, as well as drug uptake and elimination, on an approximation of the liver. Extending on previously developed computational approaches, we generate an approximation of a liver, which consists of a portal and hepatic vein vasculature structure, embedded in the surrounding liver tissue. The vasculature is generated via constrained constructive optimization, and then converted to a spatial grid of a selected grid size. Estimates for surrounding upscaled lobule tissue properties are then presented appropriate to the same grid size. Simulation of fluid flow and drug metabolism (hepatic clearance) are completed using discretized forms of the relevant convective-diffusive-reactive partial differential equations for these processes. This results in a single stage, uniformly consistent method to simulate equations for blood and drug flow, as well as drug metabolism, on a 3D structure representative of a liver.

Background: Pregnancy is associated with considerable changes in the physiological, anatomical and biochemical attributes in women. These may alter the exposure to xenobiotics between pregnant and non-pregnant women who receive similar doses, with implications for different susceptibility to environmental pollutants or therapeutic agents. Physiologically based pharmacokinetic (PBPK) models together with in vitro in vivo extrapolation (IVIVE) of absorption, distribution, metabolism and excretion (ADME) characteristics may capture the likely changes. However, such models require comprehensive information on the longitudinal variations of PBPK parameter values; a set of data that are as yet not available from a singular source. Aim: The aim of this article was to collect, integrate and analyse the available time-variant parameters that are needed for the PBPK modelling of xenobiotic kinetics in a healthy pregnant population. Methods: A structured literature search was carried out on anatomical, physiological and biochemical parameters likely to change in pregnancy and alter the kinetics of xenobiotics. Collated data were carefully assessed, integrated and analysed for trends with gestational age. Algorithms were generated to describe the changes in parameter values with gestational age. These included changes in maternal weight, the individual organ volumes and blood flows, glomerular filtration rates, and some drug-metabolising enzyme activities. Results: Articles were identified using relevant keywords, quality appraised and data were extracted by two investigators. Some parameters showed no change with gestational age and for others robust data were not available. However, for many parameters significant changes were reported during the course of pregnancy, e.g. cardiac output, protein binding and expression/activity of metabolizing enzymes. The trend for time-variant parameters was not consistent (with respect to direction and mono-tonicity). Hence, various mathematical algorithms were needed to describe individual parameter values. Conclusion: Despite the limitations identified in the availability of some values, the collected data presented in this paper provide a potentially useful singular resource for key parameters needed for PBPK modelling in pregnancy. This facilitates the risk assessment of environmental chemicals and therapeutic drug dose adjustments in the pregnant population.

In most computer-controlled pharmacokinetic model-driven drug infusion pumps, simulation of a linear compartmental pharmacokinetic model is used to compute the rate of intravenous drug infusion required to achieve setpoint central compartment (plasma) drug concentrations. For many drugs, it has been suggested that it is the drug concentration in a hypothetical "effect" compartment, rather than in the plasma, that should be manipulated to achieve maximum control over pharmacologic action. Controlling the effect compartment drug concentration is algorithmically more difficult than controlling the central compartment drug concentration because of the time delay between administration of drug into the central compartment and its subsequent appearance in the effect compartment. Presented in this paper is a model-based dosing algorithm for use in pharmacokinetic model-driven drug infusion devices that target the theoretical effect compartment drug concentration.

This paper reviews some current methods for the in vivo assessment of local cutaneous bioavailability in humans after topical drug application. After an introduction discussing the importance of local drug bioavailability assessment and the limitations of the predictive calculations, the focus turns to the relevance of experimental studies. The available techniques are then reviewed in detail, with particular emphasis on the tape stripping and microdialysis methodologies. Other less developed techniques, including the skin biopsy, suction blister, follicle removal and confocal Raman spectroscopy techniques are also described.

Therapeutic drug monitoring of factor VIII is well established in the treatment of patients with hemophilia attributable to important interindividual variability. The individual initial factor VIII dosage is usually calculated according to individual pharmacokinetic parameters obtained after a dose test administered before the surgery, using at least five-concentration data. The authors proposed a limited sampling strategy to estimate individual pharmacokinetic parameters from one- or two-concentration data in patients with hemophilia A before surgery. The mean population pharmacokinetic parameters and the interindividual variability (CV) were obtained from a group of 33 patients according to a two-compartment model using NONMEM. Eighteen additional patients were used to estimate the predictive performances of the population parameters and to evaluate the limited sampling strategies. Population parameters were clearance 2.6 mL/h per kilogram (CV 45.4%), initial volume of distribution 2.8 L (CV 21.1%). From two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion), the estimation of pharmacokinetic parameters was precise and not biased. Until now, in the hemophilic center of Lyon, the factor VIII dosage before surgery was based on the determination of the clearance, estimated from five- to nine-concentration data and on the target concentration (infusion rate = clearance x target). Ruffo et al proposed a limited sampling strategy (two-stage method) to estimate pharmacokinetic parameters from two concentration measurements drawn 3 and 9 hours after the dose. No information was given on the bias and precision of the estimation. This paper reports a one-stage method for a population pharmacokinetic study of factor VIII. The Bayesian estimation of individual pharmacokinetic parameters based on only two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion) is useful to define the best factor VIII dosage in hemophilic patients before surgery.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful imaging modality to study the pharmacokinetics in a suspected cancer/tumor tissue. The pharmacokinetic (PK) analysis of prostate cancer includes the estimation of time activity curves (TACs), and thereby, the corresponding kinetic parameters (KPs), and plays a pivotal role in diagnosis and prognosis of prostate cancer. In this paper, we endeavor to develop a blind source separation algorithm, namely convex-optimization-based KPs estimation (COKE) algorithm for PK analysis based on compartmental modeling of DCE-MRI data, for effective prostate tumor detection and its quantification. The COKE algorithm first identifies the best three representative pixels in the DCE-MRI data, corresponding to the plasma, fast-flow, and slow-flow TACs, respectively. The estimation accuracy of the flux rate constants (FRCs) of the fast-flow and slow-flow TACs directly affects the estimation accuracy of the KPs that provide the cancer and normal tissue distribution maps in the prostate region. The COKE algorithm wisely exploits the matrix structure (Toeplitz, lower triangular, and exponential decay) of the original nonconvex FRCs estimation problem, and reformulates it into two convex optimization problems that can reliably estimate the FRCs. After estimation of the FRCs, the KPs can be effectively estimated by solving a pixel-wise constrained curve-fitting (convex) problem. Simulation results demonstrate the efficacy of the proposed COKE algorithm. The COKE algorithm is also evaluated with DCE-MRI data of four different patients with prostate cancer and the obtained results are consistent with clinical observations.

Aim: To study the pharmacokinetic (PK) properties in rabbits treated with N-Ile1-Thr2-63-desulfato-r-hirudin (rH) newly developed in China by means of bioassay in order to provide preclinical experiment basis for its development as a novel anticoagulant agent. Methods: rH plasma concentration was determined using bioassay based on ex vivo antithrombin activity of rH. Normal rabbits received iv rH 4.0, 2.0 and 1.0 mg/kg or sc rH 2.0 mg/kg, respectively. The rabbits with acute severe renal failure were given iv rH 2.0 mg/kg. Results: The bioassay described in this paper met requirements for study of PK in rabbits. The major PK parameters after iv dosing were as follows: t1/2β 58.4~59 min. Vd 0.09~0.12 L/kg, CL 0.0035~0.0040 L/(kg·min); AUC were proportional to the doses, t1/2 and CL did not change significantly with the doses. The sc bioavailability reached 94%. The rabbits suffering from acute severe renal failure presented 11-fold longer t1/2β and 13-fold greater AUC than normal healthy rabbits. Conclusion: rH exhibited rapid elimination, distribution was only limited to extracellular space and good absorption from sc site. The excretion of rH by kidneys played a very important role in the elimination of rH. The PK of rH could be described by the two- and one-compartment model after iv and sc dosing, respectively, and followed linear kinetics.

Over the past three decades, the Isolated Perfused Rat Kidney (IPK) has been used to study numerous aspects of renal drug disposition. Among the available ex-vivo methods to study renal transport, the IPK allows for elucidation of the overall contributions of renal transport mechanisms on drug excretion. Therefore, IPK studies can provide a bridge between in vitro findings and in vivo disposition. This review paper begins with a detailed overview of IPK methodology (system components, surgical procedure, study design). Various applications of the IPK are then presented. These applications include characterizing renal excretion mechanisms, screening for clinically significant drug interactions, studying renal drug metabolism, and correlating renal drug disposition with drug-induced changes in kidney function. Lastly, the role of IPK studies in drug development is discussed. Demonstrated correlations between IPK data and clinical outcomes make the IPK model a potentially useful tool for drug discovery and evaluation.

The mathematical model structure selected to describe system behavior is at least partially dependent on the proposed use of the model. In this paper, a pharmacokinetic(PK)/pharmacodynamic (PD) model for use in drug delivery algorithm synthesis is developed. The antitumor agent 9-nitrocamptothecin (9NC) was administered orally to severe combined immunodeficient (SCID) mice bearing subcutaneously implanted HT29 human colon xenografts, and the effect of 9NC on those xenografts was characterized. Different PK model structures were considered in characterizing the dynamics of the drug concentration in the plasma. Akaike's Information Criterion (AIC) was used to select the model structure maximizing fit accuracy while simultaneously minimizing the number of model parameters. The resulting PK model was a set of coupled linear ordinary differential equations able to describe the nonlinear dynamic behavior (e.g. plateauing, etc.) of the drug concentrations observed in the plasma. Pharmacodynamics were modeled by characterizing tumor growth in both the untreated and drug-treated animals. The resulting PK/PD model related drug administration to effect, and this model has a structure that facilitates future control algorithm synthesis. Control algorithms in this context would directly utilize PK/PD model predictions. These predictions would be used to determine the amount and frequency of drug administration in order to reduce the tumor burden without violating clinically relevant constraints. This methodology could then be used to aid the clinician in selecting dose levels and schedules, and extension to patient tailored treatment may eventually be feasible with this approach.

Cyclosporin, the drug of first choice in transplantation surgery, is characterized by a low therapeutic index and variable absorption, so close monitoring of the drug is required to optimize the dosing. Predose blood cyclosporin levels are measured routinely for therapeutic monitoring, but this approach is not optimal because the area under the concentration-time curve (AUC) correlates better with clinical events. However, conventional methods of measuring AUC require many blood samples, which is not viable in a routine clinical setting. AUC monitoring can be simplified for use in a clinical setting by using a limited sampling strategy (LSS) that allows AUC to be estimated using a small number of blood samples collected at specific times. This article reviews the current literature on estimating cyclosporin AUC using LSS. Thirty-eight papers suggesting the use of specific time points were found. LSS has been developed for different transplant types, with different dosing regimens, and with different assays. Most authors suggested either two- or three-sample equations. Results from authors who validated their models suggest that equations defined on one transplant type may be applicable to other transplant types, to both adults and children, and to early or late after transplantation. Moreover, it seems that there is flexibility in the choice of equations available to clinicians. The number of samples to collect for accurate estimations is a matter of debate, but a wise choice can minimize the number. The choice of the optimal LSS and validation are discussed.

A sequential algorithm is developed for the non-linear dual-sorption model developed by Chandrasekaran et al. 1 , 2 which monitors pharmacokinetic profiles in percutaneous drug absorption. In the experimental study of percutaneous absorption, it is often observed that the lag-time decreases with the increase in the donor concentration when two or more donor concentrations of the same compound are used. The dual-sorption model has sometimes been employed to explain such experimental results. In this paper, it is shown that another feature observed after vehicle removal may also characterize the dual-sorption model. Soon after vehicle removal, the plots of the drug flux versus time become straight lines on a semilogarithmic scale as in the linear model, but the half-life is prolonged thereafter when the dual-sorption model prevails. The initial half-life after vehicle removal with a low donor concentration is longer than that with a higher donor concentration. These features, if observed in experiments, may be used as evidence to confirm that the dual-sorption model gives an explanation to the non-linear kinetic behaviour of a permeant.

One of the applications of pharmacokinetics and pharmacodynamics is the design of improved drug administration regimens. In its simplest form only global descriptors of the response to a bolus are used to obtain the dosing scheme leading to the desired drug plasma concentration level. For further improvement on these simple administration regimens it is necessary to have additional information on the drug kinetics in terms of either a disposition equation model or a compartmental model. Due to the high variability of individual responses to the same dosing scheme the design of drug administration regimens is often based on the average pharmacokinetic parameters of a data base of individuals. Although widely used, this approach presents some problems since the average disposition equation model and the average compartmental model are inconsistent between themselves and with the global descriptors of the average response. In this paper methods and algorithms for the estimation of alternative population pharmacokinetic models are developed and their use is illustrated using pharmacokinetic data available from the literature.