Aim: Intersex and interspecies metoprolol pharmacokinetics following intravenous and oral dose administration in rodents. Materials & methods: Oral and intravenous dose studies were conducted in rats and mice. Significant intersex differences were observed in peak plasma levels of metoprolol after oral dose in both the species. The plasma concentration (Cmax) was approximately sevenfold higher (270.356 ng/ml) in female compared with male rats (40.981 ng/ml) following oral dose administration. The Cmax observed for male (878.822 ± 75.5 ng/ml) was approximately twofold higher than in female mouse (404.016 ± 113.5 ng/ml) after oral dose administration. Conclusion: Sex and species related physioanatomical characteristics alters metoprolol pharmacokinetics. Such differences should be addressed in studies related to metoprolol interactions with concurrently administered drug candidates.
Aim:We performed a real-world data analysis to evaluate the relationship between simulated ketamine exposures and oxygen desaturation in children.Materials & methods:A previously developed population pharmacokinetic model was used to simulate exposures and evaluate target attainment, as well as the association with oxygen desaturation in children ≤17 years treated with intravenous ketamine.Results:In 2022 children, there was no significant association between simulated plasma ketamine concentrations and oxygen saturation; however, a higher cumulative area under the curve was associated with increased odds of progression to significant desaturation (<85%), though magnitude of effect was small.Conclusion:By leveraging a population pharmacokinetic model and real-world data, we confirmed there is no relationship between simulated ketamine plasma concentration and oxygen desaturation.
Conducting bioequivalence trials under traditional crossover study designs without exposing a large number of healthy volunteers to demonstrate two highly variable (%coefficient of variability greater than 30) test/reference (branded) drug products in different formulations to meet the standard 90% confidence interval criteria of relevant pharmacokinetic metrics between 0.80 and 1.25 and to maintain the consumer risk smaller than 5% has been a challenging task. Genetic polymorphisms encoding key drug-metabolizing enzymes can significantly influence absorption, distribution, metabolism and elimination of many highly variable generic drugs after administration. This article briefly reviews the case studies and examples of utilizing pharmacogenetic screening approaches in the recent literature to alleviate the resources and ethical burden of recruiting larger numbers of subjects in bioequivalence trials needed to perform pharmacokinetic studies for formulations of highly variable drug products without widening the bioequivalence acceptance limits.
Biography: Maria Veneziano is currently a Research Investigator in the DMPK unit at IRBM (Pomezia, Italy), an Italian CRO and biotech company specializing in preclinical drug discovery of small molecules, peptides and antibodies. She studied Biological Science at 'Federico II' University of Naples (Italy) and completed her PhD in Medical Biotechnologies at Merck Research Laboratories (MRL) in Rome (Italy) developing bioanalytical methods used to identify and quantify amino acids and acylcarnitines for the diagnosis and follow-up of inborn errors of metabolism. As part of the DMPK team at MRL, she was involved in PK and ADME profiling of small molecule and peptide candidates for drug-discovery programs. Presently, Maria leads a group supporting PK and PK/PD studies for small molecules and peptides. Maria Veneziano speaks to the International Journal of Pharmacokinetics about her experience working on pharmacokinetic studies. She starts by discussing the conventional bioanalytical methods used for the quantitative analysis of small molecules and peptides and she highlights the important role of LC–MS detection and sample preparation in the bioanalysis of pharmacokinetic studies. She also speaks about the role of high-resolution mass spectrometry in the bioanalysis of peptides as an important tool in a drug-discovery program to simultaneously define pharmacokinetic and metabolic profiles of the same drug candidate. She also describes cassette dosing and cassette analysis approaches as strategies to increase sample throughput, highlighting advantages and limits of each of these strategies. Finally, Maria speaks about her idea of 'simplified PK workflow' based on the miniaturization and automation of all the steps in a PK study, from in vivo administration to sample analysis.
Biography: John Kellie is currently a GlaxoSmithKline (GSK) fellow in the Bioanalysis, Immunogenicity, and Biomarkers group at GSK. John received his B.Sc. in Biochemistry from Indiana University (USA) and his PhD in Chemistry from Northwestern University (USA) studying under Dr Neil Kelleher. He was a post-doctoral scientist at Eli Lilly and Company, where he developed methods for intact protein quantitation of a Parkinson's Disease biomarker from human brain tissue. At GSK, John utilizes mass spectrometry for development of novel bioanalytical methods for biotherapeutic and protein quantitation from pre-clinical and clinical samples, with a focus on intact protein and large mass quantitation for pharmacokinetics, catabolism, biotransformation and product quality attribute support. John Kellie speaks to the International Journal of Pharmacokinetics about intact protein LC–MS for pharmacokinetic application.
Aim: The study evaluated whether a single dose pretransplant 2-h tacrolimus concentration (C2) could predict the post-transplantation trough concentration (C0). Materials & methods: C2 concentration of tacrolimus was measured after single-dose administration (0.1 mg/kg) in 20 patients, 4–7 days prior to renal transplantation. Tacrolimus C0 monitoring was done on post-transplant day 2, 5, 10, 15 and 30. Results: The mean C2 was 21.79 ± 16.83 ng/ml (4.25–69.46) and the mean C0 obtained 48 h after transplant was 10.2 ± 6.27 (1.63–22.07) ng/ml. The Spearman correlation between C2 and C0 was 0.71 (p < 0.01). Conclusion: Pretransplant C2 could explain only 50% of the total variation in the post-transplant dose requirement hence it may not be sole predictor of the post-transplant dose.
Aim: The objective of this study was to determine whether the use of a self-emulsifying drug delivery system AquaCelle®, could improve the absorption of CoQ10. Materials & methods: Fifty-seven healthy males and females completed this study with the primary outcome as change in plasma absorption of CoQ10 over a 10-h period. Results: All AquaCelle groups significantly increased CoQ10 concentrations up to three-times that of the standard CoQ10 supplement. Ubiquinone with AquaCelle achieved an equivalent absorption to ubiquinol. Conclusion: The novel delivery system AquaCelle demonstrates superior absorption for the supply of ubiquinone when compared with a standard ubiquinone extract. These results further indicate that ubiquinone with AquaCelle absorbs as effectively as the typically superior absorbing ubiquinol at the same 100 mg dose.
International Journal of PharmacokineticsVol. 4, No. 1 EditorialOpen AccessThe impact of biotin interference on laboratory testing and patient diagnosis in clinical practiceLuca GiovanellaLuca Giovanella*Author for correspondence: Tel: +41 91 811 8672; E-mail Address: Luca.Giovanella@eoc.chClinic for Nuclear Medicine & Competence Centre for Thyroid Diseases, Imaging Institute of Southern Switzerland/Ente Ospedaliero Cantonale, Via Ospedale 6, 6500 Bellinzona, SwitzerlandPublished Online:26 Jul 2019https://doi.org/10.4155/ipk-2019-0001AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit Keywords: biotinclinical practicelaboratory errorslaboratory testingimmunoassayinterferenceBiotin-streptavidin coupling has been utilized for decades by diagnostic immunoassay manufacturers. However, concerns have been raised regarding the risk of biotin interference with biotin/streptavidin-based immunoassays due to the use of high-dose biotin in cosmetic supplements, and treatments for multiple sclerosis [1–4], and some inherited metabolic diseases (e.g., biotinidase, multiple carboxylase and holocarboxylase synthetase deficiencies) [5]. A recent study characterized the biotin pharmacokinetic profile in healthy participants following high-dose biotin administration and provided guidance on washout periods to avoid false assay results from biotin interference [6].Here, we discuss the prevalence of assay interferences, particularly biotin interference, consider its impact on clinical practice, and suggest potential strategies to reduce this impact and ensure accurate patient diagnosis.Incidence & types of laboratory errorsThe total laboratory testing error rate is estimated to vary widely from 0.012 to 0.6% [7]. Most errors occur in the preanalytical phase (e.g., inappropriate sample collection/handling; up to 68% of total errors), followed by the postanalytical phase (e.g., erroneous data entry/reporting; up to 47% of total errors); analytical errors (e.g., interference from endogenous/exogenous substances) constitute 7–13% of total errors, with errors caused by interferences comprising a small proportion of these [8].Immunoassays are susceptible to many interferences. Although it is difficult to estimate the overall frequency with which interferences occur [7], hemolysis is thought to be the leading cause of unsuitable samples for analysis (40–70% of cases) [9]. Importantly, the prevalence of clinically relevant interferences can be substantially lower than the overall interference prevalence. For example, prevalence estimates for human antimouse antibodies range from <1 to 80% [10], while the proportion of clinically relevant interferences from these antibodies is considerably lower (0.03–0.05%) [11]. In our specialist clinic in Switzerland, we typically observe four to five cases of clinically relevant interferences per 1000 patients each year. In the last 2 years, we have observed six clinically relevant interference cases: two due to biotin interference in patients with multiple sclerosis who were taking high-dose biotin as part of a clinical trial, and the others due to heterophilic antibodies.Interferences should be suspected when the test result is not consistent with the wider clinical picture (e.g., a patient with no thyroid dysfunction symptoms but abnormal thyroid function test results). Identifying interferences can be more challenging in patients with an established diagnosis, such as cancer, where biomarkers are used to evaluate disease progression or guide treatment. For example, thyroglobulin and calcitonin are used as early indicators of disease relapse in patients with differentiated and medullary thyroid cancer, respectively. Similarly, in prostate and testicular cancer, early identification of disease relapse is based on prostate-specific antigen and human chorionic gonadotropin, respectively. The uncertainty caused by a potentially false test result can be particularly challenging for these patients, so it is important that clinicians are aware of potential interferences and interpret anomalous results correctly. Additional imaging and/or complementary tests should be performed and interference should be considered before proceeding with invasive procedures/therapies.Some interferences are relatively easy for laboratories to identify using techniques such as dilution experiments. Unfortunately, sophisticated imaging can be negative in some cases as the biomarkers are more sensitive; serial biomarker evaluation may be informative in these cases, as a continuous rise is rarely due to interferences. It is also useful to have a reference test to confirm the original results; for example, simultaneous procalcitonin measurement can help exclude false-positive calcitonin results due to heterophilic antibody interference [12].The impact of biotin interference on clinical practiceAll biotin/streptavidin-based immunoassays are susceptible to biotin interference. Until recently, this risk was considered relatively rare, as assay biotin interference thresholds are considerably higher than serum biotin concentrations in individuals taking no more than the recommended adequate biotin intake in adults of 30 μg daily [13]. Therefore, biotin concentrations required to pose a risk to immunoassay results are virtually impossible to achieve from a biotin-rich diet alone. However, biotin interference is of increasing concern due to the marketing of high-dose biotin supplementation (up to 10 mg in single-ingredient preparations) and trials of experimental multiple sclerosis treatments containing very high biotin doses (up to 300 mg daily) [1–4].Biotin interference can be particularly challenging with the use of thyroid hormone biomarkers as it is possible to mimic Graves' disease without having the condition [4,14,15]. The two biotin interference cases observed in our clinic resulted in erroneous thyroid hormone results [14]. In both cases, the patients were referred to our center with very high thyroid hormones and suppressed thyroid-stimulating hormone, mimicking hyperthyroidism, but with no typical symptoms of thyroid overactivity and an unremarkable physical examination. Both patients were being treated at a specialist neurologic center for multiple sclerosis and had received high-dose biotin as part of a clinical trial, but the patients and their clinicians were unaware of the possibility of biotin interference, illustrating that this risk is not well recognized. As with any interference, it is important that thyroid disorders indicated by biomarker testing are confirmed by clinical examination and imaging (e.g., ultrasound scan of the thyroid gland and, if indicated, thyroid scan/uptake test). Although this may involve the patient undergoing unnecessary investigations, it is preferable to start inappropriate treatments. Of course, it may be possible to start treatment based on a positive test result, without other confirmatory examinations, if the patient has clear symptoms of hyperthyroidism (e.g., increased heartbeat, weight loss). However, in the absence of clear signs and symptoms, clinicians should avoid starting treatment based on biomarker results alone.A key question is the distinction between the risk of analytical interference versus the risk of clinically relevant misclassification of patients. Physicians are primarily interested in detecting clinically relevant interferences. A prevalence study of 1442 patients presenting to a US emergency department showed that 7.4% had biotin ≥10 ng/ml (lowest biotin interference threshold among Roche Diagnostics immunoassays) [16]. However, only a few assays have biotin interference thresholds as low as 10 ng/ml; most have thresholds much higher than this and the proportion of samples with biotin >30 ng/ml was only 0.5%. Therefore, very few of these biotin concentrations would likely lead to clinical misclassification. The misclassification risk due to biotin interference can vary considerably between assays, and a combination of high peak biotin concentrations and a sensitive assay is needed for a clinically relevant interference to occur; for example, biotin interference thresholds for cardiac troponin assays range from 2.5 to 10,000 ng/ml [17,18]. Although most Roche Diagnostics assays are minimally affected at biotin concentrations of 15.6 and 31.3 ng/ml (simulating 5 and 10 mg biotin intake, respectively), some assays exhibit greater sensitivity, including troponin T, thyroid-stimulating hormone and antithyroid antibodies [18]. Regarding troponin T, a recent study showed that elevated biotin (>20 ng/ml) is rare in US patients with suspected acute coronary syndrome and the likelihood of false-negative acute myocardial infarction prediction due to biotin interference with the Elecsys® Troponin T Gen 5 assay was very low (0.026%), having a minimal effect on the assay's negative predictive value (93.4%) at 3 h [19]. It is important to note that the benefit of diagnostic immunoassay testing largely outweighs the risk of potential misclassification of patients due to interferences, including biotin interference, and laboratory error rates are considerably lower than those seen in overall clinical healthcare [20].Strategies to reduce the risk of biotin interferenceLaboratory staff and clinicians should be aware of the risk for biotin interference, paying particular attention to results from certain patient groups (e.g., patients with multiple sclerosis) or results that do not fit the overall clinical picture. A focus on increased reporting and education on biotin interference, as recommended by the US FDA [2], will hopefully improve awareness and reduce the likelihood of errors occurring. There is also increasing emphasis on education around biotin interference in the multiple sclerosis patient population, with participants in trials of high-dose biotin receiving a card detailing their involvement and the risk for assay interference. This approach has been extended to wider patient groups not directly involved in these trials. However, the need to raise awareness should be balanced against the fact that the risk of clinically relevant misclassification due to biotin interference is low and the overall laboratory error rate has fallen in the past few decades [8]. If biotin interference is suspected, clinicians should enquire about recent biotin consumption. It may be helpful to provide guidance to patients and ask them to avoid taking certain medications or supplements before blood tests. Lastly, many biomarker and/or disease algorithms involve serial testing, which may help to reduce the impact of interference with a single result.Future perspectiveThe use of high-dose biotin in cosmetic supplements and multiple sclerosis treatments has raised concerns regarding the risk of assay interference. However, recent research suggests the risk of clinically relevant misclassification due to biotin interference may be low. Crucially, this issue reiterates that laboratory results should not be considered in isolation. Instead, patient symptoms, clinical examination findings, laboratory results and imaging should be evaluated in concert to gain a holistic clinical picture and avoid misdiagnosis.Open accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/Financial & competing interests disclosureThe author has served as a member of Scientific Advisory Boards for Eisai, Roche Diagnostics, and Sanofi Genzyme, has received speaker fees from BRAHMS, Roche Diagnostics and Sanofi Genzyme, and received research grants from Roche Diagnostics. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Writing assistance was utilized in the production of this manuscript. Third-party medical writing assistance, under the direction of the author, was provided by Thomas Burton, BMBS (Gardiner-Caldwell Communications, Macclesfield, UK), and was funded by Roche Diagnostics International Ltd, Rotkreuz, Switzerland.References1. 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Chem. 48(11), 1871–1872 (2002).Crossref, Google ScholarFiguresReferencesRelatedDetailsCited BySelective quantification of the 22-kDa isoform of human growth hormone 1 in serum and plasma by immunocapture and LC–MS/MS15 July 2022 | Analytical and Bioanalytical Chemistry, Vol. 414, No. 20Serum Biotin Levels in General Korean PopulationLaboratory Medicine Online, Vol. 12, No. 3 Vol. 4, No. 1 Follow us on social media for the latest updates Metrics History Received 31 January 2019 Accepted 1 May 2019 Published online 26 July 2019 Published in print December 2019 Information© 2019 Newlands PressKeywordsbiotinclinical practicelaboratory errorslaboratory testingimmunoassayinterferenceOpen accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/Financial & competing interests disclosureThe author has served as a member of Scientific Advisory Boards for Eisai, Roche Diagnostics, and Sanofi Genzyme, has received speaker fees from BRAHMS, Roche Diagnostics and Sanofi Genzyme, and received research grants from Roche Diagnostics. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Writing assistance was utilized in the production of this manuscript. Third-party medical writing assistance, under the direction of the author, was provided by Thomas Burton, BMBS (Gardiner-Caldwell Communications, Macclesfield, UK), and was funded by Roche Diagnostics International Ltd, Rotkreuz, Switzerland.PDF download
Biography: Hua Li is currently a Bioanalytical Research Scientist in the NBE Pharmacokinetics Group in the Biotherapeutics Discovery Department at Boehringer Ingelheim (CT, USA). She earned her MA in molecular, cellular and developmental biology from the University of Kansas (KS, USA). While pursuing her master's degree, she worked as a research assistant on Caenorhabditis elegans genetics. After graduation, she started my career as a research associate and laboratory manager at the Stem Cell Center of Yale University (CT, USA). Her main roles included investigating the essential proteins that play a critical role in the division and differentiation of mouse testes stem cells, as well as administrative responsibilities for a laboratory of around 12 people including graduate students, post-docs and laboratory technician. Since 2008, her career has been focusing on the quantitation of pharmacokinetics and pharmacodynamics study of protein therapeutics. Over the past 12 years, she has witnessed a tremendous expansion of new technologies, devices and theories in the pharmacokinetics/pharmacodynamics field, all of which have helped us better serve the patient community all over the world. Hua Li speaks to the International Journal of Pharmacokinetics about the use of volumetric absorptive microsampling in pharmacokinetic studies and their methodology on the application of Mitra® microsampling for pharmacokinetic bioanalysis of monoclonal antibodies in rats.
Multiparametric flow cytometry is a powerful cellular analysis tool used in various stages of drug development. In adoptive cell therapies, the flow cytometry methods are used for the evaluation of advanced cellular products during manufacturing and to monitor cellular kinetics after infusion. In this report, we discussed the bioanalytical method development challenges to monitor cellular kinetics in CAR-T cell therapies. These method development challenges include procuring positive control samples for the development of the method, flow cytometry panel design, LLOQ, prestain sample stability, staining reagents and data analysis.
International Journal of PharmacokineticsVol. 3, No. 4 CommentaryBayesian therapeutic drug monitoring software: past, present and futurePhilip Drennan, Matthew Doogue, Sebastiaan J van Hal & Paul ChinPhilip Drennan*Author for correspondence: E-mail Address: pgdrennan@gmail.com Department of Microbiology & Infectious Diseases, Royal Prince Alfred Hospital, Sydney, Australia, Matthew Doogue Department of Clinical Pharmacology, Christchurch Hospital, Christchurch, New Zealand Christchurch School of Medicine, University of Otago, Christchurch, New Zealand, Sebastiaan J van Hal Department of Microbiology & Infectious Diseases, Royal Prince Alfred Hospital, Sydney, Australia & Paul Chin Department of Clinical Pharmacology, Christchurch Hospital, Christchurch, New Zealand Christchurch School of Medicine, University of Otago, Christchurch, New ZealandPublished Online:12 Dec 2018https://doi.org/10.4155/ipk-2018-0005AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: Bayesiandrug monitoringpharmacokineticsprecision medicinesoftwareReferences1 Holford NHG, Buclin T. 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Comparison of gentamicin dose estimates derived from manual calculations, the Australian 'Therapeutic Guidelines: antibiotic' nomogram and the SeBA-GEN and DoseCalc software programs. Br. J. Clin. Pharmacol. 58(5), 521–527 (2004).Crossref, Google ScholarFiguresReferencesRelatedDetailsCited ByPersonalized Dosing of Infliximab in Patients With Inflammatory Bowel Disease Using a Bayesian Approach: A Next Step in Therapeutic Drug Monitoring29 December 2022 | The Journal of Clinical Pharmacology, Vol. 162Personalized tobramycin dosing in children with cystic fibrosis: a comparative clinical evaluation of log-linear and Bayesian methods29 September 2022 | Journal of Antimicrobial Chemotherapy, Vol. 77, No. 12Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically IllCritical Care Research and Practice, Vol. 2022Assessment of an institutional guideline for vancomycin dosing and identification of predictive factors associated with dose and drug trough levelsJournal of Infection, Vol. 85, No. 4Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring9 May 2022 | Pharmaceutics, Vol. 14, No. 5Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management5 May 2022 | Pharmaceutics, Vol. 14, No. 5Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring21 January 2022 | Pharmaceuticals, Vol. 15, No. 2Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data18 February 2022 | Pharmaceutics, Vol. 14, No. 2Therapeutic Drug Monitoring of Antimicrobial Drugs in Neonates: An Opinion Article9 August 2021 | Therapeutic Drug Monitoring, Vol. 44, No. 1Appropriate Use of Glycopeptide Antibiotics and Therapeutic Drug Monitoring for Invasive InfectionsThe Korean Journal of Medicine, Vol. 96, No. 6PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment22 November 2021 | Processes, Vol. 9, No. 11Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing IntervalTherapeutic Drug Monitoring, Vol. 43, No. 4Theoretical Performance of Nonlinear Mixed-Effect Models Incorporating Saliva as an Alternative Sampling Matrix for Therapeutic Drug Monitoring in Pediatrics: A Simulation StudyTherapeutic Drug Monitoring, Vol. 43, No. 4Predictive Performance of Bayesian Vancomycin Monitoring in the Critically IllCritical Care Medicine, Vol. Publish Ahead of PrintDevelopment of a Population Pharmacokinetic Model for Cyclosporine from Therapeutic Drug Monitoring DataBioMed Research International, Vol. 2021Therapeutic drug monitoring of oral targeted antineoplastic drugs9 November 2020 | European Journal of Clinical Pharmacology, Vol. 77, No. 4Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients15 May 2020 | Frontiers in Pharmacology, Vol. 11A Critique of Pharmacokinetic Calculators for Drug Dosing Individualization26 November 2019 | European Journal of Drug Metabolism and Pharmacokinetics, Vol. 45, No. 2The dosing and monitoring of vancomycin: what is the best way forward?International Journal of Antimicrobial Agents, Vol. 53, No. 4 Vol. 3, No. 4 Follow us on social media for the latest updates Metrics Downloaded 244 times History Received 26 October 2018 Accepted 19 November 2018 Published online 12 December 2018 Published in print December 2018 Information© 2018 Newlands PressKeywordsBayesiandrug monitoringpharmacokineticsprecision medicinesoftwareFinancial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.No writing assistance was utilized in the production of this manuscript.PDF download
International Journal of PharmacokineticsVol. 3, No. 1 EditorialFree AccessAntibiotic PK/PD research in critically ill neonates and children: how do we proceed?Anne Smits & Pieter A J G De CockAnne Smits*Author for correspondence: Tel.: +32 1634 3565; E-mail Address: anne.smits@uzleuven.be Neonatal Intensive Care Unit, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium & Pieter A J G De Cock Department of Pharmacy, Ghent University Hospital, Ghent, Belgium Heymans Institute of Pharmacology, Ghent University, Ghent, Belgium Department of Paediatric Intensive Care, Ghent University Hospital, Ghent, BelgiumPublished Online:26 Jan 2018https://doi.org/10.4155/ipk-2017-0019AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit Keywords: antibioticscritical illnesspharmacokineticsAntibiotics are one of the most frequently used drugs in critically ill neonates and children. Nevertheless, they are often used off-label in these patients, with the highest incidence in the smallest patients. Laine et al. recently reported that an increase in birth weight significantly decreases the probability of off-label antibiotic use in a tertiary neonatal intensive care unit [1]. In addition, antibiotic prescribing practices are highly variable between intensive care units, due to the lack of evidence-based data on optimal antimicrobial therapy in neonates and children [2,3].Efficacy of antimicrobial treatment depends on the achievement of therapeutic concentrations at the infection site. In critically ill neonates and children, this achievement of optimal exposure is challenging due to an altered drug disposition. From birth to adulthood, the body composition changes dramatically, mainly reflecting a decrease in body water content and an increase in body fat content. Furthermore, renal and hepatic maturation need to be taken into account. Nephrogenesis is completed by the end of the 34th week of gestation. Postnatally, changes in (intra)renal blood flow contribute to the increase in glomerular filtration rate and clearance of hydrophilic drugs. Concerning hepatic maturation, Phase I and II drug metabolizing enzymes display isoenzyme-specific developmental changes. Overall, all these physiological alterations are most prominent in the first year of life and result in a large between and within patient variability in antimicrobial pharmacokinetics (PK, concentration vs time) [4]. Second, besides growth and maturation, also disease (e.g., sepsis), co-medication and treatment approaches (e.g., whole body cooling, extra-corporeal membrane oxygenation) may impact the PK of antibiotics and target attainment [3].After illustrating some population-specific (e.g., protein-binding capacity) and disease-specific aspects (e.g., augmented renal clearance) influencing antimicrobial PK in neonatal and/or pediatric intensive care unit patients, we provide future research directions to improve antimicrobial therapy in this specific population.From population- to disease-related covariates influencing antimicrobial disposition: some examplesDrug protein binding is of specific interest when evaluating antimicrobial exposure, since only the unbound drug can exert a therapeutic effect [5]. In neonates, the median unbound fraction of cefazolin and flucloxacillin in plasma are reported to be higher compared with adult values [6–8]. Recently, the unbound fraction of vancomycin in critically ill children was also found to be higher compared with adults without criticall illness [9]. The decreased drug–plasma protein binding in neonates and young infants can in part be explained by overall lower albumin concentrations compared with adults; for compounds like cefazolin also an altered binding affinity is considered [10]. In critical illness, protein binding might be additionally impaired due to disease-related hypoalbuminemia. For all the above reasons, integration of protein binding in PK modeling analyses should be promoted.Furthermore, fluid changes (e.g., hypovolemia, fluid boluses, edema), inflammation, the use of inotropes and augmented renal clearance (ARC) are reported to cause antimicrobial pharmacokinetic alterations and changes in target attainment in critically ill adults [5]. Augmented renal clearance, defined as enhanced renal elimination of a circulating solute, is being described with increasing regularity in critically ill adults and may result in subtherapeutic antibiotic exposure. The underlying pathophysiological mechanism of ARC is not yet completely unveiled. However, the biological response to inflammation and infection, combined with the administration of fluid boluses and vasoactive compounds increase cardiac output and renal blood flow, resulting in an increased glomerular filtration rate, are considered to be involved [11]. In critically ill children, ARC was recently described in 50 patients receiving amoxicillin–clavulanic acid [12]. Besides maturational changes, also plasma cystatin C (as renal biomarker) and concomitant treatment with vasopressors (as measure of disease severity, irrespective of renal function) were identified as covariates of amoxicillin clearance. A dose of 25 mg/kg every 4 h was needed to achieve therapeutic targets for amoxicillin and clavulanic acid, which is a higher total daily dose compared with available references since ARC was not yet taken into account [12]. To the best of our knowledge, in neonates, reports on ARC are currently not available.Future perspectives & remaining challengesThe introduction of PK modeling and simulation methods, alternative and low-volume PK sampling approaches improved the feasibility of clinical pharmacology research in vulnerable populations. Population PK analyses using nonlinear mixed effects modeling has become the standard method to explore drug disposition in neonates and young children. Recently, 'model-based bridging approaches' have been reported in which PK in younger patients are extrapolated from PK models using data from older children, adults, animals and/or in vitro data. Zhao et al. used this 'bridging approach' to predict neonatal fluconazole PK and dosing [13]. This proof-of-concept study illustrates that combining data can be of added value in predicting antimicrobial exposure in neonates and young children, while limiting the patient burden.As alternative PK sampling approaches, dried blood spot and scavenged sampling become more popular. Scavenged sampling, defined as the use of remnant blood from laboratory tests of standard care that would otherwise be discarded, has been applied in neonates for population PK analyses of metronidazole, piperacillin and fluconazole [14]. Furthermore, alternative matrices like saliva may be considered to determine antibiotic concentrations. Although, this approach has been studied for therapeutic drug monitoring of specific antibiotics in (healthy) adults, and to a limited extent in children, data are often insufficient to evaluate the suitability. Cross-validation between the standard (plasma) and alternative matrix are needed [15].Although progression has been made in the field of developmental PK, there are remaining challenges. First, while dosing regimens usually account for changes in growth and maturation, disease-related covariates related to critical illness are often not accounted for and warrant further study. In particular, knowledge on tissue penetration in critically ill neonates and children is required. Therefore, antibiotic concentration measurement at the effect site is of interest (e.g., cerebrospinal fluid by lumbar puncture, bronchial epithelial lining fluid by bronchoalveolar lavage). To measure interstitial tissue concentrations, microdialysis becomes more and more popular. Using this technique, a microdialysis catheter is inserted in the interstitial space of the tissue of interest, after which a physiological solution is continuously infused through the catheter at a fixed rate, which is then collected outside the body. Through a semipermeable membrane at the top of the probe, drugs can diffuse from the interstitial to the perfused fluid, in which the unbound drug concentration can be quantified at specific time points [16]. Although technically challenging and invasive, the method has been used to quantify cefazolin in skeletal muscle in children [17,18]. Currently, it is not adapted to be applied in younger children.Second, dosing regimens derived from population PK modeling and simulation analyses need prospective validation. Wilbaux et al. recently systematically reviewed available neonatal PK models for antibiotics. Although for vancomycin 17 models were published, a prospective validation of these models was absent [19]. This, in part, explains why there is still no consensus on optimal vancomycin dosing recommendations.Third, although PK models for many antibiotics become currently available, the exposure–response (pharmacodynamics [PD]) relationship of these compounds is less well understood in neonates and young children. Harmonization of currently used antimicrobial PD target indices and target values is needed to properly evaluate target attainment of current and alternative dosing regimens. In addition, building more sophisticated mechanistic PK/PD models should be the next step in the antibiotic dose optimization process. In these models, the PK is linked to the microbiological response over time from in vitro or animal infection experiments [20]. Reports on models describing clinical PK/PD relationships in critically ill neonates and young infants are currently limited [19]. Ultimately, the impact of new dosing regimens on morbidity (i.e., short and long-term effects) and mortality should be investigated.Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.References1 Laine N, Kaukonen AM, Hoppu K, Airaksinen M, Saxen H. Off-label use of antimicrobials in neonates in a tertiary children's hospital. Eur. J. Clin. Pharmacol. 73, 609–614 (2017).Crossref, CAS, Google Scholar2 Metsvaht T, Nellis G, Varendi H et al. High variability in the dosing of commonly used antibiotics revealed by a Europe-wide point prevalence study: implications for research and dissemination. BMC Pediatr. 15, 41 (2015).Crossref, Google Scholar3 Kontou A, Sarafidis K, Roilides E. Antimicrobial dosing in neonates. Exp. Rev. Clin. Pharmacol. 10(3), 239–242 (2017).CAS, Google Scholar4 Smits A, Annaert P, Allegaert K. Drug disposition and clinical practice in neonates: cross talk between developmental physiology and pharmacology. Int. J. Pharmaceutics 452, 8–13 (2013).Crossref, CAS, Google Scholar5 Uldemolins M, Roberts JA, Rello J, Paterson DL, Lipman J. The effects of hypoalbuminaemia on optimizing antibacterial dosing in critically ill patients. Clin. Pharmacokinet. 50(2), 99–110 (2011).Crossref, Google Scholar6 Smits A, Kulo A, Verbesselt R et al. Cefazolin plasma protein binding and its covariates in neonates. Eur. J. Clin. Microbiol. Infect. 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Rev. 65(3), 1053–1090 (2013).Crossref, Google ScholarFiguresReferencesRelatedDetailsCited ByEuropean Journal of Drug Metabolism and Pharmacokinetics, Vol. 47, No. 1Clinical audit of gentamicin use by Bayesian pharmacokinetic approach in critically ill childrenJournal of Infection and Chemotherapy, Vol. 26, No. 6Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data21 January 2019 | Journal of Antimicrobial Chemotherapy, Vol. 74, No. 4Physiologically based pharmacokinetic (PBPK) modeling and simulation in neonatal drug development: how clinicians can contribute17 December 2018 | Expert Opinion on Drug Metabolism & Toxicology, Vol. 15, No. 1Key Components for Antibiotic Dose Optimization of Sepsis in Neonates and Infants29 October 2018 | Frontiers in Pediatrics, Vol. 6 Vol. 3, No. 1 Follow us on social media for the latest updates Metrics History Received 29 October 2017 Accepted 1 December 2017 Published online 26 January 2018 Published in print February 2018 Information© 2018 Future Science LtdKeywordsantibioticscritical illnesspharmacokineticsFinancial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download
Aim: Mycophenolate mofetil is widely used in organ transplantation to prevent acute rejection. Following oral administration, it is rapidly converted to mycophenolic acid (MPA). Effectiveness and the incidence of rejection is influenced by area under the concentration–time curve (AUC). Effect of time after transplantations on pharmacokinetic (PK) parameters is considered. Method: PK parameters of MPA was characterized in two different groups of Iranian kidney transplant patients. MPA plasma concentrations following oral administration of 1 g mycophenolate mofetil in a multiple dosage regimen were measured in 21 (group 1) and 31 (group 2) individuals. Group 1 for more than 3 months and group 2 had been transplanted for 9–10 days. Blood samples were taken before and at different time points following dose administration (0–12 h). Peak plasma concentration (Cmax), time to reach Cmax (Tmax), and AUC were calculated for each patient. Results: The first plasma peak was reached in the range of 0.6–2 and 0.3–2 h ...
Aim: To develop a limited regression model of evogliptin for prediction of AUC data for internal (within study) and external studies. Method: Regression analyses (linear/power/polynomial) were performed in multitiered approach using paired peak plasma concentration (Cmax) versus AUC data of evogliptin. For all models, correlation co-efficient (r) and root mean square error (%RMSE) were used in predicting internal/external data. Bland–Altman analysis was performed for all the models. Results: Limited power model showed highest predictability (r = >0.98 and ≤15.5% RMSE), followed by linear model (r = >0.98 and ≤20.5% RMSE) and polynomial (r = >0.96 and ≤27.0% RMSE). Bland–Altman plots confirmed acceptable bias and precision. Conclusion: Limited regression models were successfully developed for prediction of AUC of evogliptin.
Aim: Evaluate steady-state pharmacokinetics and potential interactions between select statins and evacetrapib. Patients & methods: This open-label, two-part study included 62 healthy native Chinese subjects. Part 1 evaluated pharmacokinetics and pharmacodynamics of evacetrapib after 1 or 14 once-daily doses. Part 2 evaluated pharmacokinetics and pharmacodynamics of simvastatin, atorvastatin and evacetrapib administered alone, and of statin + evacetrapib coadministered. Results: Evacetrapib estimated accumulation ratio following once-daily dosing was 2.7. Simvastatin or atorvastatin coadministration reduced evacetrapib AUC0–24 by 12% (90% CI: -0.1 to -22%) or 10% (90% CI: -23–5%), respectively. Evacetrapib coadministration increased simvastatin or atorvastatin AUC0–24 by 123% (90% CI: 91–159%) or 16% (90% CI: 6–27%), respectively. Evacetrapib administered alone and with a statin increased high-density lipoprotein cholesterol, decreased low-density lipoprotein cholesterol, and was well tolerated. Conclusion: The significant increase in simvastatin exposure after evacetrapib coadministration was unexpected compared with previous evacetrapib and simvastatin interaction studies.
International Journal of PharmacokineticsVol. 3, No. 3 CommentaryExtrapolation of pharmacokinetic interaction data of proton pump inhibitors obtained in healthy subjects for oral targeted therapies in cancer patientsNuggehally R SrinivasNuggehally R Srinivas*Author for correspondence: E-mail Address: srini.suramus@yahoo.com Drug Metabolism & Pharmacokinetics, Zydus Research Centre, Sarkhej-Bavla NH 8A, Moraiya, Ahmedabad 382210, Gujarat, India Current affiliation: Innovation and Technology, Jubilant Life Sciences, D-12, Sector-59, Noida 201301 UP, IndiaPublished Online:30 Nov 2018https://doi.org/10.4155/ipk-2018-0003AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleReferences1 Wang X, Zhang ZY, Arora S et al. Effects of rolapitant administered intravenously on the pharmacokinetics of a modified Cooperstown cocktail (midazolam, omeprazole, warfarin, caffeine, and dextromethorphan) in healthy subjects. J. Clin. Pharmacol. (2018). doi: 10.1002/jcph.1114 (Epub ahead of print).Crossref, Google Scholar2 Srinivas NR, Mullangi R. Bioanalysis in oncology drug discovery. Biomark. Med. 9(9), 877–886 (2015).Crossref, CAS, Google Scholar3 Wisinski KB, Cantu CA, Eickhoff J et al. Potential cytochrome P-450 drug–drug interactions in adults with metastatic solid tumors and effect on eligibility for Phase I clinical trials. Am. J. Health Syst. Pharm. 72(11), 958–965 (2015).Crossref, CAS, Google Scholar4 Thomas-Schoemann A, Blanchet B, Bardin C et al. Drug interactions with solid tumour-targeted therapies. Crit. Rev. Oncol. Hematol. 89(1), 179–196 (2014).Crossref, Google Scholar5 Srinivas NR. 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Prevalence of acid-reducing agents (ARA) in cancer populations and ARA drug–drug interaction potential for molecular targeted agents in clinical development. Mol. Pharm. 10, 4055–4062 (2013).Crossref, CAS, Google Scholar10 Budha NR, Frymoyer A, Smelick GS et al. Drug absorption interactions between oral targeted anticancer agents and PPIs: is pH-dependent solubility the Achilles heel of targeted therapy? Clin. Pharmacol. Ther. 92, 203–213 (2012).Crossref, CAS, Google Scholar11 Lau YY, Gu W, Lin T et al. Assessment of drug-drug interaction potential between ceritinib and proton pump inhibitors in healthy subjects and in patients with ALK-positive non-small cell lung cancer. Cancer Chemother. Pharmacol. 79(6), 1119–1128 (2017).Crossref, CAS, Google Scholar12 Xin Y, Shao L, Maltzman J et al. The relative bioavailability, food effect, and drug interaction with omeprazole of momelotinib tablet formulation in healthy subjects. Clin. Pharmacol. Drug Dev. 7(3), 277–286 (2018).Crossref, CAS, Google Scholar13 Ohgami M, Kaburagi T, Kurosawa A et al. Effects of proton pump inhibitor co-administration on the plasma concentration of erlotinib in patients with non-small cell lung cancer. 40(6), 699–704 (2018).Crossref, Google Scholar14 Kletzl H, Giraudon M, Ducray PS et al. Effect of gastric pH on erlotinib pharmacokinetics in healthy individuals: omeprazole and ranitidine. Anticancer Drugs 26(5), 565–572 (2015).Crossref, CAS, Google Scholar15 Yin OQ, Giles FJ, Baccarani M et al. Concurrent use of proton pump inhibitors or H2 blockers did not adversely affect nilotinib efficacy in patients with chronic myeloid leukemia. Cancer Chemother. Pharmacol. 70(2), 345–350 (2012).Crossref, CAS, Google Scholar16 Yin OQ, Gallagher N, Fischer D et al. Effect of the proton pump inhibitor esomeprazole on the oral absorption and pharmacokinetics of nilotinib. J. Clin. 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Dispos. 42(12), 2041–2048 (2014).Crossref, Google ScholarFiguresReferencesRelatedDetails Vol. 3, No. 3 Follow us on social media for the latest updates Metrics Downloaded 91 times History Received 3 October 2018 Accepted 26 October 2018 Published online 30 November 2018 Published in print September 2018 Information© 2018 Newlands PressFinancial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download
Aim: Influence of cisplatin-induced acute kidney injury (AKI) on the pharmacokinetics of ciprofloxacin/fasiglifam was investigated. Methods: Normal and AKI rats received oral/intravenous doses of 5 and 1 mg/kg for ciprofloxacin and 3 and 1 mg/kg for fasiglifam, respectively. Plasma samples were subjected to LC–MS/MS analysis, followed by pharmacokinetic analysis. Liver S-9 fractions were used for stability assessment. Results: Regardless of intravenous/oral dosing, ciprofloxacin concentrations increased in AKI relative to normal rats. Oral bioavailability of ciprofloxacin was 79/24% in AKI/normal rats. With fasiglifam, pharmacokinetics was not altered in AKI versus normal rats after intravenous dosing. However, oral dosing showed 25–36% lower exposure in AKI rats. Liver S-9 fractions obtained from AKI/normal rats showed metabolic stability. Conclusion: AKI rats showed enhanced/decreased bioavailability of ciprofloxacin/fasiglifam.
Aim: New approaches are required to improve compliance in older patients with problems in swallowing traditional formulations. A novel memantine orodispersible tablet (ODT) was formulated, and its bioavailability and taste acceptability were evaluated. Materials & methods: In vitro characterization of ODT comprised dispersion in simulated saliva prior to dissolution assay in a limited volume of biorelevant media. A single oral dose of 20-mg memantine ODT exhibits similar bioavailability to that of an immediate release 20-mg tablet in a healthy population under fasting conditions. Results: 90% confidence interval for Cmax was of 96.78–106.52% and 98.27–104.78% for AUC0–72. An applied palatability survey showed exceptional acceptance of the formulation. Conclusion: Memantine microspheres prepared by a solid-dispersion technique results in ODT with adequate biopharmaceutical performance.
International Journal of PharmacokineticsVol. 3, No. 3 EditorialPharmacokinetic–pharmacodynamic modeling and it is relevance in the drug discoverySunil Kumar Dubey, Kommera Sai Pradyuth, Sreehari Krishna Kulkarni & Gautam Singhvi, Sunil Kumar Dubey*Author for correspondence: E-mail Address: skdubey@pilani.bits-pilani.ac.in Industrial Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science, Pilani 333031, Rajasthan, IndiaSearch for more papers by this author, Kommera Sai Pradyuth Industrial Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science, Pilani 333031, Rajasthan, IndiaSearch for more papers by this author, Sreehari Krishna Kulkarni Industrial Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science, Pilani 333031, Rajasthan, IndiaSearch for more papers by this author & Gautam SinghviSearch for more papers by this author, Published Online:12 Dec 2018https://doi.org/10.4155/ipk-2018-0004AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View article"Pharmacokinetic–pharmacodynamic modeling and it is relevance in the drug discovery." , 3(3), pp. 91–92Keywords: clinical trialdiscoverydrugmodelingpharmacodynamicspharmacokineticsReferences1 Derendorf H, Meibohm B. Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives. Pharm. Res. 16(2), 176–185 (1999).Crossref, CAS, Google Scholar2 Krishna KV, Saha RN, Singhvi G, Dubey SK. Pre-clinical pharmacokinetic–pharmacodynamic modelling and biodistribution studies of donepezil hydrochloride by a validated HPLC method. RSC Adv. 8(44), 24740–24749 (2018).Crossref, CAS, Google Scholar3 Lavé T, Caruso A, Parrott N, Walz A. Translational PK/PD modeling to increase probability of success in drug discovery and early development. Drug Discov. Today Technol. 21, 27–34 (2016).Crossref, Google Scholar4 Derendorf H, Lesko LJ, Chaikin P, Colburn WA, Lee P, Miller R et al. Pharmacokinetic/pharmacodynamic modeling in drug research and development. J. Clin. Pharmacol. 40(12), 1399–1418 (2000).CAS, Google Scholar5 Velkov T, Bergen PJ, Lora-Tamayo J, Landersdorfer CB, Li J. PK/PD models in antibacterial development. Curr. Opin. Microbiol. 16(5), 573–579 (2013).Crossref, CAS, Google Scholar6 Clewe O, Aulin L, Hu Y, Coates AR, Simonsson US. A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro. J. Antimicrob. Chemother. 71(4), 964–974 (2015).Crossref, Google Scholar7 Rathi C, Lee RE, Meibohm B. Translational PK/PD of anti-infective therapeutics. Drug Discov. Today Technol. 21, 41–49 (2016).Crossref, Google ScholarFiguresReferencesRelatedDetails Vol. 3, No. 3 Follow us on social media for the latest updates Metrics Downloaded 47 times History Received 20 October 2018 Accepted 21 November 2018 Published online 12 December 2018 Published in print September 2018 Information© 2018 Newlands PressKeywordsclinical trialdiscoverydrugmodelingpharmacodynamicspharmacokineticsFinancial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download
Biography Sourav Bhattacharjee is a physician (MBBS) and graduated from Medical College and Hospital Kolkata (India). After postgraduate residential training in orthopedic surgery, he finished MSc in Biomolecular Sciences/Cell Biology from the Vrije Universiteit Amsterdam (2006–2008). His MSc thesis work was done in the Napier University (Edinburgh, UK). He began his PhD in the Wageningen University (The Netherlands) in 2008, which he successfully defended in 2012. Following that he worked for almost a year as postdoc in the University of Twente (The Netherlands). From March 2014, he joined UCD (Ireland) as postdoc trying to develop nanoparticulate platforms for oral insulin delivery as part of EU FP7 funded TRANS-INT consortium. From February 2016, he was appointed as Assistant Professor in the UCD where he is engaged now in developing a broad range of advanced nanobiotechnology-based and microscopic tools for effective diagnostic and therapeutic purposes in various diseases.
