Biosensor 2001 - a Retrospect and Foresight

This editorial gives a short retrospect of the BioSensor Symposium 2001 from the point of view of the organizing committee. The symposium was held from 1st to 3rd of April 2001 at the University of Tübingen. The conference is the only German-speaking forum of its kind and provides a unique setting for new research, trends, and perspectives to be shared with peers from both industrial and educational institutions. The new approach of publishing the conference proceedings electronically by the Universitätsbibliothek Tübingen is explained in detail and the possibilities resulting are discussed

Multianalyte Quantifications by Means of Integration of Artificial Neural Networks, Genetic Algorithms and Chemometrics for Time-Resolved Analytical Data

During the last decade the application of sensors for the detection and determination of various substances has gained an increasing popularity not only in the field of analytical chemistry but also in our daily life. Most sensor systems such as exhaust gas sensors for automobiles are based on single sensors, which are as selective as possible for the analyte of interest. The problems of interfering cross-reactive analytes and the lack of specific sensors for many analytes have ended up in the development of so-called sensor-arrays. Thereby, several analytes can be simultaneously quantified by the multivariate data analysis of the signal patterns of several cross-reactive sensors. Yet, this approach is also limited since the number of sensors in the array has to exceed the number of cross-reacting analytes. In this work, a new approach is presented, which allows multi-analyte quantifications on the basis of single-sensor systems. Thereby, differences of interaction kinetics of the analytes and sensor are exploited using time-resolved measurements and time-resolved data analyses. This time-resolved evaluation of sensor signals together with suitable sensor materials combines the sensory principle with the chromatographic principle of separating analytes in space or time. The main objectives of this work can be subsumed into two focuses concerning the measurement principle and the data analysis. The first focus is the introduction of time-resolved measurements in the field of chemical sensing. In this work the time-resolved measurements are based on the microporous polymer Makrolon as sensitive sensor coating, which allows a kinetic separation of the analytes during the sorption and desorption on the basis of the size of analytes. Multi-analyte determinations using single sensors are successfully performed for three different setups and for many multicomponent mixtures of the low alcohols and the refrigerants R22 and R134a. The second focus concerns the multivariate data analysis of the data. It is demonstrated that a highest possible scanning rate of the time-resolved sensor responses is desirable resulting in a high number of variables. It is shown that wide-spread data analysis methods cannot cope with the amount of variables and with the nonlinear relationship between the sensor responses and the concentrations of the analytes. Thus, three different algorithms are innovated and optimized in this study to find a calibration with the highest possible generalization ability. These algorithms perform a simultaneous calibration and variable selection exploiting a data set limited in size to a maximum extend. One algorithm is based on many parallel runs of genetic algorithms combined with neural networks, one algorithm bases on many parallel runs of growing neural networks and the third algorithm uses several runs of the growing neural networks in a loop. All three algorithms show by far better calibrations than all common methods of multivariate calibration and than simple non-optimized neural networks for all data sets investigated. Additionally, the variable selection of these algorithms allows an insight into the relationship between the time-resolved sensor responses and the concentrations of the analytes. The variable selections also suggest optimizations in terms of shorter measurements for several data sets. All three algorithms successfully solve the problems of too many variables for too few samples and the problems caused by the nonlinearities present in the data with practically no input needed by the analyst. Together, both main focuses of this work impressively demonstrate how the combination of an advanced measurement principle and of an intelligent data analysis can improve the results of measurements at reduced hardware costs. Thereby the principle of single-sensor setups or few-sensor setups is not only limited to a size-selective recognition but can be extended to many analyte discriminating principles such as temperature-resolved measurements leaving room for many further investigations.Während des letzten Jahrzehnts haben Sensoren zur Detektion und Bestimmung von verschiedenen Substanzen nicht nur auf dem Gebiet der analytischen Chemie sondern auch im täglichen Leben rasend Verbreitung gefunden. Die meisten Sensorsysteme, wie zum Beispiel Abgasdetektoren für Automobile beruhen auf einzelnen Sensoren, welche möglichst spezifisch für den interessanten Analyten sind. Probleme auf Grund störender kreuzreaktiver Analyte und auf Grund eines Mangels an spezifischen Sensoren für viele Analyte führten zur Entwicklung so genannter Sensor-Arrays. Dabei können mehrere Analyte gleichzeitig quantifiziert werden, indem die Signalmuster von mehreren kreuzreaktiven Sensoren ausgewertet werden. Dieser Ansatz ist jedoch auch limitiert, da die Anzahl der Sensoren im Array größer als die Anzahl der kreuzreaktiven Analyte sein muss. In dieser Arbeit wird ein neuer Ansatz präsentiert, welcher es erlaubt, Multi-Analyt Quantifizierungen mit einem Einsensor-System durchzuführen. Hierbei werden Unterschiede der Wechselwirkungskinetiken zwischen den Analyten und dem Sensor mit Hilfe von zeitaufgelösten Messungen und zeitaufgelösten Datenauswertungen ausgenutzt. Zusammen mit geeigneten Sensormaterialien kombiniert die zeitaufgelöste Auswertung das Prinzip der Sensoren mit dem Prinzip der Chromatographie, welche Analyte räumlich oder zeitlich trennt. Die wichtigsten Zielsetzungen dieser Arbeit können unter den zwei Hauptgesichtspunkten Messprinzip und die Datenauswertung gestellt werden. Der erste Hauptgesichtspunkt ist die Einführung der zeitaufgelösten Messungen in die Sensorik. In dieser Arbeit basieren die zeitaufgelösten Messungen auf dem mikroporösen Polymer Makrolon als sensitive Sensorbeschichtung, welches eine kinetische Trennung der Analyte während der Sorption und der Desorption auf Grund der Analytgröße erlaubt. Es werden mit drei verschiedenen Einsensor-Aufbauten und vielen Mischungen der niederen Alkohole und der Kühlmittel R22 und R134a erfolgreich Mehrkomponentenanalysen erfolgreich durchgeführt. Der zweite Hauptgesichtspunkt betrifft die multivariate Datenauswertung. Es wird gezeigt, dass eine höchstmögliche Scanrate der zeitaufgelösten Sensorantworten wünschenswert ist, was zu einer hohen Anzahl an Variablen führt. Es wird demonstriert, dass weit verbreitete Datenauswertungsmethoden nicht mit der großen Anzahl an Variablen und mit dem nichtlinearen Zusammenhang zwischen den Sensorsignalen und den Analytkonzentrationen zurechtkommen. Deshalb werden in dieser Arbeit drei verschiedene Algorithmen entwickelt und optimiert, um eine Kalibration mit der höchstmöglichen Generalisierung zu finden. Diese Algorithmen führen eine gleichzeitige Kalibrierung und Variablenselektion durch, wobei sie einen Datensatz, welcher in der Größe limitiert ist, bestmöglich ausnutzen. Ein Algorithmus basiert auf vielen parallelen Läufen von genetischen Algorithmen kombiniert mit neuronalen Netzen. Der zweite Algorithmus beruht auf vielen parallelen Läufen von wachsenden neuronalen Netzen, während der dritte Algorithmus mehrere wachsende neuronale Netze in einer Schleife benutzt. Alle drei Algorithmen zeigen eine bei weitem bessere Kalibration als gewöhnliche Methoden der multivariaten Kalibration und als einfache nicht optimierte neuronale Netze für alle Datensätze, welche untersucht wurden. Zusätzlich erlaubt die Variablenselektion einen Einblick in den Zusammenhang zwischen den zeitaufgelösten Sensorantworten und den Konzentrationen der verschiedenen Analyte. Außerdem schlägt die Variablenselektion Optimierungen bezüglich kürzerer Messungen für mehrere Datensätze vor. Alle drei Algorithmen meistern erfolgreich das Problem von zu vielen Variablen für zu wenige Proben und die Probleme, welche von den in den Daten vorhandenen Nichtlinearitäten verursacht werden. Dabei sind praktisch keine Eingaben des Benutzers nötig. Zusammen liefern beide Hauptaspekte dieser Arbeit eine beeindruckende Demonstration, wie die Kombination eines fortschrittlichen Messprinzips mit einer intelligenten Datenauswertung die Ergebnisse von Messungen bei reduzierten Kosten für die Hardware verbessern kann. Dabei ist das Prinzip der Einsensor-Aufbauten beziehungsweise der Aufbauten mit wenigen Sensoren nicht auf ein größenselektives Erkennungsprinzip limitiert, sondern kann auf viele Prinzipien der Unterscheidung von Analyten wie zum Beispiel temperaturaufgelöste Messungen erweitert werden, was weiteren Untersuchungen ein nahezu endloses Feld eröffnet

Metabolomic serum abnormalities in dogs with hepatopathies

Hepatopathies can cause major metabolic abnormalities in humans and animals. This study examined differences in serum metabolomic parameters and patterns in left-over serum samples from dogs with either congenital portosystemic shunts (cPSS, n = 24) or high serum liver enzyme activities (HLEA, n = 25) compared to control dogs (n = 64). A validated targeted proton nuclear magnetic resonance spectroscopy platform was used to assess 123 parameters. Principal component analysis of the serum metabolome demonstrated distinct clustering among individuals in each group, with the cluster of HLEA being broader compared to the other groups, presumably due to the wider spectrum of hepatic diseases represented in these samples. While younger and older adult control dogs had very similar metabolomic patterns and clusters, there were changes in many metabolites in the hepatopathy groups. Higher phenylalanine and tyrosine concentrations, lower branched-chained amino acids (BCAAs) concentrations, and altered fatty acid parameters were seen in cPSS dogs compared to controls. In contrast, dogs with HLEA had increased concentrations of BCAAs, phenylalanine, and various lipoproteins. Machine learning based solely on the metabolomics data showed excellent group classification, potentially identifying a novel tool to differentiate hepatopathies. The observed changes in metabolic parameters could provide invaluable insight into the pathophysiology, diagnosis, and prognosis of hepatopathies.Peer reviewe

Metabolomic Abnormalities in Serum from Untreated and Treated Dogs with Hyper- and Hypoadrenocorticism

The adrenal glands play a major role in metabolic processes, and both excess and insufficient serum cortisol concentrations can lead to serious metabolic consequences. Hyper- and hypoadrenocorticism represent a diagnostic and therapeutic challenge. Serum samples from dogs with untreated hyperadrenocorticism (n = 27), hyperadrenocorticism undergoing treatment (n = 28), as well as with untreated (n = 35) and treated hypoadrenocorticism (n = 23) were analyzed and compared to apparently healthy dogs (n = 40). A validated targeted proton nuclear magnetic resonance (H-1 NMR) platform was used to quantify 123 parameters. Principal component analysis separated the untreated endocrinopathies. The serum samples of dogs with untreated endocrinopathies showed various metabolic abnormalities with often contrasting results particularly in serum concentrations of fatty acids, and high- and low-density lipoproteins and their constituents, which were predominantly increased in hyperadrenocorticism and decreased in hypoadrenocorticism, while amino acid concentrations changed in various directions. Many observed serum metabolic abnormalities tended to normalize with medical treatment, but normalization was incomplete when compared to levels in apparently healthy dogs. Application of machine learning models based on the metabolomics data showed good classification, with misclassifications primarily observed in treated groups. Characterization of metabolic changes enhances our understanding of these endocrinopathies. Further assessment of the recognized incomplete reversal of metabolic alterations during medical treatment may improve disease management.Peer reviewe

Quasi-free photoproduction of η -mesons off the deuteron

Abstract.: Precise data for quasi-free photoproduction of η-mesons off the deuteron have been measured at the Bonn ELSA accelerator with the combined Crystal Barrel/TAPS detector for incident photon energies up to 2.5GeV. The η-mesons have been detected in coincidence with recoil protons and neutrons. Possible nuclear effects like Fermi motion and re-scattering can be studied via a comparison of the quasi-free reaction off the bound proton to η-production off the free proton. No significant effects beyond the folding of the free cross-section with the momentum distribution of the bound protons have been found. These Fermi motion effects can be removed by an analysis using the invariant mass of the η-nucleon pairs reconstructed from the final-state four-momenta of the particles. The total cross-section for quasi-free η-photoproduction off the neutron reveals even without correction for Fermi motion a pronounced bump-like structure around 1GeV of incident photon energy, which is not observed for the proton. This structure is even narrower in the invariant-mass spectrum of the η-neutron pairs. Position and width of the peak in the invariant-mass spectrum are W ≈ 1665 MeV and FWHM Γ ≈ 25 MeV. The data are compared to the results of different model

Quasi-free photoproduction of eta-mesons off the deuteron

Precise data for quasi-free photoproduction of $\eta$ mesons off the deuteron have been measured at the Bonn ELSA accelerator with the combined Crystal Barrel/TAPS detector for incident photon energies up to 2.5 GeV. The $\eta$-mesons have been detected in coincidence with recoil protons and neutrons. Possible nuclear effects like Fermi motion and re-scattering can be studied via a comparison of the quasi-free reaction off the bound proton to $\eta$-production off the free proton. No significant effects beyond the folding of the free cross section with the momentum distribution of the bound protons have been found. These Fermi motion effects can be removed by an analysis using the invariant mass of the $\eta$-nucleon pairs reconstructed from the final state four-momenta of the particles. The total cross section for quasi-free $\eta$-photoproduction off the neutron reveals even without correction for Fermi motion a pronounced bump-like structure around 1 GeV of incident photon energy, which is not observed for the proton. This structure is even narrower in the invariant mass spectrum of the $\eta$-neutron pairs. Position and width of the peak in the invariant mass spectrum are $W\approx 1665$ MeV and FWHM $\Gamma\approx 25$ MeV. The data are compared to the results of different models.Comment: accepted for publication in Eur. Phys. J.

Biomarkers in oncology drug development: rescuers or troublemakers?

Oncology is considered as the pioneer indication for the clinical application of molecular biomarkers. Newly developed targeted anticancer therapies call for the implementation of molecular biomarker strategies but even novel cytotoxic treatments use biomarkers for the assessment of efficacy and toxicity. Biomarkers may play several roles in the progression of a drug from research to personalised medicine. In particular biomarkers are used to understand the mechanism of action of a drug, monitor the modulation of the intended target, assess efficacy and safety, adapt dosing and schedule, select patients and prognosticate the clinical outcome. Nowadays, the use of biomarkers in oncology is still challenged as only a limited number of oncology drugs on the market have a companion biomarker test to be mandatorily performed before treatment. This is in contradiction with the current major investment the pharmaceutical sector is devoting to biomarker identification and development. What are the measurable milestones and outcomes of these investments? How does biomarker development contribute to reaching the ultimate goal of finding the right molecules for the right targets at the right doses and schedules for the right patients? This review provides a critical overview of recent salient achievements in the identification and development of biomarkers

Impact of biomarker development on drug safety assessment

Drug safety has always be a key aspect of drug development. Vioxx case and several cases of serious adverse events being linked to high-profile products have increased the importance of drug safety, especially in the eyes of drug development companies and global regulatory agencies. Safety biomarkers are increasingly being seen as helping to provide clarity, predictability, and certainty needed to gain confidence in decision making: early-stage projects can be stopped quicker, late-stage projects become less risky. Public and private organizations are investing heavily in terms of time, money and manpower on biomarker development. A illustrative and “door opening” safety biomarker success story is the recent endorsement of kidney safety biomarkers for pre-clinical and limited translational contexts by FDA and EMEA. This milestone achieved for kidney biomarkers and the “know how” acquired is being transferred to other organ toxicities, namely liver, heart, vascular system. New technologies and molecular-based approaches, i.e. molecular pathology as a complement to the classical toolbox allow promising discoveries in the safety biomarker field. This review will focuse on the utility and use of safety biomarkers all along drug development, highlighting the present gaps and opportunities identified in organ toxicity monitoring. A last part will be dedicated to safety biomarker development in general, from identification to diagnostic tests, using the kidney safety biomarkers success as an illustrative example