Population pharmacokinetic study to evaluate dosing strategies of imipenem in neonates and infants

Objective: Imipenem is a broad spectrum antibiotic used to treat severe infections in critically ill patients. Imipenem pharmacokinetics (PK) was evaluated in a cohort of neonates treated in the Neonatal Intensive Care Unit of the Lausanne University Hospital. The objective of our study was to identify key demographic and clinical factors influencing imipenem exposure in this population. Method: PK data from neonates and infants with at least one imipenem concentration measured between 2002 and 2013 were analyzed applying population PK modeling methods. Measurement of plasma concentrations were performed upon the decision of the physician within the frame of a therapeutic drug monitoring (TDM) programme. Effects of demographic (sex, body weight, gestational age, postnatal age) and clinical factors (serum creatinine as a measure of kidney function; co-administration of furosemide, spironolactone, hydrochlorothiazide, vancomycin, metronidazole and erythromycin) on imipenem PK were explored. Model-based simulations were performed (with a median creatinine value of 46 μmol/l) to compare various dosing regimens with respect to their ability to maintain drug levels above predefined minimum inhibitory concentrations (MIC) for at least 40 % of the dosing interval. Results: A total of 144 plasma samples was collected in 68 neonates and infants, predominantly preterm newborns, with median gestational age of 27 weeks (24 - 41 weeks) and postnatal age of 21 days (2 - 153 days). A two-compartment model best characterized imipenem disposition. Actual body weight exhibited the greatest impact on PK parameters, followed by age (gestational age and postnatal age) and serum creatinine on clearance. They explain 19%, 9%, 14% and 9% of the interindividual variability in clearance respectively. Model-based simulations suggested that 15 mg/kg every 12 hours maintain drug concentrations over a MIC of 2 mg/l for at least 40% of the dosing interval during the first days of life, whereas neonates older than 14 days of life required a dose of 20 mg/kg every 12 hours. Conclusion: Dosing strategies based on body weight and post-natal age are recommended for imipenem in all critically ill neonates and infants. Most current guidelines seem adequate for newborns and TDM should be restricted to some particular clinical situations.

Ab initio modeling and molecular dynamics simulation of the alpha 1b-adrenergic receptor activation.

This work describes the ab initio procedure employed to build an activation model for the alpha 1b-adrenergic receptor (alpha 1b-AR). The first version of the model was progressively modified and complicated by means of a many-step iterative procedure characterized by the employment of experimental validations of the model in each upgrading step. A combined simulated (molecular dynamics) and experimental mutagenesis approach was used to determine the structural and dynamic features characterizing the inactive and active states of alpha 1b-AR. The latest version of the model has been successfully challenged with respect to its ability to interpret and predict the functional properties of a large number of mutants. The iterative approach employed to describe alpha 1b-AR activation in terms of molecular structure and dynamics allows further complications of the model to allow prediction and interpretation of an ever-increasing number of experimental data.

Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.