Explaining clinical behaviors using multiple theoretical models

PMID: 23075284 [PubMed - indexed for MEDLINE] PMCID: PMC3500222 Free PMC ArticlePeer reviewedPublisher PD

Pharmacokinetic Properties of Liraglutide as Adjunct to Insulin in Subjects with Type 1 Diabetes Mellitus.

BACKGROUND: The pharmacokinetic properties of liraglutide, a glucagon-like peptide-1 receptor agonist approved for the treatment of type 2 diabetes mellitus (T2D), have been established in healthy individuals and subjects with T2D. Liraglutide has been under investigation as adjunct treatment to insulin in type 1 diabetes mellitus (T1D). This single-center, double-blind, placebo-controlled, crossover, clinical pharmacology trial is the first to analyze the pharmacokinetic properties of liraglutide as add-on to insulin in T1D. METHODS: Subjects (18-64 years; body mass index 20.0-28.0 kg/m(2); glycated hemoglobin ≤9.5 %) were randomized 1:1:1 to 0.6, 1.2, or 1.8 mg liraglutide/placebo. Each group underwent two 4-week treatment periods (liraglutide then placebo or placebo then liraglutide) separated by a 2- to 3-week washout. Both trial drugs were administered subcutaneously, once daily, as adjunct to insulin. A stepwise hypoglycemic clamp was performed at the end of each treatment period (data reported previously). Pharmacokinetic endpoints were derived from liraglutide concentration-time curves after the final dose and exposure was compared with data from previous trials in healthy volunteers and subjects with T2D. RESULTS: The pharmacokinetic properties of liraglutide in T1D were comparable with those observed in healthy volunteers and subjects with T2D. Area under the steady-state concentration-time curve (AUC) and maximum plasma concentration data were consistent with dose proportionality of liraglutide. Comparison of dose-normalized liraglutide AUC suggested that exposure in T1D, when administered with insulin, is comparable with that observed in T2D. CONCLUSIONS: Liraglutide, administered as adjunct to insulin in subjects with T1D, shows comparable pharmacokinetics to those in subjects with T2D. ClinicalTrials.gov Identifier: NCT01536665

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools)

Substrate Profiling of Tobacco Etch Virus Protease Using a Novel Fluorescence-Assisted Whole-Cell Assay

Site-specific proteolysis of proteins plays an important role in many cellular functions and is often key to the virulence of infectious organisms. Efficient methods for characterization of proteases and their substrates will therefore help us understand these fundamental processes and thereby hopefully point towards new therapeutic strategies. Here, a novel whole-cell in vivo method was used to investigate the substrate preference of the sequence specific tobacco etch virus protease (TEVp). The assay, which utilizes protease-mediated intracellular rescue of genetically encoded short-lived fluorescent substrate reporters to enhance the fluorescence of the entire cell, allowed subtle differences in the processing efficiency of closely related substrate peptides to be detected. Quantitative screening of large combinatorial substrate libraries, through flow cytometry analysis and cell sorting, enabled identification of optimal substrates for TEVp. The peptide, ENLYFQG, identical to the protease's natural substrate peptide, emerged as a strong consensus cleavage sequence, and position P3 (tyrosine, Y) and P1 (glutamine, Q) within the substrate peptide were confirmed as being the most important specificity determinants. In position P1′, glycine (G), serine (S), cysteine (C), alanine (A) and arginine (R) were among the most prevalent residues observed, all known to generate functional TEVp substrates and largely in line with other published studies stating that there is a strong preference for short aliphatic residues in this position. Interestingly, given the complex hydrogen-bonding network that the P6 glutamate (E) is engaged in within the substrate-enzyme complex, an unexpectedly relaxed residue preference was revealed for this position, which has not been reported earlier. Thus, in the light of our results, we believe that our assay, besides enabling protease substrate profiling, also may serve as a highly competitive platform for directed evolution of proteases and their substrates

Towards a science of climate and energy choices

The linked problems of energy sustainability and climate change are among the most complex and daunting facing humanity at the start of the twenty-first century. This joint Nature Energy and Nature Climate Change Collection illustrates how understanding and addressing these problems will require an integrated science of coupled human and natural systems; including technological systems, but also extending well beyond the domain of engineering or even economics. It demonstrates the value of replacing the stylized assumptions about human behaviour that are common in policy analysis, with ones based on data-driven science. We draw from and engage articles in the Collection to identify key contributions to understanding non-technological factors connecting economic activity and greenhouse gas emissions, describe a multi-dimensional space of human action on climate and energy issues, and illustrate key themes, dimensions and contributions towards fundamental understanding and informed decision making

Design Constraints on a Synthetic Metabolism

A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design

Does Pathogen Spillover from Commercially Reared Bumble Bees Threaten Wild Pollinators?

The conservation of insect pollinators is drawing attention because of reported declines in bee species and the ‘ecosystem services’ they provide. This issue has been brought to a head by recent devastating losses of honey bees throughout North America (so called, ‘Colony Collapse Disorder’); yet, we still have little understanding of the cause(s) of bee declines. Wild bumble bees (Bombus spp.) have also suffered serious declines and circumstantial evidence suggests that pathogen ‘spillover’ from commercially reared bumble bees, which are used extensively to pollinate greenhouse crops, is a possible cause. We constructed a spatially explicit model of pathogen spillover in bumble bees and, using laboratory experiments and the literature, estimated parameter values for the spillover of Crithidia bombi, a destructive pathogen commonly found in commercial Bombus. We also monitored wild bumble bee populations near greenhouses for evidence of pathogen spillover, and compared the fit of our model to patterns of C. bombi infection observed in the field. Our model predicts that, during the first three months of spillover, transmission from commercial hives would infect up to 20% of wild bumble bees within 2 km of the greenhouse. However, a travelling wave of disease is predicted to form suddenly, infecting up to 35–100% of wild Bombus, and spread away from the greenhouse at a rate of 2 km/wk. In the field, although we did not observe a large epizootic wave of infection, the prevalences of C. bombi near greenhouses were consistent with our model. Indeed, we found that spillover has allowed C. bombi to invade several wild bumble bee species near greenhouses. Given the available evidence, it is likely that pathogen spillover from commercial bees is contributing to the ongoing decline of wild Bombus in North America. Improved management of domestic bees, for example by reducing their parasite loads and their overlap with wild congeners, could diminish or even eliminate pathogen spillover

Immune mechanisms in malaria: new insights in vaccine development.

Early data emerging from the first phase 3 trial of a malaria vaccine are raising hopes that a licensed vaccine will soon be available for use in endemic countries, but given the relatively low efficacy of the vaccine, this needs to be seen as a major step forward on the road to a malaria vaccine rather than as arrival at the final destination. The focus for vaccine developers now moves to the next generation of malaria vaccines, but it is not yet clear what characteristics these new vaccines should have or how they can be evaluated. Here we briefly review the epidemiological and immunological requirements for malaria vaccines and the recent history of malaria vaccine development and then put forward a manifesto for future research in this area. We argue that rational design of more effective malaria vaccines will be accelerated by a better understanding of the immune effector mechanisms involved in parasite regulation, control and elimination