A backoff strategy for model‐based experiment design under parametric uncertainty

Model‐based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model‐based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Caffeic Acid Enhances the Anti-Leukemic Effect of Imatinib on Chronic Myeloid Leukemia Cells and Triggers Apoptosis in Cells Sensitive and Resistant to Imatinib

Among the phenolic acids tested on the K562 cell line, a model of chronic myeloid leukemia (CML), caffeic acid (CA) was biologically active on sensitive and imatinib (IM)-resistant cells at micro-molar concentration, either in terms of reduction of cell proliferation or triggering of apoptosis. The CA treatment provoked mitochondrial membrane depolarization, genomic DNA fragmentation and phosphatidylserine exposure, hallmarks of apoptosis. Cell cycle analysis following the treatment with comparable cytotoxic concentrations of IM or CA showed marked differences in the distribution profiles. The reduction of cell proliferation by CA administration was associated with increased expression of two cell cycle repressor genes, CDKN1A and CHES1, while IM at a cytotoxic concentration increased the CHES1 but not the CDKN1A expression. In addition, CA treatment affected the proliferation and triggered the apoptosis in IM-resistant cells. Taken together, these data suggested that CA induced the anti-proliferative effect and triggered apoptosis of CML cells by a different mechanism than IM. Finally, the combined administration of IM and CA at suboptimal concentrations evidenced a synergy of action in determining the anti-proliferative effect and triggering apoptosis. The ability of CA to potentiate the anti-leukemic effect of IM highlighted the nutraceutical potential of CA in CML.     

Structural Cues for Understanding eEF1A2 Moonlighting

Spontaneous mutations in the EEF1A2 gene cause epilepsy and severe neurological disabilities in children. The crystal structure of eEF1A2 protein purified from rabbit skeletal muscle reveals a post-translationally modified dimer that provides information about the sites of interaction with numerous binding partners, including itself, and maps these mutations onto the dimer and tetramer interfaces. The spatial locations of the side chain carboxylates of Glu301 and Glu374, to which phosphatidylethanolamine is uniquely attached via an amide bond, define the anchoring points of eEF1A2 to cellular membranes and interorganellar membrane contact sites. Additional bioinformatic and molecular modeling results provide novel structural insight into the demonstrated binding of eEF1A2 to SH3 domains, the common MAPK docking groove, filamentous actin, and phosphatidylinositol-4 kinase IIIβ. In this new light, the role of eEF1A2 as an ancient, multifaceted, and articulated G protein at the crossroads of autophagy, oncogenesis and viral replication appears very distant from the “canonical” one of delivering aminoacyl-tRNAs to the ribosome that has dominated the scene and much of the thinking for many decades.     

Modulating drug release from poly(lactic-co-glycolic) acid microparticles by the addition of alginate and pectin

The aim of the present work is the characterization of PLGA microparticles including biopolymers for the controlled release of tilmicosin, a broad-spectrum antibiotic. Microparticles were prepared using the double-emulsion solvent evaporation technique. The effect of alginate and pectin incorporation over particle size and porosity, encapsulation efficiency (EE) and pH-responsive drug release was evaluated. Formulations presented a mean particle size of 5.5 μm approximately and a drug EE ranged from 22%–57%. PLGA-Alginate particles showed an increased porosity. Tilmicosin release profiles from PLGA and PLGA-biopolymer microparticles were affected by the particular combination of polymers and the pH of the release medium. The experimental data was simulated using a mathematical model, which takes into account the autocatalytic polymer degradation and the different mechanisms of drug transport. The combination of PLGA and biopolymers strongly influenced the morphology of the particles, offering the possibility of controlling the drug release profiles according to the therapy.     

The Contribution of Small Vessel Disease to Neurodegeneration: Focus on Alzheimer’s Disease,Parkinson’s Disease and Multiple Sclerosis

Brain small vessel disease (SVD) refers to a variety of structural and functional changes affecting small arteries and micro vessels, and manifesting as white matter changes, microbleeds and lacunar infarcts. Growing evidence indicates that SVD might play a significant role in the neurobiology of central nervous system (CNS) neurodegenerative disorders, namely Alzheimer’s disease (AD) and Parkinson’s disease (PD), and neuroinflammatory diseases, such as multiple sclerosis (MS). These disorders share different pathophysiological pathways and molecular mechanisms (i.e., protein misfolding, derangement of cellular clearance systems, mitochondrial impairment and immune system activation) having neurodegeneration as biological outcome. In these diseases, the actual contribution of SVD to the clinical picture, and its impact on response to pharmacological treatments, is not known yet. Due to the high frequency of SVD in adult-aged patients, it is important to address this issue. In this review, we report preclinical and clinical data on the impact of SVD in AD, PD and MS, with the main aim of clarifying the predictability of SVD on clinical manifestations and treatment response.