A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this article, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomizationbased methods.Postprint (published version

Sequence Effect of Self-Assembling Peptides on the Complexation and In Vitro Delivery of the Hydrophobic Anticancer Drug Ellipticine

A special class of self-assembling peptides has been found to be capable of stabilizing the hydrophobic anticancer agent ellipticine in aqueous solution. Here we study the effect of peptide sequence on the complex formation and its anticancer activity in vitro. Three peptides, EAK16-II, EAK16-IV and EFK16-II, were selected to have either a different charge distribution (EAK16-II vs. EAK16-IV) or a varying hydrophobicity (EAK16-II vs. EFK16-II). Results on their complexation with ellipticine revealed that EAK16-II and EAK16-IV were able to stabilize protonated ellipticine or ellipticine microcrystals depending on the peptide concentration; EFK16-II could stabilize neutral ellipticine molecules and ellipticine microcrystals. These different molecular states of ellipticine were expected to affect ellipticine delivery. The anticancer activity of these complexes was tested against two cancer cell lines: A549 and MCF-7, and related to the cell viability. The viability results showed that the complexes with protonated ellipticine were effective in eradicating both cancer cells (viability <0.05), but their dilutions in water were not stable, leading to a fast decrease in their toxicity. In contrast, the complexes formulated with EFK16-II were relatively stable upon dilution, but their original toxicity was relatively low compared to that with protonated ellipticine. Overall, the charge distribution of the peptides seemed not to affect the complex formation and its therapeutic efficacy in vitro; however, the increase in hydrophobicity of the peptides significantly altered the state of stabilized ellipticine and increased the stability of the complexes. This work provides essential information for peptide sequence design in the development of self-assembling peptide-based delivery of hydrophobic anticancer drugs

Synthesis of high-quality libraries of long (150mer) oligonucleotides by a novel depurination controlled process

We have achieved the ability to synthesize thousands of unique, long oligonucleotides (150mers) in fmol amounts using parallel synthesis of DNA on microarrays. The sequence accuracy of the oligonucleotides in such large-scale syntheses has been limited by the yields and side reactions of the DNA synthesis process used. While there has been significant demand for libraries of long oligos (150mer and more), the yields in conventional DNA synthesis and the associated side reactions have previously limited the availability of oligonucleotide pools to lengths <100 nt. Using novel array based depurination assays, we show that the depurination side reaction is the limiting factor for the synthesis of libraries of long oligonucleotides on Agilent Technologies’ SurePrint® DNA microarray platform. We also demonstrate how depurination can be controlled and reduced by a novel detritylation process to enable the synthesis of high quality, long (150mer) oligonucleotide libraries and we report the characterization of synthesis efficiency for such libraries. Oligonucleotide libraries prepared with this method have changed the economics and availability of several existing applications (e.g. targeted resequencing, preparation of shRNA libraries, site-directed mutagenesis), and have the potential to enable even more novel applications (e.g. high-complexity synthetic biology)

CNS Delivery Via Adsorptive Transcytosis

Adsorptive-mediated transcytosis (AMT) provides a means for brain delivery of medicines across the blood-brain barrier (BBB). The BBB is readily equipped for the AMT process: it provides both the potential for binding and uptake of cationic molecules to the luminal surface of endothelial cells, and then for exocytosis at the abluminal surface. The transcytotic pathways present at the BBB and its morphological and enzymatic properties provide the means for movement of the molecules through the endothelial cytoplasm. AMT-based drug delivery to the brain was performed using cationic proteins and cell-penetrating peptides (CPPs). Protein cationization using either synthetic or natural polyamines is discussed and some examples of diamine/polyamine modified proteins that cross BBB are described. Two main families of CPPs belonging to the Tat-derived peptides and Syn-B vectors have been extensively used in CPP vector-mediated strategies allowing delivery of a large variety of small molecules as well as proteins across cell membranes in vitro and the BBB in vivo. CPP strategy suffers from several limitations such as toxicity and immunogenicity—like the cationization strategy—as well as the instability of peptide vectors in biological media. The review concludes by stressing the need to improve the understanding of AMT mechanisms at BBB and the effectiveness of cationized proteins and CPP-vectorized proteins as neurotherapeutics

A Unique Carrier for Delivery of Therapeutic Compounds beyond the Blood-Brain Barrier

BACKGROUND: Therapeutic intervention in many neurological diseases is thwarted by the physical obstacle formed by the blood-brain barrier (BBB) that excludes most drugs from entering the brain from the blood. Thus, identifying efficacious modes of drug delivery to the brain remains a "holy grail" in molecular medicine and nanobiotechnology. Brain capillaries, that comprise the BBB, possess an endogenous receptor that ferries an iron-transport protein, termed p97 (melanotransferrin), across the BBB. Here, we explored the hypothesis that therapeutic drugs "piggybacked" as conjugates of p97 can be shuttled across the BBB for treatment of otherwise inoperable brain tumors. APPROACH: Human p97 was covalently linked with the chemotherapeutic agents paclitaxel (PTAX) or adriamycin (ADR) and following intravenous injection, measured their penetration into brain tissue and other organs using radiolabeled and fluorescent derivatives of the drugs. In order to establish efficacy of the conjugates, we used nude mouse models to assess p97-drug conjugate activity towards glioma and mammary tumors growing subcutaneously compared to those growing intracranially. PRINCIPAL FINDINGS: Bolus-injected p97-drug conjugates and unconjugated p97 traversed brain capillary endothelium within a few minutes and accumulated to 1-2% of the injected by 24 hours. Brain delivery with p97-drug conjugates was quantitatively 10 fold higher than with free drug controls. Furthermore, both free-ADR and p97-ADR conjugates equally inhibited the subcutaneous growth of gliomas growing outside the brain. Evocatively, only p97-ADR conjugates significantly prolonged the survival of animals bearing intracranial gliomas or mammary tumors when compared to similar cumulated doses of free-ADR. SIGNIFICANCE: This study provides the initial proof of concept for p97 as a carrier capable of shuttling therapeutic levels of drugs from the blood to the brain for the treatment of neurological disorders, including classes of resident and metastatic brain tumors. It may be prudent, therefore, to consider implementation of this novel delivery platform in various clinical settings for therapeutic intervention in acute and chronic neurological diseases

A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this article, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomizationbased methods

Self-stabilized antisense oligodeoxynucleotide phosphorothioates: properties and anti-HIV activity.

A new class of oligodeoxyribonucleotides has been designed, referred to here as 'self-stabilized' oligonucleotides. These oligonucleotides have hairpin loop structures at their 3' ends, and show increased resistance to degradation by snake venom phosphodiesterase, DNA polymerase I and fetal bovine serum. The self-stabilized region of the oligonucleotide does not interfere in hybridization with complementary nucleic acids as shown by melting temperature, mobility-shift and RNase H cleavage studies. Various self-stabilized oligonucleotides containing increasingly stable hairpin loop regions were studied for their anti-HIV activity. Pharmacokinetic and stability studies in mice showed increased in vivo persistence of self-stabilized oligonucleotides with respect to their linear counterparts

A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this article, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomizationbased methods