Adsorption and surface dissociation of HNCO on Pt(110) surfaces: LEED, AES, ELS and TDS studies

Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid a parts per thousand yen LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs

A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro.

OBJECTIVES: Mycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states. METHODS: The natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time-kill curves from log-phase (0.25-16 mg/L) and stationary-phase (0.5-64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure-response relationships. RESULTS: The final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R(2) = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively. CONCLUSIONS: The model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro

The implications of model-informed drug discovery and development for tuberculosis

Despite promising advances in the field and highly effective first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic–pharmacodynamic (PK/PD) relations. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on various stages in TB drug development and on the dose rationale for drug combinations

A model-based analysis identifies differences in phenotypic resistance between in vitro and in vivo: implications for translational medicine within tuberculosis.

Proper characterization of drug effects on Mycobacterium tuberculosis relies on the characterization of phenotypically resistant bacteria to correctly establish exposure-response relationships. The aim of this work was to evaluate the potential difference in phenotypic resistance in in vitro compared to murine in vivo models using CFU data alone or CFU together with most probable number (MPN) data following resuscitation with culture supernatant. Predictions of in vitro and in vivo phenotypic resistance i.e. persisters, using the Multistate Tuberculosis Pharmacometric (MTP) model framework was evaluated based on bacterial cultures grown with and without drug exposure using CFU alone or CFU plus MPN data. Phenotypic resistance and total bacterial number in in vitro natural growth observations, i.e. without drug, was well predicted by the MTP model using only CFU data. Capturing the murine in vivo total bacterial number and persisters during natural growth did however require re-estimation of model parameter using both the CFU and MPN observations implying that the ratio of persisters to total bacterial burden is different in vitro compared to murine in vivo. The evaluation of the in vitro rifampicin drug effect revealed that higher resolution in the persister drug effect was seen using CFU and MPN compared to CFU alone although drug effects on the other bacterial populations were well predicted using only CFU data. The ratio of persistent bacteria to total bacteria was predicted to be different between in vitro and murine in vivo. This difference could have implications for subsequent translational efforts in tuberculosis drug development

A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations

Background Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development. Methods In vitro time–kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure–response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug’s potency (EC50) by the combining drug(s). Results All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity. Conclusions With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens

Phosphorylated Nucleolin Interacts with Translationally Controlled Tumor Protein during Mitosis and with Oct4 during Interphase in ES Cells

BACKGROUND: Reprogramming of somatic cells for derivation of either embryonic stem (ES) cells, by somatic cell nuclear transfer (SCNT), or ES-like cells, by induced pluripotent stem (iPS) cell procedure, provides potential routes toward non-immunogenic cell replacement therapies. Nucleolar proteins serve as markers for activation of embryonic genes, whose expression is crucial for successful reprogramming. Although Nucleolin (Ncl) is one of the most abundant nucleolar proteins, its interaction partners in ES cells have remained unidentified. METHODOLOGY: Here we explored novel Ncl-interacting proteins using in situ proximity ligation assay (PLA), colocalization and immunoprecipitation (IP) in ES cells. PRINCIPAL FINDINGS: We found that phosphorylated Ncl (Ncl-P) interacted with translationally controlled tumor protein (Tpt1) in murine ES cells. The Ncl-P/Tpt1 complex peaked during mitosis and was reduced upon retinoic acid induced differentiation, signifying a role in cell proliferation. In addition, we showed that Ncl-P interacted with the transcription factor Oct4 during interphase in human as well as murine ES cells, indicating of a role in transcription. The Ncl-P/Oct4 complex peaked during early stages of spontaneous human ES cell differentiation and may thus be involved in the initial differentiation event(s) of mammalian development. CONCLUSIONS: Here we described two novel protein-protein interactions in ES cells, which give us further insight into the complex network of interacting proteins in pluripotent cells

GRSDB2 and GRS_UTRdb: databases of quadruplex forming G-rich sequences in pre-mRNAs and mRNAs

G-quadruplex motifs in the RNA play significant roles in key cellular processes and human disease. While sequences capable of forming G-quadruplexes in the pre-mRNA are involved in regulation of polyadenylation and splicing events in mammalian transcripts, the G-quadruplex motifs in the UTRs may help regulate mRNA expression. GRSDB2 is a second-generation database containing information on the composition and distribution of putative Quadruplex-forming G-Rich Sequences (QGRS) mapped in ∼29 000 eukaryotic pre-mRNA sequences, many of which are alternatively processed. The data stored in the GRSDB2 is based on computational analysis of NCBI Entrez Gene entries with the help of an improved version of the QGRS Mapper program. The database allows complex queries with a wide variety of parameters, including Gene Ontology terms. The data is displayed in a variety of formats with several additional computational capabilities. We have also developed a new database, GRS_UTRdb, containing information on the composition and distribution patterns of putative QGRS in the 5′- and 3′-UTRs of eukaryotic mRNA sequences. The goal of these experiments has been to build freely accessible resources for exploring the role of G-quadruplex structure in regulation of gene expression at post-transcriptional level. The databases can be accessed at the G-Quadruplex Resource Site at: http://bioinformatics.ramapo.edu/GQRS/