50 research outputs found

    Application of Physiologically Based Absorption Modeling to Characterize the Pharmacokinetic Profiles of Oral Extended Release Methylphenidate Products in Adults

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    <div><p>A previously presented physiologically-based pharmacokinetic model for immediate release (IR) methylphenidate (MPH) was extended to characterize the pharmacokinetic behaviors of oral extended release (ER) MPH formulations in adults for the first time. Information on the anatomy and physiology of the gastrointestinal (GI) tract, together with the biopharmaceutical properties of MPH, was integrated into the original model, with model parameters representing hepatic metabolism and intestinal non-specific loss recalibrated against in vitro and in vivo kinetic data sets with IR MPH. A Weibull function was implemented to describe the dissolution of different ER formulations. A variety of mathematical functions can be utilized to account for the engineered release/dissolution technologies to achieve better model performance. The physiological absorption model tracked well the plasma concentration profiles in adults receiving a multilayer-release MPH formulation or Metadate CD, while some degree of discrepancy was observed between predicted and observed plasma concentration profiles for Ritalin LA and Medikinet Retard. A local sensitivity analysis demonstrated that model parameters associated with the GI tract significantly influenced model predicted plasma MPH concentrations, albeit to varying degrees, suggesting the importance of better understanding the GI tract physiology, along with the intestinal non-specific loss of MPH. The model provides a quantitative tool to predict the biphasic plasma time course data for ER MPH, helping elucidate factors responsible for the diverse plasma MPH concentration profiles following oral dosing of different ER formulations.</p></div

    Fraction of ER component released and total MPH absorbed in each section.

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    <p>Fraction of ER component released and total MPH absorbed in each section.</p

    Schematic depicting the physiological absorption model for immediate release (IR) and extended release (ER) MPH formulations.

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    <p>The oral absorption model is composed of nine compartments, representing the stomach, duodenum, jejunum divided into two compartments, ileum divided into three compartments, cecum, and ascending colon. Dissolution of the IR component occurs throughout the GI tract, primarily in the stomach, whereas the release/dissolution of the ER component takes place only in the intestine accompanied by a delay time. M, metabolism; Loss, non-specific loss, IR immediate release.</p

    Model simulated versus observed <i>d</i>- MPH (A, B, C) and <i>l</i>-MPH (D, E, F) plasma concentration profiles in adults receiving a single oral dose of IR MPH at 0.3 μg/kg [17, 18] and 40 mg [19] under fasted conditions as well as model predictability of the pharmacokinetic parameters <i>AUC</i> (G) and <i>C</i><sub><i>max</i></sub> (H).

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    <p>Solid lines in A-F represent simulated mean plasma concentration-time profiles, whereas dashed lines represent the 5<sup>th</sup> and 95<sup>th</sup> percentiles for the predicted values. The observed data points (●) are shown as mean ± SD (error bars) or means only. The solid lines in G and H illustrate unity as well as a twofold deviation from unity; whereas the data points represent observed mean ± SD values with respective to the simulated mean ± SD values for <i>AUC</i> (G) and <i>C</i><sub><i>max</i></sub> (H). In the study of Patrick, Straughn et al. (2007), <i>AUC</i> and <i>C</i><sub><i>max</i></sub> values only reported for <i>d</i>-MPH.</p

    Model simulated versus observed total MPH plasma concentration profiles in adults receiving a single oral dose of Medikinet Retard at (A) 40 mg [23] and 20 mg [27, 28] (B and C) under fed conditions, as well as model predictability of the pharmacokinetic parameters <i>AUC</i> (D) and <i>C</i><sub><i>max</i></sub> (E).

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    <p>Solid lines in A-C represent simulated mean plasma concentration-time profiles, whereas dashed lines represent the 5<sup>th</sup> and 95<sup>th</sup> percentiles for the predicted values. The observed data points (●) are shown as mean except for Schutz et al (2009) where the observed data points are expressed as geometric mean. The solid lines in D and E illustrates unity, as well as a twofold deviation from unity; whereas the data points represent observed mean ± SD values (geometric mean ± SD values for the study of Schutz et al., 2009) with respective to the simulated mean ± SD values (geometric mean ± SD values for the study of Schutz et al., 2009) for <i>AUC</i> (D) and <i>C</i><sub><i>max</i></sub> (E).</p

    Model simulated versus observed total MPH plasma concentration profiles in adults receiving a single oral dose of Medikinet Retard at (A) 40 mg [23] and 20 mg [27, 28] (B and C) under fed conditions, as well as model predictability of the pharmacokinetic parameters <i>AUC</i> (D) and <i>C</i><sub><i>max</i></sub> (E).

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    <p>Solid lines in A-C represent simulated mean plasma concentration-time profiles, whereas dashed lines represent the 5<sup>th</sup> and 95<sup>th</sup> percentiles for the predicted values. The observed data points (●) are shown as mean except for Schutz et al (2009) where the observed data points are expressed as geometric mean. The solid lines in D and E illustrates unity, as well as a twofold deviation from unity; whereas the data points represent observed mean ± SD values (geometric mean ± SD values for the study of Schutz et al., 2009) with respective to the simulated mean ± SD values (geometric mean ± SD values for the study of Schutz et al., 2009) for <i>AUC</i> (D) and <i>C</i><sub><i>max</i></sub> (E).</p

    Sensitivity analysis.

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    <p>Model parameters with absolute normalized sensitivity coefficient (NSC) values greater than 0.1 are listed in the Figure. </p

    Physiological model parameters for the gastrointestinal tract [31, 32].

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    <p>Physiological model parameters for the gastrointestinal tract [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164641#pone.0164641.ref031" target="_blank">31</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164641#pone.0164641.ref032" target="_blank">32</a>].</p

    SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues

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    <div><p>Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinder<sup>F</sup> and a template predictor SNBRFinder<sup>T</sup>. SNBRFinder<sup>F</sup> was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinder<sup>T</sup> was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinder<sup>F</sup> was clearly superior to the commonly used sequence profile-based predictor and SNBRFinder<sup>T</sup> can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at <a href="http://ibi.hzau.edu.cn/SNBRFinder" target="_blank">http://ibi.hzau.edu.cn/SNBRFinder</a>.</p></div

    Residue-based evaluation of different feature-based predictors on DB312 (RB264).

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    <p><sup>a</sup>PSSM: position specific scoring matrix, CS: residue conservation scores, PS: predicted structural features, PC: physicochemical properties,</p><p>IP: interface propensity, SP: sequential position, and GF: global features.</p><p>Residue-based evaluation of different feature-based predictors on DB312 (RB264).</p
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