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Tran D.Q.H.,Montreal General Hospital | Dugani S.,Montreal General Hospital | Correa J.A.,McGill University | Dyachenko A.,Pharsight | And 2 more authors.
Regional Anesthesia and Pain Medicine | Year: 2011

BACKGROUND: The aim of this study was to determine the minimum effective volume of lidocaine 1.5% with epinephrine 5 μg/mL in 90% of patients (MEV90) for double-injection ultrasound-guided supraclavicular block (SCB). METHODS: Using an in-plane technique and a lateral to medial direction, a double-injection ultrasound-guided SCB was performed. A 17-gauge, 8-cm Tuohy needle was initially advanced until its tip was positioned at the intersection of the first rib and subclavian artery ("corner pocket"). Half the volume of lidocaine was injected in this location. Subsequently, the needle was redirected toward the neural cluster formed by the trunks and divisions of the brachial plexus. The remaining volume of lidocaine was deposited in this location. Volume assignment was carried out using a biased coin design up-and-down sequential method, where the total volume of local anesthetic administered to each patient depended on the response of the previous one. In case of failure, the next subject received a higher volume (defined as the previous volume with an increment of 2.5 mL). If the previous patient had a successful block, the next subject was randomized to a lower volume (defined as the previous volume with a decrement of 2.5 mL), with a probability of b = 0.11, or the same volume, with a probability of 1-b = 0.89. Each increment or decrement was evenly distributed between the "corner pocket" (1.25 mL) and neural cluster (1.25 mL). Lidocaine 1.5% with epinephrine 5 μg/mL was used in all subjects. Success was defined, at 30 minutes, as a minimal score of 14 of 16 points using a composite scale encompassing sensory and motor block. Patients undergoing surgery of the elbow, forearm, wrist, or hand were prospectively enrolled until 45 successful blocks were obtained. RESULTS: Fifty-four patients were included in the study. Using isotonic regression and bootstrap confidence interval, the MEV90 for double-injection ultrasound-guided SCB was estimated to be 32 mL (95% confidence interval, 30-34 mL). All patients with a minimal composite score of 14 points at 30 minutes achieved surgical anesthesia intraoperatively. CONCLUSIONS: For double-injection ultrasound-guided SCB, the MEV90 of lidocaine 1.5% with epinephrine 5 μg/mL is 32 mL. Further dose finding studies are required for other concentrations of lidocaine, other local anesthetic agents and single-injection techniques. Copyright © 2011 by American Society of Regional Anesthesia and Pain Medicine. Source

The purpose of bioanalysis in the pharmaceutical industry is to provide 'raw' data about the concentration of a drug candidate and its metabolites as input for studies of drug properties such as pharmacokinetic (PK), toxicokinetic, bioavailability/bioequivalence and other studies. Building a seamless workflow from the laboratory to final reports is an ongoing challenge for IT groups and users alike. In such a workflow, PK automation can provide companies with the means to vastly increase the productivity of their scientific staff while improving the quality and consistency of their reports on PK analyses. This report presents the concept and benefits of PK automation and discuss which features of an automated reporting workflow should be translated into software requirements that pharmaceutical companies can use to select or build an efficient and effective PK automation solution that best meets their needs. © 2011 Future Science Ltd. Source

Houk B.E.,Pfizer | Bello C.L.,Pfizer | Poland B.,Pharsight | Rosen L.S.,Premiere Oncology | And 2 more authors.
Cancer Chemotherapy and Pharmacology | Year: 2010

Purpose: In this pharmacokinetic/pharmacodynamic metaanalysis, we investigated relationships between clinical endpoints and sunitinib exposure in patients with advanced solid tumors, including patients with gastrointestinal stromal tumor (GIST) and metastatic renal cell carcinoma (mRCC). Methods: Pharmacodynamic data were available for 639 patients of whom 443 had pharmacokinetic data. Sunitinib doses ranged from 25 to 150 mg QD or QOD. Models to express endpoint values and/or changes from baseline by the highest-correlating exposure measures were developed in S-PLUS or NONMEM using fixed- and mixed-effects modeling. Results: Tentative relationships were identified between (1) steady-state AUC of total drug (sunitinib + its active metabolite SU12662) and time to tumor progression (TTP), overall survival (OS), with AUC significantly associated with longer TTP and OS in patients with GIST and mRCC, and incidence, but not severity, of fatigue; (2) steady-state AUC of sunitinib and response probability, with AUC significantly associated with objective response in patients with mRCC and stable disease in patients with both mRCC and GIST (with no such correlations in patients with solid tumors); (3) dose and tumor size reductions; (4) total drug concentration and diastolic blood pressure (DBP), with a typical patient on sunitinib 50 mg QD (the recommended dose) predicted to experience a maximum DBP increase of 8 mmHg; and (5) cumulative AUC of total drug and absolute neutrophil count (ANC), with ANC reductions occurring predominantly after one treatment cycle. Conclusions: The results of this meta-analysis indicate that increased exposure to sunitinib is associated with improved clinical outcomes (longer TTP, longer OS, greater chance of antitumor response), as well as some increased risk of adverse effects. A sunitinib 50-mg starting dose seems reasonable, providing clinical benefit with acceptably low risk of adverse events. Source

Kakhi M.,U.S. Food and Drug Administration | Marroum P.,Marroum Pharmaceutical Consulting | Chittenden J.,Pharsight
Biopharmaceutics and Drug Disposition | Year: 2013

A two-stage, numerical deconvolution approach was employed to develop level A in vitro-in vivo correlations using data for three formulations of an extended-release oral dosage form. The in vitro dissolution data for all formulations exhibited near-complete dissolution within the time frame of the test. The pharmacokinetic concentration-time profiles for 16 subjects in a cross-over study demonstrated notably limited bioavailability for the slowest formulation. These data were used as the basis for the IVIVC model development. Two models were identified that satisfied the nominal requirements for a conclusive internal predictability of the IVIVC, provided that all three formulations were used as internal datasets. These were a simple linear model with absorption cut-off and a piecewise-linear variable absorption scale model. A subsequent cross-validation of the models' robustness indicated that neither model predicted satisfactorily the pharmacokinetic characteristics of all formulations in a conclusive manner. The piecewise-linear variable absorption scale model provided the most accurate results, particularly with respect to the prediction of the slowest formulation's pharmacokinetic metrics. But this latter model also involved additional free parameters compared with the simple linear model with absorption cut-off. It is argued that more complex IVIVC models with extra parameterization require comprehensive validation to ascertain the accuracy and robustness of the model. In order to achieve this, it is necessary to ensure a complete suite of supporting datasets for internal and external validation, irrespective of the mathematical approach used subsequently to develop the IVIVC. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd. Source

Gabrielsson J.,Swedish University of Agricultural Sciences | Weiner D.,Pharsight
Methods in Molecular Biology | Year: 2012

When analyzing pharmacokinetic data, one generally employs either model fitting using nonlinear regression analysis or non-compartmental analysis techniques (NCA). The method one actually employs depends on what is required from the analysis. If the primary requirement is to determine the degree of exposure following administration of a drug (such as AUC), and perhaps the drug's associated pharmacokinetic parameters, such as clearance, elimination half-life, T max, C max, etc., then NCA is generally the preferred methodology to use in that it requires fewer assumptions than model-based approaches. In this chapter we cover NCA methodologies, which utilize application of the trapezoidal rule for measurements of the area under the plasma concentration-time curve. This method, which generally applies to first-order (linear) models (although it is often used to assess if a drug's pharmacokinetics are nonlinear when several dose levels are administered), has few underlying assumptions and can readily be automated. In addition, because sparse data sampling methods are often utilized in toxicokinetic (TK) studies, NCA methodology appropriate for sparse data is also discussed. © 2012 Springer Science+Business Media, LLC. Source

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