TRANSFER OF A MULTIDIMENSIONAL ON-LINE SPE-LC-ECD METHOD FOR THE DETERMINATION OF THREE MAJOR CATECHOLAMINES IN NATIVE HUMAN URINE. E. Rozet 1, R. Morello.

Slides:



Advertisements
Similar presentations
Contact: Eric Rozet, Statistician +32 (0)
Advertisements

Sampling: Final and Initial Sample Size Determination
Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.
1 1 Slide STATISTICS FOR BUSINESS AND ECONOMICS Seventh Edition AndersonSweeneyWilliams Slides Prepared by John Loucks © 1999 ITP/South-Western College.
Conclusions Optimum conditions for derivatization were: methanol as solvent for reagent I, K 3 [Fe(CN) 6 ] 0.02M, DPE 0.05M, methanol: HCl: water 14:1:15.
PHARM 462 PART / /31 Good Manufacturing Practices (GMP) VALIDATION of ANALYTICAL TEST METHODS.
World Health Organization
TOWARDS A RISK BASED METHODOLOGY TO ASSESS THE ACCEPTABILITY OF AN ANALYTICAL METHOD TRANSFER: COMPARISON OF DIFFERENT APPROACHES. E. Rozet 1, W. Dewe²,
EPIDEMIOLOGY AND BIOSTATISTICS DEPT Esimating Population Value with Hypothesis Testing.
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
Major Points An example Sampling distribution Hypothesis testing
Inferences About Process Quality
8-1 Introduction In the previous chapter we illustrated how a parameter can be estimated from sample data. However, it is important to understand how.
QUALITY CONTROL OF PHYSICO-Chemical METHODS Introduction :Validation توثيق المصدوقية.
Qian H. Li, Lawrence Yu, Donald Schuirmann, Stella Machado, Yi Tsong
Statistical Inference Dr. Mona Hassan Ahmed Prof. of Biostatistics HIPH, Alexandria University.
Validation of Analytical Method
Testing Hypotheses I Lesson 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics n Inferential Statistics.
Chapter 8 Introduction to Hypothesis Testing
Determining Sample Size
The following minimum specified ranges should be considered: Drug substance or a finished (drug) product 80 to 120 % of the test concentration Content.
Chapter 9 Large-Sample Tests of Hypotheses
Analytical considerations
STA Lecture 161 STA 291 Lecture 16 Normal distributions: ( mean and SD ) use table or web page. The sampling distribution of and are both (approximately)
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 17 Inferential Statistics.
Population All members of a set which have a given characteristic. Population Data Data associated with a certain population. Population Parameter A measure.
The Argument for Using Statistics Weighing the Evidence Statistical Inference: An Overview Applying Statistical Inference: An Example Going Beyond Testing.
Chapter 5 Errors In Chemical Analyses Mean, arithmetic mean, and average (x) are synonyms for the quantity obtained by dividing the sum of replicate measurements.
Maximum Likelihood Estimator of Proportion Let {s 1,s 2,…,s n } be a set of independent outcomes from a Bernoulli experiment with unknown probability.
Utilization of Accuracy Profiles as a Tool for the Validation of Analytical Methods A. Ceccato, P. Jacobs, A. Flament, M. Gibella and W. Dewé Lilly Development.
Biostatistics Class 6 Hypothesis Testing: One-Sample Inference 2/29/2000.
European Union Structural Funds Ministère de la Région Wallonne Evaluating measurement uncertainty for dioxins in routine analysis by the accuracy profile.
Confidence intervals and hypothesis testing Petter Mostad
USE OF ACCURACY PROFILE FOR THE VALIDATION OF THE DIRECT QUANTITATION OF TAGITININ C IN TITHONIA DIVERSIFOLIA LEAVES BY ON-LINE COUPLING OF SUPERCRITICAL.
QC THE MULTIRULE INTERPRETATION
Statistics for Decision Making Basic Inference QM Fall 2003 Instructor: John Seydel, Ph.D.
: An alternative representation of level of significance. - normal distribution applies. - α level of significance (e.g. 5% in two tails) determines the.
Validation Defination Establishing documentary evidence which provides a high degree of assurance that specification process will consistently produce.
Analysis of Experimental Data; Introduction
C HAPTER 4  Hypothesis Testing -Test for one and two means -Test for one and two proportions.
Hypothesis Testing Steps for the Rejection Region Method State H 1 and State H 0 State the Test Statistic and its sampling distribution (normal or t) Determine.
Chapter 9, Part E. VII. Calculating the Probability of Type II Errors A common decision in business is whether to accept a shipment or not, based upon.
1 How much and how many? Guidance on the extent of validation/verification studies S L R Ellison Science for a safer world.
Lecture 10 ANALYTICAL METHOD DEVELOPMENT AND VALIDATION IN HPLC AND GC. Lecture 10 – Chromatography, Dr. Rasha Hanafi 1© Dr. Rasha Hanafi, GUC.
Implementing principles of Quality by Design (QbD) in validation context Cédric Hubert a, Pierre Lebrun a,b, Eric Rozet a,b and Philippe Hubert a a Laboratory.
SEMINAR ON PRESENTED BY BRAHMABHATT BANSARI K. M. PHARM PART DEPARTMENT OF PHARMACEUTICS AND PHARMACEUTICAL TECHNOLGY L. M. COLLEGE OF PHARMACY.
POSTER TEMPLATE BY: om New spectrophotometric method for determination of cephalosporins in pharmaceutical formulations Shazalia.
Statistical Evaluation of the Linearity for Quantitative in Vitro Diagnostic Devices in Vitro Diagnostic Devices Tsung-Cheng Hsieh ( 謝宗成 ), Prof. Jen-Pei.
REC Savannah, Febr. 22, 2006 Title Outlier Detection in Geodetic Applications with respect to Observation Imprecision Ingo Neumann and Hansjörg.
EQUIPMENT and METHOD VALIDATION
Hypothesis Testing Chapter Hypothesis Testing  Developing Null and Alternative Hypotheses  Type I and Type II Errors  One-Tailed Tests About.
Table 2. Summary of chromatographic methods of Terazosin in different matrices Alankar Shrivastava et al. Various Analytical Methods for the Determination.
이 장 우. 1. Introduction  HPLC-MS/MS methodology achieved its preferred status -Highly selective and effectively eliminated interference -Without.
7 Statistical Data Treatment and Evaluation CHAPTER.
More on Inference.
World Health Organization
Uncontrolled variation is the enemy of quality
When we free ourselves of desire,
More on Inference.
Discrete Event Simulation - 4
Virtual University of Pakistan
ANALYTICAL METHOD VALIDATION
World Health Organization
Psych 231: Research Methods in Psychology
Simultaneous determination of creatinine, iohexol and p-aminohippuric acid in animal plasma by ultra-high-performance liquid chromatography–tandem mass.
Testing Hypotheses I Lesson 9.
Selvadurai Muralidharan, Jayaraja kumar, Venugopal Vijayan
Determining the Risk Level Regarding to the Positioning of an Exam Machine Used in the Nuclear Environment, based of polynomial regression Mihai OPROESCU1,
Inference about Population Mean
Presentation transcript:

TRANSFER OF A MULTIDIMENSIONAL ON-LINE SPE-LC-ECD METHOD FOR THE DETERMINATION OF THREE MAJOR CATECHOLAMINES IN NATIVE HUMAN URINE. E. Rozet 1, R. Morello 2, W. Dewe 3, P. Chiap 4, B. Boulanger 3, K. S. Boos 2, J. Crommen 4 and Ph. Hubert 1 1 Laboratory of Analytical Chemistry and 4 Laboratory of Analytical Pharmaceutical Chemistry, Bioanalytical Chemistry Research Unit, ULg, B 36, B-4000 Liège, Belgium. 2 Laboratory of BioSeparation, Institute of Clinical Chemistry, University Hospital Grosshadern, D Munich, Germany. 3 Lilly Development Centre, Statistical department, rue Granbompre, 11, B-1348 Mont-Saint-Guibert, Belgium.  To specify the objective of an analytical method transfer.  To recommend the use of a total error based criterion for the decision of accepting/rejecting an analytical method transfer.  To demonstrate the applicability of this decision rule with the real transfer of a multidimensional on-line SPE-LC-ECD method for the quantitative determination of norepinephrine (NE), epinephrine (E) and dopamine (DA) in native human urine. OBJECTIVE ANALYTICAL METHOD TRANSFER: DEFINITION AND OBJECTIVE  The Objective of a transfer is therefore: To provide users the guarantee that each measurement ( x i ) on unknown sample is close enough to the true value (  T ).  Enough, means for example less than 15% away from sample true unknown value ( acceptance limits, e.g. 5% (dosage form), 10% (impurities) or 15% (bio- analysis)).  Guarantee, means that it is very likely that whatever the measurements, it will be close enough from the unknown true value  β  risk), e.g. 5%, 20% or 33%. TOTAL ERROR BASED APPROACH TRANSFER SETUP Method transfer is the last step before implementation of the method in routine use at the receiving laboratory. The design used to assess the acceptation of the transfer must reflect the future use of the method. At the receiver the analytical method will be used in routine by only 1 operator (B) and on 1 LC equipment (II). Therefore this setup was used during the transfer study. Another point is the choice of the number of series and repetitions per series to perform in each laboratory. This choice is based on simulations using the results of the validation of the method (bias, RSD of repeatability and intermediate precision) made by the sender to achieve the best power for the transfer. Table 1, illustrates the experimental design applied in each laboratory. CONCLUSIONS The objective of an analytical method transfer is to provide users guarantee in order to minimize the risks to have future results out of specifications. The total error approach achieve this by computing the risk of having future individual results outside the acceptance limits. This new approach was applied to the transfer of a SPE-LC-ECD method for the quantitative determination of three major catecholamines in native human urine. The risk approach gives the guarantee that the receiver masters the analytical method and furthermore allow to manage the risks of having results out of specifications during routine use [3]. Table 1. Experimental design performed in both laboratories for the method transfer.  Definition: An analytical method transfer consists in transferring a previously validated analytical method from a sending laboratory (called sender) mastering this method, to a receiving laboratory (called receiver) after having experimentally demonstrated that the receiver also masters the method. RESULTS The transferred method is an on-line SPE-LC-ECD method for the quantitative determination of NE, E and DA in native human urine. Mobile phases:  Sample clean up: 0.2M (NH 4 ) 2 HPO 4, 3.72g/L EDTA, methanol, 95/5, v/v, pH 8.7  Chromatographic separation: 50mM KH 2 PO 4, 2.5mM sodium octanesulfonate, 0.1g/L EDTA, acetonitrile, 96.5/3.5, v/v, pH 3.5 SPE column: Restricted Access Material SPE column modified with nitrophenylboronic acid as affinity ligand. Analytical column: Zorbax bonus RP-C18 (150 x 4.6 mm i.d., 5 µm particle size) Temperature: 30°C Detection: ECD at 600 mV DESCRIPTION OF THE TRANSFERRED METHOD Research grant from the Walloon Region and the European Social Fund to one of the author (E. Rozet) is gratefully acknowledged (First Europe Objective 3 project n°215269). AKNOWLEDGMENTS A new total error based approach is proposed to correctly assess the acceptation of a transfer [1,2].  Compute the confidence interval of the mean of the sender results: [L S ;U S ].  Compute the β-expectation tolerance interval of the receiver results: [L R ;U R ], i.e. the interval in which one can expect that at least a proportion β (e.g. 95%) of future individual results will lay.  Compute the decision interval:  Compute the probability P of having results falling outside the pre-specified acceptance limits λ and compare it with the maximum tolerated risk 1- β. If P  1- β  the transfer is accepted If P > 1- β  the transfer is rejected PRE-SPECIFIED PARAMETERS  Maximum risk tolerated: 1- β = 5%  Acceptance limits: = ± 15% Results of the transfer are shown in Figure 1 as risk profiles for NE, E and DA at each concentration level evaluated during method transfer. The transfer is rejected if the observed risk is above the maximum risk accepted. As can be seen, the risk is smaller than the maximum risk of 5% for all the catecholamines studied irrespective of the concentration levels. Therefore, the risk of having results out of specification during routine use of the method for the receiver is of maximum 2.5%, 3.4% and 3.1% for NE, E, DA respectively. Fig. 1. Transfer results. [1]. Dewé, W. and al. Chemom. Intell. Lab. Syst. Accepted for publication. [2]. Rozet E. and al. J. Pharm. Biomed. Anal. In press. [3]. FDA, Process Analytical Technology (PAT) Initiative, The standards used to assess the transfer were prepared in native human urine at 3 concentration levels: 50, 250 and 500 µg/L. REFERENCES