Microsoft Word - Q1E evaluation for stability data

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1 INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE ICH HARMONISED TRIPARTITE GUIDELINE 안정성데이터평가 (Evaluation for Stability Data) Q1E Current Step 4 version dated 6 February 2003 This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA. 1

2 Q1E Document History First Codification Q1E History Date New Codification November 2005 Approval by the Steering Committee under 6 Q1E Step 2 and release for public consultation February 2002 Current Step 4 version Q1E Approval by the Steering Committee under 6 Q1E Step 4 and recommendation for adoption February to the three ICH regulatory bodies

3 EVALUATION FOR STABILITY DATA ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 6 February 2003, this guideline is recommended for adoption to the three regulatory parties to ICH. 목차 1. 서론 (INTRODUCTION) 1.1 목표 (Objectives of the Guideline) 1.2 배경 (Background) 1.3 적용범위 (Scope of the Guideline) 2. 가이드라인 (GUIDELINES) 2.1 일반원칙 (General Principles) 2.2 데이터정리 (Data presentation) 2.3 외삽 (Extrapolation) 2.4 실온보관원료의약품또는완제의약품의재시험기간또는유효기간 추정을위한데이터평가 (Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products Intended for Room Temperature Storage) 가속조건에서중대한변화가없는경우 (No significant change at accelerated condition) 가속 조건에서 중대한 변화가 발생한 경우 (Significant change at accelerated condition) 2.5 실온이하조건에서보관할원료의약품또는완제의약품의재시험기간 또는유효기간추정을위한데이터평가 (Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products Intended for Storage Below Room Temperature) 냉장 보관 예정 원료의약품 또는 완제의약품 (Drug substances or products intended for storage in a refrigerator) 냉동 보관 예정 원료의약품 또는 완제의약품 (Drug substances or products intended for storage in a freezer) C 이하보관예정원료의약품또는완제의약품 (Drug substances or products intended for storage below - 20 C) 3

4 2.6 일반통계분석방법 (General Statistical Approaches) 3. 부록 (APPENDICES) Appendix A: 원료의약품또는완제의약품품 ( 동결제품제외 ) 의재시험기간또는유효기간추정을위한데이터평가의사결정도 (Decision Tree for Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products (excluding Frozen Products)) Appendix B: 안정성데이터통계분석방법의예 (Examples of Statistical Approaches to Stability Data Analysis) 4

5 EVALUATION FOR STABILITY DATA 1. 서론 (INTRODUCTION) 1.1 목표 (Objectives of the Guideline) This guideline is intended to provide recommendations on how to use stability data generated in accordance with the principles detailed in the ICH guideline Q1A(R) Stability Testing of New Drug Substances and Products (hereafter referred to as the parent guideline) to propose a retest period or shelf life in a registration application. This guideline describes when and how extrapolation can be considered when proposing a retest period for a drug substance or a shelf life for a drug product that extends beyond the period covered by available data from the stability study under the long-term storage condition (hereafter referred to as long-term data). 이가이드라인은등록신청문서에서재시험기간또는유효기간을제시하기위해 ICH 가이드라인 "Q1A(R) 새로운원료의약품과완제의약품의안정성시험 "( 이하 " 모가이드라인 ") 에기술된원칙에따라생산한안정성데이터의활용방법에대한권고사항을제시하기위한것이다. 이가이드라인은 " 장기보관조건안정성시험데이터 "( 이하 " 장기데이터 ") 의대상기간을벗어나는원료의약품재시험기간또는완제의약품유효기간을제시함에있어서, 외삽방법을언제, 어떻게고려할수있는지설명한다. 1.2 배경 (Background) The guidance on the evaluation and statistical analysis of stability data provided in the parent guideline is brief in nature and limited in scope. The parent guideline states that regression analysis is an appropriate approach to analyzing quantitative stability data for retest period or shelf life estimation and recommends that a statistical test for batch poolability be performed using a level of significance of However, the parent guideline includes few details and does not cover situations where multiple factors are involved in a full- or reduced-design study. 모가이드라인에기술된안정성데이터의평가와통계분석에관한내용은기본적으로간략하고제한적이다. 모가이드라인은재시험기간또는유효기간추정을위한정량적안정성데이터의적절한분석방법이회귀분석이라고제시하고, 0.25의유의수준을활용해배치풀링가능성의통계적검정을권고한다. 하지만모가이드라인에는자세한내용이거의없으며, 여러요소가관련된완전또는단축디자인의안정성시험상황을 5

6 다루고있지않다. This guideline is an expansion of the guidance presented in the Evaluation sections of the parent guideline. 이가이드라인은모가이드라인의평가섹션에기술된사항을확장시킨것이다. 1.3 적용범위 (Scope of the Guideline) This guideline addresses the evaluation of stability data that should be submitted in registration applications for new molecular entities and associated drug products. The guideline provides recommendations on establishing retest periods and shelf lives for drug substances and drug products intended for storage at or below room temperature *. It covers stability studies using single- or multi-factor designs and full or reduced designs. 이가이드라인은 NME(new molecular entity) 와관련완제의약품의등록신청문서에포함시켜제출해야할안정성데이터의평가에관한것이다. " 실온 " 또는그이하에서보관하는원료의약품과완제의약품의재시험기간과유효기간설정에관한권고사항을기술한다. 단일또는복합요소디자인과완전디자인또는단축디자인의안정성시험을대상으로한다. * Note: The term room temperature refers to the general customary environment and should not be inferred to be the storage statement for labeling. 주 : " 실온 " 은통상적인환경을의미하며라벨표시보관조건으로정하지않는다. ICH Q6A and Q6B should be consulted for recommendations on the setting and justification of acceptance criteria, and ICH Q1D should be referenced for recommendations on the use of full- versus reduced-design studies. 허용기준설정및타당성증명에관한사항은 ICH Q6A와 Q6B를참조하며, 완전디자인과단축디자인시험에관한권고사항은 ICH Q1D를참조한다. 2. 가이드라인 (GUIDELINES) 2.1 일반원칙 (General Principles) The design and execution of formal stability studies should follow the principles outlined in the parent guideline. The purpose of a stability study is to establish, 6

7 based on testing a minimum of three batches of the drug substance or product, a retest period or shelf life and label storage instructions applicable to all future batches manufactured and packaged under similar circumstances. The degree of variability of individual batches affects the confidence that a future production batch will remain within acceptance criteria throughout its retest period or shelf life. 공식안정성시험의디자인과실행은모가이드라인의원칙을따라야한다. 안정성시험의목적은최소 3개원료의약품또는완제의약품배치시험결과를토대로, 유사환경에서제조 / 포장될미래의모든배치에적용할보관조건과재시험기간또는유효기간을설정하는것이다. 개별배치의편차정도는미래의생산배치가재시험기간또는유효기간전체에걸쳐허용기준이내로유지될것이라는신뢰에영향을준다. Although normal manufacturing and analytical variations are to be expected, it is important that the drug product be formulated with the intent to provide 100 percent of the labeled amount of the drug substance at the time of batch release. If the assay values of the batches used to support the registration application are higher than 100 percent of label claim at the time of batch release, after taking into account manufacturing and analytical variations, the shelf life proposed in the application can be overestimated. On the other hand, if the assay value of a batch is lower than 100 percent of label claim at the time of batch release, it might fall below the lower acceptance criterion before the end of the proposed shelf life. 정상적인제조 / 분석편차가예상되기는하지만, 배치출하승인시점에 100% 의표시원료의약품함량을제공할수있도록완제의약품을조제하는것이중요하다. 등록신청문서를뒷받침하는배치분석결과가배치출하승인당시에표시량의 100% 를넘으면, 제조 / 분석편차를고려할때신청문서에제시된유효기간이과대평가된것일수있다. 반면배치의함량결과값이배치출하승인당시에표시량의 100% 이하라면, 예정유효기간이끝나기도전에하한허용기준아래로떨어질가능성이있다. A systematic approach should be adopted in the presentation and evaluation of the stability information. The stability information should include, as appropriate, results from the physical, chemical, biological, and microbiological tests, including those related to particular attributes of the dosage form (for example, dissolution rate for solid oral dosage forms). The adequacy of the mass balance should be assessed. Factors that can cause an apparent lack of mass balance should be considered, including, for example, the mechanisms of degradation and the stability-indicating capability and inherent variability of the analytical procedures. 체계적인방법으로안정성정보를정리하고평가한다. 제형별특성관련시험을포함해 ( 예, 7

8 내용고형제의용출시험 ) 물리적, 화학적, 생물학적, 미생물학적시험결과를확보한다. 물질수지의적절성을평가한다. 물질수지의명백한결여를유발할수있는요소 ( 예를 들어분해메커니즘과분석절차의안정성지시성및내재적변동성포함 ) 를고려한다. The basic concepts of stability data evaluation are the same for single- versus multifactor studies and for full- versus reduced-design studies. Data from formal stability studies and, as appropriate, supporting data should be evaluated to determine the critical quality attributes likely to influence the quality and performance of the drug substance or product. Each attribute should be assessed separately, and an overall assessment should be made of the findings for the purpose of proposing a retest period or shelf life. The retest period or shelf life proposed should not exceed that predicted for any single attribute. 단일요소 / 복합요소시험과완전 / 단축디자인시험에안정성데이터평가의기본개념을동일하게적용한다. 공식안정성시험데이터와적절한경우에는근거데이터를평가하여, 원료의약품이나완제의약품의품질과성능에영향을미칠가능성이있는핵심품질특성요소를결정한다. 각특성요소를별도로평가하고결과를전반적으로평가하여, 재시험기간또는유효기간을제시한다. 예정재시험기간또는유효기간은단일특성요소에대하여예측되는기간을초과해서는안된다. The decision tree in Appendix A outlines a stepwise approach to stability data evaluation and when and how much extrapolation can be considered for a proposed retest period or shelf life. Appendix B provides (1) information on how to analyze long-term data for appropriate quantitative test attributes from a study with a multifactor, full or reduced design, (2) information on how to use regression analysis for retest period or shelf life estimation, and (3) examples of statistical procedures to determine poolability of data from different batches or other factors. Additional guidance can be found in the references listed; however, the examples and references do not cover all applicable statistical approaches. 부록 A의의사결정도는단계별안정성데이터평가방법, 그리고재시험기간이나유효기간을설정하기위해외삽방법을언제, 어느정도적용할수있는지보여준다. 부록 B는 (1) 복합요소, 완전또는단축디자인시험에서확보한관련정량시험항목의장기데이터를분석하는방법에관한정보, (2) 재시험기간또는유효기간추정을위한회귀분석방법에관한정보, (3) 다른배치또는다른요소에서확보한데이터의풀링가능성결정을위한통계절차의예를설명한다. 참고문헌항목에다양한가이드라인문서가정리되어있다. 하지만예와참고문헌은해당되는모든통계방법을포괄하고있지않다. 8

9 In general, certain quantitative chemical attributes (e.g., assay, degradation products, preservative content) for a drug substance or product can be assumed to follow zero-order kinetics during long-term storage 1. Data for these attributes are therefore amenable to the type of statistical analysis described in Appendix B, including linear regression and poolability testing. Although the kinetics of other quantitative attributes (e.g., ph, dissolution) is generally not known, the same statistical analysis can be applied, if appropriate. Qualitative attributes and microbiological attributes are not amenable to this kind of statistical analysis. 일반적으로원료의약품또는완제의약품의정량적인화학적특성 ( 예, 정량, 분해산물, 보존제함량 ) 은장기보관기간에영차역학을따를것으로가정할수있다. 그러므로이런특성요소의데이터는선형회귀와풀링가능성검정을포함해부록 B에기술된통계분석의대상이된다. 다른정량적특성요소 ( 예, ph, 용출 ) 의역학은일반적으로알려져있지않지만, 적절한경우에동일한통계분석방법을적용할수있다. 정성적특성과미생물학적특성은이와같은종류의통계분석으로처리할수없다. The recommendations on statistical approaches in this guideline are not intended to imply that use of statistical evaluation is preferred when it can be justified to be unnecessary. However, statistical analysis can be useful in supporting the extrapolation of retest periods or shelf lives in certain situations and can be called for to verify the proposed retest periods or shelf lives in other cases. 이가이드라인에서통계적방법을권고하지만, 필요하지않음을타당하게증명할수있는상황에서도통계평가를해야한다는의미는아니다. 하지만통계분석은특정상황에서재시험기간또는유효기간의외삽법을뒷받침하는데유용하며, 예정재시험기간또는유효기간을확인하는데필요할수있다. 2.2 데이터정리 (Data presentation) Data for all attributes should be presented in an appropriate format (e.g., tabular, graphical, narrative) and an evaluation of such data should be included in the application. The values of quantitative attributes at all time points should be reported as measured (e.g., assay as percent of label claim). If a statistical analysis is performed, the procedure used and the assumptions underlying the model should be stated and justified. A tabulated summary of the outcome of statistical analysis and/or graphical presentation of the long-term data should be included. 모든특성항목의데이터를적절한형식 ( 예, 표, 그래프, 설명 ) 으로정리하며, 데이터평가내용을신청문서에기술한다. 모든시점의정량적특성항목결과값을실제측정값 ( 예, 9

10 정량 : 표시량대비퍼센트 ) 으로기록한다. 통계분석을실시한다면, 통계분석절차와통계 분석모델의기본가정을기술하고그타당성을설명한다. 통계분석결과요약표와장기 데이터그래프를포함시킨다. 2.3 외삽 (Extrapolation) Extrapolation is the practice of using a known data set to infer information about future data. Extrapolation to extend the retest period or shelf life beyond the period covered by long-term data can be proposed in the application, particularly if no significant change is observed at the accelerated condition. Whether extrapolation of stability data is appropriate depends on the extent of knowledge about the change pattern, the goodness of fit of any mathematical model, and the existence of relevant supporting data. Any extrapolation should be performed such that the extended retest period or shelf life will be valid for a future batch released with test results close to the release acceptance criteria. 외삽은기지의데이터세트를활용해미래의데이터에관한정보를추론하는방법이다. 외삽법을적용하여장기데이터대상기간을벗어나는재시험기간또는유효기간을설정해신청할수있다. 가속조건에서중대한변화가관찰되지않은경우에특히그렇게할수있다. 안정성데이터의외삽이적절한지여부는, 관련근거데이터의존재와수학모델의적합도, 변화패턴에대한지식의정도에따라결정된다. 출하허용기준에근접한시험결과를근거로출하승인되는미래의배치에대해서도확대산정한재시험기간또는유효기간이유효할수있도록외삽을실시한다. An extrapolation of stability data assumes that the same change pattern will continue to apply beyond the period covered by long-term data. The correctness of the assumed change pattern is critical when extrapolation is considered. When estimating a regression line or curve to fit the long-term data, the data themselves provide a check on the correctness of the assumed change pattern, and statistical methods can be applied to test the goodness of fit of the data to the assumed line or curve. No such internal check is possible beyond the period covered by longterm data. Thus, a retest period or shelf life granted on the basis of extrapolation should always be verified by additional long-term stability data as soon as these data become available. Care should be taken to include in the protocol for commitment batches a time point that corresponds to the end of the extrapolated retest period or shelf life. 안정성데이터의외삽은장기데이터의대상기간이지나도동일한변화패턴이계속 10

11 되리라는가정을토대로한다. 외삽을고려할때는변화패턴가정의정확성이중요하다. 장기데이터에맞춰회귀선또는곡선을추정할때, 데이터자체가변화패턴가정의정확성점검역할을하고, 통계적방법을적용하여가정회귀선또는곡선에대비하여데이터적합도를검정할수있다. 장기데이터대상기간을벗어나면이와같은내부점검이가능하지않다. 그러므로외삽에근거하여재시험기간이나유효기간이승인되면, 추가장기안정성데이터를확보하여가능한빨리재시험기간이나유효기간을확인한다. 외삽재시험기간또는유효기간말기에상응하는시점을이행배치프로토콜에포함시킨다. 2.4 실온보관원료의약품또는완제의약품의재시험기간또는유효기간추정을위한데이터평가 (Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products Intended for Room Temperature Storage) A systematic evaluation of the data from formal stability studies should be performed as illustrated in this section. Stability data for each attribute should be assessed sequentially. For drug substances or products intended for storage at room temperature, the assessment should begin with any significant change at the accelerated condition and, if appropriate, at the intermediate condition, and progress through the trends and variability of the long-term data. The circumstances are delineated under which extrapolation of retest period or shelf life beyond the period covered by long-term data can be appropriate. A decision tree is provided in Appendix A as an aid. 공식안정성시험데이터를이섹션에서설명하는바와같이체계적으로평가한다. 특성항목별안정성데이터를순차적으로평가한다. 실온보관용원료의약품이나완제의약품인경우, 가속조건과적절한경우에는중간조건에서중대한변화가발생하는지여부의평가에서시작하여, 장기데이터의경향분석과변동성분석을진행한다. 장기데이터대상기간이상의재시험기간또는유효기간외삽이적절한상황을규정한다. 이와관련하여참고할수있는의사결정도가부록 A에제시되어있다 가속조건에서중대한변화가없는경우 (No significant change at accelerated condition) Where no significant change occurs at the accelerated condition, the retest period or shelf life would depend on the nature of the long-term and accelerated data. 가속조건에서중대한변화가발생하지않으면, 재시험기간또는유효기간은장기 11

12 데이터와가속데이터의성격을고려하여결정한다 변동성이거의또는전혀없으며시간경과에따라변화가거의또는전혀없는 장기데이터및가속데이터 (Long-term and accelerated data showing little or no change over time and little or no variability) Where the long-term data and accelerated data for an attribute show little or no change over time and little or no variability, it might be apparent that the drug substance or product will remain well within the acceptance criteria for that attribute during the proposed retest period or shelf life. In these circumstances, a statistical analysis is normally considered unnecessary but justification for the omission should be provided. Justification can include a discussion of the change pattern or lack of change, relevance of the accelerated data, mass balance, and/or other supporting data as described in the parent guideline. Extrapolation of the retest period or shelf life beyond the period covered by long-term data can be proposed. The proposed retest period or shelf life can be up to twice, but should not be more than 12 months beyond, the period covered by long-term data. 어떤특성항목의장기데이터와가속데이터가변동성이거의또는전혀없으며시간경과에따른변화도거의또는전혀없는경우, 그원료의약품또는완제의약품의해당특성항목은예정재시험기간또는유효기간동안허용기준이내로유지되리라고생각할수있다. 이와같은경우에는일반적으로통계분석이필요하지않다고생각되지만, 그와같은통계분석의생략이유를타당하게설명해야한다. 이때변화패턴이나변화의결여, 가속데이터의연관성, 물질수지, 그리고모가이드라인에기술된기타근거데이터를토대로타당성을제시할수있다. 장기데이터대상기간이상의재시험기간또는유효기간외삽방법을제시할수도있다. 예정재시험기간이나유효기간을장기데이터대상기간의최대 2배까지정할수있으나, 그기간보다 12개월이상으로할수는없다 변동성및 / 또는시간경과에따른변화를보여주는장기데이터또는가속 데이터 (Long-term or accelerated data showing change over time and/or variability) If the long-term or accelerated data for an attribute show change over time and/or variability within a factor or among factors, statistical analysis of the long-term data can be useful in establishing a retest period or shelf life. Where there are differences in stability observed among batches or among other factors (e.g., strength, container size and/or fill) or factor combinations (e.g., strength-by- 12

13 container size and/or fill) that preclude the combining of data, the proposed retest period or shelf life should not exceed the shortest period supported by any batch, other factor, or factor combination. Alternatively, where the differences are readily attributed to a particular factor (e.g., strength), different shelf lives can be assigned to different levels within the factor (e.g., different strengths). A discussion should be provided to address the cause for the differences and the overall significance of such differences on the product. Extrapolation beyond the period covered by longterm data can be proposed; however, the extent of extrapolation would depend on whether long-term data for the attribute are amenable to statistical analysis. 어떤특성항목의장기데이터또는가속데이터가특정요소안에서또는여러요소사이에서변동성및 / 또는시간경과에따른변화를보이는경우, 장기데이터의통계분석이재시험기간이나유효기간을설정하는데유용할수있다. 배치또는기타요소 ( 예, 함량, 용기크기및 / 또는충전량 ) 또는요소조합 ( 예, 함량 / 용기크기및 / 또는충전량 ) 사이에안정성차이가관찰되어데이터의결합이불가능한경우, 예정재시험기간이나유효기간이특정배치, 기타요소또는요소조합에서나타난가장짧은기간을초과해서는안된다. 또는그러한안정성차이의원인이되는요소 ( 예, 함량 ) 를용이하게파악할수있다면, 그요소범위안에서 ( 예, 여러함량제품 ) 유효기간을다르게설정할수도있다. 차이의원인과그러한차이가제품과관련하여갖는전반적인의미를설명한다. 장기데이터대상기간이상의외삽기간을제시할수도있다. 하지만외삽의정도는해당특성항목장기데이터의통계분석여부에따라달라진다. 통계분석이가능하지않은데이터 (Data not amenable to statistical analysis) Where long-term data are not amenable to statistical analysis, but relevant supporting data are provided, the proposed retest period or shelf life can be up to one-and-a-half times, but should not be more than 6 months beyond, the period covered by long-term data. Relevant supporting data include satisfactory long-term data from development batches that are (1) made with a closely related formulation to, (2) manufactured on a smaller scale than, or (3) packaged in a container closure system similar to, that of the primary stability batches. 장기데이터의통계분석이가능하지않지만관련근거데이터가있다면, 예정재시험기간이나유효기간을장기데이터대상기간의최대 1 1/2배까지설정할수있지만, 그기간보다 6개월이상길게설정할수는없다. 관련근거데이터로는기본안정성배치와 (1) 밀접하게연관된조성으로제조하거나 (2) 더작은규모로제조하거나 (3) 유사한용기마개시스템으로포장한개발배치로확보한장기데이터가있다. 13

14 통계분석이가능한데이터 (Data amenable to statistical analysis) If long-term data are amenable to statistical analysis but no analysis is performed, the extent of extrapolation should be the same as when data are not amenable to statistical analysis. However, if a statistical analysis is performed, it can be appropriate to propose a retest period or shelf life of up to twice, but not more than 12 months beyond, the period covered by long-term data, when the proposal is backed by the result of the analysis and relevant supporting data. 장기데이터의통계분석이가능하지만분석을실시하지않았다면, 외삽정도는데이터의통계분석이가능하지않은경우와동일하다. 하지만통계분석을실시한다면, 예정재시험기간이나유효기간을장기데이터대상기간의최대 2배까지설정할수있지만, 그기간보다 12개월이상으로할수는없다. 다만분석결과와관련근거데이터로뒷받침해야한다 가속조건에서중대한변화가발생한경우 (Significant change at accelerated condition) Where significant change* occurs at the accelerated condition, the retest period or shelf life would depend on the outcome of stability testing at the intermediate condition, as well as at the long-term condition. 가속조건에서중대한변화가발생한다면, 장기조건과중간조건안정성시험결과를고려하여재시험기간이나유효기간을설정한다. * 주 (Note): The following physical changes can be expected to occur at the accelerated condition and would not be considered significant change that calls for intermediate testing if there is no other significant change: 가속조건에서다음과같은물리적변화가발생하리라예상할수있으며, 다른중대한변화가없는경우에는이와같은물리적변화를중간조건시험이필요한중대한변화로간주하지않는다. softening of a suppository that is designed to melt at 37ºC, if the melting point is clearly demonstrated, 녹는점이명확히증명되는경우에 37 C 에서녹도록설계된좌약의연화 failure to meet acceptance criteria for dissolution for 12 units of a gelatin capsule or gel-coated tablet if the failure can be unequivocally attributed 14

15 to cross-linking. 젤라틴캡슐또는젤코팅정제 12개의용출시험결과가허용기준에적합하지않은경우. 이때가교결합 (cross-linking) 때문에부적합이발생했다고할수있는경우. However, if phase separation of a semi-solid dosage form occurs at the accelerated condition, testing at the intermediate condition should be performed. Potential interaction effects should also be considered in establishing that there is no other significant change. 하지만가속조건에서반고형제의상분리가일어나면, 중간조건시험을실시한다. 다른중대한변화가없다고판단할때, 상호작용영향또한고려해야한다 중간조건에서중대한변화가없는경우 (No significant change at intermediate condition) If there is no significant change at the intermediate condition, extrapolation beyond the period covered by long-term data can be proposed; however, the extent of extrapolation would depend on whether long-term data for the attribute are amenable to statistical analysis. 중간조건에서중대한변화가없다면, 장기데이터대상기간을넘어서는외삽이가능하다. 하지만외삽의정도는해당특성항목장기데이터의통계분석여부에따라달라진다. 통계분석이가능하지않은데이터 (Data not amenable to statistical analysis) When the long-term data for an attribute are not amenable to statistical analysis, the proposed retest period or shelf life can be up to 3 months beyond the period covered by long-term data, if backed by relevant supporting data. 특정특성항목장기데이터의통계분석이가능하지않지만관련근거데이터가있다면, 장기데이터대상기간에최대 3개월까지추가하여재시험기간또는유효기간을제시할수있다. 통계분석이가능한데이터 (Data amenable to statistical analysis) When the long-term data for an attribute are amenable to statistical analysis but no analysis is performed, the extent of extrapolation should be the same as when data are not amenable to statistical analysis. However, if a statistical analysis is 15

16 performed, the proposed retest period or shelf life can be up to one-and-half times, but should not be more than 6 months beyond, the period covered by long-term data, when backed by statistical analysis and relevant supporting data. 특정특성항목장기데이터의통계분석이가능하지만통계분석을실시하지않은경우, 외삽정도는통계분석이가능하지않을때와동일하다. 하지만통계분석을실시한다면, 예정재시험기간또는유효기간을장기데이터대상기간의최대 1 1/2배까지설정할수있지만, 그기간보다 6개월이상으로할수는없다. 다만통계분석결과와관련근거데이터로뒷받침을해야한다 중간조건에서중대한변화가발생한경우 (Significant change at intermediate condition) Where significant change occurs at the intermediate condition, the proposed retest period or shelf life should not exceed the period covered by long-term data. In addition, a retest period or shelf life shorter than the period covered by long-term data could be called for. 중간조건에서중대한변화가발생한다면, 예정재시험기간또는유효기간은장기데이터대상기간을초과할수없다. 또한재시험기간또는유효기간은장기데이터대상기간보다더짧아야할것이다. 2.5 실온이하조건에서보관할원료의약품또는완제의약품의재시험기간또는유효기간추정을위한데이터평가 (Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products Intended for Storage Below Room Temperature) 냉장보관예정원료의약품또는완제의약품 (Drug substances or products intended for storage in a refrigerator) Data from drug substances or products intended to be stored in a refrigerator should be assessed according to the same principles as described in Section 2.4 for drug substances or products intended for room temperature storage, except where explicitly noted in the section below. The decision tree in Appendix A can be used as an aid. 냉장보관예정원료의약품이나완제의약품품의데이터를, 아래에서명확히설명한부분을제외하고는실온보관원료의약품또는완제의약품품에관한섹션 2.4에기술된원칙에따라평가한다. 부록 A의의사결정도를참조한다. 16

17 가속조건에서중대한변화가없는경우 (No significant change at accelerated condition) Where no significant change occurs at the accelerated condition, extrapolation of retest period or shelf life beyond the period covered by long-term data can be proposed based on the principles outlined in Section 2.4.1, except that the extent of extrapolation should be more limited. 가속조건에서중대한변화가발생하지않으면, 외삽의정도가더제한적인점을제외하고, 섹션 2.4.1에기술된원칙에의거하여장기데이터대상기간을넘어서는재시험기간또는유효기간의외삽이가능하다. If the long-term and accelerated data show little change over time and little variability, the proposed retest period or shelf life can be up to one-and-a-half times, but should not be more than 6 months beyond, the period covered by longterm data normally without the support of statistical analysis. 장기데이터와가속데이터가변동성과시간경과에따른변화를거의보이지않는경우, 일반적으로통계분석으로뒷받침하지않고도예정재시험기간또는유효기간을장기데이터대상기간의최대 1 1/2배까지설정할수있지만, 그기간보다 6개월이상으로할수는없다. Where the long-term or accelerated data show change over time and/or variability, the proposed retest period or shelf life can be up to 3 months beyond the period covered by long-term data if (1) the long-term data are amenable to statistical analysis but a statistical analysis is not performed, or (2) the long-term data are not amenable to statistical analysis but relevant supporting data are provided. 장기데이터또는가속데이터가변동성및 / 또는시간경과에따른변화를보이는경우, (1) 장기데이터의통계분석이가능하지만통계분석을실시하지않거나 (2) 장기데이터의통계분석이가능하지않지만관련근거데이터가있다면, 예정재시험기간또는유효기간을장기데이터대상기간보다최대 3개월까지길게설정할수있다. Where the long-term or accelerated data show change over time and/or variability, the proposed retest period or shelf life can be up to one-and-a-half times, but should not be more than 6 months beyond, the period covered by long-term data if (1) the long-term data are amenable to statistical analysis and a statistical analysis is performed, and (2) the proposal is backed by the result of the analysis and 17

18 relevant supporting data. 장기데이터또는가속데이터가변동성및 / 또는시간경과에따른변화를보이는경우, (1) 장기데이터의통계분석이가능하고통계분석을실시하며, (2) 분석결과와관련근거데이터로뒷받침이된다면, 예정재시험기간또는유효기간을장기데이터대상기간의최대 1 1/2배까지설정할수있지만, 그기간보다 6개월이상길게설정할수는없다 가속조건에서중대한변화가발생한경우 (Significant change at accelerated condition) If significant change occurs between 3 and 6 months testing at the accelerated storage condition, the proposed retest period or shelf life should be based on the long-term data. Extrapolation is not considered appropriate. In addition, a retest period or shelf life shorter than the period covered by long-term data could be called for. If the long-term data show variability, verification of the proposed retest period or shelf life by statistical analysis can be appropriate. 가속조건에서 3개월시험과 6개월시험사이에중대한변화가발생하는경우, 장기데이터에근거하여예정재시험기간또는유효기간을설정한다. 외삽은적절하지않은것으로간주된다. 또한재시험기간또는유효기간을장기데이터대상기간보다짧게할필요가있다. 장기데이터가변동성을보인다면, 통계분석으로예정재시험기간또는유효기간을확인하는것이적절할수있다. If significant change occurs within the first 3 months testing at the accelerated storage condition, the proposed retest period or shelf life should be based on longterm data. Extrapolation is not considered appropriate. A retest period or shelf life shorter than the period covered by long-term data could be called for. If the longterm data show variability, verification of the proposed retest period or shelf life by statistical analysis can be appropriate. In addition, a discussion should be provided to address the effect of short-term excursions outside the label storage condition (e.g., during shipping or handling). This discussion can be supported, if appropriate, by further testing on a single batch of the drug substance or product at the accelerated condition for a period shorter than 3 months. 가속조건에서첫 3개월시험사이에중대한변화가발생하는경우, 장기데이터에근거하여예정재시험기간또는유효기간을설정한다. 외삽은적절하지않은것으로간주된다. 재시험기간또는유효기간을장기데이터대상기간보다짧게할필요가있다. 장기데이터가변동성을보인다면, 통계분석으로예정재시험기간또는유효기간을 18

19 확인하는것이적절할수있다. 또한라벨보관조건의단기일탈에따른영향을평가하고 설명해야한다 ( 예, 운반또는취급시 ). 이때 3 개월보다짧은기간동안가속조건에서 원료의약품이나완제의약품한배치를추가로시험하여뒷받침할수있다 냉동보관예정원료의약품또는완제의약품 (Drug substances or products intended for storage in a freezer) For drug substances or products intended for storage in a freezer, the retest period or shelf life should be based on long-term data. In the absence of an accelerated storage condition for drug substances or products intended to be stored in a freezer, testing on a single batch at an elevated temperature (e.g., 5 C ± 3 C or 25 C ± 2 C) for an appropriate time period should be conducted to address the effect of short-term excursions outside the proposed label storage condition (e.g., during shipping or handling). 냉동보관예정원료의약품이나완제의약품의재시험기간또는유효기간을장기데이터에근거하여설정한다. 냉동보관예정원료의약품이나완제의약품에대하여가속조건시험을실시하지않은경우, 적절한기간동안높은온도 ( 예, 5 C ± 3 C 또는 25 C ± 2 C) 에서한배치를시험하여, 예정라벨보관조건의단기일탈에따른영향을평가한다 ( 예, 운반또는취급시 ) C 이하보관예정원료의약품또는완제의약품 (Drug substances or products intended for storage below -20 C) For drug substances or products intended for storage below -20 C, the retest period or shelf life should be based on long-term data and should be assessed on a case-by-case basis. -20 C 이하보관예정원료의약품이나완제의약품의재시험기간또는유효기간을장기데이터에근거하여설정하며각각의상황별로평가한다. 2.6 일반통계분석방법 (General Statistical Approaches) Where applicable, an appropriate statistical method should be employed to analyze the long-term primary stability data in an original application. The purpose of this analysis is to establish, with a high degree of confidence, a retest period or shelf life during which a quantitative attribute will remain within acceptance criteria for all future batches manufactured, packaged, and stored under similar circumstances. 19

20 해당되는경우에는적절한통계방법을채택하여신청문서에포함시켜제출하는장기기본안정성데이터를분석한다. 이분석의목적은유사상황에서제조, 포장, 보관되는미래의모든배치에대하여특정정량적특성이허용기준이내로유지되리라고생각되는재시험기간또는유효기간을높은수준의신뢰성있게확립하는것이다. In cases where a statistical analysis was employed to evaluate long-term data due to a change over time and/or variability, the same statistical method should also be used to analyse data from commitment batches to verify or extend the originally approved retest period or shelf life. 변동성및 / 또는시간경과에따른변화때문에통계분석방법으로장기데이터를평가한경우, 동일한통계방법을사용하여이후이행배치의데이터를분석해승인받은재시험기간또는유효기간을확인하거나연장한다. Regression analysis is considered an appropriate approach to evaluating the stability data for a quantitative attribute and establishing a retest period or shelf life. The nature of the relationship between an attribute and time will determine whether data should be transformed for linear regression analysis. The relationship can be represented by a linear or non-linear function on an arithmetic or logarithmic scale. In some cases, a non-linear regression can better reflect the true relationship. 회귀분석이정량적특성항목의안정성데이터를평가하고재시험기간또는유효기간을설정하는적절한방법으로생각된다. 특성항목과시간사이의관계를감안하여, 선형회귀분석을위한데이터변환의필요성이결정된다. 이관계를산술또는대수스케일의선형또는비선형함수로표현할수있다. 비선형회귀가진정한관계를더잘반영하는경우도있다. An appropriate approach to retest period or shelf life estimation is to analyze a quantitative attribute (e.g., assay, degradation products) by determining the earliest time at which the 95 percent confidence limit for the mean intersects the proposed acceptance criterion. 재시험기간또는유효기간추정을위한적절한방법은, 평균의 95% 신뢰한계가예정허용기준과교차하는가장이른시점을파악하는식으로정량적특성항목 ( 예, 정량, 분해산물 ) 을분석하는것이다. For an attribute known to decrease with time, the lower one-sided 95 percent confidence limit should be compared to the acceptance criterion. For an attribute 20

21 known to increase with time, the upper one-sided 95 percent confidence limit should be compared to the acceptance criterion. For an attribute that can either increase or decrease, or whose direction of change is not known, two-sided 95 percent confidence limits should be calculated and compared to the upper and lower acceptance criteria. 시간경과에따라감소되는것으로알려진특성항목인경우, 하한단측 95% 신뢰한계를허용기준과비교한다. 시간경과에따라증가하는것으로알려진특성항목인경우, 상한단측 95% 신뢰한계를허용기준과비교한다. 증가하거나감소할수있는특성항목이나변화의방향을알지못하는항목인경우, 양측 95% 신뢰한계를계산하고상하한허용기준과비교한다. The statistical method used for data analysis should take into account the stability study design to provide a valid statistical inference for the estimated retest period or shelf life. The approach described above can be used to estimate the retest period or shelf life for a single batch or for multiple batches when the data are combined after an appropriate statistical test. Examples of statistical approaches to the analysis of stability data from single or multi-factor, full- or reduced-design studies are included in Appendix B. References to current literature sources can be found in Appendix B.6. 안정성시험디자인을감안하여데이터의통계분석을실시해야, 통계적으로유효한재시험기간또는유효기간추정이가능하다. 적절한통계검정이후데이터를결합할때, 다수배치또는단일배치에대하여, 위의방법을활용해재시험기간또는유효기간을추정할수있다. 단일요소또는복합요소, 완전또는단축디자인안정성시험데이터의통계분석방법의예가부록 B에정리되어있다. 최신참고문헌은부록 B.6에정리되어있다. 21

22 3. 부록 (APPENDICES) Appendix A: 원료의약품또는완제의약품 ( 동결제품제외 ) 의재시험기간또는유효기간추정을위한데이터평가의사결정도 (Decision Tree for Data Evaluation for Retest Period or Shelf Life Estimation for Drug Substances or Products (excluding Frozen Products)) 22

23 Appendix B: 안정성데이터통계분석방법의예 (Examples of Statistical Approaches to Stability Data Analysis) Linear regression, poolability tests, and statistical modeling, described below, are examples of statistical methods and procedures that can be used in the analysis of stability data that are amenable to statistical analysis for a quantitative attribute for which there is a proposed acceptance criterion. 아래에서설명하는선형회귀, 풀링가능성테스트, 통계모델링은예정허용기준이설정되어있고통계분석이가능한정량적특성항목의안정성데이터를분석하는데사용할수있는통계방법과절차의예이다. B.1 단일배치데이터분석 (Data Analysis for a Single Batch) In general, the relationship between certain quantitative attributes and time is assumed to be linear 1. Figure 1 shows the regression line for assay of a drug product with upper and lower acceptance criteria of 105 percent and 95 percent of label claim, respectively, with 12 months of long-term data and a proposed shelf life of 24 months. In this example, two-sided 95 percent confidence limits for the mean are applied because it is not known ahead of time whether the assay would increase or decrease with time (e.g., in the case of an aqueous-based product packaged in a semi-permeable container). The lower confidence limit intersects the lower acceptance criterion at 30 months, while the upper confidence limit does not intersect with the upper acceptance criterion until later. Therefore, the proposed shelf life of 24 months can be supported by the statistical analysis of the assay, provided the recommendations in Sections 2.4 and 2.5 are followed. 일반적으로특정정량적특성항목과시간사이의관계는선형을보인다고가정한다. 그림 1은정량항목의상하한허용기준이표시량의 105% 와 95% 이고장기데이터기간이 12개월이며예정유효기간이 24개월인완제의약품의회귀선이다. 이예에서시간경과에따라정량결과가증가할지감소할지미리알수없기때문에 ( 예, 반투성용기에포장한수성제품인경우 ), 평균의양측 95% 신뢰한계를적용한다. 하한신뢰한계는 30개월시점에하한허용기준과교차하며, 상한신뢰한계는그이후까지도상한허용기준과교차하지않는다. 그러므로섹션 2.4와 2.5의권고사항에따라, 정량결과의통계분석으로예정유효기간 24개월을뒷받침할수있다. When data for an attribute with only an upper or a lower acceptance criterion are analyzed, the corresponding one-sided 95 percent confidence limit for the mean is 23

24 recommended. Figure 2 shows the regression line for a degradation product in a drug product with 12 months of long-term data and a proposed shelf life of 24 months, where the acceptance criterion is not more than 1.4 percent. The upper one-sided 95 percent confidence limit for the mean intersects the acceptance criterion at 31 months. Therefore, the proposed shelf life of 24 months can be supported by statistical analysis of the degradation product data, provided the recommendations in Sections 2.4 and 2.5 are followed. 상하한허용기준가운데하나만있는특성항목의데이터를분석할때는, 평균의단측 95% 신뢰한계만적용한다. 그림 2는분해산물의허용기준이 1.4% 이하이고장기데이터기간이 12개월이며예정유효기간이 24개월인완제의약품의회귀선이다. 평균의상한단측 95% 신뢰한계는 31개월시점에허용기준과교차한다. 그러므로섹션 2.4와 2.5의권고사항에따라, 분해산물데이터의통계분석으로예정유효기간 24개월을뒷받침할수있다. If the above approach is used, the mean value of the quantitative attribute (e.g., assay, degradation products) can be expected to remain within the acceptance criteria through the end of the retest period or shelf life at a confidence level of 95 percent. 상기방식을사용한다면정량적특성항목 ( 예, 정량, 분해산물 ) 의평균값은 95% 신뢰수준에서재시험기간또는유효기간말기까지허용기준이내를유지하리라예상할수있다. The approach described above can be used to estimate the retest period or shelf life for a single batch, individual batches, or multiple batches when combined after appropriate statistical tests described in Sections B.2 through B.5. 섹션 B.2부터 B.5까지기술한통계검정이후에결합할때, 단일배치, 개별배치, 또는복합배치의재시험기간또는유효기간을추정하는데상기방식을사용할수있다. B.2 1 개요소, 완전디자인시험의데이터분석 (Data Analysis for One-Factor, Full-Design Studies) For a drug substance or for a drug product available in a single strength and a single container size and/or fill, the retest period or shelf life is generally estimated based on the stability data from a minimum of three batches. When analyzing data from such one-factor, batch-only, full-design studies, two statistical approaches can be considered. 24

25 단일함량및단일용기크기및 / 또는충전량의완제의약품이나원료의약품인경우, 일반적으로최소 3개배치의안정성데이터에근거하여재시험기간또는유효기간을추정한다. 1개요소완전디자인시험데이터를분석할때, 두가지통계방법을고려할수있다. The objective of the first approach is to determine whether the data from all batches support the proposed retest period or shelf life. 첫번째방법의목적은모든배치의데이터가예정재시험기간또는유효기간을뒷받침하는지평가하는것이다. The objective of the second approach, testing for poolability, is to determine whether the data from different batches can be combined for an overall estimate of a single retest period or shelf life. 두번째방법 ( 풀링가능성테스트 ) 의목적은하나의재시험기간또는유효기간을전반적으로추정하기위하여서로다른배치의데이터를결합할수있는지평가하는것이다. B.2.1 모든배치가예정재시험기간또는유효기간을뒷받침하는지평가 (Evaluating whether all batches support the proposed retest period or shelf life) The objective of this approach is to evaluate whether the estimated retest periods or shelf lives from all batches are longer than the one proposed. Retest periods or shelf lives for individual batches should first be estimated using the procedure described in Section B.1 with individual intercepts, individual slopes, and the pooled mean square error calculated from all batches. If each batch has an estimated retest period or shelf life longer than that proposed, the proposed retest period or shelf life will generally be considered appropriate, as long as the guidance for extrapolation in Sections 2.4 and 2.5 is followed. There is generally no need to perform poolability tests or identify the most reduced model. If, however, one or more of the estimated retest periods or shelf lives are shorter than that proposed, poolability tests can be performed to determine whether the batches can be combined to estimate a longer retest period or shelf life. 이방법의목적은모든배치를바탕으로추정한재시험기간또는유효기간이예정기간보다더긴것인지평가하는데있다. 먼저모든배치의데이터를토대로계산한풀링평균제곱오차, 각절편과기울기로섹션 B.1에기술된절차를이용해개별배치에대한 25

26 재시험기간또는유효기간을추정한다. 각배치의추정재시험기간또는유효기간이예정기간보다더길고섹션 2.4 및 2.5의외삽가이드라인을준수하면, 예정재시험기간또는유효기간이일반적으로적절하다고간주한다. 풀링가능성테스트를수행하거나최대단축모델을파악할필요는없다. 하지만추정재시험기간또는유효기간가운데하나이상이예정기간보다짧으면, 풀링가능성테스트를실시하여여러배치를결합해더긴재시험기간또는유효기간을추정할수있는지평가할수있다. Alternatively, the above approach can be taken during the pooling process described in Section B.2.2. If the regression lines for the batches are found to have a common slope and the estimated retest periods or shelf lives based on the common slope and individual intercepts are all longer than the proposed retest period or shelf life, there is generally no need to continue to test the intercepts for poolability. 또는섹션 B.2.2에기술된풀링시에상기방법을적용할수있다. 배치들의회귀선이공통기울기를보이며공통기울기와각절편에근거하여추정한재시험기간이나유효기간이예정재시험기간또는유효기간보다모두더길다면, 일반적으로절편의풀링가능성테스트를계속할필요가없다. B.2.2 배치풀링가능성테스트 (Testing for poolability of batches) B 공분산분석 (Analysis of covariance) Before pooling the data from several batches to estimate a retest period or shelf life, a preliminary statistical test should be performed to determine whether the regression lines from different batches have a common slope and a common timezero intercept. Analysis of covariance (ANCOVA) can be employed, where time is considered the covariate, to test the differences in slopes and intercepts of the regression lines among batches. Each of these tests should be conducted using a significance level of 0.25 to compensate for the expected low power of the design due to the relatively limited sample size in a typical formal stability study. 여러배치의데이터를풀링하여재시험기간또는유효기간을추정하기전에, 예비통계검정을실시해서로다른배치의회귀선이공통기울기와공통시간-0 절편을갖는지확인한다. 시간이공변량인경우에여러배치회귀선의기울기와절편차이를검정하기위해공분산분석 (ANCOVA) 을실시할수있다. 공식안정성시험의표본크기가상대적으로제한적이기때문에디자인의낮은검정력을보상하기위하여, 유의수준 0.25를적용하여이들검정각각을실시한다. 26

27 If the test rejects the hypothesis of equality of slopes (i.e., if there is a significant difference in slopes among batches), it is not considered appropriate to combine the data from all batches. The retest periods or shelf lives for individual batches in the stability study can be estimated by applying the approach described in Section B.1 using individual intercepts and individual slopes and the pooled mean square error calculated from all batches. The shortest estimate among the batches should be chosen as the retest period or shelf life for all batches. 기울기동일성가설이기각된다면 ( 즉, 여러배치의기울기가유의미한차이를보인다면 ), 모든배치의데이터를결합하는것은적절하다고볼수없다. 안정성시험대상개별배치의재시험기간또는유효기간은, 모든배치의데이터로계산한풀링평균제곱오차와개별절편 / 기울기로, 섹션 B.1에기술된방식을적용해추정할수있다. 이가운데가장짧은추정치를전체배치의재시험기간또는유효기간으로선택한다. If the test rejects the hypothesis of equality of intercepts but fails to reject that the slopes are equal (i.e., if there is a significant difference in intercepts but no significant difference in slopes among the batches), the data can be combined for the purpose of estimating the common slope. The retest periods or shelf lives for individual batches in the stability study should be estimated by applying the approach described in Section B.1, using the common slope and individual intercepts. The shortest estimate among the batches should be chosen as the retest period or shelf life for all batches. 절편동일성가설이기각되지만기울기가동일하다는가설은기각되지않는경우 ( 즉, 여러배치의절편은유의미한차이를보이지만기울기에는유의미한차이가없는경우 ), 데이터를결합하여공통기울기를추정할수있다. 안정성시험대상개별배치의재시험기간또는유효기간은공통기울기와개별절편을활용하여섹션 B.1의방식을적용해추정한다. 이가운데가장짧은추정치를모든배치의재시험기간또는유효기간으로선택한다. If the tests for equality of slopes and equality of intercepts do not result in rejection at a level of significance of 0.25 (i.e., if there is no significant difference in slope and intercepts among the batches), the data from all batches can be combined. A single retest period or shelf life can be estimated from the combined data by using the approach described in Section B.1 and applied to all batches. The estimated retest period or shelf life from the combined data is usually longer than that from individual batches because the width of the confidence limit(s) for the mean will 27

28 become narrower as the amount of data increases when batches are combined. 기울기와절편의동일성검정이 0.25의유의수준에서기각되지않으면 ( 즉, 여러배치의기울기와절편에유의미한차이가없는경우 ), 모든배치의데이터를결합할수있다. 섹션 B.1의방식을적용하여결합데이터로부터하나의재시험기간또는유효기간을추정하고모든배치에적용할수있다. 결합데이터에서도출한추정재시험기간또는유효기간은일반적으로개별배치의것보다더길다. 배치를결합하면데이터의양이증가하므로평균신뢰한계의폭이좁아지기때문이다. The pooling tests described above should be performed in a proper order such that the slope terms are tested before the intercept terms. The most reduced model (i.e., individual slopes, common slope with individual intercepts, or common slope with common intercept, as appropriate) can be selected for retest period or shelf life estimation. 위에서설명한풀링테스트는기울기다음에절편을검정하도록순서를지켜실시한다. 재시험기간또는유효기간추정을위해최대단축모델 ( 즉, 개별기울기, 공통기울기와개별절편, 또는공통기울기와공통절편 ) 을선정할수있다. B 기타방법 (Other methods) Statistical procedures 2-6 other than those described above can be used in retest period or shelf life estimation. For example, if it is possible to decide in advance the acceptable difference in slope or in mean retest period or shelf life among batches, an appropriate procedure for assessing the equivalence in slope or in mean retest period or shelf life can be used to determine the data poolability. However, such a procedure should be prospectively defined, evaluated, and justified and, where appropriate, discussed with the regulatory authority. A simulation study can be useful, if applicable, to demonstrate that the statistical properties of the alternative procedure selected are appropriate 7. 위에서설명한것이외의다른통계방법으로도재시험기간또는유효기간을추정할수있다. 예를들어여러배치의평균재시험기간또는유효기간이나기울기의허용차이를미리정해놓을수있다면, 기울기또는평균재시험기간이나유효기간의동등성을평가하는적절한절차를활용하여데이터풀링가능성을결정할수있다. 하지만그와같은절차를미리규정하고평가하고타당성을입증해야하며, 적절한경우에는규제기관과미리협의해야한다. 해당되는경우에는시뮬레이션시험을실시하여, 대체방법의통계적특징이적절함을증명할수있다. 28

29 B.3 다요소, 완전디자인시험의데이터분석 (Data Analysis for Multi-Factor, Full-Design Studies) The stability of the drug product could differ to a certain degree among different factor combinations in a multi-factor, full-design study. Two approaches can be considered when analyzing such data. 다요소완전디자인시험에서여러요소조합사이에의약품안정성이어느정도차이날수있다. 데이터분석시에두가지방식을고려할수있다. The objective of the first approach is to determine whether the data from all factor combinations support the proposed shelf life. 첫번째방법의목적은모든요소조합의데이터가예정유효기간을뒷받침하는지평가하는것이다. The objective of the second approach, testing for poolability, is to determine whether the data from different factor combinations can be combined for an overall estimate of a single shelf life. 두번째방법 ( 풀링가능성테스트 ) 의목적은하나의유효기간을전반적으로추정하기위하여서로다른요소조합의데이터를결합할수있는지평가하는것이다. B.3.1 모든요소조합이예정유효기간을뒷받침하는지평가 (Evaluating whether all factor combinations support the proposed shelf life) The objective of this approach is to evaluate whether the estimated shelf lives from all factor combinations are longer than the one proposed. A statistical model that includes all appropriate factors and factor combinations should be constructed as described in Section B , and the shelf life should be estimated for each level of each factor and factor combination. 이방법의목표는모든요소조합의추정유효기간이예정기간보다더긴것인지평가하는데있다. 모든관련요소와요소조합을포함하는통계모델을섹션 B 에기술한바에따라구성하고, 각요소및요소조합의수준별로유효기간을추정한다. If all shelf lives estimated by the original model are longer than the proposed shelf life, further model building is considered unnecessary and the proposed shelf life will generally be appropriate as long as the guidance in Sections 2.4 and 2.5 is 29

30 followed. If one or more of the estimated shelf lives fall short of the proposed shelf life, model building as described in Section B can be employed. However, it is considered unnecessary to identify the final model before evaluating whether the data support the proposed shelf life. Shelf lives can be estimated at each stage of the model building process, and if all shelf lives at any stage are longer than the one proposed, further attempts to reduce the model are considered unnecessary. 오리지널모델로추정한모든유효기간이예정유효기간보다더길다면, 추가적인모델구축은필요하지않다고볼수있고, 섹션 2.4와 2.5의가이드라인을따른다면예정유효기간은일반적으로적절하다. 추정유효기간가운데하나이상이예정유효기간보다짧은경우에는섹션 B 에기술된모델구축방법을채택할수있다. 하지만데이터가예정유효기간을뒷받침하는지평가하기전에최종모델을파악하는것은불필요하다고생각된다. 모델구축절차의각단계에서유효기간을추정할수있으며, 특정단계에서추정한모든유효기간이예정유효기간보다더길다면, 모델단축을위한추가적인시도는불필요하다고간주된다. This approach can simplify the data analysis of a complicated multi-factor stability study compared to the data analysis described in Section B 섹션 B 에기술된데이터분석과비교하면, 이방식은복잡한다요소안정성시험의데이터분석을단순화시킬수있다. B.3.2 풀링가능성테스트 (Testing for poolability) The stability data from different combinations of factors should not be combined unless supported by statistical tests for poolability. 풀링가능성통계테스트로뒷받침되지않으면, 서로다른요소조합의안정성데이터를결합해서는안된다. B 배치요소만관련된경우의풀링가능성테스트 (Testing for poolability of batch factor only) If each factor combination is considered separately, the stability data can be tested for poolability of batches only, and the shelf life for each non-batch factor combination can be estimated separately by applying the procedure described in Section B.2. For example, for a drug product available in two strengths and four container sizes, eight sets of data from the 2x4 strength-size combinations can be analyzed and eight separate shelf lives should be estimated accordingly. If a single 30

31 shelf life is desired, the shortest estimated shelf life among all factor combinations should become the shelf life for the product. However, this approach does not take advantage of the available data from all factor combinations, thus generally resulting in shorter shelf lives than does the approach in Section B 각요소조합을별도로검토한다면, 배치요소만의풀링가능성테스트를할수있으며, 섹션 B.2에기술된절차를적용하여배치요소와관련없는조합의유효기간을별도로추정할수있다. 예를들어 2개함량과 4개용기크기의의약품이있다면, 2 x 4 함량-크기조합에서확보한 8개데이터세트를분석하여 8개의유효기간을추정한다. 하나의유효기간을원한다면, 모든요소조합가운데가장짧은추정유효기간을그제품의유효기간으로설정한다. 하지만이방법은모든요소조합의데이터를감안하지않기때문에, 섹션 B.3.2.2의방식보다더짧은유효기간이나오게된다. B 모든요소및요소조합의풀링가능성테스트 (Testing for poolability of all factors and factor combinations) If the stability data are tested for poolability of all factors and factor combinations and the results show that the data can be combined, a single shelf life longer than that estimated based on individual factor combinations is generally obtainable. The shelf life is longer because the width of the confidence limit(s) for the mean will become narrower as the amount of data increases when batches, strengths, container sizes and/or fills, etc. are combined. 모든요소및요소조합의풀링가능성을테스트하고그결과에의거하여데이터를결합할수있다면, 개별요소조합에근거하여추정한것보다더긴하나의유효기간을확보할수있다. 배치, 함량, 용기크기및 / 또는충전량등을결합하면데이터양이증가하므로, 평균의신뢰한계폭이좁아지므로, 유효기간은길어진다. B 공분산분석 (Analysis of covariance) Analysis of covariance can be employed to test the difference in slopes and intercepts of the regression lines among factors and factor combinations 7, 8. The purpose of the procedure is to determine whether data from multiple factor combinations can be combined for the estimation of a single shelf life. 요소및요소조합사이의회귀선기울기와절편차이를검정하기위하여공분산분석을실시할수있다. 이절차의목적은복합요소조합의데이터를결합하여하나의유효기간을추정할수있는지판단하는데있다. 31

32 The full statistical model should include the intercept and slope terms of all main effects and interaction effects and a term reflecting the random error of measurement. If it can be justified that the higher order interactions are very small, there is generally no need to include these terms in the model. In cases where the analytical results at the initial time point are obtained from the finished dosage form prior to its packaging, the container intercept term can be excluded from the full model because the results are common among the different container sizes and/or fills. 임의측정오류를반영한항과모든주영향및상호작용영향의절편과기울기항을포함하는통계모델을만든다. 고차상호작용이매우적음을입증할수있다면, 이와같은항을모델에포함시킬필요는없다. 초기시점의분석결과를포장이전최종제품에서확보하는경우, 그와같은결과는여러용기크기및 / 또는충전량사이에공통적이므로, 완전모델에서용기절편항을제외시킬수있다. The tests for poolability should be specified to determine whether there are statistically significant differences among factors and factor combinations. Generally, the pooling tests should be performed in a proper order such that the slope terms are tested before the intercept terms and the interaction effects are tested before the main effects. For example, the tests can start with the slope and then the intercept terms of the highest order interaction, and proceed to the slope and then the intercept terms of the simple main effects. The most reduced model, obtained when all remaining terms are found to be statistically significant, can be used to estimate the shelf lives. 요소및요소조합사이에통계적으로유의미한차이가있는지평가하기위해풀링가능성테스트를규정한다. 일반적으로기울기항을검정한다음에절편항을검정하고상호작용영향을검정한다음에주영향을검정하는식으로풀링테스트를순차적으로실시한다. 예를들어가장높은고차상호작용의기울기그리고절편항부터시작하고, 다음에간단한주영향의기울기와절편항으로진행한다. 나머지모든항이통계적으로유의미한것으로밝혀지는경우에최대단축모델을활용하여유효기간을추정할수있다. All tests should be conducted using appropriate levels of significance. It is recommended that a significance level of 0.25 be used for batch-related terms, and a significance level of 0.05 be used for non-batch-related terms. If the tests for poolability show that the data from different factor combinations can be combined, the shelf life can be estimated according to the procedure described in Section B.1 using the combined data. 32