KR20080002239A - Methods and systems for determining lot consistency - Google Patents

Abstract

Methods and systems for determining lot consistency are provided to determine similarity among three or more lots, especially determine similarity in the immunogenicity of hemagglutinin(HA) of three viral strains in a vaccine, and determine lot consistency between two or more vaccine lots in which each lot contains multiple strains. A method for determining lot consistency between a plurality of lots, in which each lot is associated with each of a plurality of analytes, comprises the steps of: receiving level data elements, in which each level data element corresponds to one of the plurality of lots and one of the plurality of analytes; calculating a plurality of test statistics by using a computer and the equation of Zmin=min{(delta-Dij)/seij}, in which each test statistics corresponds to each analyte; and determining that the plurality of lots are consistent when each of the plurality of test statistics exceeds a predetermined value, wherein Dij comprises the difference between two elements of the level data elements for an ith lot and a jth lot for given one of the plurality of analytes, seij comprises a standard error of the difference, and delta comprises an equivalence margin; the lot is a vaccine lot; the level data element is immunogenicity value of the vaccine lot; the vaccine is influenza vaccine; and the immunogenicity of the influenza vaccine is determined by hemagglutination inhibition test for anti-HA antibody titration. The system contains a data supply processor(105), a lot consistency processor(110) which contains a processing unit(125) and a memory(130), a user and a network(120).

Description

Methods and systems for determining lot consistency

1 is a block diagram illustrating an exemplary lot consistency system in accordance with an embodiment of the present invention.

2 is a flow diagram illustrating an exemplary method for determining lot consistency in accordance with an embodiment of the present invention.

Technical field

The present invention generally relates to methods and systems for determining lot consistency. More specifically, the invention relates to systems for determining lot consistency, for example between three or more vaccine lots, in particular three lots.

Prior art

Lot consistency checking is a process for determining similarity between lots. When the lots include, for example, an influenza vaccine, the lot consistency test indicates that the lots are similar in terms of the immunogenicity of hemagglutinin (HA) of the three viral species included in the vaccine. In other words, the lot consistency test may include equivalence testing. For inspection through two lots (ie two treatments) and a single result, the statistical theory of the equivalent inspection is well established. Briefly, Δ is called the true treament difference and δ is the equivalence margin. Two processes are considered equivalent if | Δ | <δ. To illustrate the equivalence, the null hypothesis H ₀ : | Δ | ≥δ may be tested for the alternative hypothesis H ₁ : | Δ | <δ. If the null hypothesis is examined by two unilateral tests procedures, this approach may include checking both sides 100 (1-2α)% confidence intervals for Δ within an equivalent range -δ to δ.

A difficult aspect of the equivalent test may be the choice of margin δ. However, in the case of influenza vaccine lot consistency tests, there may be more than one attempt, both involving diversity according to statistical testing. First of all, for the purposes of authorization, it may need to be compared with three lots rather than two. Second, there may be three results instead of one, for each of the three species in the vaccine. Ignoring these varieties can increase the overall Form I error rate (ie, the probability of erroneous conclusion that lots are equivalent).

In view of the above, there is a need for methods and systems for determining lot consistency more optimally. Moreover, there is a need to determine lot consistency between two or more vaccine lots, for example, where each lot contains several species.

In accordance with embodiments of the present invention, systems and methods for determining lot consistency are disclosed.

In a first aspect, the present invention provides a method for determining lot consistency among a plurality of lots, each plurality of lots being associated with a respective plurality of analytes. In one embodiment, the method further comprises: receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects; Computer and formulas,

Z _min = min {(δ- | D _ij |) / se _ij }

Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; And when each of the plurality of inspection statistics exceeds a predetermined value, determining that the plurality of lots is consistent.

In one embodiment of the method, the lots are vaccine lots. In another embodiment of the method, the level data elements are a measure of immunogenicity of the vaccine lots. In another embodiment of the method, the vaccine is an influenza vaccine and the level data elements correspond to the immunogenicity of the influenza vaccine. In another embodiment of the method, the immunogenicity of the influenza vaccine is determined by a test for erythrocyte aggregation inhibition against anti-HA antibody titration. In another embodiment, the method further comprises transforming the nonnormally distributed level data elements to obtain normally distributed level data elements. In still other embodiments of the method, δ is 1.3 to 1.7 or 1.4 to 1.6. In another embodiment of the method δ / se _ij is equal to or less than 0.5. In another embodiment of the method, each of the plurality of lots is associated with one or more analytes, and a level data element corresponding to each of the one or more analytes associated with each of the plurality of lots is determined.

In another embodiment, the method further comprises: receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects; Equation,

Z _min = min {(δ- | D _ij |) / se _ij }

Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for a j th lot, se _ij includes a standard error of the difference, and δ comprises an equivalent margin; And when each of the plurality of inspection statistics exceeds a predetermined value, determining that the plurality of lots is consistent.

In another aspect, the present invention provides a system for determining lot consistency between a plurality of lots, wherein each plurality of lots is associated with each of a plurality of analytes. In one embodiment, the system includes a memory store maintaining a database and a processing unit coupled to the memory store, the processing unit receiving level data elements and performing computer and math,

Z _min = min {(δ- | D _ij |) / se _ij }

Calculate a plurality of inspection statistics and determine that the plurality of lots is consistent when each of the plurality of inspection statistics exceeds a predetermined value, each of the level data elements One of a plurality of lots and one of the plurality of analysis objects, each of the plurality of inspection statistics corresponding to each of the plurality of analysis objects, and D _ij is a given one of the plurality of analysis objects Includes two differences of the level data elements for the i th lot and the j th lot for, se _ij includes the standard error of the difference, and δ includes an equivalent margin.

In one embodiment of the system, the lots are vaccine lots. In another embodiment of the system, the level data elements are a measure of the immunogenicity of the vaccine lots. In another embodiment of the method, the vaccine is an influenza vaccine and the level data elements correspond to the immunogenicity of the influenza vaccine. In another embodiment of the system, the immunogenicity of the influenza vaccine is determined by a hemagglutination inhibition test for anti-HA antibody titration. In another embodiment, the system further comprises transforming the non-normally distributed level data elements to obtain normally distributed level data elements. In still other embodiments of the system, δ is 1.3 to 1.7 or 1.4 to 1.6. In another embodiment of the system, δ / se _ij is equal to or less than 0.5. In another embodiment of the system, each of the plurality of lots is associated with one or more analytes, and a level data element corresponding to each of the one or more analytes associated with each of the plurality of lots is determined.

In another aspect, the present invention provides a computer readable medium for storing a set of instructions to be executed when a method of determining lot consistency among a plurality of lots is performed, each of the plurality of lots Associated with each of the analytes. In an embodiment, the method executed by the set of instructions comprises: receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects. The receiving step; Computer and formulas,

Z _min = min {(δ- | D _ij |) / se _ij }

Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; And when each of the plurality of inspection statistics exceeds a predetermined value, determining that the plurality of lots is consistent.

In an embodiment of the computer readable medium, the lots are vaccine lots. In another embodiment of the computer readable medium, the level data elements are a measure of immunogenicity of vaccine lots. In another embodiment of the computer readable medium, the vaccine is an influenza vaccine and the level data elements correspond to the immunogenicity of the influenza vaccine. In another embodiment of the computer readable medium, the immunogenicity of the influenza vaccine is determined by a hemagglutination inhibition (HI) test for anti HA antibody titration. In another embodiment of the computer readable medium, the instructions further comprise converting the level data elements that are typically distributed to obtain the level data elements that are typically distributed. In a further embodiment of the computer readable medium, δ is 1.3 to 1.7 or 1.4 to 1.6. In another embodiment of the computer readable medium, δ / se _ij is 0.5 or less. In another embodiment of the computer readable medium, each of the plurality of lots is associated with one or more analytes, and a level data element corresponding to each of the one or more analytes associated with each of the plurality of lots is determined. do.

In a further aspect, the present invention provides a method for determining lot consistency between a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. An embodiment of the method comprises: manufacturing the lots, taking samples from the lots, analyzing the analytes in the samples, and generating level data elements from the analyzes, wherein the level data elements Each generating corresponding to one of the plurality of lots and one of the plurality of analytes; Computer and formulas,

Z _min = min {(δ- | D _ij |) / se _ij }

Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; Determining that each of the plurality of inspection statistics exceeds a predetermined value, and that the plurality of lots is consistent; And based on the consistency determination, determining whether to use and / or discard each of the lots, and / or adjust the manufacturing process.

It is to be understood that both the foregoing general description and the following detailed description are exemplary only and should not be considered as limiting the scope of the invention as described and claimed. In addition, features and / or variations may be provided in addition to those described herein. For example, embodiments of the invention may relate to various combinations and subcombinations of the features described in detail.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various embodiments and aspects of the invention.

The following detailed description is with reference to the accompanying drawings. Whenever possible. Like reference numerals are used in the drawings and the following description to refer to the same or like parts. While some exemplary embodiments and features of the invention are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the invention. For example, substitutions, additions, or modifications may be made of the components shown in the figures, and the example methods described herein may be replaced by replacements, rearrangements, or additional steps in accordance with the disclosed methods. Can be modified. Accordingly, the following detailed description does not limit the invention. Instead, the proper scope of the invention is defined by the appended claims.

In the present specification, the "analyte" is a substance in the configuration. In the context of vaccine construction, the analyte may be, for example, a component that elicits an immune response of the vaccine. Thus, in the context of an influenza vaccine, the analyte may be a component of an influenza virus such as hemagglutinin (HA) or neuraminidase (NA). Vaccine configurations can include one or more analytes.

In this specification, "lot" is a unit of manufacture. Thus, the configuration "lot" is a bundle of manufactured configurations. For example, a vaccine lot is a manufactured bundle of vaccines.

A "level data element" is a measure of the characteristic of an analyte in a lot. Suitable features include, for example, analyte concentration, total amount of analyte present in the lot, antigenic, immunogenic, or biological activity. In the case of biological activity analytes, the level data element may be a measure of the response elicited by the analyte. The level data element can be generated by combining the measurements of the various samples of the presented lot. For example, level data elements can be generated by calculating an arithmetic or geometric mean of the concentration of analyte in a lot.

Systems and methods consistent with embodiments of the present invention may determine lot consistency. "Lot consistency" refers to changes in level data elements between lots that are within a predetermined range. For example, “consistency” can be determined in terms of amount, concentration, or biological activity of the analyte in the lot. For example, the immunogenicity of influenza vaccines can be determined by a test for hemagglutination inhibition (HI) for anti HA antibody titration. HI titer can be defined as the dilution factor of the highest dilution that continues to completely inhibit hemagglutination. For example, the dilution start can be 1:10. From this dilution, additional 2-fold dilutions can be prepared, for example 1:20, 1:40, ..., 1: 2560. Thus, HI titers can take the values 10, 20, 40, 80, ..., 2560, for example. If the onset of dilution does not inhibit erythrocyte aggregation, a titer value of 5 can be assigned. In addition, all blood samples can be titrated twice and the HI titer assigned to the blood sample can be the geometric mean of the two titrations. Thus, geometric mean titers (GMT) can be used to summarize HI titers.

One possible approach to checking the similarity of lots is to compare the GMTs between the lots for each species. Techniques for establishing equivalence can be enhanced if the data is typically distributed. However, HI titers are likely to be distorted to the right. Logarithmic transformations can make observations approximately typical. As a result, the logarithmic transformation for HI titers is

log HI titer = log ₂ (HI titer / 5)

It may include.

Log HI titers are the same as the dilution steps mentioned above, for example 0 (HI titer 5), 1 (HI titer = 10), 2 (HI titer = 20), ..., 9 (HI titer = 2560) can do. Moreover, a one-to-one relationship of the arithmetic mean of GMT of log transformed HI titers (AMLT) and untransformed titers, for example,

GMT = 2 ^AMLT x 5

Is present.

In addition, let GMT _i and GMT _j be represented by the i-th and j-th geometric mean titers, respectively, and AMLT _i and AMLT _j as the arithmetic mean of log transformed HI titers. thereafter,

2 ^{AMLTi - AMLTj} = GMT _i / GMT _j

to be.

Thus, if the equivalent of log-transformed HI titers is explained by indicating that AMLT _i -AMLT _j = 0, then the equivalent of HI titers is that the geometric mean ratio (GMR) is close to 1 (ie, GMT _i / GMT). _j = 1) and can be explained at the same time.

Instead of using HI titers to account for lot consistency, baseline-corrected fold increases can be used. E.g,

Fold Increase = HIT _P / HIT _B

HIT _B and HIT _P are HI titers after baseline and vaccination, respectively.

log fold increase = log HIT _P -log HIT _B

Note that

Let MFL _i represent the geometric mean of fold increases (ie mean fold increase), and GMTBi and GMT _Pi represent the geometric mean of the i th baseline and HI titers after vaccination, respectively. thereafter,

MFI _i / MFI _j = [GMT _Pi / GMT _Pj ] / [GMT _Bi / GMT _Bj ]

to be.

If there is no reference imbalance in the HI titers between the i th and j th lots (ie, GMT _Bi / GMT _Bj ≒ 1), then the MFI ratio may be approximately equal to GMR. E.g,

MFI _i / MFI _j ≒ GMT _Pi / GMT _Pj

to be.

However, in the case of baseline imbalance, there may be a difference between the analysis of fold increases and that of HI titers after vaccination. Thus, the use of fold increases may not eliminate bias in the GMT due to reference imbalance and may actually introduce bias. Moreover, fold increases can be negatively correlated with baseline HI titers, for example, the higher the baseline HI titers, the smaller the fold increases. This means that if the reference HI titers are unbalanced, the MFI is highest for the lot with the lowest reference values. One way to correct for reference imbalance is to use Analysis of Covariance. Nevertheless, there are also attempts associated with this approach (eg, heterogeneity of variance). Thus, if there is no baseline imbalance between lots, analysis of fold increases can yield the same results as analysis of HI titers after vaccination. However, analysis of HI titers can be more powerful because of the lower variability between observations. If the relationship between post-vaccination and baseline HI titers is not high, then these differences show statistically greater variability than the HI titers themselves after vaccination.

To illustrate the equivalence of three influenza vaccine lots, for example, differences in lot mean (of log-transformed HI titers or log-transformed fold increases) may be small. For a single species, this can be done by examining the null hypothesis H ₀ : max | Δ _ij | ≥δ for the alternative hypothesis H ₁ : max | Δ _ij | <δ, where Δ _ij is true but ith and jth Not known between lots. The board hypothesis checks statistics,

Z _min = min {(δ- | D _ij |) / se _ij }

Can be checked using

D _ij is the difference observed between the i th and j th lots, and se _ij is the standard error of this difference. sd ₁ , sd ₂ , and sd ₃ are referred to as the standard deviation of the lot mean. thereafter,

se _ij = √ [sd _i ² / n _i + sd _j ² / n _j ]

,

n _i and n _j are the i-th and j-th sample sizes, respectively.

If the three differences D ₁₂ , D ₁₃ , and D ₂₃ coincide with H ₀ (ie, at least one | D _ij | ≧ δ), then Z _min will be negative. If the differences coincide with H ₁ (ie all | D _ij | <δ), then Z _min will be positive. The smaller the differences observed, the larger Z _min will be.

Thus, H ₀ is rejected for large values of Z _min . Accurate thresholds of Z _min can be difficult to calculate. The thresholds Z _α of the standard normal distribution can be used if the test can be conservative. Thus, the 97.5th percentile Z _{0 .025} = 1.96 of the standard normal distribution, which can be used to check H ₀ at the effective level α = 0.025 as a threshold.

Shown above is how the equivalent of three lots can be explained in the case of a single species, for example in the case of a single result. However, modern influenza vaccines include, for example, three species (eg, AH ₁ N ₁ species, AH ₃ N ₂ species, and B species). Thus, there may be three results. As such, the query indicates how and whether to adjust for this form of diversity. The answer to this query is simplified if a point is taken at which point the equivalence of lots may be required if equivalence is described for all three species. In such a case, adjustment of effective levels such as inspection may not be required. If only the null hypothesis is rejected for all three species, if H0 is tested per species at effective level α and the lot's equivalence is judged, then it is likely that α is likely to attain the wrong conclusion that the three lots are equivalent accordingly. .

One difficult aspect of equivalence testing is the choice of equivalent margin. If too small an equivalent margin is chosen, the sample size required to ensure sufficient statistical power will be enormously large. If too large an equivalent margin is chosen, the results will be meaningless.

As mentioned above, for example, blood samples may be titrated twice. For example, a blood sample may be analyzed again if two interpersonal HI titers differ by two or more dilution steps (eg, 320 and 1280). However, if the two HI titers differ by only one step (eg 40 and 80), they can be considered identical. It may be justified to allow at least the same difference between HI titers between individuals and hence the lot mean, in other words, to allow 1 <| Δ _ij | <2. There are an infinite number of choices that meet this criterion, but the discrete characteristic δ = 1.5 of the observations may be an acceptable choice. This margin corresponds to an equivalent range of 2 ^-1.5 = 0.35 to 2 ^+1.5 = 2.83 for the GMR or MFI ratio.

One embodiment consistent with the present invention may include a system that provides lot consistency. The system may include a memory store for maintaining a database and a processing unit coupled to the memory store. The processing unit may be operable to receive level data elements, each level data element corresponding to one of a plurality of lots and one of a plurality of analytes. In addition, the processing unit includes a computer, a formula,

Zmin = min {(δ- | D _ij |) / se _ij }

Can be operable to calculate a plurality of inspection statistics using a plurality of inspection statistics, each of the plurality of inspection statistics corresponding to each of the plurality of analysis objects, where D _ij is the i th for the presented among the plurality of analysis objects And a difference between two of the level data elements for j th, se _ij includes the standard error of the difference, and δ comprises an equivalent margin. In addition, the processing unit may be operable to determine that the plurality of lots matches when each of the plurality of inspection statistics exceeds a predetermined value.

In accordance with an embodiment of the present invention, the above-mentioned memory, processing unit, and other components may be implemented in a lot consistency system, such as the example lot consistency system 100 of FIG. Any suitable combination of hardware, software, and / or firmware may be used to implement the memory, processing unit, or other components. By way of example, the memory, processing unit, or other components may be implemented in conjunction with data supply processor 105 or lot coherency processor 110 in combination with system 100. The above-mentioned systems and processors are exemplary, and other systems and processors may include the above-mentioned memory, processing unit, or other components in accordance with embodiments of the present invention.

Moreover, the present invention may be implemented in discrete electronic elements, packaged or integrated electronic chips containing logic gates, circuitry using a microprocessor, or a single chip containing electronic elements or microprocessors. The invention may also be practiced using other technologies that may include, but are not limited to, mechanical, optical, hydrotechnical, and quantum technologies such as, for example, AND, OR, and NOT. In addition, the present invention may be practiced in general purpose computers or any other circuits or systems.

As a non-limiting example, FIG. 1 illustrates a system 100 in which features and principles of the present invention may be implemented. As shown in the block diagram of FIG. 1, the system 100 may include a data supply processor 105, a lot coherency processor 110, a user 115, and a network 120. User 115 may be an individual who wishes to determine lot consistency using, for example, consistency processor 110. User 115 may also be an organization, corporation, or some other entity with such desires.

The lot consistency processor 110 may include a processing unit 125 and a memory 130. Memory 130 may include lot consistency software module 135 and lot consistency database 140. Software module 135 residing within memory 130 may be performed on processing unit 125, may access database 140, and determine lot consistency, such as the method described below with respect to FIG. 2. You can implement processes for Nevertheless, processor 110 may execute other software modules and implement other processes.

The data supply processor 105 or the lot consistency processor 110 (“processors”) included in the system 100 may be implemented using a personal computer, network computer, mainframe, or other similar microcomputer based workstation. have. The processors may include any form of computer operating environment, such as portable devices, multiprocessor systems, microprocessor-based or programmable sender electronic devices, minicomputers, mainframe computers, and the like. The processors may also be executed in distributed computing environments where tasks are performed by remote processing devices. Moreover, any of the processors may be a smart phone, cellular telephone, cellular telephone using wireless application protocol (WAP), personal digital assistant (PDA), intelligent pager, portable computer, portable computer, conventional telephone, or fax machine. It may include a mobile terminal such as. The systems and devices mentioned above are exemplary and may include other systems or devices.

Network 120 may include, for example, a wireless local area network (LAN) or a wide area network (WAN). Such networking environments are common in offices, enterprise wide area computer networks, intranets, and the Internet, and are known by those skilled in the art. When a LAN is used as the network 120, a network interface located on any of the processors may be used to interconnect to any of the processors. When the network 120 is implemented in a WAN networking environment such as the Internet, the processors may typically include an internal or external modem (not shown) or other means of establishing communication over the WAN. In addition, in using the network 120, data transmitted over the network 120 may be encrypted to ensure data security using known encryption / decryption techniques.

In addition to using a wired communication system as the network 120, a wireless communication system or a combination of wired and wireless may, for example, exchange web pages over the Internet, exchange e-mail over the Internet, or use other communication channels. May be used as network 120 to do so. Radio may be defined as radio transmission over airwaves. However, it will be understood that various other communication techniques may be used to provide wireless transmission, including infrared, cellular, microwave, satellite, packet radio, and spread spectrum radios in the field of view. Processors in a wireless environment may be any mobile terminal, such as the mobile terminals described above. Wireless data may include, but is not limited to, calling, text messaging, electronic mail, Internet access, and other specialized data applications, specifically to exclude or include voice transmission.

System 100 may also transmit data by methods and processes that differ from or combine with network 120. Such methods and processes may include transferring data via diskette, flash memory sticks, CD ROM, fax, conventional mail, interactive voice response system (IVR), or over voice over a public switched telephone network. Can be, but is not limited thereto.

FIG. 2 is a flow diagram illustrating general steps involved in an exemplary method 200 in accordance with the present invention for determining lot consistency among a plurality of lots using, for example, the system of FIG. 1. The plurality of lots may include, but is not limited to, influenza vaccine lots. Moreover, each lot may include, but is not limited to, a plurality of analytes including HA corresponding to different viral species. In other words, each analyte can correspond to HA for different viral species. Example manners for implementing the steps of the example method 200 will be described in more detail below.

Exemplary method 200 may begin at start block 205 and proceed to step 210 where processor 110 may receive level data elements, eg, from data supply processor 105. The data supply processor 105 may be operated, for example, in a medical laboratory. Level data elements may include, for example, the arithmetic mean of HI titers log-transmitted as described above for different lots and analytes (eg, species). For example, the arithmetic mean of the log delivered HI titers can be calculated using the GMTs of HI titers after vaccination. Table 1 shows exemplary GMTs of HI titers after vaccination with lot (eg lot 1, lot 2, lot 3) and species (eg AH ₁ N ₁ , AH ₃ N ₂ , and B). Illustrated. As shown in Table 1, GMTs are highest in species B, with the lowest in AH ₁ N ₁ . The data shown in Table 1 can be obtained from blood samples taken from subjects previously injected with the vaccine from the lots presented by anti-HA antibody titration. Moreover, Table 1 shows the sample size "n" for each lot.

TABLE 1

Vaccine Species Form Lot 1 (n = 123) Lot 2 (n = 123) Lot 3 (n = 117) AH ₁ N ₁ 151.4 163.4 150.3 AH ₃ N ₂ 192.9 162.2 202.5 B 352.6 365.0 372.7

GMTs of HI titers after vaccination shown in Table 1 can be used to calculate the arithmetic mean of the log delivered HI titers shown in Table 2 below. This calculation is described above. Table 2 shows exemplary arithmetic mean of log transformed HI titers by lot (eg, Lot 1, Lot 2, Lot 3) and species (eg, AH ₁ N ₁ , AH ₃ N ₂ , and B). And standard deviations (in parentheses). In accordance with an embodiment of the invention, the arithmetic mean of log-transmitted HI titers and / or GMTs of HI titers after vaccination may be a calculation for either the data feed processor 105 or the lot consistency processor 110. .

TABLE 2

Viral species forms Lot 1 (n = 123) Lot 1 (n = 123) Lot 1 (n = 117) AH ₁ N ₁ 4.92 (1.69) 5.03 (1.65) 4.91 (1.65) AH ₃ N ₂ 5.27 (1.57) 5.02 (1.60) 5.34 (1.57) B 6.14 (1.20) 6.19 (1.21) 6.22 (1.28)

If processor 110 receives level data elements from step 210, example method 200 may proceed to step 220 in which processor 110 may calculate a plurality of inspection statistics. Each of the plurality of inspection statistics may correspond to each of the plurality of analysis objects. Moreover, each of the plurality of inspection statistics may include, but is not limited to, Z _min calculated as mentioned above for each analyte (species). E.g,

Z _min = min {(δ- | D _ij |) / se _ij }

ego,

Where D _ij may comprise the difference between the arithmetic mean of the i th and j th log transformed HI titers, se _ij may contain the standard error of the difference, and δ may comprise the equivalent margin described above Can be.

For the AH ₁ N type ₁ data shown in Table 2, the observed differences are D ₁₂ = + 0.11, D ₁₃ = −0.01, and D ₂₃ = −0.12. The standard deviations of the lot mean are sd ₁ = 1.69, sd ₂ = sd ₃ = 1.65. Therefore, the standard errors of the differences (the calculation described above) are

^{_{s 12 = √ [1.69 2/}} 123 + 1.65 2/123] = 0.21

^{_{s 13 = √ [1.69 2/}} 123 + 1.65 2/117] = 0.22

^{_{s 23 = √ [1.65 2/}} 123 + 1.65 2/117] = 0.21

to be.

This is for the AH ₁ N type ₁ data shown in Table 2 (when δ = 1.5),

Zmin = min {(1.5-0.11) /0.21, (1.5-0.01) /0.22, (1.5-0.12) 0.21} = 6.57

To present. Therefore, the value of the inspection statistics for AH ₁ N type ₁ data shown in Table 2 is 6.57. In addition, Z _min from the data shown in Table 2 can be calculated as 5.90 and 8.88 for AH ₃ N ₂ and B species, respectively. As a result, the values of the test statistics (eg, Z _min ) for AH ₁ N ₁ , AH ₃ N ₂ , and B may be 6.57, 5.90, and 8.88, respectively.

Once the processor 110 calculates the plurality of inspection statistics at step 220, the exemplary method 200 determines that the plurality of lots matches when each of the plurality of inspection statistics exceeds a predetermined value. If 110 can determine, step 230 can continue. For example, as mentioned above, even when a conservative threshold value based on a normal distribution is used for a predetermined value (ie Z _{0 .025} = 1.96) (eg, in relation to Table 2 The null hypothesis that three lots are not equivalent (as described) may be rejected for each of AH ₁ N ₁ species, AH ₃ N ₂ species, and B species. In other words, the value of each of the plurality of inspection statistics exceeds 1.96. Thus, null hypotheses that three lots are not equivalent may also be rejected for all three species. Since the null hypothesis was rejected for all three species, the equivalent of three lots (lot 1, lot 2, and lot 3) is shown as described above in connection with Table 2. After the processor 110 determines that each of the plurality of inspection statistics exceeds a predetermined value in step 230, the exemplary method 200 may end accordingly in step 240. Can be.

Threshold values based on the normal distribution are used in accordance with the calculation in connection with step 230 in the exemplary method 200. However, as summarized, less conservative thresholds c _α may be used. For example, c _α may depend on two parameters, δ / se and p. The first parameter is

δ / se = δ / √ [2min sd _i 2 / n _j ]

And the second parameter is

p = △ ₁₂ / △ ₁₃

It can be defined as. Since this ratio p is unknown, p can be set to 1/2. For the AH ₁ N ₁ species described above in connection with Table 2, δ / se = 7.13. for δ / se> 5, c _0, so _.025 ≒ 1.96, no advantage may be present. However, the δ / if se is 2.75 Ah like, c _{0 .025} = 1.71 and therefore, this means a significant gain with respect to statistical power.

As a comparison, the arithmetic mean and standard deviation of the log transformed fold increases for the data shown in Table 1 are shown in Table 3.

TABLE 3

Viral species forms Lot 1 (n = 123) Lot 2 (n = 123) Lot 3 (n = 117) AH ₁ N ₁ 4.51 (1.82) 4.39 (1.69) 4.48 (1.89) AH ₃ N ₂ 3.73 (2.04) 3.64 (1.98) 3.65 (2.21) B 3.78 (2.27) 3.46 (2.38) 3.59 (2.38)

For AH ₁ N ₁ species, the differences observed are D ₁₂ = -0.12, D ₁₃ = -0.03, and D ₂₃ = +0.09. The standard deviations of the lot mean are sd ₁ = 1.82, sd ₂ = 1.69, and sd ₃ = 1.89. Thus, the observed differences are of the same general magnitude as those found for the log transformed HI titers shown in Table 2, but the standard deviations are greater. This may confirm that assays based on fold increases for AH ₁ N ₁ species may be less powerful than those based on HI titers (ie, smaller, less important values for Z _min ). The same applies to the two other species shown in Tables 1, 2, and 3.

In accordance with an embodiment of the present invention, example method 200 may represent a method that may handle two forms of diversity, for example. First type fold comparisons between lots can be handled, for example, by applying a statistical method to examine the equivalence of three analytes. Moreover, in accordance with an embodiment of the present invention, the exemplary method 200 may represent a second form multiple, for example, because one or more species may be ignored (ie, no adjustment is required).

In one embodiment, the present invention provides a method for determining lot consistency between a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. The method may comprise, for example, manufacturing the lots and taking samples from them, analyzing the analytes in the samples, and generating level data elements from the analyzes, respectively The level data elements of correspond to one of the plurality of lots and one of the plurality of analytes. The method is for example a computer and a formula,

Z _min = min {(δ- | D _ij | / se _ij ),

Calculating a plurality of inspection statistics by using, wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects,

Where D _ij includes the difference of two of the level data elements for the i th lot and the j th lot for the presented one of the plurality of analytes, se _ij includes the standard error of the difference, and δ is The calculation step may further comprise an equivalent margin. The method may further comprise, for example, determining whether the plurality of lots matches because each of the plurality of inspection statistics exceeds a predetermined value. Consistency determinations can be used, for example, to make decisions to use lots, and / or to discard them and / or to adjust manufacturing processes.

This aspect of the invention can be used as a basis for assays to determine the quality of a dispensed vaccine lot. In the case of influenza vaccines, the vaccine preparation process comprises, for example, propagating the virus on an egg or in a cell culture, and the antigen (which may include one or more of inactivation, solubility, and purification). It may include the step of harvesting. In this context, the analyte may be, for example, an immunogenic component of the vaccine. If the vaccine is an influenza vaccine, the analyte may be a component of an influenza virus such as HA or NA. Subsequently, subjects can be immunized via the vaccine, blood samples can be taken from the subjects, and antibody responses can be determined. The method can be carried out via vaccine configurations comprising one or more analytes.

While certain features and embodiments of the invention are described, other embodiments of the invention will be apparent to those skilled in the art in view of the specification and practicality of the embodiments of the invention disclosed herein. Moreover, although embodiments of the present invention have been described as being associated with data and other storage media stored in memory, these aspects are, for example, second storage devices such as hard disks, floppy disks, or CD-ROM. It will be appreciated that other forms of computer readable media, carriers from the Internet, or other forms of RAM or ROM may be read from or stored therein. In addition, the steps of the disclosed methods may be modified in any manner without departing from the spirit of the present invention and including reordering steps and / or inserting or deleting steps.

Therefore, the details and examples are merely illustrative and are intended to be indicated by the true scope and spirit of the invention as set forth in the following claims and their full scope of equivalents.

The present invention provides methods and systems for determining lot consistency more optimally, for example, it is possible to determine lot consistency between two or more vaccine lots where each lot contains several species.

Claims (32)

12. A method of determining lot consistency among a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. Receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects; Computer and formulas, Z _min = min {(δ- | D _ij |) / se _ij } Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; And When each of the plurality of inspection statistics exceeds a predetermined value, determining that the plurality of lots is consistent. The method of claim 1, Wherein the lots are vaccine lots. The method of claim 2, Wherein said level data elements are a measure of the immunogenicity of said vaccine lots. The method of claim 2, Wherein said vaccine is an influenza vaccine and said level data elements correspond to the immunogenicity of said influenza vaccine. The method of claim 4, wherein The immunogenicity of the influenza vaccine is determined by the hemagglutination inhibition test for anti-HA antibody titration. The method of claim 1, And converting the non-normally distributed level data elements to obtain normally distributed level data elements. The method of claim 1, δ is 1.3 to 1.7. The method of claim 1, δ is 1.4 to 1.6. The method of claim 1, δ / se _ij is 5.0 or less. The method of claim 1, Wherein each of the plurality of lots is associated with one or more analytes, and a level data element is determined corresponding to each of the one or more analytes associated with each of the plurality of lots. 10. A method of determining lot consistency among a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. Receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects; Equation, Z _min = min {(δ- | D _ij |) / se _ij } Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for a j th lot, se _ij includes a standard error of the difference, and δ comprises an equivalent margin; And When each of the plurality of inspection statistics exceeds a predetermined value, determining that the plurality of lots is consistent. 12. A system for determining lot consistency among a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. A memory store holding a database and a processing unit coupled to the memory store, The processing unit receives the level data elements, the computer and the formula, Z _min = min {(δ- | D _ij |) / se _ij } Calculate a plurality of inspection statistics, and determine that each of the plurality of inspection statistics is consistent when the plurality of inspection statistics exceeds a predetermined value, Each of the level data elements corresponds to one of the plurality of lots and one of the plurality of analytes, Each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is the difference of two of the level data elements for the i th lot and the j th lot for a given one of the plurality of analysis objects And wherein se _ij includes the standard error of the difference and δ comprises an equivalent margin. The method of claim 12, Lot consistency determination system, wherein the lots are vaccine lots. The level data elements are lot consistency determination system, which is a measure of the immunogenicity of the vaccine lots. The method of claim 13, The vaccine is an influenza vaccine and the level data elements correspond to the immunogenicity of the influenza vaccine. The method of claim 15, Lot consistency determination system, wherein the immunogenicity of the influenza vaccine is determined by a blood aggregation inhibition (HI) test for anti-HA antibody titration The method of claim 12, The processing unit is operable to transform the nonnormally distributed level data to obtain normally distributed level data elements The method of claim 12, lot consistency determination system, wherein δ is 1.3 to 1.7. The method of claim 12, lot consistency determination system, wherein δ is 1.4 to 1.6. The method of claim 12, A lot consistency determination system, wherein δ / se _ij is 5.0 or less. The method of claim 12, Wherein each of the plurality of lots is associated with one or more analytes, and a level data element corresponding to each of the one or more analytes associated with each of the plurality of lots is determined. A computer readable medium storing a set of instructions executed when a method of determining lot consistency among a plurality of lots is performed, each of the plurality of lots associated with each of a plurality of analytes. In a possible medium, The method executed by the set of instructions is: Receiving level data elements, each of the level data elements corresponding to one of the plurality of lots and one of the plurality of analysis objects; Computer and formulas, Z _min = min {(δ- | D _ij |) / se _ij } Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; And Determining that the plurality of lots is consistent when each of the plurality of inspection statistics exceeds a predetermined value. The method of claim 22, And the lots are vaccine lots. The method of claim 23, And the level data elements are a measure of immunogenicity of the vaccine lots. The method of claim 23, The vaccine is an influenza vaccine, and the level data elements correspond to the immunogenicity of the influenza vaccine. The method of claim 25, The immunogenicity of the influenza vaccine is determined by blood aggregation inhibition (HI) test for anti HA antibody titration. The method of claim 22, The instructions further comprise transforming the non-normally distributed level data elements to obtain normally distributed level data elements. The method of claim 22, δ is 1.3 to 1.7. The method of claim 22, δ is 1.4 to 1.6. The method of claim 22, δ / se _ij is 5.0 or less. The method of claim 22, Wherein each of the plurality of lots is associated with one or more analytes, and level data elements corresponding to each of the one or more analytes associated with each of the plurality of lots are determined. 10. A method of determining lot consistency among a plurality of lots, wherein each of the plurality of lots is associated with each of a plurality of analytes. Manufacturing the lots, taking samples from the lots, analyzing the analytes in the samples, and generating level data elements from the analyzes, each of the level data elements being the plurality of lots The generating step corresponding to one of the and one of the plurality of analytes; Computer and formulas, Z _min = min {(δ- | D _ij |) / se _ij } Calculating a plurality of inspection statistics by using: wherein each of the plurality of inspection statistics corresponds to each of the plurality of analysis objects, and D _ij is an i th lot for a given one of the plurality of analysis objects; the calculating step includes two differences of the level data elements for the jth lot, se _ij includes the standard error of the difference, and δ comprises an equivalent margin; Determining that each of the plurality of inspection statistics exceeds a predetermined value, and that the plurality of lots is consistent; And Based on the consistency determination, determining whether to use and / or discard each of the lots, and / or to adjust the manufacturing process.

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