Patent Application
20220328139
The invention generally relates to groups of products made in a batch according to a predefined specification.
The invention relates more specifically to such groups in which the products have a quantitative attribute taking a value representative of the preservation state of the product, the quantitative attribute value evolving as a function of time and temperature, the product having a preservation state (for instance meeting or not meeting acceptance criteria) depending on the quantitative attribute value.
It is known that pharmaceutical products for human use are examples of such products and that harmonization works were performed on the conduct of stability studies for pharmaceutical products.
In particular, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) provides guidelines on such stability studies, known as Q1A-Q1F amongst which is Q1A(R2) entitled “Stability Testing Of New Drug Substances And Products” and Q1E entitled “Evaluation for Stability Data”.
The purpose of a ICH stability study is to establish, based on testing a minimum of three batches of the drug substance or product, a retest period (drug substance) or shelf life (drug product) and label storage instructions applicable to all future batches manufactured and packaged under similar circumstances.
Q1A(R2) guideline details principles for generating stability data and Q1E describes when and how extrapolation can be considered when proposing a retest period (drug substance) or a shelf life (drug product) that extends beyond the period covered by the available data generated from a stability study under the so-called long-term storage condition, such as twelve months in a refrigerator at 5° C.±3° C.
The stability data are generated for the critical quality attributes likely to influence the quality and performance of the drug substance or product.
Q1E guideline explains that 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.
Q1E guideline further explains that data for these attributes are therefore amenable to the type of statistical analysis described in its Appendix B, including linear regression and poolability testing; that although the kinetics of other quantitative attributes (e.g., pH, dissolution) is generally not known, the same statistical analysis can be applied, if appropriate; and that qualitative attributes and microbiological attributes are not amenable to this kind of statistical analysis.
Q1E guideline thus discloses a way of extrapolating a law of evolution—as a function of time—of a quantitative attribute value. With the extrapolated law of evolution, it is possible to determine the quantitative attribute value at time points different from the experimental time points at which the stability data were generated, in particular time points beyond the period covered by the stability data. The preservation state of the product (for instance within or out of acceptance criteria) can thus be determined at a current time point different from the experimental time points.
Such extrapolation is very useful for knowing the preservation state of one product in predefined conditions of storage, for instance the so-called long-term storage condition, such as twelve months in a refrigerator at 5° C.±3° C.
More recently, the World Health Organization (WHO) Guidelines on Stability Evaluation Of Vaccines states that statistical modelling such as regression analysis may be used to analyse data from stability studies and states that modelling can be performed after three or more stability time points have been obtained.
The invention is directed to an improved method for determining the preservation state of this kind of products, such method having good precision performance even with a limited amount of experimental stability data.
The invention provides accordingly a method for determining at a current time point a preservation state of one product of a group of products made in a batch according to a predefined specification, each product of said group of products having a quantitative attribute taking a value evolving as a function of time and temperature, said preservation state of said one product depending on said quantitative attribute value of said one product at said current time point, said method including:
The method according to the invention enables determining the preservation state of the product also in conditions of storage in which the temperature did not remain constant within predefined tolerances, for instance with a period of storage in a refrigerator followed by a period of travel at ambient temperature followed by a period of storage in a refrigerator.
Indeed, in the method according to the invention, the determined law of evolution is a function of time and temperature (and not of time only) and the successive temperatures taken by the product as from the initial time are used for determining the quantitative attribute value at the current time point.
Unlike commercially available software, even based on Arrhenius law (such as software commercialized by companies called AKTS and ASAP), the invention enables to obtain good precision performance even with a limited amount of experimental stability data.
Indeed, the invention is based on the observation that having—as in commercially available software—a single model involving an important number of parameters while providing a single statistical score for evaluating the quality of the found law of evolution, is not appropriate here because the amount of the experimental data set is low; and that in fact it is better to have a plurality of phenomenological models and to provide a plurality of estimators for discriminating the most appropriate model.
It is understood that in the present memorandum a parameter is a constant value initially unknown making part of a relation which becomes—further to the determination of the value of each parameter on the basis of an experimental data set—a law of evolution giving the quantitative attribute value as a function of time and temperature.
It is also understood that in the present memorandum a model is different from another model if they are expressed with different relations.
For instance, if a first relation is 1/(1/C02+2k t)1/2 and a second relation is 1/(1/C0n−1+k (n−1) t)1/(n−1) with k=A exp(−Ea/RT), with C which is the quantitative attribute value, t which is the time, T which is the temperature, C0 which is a first parameter, A which is a second parameter, Ea which is a third parameter and n which is a fourth parameter, the first relation is the expression of a first model with three parameters and the second relation is the expression of a second model with four parameters, different from the first model.
It is noted that if the fourth parameter n is equal to 3 and the first to third parameters have the same values in the first model and the second model, the law of evolution given by the first model and the second model is the same, but this does mean that the second model is the same as the first model.
According to advantageous features:
The invention is also directed, in a second aspect, to a computer system for carrying out the method as disclosed above, including a phenomenological library in which is stored a plurality of phenomenological models of evolution of a quantitative attribute value as a function of time and temperature, each said model having between three and nine parameters, said parameters including a quantitative attribute value at an initial time point, hereinafter termed initial quantitative attribute value parameter, said computer system further including an interface for entering an experimental stability data set, an equation resolution tool for computing a previously entered experimental stability data set for finding for each said model best fitting values for its parameters, and including an estimator production tool for finding predetermined estimators for each model, said estimators including a physico-chemical parameter likelihood estimator and a fit distance, said physico-chemical parameter likelihood estimator being based at least on a comparison between initial quantitative attribute values in the experimental data set and the best fitting value found for said each model for the initial quantitative attribute value parameter, said fit distance being based on a comparison between the values in the experimental data set and the corresponding values given by said each model.
The description of the invention continues now with a detailed description of example embodiments given hereinafter by way of non-limiting illustration and with reference to the appended drawings. In the drawings:
The products 11 are perishable and there is thus a need to determine at a current time point, such as the present time point or a time point in the future, if a product 11 still has a good preservation state.
The preservation state of a product 11 at a current time point depends on the value taken by a quantitative attribute of the product 11 evolving as a function of time and temperature.
Here, the product 11 is a vaccine dose to be administered to a patient and the quantitative attribute is the concentration of an active component in the vaccine dose.
Indeed, some active components of the vaccine dose are subject to a degradation reaction so that their quantity in the vaccine dose decreases over time and may fall below a specified efficacy threshold under which the therapeutic efficacy of the vaccine is considered lost.
The preservation state of the product 11 is considered good if the concentration of active component is over the threshold and considered bad if the concentration of active component is below the threshold.
For enabling to determine the preservation state of one product 11 at a current time point, a known method is illustrated on
This method is disclosed in the guidelines provided by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) known as Q1A-Q1F amongst which is Q1A(R2) entitled “Stability Testing Of New Drug Substances And Products” and Q1E entitled “Evaluation for Stability Data”.
This known method includes:
For carrying out step (i), experiments are conducted on the stability of the products 11.
In these experiments, certain products 11 randomly selected in the group 10 are stored at different constant temperatures within predefined tolerances. At a plurality of different experimental time points—including an experimental initial time point—the quantitative attribute value of the respective products under experiment are determined and recorded.
An experimental data set is thus produced, each data being a quantitative attribute value at time t for a product 11 stored at a temperature T.
Then, step (i) is finalised by carrying out the step of extrapolating the law of evolution from the experimental data set, assuming that the quantitative attribute value follows zero-order kinetics during long-term storage and that the quantitative attribute value is amenable to certain type of statistical analysis described in Appendix B of Q1E ICH guideline, including linear regression and poolability testing.
With the extrapolated law of evolution, carrying out step (ii) enables to determine the quantitative attribute value at time points different from the experimental time points at which the experimental stability data were generated, in particular time points beyond the period covered by the experimental stability data.
From the quantitative attribute value determined at step (ii), step (iii) gives the preservation state of the product, for instance within or out of acceptance criteria involving a threshold for the quantitative attribute value.
In practice, the law of evolution determined on the basis of one group of products such as the group 10 is used for other groups of products made in a batch according to the same predefined specification, the samples (product 11 on which the experiments were conducted) taken from group 10 being considered as reference products for the other group of products.
In practice, it is often checked that the determined law of evolution remains the same when it is determined from experimental stability data set generated from samples taken in other groups of products made in a batch according to the same predefined specification, for instance the determined law of evolution is determined with three successive groups of products made in a batch according to the same predefined specification.
In the method according to the invention, the law of evolution is also extrapolated from an experimental data set but on basis much more advanced than merely assuming that the quantitative attribute value follows zero-order kinetics during long-term storage and that the quantitative attribute value is amenable to certain type of statistical analysis described in Appendix B of Q1E ICH guideline, including linear regression and poolability testing.
Computer System Involved in the Method According to the Invention
The computer system 15 includes an interface 16 for input and output of data, a phenomenological models library 17, an equation resolution tool 18, an estimator production tool 19, a storage 20 for the experimental stability data set, a storage 21 for best fitting values of parameters found by the equation resolution tool 18 and a storage 22 for estimators found by the estimator production tool 19.
Here, the computer system 15 further includes a predictive quality value production tool 26 and a storage 27 for a confidence interval mapping produced by the predictive quality value production tool 26.
Here, further to helping to extrapolate the law of evolution of the quantitative attribute value as a function of time and temperature, the computer system 15 is also able to determine at a current time point the quantitative attribute value using the previously determined law of evolution and to determine the preservation state of one product 11 at this current time point using the previously determined quantitative attribute value at this current time point.
The computer system 15 further includes accordingly a quantitative value calculation tool 28, a preservation state determination tool 29, a storage 30 for a temperature profile, a storage 31 for a determined quantitative value at a current time point and a storage 32 for a determined preservation state at a current time point.
It is noted that a distinct computer system such as an electronic Time Temperature Indicator (eTTI) can be configured with a similar quantitative value calculation tool, a similar preservation state determination tool, a similar storage for a temperature profile, a similar storage for a determined quantitative value at a current time point and a similar storage for a determined preservation state at a current time point, with of course an interface for input and output of data.
The computer system 15 is carried out here with a stand-alone computer such as a PC or Mac running a suitable program (for instance based on Matlab®), the interface 16 being the local display, keyboard and ports of the stand-alone computer. Alternatively, the computer system is carried out differently, for instance with a server running a suitable program (for instance based on Matlab®), the interface 16 being carried out with a client, a communication link and the local interface of the server for connecting the client through the communication link.
Library 17
In the library 17 are stored a plurality of phenomenological models of evolution of the quantitative attribute value of a product 11, here the concentration of active component in a vaccine dose.
The phenomenological models are each based on the Arrhenius relationship between the kinetic constant k of a reaction and the temperature T. The Arrhenius relationship is based on the collision theory explaining that, for a reaction to occur between two particles or more, it is necessary that a collision occurs between these particles, in the right direction and with enough energy.
The Arrhenius relationship is:
k=A exp(−Ea/RT)
the kinetic constant k being the number of reactions per unit of time; the pre-exponential factor A being the total number of collisions per unit of time; and the exponential factor exp(−Ea/RT) being the probability that a collision will result in a reaction, Ea being the energy of activation of the reaction, T the temperature in ° K and R the perfect gas constant.
Models called Order 0, Order 1, Order 2, Order 3 and Order n
For certain models in the library 17, called Order 0, Order 1, Order 2, Order 3 and Order n, it is assumed that the reaction degradation of the quantitative attribute value of a product 11, here the concentration of active component in a vaccine dose, follows the below law of chemical kinetics:
dC/dt=kf(C)
C being the quantitative attribute value (here the concentration of active component in a vaccine dose), t being the time and f being the respective function on which is based the model called Order 0, Order 1, Order 2, Order 3 or Order n.
Table 1 below gives for each of these five models the expression of f(C), the expression of dC/dt and the expression of C(t) which is the analytic solution of integrating dC/dt at constant temperature.
In the expression of C(t), C0 is the quantitative attribute value (concentration of active component in a vaccine dose) at the initial time point.
It results from the expression of C(t) and from the Arrhenius relationship giving k as a function of the pre-exponential factor A, of the activation energy Ea and of the temperature T, that the models called Order 0, Order 1, Order 2 and Order 3 have three parameters, namely A, Ea and C0; and that the model called order n has four parameters, namely n further to the parameters of the models called Order 0, Order 1, Order 2 and Order 3 (A, Ea and C0).
The value of the temperature T at which the product is stored and the values of parameters A, Ea and C0 (and also n for the model called Order n) enable to determine the quantitative attribute value C at a time t.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known (for example 60 days at 5° C. as from the initial time, then 1 day at 25° C. and then 10 days at 5° C.) and provided that the determination is made iteratively, that is successively for each temperature step (for instance in the above example first determining the quantitative attribute value at 60 days starting with C0, then determining the quantitative attribute value at 61 days starting with the quantitative attribute value at 60 days for a duration of 1 day, then determining the quantitative attribute value at 71 days starting with the quantitative attribute value at 61 days for a duration of 10 days).
The model called Order 0 corresponds to a reaction of which the kinetics is independent of the concentrations of the components of the couple reactant/substrate. In biology, this is the case for certain enzyme-catalyzed reactions. In practice, this model applies as long as the substrate concentration remains high, for instance at the beginning of the reaction.
The model called Order 1 corresponds to a reaction of which the kinetics depends on the concentration of only one of the components of the couple reactant/substrate. This is the case for certain reactions of the same type as hydrolysis in aqueous media, oxidation at the air/water interface or enzyme catalysis.
The model called Order 2 corresponds to a reaction of which the kinetics depends on the concentrations of both of the components of the couple reactant/substrate if the reaction is of the same type as polysaccharide saponification or as protein deamidation, or depends on the concentration of only one of the components of the couple reactant/substrate if the reaction is of the same type as protein dimerization.
The model called Order 3 corresponds to a reaction of which the kinetics depends on the concentration of both of the components of the couple reactant/substrate if one of them is bimolecular and if the reaction is of the same type as protein trimerisation.
The model called Order n corresponds to a reaction of which the kinetics depends on the concentration of both of the components of the couple reactant/substrate if one or both of them is/are bimolecular or multimolecular and if the reaction is of the same type as an α-helix protein structure break or an oligomerization greater than 3.
It is noted that the model called Order n gives the same law of evolution as the model called Order 0, Order 1, Order 2 and Order 3 if n is respectively equal to zero, one, two and three. In practice, the model called Order n is useful for n which is not an integer number and/or is greater than three.
Model Called Parallel Reaction
Another model in the library 17 is called Parallel reaction. It is assumed the same as for the model called Order n except that two reactions (and not a single one) are considered as simultaneously going on, respectively reaction 1 involving parameters A1, Ea1, C01 and n1 for a partial quantitative attribute C1 and reaction 2 involving parameters A2, Ea2, C02 and n2 for a partial quantitative attribute C2, the quantitative attribute value C being the sum of the partial quantitative attributes C1 and C2 (in other words C=C1+C2) with C1=αC and C2=(1−α) C, α being called the proportion of subpopulation.
There are thus eight parameters for the model called Parallel reaction, namely C0, α, A1, Ea1, n1, A2, Ea2 and n2.
The value of the temperature T at which the product is stored and the values of parameters C0, α, A1, Ea1, n1, A2, Ea2 and n2 enable to determine the quantitative attribute value C at a time t, by numerical integration.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known and provided that the determination is made iteratively as explained above.
Model Called Finke-Watzky
Another model in the library 17 is called Finke-Watzky. Further to the quantitative attribute value C (here the concentration of active component in a vaccine dose) a further quantitative attribute value N is considered (here the concentration of degraded active component in a vaccine dose), with the following relationships between N and C:
dN/dt=k1C
dC/dt=−k1C−k2CN
k1 and k2 being respective kinetic constants of two different Arrhenius relationship, namely:
k1=A1 exp(−Ea1/RT)
k2=A2 exp(−Ea2/RT)
There are thus five parameters for the model called Finke-Watzky, namely C0, A1, Ea1, A2 and Ea2.
The value of the temperature T at which the product is stored and the values of parameters C0, A1, Ea1, A2 and Ea2 enable to determine the quantitative attribute value C at a time t, by numerical integration.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known and provided that the determination is made iteratively as explained above.
The model called Finke-Watzky corresponds to a reaction of aggregation, like nucleation or germination, which is common in media with proteins, in particular if the latter are concentrated.
Model Called Prout-Tompkins
Another model in the library 17 is called Prout-Tompkins. This model is also known as Sestak n/m. It is assumed that the reaction degradation of the quantitative attribute value (here the concentration of active component in a vaccine dose) is at a fraction ξ at a time t and that the fraction ξ follows the below law of chemical kinetics:
gξ/dt=−kξ
n(1−ξ)m
with ξ=1 when the reaction is finished and ξ greater than 0 (and lesser than 1) at the start of the reaction.
For simplifying the notations, the value of ξ at the initial time is noted α in the following. It is noted that this a has not the same phenomenological significance as α in the model called Parallel reaction.
There are thus six parameters for the model called Prout-Tompkins, namely C0, A, Ea, n, m and α.
The value of the temperature T at which the product is stored and the values of parameters C0, A, Ea, n, m and α enable to determine the quantitative attribute value C at a time t, by numerical integration.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known and provided that the determination is made iteratively as explained above.
The model called Prout-Tompkins corresponds to a reaction that is autocatalytic or progressively accelerated.
Model Called Sestak n/p
Another model in the library 17 is called Sestak n/p. It is assumed the same as for the model called Prout-Tompkins except that the law of chemical kinetics followed by the fraction ξ is:
dξ/dt=−kξ
n log(1−ξ)p
There are thus six parameters for the model called Sestak n/p, namely C0, A, Ea, n, p and α.
The value of the temperature T at which the product is stored and the values of parameters C0, A, Ea, n, p and α enable to determine the quantitative attribute value C at a time t, by numerical integration.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known and provided that the determination is made iteratively as explained above.
The model called Sestak n/p corresponds to a reaction of order n that is progressively accelerated with a factor p limiting the diffusion of the reactants or of the degradation products. This reaction is of the same type as a hydrolysis in solid formulation.
Model Called Sestak m/p
Another model in the library 17 is called Sestak m/p. It is assumed the same as for the model called Prout-Tompkins except that the law of chemical kinetics followed by the fraction ξ is:
dξ/dt=k(1−ξ)m log(1−ξ)p
There are thus six parameters for the model called Sestak m/p, namely C0, A, Ea, m, p and α.
The value of the temperature T at which the product is stored and the values of parameters C0, A, Ea, m, p and α enable to determine the quantitative attribute value C at a time t, by numerical integration.
It is also possible to determine the quantitative attribute value C at a time t if the temperature did not remain constant, provided that the temperature profile over the time is known and provided that the determination is made iteratively as explained above.
The model called Sestak m/p corresponds to a reaction of order m that is autocatalytic with a factor p limiting the reaction product or the diffusion of the reactant or of the degradation products. This reaction is of the same type as an oligomerization in solid formulation.
Model Parameters Recapitulation
Table 2 below recapitulates the parameters in the ten phenomenological models in the library 17:
The phenomenological models in the library 17 thus have between three and height parameters.
Equation Resolution Tool 18
In the computer system 15, the equation resolution tool 18 is able to compute a previously entered stability data set present in the storage 20 so as to find for each model in the library 17 best fitting values for the parameters.
The best fitting values for the parameters are found by optimizing a function of the parameters selected as representative of the fit of C(t) to the experimental data set; the best fitting values of the parameters being the values for which the function is optimized.
Here, such function is the sum of the square of the differences between the values in the experimental data set and the corresponding values given by C(t); and the function is optimized by minimizing the sum of the square of the differences.
The optimization of the function is performed by running an iterative algorithm, here carried out with the fminsearch function of Matlab® which is based on the Nelder-Mead simplex algorithm, from a starting set of values for the parameters.
The values of the starting set are here randomly generated by the computer system 15, for instance by using a clock drift as a source of randomness.
The iterative algorithm is then run for each model from this starting set of values and the found best fitting values are recorded in the storage 21.
Estimator Production Tool 19
In the computer system 15, the estimator production tool 19 is able to find for each model in the library 17 various estimators for helping to evaluate the modelling quality of the model.
One estimator is directed to the physico-chemical likelihood of the found parameters. It is recalled that since the models are phenomenological, the parameters each have a physico-chemical meaning so that it is possible to assign to each parameter a range of likely values, the values out of the range being unlikely, for instance a negative value for the activation energy Ea.
Other estimators are based on quantitative statistics and qualitative (probabilistic) statistics.
Phisico-Chemical Likelihood Estimator of the Found Best Fitting Values for The Parameters
This estimator can be positive (good likelihood) or negative (bad likelihood).
The physico-chemical likelihood estimator is positive except in the cases stated below for which the physico-chemical likelihood estimator is negative.
For the found initial quantitative attribute value parameter C0, a comparison is made with the initial experimental quantitative attribute value (initial quantitative attribute value in the experimental stability data set).
The physico-chemical likelihood estimator is negative if the difference between the found initial quantitative attribute value parameter C0 and the initial experimental quantitative attribute value is greater than a predetermined threshold. The physico-chemical likelihood estimator is also negative if the found initial quantitative attribute value parameter C0 is lesser than 0.
Here, if the analytical standard deviation of the experimental stability data set is unknown, the threshold is 30% of the initial experimental quantitative attribute value (or 0.3 if the values are given on a logarithmic scale).
If the analytical standard deviation of the experimental stability data set is known, then the threshold is the value of the analytical standard deviation.
The analytical standard deviation is conventionally a statistical measure of the precision of the measurements from which was obtained the experimental stability data set.
For the found parameter Ea in principle representative of an activation energy, the physico-chemical likelihood estimator is negative if Ea is lesser than 50 or greater than 1000 kJ/mol. Alternatively, the range 50-1000 kJ/mol is replaced by a narrower range, such as 50-500 kJ/mol or 50-300 kJ/mol.
For the found parameter A in principle representative of a pre-exponential factor, the physico-chemical likelihood estimator is negative if A is lesser than 1 or greater than 300 kJ/mol. Alternatively, the range 1-300 kJ/mol is replaced by a narrower range, such as 20-200 kJ/mol.
For the found parameter α in principle representative of the proportion of subpopulation (model called Parallel reaction) or representative of the fraction value at the initial time (models called Prout-Tompkins, Sestak n/p and Sestak m/p), the physico-chemical likelihood estimator is negative if a is lesser than 0 or greater than 1.
For the found parameters n, m and p in principle each representative of an order of reaction, the physico-chemical likelihood estimator is negative if n, m or p is lesser than 0 or greater than 10. Alternatively, the range 0-10 is replaced by a narrower range, such as 0-3.
Estimators Based on Qualitative Statistics
One estimator based on qualitative statistics is a fit distance based on a comparison between the values in the experimental stability data set present in the storage 20 and the corresponding values given by the model.
The fit distance is here the square root of the sum of the squares of the differences between the values in the experimental stability data set and the corresponding values given by the model. The smaller the value of the fit distance, the better the model fits to the experimental data set.
Another estimator based on qualitative statistics is the Bayesian Information Criterion (BIC). As is well known, this criterion makes it possible to estimate the bias induced by the number of parameters on the modelling quality. The lower the value of the BIC, the lower the bias induced by the number of parameters.
The BIC is interesting when comparing two models having a different number of parameters: if the lowest value of the BIC is for the model having the highest number of parameters, the modelling quality is in principle better for the model having the highest number of parameters.
Other estimators based on qualitative statistics useful for comparing two models are the well known Akaike Information Criterion (AIC) and the likelihood ratio test (LLH). The AIC and LLH may be calculated using the respective functions available in Matlab®.
Estimators Based on Quantitative Statistics
One estimator based on quantitative statistics is the result (positive or negative) of the Fisher test.
As is well known, the Fisher test is based on a comparison of the standard deviation of the values in the experimental stability data set (analytical standard deviation) with the standard deviation of the set of values given by the model at the corresponding time points (model standard deviation), so as to determine whether the analytical standard deviation is similar or lesser than the model standard deviation.
Another estimator based on qualitative statistics is the result (positive or negative) of the Student t-test.
The Student t-test is made only if the result of the Fisher test is positive.
As is well known, the Student t-test is based on a comparison of the mean of the values in the experimental stability data set (analytical mean) with the mean of the set of values given by the model at the corresponding time points (model mean). The Student t-test is here made using a 95% confidence level.
The estimators found by the estimator production tool 19 are recorded in storage 22.
Predictive Quality Value Production Tool 26
In the computer system 15, the predictive quality value production tool 26 is able to find for a selected model in the library 17 for which best fitting values are found a confidence interval mapping.
The confidence interval mapping includes a confidence interval (CI) for each of the found best fitting values. Such confidence intervals include each a lower value and an upper value between which is the best fitting value.
The lower and upper values are here found using a Bayesian method.
In a variant, a Bootstrap method or a Jackknife method may be used instead of a Bayesian method.
It should be noted that in certain cases, the experimental data set does not enable to find lower and upper values of the confidence interval which are significant, in particular for the models having the most numerous parameters.
The found confidence interval mapping or the absence of significant interval mapping is stored in the storage 27.
Quantitative Value Calculation Tool 28
Based on the respective estimators found by the estimator production tool 19 and based on the initial quantitative attribute value parameter as compared with initial quantitative attribute values in the experimental stability data set, a best model amongst the models is selected.
Should the experimental data set does not enable to find significant confidence intervals for the parameters, it could be tried to modify the experimental data set such as by adding one or more experimental point(s) and/or decided to select another model for which the experimental data set enables to find significant confidence intervals.
The selected best model with the found best fitting values for its parameters thus becomes the determined law of evolution of the quantitative attribute value.
The quantitative value calculation tool 28 is accordingly configured through the interface 16.
The computer system 15 and in particular the quantitative value calculation tool 28 is thus aware of the determined law of evolution together with the confidence interval of each parameter value.
It thus becomes possible to ask to the quantitative value calculation tool 28 to determine the quantitative attribute value C at a current time point t by entering into the computer system 15 through the interface 16 the time t and the value of the constant temperature T at which the product is stored.
It is also possible to ask to the quantitative value calculation tool 28 to determine the quantitative attribute value C at a current time point t by entering into the computer system 15 through the interface 16 the time t and a temperature profile over the time (for example 60 days at 5° C. as from the initial time, then 1 day at 25° C. and then 10 days at 5° C.), this profile being recorded in the storage 30.
The quantitative value calculation tool 28 determines the quantitative attribute value C at the current time point t by successive iterations, that is successively for each temperature step (for instance in the above example first determining the quantitative attribute value at 60 days starting with C0, then determining the quantitative attribute value at 61 days starting with the quantitative attribute value at 60 days for a duration of 1 day, then determining the quantitative attribute value at 71 days starting with the quantitative attribute value at 61 days for a duration of 10 days).
It is thus possible to determine the quantitative attribute value at time points different from the experimental time points at which the experimental stability data were generated, in particular time points beyond the period covered by the experimental stability data; and also to determine the quantitative attribute value with storage conditions in which the temperature did not remain constant.
The determined quantitative attribute value C at the current time point t is recorded in the storage 31.
Further to the quantitative attribute value C at the current time point t the quantitative value calculation tool is able, from the confidence intervals of the parameter values, to determine the confidence interval of the found quantitative attribute value C at the current time point. The determined confidence interval is recorded in the storage 31 together with determined quantitative attribute value C.
Preservation State Determination Tool 29
From the quantitative attribute value and associated confidence interval recorded in the storage 31, the preservation state determination tool 29 is able to determine the preservation state of the product, for instance within or out of acceptance criteria involving a threshold for the quantitative attribute value taking into account the associated confidence interval.
The determined preservation state of the product is recorded in the storage 32 and communicated to the user through the interface 16.
An example of carrying out the method according to the invention with the computer system 15 will now be described.
In this example, certain products 11 randomly selected in the group 10 were stored at different constant temperatures within predefined tolerances and at a plurality of different experimental time points—including an experimental initial time point—the concentration in active component of the respective products under experiment were determined and recorded.
The experimental data set listed in Table 3 below was produced from such experiments in which the products 11 in the group 10 are doses of a formulation of vaccine against flu.
It is seen that the experiments were conducted with four different constant temperatures, respectively 4° C., 25° C., 37° C. and 45° C., with one product 11 for each temperature. The tolerance for each constant temperature was ±3° C. For each of the four different temperatures, the experiments were conducted during 37 days as from the initial time point (time of release of the group 10).
The quantitative attribute in the experimental data set listed in Table 3 is the HA Flu potency, determined by the SRID assay.
For each sample (product 11 on which the experiments were conducted), the initial HA Flu potency (value at the initial time point) is the same, namely 798 μg/ml.
Due to a degradation reaction, the HA Flu potency decreases over time but it is noted that certain values in the experimental data set are over the initial HA Flu potency value or over preceding values (see for instance at 20 days for 4° C. and for 25° C.). This is due to incertitude of measurement.
The experimental data set listed in Table 3 above was entered into the computer system 15 and the equation resolution tool 18 was used for computing this experimental stability data set for finding for each model in the library 17 best fitting values for the parameters.
Best fitting values were found for each model in the library 17 and stored in the storage 21 of the computer system 15.
The found best fitting values and—when significant—the lower value and upper value of the confidence interval (CI) are listed in Tables 4 to 13 below, in which the values are in J/Mol:
No lower value and upper value of the confidence interval (CI) is listed in Tables 4 to 13 because the found values are too high for being significant.
In the computer system 15, the estimator production tool 19 was used for finding for each model the physico-chemical likelihood estimator, the fit distance, the BIC and the Fisher test and the t-test.
Table 14 below lists for each model the found distance, an aggregated likelihood estimator, the statistical distance, the BIC and a combined result of the Fisher test and the t-test. Also listed is the found initial quantitative attribute value parameter C0.
The combined result of the Fisher test and the t-test is positive if the result of the Fisher test is positive and the result of the t-test is positive. The combined result of the Fisher test and the t-test is negative if the result of the Fisher test is negative (it is recalled that here the t-test is made only if the result of the Fisher test is positive).
The aggregated likelihood estimator is positive except if at least one of the physico-chemical likelihood estimator and the combined result of the Fisher test and the t-test is negative.
Here, the computer system 15 displays Table 14 to the user so as to give him a basis for selecting one model amongst the ten models in the library 17.
One possibility for displaying the aggregated likelihood estimator is in the same column as the name of the models, with each cell giving the name of a respective model having a background taking one out of two predetermined colors, such as green for positive and orange for negative.
As mentioned above, further to the estimators generated by the estimator production tool 19, Table 14 lists also the found initial quantitative attribute value parameter C0.
It is indeed interesting for the user to make its own comparison between the value of the found initial quantitative attribute value parameter C0 and the experimental initial quantitative attribute value, for instance for taking into account experimental difficulties in measuring the experimental initial quantitative attribute value which are not reflected by the analytical standard deviation considered for generating the physico-chemical likelihood estimator of the found best fitting values for the parameters.
The user might thus keep a model despite a negative aggregated likelihood estimator.
In the present example, the aggregated likelihood estimator is positive only for Parallel reaction (it is negative for all the other models).
This is in favor of Parallel reaction.
As to the found initial quantitative attribute value parameter C0, it is recalled that in this example for each sample (product 11 on which the experiments were conducted), the initial HA Flu potency is 798 μg/ml.
The models having the found initial quantitative attribute value parameter C0 closest to 798 is Parallel reaction (796.3) followed by Sestak n/p (779.6), Order 3 (769.7) and Order n (768.1).
This is in favor of Parallel reaction.
As to the fit distance, the lower the fit distance the better the modelling quality.
The model having the lowest fit distance is Parallel reaction (294.81) followed by Order n (360.09), Sestak n/p (360.91) and Order 3 (362.46).
This is in favor of Parallel reaction.
As to BIC, the lower the BIC the better the modelling quality if the number of parameters is the same.
The model having the lowest BIC is Order 3 (438.43) followed by Parallel reaction (440.64), Order n (441.5876) and Order 2 (442.63).
It is recalled that the number of parameters is three for Order 3 and Order 2, is four for Order n and is eight for Parallel reaction.
This is in favor of Parallel reaction.
As to the Fisher test and t-test, only Parallel reaction is positive (it is negative for all the other models).
It thus appears that the best modelling quality is for Parallel reaction.
It is seen that such modelling follows relatively closely the experimental data set.
However, as visible in table 9 above, the experimental data set listed in Table 3 does not enable to find significant values for the confidence intervals of the parameters of the model called Parallel reaction.
It was thus decided to select another model for which significant values were found, namely the model called Order n which is just behind the model called Parallel reaction as regards modelling quality.
With the selection of the model called Order n with the found best fitting values for the parameters, it becomes possible as explained above to ask to the quantitative value calculation tool 28 to determine the quantitative attribute value C at a current time point t by entering into the computer system 15 through the interface 16 the time t and the value of the constant temperature T at which the product is stored; and also to ask to the quantitative value calculation tool 28 to determine the quantitative attribute value C at a current time point t by entering into the computer system 15 through the interface 16 the time t and a temperature profile over the time recorded in the storage 30.
When the determined quantitative attribute value C at the current time point t is determined and recorded in the storage 31 together with the associated confidence interval, the preservation state determination tool 29 is able to determine the preservation state of the product, for instance within or out of acceptance criteria involving a threshold for the quantitative attribute value taking into account the associated confidence interval.
The determined preservation state of the product is recorded in the storage 32 and communicated to the user through the interface 16.
For finding a better law of evolution of the quantitative attribute value, it is possible to determine a modified experimental stability data set, to provide the modified experimental data set and to carry out again the method with the modified experimental stability data set.
For instance, the modified experimental stability data set is:
The modified experimental data set is for instance determined:
In variants to the embodiments disclosed above, the product is different from a vaccine dose to be administered to a patient, for instance another pharmaceutical product, a cosmetic product, a synthetic chemistry product or a food product, such products different from a vaccine dose having a degradation following the Arrhenius law, with in particular the direction of variation of the quantitative attribute value remaining the same (always decrease or always growth), unlike microbiological degradation for which there is a change in the direction of variation.
In variants to the embodiments disclosed above, the phenomenological models of evolution of the quantitative attribute value as a function of time and temperature are different, for instance with up to nine parameters.
In variants to the embodiments disclosed above, the computer system includes also models of evolution of the quantitative attribute value as a function of time and temperature which are phenomenological and include more than nine parameters, for example ten parameters; and/or models that are not phenomenological; and/or models of evolution of the quantitative attribute value as a function of time and temperature and other factors such as residual moisture, lighting and/or radiation.
In variants to the embodiments disclosed above, the estimators are different and/or displayed differently, for instance the display of the aggregated likelihood estimator is replaced by the display of the physico-chemical parameter likelihood estimator.
Many other variants are possible and it is recalled in this respect that the invention is not limited to the examples disclosed and illustrated.
| Number | Date | Country | Kind |
|---|---|---|---|
| 19305709.8 | Jun 2019 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/065263 | 6/3/2020 | WO |
