Research Article 
Corresponding author: Nataliia Behei ( beheinatalia@ukr.net ) Academic editor: Milen Dimitrov
© 2022 Nataliia Behei, Oksana Tryhubchak, Bogdan Pryymak.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Behei N, Tryhubchak O, Pryymak B (2022) Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition. Pharmacia 69(3): 779789. https://doi.org/10.3897/pharmacia.69.e86876

Using approaches of Quality by design, namely dispersion analysis, random balance method, regression analysis and neural networks, the composition and technology tablets based on amlodipine besylate and enalapril maleate have been developed. Using dispersion analysis was determined the effect of 30 excipients on 10 pharmacotechnological parameters of tablets. With the help of the desirability function, the leaders of the excipients were selected from 6 functional groups. It was confirmed that the composition with the best pharmacotechnological parameters, determined by using statistical methods, coincides with the results studies the synthesized Feedforward neural network. The quantities of preferable excipients at 3 levels were identified by the random balance method. The relationship between the studied factors and the quality of the tablets was described by regression equations. Based on the placement of equal output lines, the optimal composition of amlodipine tablets with enalapril was established.
amlodipine and enalapril tablets, artificial intelligence, pharmaceutical development, quality by design
Every year 17 million people worldwide die from the cardiovascular disease, including heart attacks and strokes. Cardiovascular disease is the leading cause of death and disability in most countries. By 2030, more than 23 million people are expected to die from these diseases. They will be the leading cause of death on the planet (
In order to increase the effectiveness of pharmacotherapeutic benefit and rise safety combined drugs are obtained. The main advantage of combined antihypertensive therapy is the merging of active pharmaceutical ingredients with different mechanisms of action to achieve additional antihypertensive activities and reduce side effects (
Angiotensinconverting enzyme inhibitors (enalapril) and calcium antagonists (amlodipine) are the most often compounded in combination therapy (
The quality and bioavailability of tablets depends on many factors. The main are the physicochemical properties of the active pharmaceutical ingredients, correctly selected excipients and obtaining tablets with a given quality and the stability of the drug in the future. The quality of the medicinal product is ensured primarily by compliance with the pharmacotechnological parameters and their indicators for each dosage form.
According to the pharmacopoeial requirements, the main pharmacotechnological indicators of powders are bulk density and tapped density. These indicators characterize the ability of raw materials to compact. They will allow further calculation of such indicators as the loading volume of technological equipment. The criterion for evaluating the flowability of the tableting blend is flowability. The flowability study will enables to investigate the ability to fill the appropriate press form. The angle of angle of repose is an additional characteristic of the flowability of the tableting blend or granulate. For wellflowing materials, its value is 25–40° (
The main pharmacotechnological indicators of tablet quality are: uniformity of mass, resistance of tablets to crushing, friability of uncoated tablets, disintegration. Uniformity of mass shows the deviation of each of the 20 tablets separately from the average weight and should not exceed 5%. The resistance of tablets to crushing characterizes their strength and is defined as the average value for 10 tablets. The pharmacopoeia describes the acceptance criteria for tablets with different diameters (for tablets with a diameter of 8 mm  at least 25 N). Friability of uncoated tablets is carried out in order to find out the resistance of tablets to the action of mechanical impact, or abrasion. Friability should not exceed 1% and tablets should not have chips or cracks. Disintegration allows you to determine the time of disintegration of tablets in a liquid environment, usually water. Disintegration requirements for each dosage form are different. Uncoated tablets should disintegrate as quickly as 15 minutes (
Quality by design (QbD) is a sound approach to pharmaceutical implementation. It is taken by the developers as a basis from the beginning of the formation of research objectives till the release of final drugs (
As a general rule, the practical implementation of QbD in the development of new pharmaceutical products can go through the following steps:
The principles of QbD are enshrined in the regulations of pharmaceutical development (ICH Q8 (R2)), quality risk management (ICH Q9) and pharmaceutical quality system (ICH Q10) (
One of the modern methods of medicines manufacturing is an area of artificial intelligence known as artificial neural networks. Artificial neural networks use personalized knowledge and learn from experimental data to solve complex problems. Technologies involving artificial intelligence have become universal tools that can be used everywhere at different stages of drug development, such as the identification and verification of target medicine, developing of new drugs, drug reprofiling, improving the research and development efficiency, aggregating, and analyzing biomedical information and refining the decisionmaking process for involving patients in clinical research (
In connection with the above, the creation of medicine based on QbD for the treatment of hypertension, namely the combined tablets based on antihypertensive substances of different pharmacological groups is relevant for pharmaceutical science and practice.
The aim of the work was to develop the qualitative and quantitative composition of amlodipine and enalapril combined tablets based on QbD using an artificial neural network.
Enalapril maleate from Zhejiang Huahai Pharmaceutical Co., Ltd, China and Amlodipine besylate from Anek Prayog, India were used as APIs for the development of the drug.
For the study, excipients were grouped into 6 factors by functional purpose (Table
Factors and their levels studied in the development of amlodipine and enalapril combined tablets.
Factor  Factor level 

A – filler  a_{1} – microcrystalline cellulose 101 
a_{2} – sucrose  
a_{3} – calcium hydrogen phosphate  
a_{4} – corn starch  
a_{5} – pregelatinized starch  
B – disintegrant  b_{1} – croscarmellose sodium 
b_{2} – povidone К17  
b_{3} – crospovidone XL10  
b_{4} – sodium starch glycolate  
b_{5} – potato starch  
C – binder  c_{1} – pregelatinized starch 
c_{2} – macrogol 20  
c_{3} – povidone К17  
c_{4} – povidone К30  
c_{5} – hypromellose Е5  
D – glidant  d_{1} – aerosil 200 
d_{2} – aeroperl 300  
d_{3} – talc  
d_{4} – neusilin US2  
d_{5} – aerosil 200 + talc (1:1)  
E – lubricant  e_{1} – magnesium stearate 
e_{2} – calcium stearate  
e_{3} – stearic acid  
e_{4} – sodium stearyl fumarate  
e_{5} – polyethylene glycol 4000  
F – stabilizing  f_{1} – sodium bicarbonate 
f_{2} – maleic acid  
f_{3}– citric acid monohydrate  
f_{4} – magnesium carbonate  
f_{5} – lactic acid 
A generalized scheme of research on the development of amlodipine and enalapril combined tablets is shown in Fig.
To implement the experiment mathematical and statistical methods of planning the experiment and processing the results of the study were used.
Method of dispersion analysis based on the secondorder hyperGreekLatin square. Analysis of variance is a statistical method used to divide the total sum of squares of observations into components due to the influence of various factors, their interactions and random variables. Dispersion analysis was used to be able to statistically identify the influence of various factors on the variability of the studied feature (
Factor / Batch  A  B  C  D  E 

1  a_{1}  b_{1}  c_{1}  d_{1}  e_{1} 
2  a_{1}  b_{2}  c_{2}  d_{2}  e_{2} 
3  a_{1}  b_{3}  c_{3}  d_{3}  e_{3} 
4  a_{1}  b_{4}  c_{4}  d_{4}  e_{4} 
5  a_{1}  b_{5}  c_{5}  d_{5}  e_{5} 
6  a_{2}  b_{1}  c_{2}  d_{3}  e_{4} 
7  a_{2}  b_{2}  c_{3}  d_{4}  e_{5} 
8  a_{2}  b_{3}  c_{4}  d_{5}  e_{1} 
9  a_{2}  b_{4}  c_{5}  d_{1}  e_{2} 
10  a_{2}  b_{5}  c_{1}  d_{2}  e_{3} 
11  a_{3}  b_{1}  c_{3}  d_{5}  e_{2} 
12  a_{3}  b_{2}  c_{4}  d_{1}  e_{3} 
13  a_{3}  b_{3}  c_{5}  d_{2}  e_{4} 
14  a_{3}  b_{4}  c_{1}  d_{3}  e_{5} 
15  a_{3}  b_{5}  c_{2}  d_{4}  e_{1} 
16  a_{4}  b_{1}  c_{4}  d_{2}  e_{5} 
17  a_{4}  b_{2}  c_{5}  d_{3}  e_{1} 
18  a_{4}  b_{3}  c_{1}  d_{4}  e_{2} 
19  a_{4}  b_{4}  c_{2}  d_{5}  e_{3} 
20  a_{4}  b_{5}  c_{3}  d_{1}  e_{4} 
21  a_{5}  b_{1}  c_{5}  d_{4}  e_{3} 
22  a_{5}  b_{2}  c_{1}  d_{5}  e_{4} 
23  a_{5}  b_{3}  c_{2}  d_{1}  e_{5} 
24  a_{5}  b_{4}  c_{3}  d_{2}  e_{1} 
25  a_{5}  b_{5}  c_{4}  d_{3}  e_{2} 
Statistical processing of the results of experimental studies of intermediates and tablets was performed by the method of dispersion analysis. Ranked batches of advantages were built for each parameter using Duncan’s criterion. It is one of the multiple comparison procedures that are used in statistical analysis. Duncan’s multiple range test or Duncan’s test, or Duncan’s new multiple range test, provides significance levels for the difference between any pair of means (
The leaders in all respects were selected from each group of excipients for further studies on the selection of the optimal quality composition of amlodipine and enalapril combined tablets. The choice of the best combinations of excipients in the production of tablets was carried out using a generalized quality indicator such as the desirability function. The research results were converted into dimensionless values from 0 to 1 by using the scale shown in Fig.
The root of the tenth degree of the product of the obtained values was subjected to dispersion analysis. Based on these results, ranked batches of benefits for each functional group of excipients were built. That allowed to identify leaders among them for the introduction of tablets.
In applied fields of science, the problem of approximating experimental data often arises. For a qualitative solution to this problem, feedforward neural networks (multilayer perceptron). They are often called universal approximators. It is mathematically proven that with a sufficiently large number of neurons in the hidden layer, the feedforward neural network is able to approximate any vector nonlinear function with a limited number of breakpoints with a given accuracy. The minimum required number of neurons in the hidden layer is determined heuristically based on the permissible approximation error.
The ability of neural network to highquality approximation of complex dependencies is achieved due to the segmentation of the input space into subspaces by the action of nonlinear functions of neuron activation and the subsequent interpolation of neuron data on each segment. The analysis of the work of neural network proves that in some cases input signals, some neurons work and others are passive, however, other neurons are already working for other input signals.
In this work the set task is to design a feedforward neural network that approximates the complex nonlinear dependence of ten pharmacotechnological indicators of tablets on their sixfactors qualitative composition.
The design of the artificial neural network was carried out using the commercially available Matlab software product the Neural Network Toolbox through the following five main steps: 1  training data set collection, 2  data processing, 3  selection of network architecture, 4  network training, 5  evaluation of the quality of learning.
The training set of data was the result of 25 experiments for different level combinations of 6 qualitative factors  A, B, C, D, E, F. Each of factors had 5 levels  1, 2, 3, 4, 5. As a result of each experiment, 10 pharmacotechnological indicators of tablets y1 ... y10 were determined. So, the set of data for training the network includes an input array of size 6×25=150 and an output array of size 10×25=250. Such a set of data is not large. However, it is representative since it was obtained based on the mathematical theory of experiment planning. As is well known the representativeness of the training data set is fundamentally necessary for the successful synthesis of a neural network.
The architecture of a neural network includes the number of inputs and outputs of the network, the number of neurons in the hidden and output layers, and the type of their activation function. The inputs to the network are 6 factors, so it will have 6 inputs. The output values of the network are 10 indicators of the tablets, so it will have 10 outputs. Accordingly, there will be 10 neurons in the output layer. To date, there is no theoretically justified method for choosing the number of neurons in the hidden layer of the S1 network. Some practical recommendations are known, but they are rather inaccurate. In general, the choice of S1 must satisfy 2 conditions. First, S1 should be small enough to avoid the phenomenon of overlearning, when the network remembers the training data but does not generalize it. Second, S1 must be large enough for the network to be able to approximate complex functional dependence.
Taking into account the specified conditions, the number of neurons in the hidden layer S1=11 was empirically selected with an activation function of the hyperbolic tangent (“tansig”) type. Neurons of the output layer usually have a linear activation function. However, we applied the “tansig” activation function because the network had higher accuracy in this case. The scheme of the synthesized feedforward neural network with the architecture of the 61110 type is shown in Fig.
The learning quality of the designed neural network was evaluated by regression analysis. The results are shown in Fig.
With the help of the synthesized neural network, estimates of indicators of tablets for all possible combinations of 5 levels of 6 factors by quantity 5^{6} = 15625 were obtained. From the obtained data, variants of the combination of investigated excipients that ensure finding of all 10 pharmacotechnological indicators in the established ranges were selected.
The method of random balance was used in the study of many quantitative factors that significantly affect the object of research. The use of this method makes it possible to reduce the number of test subjects and to make a research plan to optimize the processes of tablet technology. Construction of the experimental plan was performed by random mixing of complete factor plans. Significant factors were determined using scatter plots. The significance of the selected factors was checked using the ttest (
Based on the results of previous studies, the excipients that showed the best results were allocated in 7 factors, that were studied at the lower «», basic «0» upper «+» according to Table
Quantitative factors and their levels studied in the development of amlodipine and enalapril combined tablets.
Factor  Factor level  

lower «»  basic «0»  upper «+»  
х_{1} – amount of calcium hydrogen phosphate, g  0.14  0.15  0.16 
х_{2} – amount of croscarmellose sodium, g  0.006  0.008  0.010 
х_{3} – amount of povidone К17, g  0.004  0.005  0.006 
х_{4} – amount of aerosil 200, g  0.001  0.0015  0.002 
х_{5} – amount of talc, g  0.001  0.0015  0.002 
х_{6} – amount of sodium stearyl fumarate, g  0.001  0.0015  0.002 
х_{7} – amount of citric acid monohydrate, g  0.004  0.005  0.006 
Using the method of random balance, an experimental plan was drawn up to study seven factors (Table
Experimental plan for the development of amlodipine and enalapril combined tablets.
Batch  х_{1}  х_{2}  х_{3}  х_{4}  х_{5}  х_{6}  х_{7} 

26  –  –  –  +  +  +  – 
27  –  +  –  +  –  +  – 
28  +  –  –  –  –  –  + 
29  +  +  –  –  +  –  + 
30  –  –  +  +  –  –  + 
31  –  +  +  –  +  +  – 
32  +  –  +  +  +  –  – 
33  +  +  +  –  –  +  + 
34  0  0  0  0  0  0  0 
35  0  0  0  0  0  0  0 
10 batches of experiments were performed, that differed in the quantitative ratio of excipients. The active substances were added to all batches in equal amounts, the amount of sucrose was added to obtain an average weight of 0.2 g per 1 tablet.
The method of regression analysis was used to analyze and process experimental data when influencing the response of only quantitative factors. Regression analysis allows you to get a mathematical model of the process in the form of a regression equation and analyze this equation.
For a detailed study of the impact of excipients, croscarmellose sodium, povidone K17 and anhydrous citric acid were selected. A list of three quantitative factors, each of which was studied at five levels, is given in Table
Factors and their levels studied in the development of amlodipine and enalapril combined tablets.
Factor  Lower star point «α»  Lower level «1» 
Basic level «0»  Upper level «+1»  Upper star point «+α» 

х_{1} – amount of croscarmellose sodium, mg  4.318  5  6  7  7.682 
х_{2} – amount of povidone К17, mg  4.318  5  6  7  7.682 
х_{3} – amount of citric Acid monohydrate, mg  3.659  4  4.5  5  5.341 
A symmetrical rotatable uniforms composite plan with second order was used for the study. The planning matrix of the experiment of amlodipine and enalapril combined tablets is shown in Table
Experiment planning matrix based on symmetrical rotatable uniforms composite plan with second order.
Batch  х_{1}  х_{2}  х_{3} 

36  +  +  + 
37  –  +  + 
38  +  –  + 
39  –  –  + 
40  +  +  – 
41  –  +  – 
42  +  –  – 
43  –  –  – 
44  +α  0  0 
45  –α  0  0 
46  0  +α  0 
47  0  –α  0 
48  0  0  +α 
49  0  0  –α 
50  0  0  0 
51  0  0  0 
52  0  0  0 
53  0  0  0 
54  0  0  0 
55  0  0  0 
The relationship between the studied factors and the quality of amlodipine and enalapril combined tablets was described by regression equations. After checking the statistical significance of the coefficients, taking into account the Student’s test (t_{5} = 2.571; p = 0.05), the adequacy of the models was checked using the Ftest (F_{0.05; 10; 5} = 4.74). The regression equations were adequate if F_{experimental} <F_{tabular}.
The nature of the influence of the studied factors is determined by the values and signs of regression coefficients. The secondorder model for three factors is:
y=b_{0}x_{0}+b_{1}x_{1}+ b_{2}x_{2}+ b_{3}x_{3}+ b_{12}x_{1}x_{2}+ b_{13}x_{1}x_{3}+ b_{23}x_{2}x_{3}+ b_{11}x_{1}^{2}+ b_{22}x_{2}^{2}+ b_{33}x_{3}^{2} (1),
where y is the value of the response (indicator);
b_{0} – zero regression equation coefficient;
b_{1}, b_{2}, b_{3} – factorial regression equation coefficients;
b_{12}, b_{13}, b_{23} – interaction coefficients of independent factors;
b_{11}, b_{22}, b_{33} – quadratic regression equation coefficients;
x_{0} – zero factor;
x_{1}, x_{2}, x_{3}  independent factors.
The magnitude of the coefficients and the signs in front of them indicate the nature and strength of the influence of the studied factors (
To be able to obtain information about the interaction between factors and to obtain the optimal composition of amlodipine and enalapril combined tablets, it is necessary to determine whether there is an extremum. If so, to find its coordinates. For this regression equation, obtained by the results of the experiment, was led to the canonical (standard) expression. The canonical transformation consists in choosing a coordinate system in which the geometric analysis of the equation is greatly facilitated. When making decisions on the model of the second order, the regression equation is transformed into a model for two factors with the stabilization of others at the optimal levels for the study area. Instead of the factor corresponding to the condition b_{ii}>0 і b_{i}∑b_{ij}>2b_{ii} the optimal value was entered, and the equation was converted into an expression with two variables. Based on the transformed regression equations, equal exit lines were constructed. According to the location of the lines, the optimal amounts of the other two studied factors were chosen.
Amlodipine and enalapril combined tablets were made by wet granulation. This method of obtaining tablets includes the following stages: 1) mixing powders of active pharmaceutical ingredients with excipients from groups A and B; 2) moistening the mixture of powders with solutions of binders and stabilizing substances (groups C and F) to obtain a mass that sticks to the lump, but does not stick to the fingers; 3) obtaining wet granules, i.e. wiping the wet mass through the perforated plates; drying of wet granules; 4) obtaining dry granules, for which the dry mass is wiped through perforated plates to destroy lumps and obtain homogeneous granules; 5) dusting of dry granules with substances from groups D and E; 6) compression of tablets.
The obtained granulate was investigated for loss of drying (
The results of the study of pharmacotechnological parameters of intermediates and tablets, and data of the desirability function, obtained on the basis of the dispersion analysis are shown in Suppl. material
Top 10 best formulations and corresponding pharmacotechnological indicators of tablets determined by means of an artificial neural network.
Batch  у_{1}  у_{2}  у_{3}  у_{4}  у_{5}  у_{6}  у_{7}  у_{8}  у_{9}  у_{10} 

a_{2}b_{1}c_{3}d_{5}e_{4}f_{3}  0.6231  0.7027  0.7854  8.1976  40.9032  4.7546  0.9079  215.3673  0.0689  1.1836 
a_{2}b_{1}c_{1}d_{3}e_{4}f_{1}  0.5992  0.7145  0.7942  8.2304  38.7178  4.0668  1.1343  208.9395  0.0436  1.3524 
a_{2}b_{1}c_{1}d_{4}e_{4}f_{1}  0.6164  0.6072  0.8266  8.8173  37.1463  3.9409  1.2119  176.8341  0.0332  2.4224 
a_{2}b_{1}c_{2}d_{3}e_{4}f_{1}  0.6085  0.6980  0.7948  8.2771  43.4693  4.3995  1.2394  218.2921  0.0505  2.1201 
a_{2}b_{1}c_{4}d_{1}e_{5}f_{2}  0.7954  0.7412  0.7512  7.6007  40.8676  4.7813  0.6644  236.1660  0.0674  2.0053 
a_{2}b_{2}c_{4}d_{1}e_{5}f_{2}  0.7917  0.6909  0.7545  7.8050  39.6801  4.5692  0.6215  224.3214  0.0596  2.4497 
a_{2}b_{3}c_{2}d_{2}e_{5}f_{2}  0.6685  0.7295  0.8038  8.5070  37.6365  4.9435  0.7308  193.9313  0.0669  2.8729 
a_{1}b_{1}c_{1}d_{2}e_{4}f_{1}  0.6021  0.6917  0.8049  8.3470  41.9503  4.1000  0.9881  212.3585  0.0699  2.5884 
a_{1}b_{2}c_{2}d_{1}e_{5}f_{2}  0.6679  0.7069  0.7687  7.8222  38.3734  4.9176  0.6228  229.7394  0.0512  1.9746 
a_{1}b_{3}c_{1}d_{1}e_{5}f_{2}  0.8319  0.7510  0.7294  8.1947  37.2146  5.1709  0.6183  218.0585  0.0573  2.4652 
The results of the study of the amounts of excipients by random balance in the development of amlodipine and enalapril combined tablets are shown in Table
The results of the study of the amounts of excipients by random balance.
Batch  y_{1}  у_{2}  y_{3}  y_{4}  y_{5}  y_{6}  y_{7}  y_{8}  y_{9}  y_{10} 

26  2.03  0.720  0.787  9.3  40.4  4.5  1.48  157  0.043  10.8 
27  2.62  0.704  0.790  9.6  42.6  4.5  2.05  125  0.003  8.6 
28  2.81  0.643  0.693  9.2  39.3  4  0.86  104  0.006  6.3 
29  2.61  0.659  0.807  8.8  40.2  5  1.17  103  0.126  6.2 
30  1.73  0.721  0.831  8.6  41.0  4  1.15  135  0.009  5.2 
31  2.65  0.658  0.750  9.1  37.9  5  1.26  119  0.011  7.3 
32  2.30  0.692  0.764  9.5  40.6  4.5  0.73  123  0.004  6.0 
33  1.30  0.646  0.778  11.1  42.0  5  1.49  60  0.100  5.5 
34  2.16  0.666  0.788  10.4  42.0  5  1.42  126  0.009  7.8 
35  2.16  0.711  0.807  10.3  42.3  5  1.51  123  0.028  8.0 
The results of the study of the amounts of excipients by regression analysis in the development of amlodipine and enalapril combined tablets are shown in Table
The results of the study of the amounts of excipients by regression analysis.
Batch  у_{1}  у_{2}  у_{3}  у_{4}  у_{5}  у_{6}  у_{7}  у_{8}  у_{9}  у_{10} 

36  1.44  0.642  0.768  11.6  40.6  5  1.13  146  0.139  8.05 
37  0.90  0.687  0.801  10.6  41.2  5  1.21  151  0.163  8.88 
38  2.43  0.626  0.767  14.4  41.0  5  0.79  142  0.165  6.48 
39  2.92  0.677  0.792  14.8  39.0  5  0.79  177  0.163  6.23 
40  2.07  0.630  0.793  18.6  41.8  5  1.16  125  0.173  6.60 
41  1.16  0.668  0.781  13.4  41.6  5  0.92  153  0.161  8.23 
42  2.33  0.639  0.819  18.5  41.0  5  0.86  121  0.197  5.62 
43  1.75  0.665  0.774  13.8  42.0  5  0.92  152  0.172  7.38 
44  1.87  0.655  0.786  19.5  42.8  5  0.86  127  0.172  5.93 
45  1.67  0.655  0.791  14.8  41.0  5  1.29  140  0.167  7.42 
46  2.04  0.687  0.793  13.7  40.8  5  1.11  142  0.153  7.18 
47  1.34  0.653  0.818  18.2  42.2  5  1.17  104  0.219  5.10 
48  1.27  0.642  0.768  14.7  42.6  5  0.82  128  0.139  6.78 
49  1.06  0.635  0.775  16.8  41.3  5  1.31  119  0.181  6.22 
50  1.75  0.639  0.799  14.9  41.0  5  1.19  118  0.210  5.83 
51  1.17  0.649  0.777  16.2  41.3  5  0.81  131  0.180  6.15 
52  1.08  0.643  0.790  12.0  40.5  5  0.99  133  0.181  6.53 
53  1.58  0.646  0.781  15.2  41.9  5  1.30  118  0.219  6.10 
54  1.50  0.646  0.774  14.4  41.2  5  0.77  135  0.146  6.95 
55  1.71  0.661  0.780  13.2  41.4  5  0.90  136  0.162  6.17 
Based on the dispersion analysis of experimental data, the significance of the studied factors on the pharmacotechnological properties of powder masses and tablets was determined (responses). An analysis of these reviews revealed that the same excipient may improve one response but worsen another response at the same time. For example, when one of the studied excipients (povidone XL10) has the best effect on the resistance of tablets to crushing but gives the worst result of the abrasion of tablets.
Statistical processing of the summary results obtained by the desirability function shows that the factors in the study sequence have the greatest influence on the studied indicators: A > E > D > F > C > B. According to the results of the desirability function among calcium fillers anhydrous dihydrogen phosphate (a_{3}) and sucrose (a_{2}) have the same effect. Unequivocal leader among lubricants is sodium stearyl fumarate (e_{4}). Aerosil 200 + talc (1:1) (d_{5}) was preferred in the group of glidants. Among the stabilizing substances, citric acid predominates (f_{3}). Of the selected binders, povidone K17 (c_{3}) had the greatest effect on amlodipine and enalapril combined tablets. Analyzing studies on the use of these leavening agents, croscarmellose sodium is preferred (b_{1}).
Among the top 10 combinations with optimal pharmacotechnological parameters that were shown by the artificial neural network, the composition of a_{2}b_{1}c_{3}d_{5}e_{4}f_{3} coincides with the results of previous studies using statistical methods of dispersion analysis and the desirability function. The obtained data confirm that for the selection of the qualitative composition of the drug statistical processing of the experimental results can be analyzed using statistical methods of dispersion analysis and desirability function or as an alternative using artificial neural networks.
The method of random balance made it possible to reduce the number of experimental batches to 10. Using scattering diagrams, the dependence of the studied quality indicators on the change in the quantities of excipients is shown and significant factors are selected. In order to select the amounts of excipients that provided indicators in accordance with pharmacopoeial requirements, Table
Factor / Indicator  х_{1}  х_{2}  х_{3}  х_{4}  х_{5}  х_{6}  х_{7} 

у_{1}  –  –  +*  +  –  +  + 
у_{2}  –*  –*  –  +*  0  +  –* 
у_{3}  –  +  –  +  –  –  + 
у_{4}  –  –  –  –  +  –  + 
у_{5}  –  +  +  +*  –*  +*  + 
у_{6}  +  +*  +  –  +*  +*  0 
у_{7}  +  –*  +  –  +  –*  + 
у_{8}  –*  –*  +  +*  +  +  –* 
у_{9}  –*  –*  +  +*  –  –  –* 
у_{10}  +*  –  +*  –  –  –*  +* 
general  –  –  +  +  0  0  – 
As a result of the research conducted by the method of random balance, the amount of investigated excipients in general for all quality indicators was determined.
Croscarmellose sodium, povidone K17 and anhydrous citric acid were selected for detailed study of the effect of excipients. The relationship between the studied factors and the loss in mass during drying of the granulate (y_{1}) is described by the regression equation (F_{Experimental} = 1.95): у_{1}=1.450.20х_{2}0.27х_{2}х_{3}.
The regression equation describing the relationship between the studied factors and the bulk density of the tableting blend (y_{2}) is as follows (F_{Experimental} = 2.19): у_{2}=0.6470.012x_{1}+0.006x_{2}+0.08x_{2}^{2}.
The nature of the influence of the quantities of the studied factors on the tapped density of the tableting blend (y_{3}) is expressed by the regression equation (F_{Experimental} = 0.50): у_{3}=0.7830.014х_{1}х_{3}+0.007х_{2}^{2}.
The flowability of the tableting blend (y_{4}) depends on the amount of test excipients as follows (F_{Experimental} = 0.80): у_{4}=14.4+1.3х_{1}1.1х_{2}1.2х_{3}.
The regression equation describing the angle of repose of the tableting blend (y_{5}) has the form: у_{5} = 41.2 (F_{Experimental} = 2.66).
The appearance of the tablets on a 5point rating system was excellent in all batches studied.
The regression equation for uniformity of mass (F_{Experimental} = 0.63) has the form: y_{7} = 1.00. Therefore, this indicator is not affected by the studied factors, and the average value of uniformity of mass is 1.00%.
The influence of the studied factors on the resistance of tablets to crushing (y_{8}) illustrates the regression equation (F_{Experimental} = 2.47): у_{8} = 127.8288.850x_{1}+5.868x_{3}+5.838x_{1}^{2}.
The studied factors do not have a significant effect on the friability of uncoated tablets index (y_{9}), as y_{9} = 0.18 (F_{Experimental} = 0.32).
The relationship between the studied factors and the disintegration (y_{10}) is described by the following regression equation: у_{10}= 6.2620.474x_{1}+0.699x_{2}+0.292x_{1}^{2}.
To translate the regression equations to the canonical standard expression instead of x_{2} in the model enter +1. We are building new models. On the basis of the transformed models, equal exit lines were built in the x_{1}x_{3} coordinate system (Fig.
Taking into account the results of placement of equal output lines, the optimum point is set at x_{1} = α and x_{3} = + α is established. This allowed us to calculate the optimal composition of amlodipine and enalapril combined tablets (Table
Ingredient  Amount  

Enalapril maleate  6.950 mg  3.48% 
Amlodipine besylate  5.010 mg  2.51% 
Croscarmellose Sodium  4.318 mg  2.16% 
Calcium Hydrogen Phosphate  166.381 mg  83.19% 
Povidone К17  7.000 mg  3.50% 
Citric Acid Monohydrate  5.341 mg  2.67% 
Aerosil 200  2.000 mg  1.00% 
Talc  1.500 mg  0.75% 
Sodium Stearyl Fumarate  1.500 mg  0.75% 
Total  200.000 mg  100.00% 
The proposed composition was tested experimentally and the following results were obtained: loss of drying of the granules 0.81%, bulk density of the tableting blend of 0.753 g / ml, tapped density of the tableting blend of 0.807 g / ml, flowability of the tableting blend of 10.3 s / 100 g, angle of repose of the tableting blend of 41.3°, appearance of the tablets 5 points, uniformity of mass of 1.18%, resistance of tablets to crushing 142.77 N, friability of uncoated tablets of 0.17%, disintegration of 7 minutes 27 seconds.
The composition and technology of amlodipine and enalapril combined tablets by wet granulation were developed with the help of QbD. The effect of 25 excipients on the pharmacotechnological parameters of amlodipine tablets with enalapril was studied. The desirability function was used to select the best combinations of excipients in the tablets. Qualitative composition is confirmed by an artificial neural network. The choice of the quantities of excipients is made by the method of random balance. Using the method of regression analysis, the optimal amount of excipients for tablets was determined: amlodipine besylate  5.010 mg, enalapril maleate  6.950 mg, croscarmellose sodium  4.318, calcium dihydrogen phosphate  166.381 mg, K17  7 mg, citric acid anhydrous  5 mg aerosil 200 – 2.000 mg, talc – 1.500 mg, sodium stearyl fumarate – 1.500 mg.
The authors report there are no competing interests to declare.
The results of the study of pharmacotechnological parameters of intermediates and amlodipine tablets with enalapril, data of the functions of desirability
Data type: pharmacotechnological parameters in the table
Explanation note: Results of the study of pharmacotechnological parameters of intermediates and amlodipine tablets with enalapril.