Research Note pubs.acs.org/IECR
A Systematic Computer-Aided Product Design and Development Procedure: Case of Disinfectant Formulations Navid Omidbakhsh,* Thomas A. Duever, Ali Elkamel, and Park M. Reilly Department of Chemical Engineering, University of Waterloo, Waterloo, Canada ABSTRACT: Product formulation design involves selecting a few ingredients from a large set through screening based on several criteria, and using the optimal proportion in the formulation. The limitation in the traditional strategy for chemical product formulation design is to carry out a large number of trials, which in most practical cases, is either economically infeasible or a very slow process. Furthermore, the presence of constraints, sometimes contradictory to some extent, further complicates the formulation design process. Such traditional trial-and-error and one-factor-at-a-time methodologies can be very cumbersome. They can also lead to a slow and high-cost process. The outcome of following such techniques does not usually lead to optimal designs. In this work, a methodology that deals with the complexity of product formulation design problem with contradictory constraints is presented and illustrated in a real case study. This methodology starts with defining needs for a new product and generating ideas. It then screens the candidate ingredients, using design of experiment techniques, and develops a model for each response. It then inverts the models using a nonlinear optimization technique, and obtains an optimal design for the product based on the desired properties. This methodology is proven to be more effective, faster, and less expensive in the development of new products or improvements on the existing ones. Furthermore, the final product is an optimal formulation, with respect to a preset performance measure and desired properties. The procedure is illustrated and tested on the case of disinfectant formulations. The optimized formulation was prepared, tested, and compared to an existing formulation. The optimized formulation faired significantly better than the existing product, in terms of technical and economic preference.
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materials,4 lipstick formulation,5 and several other products have been optimized using mixture designs. In the application disinfectant formulations, which is our focus in this study, only limited knowledge and knowhow exists about a systematic approach to design such chemical formulations. A methodology is presented here that systematically takes advantage of prior data and then augments the existing dataset using efficient experimental design techniques for each response and produces a separate model for each response. It then uses a nonlinear optimization technique to develop an optimal formulation, which satisfies the specified objective function, which incorporates the desired properties, including cost, subject to one or more constraints. This formulation is finally tested to ensure that it meets its desired specifications. The methodology that will be discussed is based on recently developed chemical product design concepts in conjunction with statistical techniques. The objective is to develop a systematic approach that can be employed to obtain new formulations based on market demands in the shortest possible time and the least resources. The remainder of this paper is organized as follows. First, a description of the concept of product formulation design is given and the proposed methodology is explained. Subsequently, the methodology is tested on a case study, which deals with disinfectant formulations.
INTRODUCTION Tightening markets and growing competition require differentiation of new products for business success. The first product based on a new technology that is entered to a market is believed to capture the majority of the market and the rival products coming after gain a much lesser share of the market. On the other hand, economic conditions dictate to reduce the product development expenses as much as possible; therefore, the trend is toward lean product development. Current practice in designing formulations is predominantly based on the trialand-error approach. Although the needs may be satisfied, there is no guarantee that the solution is optimal.1 This means that formulations may contain ingredients with concentrations greater than what are required. This results in the unnecessary consumption of raw materials, and exposure of more than the required amounts of chemicals to the environment and is against the green product development concept, which is the goal of the modern chemical industry. Furthermore, in most product development cases, some prior data are available from past experiments. However, the traditional techniques do not systematically take advantage of the prior information, leading to further unnecessary experimentation. In cases where there is more than one objective function in product development, such techniques are even less efficient. Consequently, such methods cannot suitably fulfill the market requirements for new product development. Better techniques are needed for higher efficiency to develop new product formulations in the shortest possible time, while minimizing development costs. Computer-aided chemical design techniques have received special attention in recent years to address such problems. Granule microstructures,2 pharmaceutical formulation,3 friction © 2012 American Chemical Society
Received: Revised: Accepted: Published: 14925
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Research Note
Figure 1. Systematic disinfecting formulation product design.
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PRODUCT FORMULATION DESIGN The chemical process industry produces many millions of products from a few thousand ingredients and bulk commodities.1 Many final products are formulated from these ingredients such as adhesives, detergents, fragrances, drugs, pesticides, cosmetic products, and paints. Such complex mixtures can result in creating or enhancing one or more desirable features. These features can be performance-related (such as antimicrobial efficacy and cleaning performance of a disinfecting solution), environmentally related (such as biodegradability, aquatic toxicity), or based on convenience (such as controlled release, ease of handling). In formulation product design, the design process involves defining the needs
of the product, generating ideas to meet the needs, selecting among ideas, manufacturing the product and testing it.6 In defining needs, the expectations of the customers, regulatory limitations, environmental issues, and product safety must be considered. In general, the needs can be defined as critical and optional. The needs are often ranked according to their importance as essential, desirable, and useful. The essential needs are those without which the product cannot succeed, and these cannot be neglected.7 Desirable and useful needs differentiate the product from the competitor products, and can have more of a marketing value. Next, the qualitative list of needs must be converted to specifications, including as many quantitative and chemical details as possible. The next step is to 14926
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Research Note
the optimization objective. Antimicrobial activity, stability, corrosion, and toxicity are used as the optimization constraints. The optimized formulation is then prepared and tested to ensure that it delivers the expected specifications. To facilitate the introduction of the methodology, the discussions below will pertain to the development of a peroxide-based disinfection formulation but keeping in mind that the procedure is general and can be applied to other types of formulations. Disinfectant products are widely used in health care facilities to reduce the risk of infection. Furthermore, they are becoming increasingly popular in household applications, because of the general awareness about the emerging pathogens and bacteria widely discussed in the media. As a result, the market for disinfectants and antiseptics is continuously increasing and has been estimated to reach 7.1 billion U.S. dollars ($) in 2016. A disinfectant formulation generally should deliver acceptable microbial kill, cleaning performance, and materials compatibility, as well as having minimum toxicity to end users and the environment. To design a successful formulation, a delicate balance between product safety, microbial activity, and product shelf life must be made.8 For example, by using more active ingredients in the formulation, it is possible to develop a very effective formulation, in terms of its antimicrobial activity; however, such a product will have a high toxicity and/or corrosivity. On the other hand, if a low content of ingredients is used in the mixture, even though less toxic, the formulation may not be effective enough to inactivate pathogenic microorganisms. Therefore, it is very important to find the “optimum” formulation in designing such products. The process even becomes more challenging since antimicrobial screening tests are expensive and slow and therefore tests should be as few as possible.
specify a benchmark. The benchmark can be a leading product, or a hypothetical one with all the advantages and without any known disadvantages of the existing products. The specifications of the new product are set different from that of the benchmark. If the benchmark is a leading product, the specifications can be the same or even better for a more competitive advantage. In the case of a hypothetical benchmark, the specifications must be separated into critical and optional, since not all of those are achievable. Once the specifications of the desired product are set, ideas to deliver such a product should be generated. For chemical formulations, these ideas are about the individual ingredients and their contribution in the mixture. These ideas can come from past experiments, books, patents, journal publications, etc. This phase might result in proposing a large number of ingredients in the formulation. Next, these raw materials must be screened and only a few of them should be kept for the experimentation stage, i.e., the best ingredients in each class are kept, so that the number of experiments is limited. If too many raw materials are considered for experimentation, the number of experiments will not be affordable. Different criteria can be selected for potential raw materials screening, such as their individual toxicity profile, regulatory status, environmental impact such as biodegradability, and cosmetic features (such as odor, etc.). In general, if few raw materials have the same properties in a formulation, the one with better environmental profile should be selected, provided the cost difference is not significant. Besides these criteria, patent infringement of the final formulation should be also taken into account. Various possible combinations of the ingredients must be checked for possible patent infringements by reviewing the claims of unexpired competitor patents. It is very important to do this exercise in the screening stage; otherwise entire tests may be wasted, if the final product is infringing upon a competitor’s patent. Once the ideas are screened (in formulation development case, the number of potential raw materials is reduced), the experiments can be carried out to map the relationship between individual ingredients and their proportions, as well as their interactions versus the product specifications. The optimal formulation then will be obtained by analyzing the experimental data. Finally, the product should be tested for its desired characteristics to ensure that it meets its targeted criteria. A schematic of this methodology is depicted in Figure 1. In summary, the product needs are first translated into product specifications. In the case of disinfecting solution, the product must have a minimum antimicrobial activity in order to inactivate pathogenic micro-organisms. The product should also have reasonable materials compatibility. In the case of acidic formulations, a class of the most susceptible materials is soft metals, and therefore the corrosion test criterion is to have lower than a certain limit of brass corrosion. The active ingredient(s) in the formulation should remain stable during shelf life, and therefore another criterion is to set a limit for the decomposition of the active ingredients. An important criterion for a successful product is its low toxicity and irritation. An ideal disinfectant solution should be non-irritateing to skin/eyes, and nontoxic if inhaled. The product should also be competitive cost-wise and another criterion should be to minimize the cost of the formulation. Next, designed experiments are used to generate data for antimicrobial activity, stability and corrosion. Linear and nonlinear regression analyses are performed and empirical models are developed for product properties. Later, the formulation is optimized by setting the formulation cost as
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CASE STUDY FOR DISINFECTANTS Antimicrobial Tests. Antimicrobial products are generally advertised by their level of micro-organism inactivation, such as 99%, 99.9%, etc. To measure the inactivation capability of the formulations, a quantitative carrier test method was used.9 This method can be applied for different types of microorganisms such as bacteria, fungi, mycobacteria, and spores. However, in order to reduce the experimental load in the screening process, for each class, one or a few surrogates are used. These surrogate microorganisms are known to have the highest biocide resistance against antimicrobial agents in their class. Standard strain of Staphylococcus aureus (ATCC 6538) was used in this study as the gram-positive bacterial surrogate. Some historical data from previous experiments are shown in Table 1. The raw materials (w/w %) in the formulations are denoted by variables x1, x2, ..., x8. Variable x1 is an oxidizing agent, x, x7, x8 are surfactants, x3 is a chelating agent, x4 denotes the pH of the solution, x5 is a stabilizer, and x6 is a buffering agent. When shown in capital letters (X1 to X8) denote the levels of these factors in coded format. The relationship between the coded variable Xi and its actual variable xi is defined by the following equation: Xi =
xi − (xi ,high + xi ,low )/2 (xi ,high + xi ,low )/2
(1)
where xj,low and xj,high are the lowest and highest values of actual variable xj, specified at factorial design boundaries. LR is the microbial log reduction at a contact time of 3 min, which is tested using the QCT test method. 14927
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Table 1. Historical Data for Antimicrobial Tests against Staph in Coded Format
Table 2. Fractional Factorial Design To Augment the Historical Data for Antimicrobial Tests
X1
X2
X3
X4
X5
X6
y
X1
X2
X3
X4
X5
X7
X8
X6
LR
0.88 1.2 0.88 0.88 1.2 1.2 2.2 1.4 0.6 2.2
0.73 1 0.73 0.73 1 1 0.67 0.67 0.67 0.67
0.8 0.92 0.87 0.87 −1 0.92 −1 −1 −1 0.92
−1 1.63 −1 −1 −0.94 1 −1 −1 −1 −1
0.73 1 1 1 1 1 −1 −1 −1 −1
0.67 1 0.67 −1 −1 1 −1 −1 −1 −1
6.13 4.3 6.24 6.24 6.85 3.5 6.76 6 5 7
−1 1 −1 0 1 −1 0 −1 1 0 1 −1 −1 1 1 0 −1 1 1 −1
1 −1 1 0 −1 −1 0 −1 1 0 1 1 1 −1 −1 0 −1 1 1 −1
1 −1 1 0 −1 1 0 1 −1 0 −1 −1 −1 1 1 0 −1 1 1 −1
1 −1 −1 0 1 −1 0 1 −1 0 1 1 −1 −1 1 0 −1 1 −1 1
−1 1 −1 0 1 1 0 1 −1 0 −1 1 1 −1 −1 0 −1 1 1 −1
−1 1 1 0 −1 −1 0 1 −1 0 1 −1 1 1 −1 0 −1 1 −1 1
−1 1 1 0 −1 1 0 −1 1 0 −1 1 −1 −1 1 0 −1 1 −1 1
−1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1
1.5 7 3.24 4 3.11 3.9 3.89 1 6.28 3 2.34 1.33 6.46 4.5 1.12 4 2.04 4.1 6.68 1.8
Table 1 shows that X1 and X2 have not significantly changed in the historical data, which results in poor estimation for these factors. Since it is known by experience, and from the literature, that these two factors are very effective in antimicrobial test results, their estimates are very important. Therefore, the historical data must be augmented to lead to reliable conclusions. It should be noted that the historical data are not based on statistically designed experiments. As mentioned, the historical data will be augmented with further experiments. In this study, two new raw materials will be added to the existing list. A recent literature search has shown that these two ingredients have both potential antimicrobial activity and low toxicity. Variable X6 was removed from further antimicrobial experiments, since it is known from the literature that it does not have any antimicrobial activity. It is well-known that a disinfectant formulation is made of a combination of several raw materials, and each of which may have one or several functions such as antimicrobial activity, wetting capability, pH buffering, corrosion inhibition, etc. Ordinarily, the general approach to designing experiments for determining an optimal disinfectant recipe is to use mixture designs.10 Recipe formulation experiments differ from other experiments involving process variables such as pressure and temperature in that we are interested in the effects of the proportions rather than the absolute level of a factor. Of course the proportion over all the ingredients must equal 100%. However, in the case where the majority of the ingredients is present in small quantities and can be considered additive to one component like water in the disinfectant formulation, the strategy that can be used is to study the effects of the different amounts of the additive using a factorial design. Therefore, the amount of water used is equal to 100% minus the sum of the additives. This approach is used here. With this assumption, a factorial design can be used to study the effect of the additives in the formulation development process. The fractional factorial design experiments data (Table 2) are added to the historical data and the entire dataset is then analyzed using a multiple regression analysis,11 and a model of the microbial log reduction versus ingredient concentrations and pH is developed. Least-squares multiple regression analysis has been extensively described in the literature y. In this study, it was known that the log reduction varies with the reciprocal of x4. Therefore, new manipulated variables were created by dividing each variable by x4. The resulting variables are denoted by z1 to z8. There are many cases where the original metric of the response variable (y) may violate the standard assumptions of linear regression. Such a problem may be overcome by transforming the response variable (y). One such trans-
formation method is the Box-Cox technique,11 which is used in this study. The optimum value for λ, which maximizes the log likelihood function was found to be 1.7. Note that the value λ = 2 is included in the confidence interval for this parameter and is the value used in the model. The fitted model is LR2 = −3.24 87.4x1 + 145x 2 − 15.8x3 + 89.53x5 + 647.7x 7 + x4 (2) 2
2
2
where R = 93.7%, adjusted R = 92.4%, and predicted R = 88.92%. In the above model, all of the manipulated variables are in uncoded format. Both R2 and adjusted R2 values are reasonably large, indicating that the model fits the input data well. The predicted R2 is a measure of the prediction capability of the model for the new observations, and is defined as 1 − PRESS/ SST, where PRESS represents the prediction error sum of squares and SST is the total sum of squares.12,13 PRESS is known as an indication of model prediction accuracy. A model with a small PRESS value is desired. The PRESS statistic can be used to compute an R2-like statistic for prediction. One of the most important features of the PRESS statistic is revealed in comparing regression models. Generally, a model with a small PRESS value is preferable to one where the PRESS value is large. Here, the predicted R2 is reasonably high, which indicates that the model has good predictive capability. Box-Cox transformation plot, normal probability plot of the residuals, and measured log reduction versus calculated were previously illustrated in the earlier publication.7 Table 3 shows the analysis of variance for the regression analysis. As shown in this table, the model shows a p-value of 0.95 was used as the program termination criterion. To ensure that the network is not overtrained, another criterion was also incorporated by generating an arbitrary set of 100 formulations. When the ANN model gave an R2 value of >0.95, the network was then used to predict the corrosion of this dataset. If the corrosion results were higher than 500 mpy or less than −30 mpy, then the model was assumed to be unstable and the training was started over. Among several tested number of neurons, the best result was achieved at 4 neurons. Data from the fractional factorial design (FFD) and 10 of the BB trials were used as the training set, and the rest of the BB design was used as the validation set. Table 9 shows the BB
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OPTIMIZATION In the previous sections, empirical models were developed to predict the microbial kill, peroxide stability, and brass corrosion. In this section, the question of how to design a product that will possess certain specified properties will be considered. In order to accomplish this inversion, and predict the formulation based on desired product specifications, optimization techniques are employed. The objective of the case study is to minimize the cost of the formulation. Therefore, the objective function can be written as
Table 9. Ten (10) Selected Trials from Box−Behnken (BB) Design X1
X3
X4
X6
y
−1 1 1 0 0 0 −1 0 −1 0
−1 −1 1 0 −1 −1 0 −1 0 1
0 0 0.5 −0.9 0 0 0 1.2 0 −0.9
0 0 0 1 −1 1 1 0 −1 0
0 7.1 195.6 284.6 7.3 9.6 2.1 0 0.7 337.4
Min(∑ cixi)
(8)
where ci is the cost of ingredient i per weight unit, and xi is the percentage of the ingredient in the formulation. In addition, the following constraints are applied: microbial log reduction ≥ 5
trials that were used for training in addition to the FFD trials. Figure 2 shows the actual versus predicted corrosion for both
(9)
Desired log reduction is based on marketing demand. Here, the objective is to reach 99.999% reduction in the bacterial count. To compensate for the model error, the model prediction error of (t(α/2),n−p{σ̂2[xT0 (XTX)−1x0]}1/2)1/2 is used in calculations where x0 is an arbitrary new observation. The average prediction error for 1000 random values of x0 is calculated as 1.55. microbial log reduction ≥ 5 +
∑
t(α /2), n − p σ ̂ 2⎡⎣x0T(XTX )−1x0⎤⎦ 100
x0
(10)
Therefore, the minimum required antimicrobial activity would be 6.55. peroxide loss ≤ 10%
(11)
A maximum active ingredient loss of 10% is required for having a one-year shelf life in disinfectant formulations.24 However, to compensate for the model error, a safety factor was added to the peroxide loss limit. The safety factor was calculated as the average prediction error for 1000 random trials (x0) and resulted in the value of 2.2%.
Figure 2. Training and test datasets for ANN.
training and validation data based on ANN. The data indicate that the predictions for the validation data are good enough and the model can be reliably used. This model will be used in the next section as one of the constraints to optimize the disinfectant formulation.
peroxide loss ≤ 10 −
∑
10t(α/2),n−p
x0
σ̂ 2⎡⎣x0T(XTX )−1x0⎤⎦
1000
(12)
Therefore, the peroxide loss maximum was 7.8% (rounded to 7%).
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TOXICITY CALCULATIONS Disinfecting products, ideally, must be nonirritating to skin and eyes, not cause burns, and not cause inhalation/respiration issues. There are different in vivo methods to test for each of
brass corrosion ≤ 100 mpy
Based on experience, corrosion rates of higher than 100−150 mpy result in moderate cosmetic damage to yellow metals upon 14931
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⎛ ⎜ x4 = −log10⎜[H+]s ⎜ ⎝
extended exposure. In the corrosion model, the highest discrepancy between a measured and predicted value was 38. This value was increased by 50% and used as the safety factor. The product should not be harmful if ingested, should not cause severe eye damage, and should not be a skin and respiratory irritant. The constraints given below satisfy these requirements. ⎡ P + PX ⎤ P ∑⎢ T + + T + n⎥ < 1 L Xn / T L Xn ⎥⎦ ⎢L j = 1 ⎣ Xn / T
−(K a + [H+]s ) +
([H+]s + K a)2 +
+
40x6K a MWx6
2
k
⎞ ⎟ ⎟ ⎟ ⎠ (22)
(13) +
where [H ]s = 10x2/MWx2 + 10x3/MWx1. The optimization problem results in the following formulation:
⎡ P PXi , R 41 PX , R 36 ⎤ P ⎥
