Cosmetic Coating: Saving Money by Employing a ‘Digital Twin’

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October 01, 2023

Cosmetic Coating: Saving Money by Employing a ‘Digital Twin’

Michael Choi, Stuart C. Porter, and Axel Meisen -PCTS, Inc.

Pharma execs generally consider cosmetic tablet coating a less essential aspect of manufacturing because cosmetic coatings typically do not affect drug delivery. Perhaps they should reconsider; employing “digital twins” for the tablet coating processes can lead to significant cost savings.

Companies generally employ cosmetic coating to achieve good color uniformity— which is not the same as coating uniformity in terms of mass. Achieving a uniform color endpoint depends as much on the actual coating formulation, especially color. Different colorants have different degrees of hiding power, and the mass of coating required to achieve a target color uniformity endpoint therefore varies. The color of the tablet core is another important factor. From an overall quality perspective, visual quality—such as smoothness of the coating, gloss, legend definition, edge chipping, picking, and sticking—is highly important and determined mostly by process thermodynamics, tablet movement in the coating pan, and rate of coating application.1

Because these coatings are, by definition, primaFigure 1 Schematic of the coating process for the base run (Step 1)rily for appearance, their coating processes receive less attention. As a result, issues like coating weight gain and weight uniformity are often overlooked, because they have little or no impact on drug delivery, unlike, say, controlled-release coatings. However, there are significant benefits to addressing weight uniformity and coating thickness in cosmetic coatings.

The size and shape of tablets determine the total batch surface area and therefore, for given operating conditions, the ultimate film thickness on each tablet. While coating weight per unit surface area should be the target and would be the same for tablets of all shapes and sizes, percent weight gain is typically used. For example, a large vitamin tablet might only need a weight gain of 2.5%, but a much smaller Rosuvastatin tablet would require a weight gain of 5 or 6% to achieve the same film thickness. We also know that improving weight uniformity potentially achieves better color uniformity with less coating.1 This obviously has throughput and cost benefits. The potential benefits include:

  • Avoiding costly redevelopment while ensuring the same product quality during scaleup and tech transfer
  • Optimizing cost of goods, operating cost, and capital investment
  • Aligning process parameters with production goals

Digital twins, i.e., computer models and apps based on rigorous process science, can be used to demonstrate these benefits. Here, we demonstrate how predictions of cosmetic coating weight gain and uniformity reveal optimal manufacturing costs (subsequently called the cost of goods, COG) and production rates.

Method

To determine technically feasible and cost-effective cosmetic coating, we conducted one actual run (designated Run #1) at scale and with given coating equipment to obtain coating and col

Figure 2 Coating weight gain at a fixed f∆E (shown in this digital twin screenshot as the defect rate) at various CV values.

or uniformity information. This run can be omitted when data from other similar formulations and using the same equipment are available. The steps for our method are:

  1. Conduct Run #1 using the target equipment at the target production rate.
  2. Measure the coefficient of variation (CV) of coating weight for Run #1. Note that CV is also referred to as relative standard deviation.
  3. For Run #1, measure the color uniformity and determine the limit of color-difference (∆E) against the target color level. See, for example, Cunningham et al.2 for color-difference measurements.
  4. Calculate the spray zone coefficient (k∆) for Run #1 using the CV value from step 2 and equations provided by Choi et al.3
  5. Calculate the fraction greater than ∆E (denoted by f∆E) based on the color uniformity distribution and determine the corresponding coating weight gain.
  6. Calculate the weight gain values and coating times for a range of CV values that satisfy f∆E.
  7. Calculate COG based on material cost and operating cost using the coating time.
  8. Calculate the production rate based on the coating time and batch changeover time.
  9. Determine the minimum COG and the corresponding production rate.
  10. Calculate the range of processing conditions that result in cost savings and production increases relative to the results from Run #1.

The calculation for step 4 can be performed by using our PCTSUni II digital twin simulator, which gives the coating efficiency, uniformity, and thermodynamics for pan-coating processes. Calculations for steps 5-10 can be performed with our Formulation Quantity Optimization (FQO) app, which is an add-on module for PCTSUni II. FQO uses the results from steps 1-3 and PCTSUni II to calculate different combinations of CV and 

weight gain at the target f∆E. Descriptions of PCTSUni II and FQO are provided in our earlier article4 and at www. particlecoating.com.

Results

Based on the process shown in Figure 1, the following example illustrates our method.

      1. An experiment (simulated for this example) was conducted at the target production rate under the following conditions: 410 kg batch size, 1.5 m diameter pan, spray rate of 650 g/min, 19% solids content, and 90 min coating time that resulted in weight gain of 2.5% (90% coating efficiency). The uncoated tablet shape was standard round convex with the diameter and weight of 10 mm and 400 mg, respectively. These conditions resulted in the final film weight of 10 mg per tablet. The production rate and COG are 133kg/h and $326/kg, respectively.
      2. The CV was measured to be 19.1%.
      3. The color uniformity distribution was measured using 30 tablets and ∆E < 3 was determined to be the cutoff point for acceptable color level.
      4. The spray zone coefficient (k∆) using the relationship given by Choi et al.3 was 0.11.
      5. f ∆E was 0.1% based on the color uniformity distribution measurement, which corresponded to 4.1 mg coating weight.
      6. The target f∆E is 1%. The weight gain and coating times were calculated for various CV values using the FQO app with k∆ = 0.11 and f∆E (shown as ‘defect rate’ in FQO) = 1%. The results are shown in Figure 2.
      7. COG was also calculated using the FQO app and the results are shown in Figure 3. The fixed and variables costs are shown in Figures 1 and 2.
      8. The production rate, calculated using the FQO app, is also shown in Figure 3. The calculations were based on the cycle times in Figure 2 and coating times calculated from step 6.
      9. The minimum COG was $316/kg of uncoated tablets processed at the production rate of 110 kg/h and with a coating weight gain of 1.6%.
      10. At the target production rate of 133 kg/h, the coating weight gain is 1.9% (24% material savings) and the cost is of $316/kg of uncoated tablets processed (3% cost reduction). The production rate can be increased to 203 kg/h (53% increase) at 5% weight gain without increasing the COG in #1, giving the potential improvement range of 133-203 kg/h production rate and 0-3% COG reduction.

Discussion

For the present example, Figure 3 shows that even for cosmetic coatings, one can lower the cost of goods (COG) substantially. By using coating uniformity (CV) and color-difference limit information, an ‘ideal production’ region is revealed by the intersection of the pFigure 3 Cost of goods (COG) and production rate vs coating weight gain at the f∆E of 1%.roduction rate and COG curves. The improvements in production rate and COG from the base case are as high as 53% and 3%, respectively.

Cost Rate versus COG

A question that arises naturally is ‘where within this region should we operate?’ The answer depends on the production goal or the ‘capacity needs’ expressed in the number of tablets produced per year. If the goal is to minimize COG with no capacity constraint, the lowest point in the COG curve is recommended. However, if capacity utilization is important, the optimum cost savings rate (CSR) should be determined. To calculate the optimum CSR, the maximum point on the curve generated by graphing CSR against weight gain is recommended. CSR is given by

CSR=(COG0-COG)∙PR∙t

where COG0 = base cost of goods (from #1) = $326/kg

COG = cost of goods, $/kg

  PR = production rate, kg/h

t = time, h

The results in Figure 4 show that the optimal cost savings rate for year-round, non-stop production occurs at the coating weight gain of 2.3%. This results in an annual cost saving of $13.2MM and 23% increase in production rate compared to the base run. We should note that the corresponding COG is 0.3% higher than the minimum COG, but the CSR improves by 30%.

Figure 4 Yearly cost savings as a function of weight gain for 52 weeks/ year operation with 100% equipment utilization.

While it is possible to increase the production rate further from the maximum CSR, the cost savings diminish quickly with increasing weight gain. In cases where the production rate at the optimal CSR does not meet the capacity needs, a choice between higher-cost manufacturing and the capital cost of new equipment should be analyzed. Suppose, for example, if the product demand increases requiring production rate of 210 kg/h, CSR at 210 kg/h can be determined by

      1. looking up the weight gain (= 5%) corresponding to 210 kg/h in Figure 3
      2. looking up CSR (= ~0) at this weight gain in Figure 4

The difference between optimal CSR and CSR at 210 kg/h is ~$13MM/a. The capital cost of a new pan must be weighed against the $13 MM/a cost savings to justify the capital project.

Effect of Material and Operating Cost on COG

The manufacturing cost (COG) is dominated by the core tablet cost (94%) as shown in Figure 1 for the base run. When the cost of coating material changes, the minimum COG also changes as shown in Figure 5, i.e., the higher the cost of coating, the lower the weight gain at which the minimum COG occurs. A similar effect was found with changing operating cost and the inclusion of equipment depreciation cost. The minimum COG also changes when, after the initial optimization, changes are made to

  • Cost of materials or labor
  • Production rate due to:
  • Automation 
    • Operations 
    • Batch/Continuous manufacturing technology

Accordingly, process re-optimization (e.g., under a Lean Figure 5 Weight gain and cost of goods as function of coating-to-tablet cost ratio.Manufacturing program) is recommended in those cases. Since the same data from steps 1-3 can be used for future changes, additional experiments are unnecessary; only the analysis needs to be done in those cases.

Conclusions

Characterizing coating weight gain and color uniformity by using a digital twin simulator for coating processes is beneficial—even for cosmetic coatings. Using the coating uniformity and color-difference limit information determined from a base run, cost of goods and production rate analysis showed savings of over $13MM per year relative to the base case at 100% equipment utilization. Since material and operating costs fluctuate, this analysis should be repeated periodically as part of a Lean Manufacturing program.

References

      1. Porter, S.C. Trends in continuous film-coating processes. Tablets and Capsules. 2021:24:5.
      2. Cunningham, C., Croenlein, J., and Nohynek, O., “Evaluation of a Continuous-Cycled Film Coater in Applying a High Solids Coating Formulation,” Tablets & Capsules, Oct. 2015.
      3. Choi, M., Porter S.C., Macht, B., and Meisen, A., “Novel Coating Uniformity Models for Tablet Pan Coaters”, AAPS PharmSciTech 2021;22:7.
      4. Choi, M., Porter, S.C., Meisen, A., Managing Uncertainty: Mathematical Modeling Can Guide Key Tablet Coating Decisions, Tablets and Capsules. 2022:18;3.

About the Authors

Dr. Michael Choi is the president of PCTS, Inc., Dr. Stuart Porter and Dr. Axel Meisen serve as the strategic advisors to PCTS. Dr. Porter is the president of PPT, and Dr. Meisen is the president of Fusion Energy Council of Canada and Emeritus Professor, UBC. Contact: michael.choi@particlecoating.com. Company Website: www.particlecoating.com. LinkedIn: www.linkedin. com/company/particle-coating-technology-solutions.

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