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Article

Effect of Geometry on the Dissolution Behaviour of Complex Additively Manufactured Tablets

by
Seyedebrahim Afkhami
1,
Meisam Abdi
1,* and
Reza Baserinia
2,*
1
School of Engineering and Sustainable Development, Faculty of Computing, Engineering, and Media, De Montfort University, Leicester LE1 9BH, UK
2
School of Engineering, College of Science and Engineering, University of Leicester, Leicester LE1 7RH, UK
*
Authors to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2025, 9(1), 11; https://doi.org/10.3390/jmmp9010011
Submission received: 20 November 2024 / Revised: 27 December 2024 / Accepted: 31 December 2024 / Published: 3 January 2025
(This article belongs to the Special Issue Recent Advances in 3D Printing Technologies in Bioengineering with Selected Papers from the 29th Congress of the European Society of Biomechanics (ESB 2024))
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Abstract

Additive manufacturing (AM) processes, such as fused deposition modelling (FDM), have emerged as transformative technologies in pharmaceutical manufacturing, enabling the production of drug delivery systems with complex and customised geometries. These advancements provide precise control over drug release profiles and facilitate the development of patient-specific medicines. This study investigates the dissolution behaviour of AM-fabricated tablets made from polyvinyl alcohol (PVA), a hydrophilic and biocompatible polymer widely used in drug delivery systems. The influence of the initial mass, surface area, and surface-area-to-volume ratio (S/V) on dissolution kinetics is evaluated for tablets with intricate geometries. Our findings demonstrate that these parameters, while critical for conventional tablet shapes, are insufficient to fully predict the dissolution behaviour of complex geometries. Furthermore, this study highlights how geometric modifications can enable the administration of the same drug dosage through sustained or immediate release profiles, offering enhanced versatility in drug delivery. By leveraging the geometric design freedom provided by AM technologies, this research underscores the potential for optimising drug delivery systems to improve therapeutic outcomes and patient compliance.

1. Introduction

A large majority of the drugs developed are administered to patients in tablet form. To manufacture these tablets, the active pharmaceutical ingredient (API) is processed into powder and mixed with excipients (binders, gliders, bulking agents, etc.) to formulate the tablet ingredients [1,2]. The formulation is then introduced into dies of specific geometries before compaction using a pair of solid punches in a system known as a rotary tablet press [3,4,5]. Despite advancements in process control technologies used in these machines and better understanding of the parameters influencing the quality of tablet products [6,7], the process has an inherent limitation. Due to the nature of the compaction process, the geometry of the tablets that can be manufactured is limited to a few simple shapes such as square, round, oval, triangle, caplet, etc. Consequently, drug release and absorption are mostly controlled via processing parameters including compression stress and tablet size and the scope for changing the shape is limited. For instance, Molavi et al. [8] investigated the influence of size and mass on the dissolution of oblong, biconvex, round, and flat tablets and observed a higher rate of drug release for samples with a larger size and mass. Comparing the shapes, they observed a higher dissolution efficiency for biconvex tablets compared to flat tablets.
Additive manufacturing (AM), also known as 3D printing, has evolved significantly over the past decade and its constituent technologies have been adopted by many industries, such as the aerospace, automotive, food, and fashion industries, for manufacturing prototypes and functional products with enhanced properties [9,10,11]. For pharmaceuticals, there is an increasing interest toward utilising AM for manufacturing tablets in both the R&D [12] and production stages [13]. The high degree of design freedom offered by AM allows for the development of complicated structures and multi-drug compositions, enhancing the effectiveness and safety of pharmaceuticals [9,14,15,16]. These technologies have been utilised by researchers to fabricate pharmaceutical solid dosage forms and evaluate the changes to the critical quality attributes of products. For example, Kyobula et al. [17] used the 3D inkjet printing method to fabricate fenofibrate in a beeswax carrier matrix of honeycomb architecture to control drug dissolution for personalised medicine applications. Crișan et al. [18] used different polyvinyl alcohol (PVA) particle sizes whilst preparing drug-loaded filaments and examined how it influenced the dissolution behaviour of tablets with open channels manufactured using the fused deposition modelling (FDM) technique. Moreover, McDonagh et al. [19] investigated the influence of infill geometry and density on the dissolution of tablets 3D printed using the Arburg Plastic Freeforming (APF) system and found the release profile to be affected significantly for different geometries with a similar infill density. While FDM is widely regarded as suitable for producing proof-of-concept prototypes, it has demonstrated substantial promise for the production of final pharmaceutical applications, particularly in personalised medicine [20].
AM provides an excellent route toward controlling the release profile and improving pharmaceutical efficacy through fabricating solids with complex geometries that cannot be manufactured alternatively. Such geometrical freedom facilitates optimising the dissolution behaviour of drugs [21]. There is a limited number of studies in the literature looking at geometrical effects on dissolution behaviour of tablets made by AM. Lee et al. [22] investigated the changes in the drug release rate of microplates of a similar mass but with different surface areas manufactured by piezoelectric inkjet printing and concluded that the release rate is predominantly influenced by the sample surface area. However, Zhang et al. [23] showed that dissolution behaviour is governed by the surface-area-to-volume ratio (S/V) of tablets. They examined the impact of porosity, pore shape, pore length, and pore alignment on drug release behaviour in 3D-printed constructs and recorded faster drug release rates for samples with larger porosities. They showed that under a fixed S/V the dissolution rate is not affected notably. Martinez et al. [24] used the stereolithographic (SLA) process to fabricate solid dosage forms in simple shapes, including cube, pyramid, and sphere. They also reported the S/V to be the main geometrical factor affecting the dissolution behaviour. In an earlier work, they ruled out the influence of surface area and stated that the S/V is the sole important parameter affecting the dissolution behaviour [25].
Polymer-based tablets often release drugs via complex mechanisms, including diffusion, swelling, erosion, or combinations thereof, depending on the material properties and environmental conditions. Diffusion-based models have gained significant attention due to their ability to predict drug release profiles effectively. These models range from analytical solutions of Fick’s laws for simple geometries to advanced numerical models capable of addressing non-linear and dynamic systems. For example, Siepmann and Peppas [26] developed a model integrating Fickian diffusion and polymer swelling to describe drug release from hydrophilic matrices, which has since been applied widely. Similarly, Colombo et al. [27] utilised numerical simulations to capture time-dependent diffusion behaviour in swellable polymers. These approaches have proven efficient for understanding and predicting dissolution dynamics in 3D-printed tablets, offering insights into the effects of geometry, porosity, and material composition. Expanding on these methodologies could further enhance the precision of dissolution predictions, particularly for complex geometries fabricated using AM technologies.
The reviewed literature highlights the significance of understanding how tablet geometry influences dissolution behaviour, particularly in the context of 3D-printed polymer tablets. Despite advancements in AM technologies and prior research efforts, there remains a critical gap in the knowledge regarding the precise interplay between the tablet volume, surface area, and surface-to-volume ratio and the dissolution behaviour of 3D-printed tablets. Moreover, the current literature is mainly focused on simple geometries whilst additional factors arising from utilising complex shapes have not been considered. To address the gaps, our study aims to experimentally investigate the dissolution behaviour of FDM-printed cylindrical PVA tablets with varying geometrical features while maintaining a consistent mass, surface area, and surface-area-to-volume ratio. Through the comparison of concentration and dissolved mass profiles across time for each set of samples, our aim is to improve the critical impacts of geometry on tablet dissolution.
This holistic approach seeks to offer valuable insights into how tablet geometry influences the dynamics of drug release, thereby aiding in the refinement of geometric parameters and optimising tablet design for enhanced dissolution behaviour. While this study focuses on the dissolution behaviour of PVA tablets with different geometries without incorporating active pharmaceutical ingredients (APIs), the primary aim is to examine the fundamental effects of tablet geometry on dissolution dynamics. By using PVA as a model material, the study isolates the geometric influence on the dissolution process, allowing for a controlled investigation that excludes the complexity of drug-solvent interactions. This approach serves as a foundation for future research, where drug-loaded systems can be explored, building on these insights to optimise the design of drug delivery systems using 3D-printed polymers.

2. Materials

Polyvinyl alcohol (PVA), a water-soluble polymer with excellent biocompatibility, was selected for this study to evaluate how the geometry of the dosage form influences its dissolution behaviour, with distilled water used as the solvent. PVA plays a crucial role in controlled drug release systems through mechanisms primarily driven by matrix swelling and diffusion. Its hydrophilic nature allows water absorption, leading to polymer swelling and the formation of hydrated layers that facilitate the diffusion of the active pharmaceutical ingredient. This dual mechanism provides versatility in tailoring drug release profiles for both immediate and sustained delivery systems, making PVA an ideal material for studying the influence of geometry on drug release, particularly in relation to surface-to-volume ratios in polymeric matrices [28,29]. Furthermore, PVA’s filaments are commercially available and commonly used in FDM additive manufacturing due to their water solubility, high biocompatibility, and low toxicity, which further underscores its desirability for controlled drug delivery applications [30,31].
Here, a commercially available filament of PVA (manufactured by Ultimaker, the Netherlands) with a diameter of 2.85 mm was used to fabricate different geometries. The PVA filament properties provided by the manufacturer are summarised in Table 1.

3. Methodology

3.1. Design and Fabrication of the Specimens

For this research project, four cylindrical geometries were considered to represent the solid dosage forms. These were the Solid Cylinder (SC), a cylindrical shell named Hollow Cylinder 1 (HC1), a cylinder with a central opening named Hollow Cylinder 2 (HC2), and a cylindrical shell with four square openings equally spaced in the middle named Hollow Cylinder 3 (HC3). The selection of the four geometries was specifically aimed at achieving significant variation in the mass, surface area, and surface-area-to-volume ratio to enable a detailed investigation of their effects on dissolution behaviour. The dimensions of the geometries were also tailored to the resolution capabilities of the 3D printer to ensure precise fabrication of the samples with the desired parameter variations. These geometries are demonstrated in Figure 1.
The design procedure was divided into three stages. First, geometries were manipulated to achieve a similar design mass of approximately 500 mg using the filament density (specific gravity) provided in Table 1. This was achieved by altering the dimensions of different designs and analysing the model in CAD software. Here, the modifications were such that the overall aspect ratios remained similar without considering the extreme cases where, for example, the geometry is elongated with a very small outer diameter. Similarly in the second stage, the dimensions were modified to achieve a similar surface area for the models (approximately 466 mm2). In the third and final stage, the dimensions of the models were modified to achieve a similar S/V for the four geometries (approximately 0.75 mm−1). It should be noted that the surface area considered here was the area of models that are initially exposed to the solvent when submerged, and in the case of HC3, it did not include the area of the hollow section inside the cylinder.
The four unique geometries corresponding to each set of samples were fabricated using an Ultimaker 3 FDM 3D printer with the layer height set at 0.1 mm and an infill density of 100% while utilising a 0.4 mm nozzle at a working temperature of 200 °C. This facilitated consistent layer bonding and ensured the structural integrity of the material. For each unique geometry, three replicate samples were 3D printed for further testing to improve the accuracy and reliability of the experimental analysis, making the total number of printed and analysed samples 36. An image of a set of geometries with similar surface areas is shown in Figure 2 as an example.
After production, each sample was weighted using a precision scale to measure its actual mass, which was averaged for each geometry. The design and measured properties of the samples fabricated for the three sets of designs are summarised in Table 2. In the table, the design values are rounded to the nearest integer and the tolerance values indicated for the averaged measured mass are the standard deviations. The mass error values listed in the final column compare the design values with the averaged measured masses. Discrepancies in mass can directly lead to discrepancies in concentration values, potentially affecting the accuracy of the dissolution profiles and the interpretation of results.
For simplification in the following sections, the samples with similar mass are labelled with the letter M, the samples with a similar surface area with S, and the samples with a similar surface-area-to-volume ratio with SV.

3.2. Dissolution Testing

Following the fabrication of the samples using the 3D printer, their dissolution behaviour was examined with a PTWS 120 USP (IV) dissolution tester manufactured by Pharma Test, Germany. Each sample was placed in a 250 mL distilled water bath as the solvent and kept at a constant temperature of 37 °C to mimic the physiological conditions of the body. During testing, the solutions were stirred at a constant speed of 50 rpm to ensure consistent mixing over the testing period of up to 8 h. At different intervals, 3 mL samples were extracted from the solutions and filtered with a Sartorius CA 0.45 filter. The absorbance of the dissolved PVA was then determined using an Evolution 60S UV-visible spectrophotometer manufactured by Thermo Scientific, US. Following the measurements, the calibration curve presented in Figure 3a was used to convert the absorbance values to the corresponding concentrations in the solution.
Prior to testing, the peak absorbance of the 3D-printed PVA was measured with the UV-visible spectrophotometer, as shown in Figure 3b. The peak absorbance was found to be at the UV wavelength of 206 nm followed by a minor peak at 278 nm. UV-visible spectrometry experiments were conducted at peak wavelengths of 206 where the material absorbance is maximum.

4. Results and Discussion

Following the dissolution testing of the 3D-printed geometries, the changes in the average concentration and dissolved percentage of PVA in water over time were calculated for the three sets of samples. The results obtained are compared in the following subsections.

4.1. Dissolution Behaviour of Samples with a Similar Mass

The changes in the average concentration and average dissolved percentage of PVA samples with a similar initial mass are shown in Figure 4, with the error bars indicating the standard deviation of three repeat measurements. Logically, the two figures are very similar and minor discrepancies are the result of a small difference in the initial mass of the samples, which influenced the dissolved percentage values.
The results show a similar release profile for HC1-M and HC3-M samples whereby 90% of the polymers were dissolved in under 200 min, whilst for SC-M and HC2-M the time required to reach the same threshold was double. The results highlight that despite having a similar overall mass or volume, the geometry of the tablets can be manipulated to achieve very different release profiles. This suggests that the same overall drug concentration can be administered as immediate release or controlled release by carefully tailoring the geometry of the solid dosage form.
As discussed earlier, the surface area and surface-area-to-volume ratio are widely recognised by researchers as critical factors influencing the dissolution of tablets. These factors have been examined for samples with a similar mass. For HC1-M and HC3-M, the values of these parameters are comparable and significantly larger than those of the other geometries (Table 2). The similarities in the dissolution behaviour of these two samples (Figure 4) align well with findings reported in the literature. Furthermore, Figure 4 shows that the SC-M sample, which has the lowest surface area within the set of four samples of a similar mass, exhibits the lowest diffusion rate, which is again consistent with the conclusions drawn by other researchers in previous studies [22,32,33].
The correlations between the surface area, the S/V, and the dissolution behaviour are further investigated by examining the release profiles of samples with a similar surface area and similar S/V in the following subsections.

4.2. Dissolution Behaviour of Samples with a Similar Surface Area

The changes in the concentration of the dissolved polymer and the polymer percentage dissolved over time for samples with a similar surface area are shown in Figure 5. Unlike in the previous set of samples, where the concentration–time graph closely resembled the % dissolved–time graph due to similar sample masses, in this set HC1-S and HC3-S have significantly smaller masses compared to SC-S and HC2-S. As a result, these samples with a smaller mass contribute less to the concentration of the solvent, which is reflected in the lower curves on the concentration–time graph. However, their smaller mass allows them to dissolve more rapidly percentagewise, resulting in higher curves on the % dissolved–time graph. Similar to previous results, for the HC1-S and HC3-S geometries, the release profiles suggest a rapid release response, whilst for SC-S and HC2-S the time required to fully dissolve the samples was larger and polymer release was sustained over a longer period (Figure 5b).
Comparing all four profiles shows that, despite having similar surface areas, the dissolution behaviours of the samples are significantly different. However, if these results are compared with respect to the surface-area-to-volume ratios, for samples with a larger S/V the time required to reach a fixed percentage released is shorter. This is consistent with the results obtained by Martinez et al. [24]. However, the varying masses and overall sizes of the samples examined in this section preclude a reliable comparison based solely on the percentage dissolved. Therefore, the apparent correlations between the dissolution behaviour and S/V may be inaccurate.
To evaluate the correlations, the results were compared with respect to changes in the concentration of the polymer in the solution, as shown in Figure 5a. Despite observing a sustained release behaviour for SC-S and HC2-S relative to others (Figure 5b), larger average concentrations were recorded for these geometries over the measurement period. In late stages of the dissolution measurement, this is mostly due to larger overall masses of the samples, as indicated in Table 2. However, in early stages, the higher concentration values measured for SC-S and HC2-S suggest dependency on factors other than the initial surface area or the initial S/V individually. These samples have a similar initial surface area but a smaller S/V. According to conclusions made by researchers previously, these samples should exhibit the same behaviour if the surface area is considered or reduced dissolution if the S/V is considered. Yet the results contradict both cases. Therefore, once again, it cannot be stated that the dissolution behaviour is solely a function of the initial surface area or initial S/V.
These results further consolidate the idea of achieving a desired release mechanic through geometry alteration rather than formulation or process parameter changes. Additionally, it is highlighted that the common approach of reporting the release profiles in terms of percentage release can present misleading information about dissolution kinetics.

4.3. Dissolution Behaviour of Samples with a Similar Surface-Area-to-Volume Ratio

The changes in the concentration and percentage of polymer dissolved over time for samples with a similar S/V are shown in Figure 6. From Figure 6a, it can be observed that the concentration changes differ among the four samples with a similar S/V, which can be attributed to the variations in their mass and surface area. The HC3-SV specimen, having a significantly larger mass than the other specimens, results in a much higher concentration level. The concentration–time trends also align with the Fickian diffusion model, where the diffusion rate is proportional to the surface area of the specimens. This is evident from the slopes of the concentration–time curves at the start of dissolution: specimens with larger surface areas (e.g., HC3-SV) exhibit steeper initial slopes (i.e., higher diffusion rates), while those with smaller surface areas (e.g., SC-SV) demonstrate gentler slopes (i.e., lower diffusion rates), highlighting the influence of surface area on dissolution.
Although the general trend in the concentration–time graph is a gradual reduction in slope as concentration increases due to the decreasing surface area of the specimens during dissolution, an exception is noted for the HC2-SV specimen. Around 380 min after the start of dissolution, HC2-SV shows an unexpected increase in the slope of the concentration–time curve. This is likely due to a sudden increase in the effective surface area as the internal hole of this cylindrical specimen, initially inaccessible to the solvent, becomes exposed. A similar trend—an abrupt increase in the slope of the concentration–time curve—can also be observed for the HC2-S specimen in Figure 5a, suggesting the same underlying cause. Once more, this underscores the importance of geometry design and the interplay between different parameters such as the mass, surface area, and topology of the specimens in determining dissolution kinetics.
It can be seen from Figure 6b that, despite having very different mass and surface area values, this set of specimens demonstrates a closer trend in terms of percentage dissolved over time compared with the previous sets. This observation aligns with findings by other researchers [24], suggesting that dissolution behaviour (in terms of percentage dissolved) is primarily influenced by the S/V, and the difference observed could be due to variation in the accessibility of the solvent to the internal volume of the samples. A slightly different trend is noted for the HC2-SV specimen, which is attributed to its specific design featuring an internal hole that is initially not exposed to the solvent. This unique topology impacts the dissolution behaviour by delaying solvent access to certain regions of the sample, demonstrating the interplay between geometric features and dissolution kinetics.

4.4. Further Remarks and Discussion

The dissolution behaviour of pharmaceutical tablets is a multifaceted process influenced by geometric parameters, surface properties, and solvent interactions. While earlier sections have explored specific geometric influences such as the surface area and S/V, this section synthesises these findings to provide a broader understanding of their combined impact. Figure 7 compares the average time to 50% dissolution (half dissolution time) for all 12 unique geometries, enabling a ranking of the geometries based on their dissolution performance. It can be observed that the SC-S specimen exhibits the longest half dissolution time, which correlates with its having the lowest S/V (0.61 mm−1) among the 12 samples, as shown in Table 2. Conversely, the two samples with the shortest half dissolution times, HC3-S and HC1-S, have the highest S/V, 1.69 mm−1 and 1.57 mm−1, respectively. For samples with a similar S/V, the half dissolution times remain within the same range, reinforcing the critical role of the S/V in determining dissolution rates. These findings underscore how surface area, mass, and topology can be tailored to achieve a desired dissolution response, offering valuable insights for designing solid dosage forms with specific dissolution characteristics.
To further substantiate the dissolution behaviour observed in this study, the data for the samples with a similar S/V were fitted to the Higuchi model, a well-established mathematical framework describing drug release from polymeric matrices governed by Fickian diffusion [26,34]. The Higuchi model expresses the cumulative release as proportional to the square root of time:
M t = k H t 1 / 2
where M t is the amount of drug released at time t (% dissolved), and k H is the Higuchi dissolution constant. This model assumes constant diffusivity and a homogeneous polymer matrix [35]. As shown in Figure 8, the linearity of the plotted curves for cumulative drug release ( M t ) versus the square root of time ( t 1 / 2 ) for all four unique specimens confirms their conformity to the Higuchi model, demonstrating that the dissolution mechanism for PVA tablets is primarily diffusion-controlled. The slope, intersect, and coefficient of determination (R2) for the lines fitted to the data using linear regression (LR) are summarised in Table 3. While the similarity in the slopes of these lines (Higuchi dissolution constants) indicates comparable dissolution behaviour for specimens with equivalent S/V ratios, discrepancies in the magnitude of the slopes suggest that additional factors, such as initial mass, material distribution, and internal topology, also play a significant role in influencing dissolution. These findings align with prior research indicating that the Fickian diffusion mechanism dominates dissolution in hydrophilic polymer matrices like PVA [27,36], reinforcing the suitability of this model for describing the system studied.
According to Fick’s first law of diffusion [37], the diffusion rate (i.e., the rate of change in concentration over time, represented by the slope of the concentration–time graph) is directly proportional to the surface area when comparing the dissolution of specimens at a fixed concentration. In the case of the presented experiments, such comparisons can only be made in the very early stages of dissolution when the concentration is near zero and the surface area of the specimens has not significantly changed due to dissolution, typically within the first 10 min. Theoretically, during this interval, the slopes of the concentration–time graphs for all specimens with a similar surface area should be comparable. However, this effect is not captured in the presented experimental data due to the need for a much higher number of concentration measurements within a short period of time. Figure 5b shows that shortly after the start of dissolution, within the first 50 m, a significant percentage of the polymer samples dissolve: 25–30% for the SC-S and HC2-S samples, and approximately 60% for the HC1-S and HC-3S samples. This indicates that the surface area of the specimens changes significantly from the starting point. Therefore, considering the original surface area as the sole factor affecting dissolution behaviour is invalid as the change in the shape of the samples over time is not governed solely by the original surface area. The same holds true for changes in the S/V over the experimentation period (Figure 6b).
In this context, S/V values provide further insights into the dissolution behaviour of the specimens. Higher S/V values theoretically imply that a larger percentage of the specimen is exposed to the solvent, resulting in enhanced release behaviour, as observed for the HC1-S and HC-3S samples in Figure 5b. For the same reason, higher S/V values lead to a higher rate of change in geometry and a reduction in surface area, consequently decreasing the diffusion rate (slope of concentration–time curves in Figure 5a). This effect is visible in Figure 5a, where the HC1-S and HC-3S samples, which have higher S/V values, experience a rapid change in the diffusion rate compared to the other two specimens with lower S/V values and a more sustained release behaviour over time. Consistently, for samples with a similar S/V (Figure 6a), the initial change in concentration is similar, supporting this argument; however, as the dissolution continues, the evolution of the shape of the samples and, thus, changes in the S/V become dissimilar, resulting in different concentrations.

5. Summary and Conclusions

Additive manufacturing (AM) has the potential to transform the design and production of pharmaceutical tablets by enabling the fabrication of complex geometries. These advancements offer opportunities to tailor drug release profiles, addressing the limitations of conventional manufacturing methods. This study explored the dissolution behaviour of 3D-printed polyvinyl alcohol (PVA) tablets, which were fabricated using fused deposition modelling (FDM), focusing on the effects of the geometry, surface area, and surface-area-to-volume ratio (S/V).
The findings demonstrate that while the surface area and S/V are critical factors for simple geometries, they are insufficient to fully predict the dissolution behaviour of complex shapes. Complex geometries exhibited unique dissolution dynamics influenced by solvent accessibility and internal topology, challenging the conventional reliance on the surface area or S/V as sole predictors. This highlights the importance of considering additional parameters when designing advanced drug delivery systems.
A key outcome of this research is the potential for geometric tailoring to provide precise control over drug release profiles. Tablets with an identical mass but varying geometries demonstrated distinct dissolution behaviours, enabling the delivery of the same drug concentration through either immediate or sustained release by altering the design. This approach offers a promising pathway for optimising drug release without modifying formulation or processing conditions.
The implications of this work extend beyond simple geometry. For diffusion-controlled systems like PVA-based tablets, higher S/V ratios enhance solvent penetration and diffusion, while erosion- and swelling-controlled mechanisms benefit from similar geometric optimisations. These findings underline the critical role of geometry in achieving desired dissolution outcomes and pave the way for integrating complex shapes into personalised and multi-drug delivery systems.
Future research should focus on incorporating active pharmaceutical ingredients (APIs) to evaluate how geometry influences active drug release mechanisms. Additionally, exploring multi-drug tablet designs will help expand the application of AM in complex therapeutic scenarios. Optimisation techniques, such as machine learning and evolutionary algorithms, could further enhance the design of tablet geometries to achieve specific release profiles.
This study highlights the transformative potential of AM for pharmaceutical manufacturing. By leveraging geometric design freedom and integrating experimental and computational approaches, AM could enable advanced drug delivery systems tailored to individual therapeutic needs, enhancing both efficacy and patient compliance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jmmp9010011/s1, Dissolution Data are in the Supplementary Materials.

Author Contributions

Conceptualization, S.A., M.A. and R.B.; methodology, S.A.; validation, S.A.; formal analysis, S.A.; investigation, S.A.; resources, R.B.; writing—original draft preparation, S.A.; writing—review and editing, R.B. and M.A.; visualisation, S.A. and R.B.; supervision, R.B. and M.A.; project administration, R.B. and M.A.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Faculty of Computing, Engineering and Media at De Montfort University.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Example isometric projections of the four geometries. From left to right: Solid Cylinder (SC), Hollow Cylinder 1 (HC1), Hollow Cylinder 2 (HC2), and Hollow Cylinder 3 (HC3).
Figure 1. Example isometric projections of the four geometries. From left to right: Solid Cylinder (SC), Hollow Cylinder 1 (HC1), Hollow Cylinder 2 (HC2), and Hollow Cylinder 3 (HC3).
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Figure 2. Example samples with similar surface areas. From left to right: Solid Cylinder (SC), Hollow Cylinder 1 (HC1), Hollow Cylinder 2 (HC2), and Hollow Cylinder 3 (HC3).
Figure 2. Example samples with similar surface areas. From left to right: Solid Cylinder (SC), Hollow Cylinder 1 (HC1), Hollow Cylinder 2 (HC2), and Hollow Cylinder 3 (HC3).
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Figure 3. (a) The calibration curve for converting the absorbance to the concentration of PVA dissolved in water (coefficient of determination, R2 = 0.99), and (b) the UV absorption standard curve for the PVA.
Figure 3. (a) The calibration curve for converting the absorbance to the concentration of PVA dissolved in water (coefficient of determination, R2 = 0.99), and (b) the UV absorption standard curve for the PVA.
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Figure 4. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar mass. The error bars are generated using the standard deviation of the three repeat measurements.
Figure 4. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar mass. The error bars are generated using the standard deviation of the three repeat measurements.
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Figure 5. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar surface area. The error bars are generated using the standard deviation of the three repeat measurements.
Figure 5. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar surface area. The error bars are generated using the standard deviation of the three repeat measurements.
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Figure 6. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar surface-area-to-volume ratio. The error bars are generated using the standard deviation of the three repeat measurements.
Figure 6. The changes in (a) average concentration and (b) percentage dissolved over time for samples with a similar surface-area-to-volume ratio. The error bars are generated using the standard deviation of the three repeat measurements.
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Figure 7. Time to 50% dissolution for all studied specimens.
Figure 7. Time to 50% dissolution for all studied specimens.
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Figure 8. Linear regression (LR) plots of cumulative drug release (% dissolved) versus the square root of time for specimens with a similar S/V fitted to the Higuchi model.
Figure 8. Linear regression (LR) plots of cumulative drug release (% dissolved) versus the square root of time for specimens with a similar S/V fitted to the Higuchi model.
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Table 1. Material properties of the PVA filament provided by Ultimaker.
Table 1. Material properties of the PVA filament provided by Ultimaker.
PVA Filament Specifications
Diameter2.85 ± 0.10 mm
ColourPVA Natural
Melting temperature163 °C
3D-printing temperature190–210 °C
Specific gravity1.23 gr/cm3
Table 2. The design and measured properties for manufactured specimens of four different shapes fabricated using FDM.
Table 2. The design and measured properties for manufactured specimens of four different shapes fabricated using FDM.
GeometryDesigned Mass (mg)Designed Surface Area (mm2)Surface-Area-to-Volume (mm−1)Average Measured Mass (mg)Mass Error (%)
Similar Mass
(M)		Solid Cylinder
(SC-M)		5023020.75487 ± 2.92.9
Hollow Cylinder 1
(HC1-M)		5016141.53500 ± 11.90.1
Hollow Cylinder 2
(HC2-M)		4973800.95500 ± 6.50.5
Hollow Cylinder 3
(HC3-M)		5046051.50503 ± 5.80.1
Similar Surface Area
(S)		Solid Cylinder
(SC-S)		9504660.61901 ± 10.55.1
Hollow Cylinder 1
(HC1-S)		3714661.57359 ± 2.63.2
Hollow Cylinder 2
(HC2-S)		7584660.77732 ± 8.53.4
Hollow Cylinder 3
(HC3-S)		3464681.69324 ± 10.96.2
Similar Surface-Area-to-Volume Ratio
(S/V)		Solid Cylinder
(SC-SV)		4973000.75470 ± 45.4
Hollow Cylinder 1
(HC1-SV)		9585280.75922 ± 12.33.8
Hollow Cylinder 2
(HC2-SV)		9816040.77910 ± 19.67.2
Hollow Cylinder 3
(HC3-SV)		14899230.771404 ± 35.15.6
Table 3. The slope, intercept, and coefficient of determination, R2, for the lines fitted to the data using linear regression.
Table 3. The slope, intercept, and coefficient of determination, R2, for the lines fitted to the data using linear regression.
SampleSlopeInterceptR2
SC-SV5.42−13.160.99
HC1-SV4.81−7.410.99
HC2-SV4.45−7.700.99
HC3-SV6.01−16.150.98
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MDPI and ACS Style

Afkhami, S.; Abdi, M.; Baserinia, R. Effect of Geometry on the Dissolution Behaviour of Complex Additively Manufactured Tablets. J. Manuf. Mater. Process. 2025, 9, 11. https://doi.org/10.3390/jmmp9010011

AMA Style

Afkhami S, Abdi M, Baserinia R. Effect of Geometry on the Dissolution Behaviour of Complex Additively Manufactured Tablets. Journal of Manufacturing and Materials Processing. 2025; 9(1):11. https://doi.org/10.3390/jmmp9010011

Chicago/Turabian Style

Afkhami, Seyedebrahim, Meisam Abdi, and Reza Baserinia. 2025. "Effect of Geometry on the Dissolution Behaviour of Complex Additively Manufactured Tablets" Journal of Manufacturing and Materials Processing 9, no. 1: 11. https://doi.org/10.3390/jmmp9010011

APA Style

Afkhami, S., Abdi, M., & Baserinia, R. (2025). Effect of Geometry on the Dissolution Behaviour of Complex Additively Manufactured Tablets. Journal of Manufacturing and Materials Processing, 9(1), 11. https://doi.org/10.3390/jmmp9010011

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