Peer Review Article | Open Access | Published 29th September 2026 | Submitted 11th August 2026
The Effects of Freeze-Thaw Practices on Microorganisms in Pharmaceutical Preparations
Karen Capper, AstraZeneca, Macclesfield, UK | EJPPS | 313, (2026) | Cite this article
Abstract
This study evaluated the effects of single and repeated freeze-thaw events on the viability of representative pharmaceutical microorganisms suspended in tryptone soya broth (TSB), purified water (PW), and phosphate-buffered saline (PBS). Eight microorganisms were stored at -20°C and assessed following either a single freeze-thaw event after four weeks or repeated weekly freeze-thaw cycling over four weeks using membrane filtration and colony enumeration. Repeated freeze-thaw cycling generally caused greater viability loss than a single freeze-thaw event, although responses varied by organism and matrix. Candida albicans was the most susceptible organism, declining to non-detectable levels in TSB and PBS, while Micrococcus luteus and Aspergillus brasiliensis showed greater resilience. Statistical analysis confirmed significant effects of freeze-thaw cycling on recovery for Staphylococcus aureus, C. albicans, and A. brasiliensis (p < 0.01). These findings demonstrate that freeze-thaw exposure reduces, but does not necessarily eliminate, microbial survival. Repeated cycling is generally more damaging than a single thaw, and microbial responses depend on both organism type and matrix composition. The results provide useful evidence to support microbiological risk assessments and thaw-handling controls during pharmaceutical development and manufacture.
Introduction
Microorganisms pose a significant risk to product quality, patient safety and regulatory compliance during drug product manufacturing (Tavares et al., 2020; Kumar et al., 2024). Solutions and suspensions are commonly stored frozen to suppress microbial proliferation. However, they may require thawing for activities such as batch manufacture, visual inspection, labelling and packaging. In practice, these operations may involve repeated freeze-thaw cycles and ambient handling windows, creating opportunities for contamination events to influence microbial proliferation. Despite the widespread use of frozen storage for pharmaceutical preparations, such as biologics, and in early-phase development, the microbiological consequences of thaw-handling remain insufficiently identified, resulting in uncertainty and inconsistency in microbial risk assessment (Gunnarsdóttir et al., 2012; Rayfield et al., 2017).
Freeze-thaw injury is generally attributed to ice-crystal formation, osmotic stress and cold-shock damage (He et al., 2025). Ice crystals can disrupt membranes and other cellular structures, leading to leakage of intracellular components and loss of cultivability. Freezing also concentrates extracellular solutes, increasing osmotic pressure and driving water out of cells, which may cause dehydration, shrinkage and structural injury. In addition, exposure to low and subzero temperatures has been associated with changes in membrane permeability, cell wall damage and DNA lesions (Beuchat, 2008; Song et al., 2017; Chen et al., 2022). These effects may be intensified when samples undergo repeated freeze-thaw cycling rather than a single thaw, as cumulative injury can arise over multiple episodes of freezing and recrystallisation (Walker et al., 2006).
The microorganisms selected reflect pharmacopeial method-suitability strains and credible pharmaceutical contaminants (USP <61>, Eur. Ph. 2.6.12). Staphylococcus aureus and Micrococcus luteus represent human commensals that may contaminate pharmaceutical environments. M. luteus can form dormant structures which enable the cells to survive long periods under adverse environmental conditions (Kaprelyants and Kell, 1993).
Water-associated Gram-negatives are pertinent to aqueous systems: Escherichia. coli was used due to its rapid growth rates. Pseudomonas aeruginosa and Burkholderia cepacia for persistence in low-nutrient purified water systems and links to contamination events (Price and Wildeboer, 2017; Thi, Wibowo and Rehm, 2020; Tavares et al., 2020). Fungal risks include Candida albicans (skin/mucosa yeast) and Aspergillus brasiliensis (environmental mould used in compendial tests). Bacillus subtilis, a spore former, was included for stress resistance was included as being representative of common GPR environment, air and dust contaminants (Wong Sak Hoi, Beau and Latgé, 2012). This panel enables freeze-thaw comparisons across major microbial classes.
Microbial survival during freeze-thaw exposure is influenced by cell structure and stress tolerance. Gram-positive bacteria such as S. aureus and M. luteus may better tolerate freeze-thaw stress than Gram-negative organisms because of their thicker peptidoglycan layers and differing membrane composition (Mohammadipanah et al., 2017). By contrast, Gram-negative rods such as E. coli, P. aeruginosa and B. cepacia may be more vulnerable to membrane disruption, although environmental specialists such as Pseudomonas and Burkholderia may possess adaptive responses that moderate these effects (Ray, 1883; Zhang Ru et al., 2021). B. subtilis may also display increased resilience because of its ability to form endospores, while A. brasiliensis may retain survival through the environmental tolerance of fungal spores and conidia (Sakamoto et al., 2000; Wong Sak Hoi, Beau and Latgé, 2012). Yeasts such as C. albicans show variable tolerance depending on membrane lipid composition and stress response pathways, but in the absence of cryoprotectants is generally associated with reduced cultivability (Rybalkin et al., 2023).
The suspending medium is also likely to be a critical determinant of freeze-thaw outcome. Tryptone Soya Broth (TSB) represents a nutrient-rich medium widely used in compendial testing and may provide protective solutes and proteins that improve post-thaw recovery (Murray and Gibson., 2022). The medium TSB was selected to represent media used in biological products. Purified water (PW) is a non-protective aqueous matrix with low ionic strength and no nutritive support, which may amplify osmotic stress and membrane damage (Fowler and Toner., 2006). Defined buffers control pH and ionic strength without supplying nutrients. Although freeze-thaw injury has been studied in food, environmental and cryopreservation settings, these findings are not directly transferable to pharmaceutical systems, where thaw-handling occurs in operational rather than experimental contexts.
Therefore, this study aimed to evaluate the effect of repeated freeze-thaw cycling and a single freeze-thaw event on the cultivability of representative microorganisms suspended in TSB, purified water and buffer. It was hypothesised that repeated cycling would result in greater loss of cultivability than a single thaw, and that survival would be highest in TSB and lowest in purified water. By comparing organism-specific responses across matrices, this study sought to generate evidence to support microbial risk assessment during frozen storage and thaw-handling operations.
Project Aims and Methodology
This study aimed to determine how a single freeze-thaw event and repeated freeze-thaw cycling affect the viability of representative microorganisms in TSB, PW, and a buffer, generating evidence to support microbiological risk assessments for manufacturing, labelling, and product storage, including processes requiring multiple thaws. Specifically, changes in viable counts will be quantified after 4 weeks of frozen storage followed by a single ambient thaw, and over 1-, 2-, 3, and 4-week freeze-thaw cycling with ambient thaw at each interval; assessing survival outcomes and microbial resilience across organisms and media using ≥3 replicates per organism to enable tests of statistical significance; and evaluate whether media type influences survival, applying appropriate statistical analyses to compare outcomes across conditions.
Matrix preparation
Buffer PBS was prepared at pH 6.5 by dissolving sodium phosphate dibasic dihydrate (14.24 g) and sodium chloride (56.0 g) in 7.5 L PW, adjusting pH to 6.50 ± 0.05 at ambient temperature with dilute HCl/NaOH. The solution was brought to volume with PW and pH/conductivity recorded. Buffer was dispensed into clean containers for immediate use. TSB and PW were prepared for use as comparator matrices. 500ml of each matrix was dispensed into sterile 650 mL polypropylene (Nalgene) bottles, leaving headspace. Bottles were labelled with organism, matrix and timepoint schedule
Inoculum preparation and dosing
For Micrococcus luteus a fresh purity plate (approved ID score >1.75) was used to inoculate 10 mL TSB; cultures were incubated overnight at 30-35°C. Ten‑fold serial dilutions to 105 were prepared in Buffered sodium chloride peptone (BSCP). Cell concentration was estimated with a µCount3D counter. A dilution yielding 50 CFU per 10 mL assay volume was selected, and 1.5 mL was added aseptically to each 500 mL bottle.
For Burkholderia cepacia, an overnight 10 mL TSB culture at 30-35°C was prepared, diluted to 104 in BSCP and quantified by µCount3D. A dilution targeting 50 CFU per 10 mL was selected; 2.0 mL was added aseptically to each 500 mL bottle. For the remaining organisms: One 108 CFU Bioball was dissolved in 1 L BSCP to generate a working suspension. 25µl of this suspension was added aseptically to each 500 mL bottle to achieve the target inoculum (50 CFU/10 mL). Bottles were mixed by gentle inversion.
Freeze-thaw designs
Cycling: Bottles were thawed at ambient temperature at weekly intervals (T1-T4). Upon complete thaw, 10 mL was sampled for enumeration, then the bottle was returned to -20°C within the same session. Single freeze-thaw: Parallel bottles were held at -20°C from T0 until T4, then thawed at ambient temperature and sampled (10 mL).
Enumeration and statistical analysis
Each 10 mL sample was filtered through a 0.45 µm membrane. Filters were transferred to TSA plates and incubated at 30-35°C for up to 5 days. Colonies were counted daily. Triplicate datasets were available for S. aureus, C. albicans and A. brasiliensis. The remaining microorganisms were included in the descriptive analysis to characterise the overall biological response to freeze-thaw exposure; however, a two-sided one-way sign test was applied to the T0-T4 paired differences. Full statistical outputs are presented in Appendix A (Tables A1-A4). Changes in recovery across T0-T4 during repeated freeze-thaw cycling were analysed using repeated-measures ANOVA on log10(CFU + 1) transformed data. Matrix effects were assessed by calculating log reduction from T0 to T4, defined as log10(T0 + 1) - log10(T4 + 1), and comparing these values across TSB, Buffer and PW using one-way ANOVA. Kruskal-Wallis testing was performed as a non-parametric sensitivity analysis for matrix comparisons (Appendix A, Table A4).
Results
Overview of microbial recovery
Repeated freeze-thaw cycling reduced microbial recovery across most organisms, although the extent of reduction varied by organism and matrix (Figure 1). C. albicans was the most susceptible, with recovery declining to zero in TSB and buffer and to near-zero levels in PW by T4. A. brasiliensis decreased across all matrices but remained detectable at T4, indicating greater tolerance to repeated cycling. S. aureus showed the clearest matrix-dependent pattern, with recovery maintained in TSB but reduced to zero in buffer and PW. Descriptively, E. coli, B. cepacia and P. aeruginosa also showed marked reductions, particularly in PW and buffer. M. luteus appeared comparatively more resilient, while B. subtilis showed a more variable response.
A single freeze-thaw event also reduced recovery, although reductions were generally smaller than those observed under repeated cycling (Figure 2). S. aureus was largely preserved in TSB but decreased in buffer and PW. C. albicans again showed substantial susceptibility, reaching non-detectable levels in TSB and buffer while remaining detectable in PW. A. brasiliensis remained detectable across all matrices following a single thaw. Similar descriptive declines were observed for E. coli, B. cepacia and P. aeruginosa, whereas M. luteus remained comparatively tolerant and B. subtilis again showed mixed outcomes.
Cycling versus single freeze-thaw
Comparison of T0-T4 change showed that repeated cycling generally produced greater loss than a single freeze-thaw event (Figure 3). In buffer, all eight organisms showed greater reduction under repeated cycling than under a single thaw, and this directional effect was significant (sign test, p = 0.008). In PW and TSB, six of eight organisms showed greater reduction under repeated cycling, but these differences were not significant (p = 0.289 for both). This indicates that cumulative freeze-thaw injury was most consistent in buffer.
Effect of repeated freeze-thaw cycling on Microbial recovery
Repeated freeze-thaw cycling significantly affected recovery in all replicated organism-matrix combinations (Appendix A, Table A1). For S. aureus, a significant effect of cycle number was observed in TSB (F(4,8) = 23.07, p = 0.00019), Buffer (F(4,8) = 154.63, p < 0.001) and PW (F(4,8) = 278.05, p < 0.001). In TSB, mean recovery increased from 45.0 ± 10.39 CFU at T0 to 68.67 ± 11.55 CFU at T4, whereas recovery decreased to zero in Buffer and PW.
For C. albicans, significant effects of cycle number were observed in TSB (F(4,8) = 257219.44, p < 0.001), Buffer (F(4,8) = 107.42, p < 0.001) and PW (F(4,8) = 33.14, p = 0.00005) (Appendix A, Table A1). Recovery fell to zero in TSB and Buffer and to near-zero levels in PW by T4.
For A. brasiliensis, repeated freeze-thaw cycling also had a significant effect in TSB (F(4,8) = 9.18, p = 0.0044), Buffer (F(4,8) = 20.19, p = 0.00031) and PW (F(4,8) = 38.90, p = 0.000027) (Appendix A, Table A1). Recovery decreased across all matrices but remained detectable at T4.
Effect of matrix on viability loss
During repeated freeze-thaw cycling, matrix had a significant effect on viability loss for S. aureus (F = 6918.70, p < 0.001, η² = 0.9996) and A. brasiliensis (F = 7.36, p = 0.024, η² = 0.7104) (Figure 4; Appendix A, Table A2). For S. aureus, mean log reduction values were -0.183 in TSB, 1.729 in Buffer and 1.696 in PW. For A. brasiliensis, mean log reduction values were 0.457 in TSB, 0.405 in Buffer and 0.630 in PW. No significant matrix effect was detected for C. albicans (F = 1.62, p = 0.274, η² = 0.3506), although substantial loss was observed in all matrices.
Following a single freeze-thaw event, matrix significantly influenced viability loss for all three replicated organisms (Figure 5; Appendix A, Table A3). For S. aureus, the matrix effect was significant (F = 44.63, p < 0.001, η² = 0.9370), with lower loss in TSB than in Buffer or PW. For C. albicans, matrix also had a significant effect (F = 339.63, p < 0.001, η² = 0.9912), with lower loss in PW than in TSB or Buffer. For A. brasiliensis, matrix significantly affected viability loss (F = 103.85, p < 0.001, η² = 0.9719), with the lowest reduction observed in TSB and the highest in Buffer.
Discussion
Organism-specific responses to freeze-thaw
The results showed clear organism-specific differences in freeze-thaw tolerance, likely reflecting variation in cell envelope structure and physiological resilience to sublethal injury.
Among the Gram-positive cocci, S. aureus showed the strongest matrix dependence. Recovery remained high in TSB but fell to zero by T4 in both Buffer and PW. This is consistent with the protective role of the thick peptidoglycan cell wall of Gram-positive bacteria, which may reduce osmotic injury, while the nutrients of TSB may further support recovery (Borisova et al., 2016; Parvin et al 2023; Harris et al., 2024). M. luteus also decreased across matrices, but the reduction appeared more gradual than in several Gram-negative organisms, suggesting some tolerance to freeze-thaw stress (Simon et al., 2015).
The Gram-negative bacteria were generally more susceptible, particularly in Buffer and PW. E. coli and B. cepacia showed pronounced reductions and, in several conditions, fell below detectable levels. This aligns with reports that the Gram-negative outer membrane is especially vulnerable to freeze-induced permeability changes and structural disruption (Zhang Ru et al., 2021). P. aeruginosa showed an intermediate pattern, particularly in TSB, where recovery decreased but persisted. This may reflect the barrier properties of its outer membrane, including the contribution of lipopolysaccharide to membrane stability (Ray, 1983; Weber et al., 2003).
The fungal responses differed by organism. C. albicans was among the most susceptible microorganisms tested, particularly during repeated cycling. This indicates pronounced sensitivity to freeze-thaw stress (Rybalkin et al., 2023). In contrast, A. brasiliensis retained measurable viability across all matrices even after repeated cycling, indicating substantially greater resilience. This is consistent with the environmental tolerance of filamentous fungal spores, which are generally better able to withstand osmotic stress than vegetative cells (Wong Sak Hoi, Beau and Latgé, 2012). B. subtilis showed reduced counts across all matrices, although the decline was less extreme than some Gram-negative organisms. This may reflect the mixed nature of B. subtilis populations, where vegetative cells are vulnerable to freezing stress, but endospores are highly resistant (Sakamoto et al., 2000).
Effect of matrix on viability loss
The statistical analysis confirmed that matrix composition influenced microbial viability, although the strength of this effect varied between organisms. For cycling, matrix had a distinct effect on S. aureus, with TSB as expected showing substantially lower log reduction than Buffer and PW, indicating a protective effect. By contrast, no significant matrix effect was detected for C. albicans during repeated cycling, despite PW appearing slightly less damaging, suggesting that all three matrices were associated with substantial loss of viability. For A. brasiliensis, matrix also influenced recovery, with PW associated with greater loss than TSB or Buffer. This pattern is consistent with literature indicating that the physicochemical properties of the surrounding medium and nutrient availability, can strongly influence survival during freeze-thaw (Mohammadipanah et al., 2017; Hallsworth et al., 2021). Critically, the persistence and occasional increase of Gram‑positives in TSB during cycling highlights the importance of selecting storage and handling conditions that are appropriate for the matrix in which the drug it is suspended in.
Following a single freeze-thaw, matrix effects were again evident, but the pattern remained organism-specific. TSB provided the greatest protection for S. aureus, while PW appeared more protective for C. albicans than either TSB or Buffer. For A. brasiliensis, TSB was again the most protective and Buffer the least protective. Collectively, these findings suggest that no single matrix was universally optimal across all microorganisms. Instead, matrix performance depended on the interaction between the physiological characteristics of the organism and the physicochemical properties of the suspending medium (E. Jingjing et al., 2020).
Repeated freeze-thaw cycling compared with a single freeze-thaw event
Comparison of the T0-T4 reduction under repeated cycling following a single freeze-thaw indicates that repeated cycling was generally more damaging than a single thaw, although the consistency of this pattern differed by matrix. In Buffer, all eight microorganisms showed larger T0-T4 reductions under repeated cycling than following a single freeze-thaw event, indicating a highly consistent directional effect in a non-nutritive ionic environment. This is consistent with previous studies showing that repeated freeze-thaw cycles result in cumulative cellular damage and significantly greater reductions in viability compared to a single freezing event (Saliba et al., 2020). Overall, the data support the conclusion that repeated freeze-thaw exposure generally results in greater loss of cultivability than a single thaw, but that the extent of this effect is strongly moderated by matrix composition.
Conclusion
The findings show no microbial proliferation was observed during thawing or while thawed under the conditions studied (-20°C storage, closed containers, ambient thaw). Across organisms, repeated freeze-thaw cycling produced larger T0→T4 losses than a single thaw, but the magnitude of loss depended on both the microorganism and the suspending matrix. Nutrient‑rich TSB was most permissive (e.g., Gram‑positives persisted), whereas purified water and buffer were more damaging, and mould conidia remained detectable across matrices. Gram negative organisms are a concern in aqueous environments and non-sterile water-based products because of the potential for these microorganisms to be present in pharmaceutical water systems. The study data shows that overall, Gram-negative organisms were more susceptible to the effects of freeze/thaw and single thaw. Even at high numbers B.cepacia died off rapidly. Taken together, these results support the freeze-thaw handling steps used from a microbial proliferation standpoint. The dataset provides an evidence‑based foundation for justifying freeze-thaw stages during manufacture and storage.
Future work
Future studies should investigate a wider range of operational conditions relevant to pharmaceutical handling. These could include different thaw durations, storage temperatures and greater numbers of freeze-thaw cycles. As the present findings suggest that repeated cycling is generally more damaging than a single thaw, further work should also examine the role of matrix composition in greater detail. It would be useful to determine which components of TSB contribute most strongly to improved post-thaw recovery. Finally, extending the work to include product formulations would improve the direct applicability of the findings to early-stage drug development and manufacturing environments.
Appendix
Appendix A
Table A1. Repeated-measures ANOVA of log10-transformed CFU counts across freeze-thaw cycles (T0-T4) for S. aureus, C. albicans and A. brasiliensis in TSB, Buffer and PW.
Organism | Matrix | F(df = 4,8) | p value | Mean T0 CFU ± SD | Mean T4 CFU ± SD |
S. aureus | TSB | 23.07 | 0.00019 | 45.0 ± 10.39 | 68.67 ± 11.55 |
S. aureus | Buffer | 154.63 | <0.001 | 52.67 ± 2.08 | 0.00 ± 0.00 |
S. aureus | PW | 278.05 | <0.001 | 48.67 ± 3.06 | 0.00 ± 0.00 |
C. albicans | TSB | 257219.44 | <0.001 | 43.67 ± 0.58 | 0.00 ± 0.00 |
C. albicans | Buffer | 107.42 | <0.001 | 45.33 ± 21.78 | 0.00 ± 0.00 |
C. albicans | PW | 33.14 | 0.00005 | 48.67 ± 1.16 | 1.67 ± 1.53 |
A. brasiliensis | TSB | 9.18 | 0.0044 | 45.00 ± 6.00 | 15.00 ± 1.00 |
A. brasiliensis | Buffer | 20.19 | 0.00031 | 51.00 ± 6.08 | 19.67 ± 4.16 |
A. brasiliensis | PW | 38.9 | 0.000027 | 40.33 ± 4.16 | 8.67 ± 0.58 |
Table A2. One-way ANOVA of log reduction values comparing the effect of matrix on viability loss during repeated freeze-thaw cycling.
Organism | Mean log reduction, TSB | Mean log reduction, Buffer | Mean log reduction, PW | F | p value | η² |
S. aureus | -0.183 | 1.729 | 1.696 | 6918.7 | <0.001 | 0.9996 |
C. albicans | 1.65 | 1.624 | 1.336 | 1.62 | 0.274 | 0.3506 |
A. brasiliensis | 0.457 | 0.405 | 0.63 | 7.36 | 0.024 | 0.7104 |
Table A3. One-way ANOVA of log reduction values comparing the effect of matrix on viability loss following a single freeze-thaw event.
Organism | Mean log reduction, TSB | Mean log reduction, Buffer | Mean log reduction, PW | F | p value | η² |
S. aureus | 0.012 | 1.318 | 1.143 | 44.63 | <0.001 | 0.937 |
C. albicans | 1.435 | 1.454 | 0.514 | 339.63 | <0.001 | 0.9912 |
A. brasiliensis | 0.42 | 1.454 | 0.581 | 103.85 | <0.001 | 0.9719 |
Table A4. Non-parametric analysis of matrix effects using Kruskal-Wallis testing.
Design | Organism | H | p value |
Cycling | S. aureus | 6.31 | 0.0427 |
Cycling | C. albicans | 2.24 | 0.3261 |
Cycling | A. brasiliensis | 5.6 | 0.0608 |
Single freeze-thaw | S. aureus | 5.6 | 0.0608 |
Single freeze-thaw | C. albicans | 5.6 | 0.0608 |
Single freeze-thaw | A. brasiliensis | 5.96 | 0.0509 |
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Author Information
Corresponding Author: Karen Capper,
AstraZeneca, Macclesfield, UK
Email: karen.capper@astrazeneca.com
Telephone: +44 7384 438531
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