Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Rahban, Mahdie; Ahmad, Faizan; Piatyszek, Mieczyslaw A.; Haertlé, Thomas; Saso, Luciano; Saboury, Ali Akbar

doi:10.1039/d3ra06476j

Cited by 12 publications

(4 citation statements)

References 178 publications

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“…Protein-based therapeutics, vaccines, and enzymes have many important biomedical and biotechnological 1,2 applications. In most cases, it is desirable to stabilize the protein to prevent its unfolding or aggregation in non-native environments and improve product efficacy [3][4][5][6][7] . Experimental identification of stabilizing mutations is often time-consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding

Ganesan,

Mittal,

Bhat

et al. 2024

Preprint

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While many computational methods accurately predict destabilizing mutations, identifying stabilizing mutations has remained a challenge, due to their relative rarity. We tested DeltaDeltaG0 predictions from computational predictors such as Rosetta, ThermoMPNN, RaSP, and DeepDDG, using eighty-two mutants of the bacterial toxin CcdB as a test case. On this dataset, the best computational predictor is ThermoMPNN which identifies stabilizing mutations with a precision of 68%. However, the average increase in Tm for these predicted mutations was only 1 degrees C for CcdB, and predictions were poorer for a more challenging target, influenza neuraminidase. Using data from multiple previously described yeast surface display libraries and in vitro thermal stability measurements, we trained logistic regression models to identify stabilizing mutations with a precision of 90% and an average increase in Tm of 3 degrees C for CcdB. When such libraries contain a population of mutants with significantly enhanced binding relative to the corresponding wild type, there is no benefit in using computational predictors. It is then possible to predict stabilizing mutations without any training, simply by examining the distribution of mutational binding scores. This avoids laborious steps of in vitro expression, purification, and stability characterization. When this is not the case, combining data from computational predictors with high-throughput experimental binding data enhances the prediction of stabilizing mutations. However, this requires training on stability data measured in vitro with known stabilized mutants. It is thus feasible to predict stabilizing mutations rapidly and accurately for any system of interest that can be subjected to a binding selection or screen.

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Section: Introductionmentioning

confidence: 99%

Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding

Ganesan,

Mittal,

Bhat

et al. 2024

Preprint

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“…Protein or peptide aggregation is a concerning issue encountered in nearly all stages of drug development [ 1 , 2 ]. The occurrence of aggregation reduces physical stability and functionality of the peptide or protein in question, which not only may lead to a loss in activity but can also create critical problems such as toxicity and immunogenicity [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…The occurrence of aggregation reduces physical stability and functionality of the peptide or protein in question, which not only may lead to a loss in activity but can also create critical problems such as toxicity and immunogenicity [1]. Many factors can compromise the physical stability of therapeutic protein/peptides being developed including ionic strength, concentration, pH, and temperatures [1][2][3][4]. Therefore, the development of essential therapeutic protein/peptides such as glucagon, can be challenging and difficult due to their high tendency for aggregation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of aggregation profile of glucagon using SEC-HPLC and FFF-MALS methods

Bao,

Cheng,

Luo

et al. 2024

PLoS ONE

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Recently, the first generic glucagon for injection was approved for the treatment of severe hypoglycemia. Unlike its brand name recombinant glucagon, the generic glucagon is synthetic. Since glucagon has a high propensity to form aggregates in solution, it is essential to assess the aggregation profile of the synthetic glucagon compared to the recombinant glucagon. In this study, two robust separation methods, size-exclusion chromatography (SEC-HPLC) and field-flow fractionation coupled with a multi-angle light scattering detector (FFF-MALS), were employed to characterize generic and brand glucagon aggregation in six lots (three newly released, three expired). The presence of aggregation in samples was determined from the generated chromatograms and analyzed. The study showed that both products have comparable aggregation profiles. The SEC-HPLC demonstrated that in both glucagon versions, the expired lots had a higher percentage of dimers than the newly released lots, but even at expiration, the amount was negligible (∼0.1%). The FFF-MALS method did not detect any dimers or higher molecular weight aggregates. Further evaluation of the detection limit found that FFF-MALS was unable to detect aggregates at amounts lower than 0.5% of total glucagon. The negligible amounts of dimer detected in the generic and brand glucagon indicate that both versions are physically stable and are not prone to aggregation under clinically relevant conditions.

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“…Besides aggregation, chemical degradation such as deamidation, oxidation, and fragmentation can also take place, leading to significant changes in the tertiary structure of biologics. These degradation processes not only reduce the drug’s efficacy but can also induce immunogenic responses, posing risks to patient safety [ 4 , 5 , 6 ]. Excipients have been routinely used to increase the conformational stability of mAbs and to inhibit interfacial-induced aggregation.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Combined Effect of Hydroxypropyl Beta-Cyclodextrin (HPβCD) and Polysorbate in Monoclonal Antibody Formulation

Huang,

Hong,

Goh

et al. 2024

Pharmaceuticals

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Monoclonal antibodies require careful formulation due to their inherent stability limitations. Polysorbates are commonly used to stabilize mAbs, but they are prone to degradation, which results in unwanted impurities. KLEPTOSE® HPβCD (hydroxypropyl beta-cyclodextrin) has functioned as a stable stabilizer for protein formulations in our previous research. The current study investigates the collaborative impact of combining polysorbates and HPβCD as excipients in protein formulations. The introduction of HPβCD in formulations showed it considerably reduced aggregation in two model proteins, bevacizumab and ipilimumab, following exposure to various stress conditions. The diffusion interaction parameter revealed a reduction in protein–protein interactions by HPβCD. In bevacizumab formulations, the subvisible particle counts per 0.4 mL of samples in commercial formulations vs. formulations containing both HPβCD and polysorbates subjected to distinct stressors were as follows: agitation, 87,308 particles vs. 15,350 particles; light, 25,492 particles vs. 6765 particles; and heat, 1775 particles vs. 460 particles. Isothermal titration calorimetry (ITC) measurement indicated a weak interaction between PS 80 and HPβCD, with a KD value of 74.7 ± 7.5 µM and binding sites of 5 × 10–3. Surface tension measurements illustrated that HPβCD enhanced the surface activity of polysorbates. The study suggests that combining these excipients can improve mAb stability in formulations, offering an alternative for the biopharmaceutical industry.

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Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Abstract: In this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Cited by 12 publications

References 178 publications

Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding

Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding

Analysis of aggregation profile of glucagon using SEC-HPLC and FFF-MALS methods

Investigation on the Combined Effect of Hydroxypropyl Beta-Cyclodextrin (HPβCD) and Polysorbate in Monoclonal Antibody Formulation

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