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Calculation of injection forces for highly concentrated protein solutions
Accepted Manuscript Title: Calculation of Injection Forces for Highly Concentrated Protein Solutions Author: Ingo Fischer Astrid Schmidt Andrew Bryant Ahmed Besheer PII: DOI: Reference:
S0378-5173(15)30072-7 http://dx.doi.org/doi:10.1016/j.ijpharm.2015.07.054 IJP 15058
To appear in:
International Journal of Pharmaceutics
Received date: Revised date: Accepted date:
16-6-2015 16-7-2015 17-7-2015
Please cite this article as: Fischer, Ingo, Schmidt, Astrid, Bryant, Andrew, Besheer, Ahmed, Calculation of Injection Forces for Highly Concentrated Protein Solutions.International Journal of Pharmaceutics http://dx.doi.org/10.1016/j.ijpharm.2015.07.054 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Calculation of Injection Forces for Highly Concentrated Protein Solutions
Ingo Fischer1, Astrid Schmidt1, Andrew Bryant2, Ahmed Besheer1*,
1
Biologics Process Research and Development, 2Pharmaceutical and Analytical Development
Novartis Pharma AG, 4002 Basel, Switzerland
*Correspondence to: Ahmed Besheer, Tel: +41 61 6969513, Fax: +41 61 6969697, e-mail: [email protected].
Abstract Protein solutions often manifest a high viscosity at high solution concentrations, thus impairing injectability. Accordingly, accurate prediction of the injection force based on solution viscosity can greatly support protein formulation and device development. In this study, the sheardependent viscosity of three concentrated protein solutions is reported, and calculated injection forces obtained by two different mathematical models are compared against measured values. The results show that accurate determination of the needle dimensions and the shear-thinning behavior of the protein solutions is vital for injection force prediction. Additionally, one model delivered more accurate results, particularly for solutions with prominent shear-thinning behavior.
Many therapeutic proteins require high doses to achieve clinical efficacy (Chames et al., 2009). If self-administration by subcutaneous injection is desired, high protein concentrations (≥ 100 mg/mL) should be administered due to the limited injection volume (Heise et al., 2014; Narasimhan, 2009). These concentrated protein solutions can have a high viscosity, leading to high injection forces, thus hampering self-administration by prefilled syringes (PFS) or autoinjectors (Jezek et al., 2011). Accordingly, there is a need to identify the relation between solution viscosity and injection forces. Rathore et al. used the relation in Equation 1 to calculate injection forces. It is based on the Hagen-Poiseuille’s law for the relation between pressure drop and the volumetric flow rate.
Equation 1
where Finject is the injection force, Ffriction is the force needed to overcome friction, Fhydrodynamic is the hydrodynamic force, µLiquid is the viscosity of the liquid, Lneedle is needle length, rbarrel is the inner radius of the syringe barrel, rneedle is the inner radius of the needle and ν is the injection speed. Many concentrated protein solutions show shear-thinning behavior (Zarraga et al., 2013), where µLiquid decreases with increasing shear rate. The “power law” model can be used to describe the dependence of viscosity on shear rate
(see Equation 2).
Equation 2
where K is the flow consistency index and n is the power law index, with n = 1 for Newtonian liquids, and n<1 for shear-thinning solutions. For the latter, the maximum shear rate at the wall is dependent on shear behavior of the fluid (and accordingly on the power law index). Thus, the shear rate at the wall
is expressed by:
Equation 3
Rathore et al. reported Equations 1 to 3 to express the injection force for shear thinning fluids (Rathore et al., 2012; Rathore et al., 2011). In a recent study, Allmendinger et al. employed a slightly different approach, where they did not use the shear rate at the wall, but used an average shear rate, which takes into consideration the non-linear shear rate changes throughout the needle diameter, thus coming up with the “effective shear rate”
(Allmendinger et al.,
2014) :
Equation 4
Interestingly, the reports by Rathore et al. and Almendinger et al. prove their mathematical models using surrogate solutions only, but do not show the ability of the equations to predict the injection force for shear-thinning concentrated protein solutions. In this study, we report the viscosity of three concentrated protein solutions at high shear rates (up to 100,000 s-1), and compare the measured and calculated injection forces for both models over a wide range of viscosities, needle and syringe dimensions at pharmaceutically-relevant injection rates. Initially, the experimental setup was validated using Newtonian solutions, and the effect of different geometries and speeds on the injection force was investigated. These parameters included five solution viscosities (between 1 and 95 mPas), four different needle dimensions (Needle gauge: 23G, 25G, 27G and 30G, and Needle length: 3.8, 2.2, 1.8, and 1.9 cm, respectively), three injection rates (0.05, 0.1 and 0.2 mL/s corresponding to the injection of 1 mL in 20, 10 and 5 seconds, respectively), and two syringes with different dimensions (See Supporting Information). X-ray Tomography was employed to measure the needle dimensions. Figure 1 shows a tomograph of a 23G needle, with the actual needle length extending over the nominal length by the part buried in the needle hub. Additionally, the measured needle internal
diameters (IDs) are plotted together with the nominal range of diameters obtained from (SigmaAldrich, Last accessed on 09.06.2015). Results show that the ID can deviate significantly from the nominal diameter, probably due to the manufacturer’s employment of thin-walled needles, leading to larger ID. The experimental setup was validated applying Equation 1 and using the measured needle dimensions and reference solutions of known viscosity. The calculated injection forces were correlated to the measured injection forces for all the different combinations employed by determining the concordance correlation coefficient ρC (Lin, 1989). A good correlation is obtained between calculated and measured injection forces for all combinations using a 23G (Figure 2A, ρC = 0.98), a 25G (Figure 2B, ρC = 0.97) a 27G (Figure 2D, ρC = 0.97) or 30G needle (Figure 2C, ρC = 0.99). To calculate the injection force of concentrated protein solutions, the viscosity of three concentrated monoclonal antibody (mAB) solutions was measured at shear rates up to 100,000 s-1 (See Supporting information). In Figure 3, the measured viscosity vs. shear rate curves and the power law plots are depicted. Results show that mAB1 has a higher viscosity, with the onset of thinning starting at lower shear rate compared to mAB2. Meanwhile, mAB3 shows Newtonian behavior up to the highest measured shear rate of 100,000 s-1. The power law values of K and n in Table 1 were calculated by curve fitting through the data points in the shear thinning region using equation 2.
Equations 1 to 4 were used for calculating the injection force for the antibody solutions at varying injection speeds, and compared to the measured injection force. The two models described in the literature were compared for mAB1 and mAB2, while Equation 1 with a constant viscosity of a Newtonian fluid was used for mAB3. Figure 4 depicts the correlation between measured and calculated force for the three antibody solutions tested. For the shear-thinning protein solutions
(mAB1 and mAB2), the injection force calculated using the maximum shear rate at the wall (Equation 3) correlates well with the measured injection forces for all the tested conditions (ρC = 0.97 for mAB1 and 0.99 for mAB2). Meanwhile, using the “effective” shear rate (Equation 4) leads to overestimation of the injection force (ρC = 0.64 for mAB1 and 0.96 for mAB2). This overestimation is more pronounced for mAB1, due to its smaller “n”-value. This can be explained by the fact that Equation 4 calculates smaller shear rate with smaller “n”-value compared to Equation 3, and thus results in a higher calculated viscosity; and a higher calculated injection force. For the Newtonian mAB3 solution, the correlation between the calculated and measured injection forces leads to a ρC = 0.99. In summary, the presented data show that by using accurately determined needle dimensions and well-characterized shear-thinning viscosity behavior, the prediction of the injection force of highly concentrated protein solutions is possible. Using the shear rate at the wall (Equation 3) results in more accurate prediction of injection force compared to using the effective shear rate (Equation 4). This is of particular importance for solutions with more pronounced shear thinning properties, e.g. solutions with high concentration and high viscosity at low shear rates. The ability to accurately predict injection force can be of great help during development of protein drug products, allowing informed decision-making regarding the protein formulation and the injection device. For instance, once a target injection force is identified, it is possible to predict early during development whether a candidate protein formulation with a specific rheological profile is injectable using a particular syringe/needle within a pre-defined injection time. Additionally, if the solution viscosity is high, the above equations shed light on the possible design adaptations of the device to reduce the force and facilitate injection.
References Allmendinger, A., Fischer, S., Huwyler, B., Mahler, H.C., Schwarb, E., Zarraga, I.E., Mueller, R., 2014. Rheological characterization and injection forces of concentrated protein formulations: An alternative predictive model for non-Newtonian solutions. Eur J Pharm Biopharm 87, 318-328.
Chames, P., Van Regenmortel, M., Weiss, E., Baty, D., 2009. Therapeutic antibodies: successes, limitations and hopes for the future. Brit J Pharmacol 157, 220-233.
Heise, T., Nosek, L., Dellweg, S., Zijlstra, E., Praestmark, K.A., Kildegaard, J., Nielsen, G., Sparre, T., 2014. Impact of injection speed and volume on perceived pain during subcutaneous injections into the abdomen and thigh: a single center, randomised controlled trial. Diabet Obes Metab 10.1111/dom.12304.
Jezek, J., Rides, M., Derham, B., Moore, J., Cerasoli, E., Simler, R., Perez-Ramirez, B., 2011. Viscosity of concentrated therapeutic protein compositions. Adv Drug Deliver Rev 63, 11071117.
Lin, L.I., 1989. A Concordance Correlation-Coefficient to Evaluate Reproducibility. Biometrics 45, 255-268.
Narasimhan, C.M., H.; Shameem, M., 2009. High-dose monoclonal antibodies via the subcutaneous route: challenges and technical solutions, an industry perspective. Therapeutic Delivery 3, 889-900.
Rathore, N., Pranay, P., Bernacki, J., Eu, B., Ji, W.C., Walls, E., 2012. Characterization of protein rheology and delivery forces for combination products. J Pharm Sci-Us 101, 4472-4480. Rathore, N., Pranay, P., Eu, B., Ji, W., Walls, E., 2011. Variability in syringe components and its impact on functionality of delivery systems. PDA journal of pharmaceutical science and technology / PDA 65, 468-480.
Sigma-Aldrich;
Syringe
Needle
Gauge
Chart,
Last
accessed
on
09.06.2015..
http://www.sigmaaldrich.com/chemistry/stockroom-reagents/learning-center/technicallibrary/needle-gauge-chart.html
Zarraga, I.E., Taing, R., Zarzar, J., Luoma, J., Hsiung, J., Patel, A., Lim, F.J., 2013. High shear rheology and anisotropy in concentrated solutions of monoclonal antibodies. J.Pharm.Sci. 102, 2538-2549.
Figure Caption Figure 1. (A) X-ray tomography image of a 23G needle, showing the nominal needle length, the hub and the internal diameter, ID. (B) ID of different needles as measured by X-ray tomography, represented as black circles. The bars represent the nominal ID range according to (SigmaAldrich, Last accessed on 09.06.2015). Figure 2. Validation of experimental setup using reference solutions for needle gauges: (A) 23G, (B) 25G, (C) 27G (D) 30G. Figure 3. Shear-viscosity profile of mAB1, mAB2 and mAB3 measured at 20°C. Measured data points are shown in black. Red lines represent the curve fitting according to equation 2 in the shear thinning region for mAB1 and mAB2, or the constant viscosity of mAB3. Figure 2. Correlation between measured and calculated Injection Force for A) mAB1, B) mAB2 and C) mAB3. Black squares represent the injection force calculated using the shear rate at the wall (
according to Equation 3) and circles represent the forces calculated using the effective
shear rate ( used.
according to Equation 4). For mAB3, Equation 1 with a constant viscosity was
Figure 1 .
Figure 2 .
Figure 3 .
Figure 4 .
Graphical abstract .
Table 1. Power law parameters obtained from curve fitting using Equation 3 for mAb1 and mAB2, as well as viscosity of mAB3.
mAB
K
n
µ [mPa s]
mAB1
6748
0.570
-
mAB2
279.3
0.814
-
mAB3
-
-
8.06
S0378-5173(15)30072-7 http://dx.doi.org/doi:10.1016/j.ijpharm.2015.07.054 IJP 15058
To appear in:
International Journal of Pharmaceutics
Received date: Revised date: Accepted date:
16-6-2015 16-7-2015 17-7-2015
Please cite this article as: Fischer, Ingo, Schmidt, Astrid, Bryant, Andrew, Besheer, Ahmed, Calculation of Injection Forces for Highly Concentrated Protein Solutions.International Journal of Pharmaceutics http://dx.doi.org/10.1016/j.ijpharm.2015.07.054 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Calculation of Injection Forces for Highly Concentrated Protein Solutions
Ingo Fischer1, Astrid Schmidt1, Andrew Bryant2, Ahmed Besheer1*,
1
Biologics Process Research and Development, 2Pharmaceutical and Analytical Development
Novartis Pharma AG, 4002 Basel, Switzerland
*Correspondence to: Ahmed Besheer, Tel: +41 61 6969513, Fax: +41 61 6969697, e-mail: [email protected].
Abstract Protein solutions often manifest a high viscosity at high solution concentrations, thus impairing injectability. Accordingly, accurate prediction of the injection force based on solution viscosity can greatly support protein formulation and device development. In this study, the sheardependent viscosity of three concentrated protein solutions is reported, and calculated injection forces obtained by two different mathematical models are compared against measured values. The results show that accurate determination of the needle dimensions and the shear-thinning behavior of the protein solutions is vital for injection force prediction. Additionally, one model delivered more accurate results, particularly for solutions with prominent shear-thinning behavior.
Many therapeutic proteins require high doses to achieve clinical efficacy (Chames et al., 2009). If self-administration by subcutaneous injection is desired, high protein concentrations (≥ 100 mg/mL) should be administered due to the limited injection volume (Heise et al., 2014; Narasimhan, 2009). These concentrated protein solutions can have a high viscosity, leading to high injection forces, thus hampering self-administration by prefilled syringes (PFS) or autoinjectors (Jezek et al., 2011). Accordingly, there is a need to identify the relation between solution viscosity and injection forces. Rathore et al. used the relation in Equation 1 to calculate injection forces. It is based on the Hagen-Poiseuille’s law for the relation between pressure drop and the volumetric flow rate.
Equation 1
where Finject is the injection force, Ffriction is the force needed to overcome friction, Fhydrodynamic is the hydrodynamic force, µLiquid is the viscosity of the liquid, Lneedle is needle length, rbarrel is the inner radius of the syringe barrel, rneedle is the inner radius of the needle and ν is the injection speed. Many concentrated protein solutions show shear-thinning behavior (Zarraga et al., 2013), where µLiquid decreases with increasing shear rate. The “power law” model can be used to describe the dependence of viscosity on shear rate
(see Equation 2).
Equation 2
where K is the flow consistency index and n is the power law index, with n = 1 for Newtonian liquids, and n<1 for shear-thinning solutions. For the latter, the maximum shear rate at the wall is dependent on shear behavior of the fluid (and accordingly on the power law index). Thus, the shear rate at the wall
is expressed by:
Equation 3
Rathore et al. reported Equations 1 to 3 to express the injection force for shear thinning fluids (Rathore et al., 2012; Rathore et al., 2011). In a recent study, Allmendinger et al. employed a slightly different approach, where they did not use the shear rate at the wall, but used an average shear rate, which takes into consideration the non-linear shear rate changes throughout the needle diameter, thus coming up with the “effective shear rate”
(Allmendinger et al.,
2014) :
Equation 4
Interestingly, the reports by Rathore et al. and Almendinger et al. prove their mathematical models using surrogate solutions only, but do not show the ability of the equations to predict the injection force for shear-thinning concentrated protein solutions. In this study, we report the viscosity of three concentrated protein solutions at high shear rates (up to 100,000 s-1), and compare the measured and calculated injection forces for both models over a wide range of viscosities, needle and syringe dimensions at pharmaceutically-relevant injection rates. Initially, the experimental setup was validated using Newtonian solutions, and the effect of different geometries and speeds on the injection force was investigated. These parameters included five solution viscosities (between 1 and 95 mPas), four different needle dimensions (Needle gauge: 23G, 25G, 27G and 30G, and Needle length: 3.8, 2.2, 1.8, and 1.9 cm, respectively), three injection rates (0.05, 0.1 and 0.2 mL/s corresponding to the injection of 1 mL in 20, 10 and 5 seconds, respectively), and two syringes with different dimensions (See Supporting Information). X-ray Tomography was employed to measure the needle dimensions. Figure 1 shows a tomograph of a 23G needle, with the actual needle length extending over the nominal length by the part buried in the needle hub. Additionally, the measured needle internal
diameters (IDs) are plotted together with the nominal range of diameters obtained from (SigmaAldrich, Last accessed on 09.06.2015). Results show that the ID can deviate significantly from the nominal diameter, probably due to the manufacturer’s employment of thin-walled needles, leading to larger ID. The experimental setup was validated applying Equation 1 and using the measured needle dimensions and reference solutions of known viscosity. The calculated injection forces were correlated to the measured injection forces for all the different combinations employed by determining the concordance correlation coefficient ρC (Lin, 1989). A good correlation is obtained between calculated and measured injection forces for all combinations using a 23G (Figure 2A, ρC = 0.98), a 25G (Figure 2B, ρC = 0.97) a 27G (Figure 2D, ρC = 0.97) or 30G needle (Figure 2C, ρC = 0.99). To calculate the injection force of concentrated protein solutions, the viscosity of three concentrated monoclonal antibody (mAB) solutions was measured at shear rates up to 100,000 s-1 (See Supporting information). In Figure 3, the measured viscosity vs. shear rate curves and the power law plots are depicted. Results show that mAB1 has a higher viscosity, with the onset of thinning starting at lower shear rate compared to mAB2. Meanwhile, mAB3 shows Newtonian behavior up to the highest measured shear rate of 100,000 s-1. The power law values of K and n in Table 1 were calculated by curve fitting through the data points in the shear thinning region using equation 2.
Equations 1 to 4 were used for calculating the injection force for the antibody solutions at varying injection speeds, and compared to the measured injection force. The two models described in the literature were compared for mAB1 and mAB2, while Equation 1 with a constant viscosity of a Newtonian fluid was used for mAB3. Figure 4 depicts the correlation between measured and calculated force for the three antibody solutions tested. For the shear-thinning protein solutions
(mAB1 and mAB2), the injection force calculated using the maximum shear rate at the wall (Equation 3) correlates well with the measured injection forces for all the tested conditions (ρC = 0.97 for mAB1 and 0.99 for mAB2). Meanwhile, using the “effective” shear rate (Equation 4) leads to overestimation of the injection force (ρC = 0.64 for mAB1 and 0.96 for mAB2). This overestimation is more pronounced for mAB1, due to its smaller “n”-value. This can be explained by the fact that Equation 4 calculates smaller shear rate with smaller “n”-value compared to Equation 3, and thus results in a higher calculated viscosity; and a higher calculated injection force. For the Newtonian mAB3 solution, the correlation between the calculated and measured injection forces leads to a ρC = 0.99. In summary, the presented data show that by using accurately determined needle dimensions and well-characterized shear-thinning viscosity behavior, the prediction of the injection force of highly concentrated protein solutions is possible. Using the shear rate at the wall (Equation 3) results in more accurate prediction of injection force compared to using the effective shear rate (Equation 4). This is of particular importance for solutions with more pronounced shear thinning properties, e.g. solutions with high concentration and high viscosity at low shear rates. The ability to accurately predict injection force can be of great help during development of protein drug products, allowing informed decision-making regarding the protein formulation and the injection device. For instance, once a target injection force is identified, it is possible to predict early during development whether a candidate protein formulation with a specific rheological profile is injectable using a particular syringe/needle within a pre-defined injection time. Additionally, if the solution viscosity is high, the above equations shed light on the possible design adaptations of the device to reduce the force and facilitate injection.
References Allmendinger, A., Fischer, S., Huwyler, B., Mahler, H.C., Schwarb, E., Zarraga, I.E., Mueller, R., 2014. Rheological characterization and injection forces of concentrated protein formulations: An alternative predictive model for non-Newtonian solutions. Eur J Pharm Biopharm 87, 318-328.
Chames, P., Van Regenmortel, M., Weiss, E., Baty, D., 2009. Therapeutic antibodies: successes, limitations and hopes for the future. Brit J Pharmacol 157, 220-233.
Heise, T., Nosek, L., Dellweg, S., Zijlstra, E., Praestmark, K.A., Kildegaard, J., Nielsen, G., Sparre, T., 2014. Impact of injection speed and volume on perceived pain during subcutaneous injections into the abdomen and thigh: a single center, randomised controlled trial. Diabet Obes Metab 10.1111/dom.12304.
Jezek, J., Rides, M., Derham, B., Moore, J., Cerasoli, E., Simler, R., Perez-Ramirez, B., 2011. Viscosity of concentrated therapeutic protein compositions. Adv Drug Deliver Rev 63, 11071117.
Lin, L.I., 1989. A Concordance Correlation-Coefficient to Evaluate Reproducibility. Biometrics 45, 255-268.
Narasimhan, C.M., H.; Shameem, M., 2009. High-dose monoclonal antibodies via the subcutaneous route: challenges and technical solutions, an industry perspective. Therapeutic Delivery 3, 889-900.
Rathore, N., Pranay, P., Bernacki, J., Eu, B., Ji, W.C., Walls, E., 2012. Characterization of protein rheology and delivery forces for combination products. J Pharm Sci-Us 101, 4472-4480. Rathore, N., Pranay, P., Eu, B., Ji, W., Walls, E., 2011. Variability in syringe components and its impact on functionality of delivery systems. PDA journal of pharmaceutical science and technology / PDA 65, 468-480.
Sigma-Aldrich;
Syringe
Needle
Gauge
Chart,
Last
accessed
on
09.06.2015..
http://www.sigmaaldrich.com/chemistry/stockroom-reagents/learning-center/technicallibrary/needle-gauge-chart.html
Zarraga, I.E., Taing, R., Zarzar, J., Luoma, J., Hsiung, J., Patel, A., Lim, F.J., 2013. High shear rheology and anisotropy in concentrated solutions of monoclonal antibodies. J.Pharm.Sci. 102, 2538-2549.
Figure Caption Figure 1. (A) X-ray tomography image of a 23G needle, showing the nominal needle length, the hub and the internal diameter, ID. (B) ID of different needles as measured by X-ray tomography, represented as black circles. The bars represent the nominal ID range according to (SigmaAldrich, Last accessed on 09.06.2015). Figure 2. Validation of experimental setup using reference solutions for needle gauges: (A) 23G, (B) 25G, (C) 27G (D) 30G. Figure 3. Shear-viscosity profile of mAB1, mAB2 and mAB3 measured at 20°C. Measured data points are shown in black. Red lines represent the curve fitting according to equation 2 in the shear thinning region for mAB1 and mAB2, or the constant viscosity of mAB3. Figure 2. Correlation between measured and calculated Injection Force for A) mAB1, B) mAB2 and C) mAB3. Black squares represent the injection force calculated using the shear rate at the wall (
according to Equation 3) and circles represent the forces calculated using the effective
shear rate ( used.
according to Equation 4). For mAB3, Equation 1 with a constant viscosity was
Figure 1 .
Figure 2 .
Figure 3 .
Figure 4 .
Graphical abstract .
Table 1. Power law parameters obtained from curve fitting using Equation 3 for mAb1 and mAB2, as well as viscosity of mAB3.
mAB
K
n
µ [mPa s]
mAB1
6748
0.570
-
mAB2
279.3
0.814
-
mAB3
-
-
8.06