Multi-dimensional population balance model development using a breakage mode probability kernel for prediction of multiple granule attributes

References

• Ansari MA, Stepanek F. 2008. The effect of granule microstructure on dissolution rate. Powder Technol. 181(2):104–114. doi: 10.1016/j.powtec.2006.12.012.
   Web of Science ®Google Scholar
• Barrasso D, El Hagrasy A, Litster JD, Ramachandran R. 2015. Multi-dimensional population balance model development and validation for a twin screw granulation process. Powder Technol. 270:612–621. doi: 10.1016/j.powtec.2014.06.035.
   Web of Science ®Google Scholar
• Barrasso D, Oka S, Muliadi A, Litster JD, Wassgren C, Ramachandran R. 2013. Population balance model validation and predictionof cqas for continuous milling processes: toward qbdin pharmaceutical drug product manufacturing. J Pharm Innov. 8(3):147–162. doi: 10.1007/s12247-013-9155-0.
   Web of Science ®Google Scholar
• Barrasso D, Ramachandran R. 2012. A comparison of model order reduction techniques for a four-dimensional population balance model describing multi-component wet granulation processes. Chem Eng Sci. 80:380–392. doi: 10.1016/j.ces.2012.06.039.
   Web of Science ®Google Scholar
• Barrasso D, Tamrakar A, Ramachandran R. 2014. A reduced order pbm–ann model of a multi-scale pbm–dem description of a wet granulation process. Chem Eng Sci. 119:319–329. doi: 10.1016/j.ces.2014.08.005.
   Web of Science ®Google Scholar
• Bilgili E, Scarlett B. 2005. Population balance modeling of non-linear effects in milling processes. Powder Technol. 153(1):59–71. doi: 10.1016/j.powtec.2005.02.005.
   Web of Science ®Google Scholar
• Boukouvala F, Gao Y, Muzzio F, Ierapetritou MG. 2013. Reduced-order discrete element method modeling. Chem Eng Sci. 95:12–26. doi: 10.1016/j.ces.2013.01.053.
   Web of Science ®Google Scholar
• Capece M, Bilgili E, Dave R. 2011. Identification of the breakage rate and distribution parameters in a non-linear population balance model for batch milling. Powder Technol. 208(1):195–204. doi: 10.1016/j.powtec.2010.12.019.
   Web of Science ®Google Scholar
• Capece M, Bilgili E, Davé RN. 2014. Formulation of a physically motivated specific breakage rate parameter for ball milling via the discrete element method. AIChE J. 60(7):2404–2415. doi: 10.1002/aic.14451.
   Web of Science ®Google Scholar
• Capece M, Davé R, Bilgili E. 2018. A pseudo-coupled dem–non-linear pbm approach for simulating the evolution of particle size during dry milling. Powder Technol. 323:374–384. doi: 10.1016/j.powtec.2017.10.008.
   Web of Science ®Google Scholar
• Chaudhury A, Kapadia A, Prakash AV, Barrasso D, Ramachandran R. 2013. An extended cell-average technique for a multi-dimensional population balance of granulation describing aggregation and breakage. Adv Powder Technol. 24(6):962–971. doi: 10.1016/j.apt.2013.01.006.
   Web of Science ®Google Scholar
• Chu KR, Lee E, Jeong SH, Park ES. 2012. Effect of particle size on the dissolution behaviors of poorly water-soluble drugs. Arch Pharm Res. 35(7):1187–1195. doi: 10.1007/s12272-012-0709-3.
   PubMed Web of Science ®Google Scholar
• Courant R, Friedrichs K, Lewy H. 1967. On the partial difference equations of mathematical physics. IBM J Res & Dev. 11(2):215–234. doi: 10.1147/rd.112.0215.
   Web of Science ®Google Scholar
• Dan A, Kotamarthy L, Ramachandran R. 2022. Understanding the effects of process parameters and material properties on the breakage mechanisms and regimes of a milling process. Chem Eng Res Des. 188:607–619. doi: 10.1016/j.cherd.2022.10.015.
   Web of Science ®Google Scholar
• de Koster SA, Pitt K, Litster JD, Smith RM. 2019. High-shear granulation: an investigation into the granule consolidation and layering mechanism. Powder Technol. 355:514–525. doi: 10.1016/j.powtec.2019.07.076.
   Web of Science ®Google Scholar
• Fda U. 2009. Guidance for industry. q8 (r2) pharmaceutical development. Maryland: food and Drug Administration.
   Google Scholar
• Hill PJ, Ng KM. 1996. Statistics of multiple particle breakage. AIChE J. 42(6):1600–1611. doi: 10.1002/aic.690420611.
   Web of Science ®Google Scholar
• Iveson SM. 2002. Limitations of one-dimensional population balance models of wet granulation processes. Powder Technol. 124(3):219–229. doi: 10.1016/S0032-5910(02)00026-8.
   Web of Science ®Google Scholar
• Kašpar O, Tokárová V, Oka S, Sowrirajan K, Ramachandran R, Štěpánek F. 2013. Combined uv/vis and micro-tomography investigation of acetaminophen dissolution from granules. Int J Pharm. 458(2):272–281. doi: 10.1016/j.ijpharm.2013.10.032.
   PubMedGoogle Scholar
• Kotamarthy L, Metta N, Ramachandran R. 2020. Understanding the effect of granulation and milling process parameters on the quality attributes of milled granules. Processes. 8(6):683. doi: 10.3390/pr8060683.
   Web of Science ®Google Scholar
• Krishna KB, Prabhakar C. 2011. A review on nanosuspensions in drug delivery. Int J Pharma and Bio Sci. 2(1):549–558.
   Google Scholar
• Metta N, Ierapetritou M, Ramachandran R. 2018a. A multiscale dem-pbm approach for a continuous comilling process using a mechanistically developed breakage kernel. Chem Eng Sci. 178:211–221. doi: 10.1016/j.ces.2017.12.016.
   Web of Science ®Google Scholar
• Metta N, Verstraeten M, Ghijs M, Kumar A, Schafer E, Singh R, De Beer T, Nopens I, Cappuyns P, Van Assche I, et al. 2018b. Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process. Int J Pharm. 549(1-2):271–282. doi: 10.1016/j.ijpharm.2018.07.056.
   PubMedGoogle Scholar
• Mirtič A, Reynolds GK. 2016. Determination of breakage rate and breakage mode of roller compacted pharmaceutical materials. Powder Technol. 298:99–105. doi: 10.1016/j.powtec.2016.04.033.
   Web of Science ®Google Scholar
• Mosharraf M, Nyström C. 1995. The effect of particle size and shape on the surface specific dissolution rate of microsized practically insoluble drugs. Int J Pharm. 122(1-2):35–47. doi: 10.1016/0378-5173(95)00033-F.
   Web of Science ®Google Scholar
• Murugesu B. 2008. Milling. In: Augsburger LL, Hoag SW, editors. Pharmaceutical dosage forms-tablets. CRC Press; p. 191–210.
   Google Scholar
• Muthancheri I, Ramachandran R. 2021. A hybrid model to predict formulation dependent granule growth in a bi-component wet granulation process. Pharmaceutics. 13(12):2063. doi: 10.3390/pharmaceutics13122063.
   PubMed Web of Science ®Google Scholar
• Naik S, Chaudhuri B. 2015. Quantifying dry milling in pharmaceutical processing: a review on experimental and modeling approaches. J Pharm Sci. 104(8):2401–2413. doi: 10.1002/jps.24512.
   PubMed Web of Science ®Google Scholar
• Oka S, Kašpar O, Tokárová V, Sowrirajan K, Wu H, Khan M, Muzzio F, Štěpánek F, Ramachandran R. 2015. A quantitative study of the effect of process parameters on key granule characteristics in a high shear wet granulation process involving a two component pharmaceutical blend. Adv Powder Technol. 26(1):315–322. doi: 10.1016/j.apt.2014.10.012.
   Web of Science ®Google Scholar
• Oka S, Smrčka D, Kataria A, Emady H, Muzzio F, Štěpánek F, Ramachandran R. 2017. Analysis of the origins of content non-uniformity in high-shear wet granulation. Int J Pharm. 528(1–2):578–585. doi: 10.1016/j.ijpharm.2017.06.034.
   PubMedGoogle Scholar
• Olaleye B, Wu CY, Liu LX. 2020. Impact of feed material properties on the milling of pharmaceutical ribbons: a pbm analysis. Int J Pharm. 590:119954. doi: 10.1016/j.ijpharm.2020.119954.
   PubMedGoogle Scholar
• Pistikopoulos EN, Barbosa-Povoa A, Lee JH, Misener R, Mitsos A, Reklaitis GV, Venkatasubramanian V, You F, Gani R. 2021. Process systems engineering–the generation next? Comput Chem Eng. 147:107252. doi: 10.1016/j.compchemeng.2021.107252.
   Web of Science ®Google Scholar
• Ramachandran R, Barton PI. 2010. Effective parameter estimation within a multi-dimensional population balance model framework. Chem Eng Sci. 65(16):4884–4893. doi: 10.1016/j.ces.2010.05.039.
   Web of Science ®Google Scholar
• Ramachandran R, Immanuel CD, Stepanek F, Litster JD, Doyle IF. 2009. A mechanistic model for breakage in population balances of granulation: theoretical kernel development and experimental validation. Chem Eng Res Des. 87(4):598–614. doi: 10.1016/j.cherd.2008.11.007.
   Google Scholar
• Ramkrishna D, Mahoney AW. 2002. Population balance modeling. promise for the future. Chem Eng Sci. 57(4):595–606. doi: 10.1016/S0009-2509(01)00386-4.
   Web of Science ®Google Scholar
• Reynolds GK. 2010. Modelling of pharmaceutical granule size reduction in a conical screen mill. Chem Eng J. 164(2–3):383–392. doi: 10.1016/j.cej.2010.03.041.
   Web of Science ®Google Scholar
• Sam T, Ernest TB, Walsh J, Williams JL, Initiative EPF, et al. 2012. A benefit/risk approach towards selecting appropriate pharmaceutical dosage forms–an application for paediatric dosage form selection. Int J Pharm. 435(2):115–123. doi: 10.1016/j.ijpharm.2012.05.024.
   PubMed Web of Science ®Google Scholar
• Samanta A, Ng K, Heng P. 2012. Cone milling of compacted flakes: process parameter selection by adopting the minimal fines approach. Int J Pharm. 422(1–2):17–23. doi: 10.1016/j.ijpharm.2011.10.015.
   PubMedGoogle Scholar
• Schenck LR, Plank RV. 2008. Impact milling of pharmaceutical agglomerates in the wet and dry states. Int J Pharm. 348(1-2):18–26. doi: 10.1016/j.ijpharm.2007.07.029.
   PubMedGoogle Scholar
• Tao J, Pandey P, Bindra DS, Gao JZ, Narang AS. 2015. Evaluating scale-up rules of a high-shear wet granulation process. J Pharm Sci. 104(7):2323–2333. doi: 10.1002/jps.24504.
   PubMed Web of Science ®Google Scholar
• Thiel W, Nguyen L. 1982. Fluidized bed granulation of an ordered powder mixture. J Pharm Pharmacol. 34(11):692–699. doi: 10.1111/j.2042-7158.1982.tb06202.x.
   PubMed Web of Science ®Google Scholar
• Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J R Statis Soc. 58(1):267–288.
   Google Scholar
• Vanarase A, Aslam R, Oka S, Muzzio F. 2015. Effects of mill design and process parameters in milling dry extrudates. Powder Technol. 278:84–93. doi: 10.1016/j.powtec.2015.02.021.
   Web of Science ®Google Scholar
• Varinot C, Hiltgun S, Pons MN, Dodds J. 1997. Identification of the fragmentation mechanisms in wet-phase fine grinding in a stirred bead mill. Chem Eng Sci. 52(20):3605–3612. doi: 10.1016/S0009-2509(97)89693-5.
   Web of Science ®Google Scholar
• Vdović N, Jurina I, Škapin SD, Sondi I. 2010. The surface properties of clay minerals modified by intensive dry milling—revisited. Appl Clay Sci. 48(4):575–580. doi: 10.1016/j.clay.2010.03.006.
   Web of Science ®Google Scholar
• Vogel L, Peukert W. 2003. Breakage behaviour of different materials—Construction of a mastercurve for the breakage probability. Powder Technol. 129(1–3):101–110. doi: 10.1016/S0032-5910(02)00217-6.
   Web of Science ®Google Scholar
• Vogel L, Peukert W. 2005. From single particle impact behaviour to modelling of impact mills. Chem Eng Sci. 60(18):5164–5176. doi: 10.1016/j.ces.2005.03.064.
   Web of Science ®Google Scholar
• Wong TT, Yeh PY. 2020. Reliable accuracy estimates from k-fold cross validation. IEEE Trans Knowl Data Eng. 32(8):1586–1594. doi: 10.1109/TKDE.2019.2912815.
   Web of Science ®Google Scholar
• Yu LX. 2008. Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res. 25(4):781–791. doi: 10.1007/s11095-007-9511-1.
   PubMed Web of Science ®Google Scholar