Computers in Pharmaceutical Analysis

Automation of analytical techniques becomes a necessity both in research and pharmaceuticals manufacturing especially when a large number of analyses have to be carried out as rationally and reliably as possible. With the evolution of technology, there is a simultaneous increase in the levels of quality, safety, and reliability. Additionally, the revolution of the use of computers in pharmaceutical analysis provided by the development of flow analysis concepts and process analysis strategies offer a link between modern instrumentation and social or technological problems. Automation of computer in analysis as well as analytical methodology provides an opportunity to the pharmaceutical industry in its attempts to use risk management and try scientifically designed manufacturing processes. Such attempts often lead to a better understanding of the product and thereby promote quality assurance. With an aim to reduce the increasing costs for product development and to overcome the regulatory hurdles toward invention and creativity, the Federal regulatory agency of the USA, that is, FDA, is promoting automation, and computers are an integral part of achieving this objective. This chapter summarizes current state of automation and computer-aided analysis, computer-assisted analysis of drug delivery systems, different chromatographic data systems, use of computer-/software-assisted analytical method development, role of analytical QbD as well as its application in analytical process, and importance of nanoparticle tracking analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

eBook EUR 213.99 Price includes VAT (France)

Softcover Book EUR 263.74 Price includes VAT (France)

Hardcover Book EUR 263.74 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

Guide to Pharmaceutical Product Quality

Chapter © 2018

Improving Robustness of Pharmaceutical Dosage form Sample Preparation Using Experimental Design and Process Understanding Tools

Article 06 October 2020

Trends in Process Analytical Technology: Present State in Bioprocessing

Chapter © 2018

References

  1. Bruno A, Costantino G, Sartori L, Radi M (2019) The insilico drug discovery toolbox: applications in lead discovery and optimization. Curr Med Chem 26:3838–3873. https://doi.org/10.2174/0929867324666171107101035ArticleCASPubMedGoogle Scholar
  2. Maia EH, Assis LC, de Oliveira TA, da Silva AM (2020) Taranto AG Structure-based virtual screening: from classical to artificial intelligence. Front Chem 8:343. https://doi.org/10.3389/fchem.2019.00343ArticleCASPubMedPubMed CentralGoogle Scholar
  3. Ekins S, Wang B (2006) In: Ekins S (ed) Computer applications in pharmaceutical research and development. Wiley ChapterGoogle Scholar
  4. Rahman MM, Karim MR, Ahsan MQ, Khalipha ABR, Chowdhury MR, Saifuzzaman M (2012) Use of computer in drug design and drug discovery: a review. Int J Pharm Life Sci 1:1–21. https://doi.org/10.3329/ijpls.v1i2.12955ArticleCASGoogle Scholar
  5. Tang Y, Zhu W, Chen K, Jiang H (2006) New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug Disc Today Technol 3:307–313. https://doi.org/10.1016/j.ddtec.2006.09.004ArticleGoogle Scholar
  6. Geldenhuys WJ, Gaasch KE, Watson M, Allen DD, der Schyf CJV (2006) Optimizing the use of open-source software applications in drug discovery. Drug Disc Today 11:127–132. https://doi.org/10.1016/S1359-6446(05)03692-5ArticleCASGoogle Scholar
  7. Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591. https://doi.org/10.1093/bib/bbp023ArticleCASPubMedGoogle Scholar
  8. Shah P, Mistry J, Reche PA, Gatherer D, Flower DR (2018) In silico design of Mycobacterium tuberculosis epitope ensemble vaccines. Mol Immunol 97:56–62. https://doi.org/10.1016/j.molimm.2018.03.007ArticleCASPubMedGoogle Scholar
  9. Mali AS, Jagtap M, Karekar P, Maruška A (2016) A brief review on process analytical technology (PAT). Int J Curr Pharm Res 8:10–15. https://doi.org/10.1016/j.molimm.2018.03.007ArticleCASGoogle Scholar
  10. Blundell TL (1996) Structure-based drug design. Nature 384:23–26. https://doi.org/10.1038/384023a0ArticleCASPubMedGoogle Scholar
  11. Zöldhegyi A, Rieger HJ, Molnár I, Fekhretdinova L (2018) Automated UHPLC separation of 10 pharmaceutical compounds using software-modeling. J Pharm Biomed Anal 156:379–388. https://doi.org/10.1016/j.jpba.2018.03.039ArticleCASPubMedGoogle Scholar
  12. Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P (2019) Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatol 155:1291–1299. https://doi.org/10.1001/jamadermatol.2019.137ArticlePubMedPubMed CentralGoogle Scholar
  13. Bakan E, Bayraktutan Z, KilicBaygutalp N, Gul MA, Umudum FZ, Bakan N (2018) Evaluation of the analytical performances of Cobas 6500 and Sysmex UN series automated urinalysis systems with manual microscopic particle counting. Biochem Med 28:329–339. https://doi.org/10.11613/BM.2018.020712ArticleGoogle Scholar
  14. Perone SP (1971) Computer applications in the chemistry laboratory. Survey. Anal Chem 43:1288–1299. https://doi.org/10.1021/ac60304a036ArticleCASGoogle Scholar
  15. Podlogar BL, Muegge I, Brice LJ (2001) Computational methods to estimate drug development parameters. Curr Opin Drug Discov Devel 4:102–109. PMID: 11727315 CASPubMedGoogle Scholar
  16. Shelmerdine SC, Singh M, Norman W, Jones R, Sebire NJ, Arthurs OJ (2019) Automated data extraction and report analysis in computer-aided radiology audit: practice implications from post-mortem paediatric imaging. Clin Radiol 74:733e11–733e18. https://doi.org/10.1016/j.crad.2019.04.021ArticleGoogle Scholar
  17. Lee AC, Harris JL, Khanna KK, Hong JH (2019) A comprehensive review on current advances in peptide drug development and design. Int J Mol Sci 20:2383. https://doi.org/10.3390/ijms20102383ArticleCASPubMed CentralGoogle Scholar
  18. Engel KM, Grunewald S, Schiller J, Paasch U (2019) Automated semen analysis by SQA Vision® versus the manual approach—a prospective double-blind study. Andrologia 51:e13149. https://doi.org/10.1111/and.13149ArticleCASPubMedGoogle Scholar
  19. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98. https://doi.org/10.1038/nchem.1243ArticleCASPubMedPubMed CentralGoogle Scholar
  20. Garofalo M, Grazioso G, Cavalli A, Sgrignani J (2020) How computational chemistry and drug delivery techniques can support the development of new anticancer drugs. Molecules 25:1756. https://doi.org/10.3390/molecules25071756ArticleCASPubMed CentralGoogle Scholar
  21. Yang X, Wang Y, Byrne R, Schneider G, Yang S (2019) Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev 119:10520–10594. https://doi.org/10.1021/acs.chemrev.8b00728ArticleCASPubMedGoogle Scholar
  22. Fekete S, Veuthey JL, Beck A, Guillarme D (2016) Hydrophobic interaction chromatography for the characterization of monoclonal antibodies and related products. J Pharm Biomed Anal 130:3–18. https://doi.org/10.1016/j.jpba.2016.04.004ArticleCASPubMedGoogle Scholar
  23. Pinzi L, Rastelli G (2019) Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci 20:4331. https://doi.org/10.3390/ijms20184331ArticleCASPubMed CentralGoogle Scholar
  24. Gillings N, Todde S, Behe M, Decristoforo C, Elsinga P, Ferrari V et al (2020) EANM guideline on the validation of analytical methods for radiopharmaceuticals. EJNMMI Radiopharm Chem 5(1):7. https://doi.org/10.1186/s41181-019-0086-zArticlePubMedPubMed CentralGoogle Scholar
  25. Fekete S, Guillarme D (2012) Reversed-phase liquid chromatography for the analysis of therapeutic proteins and recombinant monoclonal antibodies. LCGC Europe 25:540–550. https://doi.org/10.1039/C6AN01520DArticleCASGoogle Scholar
  26. Graves PR, Haystead TA (2002) Molecular biologist’s guide to proteomics. Microbiol Mol Biol Rev 66:39–63. https://doi.org/10.1128/MMBR.66.1.39-63.2002ArticleCASPubMedPubMed CentralGoogle Scholar
  27. Ambrose J (2006) Radiologists who co-developed computer tomography and performed the first scan. Br Med J 332:977. https://doi.org/10.1016/j.jpba.2015.01.045ArticleCASGoogle Scholar
  28. Hindelang F, Zurbach R, Roggo Y (2015) Microcomputer tomography for medical device and pharmaceutical packaging analysis. J Pharm Biomed Anal 108:38–48. https://doi.org/10.1016/j.jpba.2015.01.045ArticleCASPubMedGoogle Scholar
  29. Stock SR (2012) Trends in the micro-and nano Computed Tomography 2010-2012. In: Developments in X-ray tomography VIII, vol 8506. Int Society Optics and Photonics, p 850602. https://doi.org/10.1117/12.930157
  30. Schambach SJ, Bag S, Schilling L (2010) Application of micro-CT in small animal imaging. Methods 50:2–13. https://doi.org/10.1016/j.ymeth.2009.08.007ArticleCASPubMedGoogle Scholar
  31. Baker DR, Mancini L, Polacci M (2012) An introduction to the application of X-ray microtomography to the three-dimensional study of igneous rocks. Lithos 148:262–276. https://doi.org/10.1016/j.lithos.2012.06.008ArticleCASGoogle Scholar
  32. Naik NN, Jupe AC, Stock SR, Wilkinson AP, Lee PL, Kurtis KE (2006) Sulfate attack monitored by microCT and EDXRD: influence of cement type, water-to-cement ratio, and aggregate. Cem Concr Res 36:144–159. https://doi.org/10.1016/j.cemconres.2005.06.004ArticleCASGoogle Scholar
  33. Salvo L, Cloetens P, Maire E, Zabler S, Blandin JJ, Buffiere J et al (2003) X-ray micro-tomography an attractive characterisation technique in materials science. Nucl Instrum Methods Phys Res B 200:273–286. https://doi.org/10.1016/S0168-583X(02)01689-0ArticleCASGoogle Scholar
  34. De Chiffre L, Carmignato S, Kruth JP, Schmitt R, Weckenmann A (2014) Industrial applications of computed tomography. CIRP Ann 63:655–677. https://doi.org/10.1016/j.cirp.2014.05.011ArticleGoogle Scholar
  35. Lee SH, Bajracharya R, Min JY, Han J, Park BJ, Han H (2020) Strategic approaches for colon targeted drug delivery: an overview of recent advancements. Pharmaceutics 12:68. https://doi.org/10.3390/pharmaceutics12010068ArticleCASPubMed CentralGoogle Scholar
  36. Acharya C, Coop A, Polli JE, MacKerell AD (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 7(1):10–22. https://doi.org/10.2174/157340911793743547ArticleCASPubMedPubMed CentralGoogle Scholar
  37. Muehlwald S, Meyburg N, Rohn S, Buchner N (2020) Comparing a two-dimensional liquid chromatography with a quick, easy, cheap, effective, rugged, and safe protocol-based liquid chromatography method for matrix removal in pesticide analysis using time-of-flight mass spectrometry. J Chromatogr A 3:461153. https://doi.org/10.1016/j.chroma.2019.461153ArticleGoogle Scholar
  38. Davis JM, Giddings JC (1983) Statistical theory of component overlap in multicomponent chromatograms. Anal Chem 55:418–424. https://doi.org/10.1021/ac00254a003ArticleCASGoogle Scholar
  39. Tyrkkö E, Pelander A, Ojanperä I (2012) Prediction of liquid chromatographic retention for differentiation of structural isomers. Anal Chim Acta 720:142–148. https://doi.org/10.1016/j.aca.2012.01.024ArticleCASPubMedGoogle Scholar
  40. Goga-Remont S, Heinisch S, Rocca JL (2000) Use of optimization software to determine rugged analysis conditions in high-performance liquid chromatography. J Chromatogr A 868:13–29. https://doi.org/10.1016/S0021-9673(99)01203-0ArticleCASPubMedGoogle Scholar
  41. Wang X, Yang B, Sun H, Zhang A (2012) Pattern recognition approaches and computational systems tools for ultra-performance liquid chromatography–mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets. Anal Chem 84:428–439. https://doi.org/10.1021/ac202828rArticleCASPubMedGoogle Scholar
  42. Rogers CA, Ahearn JD, Bartlett MG (2020) Data integrity in the pharmaceutical industry: analysis of inspections and warning letters issued by the bioresearch monitoring program between fiscal years 2007–2018. Ther Innov Regul Sci:1–1. https://doi.org/10.1007/s43441-020-00129-z
  43. Khin NA, Francis G, Mulinde J, Grandinetti C, Skeete R, Yu B et al (2020) Data integrity in global clinical trials: discussions from joint US Food and Drug Administration and UK medicines and healthcare products regulatory agency good clinical practice workshop. Clin Pharmacol Ther 108:949–963. https://doi.org/10.1002/cpt.1794ArticlePubMedGoogle Scholar
  44. Shafiei N, De Montardy R, Rivera-Martinez E (2015) Data integrity—a study of current regulatory thinking and action. PDA J Pharm Sci Technol 69:762–770. https://doi.org/10.5731/pdajpst.2015.01082ArticlePubMedGoogle Scholar
  45. Steinwandter V, Herwig C (2019) Provable data integrity in the pharmaceutical industry based on version control systems and the blockchain. PDA J Pharm Sci Technol 73:373–390. https://doi.org/10.5731/pdajpst.2018.009407ArticlePubMedGoogle Scholar
  46. Achey TS, McEwen CL, Hamm MW (2019) Implementation of a workflow system with electronic verification for preparation of oral syringes. Am J Health Syst Pharm 76:S28–S33. https://doi.org/10.1093/ajhp/zxy019ArticlePubMedGoogle Scholar
  47. Spjuth O, Bongcam-Rudloff E, Hernández GC, Forer L, Giovacchini M, Guimera RV et al (2015) Experiences with workflows for automating data-intensive bioinformatics. Biol Direct 10:1–2. https://doi.org/10.1186/s13062-015-0071-8ArticleCASGoogle Scholar
  48. Velghe S, Deprez S, Stove CP (2019) Fully automated therapeutic drug monitoring of anti-epileptic drugs making use of dried blood spots. J Chromatogr A 1601:95–103. https://doi.org/10.1016/j.chroma.2019.06.022ArticleCASPubMedGoogle Scholar
  49. Tyteca E, Veuthey JL, Desmet G, Guillarme D, Fekete S (2016) Computer assisted liquid chromatographic method development for the separation of therapeutic proteins. Analyst 141:5488–5501. https://doi.org/10.1039/C6AN01520DArticleCASPubMedGoogle Scholar
  50. Shi Y, Xiang R, Horváth C, Wilkins JA (2004) The role of liquid chromatography in proteomics. J Chromatogr A 1053:27–36. https://doi.org/10.1016/j.chroma.2004.07.044ArticleCASPubMedGoogle Scholar
  51. Van Heukelem L, Thomas CS (2001) Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. J Chromatogr A 910:31–49. https://doi.org/10.1016/S0378-4347(00)00603-4ArticlePubMedGoogle Scholar
  52. Płotka J, Tobiszewski M, Sulej AM, Kupska M, Górecki T, Namieśnik J (2013) Green chromatography. J Chromatogr A 1307:1–19. https://doi.org/10.1016/j.chroma.2013.07.099ArticleCASPubMedGoogle Scholar
  53. Molnar I (2002) Computerized design of separation strategies by reversed-phase liquid chromatography: development of DryLab software. J Chromatogr A 965:175–194. https://doi.org/10.1016/S0021-9673(02)00731-8ArticleCASPubMedGoogle Scholar
  54. Torres-Lapasió JR, García-Álvarez-Coque MC (2006) Levels in the interpretive optimisation of selectivity in high-performance liquid chromatography: a magical mystery tour. J Chromatogr A 1120:308–321. https://doi.org/10.1016/j.chroma.2006.03.008ArticleCASPubMedGoogle Scholar
  55. Tyteca E, Liekens A, Clicq D, Fanigliulo A, Debrus B, Rudaz S et al (2012) Predictive elution window stretching and shifting as a generic search strategy for automated method development for liquid chromatography. Anal Chem 84:7823–7830. https://doi.org/10.1021/ac301331gArticleCASPubMedGoogle Scholar
  56. Peter GA, Warren BP, Dana Y. https://www.waters.com/nextgen/es/es/library/application-notes/2010/qbd-design-experiments-approach-development-of-chromatographic-method-for-separation-impurities-vancomycin.html. Accessed 23 Mar 2021
  57. Lawrence XY (2008) Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res 25:781–791. https://doi.org/10.1007/s11095-007-9511-1ArticleCASGoogle Scholar
  58. De Beer M, Lynen F, Chen K, Ferguson P, Hanna-Brown M, Sandra P (2010) Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm. Anal Chem 82:1733–1743. https://doi.org/10.1021/ac902287vArticleCASPubMedGoogle Scholar
  59. Wang X, Stoll DR, Schellinger AP, Carr PW (2006) Peak capacity optimization of peptide separations in reversed-phase gradient elution chromatography: fixed column format. Anal Chem 78:3406–3416. https://doi.org/10.1021/ac0600149ArticleCASPubMedPubMed CentralGoogle Scholar
  60. Parr MK, Schmidt AH (2018) Life cycle management of analytical methods. J Pharm Biomed Anal 147:506–517. https://doi.org/10.1016/j.jpba.2017.06.020ArticleCASPubMedGoogle Scholar
  61. de Sousa J, Holt D, Butterworth PA (2018) Analytical method design, development, and lifecycle management in pharmaceutical quality by design: a practical approach. In: Schlindwein WS, Gibson M (eds) Pharmaceutical quality by design: a practical approach, 1st edn. Wiley, New York, pp 257–279. https://doi.org/10.1002/9781118895238ChapterGoogle Scholar
  62. Vogt FG, Kord AS (2011) Development of quality-by-design analytical methods. J Pharm Sci 100:797–812. https://doi.org/10.1002/jps.22325ArticleCASPubMedGoogle Scholar
  63. Kochling J, Bridgewater J, Naji R (2010) Introducing a science-based quality by design concept to analytical methods development. In: Pharmaceutical stability testing to support global markets. Springer, New York, pp 169–179. https://doi.org/10.1007/978-1-4419-0889-6_22ChapterGoogle Scholar
  64. Mhatre R, Rathore AS (2009) Quality by design: an overview of the basic concepts. In: Rathore AS, Mhatre R (eds) Quality by design for biopharmaceuticals: principles and case studies, 1st edn. Wiley, New York, pp 1–7. https://doi.org/10.1002/9780470466315.ch1ChapterGoogle Scholar
  65. Peraman R, Bhadraya K, Padmanabha Reddy Y (2015) Analytical quality by design: a tool for regulatory flexibility and robust analytics. Int J Anal Chem:1–10. https://doi.org/10.1155/2015/868727
  66. International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use, ICH harmonized tripartite guideline, Draft Step 4. Pharmaceutical Development Q8(R1) (2008) https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf. Accessed 23 Mar 2021
  67. International Conference on harmonization of technical requirements for registration of pharmaceuticals for human use, ICH harmonized tripartite guideline. Pharmaceutical Development Q8(R2) (2009) https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf. Accessed 23 Mar 2021
  68. International Conference on harmonization of technical requirements for registration of pharmaceuticals for human use, ICH harmonized tripartite guideline. Quality Risk Management Q9 (2005) https://database.ich.org/sites/default/files/Q9_Guideline.pdf Accessed 23 Mar 2021
  69. International Conference on harmonization of technical requirements for registration of pharmaceuticals for human use, ICH harmonized tripartite guideline (2008) Pharmaceutical Quality System Q10. https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human_en.pdf Accessed 23 Mar 2021
  70. Mishra V, Thakur S, Patil A, Shukla A (2018) Quality by design (QbD) approaches in current pharmaceutical set-up. Expert Opin Drug Deliv 15:737–758. https://doi.org/10.1080/17425247.2018.1504768ArticleCASPubMedGoogle Scholar
  71. Sun M, Liu DQ, Kord AS (2010) A systematic method development strategy for determination of pharmaceutical genotoxic impurities. Org Process Res Dev 14:977–985. https://doi.org/10.1021/op100089pArticleCASGoogle Scholar
  72. Hanna-Brown M, Borman PJ, Bale S, Szucs R, Roberts J, Jones C (2010) Development of chromatographic methods in the era of quality by design for analytical methods. Sep Sci 2:12–19. https://doi.org/10.1016/j.jscs.2012.12.001ArticleCASGoogle Scholar
  73. Mallik R, Raman S, Liang X, Grobin AW, Choudhury D (2015) Development and validation of a rapid ultra-high performance liquid chromatography method for the assay of benzalkonium chloride using a quality-by-design approach. J Chromatogr A 1413:22–32. https://doi.org/10.1016/j.chroma.2015.08.010ArticleCASPubMedGoogle Scholar
  74. Gavin PF, Olsen BA (2008) A quality by design approach to impurity method development for atomoxetine hydrochloride (LY139603). J Pharm Biomed Anal 46:431–441. https://doi.org/10.1016/j.jpba.2007.10.037ArticleCASPubMedGoogle Scholar
  75. Li Y, Liu DQ, Yang S, Sudini R, McGuire MA, Bhanushali DS et al (2010) Analytical control of process impurities in Pazopanib hydrochloride by impurity fate mapping. J Pharm Biomed Anal 52:493–507. https://doi.org/10.1016/j.jpba.2010.01.043ArticleCASPubMedGoogle Scholar
  76. Tumpa A, Stajić A, Jančić-Stojanović B, Medenica M (2017) Quality by design in the development of hydrophilic interaction liquid chromatography method with gradient elution for the analysis of olanzapine. J Pharm Biomed Anal 134:18–26. https://doi.org/10.1016/j.jpba.2016.11.010ArticleCASPubMedGoogle Scholar
  77. Jameel F, Khan M (2009) Quality-by-design as applied to the development and manufacturing of a lyophilized protein product. Am Pharm Rev 12:20–24 CASGoogle Scholar
  78. Ye C, Terfloth G, Li Y, Kord A (2009) A systematic stability evaluation of analytical RP-HPLC columns. J Pharm Biomed Anal 50:426–431. https://doi.org/10.1016/j.jpba.2009.05.028ArticleCASPubMedGoogle Scholar
  79. Kormány R, Molnár I, Rieger HJ (2013) Exploring better column selectivity choices in ultra-high performance liquid chromatography using Quality by Design principles. J Pharm Biomed Anal 80:79–88. https://doi.org/10.1016/j.jpba.2013.02.028ArticleCASPubMedGoogle Scholar
  80. Liu DQ, Chen TK, McGuire MA, Kord AS (2009) Analytical control of genotoxic impurities in the pazopanib hydrochloride manufacturing process. J Pharm Biomed Anal 50:144–150. https://doi.org/10.1016/j.jpba.2009.04.002ArticleCASPubMedGoogle Scholar
  81. Monks KE, Rieger HJ, Molnár I (2011) Expanding the term “Design Space” in high performance liquid chromatography (I). J Pharm Biomed Anal 56:874–879. https://doi.org/10.1016/j.jpba.2011.04.015ArticleCASPubMedGoogle Scholar
  82. Aydar AY (2018) Utilization of response surface methodology in optimization of extraction of plant materials. Statistical approaches with emphasis on design of experiments applied to chemical processes, vol 7. pp 157–169. https://doi.org/10.5772/intechopen.73690
  83. Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977. https://doi.org/10.1016/j.talanta.2008.05.019ArticleCASPubMedGoogle Scholar
  84. Jamkhande PG, Ghante MH, Ajgunde BR (2017) Software based approaches for drug designing and development: a systematic review on commonly used software and its applications. Bull Fac Pharm Cairo Univ 55:203–210. https://doi.org/10.1016/j.bfopcu.2017.10.001ArticleGoogle Scholar
  85. Milanowski B, Hejduk A, Bawiec MA, Jakubowska E, Urbańska A, Wiśniewska A et al (2020) Biorelevant in vitro release testing and in vivo study of extended-release niacin hydrophilic matrix tablets. AAPS PharmSciTech 21:83. https://doi.org/10.1208/s12249-019-1600-zArticleCASPubMedGoogle Scholar
  86. Espíndola B, Bortolon FF, Pinto JM, Pezzini BR, Stulzer HK (2018) New approach for the application of USP apparatus 3 in dissolution tests: case studies of three antihypertensive immediate-release tablets. AAPS PharmSciTech 19:2866–2874. https://doi.org/10.1208/s12249-018-1086-0ArticleCASPubMedGoogle Scholar
  87. Li J, Li LB, Nessah N, Huang Y, Hidalgo C, Owen A et al (2019) Simultaneous analysis of dissolution and permeation profiles of nanosized and microsized formulations of indomethacin using the in vitro dissolution absorption system. J Pharm Sci 108:2334–2340. https://doi.org/10.1016/j.xphs.2019.01.032ArticleCASPubMedGoogle Scholar
  88. Feng X, Zidan A, Kamal NS, Xu X, Sun D, Walenga R et al (2020) Assessing drug release from manipulated abuse deterrent formulations. AAPS PharmSciTech 21:1–1. https://doi.org/10.1208/s12249-019-1595-5ArticleCASGoogle Scholar
  89. Costa P, Lobo JM (2001) Modeling and comparison of dissolution profiles. Eur J Pharm Sci 13:123–133. https://doi.org/10.1016/S0928-0987(01)00095-1ArticleCASPubMedGoogle Scholar
  90. Hofsäss MA, Dressman J (2020) Suitability of the z-factor for dissolution simulation of solid oral dosage forms: potential pitfalls and refinements. J Pharm Sci 109:2735–2745. https://doi.org/10.1016/j.xphs.2019.05.019ArticleCASPubMedGoogle Scholar
  91. Zuo J, Gao Y, Bou-Chacra N, Löbenberg R (2014) Evaluation of the DDSolver software applications. Biomed Res Int 2014:204925. https://doi.org/10.1155/2014/204925ArticlePubMedPubMed CentralGoogle Scholar
  92. Zhang Y, Huo M, Zhou J, Zou A, Li W, Yao C et al (2010) DDSolver: an add-in program for modeling and comparison of drug dissolution profiles. AAPS J 12:263–271. https://doi.org/10.1208/s12248-010-9185-1ArticleCASPubMedPubMed CentralGoogle Scholar
  93. Mendyk A, Jachowicz R, Fijorek K, Dorozynski P, Kulinowski P, Polak S (2012) KinetDS: an open source software for dissolution test data analysis. Dissolut Technol 19:6–11. https://doi.org/10.14227/DT190112P6ArticleGoogle Scholar
  94. Belew S, Suleman S, Duguma M, Teshome H, Wynendaele E, Duchateau L et al (2020) Development of a dissolution method for lumefantrine and artemether in immediate release fixed dose artemether/lumefantrine tablets. Malar J 19:1–2. https://doi.org/10.1186/s12936-020-03209-5ArticleCASGoogle Scholar
  95. Nair AB, Al-Dhubiab BE, Shah J, Jacob S, Saraiya V, Attimarad M et al (2020) Mucoadhesive buccal film of almotriptan improved therapeutic delivery in rabbit model. Saudi Pharm J 28:201–209. https://doi.org/10.1016/j.jsps.2019.11.022ArticleCASPubMedGoogle Scholar
  96. Guilherme VA, Ribeiro LN, Alcântara AC, Castro SR, da Silva GH, da Silva CG et al (2019) Improved efficacy of naproxen-loaded NLC for temporomandibular joint administration. Sci Rep 9:1–1. https://doi.org/10.1038/s41598-019-47486-wArticleCASGoogle Scholar
  97. Almukainzi M, Okumu A, Wei H, Löbenberg R (2015) Simulation of in vitro dissolution behavior using DDDPlus™. AAPS Pharm Sci Technol 16:217–221. https://doi.org/10.1208/s12249-014-0241-5ArticleCASGoogle Scholar
  98. Hole P, Sillence K, Hannell C, Maguire CM, Roesslein M, Suarez G et al (2013) Interlaboratory comparison of size measurements on nanoparticles using nanoparticle tracking analysis (NTA). J Nanopart Res 15:2101. https://doi.org/10.1007/s11051-013-2101-8ArticleCASPubMedPubMed CentralGoogle Scholar
  99. Probst C, Zeng Y, Zhu RR (2017) Characterization of protein particles in therapeutic formulations using imaging flow cytometry. J Pharm Sci 106:1952–1960. https://doi.org/10.1016/j.xphs.2017.04.034ArticleCASPubMedGoogle Scholar
  100. Panchal J, Kotarek J, Marszal E, Topp EM (2014) Analyzing subvisible particles in protein drug products: a comparison of dynamic light scattering (DLS) and resonant mass measurement (RMM). AAPS J 16:440–451. https://doi.org/10.1208/s12248-014-9579-6ArticleCASPubMedPubMed CentralGoogle Scholar
  101. Xin Y, Yin M, Zhao L, Meng F, Luo L (2017) Recent progress on nanoparticle-based drug delivery systems for cancer therapy. Cancer Biol Med 14:228–241. https://doi.org/10.20892/j.issn.2095-3941.2017.0052ArticleCASPubMedPubMed CentralGoogle Scholar
  102. Zhang Z, Zhuang L, Lin Y, Yan M, Lv J, Li X, Lin H, Zhu P, Lin Q, Xu Y (2020) Novel drug delivery system based on hollow mesoporous magnetic nanoparticles for head and neck cancers—targeted therapy in vitro and in vivo. Am J Cancer Res 10:350–364. PMID: 32064172 CASPubMedPubMed CentralGoogle Scholar
  103. Varenne F, Makky A, Gaucher-Delmas M, Violleau F, Vauthier C (2016) Multimodal dispersion of nanoparticles: a comprehensive evaluation of size distribution with 9 size measurement methods. Pharm Res 33:1220–1234. https://doi.org/10.1007/s11095-016-1867-7ArticleCASPubMedGoogle Scholar
  104. Filipe V, Hawe A, Jiskoot W (2010) Critical evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm Res 27:796–810. https://doi.org/10.1007/s11095-010-0073-2ArticleCASPubMedPubMed CentralGoogle Scholar
  105. Defante AP, Vreeland WN, Benkstein KD, Ripple DC (2018) Using image attributes to assure accurate particle size and count using nanoparticle tracking analysis. J Pharm Sci 107:1383–1391. https://doi.org/10.1016/j.xphs.2017.12.016ArticleCASPubMedGoogle Scholar
  106. Tian X, Nejadnik MR, Baunsgaard D (2016) A comprehensive evaluation of nanoparticle tracking analysis (NanoSight) for characterization of proteinaceous submicron particles. J Pharm Sci 05:3366–3375. https://doi.org/10.1016/j.xphs.2016.08.009ArticleCASGoogle Scholar
  107. Bai K, Barnett GV, Kar SR, Das TK (2017) Interference from proteins and surfactants on particle size distributions measured by nanoparticle tracking analysis (NTA). Pharm Res 34:800–808. https://doi.org/10.1007/s11095-017-2109-3ArticleCASPubMedGoogle Scholar
  108. Kousaka Y, Endo Y, Ichitsubo H, Alonso M (1996) Orientation-specific dynamic shape factors for doublets and triplets of spheres in the transition regime. Aerosol Sci Technol 24:36–44. https://doi.org/10.1080/02786829608965350ArticleCASGoogle Scholar
  109. Fung J, Manoharan VN (2013) Holographic measurements of anisotropic three-dimensional diffusion of colloidal clusters. Phys Rev E 88:020302. https://doi.org/10.1103/PhysRevE.88.020302ArticleCASGoogle Scholar

Author information

Authors and Affiliations

  1. Multidisciplinary Research Unit, Veer Chandra Singh Garhwali Government Institute of Medical Science and Research, Srinagar, India Mukesh Maithani
  2. Department of Pharmaceutics, University Institute of Pharmaceutical Sciences and Research, Baba Farid University of Health Sciences, Faridkot, Punjab, India Viney Chawla
  3. Department of Pharmaceutical Chemistry and Analysis, ISF College of Pharmacy, Moga, India Pooja A. Chawla
  1. Mukesh Maithani