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Krueger L, Miles JA, Popat A (2022) 3D printing hybrid materials using fused deposition model-
ling for solid oral dosage forms. J Control Release 351:444–455
Kruth J-P, Levy G, Klocke F, Childs THC (2007) Consolidation phenomena in laser and powder-
bed based layered manufacturing. CIRP Ann 56:730–759
Kuang S, Zhou M, Yu A (2020) CFD-DEM modelling and simulation of pneumatic conveying: a
review. Powder Technol 365:186–207
Kumar S (2003) Selective laser sintering: a qualitative and objective approach. JOM 55:43–47
Kurbanoglu S, Uslu B, Ozkan SA (2017) Carbon-based nanostructures for electrochemical analy-
sis of oral medicines. In: Nanostructures for oral medicine. Elsevier, pp885–938
Lachman L, Lieberman HA, Kanig JL (1976) The theory and practice of industrial pharmacy. Lea
& Febiger Philadelphia
Lafeur F, Keckeis V (2020) Advances in drug delivery systems: work in progress still needed?
Int J Pharm 590:119912
Lamberti MJ, Wilkinson M, Donzanti BA, Wohlhieter GE, Parikh S, Wilkins RG, Getz K (2019)
A study on the application and use of articial intelligence to support drug development. Clin
Ther 41:1414–1426
Lee SL, O’Connor TF, Yang X, Cruz CN, Chatterjee S, Madurawe RD, Moore CMV, Yu LX,
Woodcock J (2015) Modernizing pharmaceutical manufacturing: from batch to continuous
production. J Pharm Innov 10:191–199
Lee H, Kim J, Kim S, Yoo J, Choi GJ, Jeong Y-S (2022) Deep learning-based prediction of physical
stability considering class imbalance for amorphous solid dispersions. J Chem 2022:1
Limongi T, Susa F, Allione M, Di Fabrizio E (2020) Drug delivery applications of three- dimensional
printed (3DP) mesoporous scaffolds. Pharmaceutics. 12:851
Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for
generic object detection: a survey. Int J Comput Vis 128:261–318
Lopez-Vidal L, Real JP, Real DA, Camacho N, Kogan MJ, Paredes AJ, Palma SD (2022)
Nanocrystal-based 3D-printed tablets: semi-solid extrusion using melting solidication printing
process (MESO-PP) for oral administration of poorly soluble drugs. Int J Pharm 611:121311
Lou H, Chung JI, Kiang Y-H, Xiao L-Y, Hageman MJ (2019) The application of machine learn-
ing algorithms in understanding the effect of core/shell technique on improving powder com-
pactability. Int J Pharm 555:368–379
Lowe M, Qin R, Mao X (2022) A review on machine learning, articial intelligence, and smart
technology in water treatment and monitoring. Water 14:1384
Ma X, Kittikunakorn N, Sorman B, Xi H, Chen A, Marsh M, Mongeau A, Piché N, Williams RO
III, Skomski D (2020) Application of deep learning convolutional neural networks for internal
tablet defect detection: high accuracy, throughput, and adaptability. J Pharm Sci 109:1547–1557
Maderuelo C, Lanao JM, Zarzuelo A (2019) Enteric coating of oral solid dosage forms as a tool to
improve drug bioavailability. Eur J Pharm Sci 138:105019
Mak K-K, Pichika MR (2019) Articial intelligence in drug development: present status and future
prospects. Drug Discov Today 24:773–780
Matsuda Y (2018) PMDA activities for implementation of continuous manufacturing. In:
Proceedings of the ISPE continuous manufacturing work, Arlington, VA. pp6–7
Mazzoli A (2013) Selective laser sintering in biomedical engineering. Med Biol Eng Comput
51:245–256
McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A proposal for the Dartmouth summer
research project on articial intelligence, august 31, 1955. AI Mag 27:12
Medendorp J, Shapally S, Vrieze D, Tolton K (2020) Process control of drug product continuous
manufacturing operations—a study in operational simplication and continuous improvement.
J Pharm Innov 1–12:85
Miller TA, York P (1988) Pharmaceutical tablet lubrication. Int J Pharm 41:1–19. https://doi.
org/10.1016/0378- 5173(88)90130- 5
Moingeon P, Kuenemann M, Guedj M (2022) Articial intelligence-enhanced drug design and
development: toward a computational precision medicine. Drug Discov Today 27:215–222
P. Saikiran etal.
139
Montez M, Willis K, Rendler H, Marshall C, Rubio E, Rajak DK, Rahman MH, Menezes PL
(2022) Fused deposition modeling (FDM): processes, material properties, and applications. In:
Tribology of additively manufactured materials. Elsevier, pp137–163
Myerson AS, Krumme M, Nasr M, Thomas H, Braatz RD (2015) Control systems engineering in
continuous pharmaceutical manufacturing May 20–21, 2014 Continuous manufacturing sym-
posium. J Pharm Sci 104:832–839
Nagy B, Farkas A, Borbás E, Vass P, Nagy ZK, Marosi G (2019) Raman spectroscopy for
process analytical technologies of pharmaceutical secondary manufacturing. AAPS
PharmSciTech 20:1–16
Nambiar AG, Singh M, Mali AR, Serrano DR, Kumar R, Healy AM, Agrawal AK, Kumar D
(2022) Continuous manufacturing and molecular modeling of pharmaceutical amorphous solid
dispersions. AAPS PharmSciTech 23:249
Nasr MM, Krumme M, Matsuda Y, Trout BL, Badman C, Mascia S, Cooney CL, Jensen KD,
Florence A, Johnston C (2017) Regulatory perspectives on continuous pharmaceutical manu-
facturing: moving from theory to practice: September 26-27, 2016, international symposium
on the continuous manufacturing of pharmaceuticals. J Pharm Sci 106:3199–3206
Ng LH, Ling JKU, Hadinoto K (2022) Formulation strategies to improve the stability and han-
dling of Oral solid dosage forms of highly hygroscopic pharmaceuticals and nutraceuticals.
Pharmaceutics. 14:2015
Norman J, Madurawe RD, Moore CMV, Khan MA, Khairuzzaman A (2017) A new chapter in
pharmaceutical manufacturing: 3D-printed drug products. Adv Drug Deliv Rev 108:39–50
O’Connor TF, Lawrence XY, Lee SL (2016) Emerging technology: a key enabler for modernizing
pharmaceutical manufacturing and advancing product quality. Int J Pharm 509:492–498
Okafor-Muo OL, Hassanin H, Kayyali R, ElShaer A (2020) 3D printing of solid oral dosage forms:
numerous challenges with unique opportunities. J Pharm Sci 109:3535–3550
Paimard G, Ghasali E, Baeza M (2023) Screen-printed electrodes: fabrication, modication, and
biosensing applications. Chemosensors 11:113
Palo HK, Sahoo S, Subudhi AK (2021) Dimensionality reduction techniques: principles, benets,
and limitations, data anal. Bioinforma A Mach Learn Perspect:77–107
Patel V, Shah M (2022) Articial intelligence and machine learning in drug discovery and develop-
ment. Intell Med 2:134–140
Paul S, Baranwal Y, Tseng Y-C (2021) An insight into predictive parameters of tablet capping by
machine learning and multivariate tools. Int J Pharm 599:120439
Paul P, Sobhan Gupta DADR, Mian S (2023) EVALUATING THE EFFECTIVENESS OF
INTERVENTIONS TO REDUCE MEDICATION ERRORS: A SYSTEMIC REVIEW. J
Pharm Negat Results:3064–3074
Pereira GG, Figueiredo S, Fernandes AI, Pinto JF (2020) Polymer selection for hot-melt extrusion
coupled to fused deposition modelling in pharmaceutics. Pharmaceutics. 12:795
Pittu V, Sharma J (2013) FORMULATION AND EVALUATION OF GASTRORETENTIVE
FLOATING TABLETS OF VALSARTAN
Poechlauer P, Colberg J, Fisher E, Jansen M, Johnson MD, Koenig SG, Lawler M, Laporte T,
Manley J, Martin B (2013) Pharmaceutical roundtable study demonstrates the value of con-
tinuous manufacturing in the design of greener processes. Org Process Res Dev 17:1472–1478
Pravin S, Sudhir A (2018) Integration of 3D printing with dosage forms: a new perspective for
modern healthcare. Biomed Pharmacother 107:146–154
Qiu C, Adkins NJE, Hassanin H, Attallah MM, Essa K (2015) In-situ shelling via selective laser
melting: modelling and microstructural characterisation. Mater Des 87:845–853
Rao D, Pathak P (2022) Evolving robotic process automation (RPA) & articial intelligence (AI)
in response to Covid-19 and its future. In: AIP conference proceedings. AIP Publishing
Rathore AS, Agarwal H, Sharma AK, Pathak M, Muthukumar S (2015) Continuous processing for
production of biopharmaceuticals. Prep Biochem Biotechnol 45:836–849
Risør BW, Lisby M, Sørensen J (2017) Cost-effectiveness analysis of an automated medication
system implemented in a Danish hospital setting. Value Heal 20:886–893
5 Advances inPharmaceutical Oral Solid Dosage Forms
140
Rogers AJ, Hashemi A, Ierapetritou MG (2013) Modeling of particulate processes for the continu-
ous manufacture of solid-based pharmaceutical dosage forms. PRO 1:67–127
Schaber SD, Gerogiorgis DI, Ramachandran R, Evans JMB, Barton PI, Trout BL (2011) Economic
analysis of integrated continuous and batch pharmaceutical manufacturing: a case study. Ind
Eng Chem Res 50:10083–10092
Schmidt A, de Waard H, Kleinebudde P, Krumme M (2018) Continuous single-step wet granula-
tion with integrated in-barrel-drying. Pharm Res 35:1–16
Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, Ringel M, Schork N (2019) Articial
intelligence and machine learning in clinical development: a translational perspective. NPJ
Digit Med 2:69
Shaheen MY (2021) Applications of articial intelligence (AI) in healthcare: a review, Sci. Prepr
Shaikh R, O’Brien DP, Croker DM, Walker GM (2018) The development of a pharmaceutical oral
solid dosage forms. In: Computer aided chemical engineering. Elsevier, pp27–65
Sheikhy S, Safekordi AA, Ghorbani M, Adibkia K, Hamishehkar H (2021) Synthesis of novel
superdisintegrants for pharmaceutical tableting based on functionalized nanocellulose hydro-
gels. Int J Biol Macromol 167:667–675. https://doi.org/10.1016/j.ijbiomac.2020.11.173
Shin S, Koo J, Kim SW, Kim S, Hong SY, Lee E (2023) Evaluation of robotic systems on cytotoxic
drug preparation: a systematic review and meta-analysis. Medicina (B.Aires) 59:431
Sng Y, Ong CK, Lai YF (2019) Approaches to outpatient pharmacy automation: a systematic
review. Eur J Hosp Pharm 26:157–162
Somnache SN, Vasantakumar KP, Godbole AM, Gajare PS, Pednekar AS (2023) COMBILOSE:
a novel lactose-based co-processed excipient for direct compression. J Appl Pharm Sci 0:1–8.
https://doi.org/10.7324/japs.2023.37613
Srai JS, Badman C, Krumme M, Futran M, Johnston C (2015) Future supply chains enabled by
continuous processing—opportunities and challenges. May 20–21, 2014 continuous manufac-
turing symposium. J Pharm Sci 104:840–849
Srai JS, Settanni E, Aulakh PK (2020) Evaluating the business case for continuous manufacturing
of pharmaceuticals: a supply network perspective. In: Continuous pharmaceutical processing.
Springer, pp477–512
Steenweg C, Seifert AI, Böttger N, Wohlgemuth K (2021) Process intensication enabling con-
tinuous manufacturing processes using modular continuous vacuum screw lter. Org Process
Res Dev 25:2525–2536
Stocker MW, Tsolaki E, Harding MJ, Healy AM, Ferguson S (2023) Combining isolation-free
and co-processing manufacturing approaches to access room temperature ionic liquid forms of
APIs. J Pharm Sci 112:2079. https://doi.org/10.1016/j.xphs.2023.01.030
Suryadinata HU (2017) The benets of automated dispensing machine as solutions for hospital
pharmacy in Indonesia: a systematic review. Proc Int Conf Appl Sci Heal 1:151–159
Thabet Y, Lunter D, Breitkreutz J (2018) Continuous manufacturing and analytical characteriza-
tion of xed-dose, multilayer orodispersible lms. Eur J Pharm Sci 117:236–244
Tracy T, Wu L, Liu X, Cheng S, Li X (2022) 3D printing: innovative solutions for patients and
pharmaceutical industry. Int J Pharm 631:122480
Treneld SJ, Awad A, Goyanes A, Gaisford S, Basit AW (2018) 3D printing pharmaceuticals: drug
development to frontline care. Trends Pharmacol Sci 39:440–451
Tsao NW, Lo C, Babich M, Shah K, Bansback NJ (2014) Decentralized automated dispens-
ing devices: systematic review of clinical and economic impacts in hospitals. Can J Hosp
Pharm 67:138
Tu P, Yan F (2022) Experimental and numerical study on the horizontal-vertical pneumatic con-
veying system with pulse excitation ow based on POD and recursive analysis. Adv Powder
Technol 33:103524
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P,
Spitzer M (2019) Applications of machine learning in drug discovery and development. Nat
Rev Drug Discov 18:463–477
P. Saikiran etal.
141
van der Lee M, Swen JJ (2023) Articial intelligence in pharmacology research and practice. Clin
Transl Sci 16:31–36
van der Merwe J, Steenekamp J, Steyn D, Hamman J (2020) The role of functional excipi-
ents in solid oral dosage forms to overcome poor drug dissolution and bioavailability.
Pharmaceutics. 12:393
Van Snick B, Holman J, Cunningham C, Kumar A, Vercruysse J, De Beer T, Remon JP, Vervaet C
(2017) Continuous direct compression as manufacturing platform for sustained release tablets.
Int J Pharm 519:390–407
Verstraeten M, Van Hauwermeiren D, Lee K, Turnbull N, Wilsdon D, Am Ende M, Doshi P, Vervaet
C, Brouckaert D, Mortier STFC (2017) In-depth experimental analysis of pharmaceutical twin-
screw wet granulation in view of detailed process understanding. Int J Pharm 529:678–693
Wang J, Goyanes A, Gaisford S, Basit AW (2016) Stereolithographic (SLA) 3D printing of oral
modied-release dosage forms. Int J Pharm 503:207–212
Wang J, Zhang Y, Aghda NH, Pillai AR, Thakkar R, Nokhodchi A, Maniruzzaman M (2021a)
Emerging 3D printing technologies for drug delivery devices: current status and future perspec-
tive. Adv Drug Deliv Rev 174:294–316
Wang W, Ye Z, Gao H, Ouyang D (2021b) Computational pharmaceutics-A new paradigm of drug
delivery. J Control Release 338:119–136
Wang G, Su W, Hu B, Al-Huqail AA, Majdi HS, Algethami JS, Jiang Y, Ali HE (2022) Assessment
in carbon-based layered double hydroxides for water and wastewater: application of articial
intelligence and recent progress. Chemosphere 308:136303
Welch CJ, Faul MM, Tummala S, Papageorgiou CD, Hicks F, Hawkins JM, Thomson N, Cote A,
Bordawekar S, Wittenberger SJ (2017) The enabling technologies consortium (ETC): fostering
precompetitive collaborations on new enabling technologies for pharmaceutical research and
development. Org Process Res Dev 21:414–419
Winarso R, Anggoro PW, Ismail R, Jamari J, Bayuseno AP (2022) Application of fused deposition
modeling (FDM) on bone scaffold manufacturing process: a review. Heliyon 8:e11701
Wirtz BW, Weyerer JC, Geyer C (2019) Articial intelligence and the public sector—applications
and challenges. Int J Public Adm 42:596–615
Xiouras C, Cameli F, Quilló GL, Kavousanakis ME, Vlachos DG, Stefanidis GD (2022)
Applications of articial intelligence and machine learning algorithms to crystallization. Chem
Rev 122:13006–13042
Ye X, Kallakunta V, Kim DW, Patil H, Tiwari RV, Upadhye SB, Vladyka RS, Repka MA (2019)
Effects of processing on a sustained release formulation prepared by twin-screw dry granula-
tion. J Pharm Sci 108:2895–2904
Yu LX, Amidon G, Khan MA, Hoag SW, Polli J, Raju GK, Woodcock J (2014) Understanding
pharmaceutical quality by design. AAPS J 16:771–783
Zaman R, Arefeen A, Quarnstrom J, Barman S, Yang J, Xiang Y (2022) Optimization-based bio-
mechanical lifting models for manual material handling: a comprehensive review. Proc Inst
Mech Eng Part H J Eng Med 236:1273–1287
Zhang GGZ, Law D, Schmitt EA, Qiu Y (2004) Phase transformation considerations during pro-
cess development and manufacture of solid oral dosage forms. Adv Drug Deliv Rev 56:371–390
Zhang B, Gao L, Ma L, Luo Y, Yang H, Cui Z (2019) 3D bioprinting: a novel avenue for manufac-
turing tissues and organs. Engineering 5:777–794
Zhang Y, Abatzoglou N, Hudon S, Lapointe-Garant P-P, Simard J-S (2021a) Dynamics of heat-
sensitive pharmaceutical granules dried in a horizontal uidized bed combined with a screw
conveyor. Chem Eng Process Intensif 167:108516
Zhang Y, Liu T, Kashani-Rahimi S, Zhang F (2021b) A review of twin screw wet granulation
mechanisms in relation to granule attributes. Drug Dev Ind Pharm 47:349–360
Zhang Z, Feng S, Almotairy A, Bandari S, Repka MA (2023) Development of multifunctional
drug delivery system via hot-melt extrusion paired with fused deposition modeling 3D printing
techniques. Eur J Pharm Biopharm 183:102–111
5 Advances inPharmaceutical Oral Solid Dosage Forms
142
Zhao L, Kim M, Zhang L, Lionberger R (2019) Generating model integrated evidence for generic
drug development and assessment. Clin Pharmacol Ther 105:338–349
Zhao J, Tian G, Qiu Y, Qu H (2021) Rapid quantication of active pharmaceutical ingredient for
sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on
machine learning techniques. Spectrochim Acta Part A Mol Biomol Spectrosc 245:118878
P. Saikiran etal.
143
6
Advances inTablet Production
andTablet Coating
NagphaseNakshatraJitendra, RohitGarg,
MdImtiyazAlam, andAweshK.Yadav
Abstract
Tablets have long been a preferred pharmaceutical dosage form due to their con-
venience, stability, and ease of administration. Signicant advancements in tab-
let production and coating technologies have revolutionized the pharmaceutical
and nutraceutical industries in recent years. This book chapter provides an over-
view of key innovations in tablet manufacturing and coating processes, high-
lighting their impact on drug delivery, product quality, and patient compliance.
Advancements in granulation methods such as spray drying, uid bed granula-
tion, and hot-melt extrusion have improved drug content uniformity, dissolution
rates, and overall product stability. The adoption of these technologies promises
continued progress in the eld of tablet manufacturing, offering new opportuni-
ties for personalized medicine and enhanced therapeutic outcomes. Notably,
coating technologies have also evolved signicantly to enhance tablet appear-
ance, taste-masking, and drug release proles. Film coating using aqueous and
organic solutions has become more efcient, environmentally friendly, and cost-
effective. Furthermore, automation involves the utilization of machinery and
tools to execute both physical and cognitive tasks within a production process.
The growing focus on automated technology in the pharmaceutical industry is
driven by the promising trend of fully automating tablet production. This book
chapter also emphasized issues concerning the process of tablet manufacturing
and recent advancements in the tablet coating process.
Nagphase Nakshatra Jitendra, Rohit Garg, and Md Imtiyaz Alam contributed equally to this work.
N. N. Jitendra · R. Garg · M. I. Alam · A. K. Yadav (*)
Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research
(NIPER) Raebareli, Lucknow, Uttar Pradesh, India
e-mail: awesh.yadav@niperraebareli.edu.in
144
Keywords
Tablet · Coating technologies · Granulation methods · Film coating · Tablet
automation
6.1 Introduction
For convenience and effectiveness, a variety of oral solid dosage forms, including
tablets, capsules, powders, and granules, are typically utilized. Tablets stand out
among them as the most popular solid oral dosage form due to their advantages,
which encompass excellent stability, therapeutic effectiveness, and ease of storage
and transport. So, it’s crucial to regulate the tablet preparation procedure. The pro-
cess of preparing tablets is a complex procedure that can be divided into multiple
operational stages. These production processes involve crushing, screening, drying,
granulating, lubricant/disintegrating agent mixing, tableting, coating, and mixing
with auxiliary ingredients. When making tablets, a variety of process issues or
material traits are present that may have an impact on the end product’s quality. For
example, factors like the material’s size, its initial water content, drying tempera-
ture, humidity, air velocity, and various other variables can inuence the transfer of
mass and heat between the liquid and gas phases during the drying process. The
nal blended product’s quality is inuenced by how the particles interact while
being mixed, which is controlled by the particle size, shape, and speed of the mixer.
Traditional tools for analyzing processes frequently encounter challenges when it
comes to measuring the transfer of mass and heat, particle interactions, and uid
multiphase ow within a system, which are expensive as well as time-consuming.
As a result, the analysis of the manufacturing process for tablets may be done better
with numerical simulation technologies. It can forecast the viability of drug manu-
facturing procedures, optimize them, and choose processing conditions that are
safer and more efcient. The spray-uidized bed granulation process was simulated
by using the CFD (computational uid dynamics) model. Investigations were done
into how starting particle size and nozzle positioning affected spray-uidized bed
granulation. In order to forecast the mixing quality between particles, studied the
ow of particles in the Ross-type static mixer and the vertical feed mixer using the
DEM model. Numerical simulation can be thought of as a computer-based experi-
ment that can faithfully depict different ow situations inside a system, the alloca-
tion of force and the transmission of energy, and other challenging issues. Despite
the widespread use of numerical technology, the fundamental steps in solving these
issues remain the same. They are as follows: (1) create model equations for response
problems and establish the conditions under which they can be solved; (2) select an
effective and precise approach to solving the issue; and (3) program and compute
(Li etal. 2023).
N. N. Jitendra et al.
145
6.2 Excipients
Traditionally, excipients such as binders, disintegrants, lubricants, llers, and gli-
dants have been utilized as formulation aids. However, in recent years, functional
excipient applications have been thoroughly investigated for modulating drug
release, prolonging the tablet’s presence through mucoadhesion, concealing taste,
and enhancing solubility. Important technical factors play a role in determining the
selection of excipients for oral solid dosage forms, integrating the critical pharma-
cokinetic efcacy of the formula with the physical or chemical properties of the
active pharmaceutical ingredient (API), as well as the dosage levels. The excipients
chosen are those that have ability to achieve desired effects, such as improved bio-
availability, modied release, or drug stability (Sohail Arshad etal. 2021). Moreover,
co-processed excipients that are prepared for immediate use have been created to
enhance tablet manufacturing.
6.2.1 Superdisintegrants
It is important for a tablet to disintegrate because this causes the drug component
to dissolve. Because natural polymers like starch are only partially soluble, their
inclusion in a formulation as a disintegrant may increase the viscosity of the ambi-
ent medium, preventing disintegration and dissolution. Incorporating cross-links
within the polymer chains can mitigate the inuence of the disintegrant on moder-
ate viscosity; introducing carboxyl groups into the polymer’s structure will improve
its ability to attract and interact with water molecules, thereby increasing its hydro-
philicity, aiding in the resolution of this issue (Quodbach and Kleinebudde 2016).
The characteristics of the disintegrant pertaining to its ability to swell and regain
its shape after deformation and its washing effect are among the mechanisms
engaged in tablet disintegration. In swelling, tablet disintegration is caused by the
particles expanding in all directions, which increases pressure within the system
and puts stress on it. Water enters the system by capillary action during wicking,
which causes van der Waals force, hydrogen bonds, and electrostatic interactions
to break down (Markl and Zeitler 2017). Van Kamp examined the disintegration
capabilities of potato starch (20%), SSG (4%), and CP (4%). The disintegration
times of CP (26s) and SSG (49s) were much less than the time measured with
potato starch (149 s), demonstrating the improved performance of cross-linked
superdisintegrants.
6.2.2 Fillers andBinders
Fillers signicantly increase the volume of the formulation, allowing it to be pro-
cessed into dosage forms and conveniently administered in unit dosages. The phar-
maceutical industry uses two types of llers: water-soluble (such as α-lactose
monohydrate, sucrose, and PEG (polyethylene glycol) 6000) and water-insoluble
6 Advances inTablet Production andTablet Coating
146
(such as calcium hydrogen phosphate in either an anhydrous or dihydrate form)
(van der Merwe et al. 2020). Through the utilization of cohesive and adhesive
forces, such as van der Waals and electrostatic forces, hydrogen bonding, solid
bridges, and mechanical interlocking, binders enhance exibility and strengthen the
cohesion of the ingredients within the formulation at the interparticulate level
(Adolfsson etal. 1998).
In tablet production, a range of binders is employed, including wet binders like
gelatin, pregelatinized starch, starch, PEG, gum acacia, okra, and xanthan, as well
as dry binders like cellulose, MCC (microcrystalline cellulose), methyl cellulose,
PVP (polyvinyl pyrrolidone), and PEG.Different materials that have undergone co-
processing, such as silicied MCC, α-lactose monohydrate-MCC, hydroxypropyl
methylcellulose (HPMC)-α-lactose monohydrate, vinyl pyrrolidone-vinyl acetate,
and corn starch-MCC-α-lactose monohydrate, have been utilized as binding agents
in tablet compositions that include tramadol HCl, hydrochlorothiazide, and acetyl
salicylic acid (Komersová etal. 2016; Mužíková etal. 2014, 2017).
6.2.3 Lubricants/Anti-adherents
To prevent sticking, picking, and capping concerns, lubricants are added in minor
quantities to the tablet formulation (0.25–0.5% w/w). They function by enclosing
particles or surfaces with a stable layer. The tablet compression process is inu-
enced by a number of variables, including the type and quantity of lubricant, the
way the lubricating agent is incorporated, as well as the lubrication technique,
including methods like either within the formulation (internal) or applied externally
by spraying onto punches and dies (external). A variety of lubricants consist of
metallic fatty acid salts like magnesium stearate, zinc stearate, and aluminum stea-
rate. Additionally, they encompass fatty compounds such as fatty acids, fatty alco-
hols, and hydrocarbons, like stearic acid. Fatty acid esters like glyceryl behenate,
sodium stearyl fumarate, and sucrose monopalmitate are also part of these lubri-
cants. Alkyl sulfates such as magnesium lauryl sulfate and sodium lauryl sulfate are
included, alongside inorganic substances like magnesium silicate. Polymers, includ-
ing polyoxyethylene-polyoxypropylene copolymer, polytetrauoroethylene, and
PEG 4000, are further examples of components used in these lubricants (Wang etal.
2010; Li and Wu 2014). Research indicated the promise of L-leucine and hexagonal
boron nitride as innovative lubricating substances (Sohail Arshad etal. 2021).
6.2.4 Solubility/Dissolution Enhancers
A signicant number of novel pharmaceutical entities fall under BCS class II, which
is distinguished by limited dissolution due to low solubility and high permeability,
with the former being the primary factor restricting overall bioavailability. To
improve the solubility of these drugs, a variety of approaches have been used,
including physical and chemical changes of the active component. Modications to
N. N. Jitendra et al.
147
a pharmaceutical compound at a physical level involve decreasing particle size
(micronization, nanosuspension), changing crystal structure (co-crystallization,
polymorphism, amorphization), and dispersing the drug within carriers (solid solu-
tions/dispersions, eutectic mixtures, cryogenic methods). The production of salts or
prodrugs, the addition of buffer, derivatization, and complexation are examples of
chemical modications to the drug molecule. Additional strategies utilized to
enhance drug dissolution encompass supercritical uid techniques and integrating
additives such as solubilizers, hydrotropic agents, surfactants, and cosolvents for
the formulation of solid dispersions (Khadka etal. 2014; Savjani etal. 2012). The
literature discusses the enhancement of solubility for diverse drugs (such as pacli-
taxel, Adriamycin, doxorubicin, lonidamine, famotidine, ondansetron, furosemide,
itraconazole, ibuprofen) through innovative additives like sulfobutylether- b-
cyclodextrin, HP-b-cyclodextrin, HPMC acetate succinate, graft copolymers of
polyethylene glycol/polyvinyl acetate/polyvinylcaprolactam, xyloglucan, dextran,
chondroitin sulfate/pluronic copolymer, and silica (Cho and Jung 2015; Basha etal.
2020; Havel 2018).
6.2.5 Drug Release Rate Modifiers
In many situations, controlled drug release is preferred because it enables appropri-
ate plasma concentrations, a longer duration of the therapeutic effect, reduced dos-
age administrations, and, ultimately, increased adherence from the patient.
Monolithic, reservoir, osmotic, ion-exchange, and membrane diffusion systems are
a few examples of different formulation strategies that are used to produce modied
releases of drugs (Wen and Park 2010).
Numerous factors regulate the discharge of the drug from the potential formula-
tions. These factors encompass the characteristics of both the excipients and the
active component, especially the loading of drugs and their solubility. Additionally,
the dimensions of the tablet (such as shape, size, and surface area) as well as the
properties of the coating membrane (including material and thickness) play a sig-
nicant role (Mužíková etal. 2014; Kaur etal. 2018).
A range of polymers, including polyvinyl alcohol, polymethacrylate, ethyl cel-
lulose, chitosan, polyethylene oxide, polyacrylic acid, polydextrose, gelatin, pectin,
sodium alginate, xanthan gum, tragacanth gum, and poloxamers, have been studied
for their ability to create structures or barrier membranes in oral controlled-release
systems such as multiparticulate, matrix, osmotic, and enteric-coated tablets (Sohail
Arshad etal. 2021).
Hydrophilic molecules called pyrogens, known for their substantial ability to
swell within a matrix, are integrated into a formulation based on hydrophobic poly-
mers. This integration aims to enhance the formulation’s porosity, as these porogens
later exit through dissolution. Hydrophilic polymers, polyethylene glycols (PEGs),
sodium chloride, sugars, and sugar alcohols, as well as L-menthol and sodium chlo-
ride, are some of the substances that result in pore formation (Vasvári etal. 2018;
Raza etal. 2020). Diverse lipid substances are also employed in the formulation of
6 Advances inTablet Production andTablet Coating