(PDF) Systematic screening of pharmaceutical polymers for hot melt extrusion processing: a comprehensive review

Pharmaceutical research, whether industrial or academic, has attempted to adopt approaches most efficient for the development of innovations. With the abundance of literature and growth of modern techniques available to minimize the number of trials, research is becoming more systematic by the day. Screening and selection of polymers for a pharmaceutical formulation can be challenging, considering the variety of polymers available and under development. Multiple considerations and experimentations are required to select a polymer to attain the target product profile. In this review, a stepwise discussion of techniques useful to screen and select polymers suitable for hot melt extrusion processing are explored and reported. First of all, selecting a range of polymers available for certain formulation types, for example, immediate release or modified release. Secondly, the screening of these selected polymers based on their physical and chemical properties as these properties should be in line with the active pharmaceutical ingredients (APIs) and the processing limitations of the equipment. Finally, selecting polymers using theoretical models such as solubility parameters and Flory Huggins modeling. Utilization of these three steps before proceeding to experimental methods will minimize the use of resources and provide a higher degree of accuracy towards the development of efficient, stable, and consistent products.

International Journal of Pharmaceutics 576 (2020) 118989 Contents lists available at ScienceDirect International Journal of Pharmaceutics journal homepage: www.elsevier.com/locate/ijpharm Review Systematic screening of pharmaceutical polymers for hot melt extrusion processing: a comprehensive review Rishi Thakkara, Ruchi Thakkara, Amit Pillaia, Eman A. Ashoura, Michael A. Repkaa,b, a b T ⁎ Department of Pharmaceutics and Drug Delivery, School of Pharmacy, The University of Mississippi, University, MS 38677, USA Pii Center for Pharmaceutical Technology, The University of Mississippi, University, MS 38677, USA A R T I C LE I N FO A B S T R A C T Keywords: Hot melt extrusion Polymer screening Thermal properties Mechanical properties Flory Huggins modeling Solubility parameters Pharmaceutical research, whether industrial or academic, has attempted to adopt approaches most eﬃcient for the development of innovations. With the abundance of literature and growth of modern techniques available to minimize the number of trials, research is becoming more systematic by the day. Screening and selection of polymers for a pharmaceutical formulation can be challenging, considering the variety of polymers available and under development. Multiple considerations and experimentations are required to select a polymer to attain the target product proﬁle. In this review, a stepwise discussion of techniques useful to screen and select polymers suitable for hot melt extrusion processing are explored and reported. First of all, selecting a range of polymers available for certain formulation types, for example, immediate release or modiﬁed release. Secondly, the screening of these selected polymers based on their physical and chemical properties as these properties should be in line with the active pharmaceutical ingredients (APIs) and the processing limitations of the equipment. Finally, selecting polymers using theoretical models such as solubility parameters and Flory Huggins modeling. Utilization of these three steps before proceeding to experimental methods will minimize the use of resources and provide a higher degree of accuracy towards the development of eﬃcient, stable, and consistent products. 1. Introduction Polymers are macromolecules constructed with repeating units or monomers having backbones yielding unique properties and characteristics. These macromolecules are either naturally derived, semisynthetically modiﬁed or synthetically manufactured (Flory, 1953). Polymer science was ﬁrst introduced by Henri Braconnot and Christian Schönbein’s work on cellulose derivatives in the 1830s. Charles Goodyear’s patent on vulcanization of natural rubber in 1844 paved the way for the industrialization of polymers (“Charles Goodyear,” 2019). The polymer industry further revolutionized the textile industry and birthed the plastic industry (Jensen, 2008). This explosive growth of the polymer industry soon reached the pharmaceutical industry. Inert polymers with good ﬂow and compressibility ﬁt the characteristics of pharmaceutical excipients. Since then polymers have been used as binders (Sinha and Kumria, 2002), ﬁllers, lubricants, solubility enhancers in the tablet industry as well as emulsifying, suspending, stabilizing agents in the liquids and semisolids industry (Guo et al., 1998; Kamel et al., 2008). Development of novel polymers further gave rise to modiﬁed release dosage forms i.e. sustained release, delayed release and targeted release systems. On the other hand synthetic and ⁎ biopolymers gave rise to nanotherapeutic agents (Linsley & Wu, 2017). Stimuli sensitive polymers enhanced the eﬃciency of targeting (e.g. Cancer targeting using light sensitive nanoparticulate drug delivery) (Linsley and Wu, 2017; Zhu et al., 2016). This boom in the polymer industry has provided formulation scientists with both an opportunity and a challenge. The opportunity lies in the myriad number of ways in which a quality target proﬁle for a certain formulation can be achieved using the available polymers (Linsley and Wu, 2017). The challenge is with the screening and selection of a polymer which is compatible with the API, manufacturing process and quality standards of the desired formulation. This increase in the number of available polymers has increased the number of experimental trials required for the selection and optimization of a drug-polymer pair which in turn has increased the cost of research. Approaches such as ‘Quality by Design’ (QbD) have directed the industrial and academic research towards the proper use of these resources in a more systematic and eﬃcient manner to reach the desired results (Sivaraman and Banga, 2015; Yu, 2008). This review is focused on the systematic screening (Fig. 1) and the selection of polymers suitable for Hot Melt Extrusion (HME) processing. The systematic approach for the screening of polymers described in this review will potentially minimize the use of resources and maximize the eﬃciency Corresponding author. E-mail address: marepka@olemiss.edu (M.A. Repka). https://doi.org/10.1016/j.ijpharm.2019.118989 Received 25 October 2019; Received in revised form 20 December 2019; Accepted 21 December 2019 Available online 11 January 2020 0378-5173/ © 2020 Elsevier B.V. All rights reserved. International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Fig. 1. Step wise approach for the screening and selection of polymers for the HME process. multiple additions and innovations to the existing HME technology such as process analytical tools (PAT) (Crowley et al., 2007; Islam et al., 2014; Repka et al., 2007), which in turn have further expanded opportunities for more research and development thereby widening its scope. of research. Pharmaceutical implementation of HME was inspired by the plastics industry which has been employing HME since the mid-19th century (Treﬀer et al., 2013). In a nutshell, HME is a semi-continuous or continuous manufacturing process which involves melting, mixing, homogenizing and pumping out of fed raw materials (Crowley et al., 2007; Repka et al., 2013). This technique of melt homogenization has proven to be useful for improving the solubility of API with poor aqueous solubility (Zhu et al., 2006). Solubilization of an API in a suitable polymer matrix (Huang et al., 2017), under controlled processing conditions ensures mixing at a molecular level. Furthermore, the application of HME is not limited to solubility enhancement (Zhu et al., 2006). The versatility of HME in pharmaceutical product processing has been extensively demonstrated in the past two decades through dedicated academic and industrial research. This research is being reﬂected in the form of over a hundred publications in just the past decade and steady growth in the pharmaceutical patents since 1980 (Crowley et al., 2007). The technique has found application in the production of amorphous solid dispersions for improving the solubility of poorly soluble API (Patel, 2011), formulation of abuse deterrent products for controlled substances (Jedinger et al., 2016; Thakral et al., 2013), taste masking of geriatric and pediatric formulations (Juluri et al., 2016; Okuda et al., 2012; Pimparade et al., 2015) as well as topical (Bhagurkar et al., 2016; Mendonsa et al., 2018; Pawar et al., 2017), transdermal and transmucosal drug delivery systems (Low et al., 2013; Repka and McGinity, 2001). This ongoing research has attracted 2. HME: A versatile technology Although the pharmaceutical HME technology was inspired from the plastics industry, there is a diﬀerence between an extruder employed in the plastics industry and that used in the pharmaceutical industry (Crowley et al., 2007; Thommes et al., 2011). Regulatory compliance is an important aspect of pharmaceutical processing. Regulations demand the metallurgy of the extruder to be inert (physically and chemically), which means that there should not be any kind of leaching, erosion, absorption or adsorption contributed by the equipment. Moreover, provisions for proper quality inspection, validation and cleaning should be provided in the equipment abiding by the core pharmaceutical regulatory requirements (Repka et al., 2013). Even though there is a major diﬀerence in the regulatory aspects of these extruders, they function on the same principle. Depending on the number of screws, extruders can be single (smooth or grooved barrel), twin (corotating or counter, with or without intermeshing), or multiscrew extruders (static or rotating central shaft). Mechanisms and working of these extruders are further discussed in this section of the review. 2 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Fig. 2. Schematic representation of the progression and evolution of the bulk fed across the HME. depends on the type of feed, thermal conductivity of the material as well as the other equipment along with processing conditions (feed rate (Reitz et al., 2013), screw design (Morott et al., 2016), number of ﬂights (Maniruzzaman et al., 2012a,b), channel depth (Patil et al., 2016), ﬂight width, barrel clearance, design and number of kneading paddles (Morott et al., 2016), length and number of mixing sections, RPM, torque, temperature set up) (Mollan et al., 2003). This is the most important part of the process as the physical mixture is fused or solubilized at a molecular level at this stage. The melting of the polymer (at processing temperatures above the Tg/Tm) may lead to solubilization (weak intermolecular interactions) of the API in the molten mass or by fusion due to the simultaneous melting of the polymer and the API (Censi et al., 2018). Melt conveying and venting (optional) are the terminal processes (Mollan et al., 2003; Repka et al., 2013; Wesholowski et al., 2018). The fused and molten mass further undergoes heating and at this point, the time spent by the molten mass in the barrel (henceforth mentioned as ‘melt residence time’) needs to be considered as it has major eﬀects on the degradation (Huang et al., 2017), polymorphic conversion (Xu et al., 2018) and hence on the appearance of the extrudates as in the case of indomethacin (Xu et al., 2018). If the melt residence time is high without venting (in presence of volatile excipients), bubbling and foaming may be observed in the extrudate (foaming may also result from starve fed and low RPM conditions where air gets incorporated in the viscous extrudates at the die) (Crowley et al., 2007; Patil et al., 2016). Low melt residence time is desired to prevent undesirable extrudate features and degradation. Vacuum conditions can be provided in this region to prevent undesired incorporation of air in the molten mass of the extrudates. This region may also be used for intentional incorporation of carbon dioxide (CO2) to the molten mass in order to achieve low density extrudates displaying buoyancy for gastroretentive drug delivery systems (GRDDS) which are discussed in a later section of this review (Ashour et al., 2017; Vo et al., 2016) (see Fig. 2). The ﬁnal step of the extrusion process is the pumping, shaping, and cooling of the extrudates. The physical attributes of the extrudates depend on the processing temperature, thermal and viscoelastic properties of the processed material or mixture, feeding approach, processing parameters (temperature, RPM), die design as well as the shape and downstream processing attachments. In the case of conventional downstream processing (milling and compression), shape or physical appearance is not of importance (Crowley et al., 2007). Die design and geometry precision would play an important role in shaping the extrudate in the form required for the desired application (e.g. ﬁlament for fused deposition modelling (FDM) based 3D printing (Zhang et al., 2016), hot melt extruded ﬁlms (Albarahmieh et al., 2016; Repka et al., 2.1. Mechanism and processing The HME process can be divided into ﬁve major sub-processes starting with the feeding (ﬂood or starve), conveying and mixing of the physical mixture. Starve feeding dispenses the material directly on the screws, which prevents the buildup of the fed material near the feed zone. Thereby the mass ﬂow rate is not dependent on the screw speed. At steady state, in a starve-fed mode, the mass ﬂow rate at the feed zone is equal to the mass exiting the barrel and thus accretion in the barrel is negligible leading to a lower overall torque and die pressure (Repka et al., 2013). A gravimetric (weight displacement) feeder or a volumetric (volume displacement feeder) can be used for feeding depending on the nature of the physical mixture (Repka et al., 2013). This physical mixture can be simply conveyed using conveying elements or mixed by incorporation of initial mixing zones. Reitz et al. established a signiﬁcant correlation between the type and rate of feeding on the residence time, uniformity of the product, quality of the extrudates as well as the overall torque of the system by developing a mathematical residence time model and discussing its application using experimental analysis along with statistical analysis (Reitz et al., 2013). It was observed that an increase in feed rate with a constant RPM signiﬁcantly reduced the residence time of the feed. Starve feeding is preferred as it provides lower torque, eﬃcient and uniform heat transfer (melting) along with mixing, uniformity in the content and physical appearance of the produced extrudate (Reitz et al., 2013; Thommes et al., 2011). Flood feeding has a higher chance of resulting in an increase in torque, ineﬃcient mixing, clogging and variability which leads to poor processing control (Crowley et al., 2007; Repka et al., 2013). Next is the conveying and softening of the physical mixtures. Melting/softening of the fed mass is initiated by the thermal input provided by the heated barrel surface and the mechanical shear provided by the mixing and conveying elements (Repka et al., 2013). Softening zones depend on the thermophysical properties of the material and set processing conditions. Equipment like ThermoScientiﬁc HAAKE MiniLab 3 operate at the same temperature throughout the barrel and hence softening is highly dependent on the properties of the material. On the other hand, ThermoScientiﬁc Process 11 parallel twinscrew extruder and other industrial grade extruders oﬀer the ﬂexibility of maintaining diﬀerent thermal conditions across the barrel. In this, materials experience softening and initiation of fusion of the constituent material (Patil et al., 2016). This softened mixture further experiences uniform conduction of the thermal and frictional input (interparticle, material/wall, material/ screw) which leads to the melting and further solubilization or fusion of the constituents (Repka et al., 2013). The mentioned phenomenon 3 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. self-wiping characteristic minimizes the stagnant zones adhered to the screws, this in turn eliminates localized overheating which can be detrimental to the feed (Breitenbach, 2002). Another advantage of complete intermeshing is the breaking of the material bed, this provides better control over the overall system pressure by keeping it below the operational limits and independent of the die pressure. This also contributes to the uniformity of the product (Mollan et al., 2003). Though most of the pharmaceutical operations prefer intermeshing twin screw designs, non-intermeshing designs show applicability in processing certain types of materials. Due to the possibility of a large vent opening as the screws are placed apart, the material with high amounts of volatile content can also be processed. The arrangement of screws also allows smooth processing of material with high density and melt viscosity, which would otherwise have led to a massive increase in the torque of the extruders along with intermeshing. Increase in torque has a damaging eﬀect on the equipment, material, and uniformity of the extrudates as well as on the processing conditions (Repka et al., 2013). 2005; etc.). The shaping of the extrudate can be achieved by using a slit die, die face cut pelletizer or circular dies with multiple strands which can be cooled and stored for further processing (Crowley et al., 2007). Other auxiliary process analytical tools (PAT) can also be synchronized with the equipment at this point. These PAT tools aid determination of uniformity of the extrudates (Near Infra-red probes (transmission and reﬂectance), N-IR) (Maniruzzaman et al., 2016) (Islam et al., 2014) and mean residence time using a UV/Vis spectrophotometer (Wesholowski et al., 2018a). The cooled extrudates can be further milled and shaped in the form of tablets, injection molds and casts, yielding diﬀerent shapes of tablets, dental adhesives, vaginal rings, ear inserts, ocular inserts, etc. The extrudate can also be fed to a 3-D or 4-D printer to print out tablets with special designs (Chronotherapy, modiﬁed release, targeted release) and patient speciﬁc applicability (Aho et al., 2015; Zhang et al., 2016). 2.2. Single screw extruder (SSE) Single screw extruders are considered to have the simplest extruder designs. A single screw element continuously rotating within a barrel provides an advantage over ram extruders in developing a good and uniform quality of melt (Mollan et al., 2003; Patil et al., 2016). The transport mechanism comprises of the frictional along with the viscous forces employed in the solid and melt conveying zones respectively. This processing equipment with suﬃcient pressure generation for extrusion along with the simplicity of design, low cost and ease of maintenance, is a good choice for processing simpler formulations, usually with binary or tertiary components of comparable ratios (Mollan et al., 2003). On the other hand, it faces the limitations of highpressure compression, agglomerate formation and improper mixing characteristics of the extrudate. With a single element responsible for conveying, melting and pumping out the feed, the sustenance of the equipment needs to be considered. Most extruder screws are driven from the hopper end. However, screws reduced to less than 18 mm becomes weak, making solid transport less reliable (Mollan et al., 2003). A vertical screw, driven from the discharge end, may be used to make the discharge, two to four times stronger, in turn increasing the transport of solids. Single-screw extruders are usually ﬂood fed i.e. the hopper resides over the feed throat and the die pressure along with the RPM have to be adjusted in order to control the output. From an engineering standpoint, ﬂood feeding provides a continuous material bed throughout the extruder which allows the transmission of pressure back from the die end to the hopper end of the extruder. Hence, the die pressure is the indicator of the overall pressure exerted throughout the system. Alternatively, the equipment can also be starve-fed, where the feeder (volumetric or gravimetric) regulates the output at a predetermined rate (Mollan et al., 2003; Patil et al., 2016; Repka et al., 2013). 2.4. Multi screw extruder (MSE) This category encompasses the instruments with more than two screws. The assembly and design of the extruder diﬀer depending on the number of screws used (Patil et al., 2016). Extruders with three to ﬁve screws are generally arranged in a linear fashion. The extruders with four screws have one control and three spur screws. The equipment with six or eight screws are generally arranged in a circumferential manner (Patil et al., 2016). These arrangements are dependent on the processed material along with processing condition requirements. MSE(s) are largely employed in the plastics industry because of the high shear requirements of the industry and the processing material. However, MSE(s) ﬁnd use in the processing of thermolabile materials which are in danger of degradation in the TSE. This is due to the highly shear dominated ﬂow in the other processes of the melted material which leads to heat generation. However, due to the positive displacement ﬂow observed between the intermeshing screws in MSE, heat generation is minimal and can be used for processing of thermolabile components (Patil et al., 2016). Changes in processing variables (RPM, feed rate and temperature) and formulation variables (Plasticizers ratio, drug-polymer ratio, polymer selection) can also help accommodate the processing of thermolabile components using TSE (see Table 1). 3. Screening of polymers based on formulation types Today pharmaceutical research has entered an era where we can manipulate the rate and time of release of the API from a pharmaceutical dosage form. This versatility of new era dosage forms is mainly attributed to pharmaceutical polymers (Karolewicz, 2016). This part of the review focuses on the diﬀerent pharmaceutical polymers available for designing a myriad of formulations using HME. The two main prerequisites of using hot melt extrusion technology are the physiochemical suitability of the polymer (inﬂuencing the processing conditions) and formulation design-based suitability of the formulation (Crowley et al., 2007). Physiochemical parameters such as thermal stability, deﬁned as the ability of a material to withstand high temperatures is an essential condition which is needs to be fulﬁlled in order for any excipient to be used with the HME (Censi et al., 2018; Meena et al., 2014). These properties are discussed in depth in the next part of this review. Another prerequisite is the quality target product proﬁle (QTPP), which aﬀects the mechanism undertaken by the polymer to operate when used with the API (Sangshetti et al., 2017; Sivaraman and Banga, 2015; Yu, 2008). While processing a formulation using HME, the API is dispersed in a carrier which can either be hydrophilic or hydrophobic depending on the requirements of the formulation (Crowley et al., 2007). A hydrophilic carrier such as HPMCAS HG does not allow the drug to be released in an acidic environment and enhances the release under 2.3. Twin screw extruders (TSE) Twin screw extruders are two screws which are either co-rotating (same direction, clockwise or counterclockwise) or counter-rotating (opposite directions, towards the center or away from the center), intermeshing or non-intermeshing and are arranged parallel to one another. Both co-rotating and counter-rotating designs have processing advantages over the single screw extruders (Repka et al., 2013). Though the material transport is dependent on the speciﬁc screw conﬁgurations, parallel screw alignment prevents the material adherence to the screw which is a known disadvantage for single screw extruders (Mollan et al., 2003). This highlights the two major advantages of twinscrew extruders i.e. positive conveying and eﬃcient mixing. Non-intermeshing extruders cannot form closed or semi-closed compartments as it reduces the positive conveying characteristics, whereas complete intermeshing extruders have excellent control over positive conveying with minimal to no backﬂow along with self-wiping characteristics. The 4 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Table 1 Types of hot melt extruders with pharmaceutical applications. Extruder type Design consideration Advantages Disadvantages Single screw extruder Usually ﬂood fed; Continuous material bed created; Die pressure controls the equipment/barrel pressure; Transport mechanism is dependent on the frictional and rheological properties of the material High pressure generation; Agglomerate formation; Non-uniform mixing of the stagnant layer; deposition on the rotating screws Non intermeshing counter rotating twin screw extruders Counter rotation (same or opposite direction); Transport mechanism is either drag ﬂow (created by screw and barrel) or pressure driven ﬂow (inter screw region); Large vent openings Intermeshing counter rotating twin screw extruders Transport mechanism is positive displacement due to intermeshing; Tightly ﬁtted, fully closed and intermeshing screw design Intermeshing corotating twin screw extruders Intermeshing self-wiping complex design due to several diﬀerent type of elements; Mass transfer via positive conveying Multiple screw extruders More than two screws with a range of arrangement and design based on application; Mass transfer depends on the way of arrangement of the screws, major mechanisms involve a combination of frictional forces, positive displacement, drag ﬂow and pressure driven ﬂow Ease of maintenance; Low cost; Simple design; Best suited for simple formulations (binary mixtures); High productivity to cost ratio Allows compounding of highly dense and viscous material; Continuous Compounding of mixtures with volatile components; Less localized heat generation High shearing action; More uniformity in product; More controlled processing condition; Larger versatility and range of application High shear; Self-cleaning so no stagnant ﬁlms and localized thermal degradation; Wide range of applications; More control over the process; Industrially, most important type Moderate to High shear; Better control over localized instrument temperature; Suitable for processing of thermolabile and viscous components; May develop dead zones based on design selection (linear, circumferential, etc.) Limited application; Less viscous materials may create a dead zone in the inter screw region; Less eﬃcient mixing as compared to intermeshing design Heat generation due to high shear; Limited application for thermolabile processing; Higher cost of equipment and maintenance Higher cost of equipment; Complex instrumentation hence higher maintenance cost Limited industrial applicability for pharmaceuticals due to fewer advantages over TSE; High cost of maintenance and operation Fig. 3. Classiﬁcation of formulations based on HME applicability. characteristics such as poly-lactic acid (PLA) and poly-glycolic acid (PGA) (Amass et al., 1998; Makadia and Siegel, 2011). Solubility enhancement is one of the most widely exploited functions of HME and some of the polymers used for it are HPMC, HPMCAS (Ghebremeskel et al., 2007; Mitra et al., 2016; Tian et al., 2013) and PEG (Van Den Mooter, 2012; Zhu et al., 2006). Polymers have been used with HME to control the release of drug from the dosage form by formulating a drug-polymer matrix (Ma et al., 2013; Tsunashima et al., 2017; Verma and Garg, 2005; Vo et al., 2016; Zhu et al., 2006). favorable conditions (Maniruzzaman et al., 2016; Sarode et al., 2014). Hydrophobic polymers (hydrogenated castor oil [HCO], ethylcellulose) have also been used in some studies to study the sustained release eﬀect of these polymers (Repka et al., 2013). In recent times, there has been a rise in the use of biodegradable polymers in drug delivery. These polymers are known to carry out their speciﬁc tasks and then degrade into non-toxic metabolites eliminated through natural pathways. There are various synthetic biodegradable polymers which can be tailor made according to the desired molecular weight and degradation 5 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. crystalline suspensions. The enhanced solubility results from the physical conﬁguration of the API in the extrudates due to the intense agitation and mixing induced by the process, rapid crystallization of the excipient ﬁxing API in a spatially uniform crystalline suspension and enhanced wettability rather than from interactions between drug and API. Ideal excipients for these processes should have high aqueous solubility, rapid crystallization behavior and lower Tm than the API. The theoretical solubility parameters (explained further in the review) of the API and excipients should have a diﬀerence of 10 MPa1/2 which recalls the feature of CSDs mentioned earlier in this review i.e. API is dispersed in an immiscible carrier (Thommes et al., 2011). Another way of overcoming the stability issues of solid amorphous dispersion is the formulation of co-crystal systems (Liu et al., 2012; Thommes et al., 2011). Co-crystals consist of a single crystalline homogenous phase with multiple neutral components which are solid in their pure forms under ambient conditions (Wu et al., 2004). These systems require the components to be in a deﬁnite stoichiometric ratio and assemble via noncovalent interactions i.e. H-bonding, π-π stacking or weak Van der Waals interactions (Liu et al., 2012). These systems usually have intermediate physical properties such as melting temperatures and solubilities of the API and the co-former. Co-crystals can be prepared by either co-solvent evaporation method or by solvent free melt-dispersion. Carbamazepine (CBZ) (Tm-190 °C) is a poorly watersoluble API which has been reported to show thermal degradation at elevated temperatures. Liu et al outline the process of employing melt extrusion at 160 °C for the preparation of an in-situ Carbamazepine (CBZ)-Nicotinamide (NIC) co-crystal systems using polymeric carriers (Kollidon® VA 64 and Soluplus®). Melt extrusion was conducted at 160 °C and 30 RPM by employing a conical co-rotating conical twin screw HAAKE Minilab II micro compounder and co-crystals were simultaneously prepared using the melting method. The thermal characterization by DSC conﬁrmed the in-situ formation of co-crystals denoted by a single melting peak at 160 °C. The CBZ-NIC-Polymer systems displayed enhanced dissolution and stability as compared to the CBZPolymer amorphous solid dispersions. This high dissolution rate (< 20 min) could be attributed to the high aqueous solubility of the polymers and hydrotropic behavior of NIC as well as the stability observed due to low processing temperature of CBZ-NIC-Polymer due to the in-situ co-crystal formation. Contrary to the crystalline suspensions, co-crystals require interaction between the API, co-former and the polymer i.e. a diﬀerence of less than 7 MPa1/2 between their theoretical solubility parameters (Liu et al., 2012). The dissolution of solid crystalline API can be described in three steps First, the breaking of the crystal lattice (Lattice energy, endothermic) followed by the solvation or hydration of the API molecules (solvation energy, exothermic) and ﬁnally the breaking of weak solvent/water hydrogen bonds to accommodate the API molecules (cavitation energy, endothermic) (Patel, 2011). Poor solubilities can thus be a result of high lattice energy (crystalline) and/or high solvation energy. Amorphous solid dispersions (ASD) are characterized by the complete conversion of the API to amorphous form i.e. the disappearance of the endothermic peak on the DSC curve. Amorphous dispersions can be produced by rapid cooling which inhibits crystal formation or high processing temperatures, where the API melts and fuses with the carrier to give a single glass transition temperature (Tg). Although ASDs have a higher dissolution rate due to their high thermodynamic activity (increased free energy state), they tend to convert back to their stable crystalline form over time (relaxation, nucleation and crystal growth) (Patel, 2011). Hence, the stability of ASDs is an essential critical quality attribute to be considered during the formulation development, i.e. optimizing the API to polymer ratio and process variables (temperature, screw conﬁguration, RPM, etc.). ASDs are also known as glass solutions and are one phase systems comprising of molecules of the API intimately mixed with an amorphous carrier molecule. To decrease the pill burden, ideal API loading is 30–40% without phase separation. For the preparation of ASDs, solubility Examples of polymers used for this purpose are ethyl cellulose (Islam et al., 2014; Kim et al., 2014), Eudragit® (Ghebremeskel et al., 2007; Miller et al., 2008; Zhang, 2016; Zhu et al., 2006) and chitosan. There has been a great amount of research going on to exploit the use of HME along with some enteric polymers to mask the taste of some bitter drugs like caﬀeine citrate (Pimparade et al., 2015). These delayed release formulations are prepared by embedding the drug in the polymer matrix and blocking the release for the initial phase of administration. Polymers such as methacrylic acid-ethyl acrylate copolymer could be used for this purpose. Eudragit® (P-4135F) or polymethacrylate has been used by researchers to target the drug release in the colon, which is one of the novel approaches used for targeted drug delivery via HME (Cassidy et al., 2011; Gately and Kennedy, 2017). Many of the carriers used in drug delivery could also be lipids such as Gelucire and other lipids which are mainly used to enhance the solubility of poorly soluble drugs (Vithani et al., 2013) (see Fig. 3). 3.1. Immediate release dosage forms As described previously, HME can be employed for the preparation of immediate release formulations by exploiting its continuous manufacturing and solubility enhancement aspect (Crowley et al., 2007; Repka et al., 2007; Shi et al., 2019; Xu et al., 2014). The previous section of this review explains how HME is used as a continuous manufacturing platform. It also explains how the processing conditions mix components of a physical mixture at a molecular level which contributes to solubility enhancement. HME has been used for the continuous manufacturing of solid and semi-solid dosage forms (Bhagurkar et al., 2016; Mendonsa et al., 2018; Pawar et al., 2017). It has also been employed for successful manufacturing of solid dispersions inducing solubility enhancement or controlled release depending on the characteristics of the extrudates. Melt extrusion (ME) can be used for the production of crystalline solid dispersions and amorphous solid dispersions (Patel, 2011). The key diﬀerence in these two extrudate systems is the dispersed state of the API in the amorphous polymer matrix. Crystalline solid dispersions (CSD) are systems where the API is distributed in crystalline state in the amorphous matrix. These systems are generally used for controlling the release of API with high aqueous solubilities (Shah et al., 2013) and their DSC proﬁles have an endothermic peak signifying the melting temperature (Tm) of the API (Mollan et al., 2003; Patel, 2011). CSDs can be prepared by employing top down or bottom up approach by using techniques such as supersaturation or controlled cooling of the molten mass. In both the cases the key for producing CSD is the complete immiscibility of API in the carrier and processing temperature lower than the Tm of the API (Patel, 2011; Shah et al., 2013; Thommes et al., 2011). Attempts have been made towards manufacturing of micro, nano and co-crystal-based systems for solubility enhancements (Wu et al., 2004). Thommes et al. describes an extrusion-based approach to accelerate the dissolution rate and enhance the thermodynamic stability of poorly soluble API (10 and 50% of Griseofulvin, Spironolactone, and Phenytoin) with high Tm using a highly water-soluble excipient (Mannitol) (Thommes et al., 2011). The feed was processed using a ram feeder of a small-scale, corotating twin screw extruder (Haake MiniLab, Thermo Electron, Newington, NH, USA) at 160 °C (below the Tm of API) subsequently raising the temperature to 165 °C. The resultant crystalline suspension on the XRD and thermal characterization displayed chromatograms conﬁrmed the retention of the crystallinity. However, β Mannitol has converted into kinetically stable α Mannitol during the process. The products demonstrated two-fold enhancement in dissolution rate as compared to pure API and enhanced thermodynamic stability as compared to amorphous API or amorphous dispersions. The solubility of an API such as Griseofulvin is not only limited because of its hydrophobicity but also due to its crystal lattice energy (Thommes et al., 2011). These two attributes serve as requirements for API suitable to be formulated as 6 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. API. The formulation demonstrated rapid disintegration times (6–11 s) and an excess of 95% dissolution in < 5 min. Pimparade et al. demonstrated the versatility of HME by developing a solvent free, continuous manufacturing process for manufacturing of ODTs (Pimparade et al., 2017). Extensive research, development and processing of sustained release, and extended release formulations using HME has been conducted in the past decade. Sustained release can be attained by developing a matrix-based system (API uniformly dispersed in a rate controlling polymeric matrix) or core-based controlled release (API trapped in the core with a rate controlling polymeric coat). Sustained release formulations are employed usually for an extended eﬀect of the API to reduce the frequency of administration which reduces side effects and cost of medication for the patient. These systems use hydrophobic polymers which erode as a slow rate releasing API with each eroded polymer layer in a matrix-based system. Polymers that are not hydrophilic at times can also absorb water on contact to form a gel-like layer and a system of small networks from the core to the dosage form environment. This allows the environmental ﬂuid to get imbibed in the core, get saturated with the API and leave the system with a small portion of API which is available for absorption and distribution in the body (Haan and Lerk, 1984; Natarajan et al., 2014). HME can be used to control the release rate of highly water-soluble API by the development of crystalline solid dispersions (Thommes et al., 2011). CSDs are produced by processing API-polymer blend at a temperature lower than the API’s Tm, Hence hydrophobic polymers with low Tm are employed for this process (Dierickx et al., 2012; Thommes et al., 2011). To prevent the solubilization (amorphous conversion) of the API in the molten polymer, theoretical parameters (> 7 MPa1/2) and thermal characterization (DSC) can be employed for the screening of the polymers. Dierickx et al. developed a multilayered (Hydrochlorothiazide (HCT), immediate release coat and Metoprolol tartrate (MPT), sustained release core) dosage form using co-extrusion. Several polymers for immediate release coat (Soluplus®, Eudragit® E PO, Polyox® WSR N10 (PEO), Kollidon® VA) and for sustained release core (CAPA® 6506, Eudragit® RS PO, Ethocel® std 10, Kollidon® SR) were considered and evaluated by coextruding in various ratios with API. From the pool, depending on the ease of processing and kinetics of release, Eudragit® E PO and PEO (with 50% PEG, this provided plasticization lowering the processing temperature and increased the rate of dissolution) were selected for immediate release, and polycaprolactone and ethylcellulose for sustained release. Polycaprolactone and PEO (with 50% PEG) displayed better control over processing (due to lower processing temperatures) and hence were selected for the ﬁnal formulation. The X-ray diﬀraction studies displayed restoration of the crystalline characteristics of Metoprolol tartrate post extrusion, which is beneﬁcial for the sustained release and thermodynamic stability of the dosage form. Whereas, Hydrochlorothiazide did not display any crystalline peaks, which signiﬁes complete amorphous conversion of the API i.e. beneﬁcial for the immediate release nature of the coat. In conclusion, HCT was released in less than 30 min whereas MPT displayed sustained release over a 24-h time period (Dierickx et al., 2012). Delayed release systems usually have a lag time after which they may follow sustained or immediate release kinetics. Taste-masked products are a classic example of these systems wherein API with disagreeable taste are masked using a hydrophilic polymer or a sweetening agent for pediatric and geriatric formulations (Pimparade et al., 2015). Enteric coated systems delay the release of an acid-labile API, usually, polymers with pH dependent solubility or time dependent dissolution are employed for the development of enteric coated formulations (Sarode et al., 2014). Sarode et al. evaluated the extrudability of HPMCAS (Hypromellose Acetate Succinate) which is generally used for the development of enteric coated formulations because of its pH dependent solubility. The L, M and H grades available of this polymer and they dissolved at pH ≥ 5.5, 6 and 6.8 respectively which was controlled by the changing the Acetyl and the Succinoyl content of the polymer. parameters can be used to choose a polymer which can accommodate the desired amount of API. The physical stability issues require a proper investigation of the solid-state properties of the produced solid dispersion. This includes DSC studies, X-ray diﬀraction studies, Scanning Electron Microscopy studies which provide an idea about the kinetics of crystal growth over time (Feng et al., 2014). Maniruzzaman et al. worked towards developing an ASD of Indomethacin (INM) and Famotidine (FMT) using hydrophilic polymers (Soluplus®, Kollidon® VA64, and Plasdone® S-630). The API polymer pairs were selected based on their respective solubility parameters and theoretical compatibilities. Both the API provided good plasticization eﬀects with the selected polymers which in turn reduced the processing temperatures. The thermal characterization conducted using DSC and XRD displayed complete disappearance of the API’s crystalline characteristics even with a 40% drug load. This was also reﬂected in the enhanced dissolution rates of the produced extrudates observed in the release proﬁles of these formulations (Maniruzzaman et al., 2013). The crucial problem with ASDs as described earlier is recrystallization due to high free energy which leads to thermodynamic instability (Patel, 2011). Feng et al. prepared ASDs of Fenoﬁbrate with diﬀerent grades of Klucel™ (LF, EF, ELF) and evaluated the stability of these formulations through thermal characterization (DSC) (Feng et al., 2014). This experimental data was merged with the improved kinetics model of a modiﬁed Avarami equation using a multivariate, nonlinear, regression method to calculate the recrystallization rate constant and activation energy using MATLAB software. The data yielded several important conclusions i.e. a decrease in recrystallization rate (k) with an increase in polymer concentration and an increase in molecular weight of the polymer (KlucelTM LF). The rate ‘k’ increased exponentially with an increase in storage temperature and linearly with an increase in relative humidity (%RH). Hence, modiﬁed Avarami equation can predict the recrystallization kinetics and hence can be used for screening and optimization of formulations in a later stage (Feng et al., 2014). 1 ⎞ α (t ) = 1 − ⎛ ⎝ 1 + kt n ⎠ (1) ∆E k = k 0exp ⎛− A ⎞ ⎝ RT ⎠ (2) where the ‘α(t)’ is the relative crystallinity, ‘k’ is the recrystallization rate constant, ‘k0′ describes the pre-exponential factor, ‘n’ represents the dimensionality of the crystal growth, ‘ΔEA’ is the activation energy, ‘R’ is the universal gas constant and ‘T’ is the absolute temperature. 3.2. Modiﬁed release dosage forms Modiﬁed release signiﬁes all kinds of formulations wherein the release is manipulated using excipients (usually polymers). Some examples of modiﬁed release formulations are orally disintegrating tablets or fast dispersing tablets (Pimparade et al., 2017), sustained release formulations (Ma et al., 2013; Maniruzzaman et al., 2016), delayed release formulations and targeted release formulations (Cassidy et al., 2011; Gately and Kennedy, 2017; Ma et al., 2013). Fast dissolving or super disintegrating systems use super disintegrants such as crosslinked cellulose (Crosscarmellose®, Ac-Di-Sol®, Nymce ZSX®, Primellose®, Solutab®, Vivasol®, L-HPC), cross linked starch (Sodium carboxy methyl starch, sodium starch glycolate), cross linked PVP (Crosspovidon M®, Kollidon®, Polyplasdone®), cross linked alginic acid and mineral salts to induce immediate disintegration of the tablet on contact with aqueous environment for rapid action (Mohanachandran et al., 2011). Pimparade et al. developed an orally disintegrating ﬁlm (ODTs) of chlorpheniramine maleate (CPM) using HME and its continuous manufacturing characteristics. CPM was processed with modiﬁed starch (Lycoat® RS 780) and glycerol (plasticizer) which on thermal evaluation displayed complete amorphous conversion of the 7 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Diﬀerential scanning calorimetry (DSC) and modulated-temperature DSC (MTDSC) are analytical tools widely used for thermal analysis and determination of Tg as well as the Tm of polymers (Thomas, 2005). The operating principle of MDSC® diﬀers from standard DSC in that MDSC® uses two simultaneous heating rates - a linear heating rate that provides information similar to a standard DSC, and a sinusoidal or modulated heating rate that permits the simultaneous measurement of the sample's heat capacity and structure (Forster et al., 2001; Fule et al., 2013; Gao et al., 2015; Ma et al., 2013). It gives information about the solid state (degree of crystallinity or the amorphous nature) of a physical mixture subjected to HME (Kasap et al., 2017; Maniruzzaman et al., 2013; Pani et al., 2012). In DSC, the solid state transitions are measured by temperature and heat ﬂow which are applied as a function of time or temperature simultaneously to the sample and the reference pan using a linear or isothermal program (Censi et al., 2018). DSC was used to evaluate the thermal behavior of the mixture of a poorly water-soluble drug which in this case was Diﬂunisal and a polymer which in this case was PVP K30 to determine the extrusion temperatures. DSC conﬁrmed that the Diﬂunisal degraded at higher temperatures but on conducting DSC studies with polymers it got solubilized in the polymeric carriers (PVP K30, PVP VA64, and Soluplus®) which reduced the processing temperature to 160 °C, a temperature far lower than Diﬂunisal’s melting or degradation temperature (Censi et al., 2018; Guo et al., 2014). Subsequently, the stability of the drug in its amorphous state was determined at various drug-polymer ratios (Kasap et al., 2017). Hence, the use of DSC as a preformulation tool for evaluating the thermal behavior of the polymer and the polymer-drug mix aids informed selection of the processing range. On the other hand, in MTDSC the sample temperature is modulated sinusoidally while being slowly ramped thus allowing better thermal characterization and heat capacity measurement (Kasap et al., 2017). DSC and MTDSC are generally applied for conﬁrming the stability of the API/polymer mixture during or after the process but using them as a preformulation tool can aid the screening of polymers and selection of processing conditions as well. Thermogravimetry also known as thermogravimetric analysis (TGA) is another analytical tool frequently applied during the design of the process (Albarahmieh et al., 2016; Gupta et al., 2014; Gupta et al., 2016). TGA is particularly interesting because it can be used to identify the thermal degradation of a molecule due to changes in the weight during heating. Thereby, TGA is used to predict the temperature limit for HME processing temperatures to avoid thermal degradation during the process. For example, the thermal stability of Diﬂunisal has been determined by TGA and it was found that the Diﬂunisal showed signiﬁcant weight loss and decomposition over the melting temperature of 215 °C. Based on this, it was concluded that the HME of diﬂunisal should not be performed at such a high temperature and hence should be extruded with a polymer having a lower Tg or Tm. The thermal stability of the PEO has also been assessed by TGA and it was found that thermal decomposition of PEO started at nearly 200 °C leading to the formation of smaller polymer segments. From this study, it was concluded that low molecular weight polymers have faster degradation rates (Censi et al., 2018). Considering this, the processing temperatures for PEO should be kept below 200 °C. Some important considerations that will help in selecting a polymer based on the thermal properties (Kolter et al., 2012): Polymers such as HPMCAS-HG can be used with acid-labile API like Lansoprazole to develop delayed release formulations to enhance the stability of the participating API. Delayed release systems have also been employed for chronotherapy (time mediated release of API) for patients with chronic metabolic disorders and arthritis. HME in combination with 3D printing technology can be used for the manufacturing of multilayered, time sensitive or stimuli sensitive dosage forms for patient speciﬁc ailments (Sarode et al., 2014). A targeted release can also be achieved using HME (Cassidy et al., 2011; Gately and Kennedy, 2017; Zhu et al., 2006). Gastro retentive systems, formulations targeting Peyer’s patches in the intestine and colon targeting systems have been previously investigated for oral drug delivery. Vo et al. developed a novel ﬂoating drug release system for anhydrous theophylline using ammonio methacrylate copolymer (Eudragit®-35RSPO) by developing foaming structures using ethanol’s liquid-vapor phase transition inside the strand towards the terminal end due to pressure reduction (Ashour et al., 2017; Vo et al., 2016). These vacant spots created by vaporization of ethanol, leading to a low density, hydrodynamically balanced system. These produced pellets were evaluated using X-ray diﬀraction to get a clear idea of their internal structures, ﬂoating strengths to evaluate the hydrodynamic stability of the system, density and porosity, and ﬁnally their drug release characteristics. The ﬁnal formulation was able to release the drug over a period of hours. The ﬂoating strengths displayed a positive correlation with the stearic acid content (processing aid) and drug release was initially (for the ﬁrst hour) controlled by the drug content and after by the HPMC concentration. Vo et al. concluded that processing parameters such as the screw speed and feeding rate had a pronounced effect on the compression of the matrix. Increasing the feeding rate compressed the matrix more, thereby decreasing the ﬂoating strength. In contrast, it was observed that increasing the screw speed led to a decrease in the density of the matrix, enhancing its buoyancy. The ﬂoating strength of the pellets after 12 h was most signiﬁcantly dependent on the stearic acid content and screw speed. The HPMC content also negatively aﬀected the ﬂoating force as its presence accelerated the water penetration into the pellet (Vo et al., 2016). Research has been conducted on the incorporation of pressurized CO2 instead of ethanol at the die terminal to induce low density and hydrodynamic stability which will make the process solvent free (Ashour et al., 2017). 4. Screening of polymers based on physiochemical properties Amongst the excipients used for HME formulations, polymers are essential and are used extensively. The wide range of physiochemical properties of the polymers oﬀer opportunities to overcome various processing limitations of HME and develop novel dosage forms (Miller et al., 2012). To process stable solid formulations using HME, it is imperative to choose a polymer with appropriate physical and chemical stability and drug–polymer miscibility (Patil et al., 2016). Furthermore, various physiochemical properties and criterion for the selection of polymers for HME are discussed in detail in this section of the review: 4.1. Thermal properties of polymers The extrusion conditions of a polymer can be assessed by its glass transition (Tg), melting temperature (Tm) or a combination of both, therefore it is crucial to understand them (Kolter et al., 2012). Tg is a transitional temperature at which the molecules within a polymer chain begins to soften and become ﬂexible from their glassy (disorganized molecular array) state. It is a characteristic of amorphous polymers and it depends on the molecular weight of the polymer as well as the mobility of the polymer chain structure. On the other hand, Tm is the temperature at which the molecules go from an ordered to a disordered state and start to melt which is depicted by crystalline polymers. Whereas semi-crystalline polymers show both Tg and Tm as they exhibit both amorphous and crystalline regions (Balani et al., 2014). 1. To make the HME process possible, the polymers should be stable at the extrusion temperature and possess thermoplastic properties. 2. High processing temperatures are required for high Tg or Tm, but they could lead to degradation of thermo-sensitive molecules. 3. For most polymers, the processing temperature during HME should be kept 20–40 °C above the Tg of the polymers (preformulation studies must be conducted to foresee any plasticizer eﬀect provided by the API). 4. The high Tg of the polymers can be reduced by using plasticizers (preferably in the solid state) (Ghebremeskel et al., 2007). 8 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. enabled the melt extrusion of clotrimazole at 140 °C by using the plasticizer polyethylene oxide (PEO) 15–55%w/v to reduce the melt viscosity of the polymer Hydroxypropyl cellulose (HPC) (Ghosh et al., 2011). Another example is that of the polymer Polyvinyl CaprolactamPolyvinyl Acetate-Polyethylene Glycol Graft Copolymer (Soluplus®) which has a very high molecular weight of 118,000 Daltons but has low melt viscosity due to the polyethylene glycol moiety in the polymer, which acts as a plasticizer (Repka et al., 2013). Length of the polymer chains also plays a signiﬁcant role in their melt viscosity since polymers vary in molecular weight depending on their chain length. Also, longer the polymer chain, more the melt viscosity as they will have a higher degree of entanglement. It is generally observed that higher temperatures lead to the disentanglement of the polymer chains and also causes them to arrange themselves in a linear fashion thereby reducing the melt viscosity of polymers (Meena et al., 2014). Just like in the case of Soluplus® at 120 °C, the viscosity of the polymer was over 600000 Pa·s making it extremely diﬃcult to extrude. With a rise in temperature to 170 °C, the viscosity dropped to 1000 Pa·s and the torque also drastically reduced by 30%. Beyond 170 °C, the viscosity further decreased and was extruded easily indicating that it was free ﬂowing (Fox and Flory, 1948; Gupta et al., 2016) (see Table 2). Choksi et al. used DSC to evaluate the miscibility of the model drug (indomethacin) with several polymers (Eudragit® EPO, PVP K30, and poloxamer 188) for their suitability for the HME process. From the results, it was concluded that all the polymers were miscible with indomethacin except poloxamer 188. One indication of this phenomenon was the appearance of two melting peaks (Tm) for various binary mixtures of the drug and polymer which can be attributed to partial miscibility of the drug in molten polymer. Since the melting point is a colligative property, the miscibility of drug in molten polymer decreased the drug’s melting point. Although, a single peak was observed at 20% drug load which suggests complete miscibility of the drug in poloxamer 188 at a lower drug load. Thus, this study proved to be a useful indicator for the selection of polymers for HME (Chokshi et al., 2005). In another study, the stability of Hypromellose acetate succinate (HPMCAS) polymer was evaluated for HME processing using DSC. It was found that HPMCAS did not degrade at the processing temperatures-160, 180, and 200 °C when extruded using HME since it showed only one Tg at 120 °C (Sarode et al., 2014). Furthermore, the thermal degradation of Gliclazide as a pure drug or in the presence of the polymer Aﬃnisol™ HPMC HME 100LV during HME was assessed using TGA. Pure Gliclazide showed degradation over 165 °C while TGA showed that the weight loss occurred between 165 °C and 240 °C was less than 10%. However, the degradation was further conﬁrmed by HPLC. Therefore, based on this result, the processing temperature for HME was optimized such that it is below the degradation temperature (Huang et al., 2017). Moreover, TGA was also useful to ﬁnd the degradation temperature of several other polymers (Soluplus®, Kollidon® VA64, Kollidon® 12 PF, and Aﬃnisol® HPMC), which would have the ability to form amorphous solid dispersions with the itraconazole during HME (Lauranne et al., 2016). 4.3. Solubilization capacity At least 40% of the API and as many as 70% of the New Chemical Entity(s) (NCE) are poorly water soluble which is a challenge long faced by the pharmaceutical industry (Patel, 2011). Therefore, there is an unmet need for novel solubilizers. Apart from the Tg and melt viscosity another critical characteristic of the polymers that has an impact on the HME process is their solubilization capacity. The solubilization capacity of the polymers is the extent to which they can solubilize the API in aqueous solution (Repka et al., 2013). The solubilization capacity of the polymers also plays a key role in the release of the API, in turn increasing their bioavailability and resorption. The most commonly used solubilizers for solid oral dosage forms are polyoxyethylene and polyoxypropylene copolymers (Poloxamers) (Hardung et al., 2010). However, their use is limited in solid dispersions due to their low Tm. While the most commonly used solubilizers for liquid formulations are usually polyethylene glycol-based surfactants, some other polymers like povidone and cyclodextrins have also been used but they have limited solubilization capacity for insoluble drugs (Hardung et al., 2010; Lim et al., 2015; Manakker et al., 2009). Amphiphilic polymers have good solubilization capacity for API as the API can reside within the hydrophobic region of the micelles while in the case of non-amphiphilic polymers, solubilization of the API occurs by complexing eﬀect with the polymer chains (Repka et al., 2013). The mechanism of solubilization is better understood by Noyes-Whitney equation which suggests that the dissolution rates of the crystalline drug in the molten carrier matrix can be increased by processing above the Tg of the carrier phase (Shah et al., 2013; Siepmann and Siepmann, 2013). This would result in an elevated equilibrium solubility and diﬀusivity of the drug in the molten carrier phase. Furthermore, increased solubility can also be achieved by micronization of the drug or addition of co-solvents and plasticizers (Ghebremeskel et al., 2007; Okuda et al., 2012). Moreover, the solubility or miscibility of the drug in the polymer matrix is also greatly dependent on the stability of the amorphous drug against crystallization as the amorphous state is highly unstable and it leads to recrystallization of the drug from the solid dispersion during storage (Feng et al., 2014). However, this can be avoided in most cases if the storage or manufacturing temperature is kept at 50 °C less than that of Tg. Apart from the polymeric properties, successful solubilization using HME is also dependent on the following (Shah et al., 2013): 4.2. Melt viscosity According to Kolter et al, it is the melt viscosity of the polymer rather than the Tg that determines whether the extrusion is possible or not (Nishioka et al., 1958; Parikh et al., 2014). If the melt viscosity of the polymer is too high, torque within the extruder will increase drastically and ultimately overload the motor and the screws (Franck, n.d.; Huang et al., 2016). For example, most cellulosic polymers, owing to their relatively high melt viscosity, generated high torque within the extruder and therefore, were not suitable for melt extrusion (Gupta et al., 2016; Meena et al., 2014). Also, if the melt viscosity is too low, it will considerably avert the extrudate formation through the extrusion die. So, the optimum range of melt viscosity of a polymer for melt extrusion is found to be between 1000 and 10,000 Pa·s (Kolter et al., 2012). In general, the melt viscosity of a polymer can be deﬁned as the rate at which the polymer chains can move relative to one another and is mainly controlled by the chain ﬂexibility as well as by the degree of entanglement. For example, polymers such as Polytetraﬂuoroethylene (PTFE), aromatic polycarbonates and aromatic polyimides are highly viscous in their melting range due to their low chain ﬂexibility as compared with polyethylene or polystyrene polymers (Gilbert, 2017). It is crucial to understand the properties of the polymers which aﬀect its melt viscosity to optimize the extrusion process (see Fig. 4). Molecular weight is the most important structural parameter for the ﬂow behavior of polymers at temperatures above their Tg or Tm (in case of an amorphous or semi-crystalline polymer) (Franck, n.d.). It is observed that with an increase in the molecular weight of the polymer, the melt viscosity also increases (Colby et al., 1987; Fox and Flory, 1948). For example, in the case of Kollidon® as its molecular weight increases from 2500 Daltons (Kollidon® 12 PF) to 1,250,000 Daltons (Kollidon® 90F), the melt viscosity also increases substantially (Kolter et al., 2012). As described earlier in the case of high Tg polymers, plasticizers can be added in order to extrude the high molecular weight polymers smoothly and to lower their melt viscosity. The higher the amount of plasticizer, the lower the melt viscosity (Repka et al., 2013). Prodduturi et al. (i) The properties of the drug (ii) The processing temperature (iii) Operating conditions 9 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Fig. 4. Factors aﬀecting melt viscosity. of the mechanical properties of the polymers, like the extent to which it can be stretched or bent and its hardness (Balani et al., 2014). These properties also aﬀect the drug product stability and drug release. Therefore, the various mechanical properties of the polymers and the factors which aﬀect some of them are explained below: (iv) Appropriate screw conﬁguration (v) Shearing action of the screw Recently introduced grades of polymer speciﬁcally designed for HME processing i.e. AFFINISOL™ HPMC HME (15LV, 100LV, 4M), have low Tg and melt viscosities which ultimately provides a broad processing window for HME (Dow Pharma and Food Solutions, n.d.; Gupta et al., 2016). Studies have also proven that it is capable of solubilizing Biopharmaceutical Classiﬁcation System (BCS) class II and class IV compounds (Amidon et al., 1995; Ritu and O’Donnell, 2016). Ritonavir has poor water solubility and it rapidly degrades if the temperature exceeds 160 °C and hence AFFINISOL™ due to its wide operating window, was used to prepare solid dispersions of ritonavir such that it did not cause degradation of the drug and optimized the HME process (Bauer et al., 2001; Ritu and O’Donnell, 2016). Solubilization of two poorly water-soluble drugs (carbamazepine and itraconazole) in various polymers and polymer-plasticizer combinations were tested using HME. It was found that the polymer (Kollidon®) and the polymerplasticizer combination Kollidon® 30 +10% PEG 1500 dissolved more than 50% w/w of itraconazole. While in case of Carbamazepine, Kollidon® VA 64 and Soluplus® not only solubilized > 30 %w/w of Carbamazepine but also kept the API stable over a period of 3 months (Kolter et al., 2012; Liu et al., 2012). a. Strength: The strength is the stress needed to break the polymer. This could mean the polymer's capacity to withstand stretching (tensile strength) which is aﬀected by the molecular weight of the polymer. The tensile strength of the polymer increases with the molecular weight until it gets saturated. This is because the polymer chains with low molecular weights have loose Van der Waals bonds and hence the chains can move easily which ultimately leads to low strength. Polymers with large molecular weights have large chains that get entangled, giving strength to the polymer. Various other strengths of the polymers include the polymer’s capacity to withstand compression (compressional strength), bending (ﬂexural strength), twisting (torsional strength) or hammering (impact strength). Network polymers have the most strength followed by cross-linked and branched polymers, while polymers with linear chains have the least strength of the bunch (Flory, 1953; Gandhi et al., 2012; González-Campos et al., 2013). b. Ultimate Elongation (Percent Elongation to Break): This is the maximum strain that the polymer can endure without breaking. It is related to the percentage elongation in the polymer length before fracture. For example, ceramics have low (< 1%), metals have moderate (1–50%) and thermoplastics have high (> 100%) values 4.4. Mechanical properties To eﬃciently optimize the HME process, it is important to be aware Table 2 Methods employed for the measurement of physiochemical properties of polymers. Polymer property Thermal properties Glass transition temperature (Tg) Melting temperature (Tm) Degradation temperature (Td) Melt viscosity Solubilization capacity Mechanical properties Methods of estimation References Diﬀerential scanning calorimetry (DSC); Modulated temperature diﬀerential scanning calorimetry (MTDSC); Dynamic mechanical analysis (DMA); Thermal mechanical analysis (TMA) Thiele tube; Fisher-Johns apparatus; Gallen Kamp (Electronic) melting-point apparatus; DSC; Thermal gravimetric analysis (TGA) DSC; MTDSC; TGA Melt ﬂow indexer (MFI); Shear stress controlled rotational rheometers with temperature control; capillary rheometers and viscometers Experimental solubility studies; theoretical parameters; thermodynamic phase diagrams (Flory Huggins theory) Texture analyzer; Three-point bend/ﬂexural test; Rheometers Ma et al. (2013) and Mahlin et al. (2009) Marsac et al. (2006) 10 Meena et al. (2014) Kolter et al. (2012) Kolter et al. (2012) and Marsac et al. (2006) Repka et al. (2005) International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. technological breakthrough was the birth of in silico experimentation and bioinformatics in 1970. In 1989 the expression was used for the ﬁrst time in public at the workshop “Cellular Automata: Theory and Applications” in Los Alamos, New Mexico by the mathematician Pedro Miramontes (Rojas and Matas, 2016). In silico is a commonly used expression for computer simulations with regards to biological or nonbiological experiments, using bioinformatics for cell models, digital genetic sequences, computer aided drug designing and molecular modelling (Danchin et al., 1991). This has been accomplished by the development of massive libraries ranging from small molecules to large proteins and peptides such as biopanning data banks (BDB) (Danchin et al., 1991; Turpin et al., 2018; Wadood et al., 2013). These methods can potentially reduce the need for expensive lab work and clinical trials which can speed up the rate of drug development. In pharmaceutics, these models are incorporated from the selection of excipients to in silico evaluation of the ﬁnal formulation using computerized dissolution models. These models have the advantages of in vivo as well as in vitro models without the need for ethical considerations and gives the user a virtually unlimited array of parameters (Wadood et al., 2013). This section of the review will focus on the computer-based and theoretical methods utilized for the selection of a suitable polymer. Theoretical parameters are mathematically derived descriptions of complex physical and chemical phenomenon which can be used as predictors for inter and intramolecular interactions e.g. solubility/ miscibility (Barton, 2000; Greenhalgh et al., 1999). With this data, processes resulting in unfavorable outcomes can be minimized and in turn can signiﬁcantly reduce the time and resources allocated when conducting experiments. These complex mathematical models are visualized computationally using in silico models (Maniruzzaman et al., 2015). During the HME process, polymers and drugs are exposed to high shear and temperatures which can result in a myriad of problems e.g. phase separation and insolubility of the drug in the polymer. The solubility of a drug in a polymer is a prerequisite when it comes to the formation of stable solid dispersions (Albarahmieh et al., 2016; Alhijjaj et al., 2015). Commonly used theoretical parameters for predicting the interactions (solubility/miscibility) between the API and polymer are: of ultimate elongation (González-Campos et al., 2013). c. Young’s Modulus (Modulus of Elasticity or Tensile Modulus): Young’s Modulus measures the stiﬀness of the polymer in the linearly elastic region and is the ratio of stress to the strain. The toughness measures the energy absorbed by the material before breaking and is given by the area under a stress–strain curve. For example, elastomers have low Young’s modulus and toughness. While both brittle polymers and ductile polymers have similar Young’s modulus. Ductile materials have higher fracture toughness as well (González-Campos et al., 2013). d. Viscoelasticity: Deformations could be either elastic or viscous. For constant stress applied to the material in the elastic deformation, the strain is generated at the moment the constant stress is applied. The material recovers its initial shape when stress is removed, that is the deformation is reversible. However, in viscous deformation, the strain generated is not instantaneous and increases with time when a constant load is applied. Furthermore, the deformation is irreversible, and the material does not recover to its original shape completely. Most polymers show both elastic and viscous deformation depending on the temperature and the strain. Elastic behavior is observed at low temperatures and high strain rates, while the viscous behavior is observed at high temperatures but low strain rates. At intermediate temperatures and strain rates, a combination of two is observed. This behavior is termed as viscoelasticity, and the polymer is termed as viscoelastic (González-Campos et al., 2013). Amongst these parameters discussed, the elastic modulus, tensile strength, and elongation of the extruded products are the most crucial parameters that need to be assessed as they play a pivotal role in the product processability, stability and release of the drug product. Some examples of how these physio-mechanical properties are aﬀected in the ﬁlms produced by HME are explained below: While formulating a hot melt extruded orally dispersible ﬁlm, it is essential for the selected polymer to provide optimum mechanical properties as well as release rates (Low et al., 2013). Orodispersible ﬁlms were formulated with HME using Hydroxypropyl cellulose (ﬁlm forming polymer) in conjugation with drug solubilizing polymers (Kollidon® VA 64 or Soluplus®) in a co-rotating twin screw extruder. The screw speed was maintained at 100 RPM, Triethyl citrate (TEC) was injected into the extruder barrel and the extrusion temperature was kept below 140 °C to avoid degradation of TEC. Indomethacin and chlorpheniramine were the model drugs that were chosen. These ﬁlms were evaluated in terms of their tensile strength, elongation percentage and Young’s modulus. It was found that both the drugs showed plasticizing eﬀects and showed lower ﬁlm stiﬀness while ﬁlms containing Kollidon® VA 64 performed better in terms of drug release. Films containing Soluplus® had better mechanical properties, this suggests that polymers play a major role in attaining a balance between desirable mechanical properties and drug release properties. Hence, careful evaluation of mechanical properties prior to designing a formulation can aid attaining desired quality target proﬁles (Low et al., 2013). Another study was conducted to evaluate the presence of Vitamin E TPGS on the physio-mechanical properties of ﬁlms containing HPC and polyethylene oxide (PEO) which were prepared using HME. Results indicated that as the concentrations of TPGS increased as the tensile strength decreased. The percent elongation of HPC/PEO ﬁlms with TPGS increased three-fold as compared to the HPC/PEO ﬁlms without TPGS. It was concluded that TPGS assisted in the processing of HPC/ PEO ﬁlms by reducing the barrel pressure and torque of the extruder (Low et al., 2013; Piccinni et al., 2016; Repka and Mcginity, 2000) (see Table 3). (1) Solubility parameters (Greenhalgh et al., 1999; Sarode et al., 2013; Turpin et al., 2018; Wlodarski et al., 2015) (2) Flory Huggins/thermodynamic modeling (Anderson, 2018; Marsac et al., 2009; Marsac et al., 2006) These theoretical estimates minimize unfavorable outcomes by giving numerical values to the likelihood of success of the drug-polymer pairing and hence signiﬁcantly reduces the wastage of time and resources commonly associated with trial and error. 5.1. Solubility parameters Joel H. Hildebrand in 1936 proposed a numerical value that can be used to indicate the speciﬁc solvency behavior of a speciﬁc solvent. It was not until 1950 that it was given the name Hildebrand solubility parameter which was described as the total Van Der Waals force and has been since considered as the simplest solubility value (Barton, 2000; Marsac et al., 2006). These parameters were used to explain the miscibility of the solute with the solvent and were later extended to polymer-based systems. Solubility parameters employ the use of individual Cohesive Energy Density (CED) values in order to predict the miscibility of two substances. Inter atomic/molecular interactions such as Van der Waals interactions, covalent bonds, ionic bonds, hydrogen bonds, electrostatic interactions, induced dipole, and permanent dipole interactions contribute to the cohesive energy of a substance (Sarode et al., 2013). The CED is the cohesive energy per unit volume required to vaporize molecules in a condensed phase (Heat of vaporization). The equation below is used for the calculation of CED: 5. Selection of polymers based on theoretical parameters With the exponential growth of technology over the past few decades, research is becoming more eﬃcient and cost-eﬀective. One such 11 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. Table 3 Thermal and theoretical properties of the polymers used in HME. Trade name Tg (°C) Tm (°C) Degradation Temperature (°C) δ (MPa1/2) References Kollidon® 12 PF 72 – 196 19.40 Kollidon® 17 PF 140 – 217 21.75 Kollidon® 25 153 – 166 22.5 Kollidon® 30 160 – 171 25.12 Kollidon® 90F 177 – 194 – Lim et al. (2015) and Miller et al. (2012) Miller et al. (2012) and Piccinni et al. (2016) Miller et al. (2012) and Wlodarski et al. (2015) Miller et al. (2012) and Shi et al. (2019) Miller et al. (2012) 105 – 270 19.60 72 – 278 19.35 Methocel™A 200 – 247 30.0 Ethocel® 4P 128 168 200 – Ethocel® 7P 128 168 205 – Ethocel® 10P 132 172 205 20.90 Hydroxypropyl cellulose (HPC) (MW 95000) Klucel® LF 111 – 227 21.27 Hydroxypropyl methyl cellulose (HPMC), 100 cps, (MW 25,000) Hydroxypropyl methyl cellulose (HPMC), 100,000 cps, (MW 150,000) Methocel™ K100LV Methocel™ K100M 147 168 259 – 96 173 259 21.10 Hydroxypropyl methyl cellulose acetate succinate (HPMCAS) AFFINISOL™ HPMC HME ~115 – > 250 29.10 Eudragit® E PO 52 – 250 18.90 Balani et al. (2014) and Maniruzzaman et al. (2015) Eudragit® RL PO 63 – 166 – Balani et al. (2014) Eudragit® RS PO 64 – 170 18.35 Balani et al. (2014) and Piccinni et al. (2016) Eudragit® L 100 125-135 – 176 22.75 Balani et al. (2014) and Maniruzzaman et al. (2015) Eudragit® S 100 173 – 173 18.38 Balani et al. (2014) and Piccinni et al. (2016) Eudragit® L 100–55 111 – 176 21.65 Balani et al. (2014) and Maniruzzaman et al. (2015) Chemical name of the polymer Poly-vinylpyrrolidones (PVP) PVP (MW 2000–3000) PVP (MW 7000–11,000) PVP (MW 28,000–34,000) PVP (MW 44,000–54,000) PVP (MW 1,000,000–1,500,000) Poly(vinylpyrrolidone-co-vinyl acetate) block copolymer (PVPcoPVA) Kollidon® VA64 Vinyl pyrrolidone: vinyl acetate 6:4 (MW 45,000–70,000) Soluplus® Poly(vinyl caprolactam-covinylacetateethylene glycol) graft polymer (MW 90,000–140,000) Cellulose Ethers Methyl Cellulose (MC) (MW 14,000) Ethyl Cellulose (EC) 4 cps Ethyl Cellulose (EC) 7 cps Ethyl Cellulose (EC) 10 cps Polyacrylates and Polyacrylic acids Butyl methacrylate:Dimethylamino ethyl methacrylate:Methyl methacrylate (1:2:1) (MW – 47,000 Da) Ethyl acrylate:Methyl Methacrylate:Trimethylammonioethyl ethacrylate chloride (1:2:0.2) (MW – 32,000 Da) Ethyl acrylate:Methyl methacrylate:Trimethylammonioethyl methacrylate chloride (1:2:0.1) (MW – 32,000 Da) Methacrylic acid:Methyl methacrylate (1:1) (MW – 125,000 Da) Methacrylic acid:Methyl methacrylate (1:2) (MW – 125,000 Da) Methacrylic acid:Ethyl acrylate (1:1) (MW – 320,000 Da) c= ∆H − RT Vm Maniruzzaman et al. (2015), Miller et al. (2012) and Piccinni et al. (2016) Miller et al. (2012) and Piccinni et al. (2016) Kolter et al. (2012), Patil et al. (2016) and Wlodarski et al. (2015) Censi et al. (2018), Kolter et al. (2012) and Patil et al. (2016) Censi et al. (2018) and Kolter et al. (2012) Kasap et al. (2017), Kolter et al. (2012) and Stortz and Marangoni, 2014) Kasap et al. (2017), Kolter et al. (2012), Shi et al. (2019) and Xiang and Anderson, 2017) Kolter et al. (2012) Kasap et al. (2017), Kolter et al. (2012) and Xiang and Anderson, 2017) Ghebremeskel et al. (2007), Patil et al. (2016) and Sarode et al. (2013) The square root of the CED is the Hildebrand solubility parameter signiﬁed as ‘δ’ for which the standard international unit (SI units) is mega-pascals (MPa) (Hildebrand and Lane, 1922). (3) where 1 δ= C = Cohesive energy density ΔH = Heat of vaporization R = Gas constant T = Temperature Vm = Molar volume. ∆H − RT ⎤2 c =⎡ ⎥ ⎢ Vm ⎦ ⎣ (4) There are multiple ways to ﬁnd the Hildebrand solubility parameter of polymer mixtures which have been proposed by Hansen, Stefanis, Panayiotou, Van Krevelen, Hoftyzer, Hoy, Fedors and Beerbower. The Hildebrand solubility parameter was found to perform poorly in the 12 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. dependent solubility parameter by using the two-dimensional approach which can be deﬁned by δv, where δp and δd were used to predict the combined thermodynamic eﬀects on the drug–polymer miscibility over hydrogen bonding energy, δh (Maniruzzaman et al., 2015). case of polar interactions and hence for pharmaceutical drugs and excipients, the solubility parameter used is the Hansen solubility parameter (HSP) which can be calculated using Van Krevelen and Hoftyzer’s group contribution methods (Stefanis and Panayiotou, 2008). Van Krevelen derived a set of atomic contributions to calculate ‘F’ in 1965. Solubility parameter for any compound can be calculated using this value of ‘F’. Van Krevelen and Hoftyzer’s group contribution method is one of the easiest methods to estimate Hansen’s solubility parameter (HSP) which is calculated as the sum of the contributions from diﬀerent moieties that make up the molecule of interest. This parameter was set by Hansen taking into consideration the three types of Van der Waals forces playing a role in the solubility which are polar forces, dispersion forces, and hydrogen bonding forces. (J/cm3)1/2 or MPa1/2 are used to express these parameters using the equation below which was introduced by Hansen (Sarode et al., 2013): δ 2 = δd2 + δp2 + δh2 δv = ∑ Fdi V δp = (5) ∑ Fpi2 V δh = ∑ Ehi V (8) These processes do have their limitations as the estimation of highly directional (e.g. hydrogen bonding) or long-range (e.g. electrostatic) interactions are challenging. Depending on the nature of the formulation, solubility parameters can be used for the screening of polymers. Ashish L. Sarode et al. evaluated the processing parameters and the impact of solid-state intermolecular drug-polymer interactions on supersaturation in hot melt extrusion (HME). Three poorly water-soluble drugs (Indomethacin, Itraconazole, and Griseofulvin) and seven hydrophilic polymers (Eudragit® EPO, Eudragit® L-100-55, Eudragit® L100, HPMCAS-LF, HPMCAS-MF, Pharmacoat 603, and Kollidon® VA64) which are of extensively diverse physiochemical properties were used for this evaluation. For this study, the solubility parameter calculation (SPC) was used to see if the values obtained corroborated with the DSC values. The method used for this calculation was Van Krevelen and Hoftyzer’s group contribution method with the help of Molecular Modelling Pro software. The partial solubility parameters were calculated for polymeric materials based on the single repeating monomer unit and the average molecular weight was used for the determination of solubility parameter (δ). The Δδ was also determined, which is the diﬀerence between the δ values of the drug and the polymer. All the Δδ were found to be below 7.0 MPa1/2 as the highest Δδ was found to be 6.9 MPa1/2 from the calculation and Itraconazole-Eudragit® EPO system was found to be immiscible as it did show the highest diﬀerence between the solubility parameters. The values obtained were found to be in line with the DSC results. This suggests that solubility parameters can be employed for the preliminary screening and selection of polymers (Sarode et al., 2013). Forester et al. determined the miscibility of the drug and excipient in order to predict the likelihood of formation of glass solutions when melt extruded. Two poorly soluble drugs were used which were Indomethacin and Lacidipine with 11 diﬀerent polymeric and nonpolymeric excipients. An average of Hoy and Hoftyzer/Van Krevelen methods were used in the determination of Hansen’s solubility parameter. For polymeric excipients, the average molecular weight was the basis of calculation. Indomethacin and Lacidipine were found to have similar solubility parameters (δ = 22.1 MPa1/2 and 20.0 MPa1/2 respectively). Hence, excipients with solubility parameters less than 27–30 MPa1/2 were considered miscible whereas solubility parameters over 30–32 MPa1/2 were considered immiscible. The excipients were grouped into three categories where δd = δd2 + δp2 (6) ‘δ’ is the total solubility parameter, δd is the contribution from the dispersion forces, δp is the contribution from the polar forces and δh is the contribution from hydrogen bonding (Sarode et al., 2013). Atom molecular dynamics (MD) can be used to estimate the CED and hence also the solubility parameter (δ). MD uses classical mechanics to show the evolution of a molecular system over time and can be calculated using MD simulations like GROMOS, OPLS, CHARMM, AMBER, and COMPASS (Maniruzzaman et al., 2015). These simulations do not explicitly take into consideration the hydrogen bonding but implicitly takes it into consideration with the polar term. Hence the MD simulations calculate Hansen parameter with only taking into consideration δp and δd. Substances with similar solubility parameters are considered to be miscible because the energy of mixing within the components is balanced by the energy released by the interaction between the components. When the Δδ which is the diﬀerence in the solubility parameters in drug-excipient blends is < 7.0 MPa1/2, it is anticipated to be miscible which is usually a requirement for the development of amorphous solid dispersions. The drug-excipient pair with Δδ > 10 MPa1/2 are predicted to be immiscible with each other, which works well with controlling the release of highly water-soluble drugs (crystalline solid dispersions) or crystalline suspension of a poorly water-soluble API in an extremely hydrophilic matrix (e.g. Mannitol) (Agarwal et al., 2015; Maniruzzaman et al., 2015; Wilkinson, 1996). Hansen parameters when combined can in fact, give the Hildebrand solubility parameter but Hansen preferred if it gave rise to a Radius for which a factor of 4 will give additional weighting to the dispersive term. Group 1 – Δδ < 2.0 MPa1/2- likely to be miscible Group 2 – Δδ > 2.0 MPa1/2 and Δδ < 10 MPa1/2-likely to be immiscible Group 3 – Δδ > 10.0 MPa1/2- borderline miscibility δ = 4(δd − polymer − δd − dug )2 + (δp − polymer − δp − dug )2 + (δh − polymer − δh − dug )2 (7) HSPiP is the Hansen computer program that uses a radius of a sphere that has been derived experimentally excluding immiscible solvents. In the absence of experimental data, this Radius can give an idea about the miscibility of drug-polymer combinations (Abbott et al., 2008). This two-dimensional approach can be used to estimate the drug-polymer miscibility by calculating the distance denoted by Ra(v) using the Pythagorean theorem. For such cases as Ra(v) ≤ 5.6 MPa1/2 renders the substances miscible (Maniruzzaman et al., 2015). Hansen solubility parameter might not always be very accurate as it is limited by the availability of data for diﬀerent group contributions. Hansen solubility parameters also fail to consider the eﬀect of chain conformation which includes branching and linkages between monomer units and the molecular weight of the compound. These factors are imperative for a complete understanding of the miscibility of drug molecule into polymer matrixes. Bagley et al. introduced volume- It was found that the diﬀerence between solubility parameters if < 2.0 MPa1/2 were likely to form glass solutions but the viscosity of the excipient may limit the drug/excipient ratio even in these cases. Therefore, thermal analysis needs to be performed experimentally before extrusion. The prediction of glass solution for a diﬀerence in solubility parameters between 5 and 10 MPa1/2 cannot be done without thermal analysis. It was seen that the experimental values corroborated with the solubility parameter predictions and that a combination of HSP and thermal analysis can be successfully used for the prediction of glass solutions with melt extrusion (Forster et al., 2001). Alazar N. Ghebremeskel et al. investigated the properties of surfactants such as Tween-80, Docusate sodium, Myrj-52, Pluronic-F68, and SLS as plasticizers for API-polymer systems during the HME process 13 International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. interaction of a given molecule and an average ﬁeld, caused by all the other molecules in the system. and characterized their performance of the extruded API-polymer surfactant matrices. Physical blends of poorly soluble API and hydrophilic polymers such as PVP-K30, Plasdone -S630, HPMC -E5, HPMCAS, and Eudragit® L100 with a mass ratio of 1:1 was used. Molecular modeling pro (1991) was the software being used and the calculations were done according to the group contribution method. Chemical structure using Hansen 3-D solubility parameter was used for the compounds. Hansen’s partial solubility parameters were used for polymeric excipients and calculated based on the single repeating monomeric unit. The solubility parameters of the API and surfactants were then matched with polymers by observing the Δδ and the partial Hansen solubility parameter values which are depicted by R (the square root of the sum of squares of diﬀerences in dispersion, polarity and hydrogen bonding values). Compounds which has similar solubility parameters were considered miscible (Δδ < 7.0 MPa1/2). The solubility parameter of the API was found to be 33.1 MPa1/2 and hence solubility parameters between 26 and 41 MPa1/2 will be miscible whereas less than 23 MPa1/2 and above 41 MPa1/2 will be immiscible. The solubility parameters were successfully correlated with the level of plasticization observed (Ghebremeskel et al., 2007). Maniruzzaman et al. used molecular modelling as a predictive tool for the development of solid dispersions. Quantum mechanical (QM) calculations were used to predict the miscibility of various drugs with various polymers. These QM calculations predicted the binding strength between drug and dimeric form of the polymer to estimate miscibility. Hansen solubility parameters were calculated by both Van Krevelen/ Hoftyzer’s group contribution method as well as by the method developed by Bagley et al. It was found that diphenhydramine HCl and propranolol HCl were miscible with Eudragit® L100 and L100-55. It was also found that paracetamol is not miscible with Eudragit® EPO. Van Krevelen and Hoftyzers’s partial solubility parameter predicts that Diclofenac sodium is miscible with Eudragit® L100, but Bagley’s theory opposes this. Paracetamol and Kollidon® VA64 polymer had a Δδ < 7 MPa1/2 but were found however to be immiscible as paracetamol existed as a separate amorphous-amorphous phase from the polymer after extrusion. Molecular modeling was able to successfully predict the drug-polymer binding energies and the preferable site of interaction between functional groups. Ultimately, QM based molecular modeling proved to be a powerful tool to predict the strength and the type of intermolecular interactions in a range of drug/polymeric systems for the development of solid dispersions (Maniruzzaman et al., 2015). The end result is an equation for the Helmholtz free energy change of mixing of polymer solutions. The theory assumes that the polymer along with the solvent molecules will arrange themselves randomly within a crystal structure. In Flory Huggins theory, the Gibbs free energy of mixing correlates (ΔGM) with the number of moles and the volume fraction of the drug (ϕ) and polymer using the equation (Wilkinson, 1996): ∆GM = ndrug lnϕdrug + npolymer lnϕpolymer + χndrug ϕpolymer RT (9) where R is the gas constant, T is the absolute temperature, n being the number of moles and χ (a dimensionless quantity) being the interaction parameter which takes enthalpy of mixing into consideration and is an indicator of drug-polymer miscibility. The ﬁrst two terms in the equation for most drug and polymer combinations remain constant as the conﬁgurational entropy of any polymer is negligible which is due to the connectivity of polymer repeat units (PRUs). The last term is an expression for the enthalpic cohesive and adhesive forces and hence is used for the determination is miscibility. The interaction parameter χ (> 0.5) when positive indicates strong adhesive interactions which favors miscibility while when negative (< 0.5), favors strong cohesive interactions and hence immiscibility is favored (Wilkinson, 1996). This theory gives information about the drug-polymer dispersions by treating the drug as a solvent for the polymer. One of the popular ways of calculating the interaction parameter (χ) is by using the Nishi-Wang equation which is based on the melting point depression data. This equation represents the interaction between two substances, speciﬁcally at the melting temperature, which may not be extrapolated to other temperatures (Maniruzzaman et al., 2012a,b, 2015). 1 1 − 0 Tm Tm =− Rvdrug 1 ⎞ ⎡ lnϕ + ⎛⎜1 − ⎟ × (1 − ϕdrug ) + χdrug − poly ∆Hdrug vpolymer ⎢ drug ⎝ mpoly ⎠ ⎣ ⎤ (1 − ϕdrug )2⎥ ⎦ (10) where m is the degree of polymerization, v is the molar volume, ϕ is the volume fraction, Tm is the crystalline melting peak of the pure drugs, and Tm0 is the melting endotherm of the drug-polymer physical blends and χ is the crystalline-amorphous polymer interaction parameter. Another popular way of calculating the interaction parameter is by using the Hildebrand and Scott equation. 5.2. Flory Huggins/Thermodynamic modeling In the early 1940 s, Paul Flory and Maurice Huggins ﬁrst investigated the thermodynamics of (binary) regular polymer solutions. Flory in his famous book “Principles of Polymer Chemistry” (1953) described this mathematical model in great detail (Flory, 1953). Flory Huggins model is a simple mean-ﬁeld lattice model based on the thermodynamics of mixing polymer solutions which can be used as an expression for the entropy of mixing and to understand the non-ideal nature of polymer mixtures and solutions. The model assumes the simple mixing rules (Flory, 1942, 1953; Huggins, 1942) χ= Vref ∆Hmix RT (11) where ∆Hmix = (δdrug − δpolymer )2 (12) where ‘Vref ’ is the volume of the lattice site and ‘∆Hmix ’ is the enthalpy of mixing. In silico evaluation of the interaction parameter (χ) for a drugpolymer system substitute ‘ΔHm’ with ‘Δδ2’ where, 1. The distribution of solvent and monomer repeat units over the lattice sites is considered to be random. 2. There is no volume change on mixing hence the total volume of the mixture is equal to the summation of the volumes of the individual components. 3. The sum of the pure component pair interactions is equal to the total number of pair interactions in the mixture. 4. The average volume and energy for cross-interaction is equal to the geometric average of the pure components volume and interaction energy. 5. The interaction between two molecules is assumed to be equal to the ∆δ 2 = (δdrug − δpolymer )2 (13) Molecular dynamics can be used for the evaluation of Flory Huggins parameter by simulating pure drug, pure polymer and an example of the mixed system. ‘ΔHmix’ (enthalpy of mixing) can be estimated by the equation, ∆E ∆E ∆E ∆Hmix = ⎛ V ⎞ − ϕpolymer ⎛ V ⎞ − ϕdrug ⎛ V ⎞ ⎝ V ⎠ ⎝ V ⎠ polymer ⎝ V ⎠drug 14 (14) International Journal of Pharmaceutics 576 (2020) 118989 R. Thakkar, et al. order to identify the relevance of temperature and drug weight fraction to phase separation within solid dispersions. The novel method was used to compare the solubility and miscibility of drug in polymeric systems. Two systems were chosen which were Hydroxypropyl methylcellulose acetate succinate HF grade (HPMCAS-HF)-Felodipine (FD) and Soluplus® (a graft copolymer of polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol)-Felodipine. The diﬀerent drug compositions were then mixed, ball milled and analyzed by Diﬀerential Scanning Calorimetry. Values for the interaction parameter (χ) were calculated from the melting point depression data and then extrapolated to lower temperatures. The interaction parameters were also calculated at 250 °C for both the systems using Van Krevelen solubility parameter. This temperature allowed the rank order of the systems to be comparable. Ultimately, diagrams of drug polymer temperature composition and energy of mixing (ΔGmix) were constructed. The value of the interaction parameter in the case of HPMCAS-FD was found to be +0.37 (r2 = 0.99) whereas for Soluplus®-FD was found to be −0.37 (r2 = 0.99) which showed greater miscibility between HPMCAS and Felodipine compared to Soluplus® and Felodipine. It was concluded that a novel small-scale method was successfully established which can be used in combination with Flory Huggins theory in order to predict the physical stability of amorphous drug solid dispersions (Tian et al., 2013). Flory Huggins theory is also frequently used in order to corroborate the data collected from the calculation of the solubility parameter. Pierro Piccini et al. attempted to calculate the solubility parameter based on screening methods for early stage formulation development of Itraconazole amorphous solid dispersions. They attempted to use both conventional and newly extended solubility parameter based (δ) methods to identify the polymeric materials which are capable of forming amorphous solid dispersions with Itraconazole (ITZ) which is a BCS class II drug. The polymers selected were Soluplus®, Eudragit® EPO, Kollidon® 17PF, and Kollidon® VA64 which were prepared using quench cooling and HME. Both the conventional and novel solubility parameter-based methods showed similar results as the rank order of miscibility was found to be 17PF > VA64 > Soluplus® ≫ EPO. This information was corroborated using Flory Huggins lattice model to ITZexcipient binary systems. The Flory Huggins interaction parameter was calculated using the solubility parameter and the interaction parameters were found to be 0.02, 0.2 and 1 for VA64, Soluplus® and EPO respectively. This information indicates stronger cohesive forces in the methacrylate matrix compared to the VA64, and Soluplus® systems which ultimately shows lower miscibility with ITZ. The results thus obtained for VA64, Soluplus® and EPO agreed with thermal analysis and storage stability studies. The study ultimately concluded that although solubility parameter-based screening may be useful in predicting the initial state of amorphous solid dispersions, assessment of the physical behavior of the formulations at relevant temperatures may be more appropriate for the successful development of commercially acceptable amorphous drug products (Piccinni et al., 2016). Even though researchers have exploited Flory Huggins theory for predicting the thermodynamic compatibility and stability of amorphous dispersions, it was never intended for systems in which hydrogen-bonding interactions are important, as is the case for most ASDs. Given the critical importance of the χ value in predicting the miscibility, major emphasis has been placed on obtaining a correct value for this parameter. This has diverted attention form the importance and reliability of its underlying framework and its inadequacy towards assessment of complex mixtures. Fortunately, new solution models based on more realistic assumptions are being explored by some pharmaceutical scientists. Thereby, even though it is an extremely helpful tool for preliminary screening of the polymers, limitations of the theory should be kept in mind during its application (Anderson, 2018). These techniques for screening and selection of polymers have been employed individually in multiple research studies as pointed out in this review. But, using these methods synergistically in a systematic and where ‘ϕ’ is the volume fraction of the relevant material in the E mixed system and VV is the CED. Estimation of Flory Huggins theory does not require any input beyond the molecular structure and hence unlike other theoretical tools like statistical associating ﬂuid theory (SAFT), locally correlated lattice theory (LCL) and the solubility parameters-based approach which require additional data input. The Flory Huggins theory does, in turn, have major limitations as the presence of strong intermolecular forces (such as H bonding) in solid dispersions limits the mobility of the chains which are then forced into non-random conﬁgurations. The presence and underestimation of these strong interactions make this theory unsatisfactory. The χ between drugpolymer blends requires the heat of fusion and melting peaks of polymers, bulk drugs and physical blends which have to be determined by thermal analysis. Hence, the Flory Huggins parameter cannot provide successful estimations when the presence of stronger interactions (such as H bonding) are seen in the drug polymer blends. Another limitation is the range of experimental data requires which ultimately makes this approach questionable. Flory Huggins theory cannot reveal the mechanism or site of interaction in drug-polymer dispersions. Therefore, the development of a novel method providing data on both the atomistic and quantitative descriptions of drug–polymer interactions is cardinal for the development of solid dispersions for various drug–polymer systems (Maniruzzaman et al., 2015). Jelena Djuris et al. used Flory Huggins Thermodynamic modelling and solubility parameters for the screening and selection of polymer for carbamazepine. Carbamazepine is a BCS class II antiepileptic drug which has high permeability and low solubility in water. The Flory Huggins parameter was calculated using the melting point depression method and the diﬀerence in Hansen solubility parameter used as the conﬁrmatory calculation. Soluplus® was selected for its capability of increasing the solubility of CBZ up to 5 times due to its non-ionic nature and independent of the pH. Furthermore the theoretical values corroborated with the experimental data obtained from hot melt extruded solid dispersion of carbamazepine and Soluplus®. Maniruzzaman et al. also used QM based molecular modelling for the prediction of the interaction parameter in the Flory Huggins theory (Djuris et al., 2013) Maniruzzaman et al. used molecular modelling as a predictive tool for the development of solid dispersions. In this study, quantum mechanical calculations were used to predict the miscibility of various drugs with various polymers. Traditional approaches such as the calculation of solubility parameters along with the calculation of Flory Huggins interaction parameter were performed to compare to the molecular modelling approach. To ﬁnd the Flory Huggins interaction parameters between the drug-polymer blends, thermal analysis was required in order to ﬁnd the heat of fusion and melting peaks of bulk drug substances, polymers, and physical blends. The observed DSC thermograms of PRP, DPD, HCS, Df-Na, PMOL and Ibuprofen showed sharp melting peaks at 166.65 °C, 170.83 °C, 226.12 °C, 289.60 °C, 170.00 °C and 79.50 °C and the heat of fusion (ΔH) were found to be 126.25 J/g, 124.59 J/g, 114.92 J/g, 161.06 J/g, 137.06 J/g and 151.17 J/g respectively. Functional group contribution method was used to ﬁnd the molar volumes of all drugs and polymers that were estimated. The interaction parameter value (χ) was calculated using Nishi-Wang equation and the Hildebrand and Scott equation and the values were averaged. Eudragit® L 100 showed stronger interactions with diﬀerent drugs compared to those of Eudragit® L100-55. 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