Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Investigating the Formation and Molecular Solubilization Mechanism of Emodin Solid Dispersions by Molecular Dynamics Simulation

The preparation of solid dispersions (SDs) of emodin (EMO) represents an effective strategy for enhancing its limited water solubility. However, there is a lack of effective strategies for carrier screening. The molecular mechanism underlying EMO-SDs has yet to be fully elucidated. In this study, we employed a molecular simulation to identify the optimal solubilizing carriers for EMO-SDs, which were subsequently validated through solubilization experiments. Gelucire 50/13 (GEL) was identified as the most effective solubilizing carrier. The formulation of the EMO-SDs was established through solubility testing, utilizing a drug-to-carrier loading ratio of 1:9. The characterization of the interactions between the drug and the carrier was conducted using DSC, FTIR, and NMR spectroscopy. The DSC results indicated that EMO molecules were dispersed within the carrier in an amorphous state, while FTIR and NMR analyses revealed the formation of hydrogen bonds between the drug and carrier molecules. The molecular mechanisms of EMO-SDs were further elucidated through an MD simulation. Findings from the formation mechanism studies demonstrated that the majority of EMO molecules were embedded within the interstices of a loosely aggregated micelle-like structure formed by the carrier molecules. The solubility enhancement mechanism indicated that the carrier molecules surrounded the EMO molecules during the solubilization process, thereby facilitating the interaction of EMO with water. The stability mechanism accounts for the fact that recrystallization of the drug may occur.

Predicting Drug-Polymer Compatibility in Amorphous Solid Dispersions by MD Simulation: On the Trap of Solvation Free Energies

Amorphous solid dispersions (ASDs) are a prevalent method for increasing the bioavailability and apparent solubility of poorly soluble drugs. Consequently, extensive research, encompassing both experimental and computational approaches, has been dedicated to developing methods for assessing the key factors influencing their stability, notably drug-polymer interactions. A common computational approach to rank the compatibility of a drug with a set of solvents or polymers is to compare thermodynamic observables, such as solvation free energies at infinite dilution. However, the impact of the molecular weight of the polymer excipient on these interactions remains underexplored. This study delves into this impact through atomistic simulations of Indomethacin in PVP(-VA) and HPMC, and through simulations using a coarse-grained model, emphasizing its critical importance. First, we demonstrate that the molecular weight of the polymer plays a pivotal role in determining the solvation free energy of the drug, at times exerting a more significant influence than the specific chemical identity of the polymer. Additionally, our simulations suggest that higher molecular weight polymers lead to lower solvation free energies and, thus, suggest better compatibility with the drug. Yet, the lower free energy of solvation of the drug in longer polymers does not translate into a higher solubility. This work highlights the subtle role polymer molecular weight plays when measuring thermodynamic observables in amorphous solid dispersions, a role which must be considered when optimizing pharmaceutical formulations.

Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion

(20 S)-Ginsenoside Rh2 is a natural saponin derived from Panax ginseng Meyer (P. ginseng), which showed significantly potent anticancer properties. However, its low water solubility and bioavailability strongly restrict its pharmaceutical applications. The aim of current research is to develop a modified (20 S)-Ginsenoside Rh2 formulation with high solubility, dissolution rate and bioavailability by combined computational and experimental methodology. The "PharmSD" model was employed to predict the optimal polymer for (20 S)-Ginsenoside Rh2 solid dispersion formulations. The solubility of (20 S)-Ginsenoside Rh2 in various polymers was assessed, and the optimal ternary solid dispersion was evaluated across different dissolution mediums. Characterization techniques included the Powder X-ray diffraction (PXRD) and Fourier transform infrared spectroscopy (FTIR). Molecular dynamics simulations were employed to elucidate the formation mechanism of the solid dispersion and the interactions among active pharmaceutical ingredient (API) and excipient molecules. Cell and animal experiments were conducted to evaluate the in vivo performance of the modified formulation. The "PharmSD" solid dispersion model identified Gelucire 44/14 as the most effective polymer for enhancing the dissolution rate of Rh2. Subsequent experiment also confirmed that Gelucire 44/14 outperformed the other selected polymers. Moreover, the addition of the third component, sodium dodecyl sulfate (SDS), in the ternary solid dispersion formulation significantly amplified dissolution rates than the binary systems. Characterization experiments revealed that the API existed in an amorphous state and interacted via hydrogen bonding with SDS and Gelucire. Moreover, molecular modeling results provided additional evidence of hydrogen bonding interactions between the API and excipient molecules within the optimal ternary solid dispersion. Cell experiments demonstrated efflux ratio (EfR) of Rh2 ternary solid dispersion was lower than that of pure Rh2. In vivo experiments revealed that the modified formulation substantially improved the absorption of Rh2 in rats. Our research successfully developed an optimal ternary solid dispersion for Rh2 with high solubility, dissolution rate and bioavailability by integrated computational and experimental tools. The combination of Artificial Intelligence (AI) technology and molecular dynamics simulation is a wise way to support the future formulation development.

Quantitative Analysis of Physical Stability Mechanisms of Amorphous Solid Dispersions by Molecular Dynamic Simulation

Amorphous solid dispersions (ASDs) represent a promising strategy for enhancing the solubility of poorly soluble drugs. However, the mechanisms underlying the physical stability of ASDs remain insufficiently understood. This study aims to investigate these mechanisms and propose quantitative thresholds to predict the maximum stable drug loading using molecular dynamics simulations. Poly(vinylpyrrolidone) (PVP) and poly (vinylpyrrolidone-co-vinyl acetate) (PVPVA64) are selected as polymeric carriers, while naproxen and acetaminophen serve as model drugs, resulting in the formulation of 18 distinct ASDs across four types for comparison with experimental results. Our findings indicate that the molecular mobility of active pharmaceutical ingredients (APIs) is the primary determinant of solid dispersion stability. High polymer concentrations limit drug molecular mobility through spatial structural constraints and ASD viscosity. As drug loading increases, the polymer concentration reaches a critical threshold (C*), beyond which drug-rich regions form, leading to potential aggregation, rearrangement, and recrystallization of drug molecules into more energetically stable forms. Notably, both the interaction energy and diffusion coefficient show sharp fluctuations at the maximum stable drug loading, which can serve as predictive indicators for ASD stability. Additionally, a search strategy is used to identify potential pre-crystalline sites. By integrating kinetic, thermodynamic, and pre-crystalline analyses through molecular dynamics simulations, this study provides a foundation for more accurate predictions of ASD stability, significantly aiding future formulation development.

Crystallization and intermolecular hydrogen bonding in carbamazepine-polyvinyl pyrrolidone solid dispersions: An experiment and molecular simulation study on drug content variation

The choice of drug content is a critical factor as far as the solid dispersion is concerned. This investigation aims to build the relationship between the drug content, intermolecular hydrogen bonding and the crystalline of the carbamazepine-polyvinyl pyrrolidone solid dispersion. In this work, the microstructural changes of solid dispersions were investigated using experimental characterization combined with molecular simulation. Experimental investigations demonstrated that increasing the drug content enhances the intermolecular hydrogen bonding between drugs, resulting in the crystalline phase of the drug emerged in the solid dispersion. This negatively affects the solubility and stability of solid dispersions. Molecular simulations were then used to analyze the changes of intermolecular hydrogen bonding at different drug content in the system. It revealed a tenfold increase in drug-drug hydrogen bonding concentration as drug content elevated from 10% to 50%, while the drug-excipient hydrogen bonding concentration decreased by 45%. The correlation analysis proves the significant relationships among the drug content, intermolecular hydrogen bonding, and crystallinity of solid dispersion. Using polynomial fitting analysis, the quantitative relationships between the drug content and crystalline properties were investigated. This study will offer valuable insights into the impact of drug content on the performance of solid dispersion.

Computational Support to Explore Ternary Solid Dispersions of Challenging Drugs Using Coformer and Hydroxypropyl Cellulose

A majority of drugs marketed in amorphous formulations have a good glass-forming ability, while compounds less stable in the amorphous state still pose a formulation challenge. This work explores ternary solid dispersions of two model drugs with a polymer (i.e., hydroxypropyl cellulose) and a coformer as stabilizing excipients. The aim was to introduce a computational approach by preselecting additives using solubility parameter intervals (i.e., overlap range of solubility parameter, ORSP) followed by more advanced COSMO-RS theory modeling. Thus, a mapping of calculated mixing enthalpy and melting points is proposed for in silico evaluation prior to hot melt extrusion. Following experimental testing of process feasibility, the selected formulations were tested for their physical stability using conventional bulk analytics and by confocal laser scanning and atomic force microscopy imaging. In line with the in silico screening, dl-malic and l-tartaric acid (20%, w/w) in HPC formulations showed no signs of early drug crystallization after 3 months. However, l-tartaric acid formulations displayed few crystals on the surface, which was likely a humidity-induced surface phenomenon. Although more research is needed, the conclusion is that the proposed computational small-scale extrusion approach of ternary solid dispersion has great potential in the formulation development of challenging drugs.

Predicting the Release Mechanism of Amorphous Solid Dispersions: A Combination of Thermodynamic Modeling and In Silico Molecular Simulation

BACKGROUND

During the dissolution of amorphous solid dispersion (ASD) formulations, the drug load (DL) often impacts the release mechanism and the occurrence of loss of release (LoR). The ASD/water interfacial gel layer and its specific phase behavior in connection with DL strongly dictate the release mechanism and LoR of ASDs, as reported in the literature. Thermodynamically driven liquid-liquid phase separation (LLPS) and/or drug crystallization at the interface are the key phase transformations that drive LoR.

METHODS

In this study, a combination of Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) thermodynamic modeling and in silico molecular simulation was applied to investigate the release mechanism and the occurrence LoR of an ASD formulation consisting of ritonavir as the active pharmaceutical ingredient (API) and the polymer, polyvinylpyrrolidone-co-vinyl acetate (PVPVA64). A thermodynamically modeled ternary phase diagram of ritonavir (PVPVA64) and water was applied to predict DL-dependent LLPS in the ASD/water interfacial gel layer. Microscopic Erosion Time Testing (METT) was used to experimentally validate the phase diagram predictions. Additionally, in silico molecular simulation was applied to provide further insights into the phase separation, the release mechanism, and aggregation behavior on a molecular level.

RESULTS

Thermodynamic modeling, molecular simulation, and experimental results were consistent and complementary, providing evidence that ASD/water interactions and phase separation are essential factors driving the dissolution behavior and LoR at 40 wt% DL of the investigated ritonavir/PVPVA64 ASD system, consistent with previous studies.

CONCLUSIONS

This study provides insights into the potential of blending thermodynamic modeling, molecular simulation, and experimental research to comprehensively understand ASD formulations. Such a combined approach can be leveraged as a computational framework to gain insights into the ASD dissolution mechanism, thereby facilitating in silico screening, designing, and optimization of formulations with the benefit of significantly reducing the number of experimental tests.

Evaluation of polymer-preservative interactions for preservation efficacy: molecular dynamics simulation and QSAR approaches

Preservatives are critical ingredients in various pharmaceutical and consumer products. In particular, a high efficacy preservative system is essential in enhancing the shelf-life and safety of these products. However, the development of such a preservative system heavily relies on experimental approaches. In this study, molecular dynamics (MD) simulation was complemented with quantitative structure-activity relationship (QSAR) modelling to comprehensively evaluate polymer-preservative interactions between three different polymers (polyethylene terephthalate, PET; polypropylene, PP; and cellulose) and a series of preservatives from the classes of aliphatic, aromatic, and organic acids. First, adsorption of preservatives onto polymer surfaces was simulated in an aqueous environment. The preservatives did not adhere to hydrophilic cellulose, but most preservatives were adsorbed by PET and PP in distinct configurations. Interaction energies (IEs) between the preservatives and the polymers generally increase from cellulose to PP and PET. The diffusion coefficients of preservatives are dependent on polymer nature, preservative structure, and their resulting molecular interactions. Linear and low molecular weight preservatives exhibit higher diffusion coefficients in polymers. For a particular preservative, diffusion coefficients increased in the order of cellulose < PET < PP. Finally, using MD properties and molecular descriptors of preservatives, QSAR models were developed to identify key descriptors of preservatives and predict their IEs and diffusion coefficients in polymers. This study demonstrates a computational approach for identifying critical materials properties, and predicting polymer-preservative molecular interactions in water. Such an approach streamlines the rational selection and design of high efficacy preservative systems for various pharmaceutical, food and cosmetic products. Furthermore, the integrated computational strategy also reduces trial-and-error experimental efforts, thereby accelerating the development of high efficacy preservative systems.

Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions

About 90% of active pharmaceutical ingredients (APIs) in the oral drug delivery system pipeline have poor aqueous solubility and low bioavailability. To address this problem, amorphous solid dispersions (ASDs) embed hydrophobic APIs within polymer excipients to prevent drug crystallization, improve solubility, and increase bioavailability. There are a limited number of commercial polymer excipients, and the structure-function relationships which lead to successful ASD formulations are not well-documented. There are, however, certain solid-state ASD characteristics that inform ASD performance. One characteristic shared by successful ASDs is a high glass transition temperature (Tg), which correlates with higher shelf stability and decreased drug crystallization. We aim to identify how polymer features such as side chain geometry, backbone methylation, and hydrophilic-lipophilic balance impact Tg to design copolymers capable of forming high-Tg ASDs. We tested a library of 50 ASD formulations (18 previously studied and 32 newly synthesized) of the model drug probucol with copolymers synthesized through automated photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. A machine learning (ML) algorithm was trained on the Tg data to identify the major factors influencing Tg, including backbone methylation and nonlinear side chain geometry. In both polymer alone and probucol-loaded ASDs, a Random Forest Regressor captured structure-function trends in the data set and accurately predicted Tg with an average R2 > 0.83 across a 10-fold cross validation. This ML model will be used to predict novel copolymers to design ASDs with high Tg, a crucial factor in predicting ASD success.

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future

Nanofiltration (NF) and reverse osmosis (RO) processes are physical separation technologies used to remove contaminants from liquid streams by employing dense polymer-based membranes with nanometric voids that confine fluids at the nanoscale. At this level, physical properties such as solvent and solute permeabilities are intricately linked to molecular interactions. Initially, numerous studies focused on developing macroscopic transport models to gain insights into separation properties at the nanometer scale. However, continuum-based models have limitations in nanoconfined situations that can be overcome by force field molecular simulations. Continuum-based models heavily rely on bulk properties, often neglecting critical factors like liquid structuring, pore geometry, and molecular/chemical specifics. Molecular/mesoscale simulations, while encompassing these details, often face limitations in time and spatial scales. Therefore, achieving a comprehensive understanding of transport requires a synergistic integration of both approaches through a multiscale approach that effectively combines and merges both scales. This review aims to provide a comprehensive overview of the state-of-the-art in multiscale modeling of transport through NF/RO membranes, spanning from the nanoscale to continuum media.

Status of Polymer Fused Deposition Modeling (FDM)-Based Three-Dimensional Printing (3DP) in the Pharmaceutical Industry

Additive manufacturing (AM) or 3D printing (3DP) is arguably a versatile and more efficient way for the production of solid dosage forms such as tablets. Of the various 3DP technologies currently available, fused deposition modeling (FDM) includes unique characteristics that offer a range of options in the production of various types of tablets. For example, amorphous solid dispersions (ASDs), enteric-coated tablets or poly pills can be produced using an appropriate drug/polymer combination during FDM 3DP. The technology offers the possibility of evolving personalized medicines into cost-effective production schemes at pharmacies and hospital dispensaries. In this review, we highlight key FDM features that may be exploited for the production of tablets and improvement of therapy, with emphasis on gastrointestinal delivery. We also highlight current constraints that must be surmounted to visualize the deployment of this technology in the pharmaceutical and healthcare industries.

Analysis of stabilization mechanisms in β-lactoglobulin-based amorphous solid dispersions by experimental and computational approaches

Our previous work shows that β-lactoglobulin-stabilized amorphous solid dispersion (ASD) loaded with 70 % indomethacin remains stable for more than 12 months. The stability is probably due to hydrogen bond networks spread throughout the ASD, facilitated by the indomethacin which has both hydrogen donors and acceptors. To investigate the stabilization mechanisms further, here we tested five other drug molecules, including two without any hydrogen bond donors. A combination of experimental techniques (differential scanning calorimetry, X-ray power diffraction) and molecular dynamics simulations was used to find the maximum drug loadings for ASDs with furosemide, griseofulvin, ibuprofen, ketoconazole and rifaximin. This approach revealed the underlying stabilization factors and the capacity of computer simulations to predict ASD stability. We searched the ASD models for crystalline patterns, and analyzed diffusivity of the drug molecules and hydrogen bond formation. ASDs loaded with rifaximin and ketoconazole remained stable for at least 12 months, even at 90 % drug loading, whereas stable drug loadings for furosemide, griseofulvin and ibuprofen were at a maximum of 70, 50 and 40 %, respectively. Steric confinement and hydrogen bonding to the proteins were the most important stabilization mechanisms at low drug loadings (≤ 40 %). Inter-drug hydrogen bond networks (including those with induced donors), ionic interactions, and a high Tg of the drug molecule were additional factors stabilizing the ASDs at drug loading greater than 40 %.

Composition Dependency of the Flory-Huggins Interaction Parameter in Drug-Polymer Phase Behavior

An innovative strategy to address recent challenges in the oral administration of poorly soluble drugs is the formulation of amorphous solid dispersions (ASDs), where the drug is dissolved in a highly soluble carrier polymer. Therefore, special knowledge of the drug-polymer phase behavior is essential for an effective product and process design, accelerating the introduction of novel efficacious ASD products. Flory-Huggins theory can be applied to model solubility temperatures of crystalline drugs in carrier polymers over the drug fraction. However, predicted solubility temperatures lack accuracy in cases of strong drug/polymer interactions that are not represented in the Flory-Huggins lattice model. Within this study, a modeling strategy is proposed to improve the predictive power through an extension of the Flory-Huggins interaction parameter by a correlation with the drug fraction. Therefore, the composition dependency of the Flory-Huggins interaction parameter was evaluated experimentally for various drug-polymer formulations that cover a wide variety of drug and polymer characteristics regarding molecular weights, glass transition temperatures and melting temperatures, as well as drug-polymer interactions of different strengths and effects. The extended model was successfully approved for nine exemplary ASD formulations containing the drugs acetaminophen, itraconazole, and griseofulvine, as well as the following polymers: basic butylated methacrylate copolymer, Soluplus®, and vinylpyrrolidone/vinyl acetate copolymer. A high correlation between the predicted solubility temperatures and experimental and literature data was found, particularly at low drug fractions, since the model accounts for composition dependent drug-polymer interactions.

Molecular Dynamics Simulations of Selected Amorphous Stilbenoids and Their Amorphous Solid Dispersions with Poly(Vinylpyrrolidone)

Amorphous solid dispersions (ASDs) are one of the promising strategies to improve the solubility and dissolution rate of poorly soluble compounds. In this study, Molecular Dynamics simulations were used to investigate the interactions between three selected stilbenoids with important biological activity (resveratrol, pinostilbene and pterostilbene) and poly(vinylpyrrolidone). The analysis of the pair distribution functions and hydrogen bond distributions reveals a significant weakening of the hydrogen bond network of the stilbenoids in ASDs compared to the pure (no polymer) amorphous systems. This is accompanied by an increase in the mobility of the stilbenoid molecules in the ASDs, both in the translational dynamics determined from the molecular mean square displacements, and in the molecular reorientations followed by analysing several torsional distributions.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Molecular Interactions between APIs and Enteric Polymeric Excipients in Solid Dispersion: Insights from Molecular Simulations and Experiments

Solid dispersion of poorly soluble APIs is known to be a promising strategy to improve dissolution and oral bioavailability. To facilitate the development and commercialization of a successful solid dispersion formulation, understanding of intermolecular interactions between APIs and polymeric carriers is essential. In this work, first, we assessed the molecular interactions between various delayed-release APIs and polymeric excipients using molecular dynamics (MD) simulations, and then we formulated API solid dispersions using a hot melt extrusion (HME) technique. To assess the potential API–polymer pairs, three quantities were evaluated: (a) interaction energy between API and polymer [electrostatic (Ecoul), Lenard-Jones (ELJ), and total (Etotal)], (b) energy ratio (API–polymer/API–API), and (c) hydrogen bonding between API and polymer. The Etotal quantities corresponding to the best pairs: NPX-Eudragit L100, NaDLO–HPMC(P), DMF–HPMC(AS) and OPZ–HPMC(AS) were −143.38, −348.04, −110.42, and −269.43 kJ/mol, respectively. Using a HME experimental technique, few API–polymer pairs were successfully extruded. These extruded solid forms did not release APIs in a simulated gastric fluid (SGF) pH 1.2 environment but released them in a simulated intestinal fluid (SIF) pH 6.8 environment. The study demonstrates the compatibility between APIs and excipients, and finally suggests a potential polymeric excipient for each delayed-release API, which could facilitate the development of the solid dispersion of poorly soluble APIs for dissolution and bioavailability enhancement.

Effect of Different Seed Crystals on the Supersaturation State of Ritonavir Tablets Prepared by Hot-Melt Extrusion

Hot-melt extrusion (HME) is a technology increasingly common for the commercial production of pharmaceutical amorphous solid dispersions (ASDs), especially for poorly water-soluble active pharmaceutical ingredients (APIs). However, recrystallization of the APIs during dissolution must be prevented to maintain the supersaturation state enabled by ASD. Unfortunately, the amorphous formulation may be contaminated by seed crystals during the HME manufacturing process, which could lead to undesirable crystal growth during the dissolution process. In this study, the dissolution behavior of ritonavir ASD tablets prepared using both Form I and Form II polymorphs was examined, and the effects of different seed crystals on crystal growth rates were investigated. The aim was to understand how the presence of seed crystals can impact the dissolution of ritonavir, and to determine the optimal polymorph and seeding conditions for the production of ASDs. The results showed that both Form I and Form II ritonavir tablets had similar dissolution profiles, which were also similar to the reference listed drug (RLD). However, it was observed that the presence of seed crystals, particularly the metastable Form I seed, led to more precipitation compared to the stable Form II seed in all formulations. The Form I crystals that precipitated from the supersaturated solution were easily dispersed in the solution and could serve as seeds to facilitate crystal growth. On the other hand, Form II crystals tended to grow more slowly and presented as aggregates. The addition of both Form I and Form II seeds could affect their precipitation behaviors, and the amount and form of the seeds had significant effects on the precipitation process of the RLD tablets, as are the tablets prepared with different polymorphs. In conclusion, the study highlights the importance of minimizing the contamination risk of seed crystals during the manufacturing process and selecting the appropriate polymorph for the production of ASDs.

The role of hydrophilic/hydrophobic group ratio of polyvinyl alcohol on the miscibility of amlodipine in orodispersible films: From molecular mechanism study to product attributes

The miscibility of the therapeutic drug in the polymer matrix is the key to the successful design and development of orodispersible films (ODFs). In the present study, four hydrolyzed polyvinyl alcohols (PVAs) with identical polymerization degree were investigated as carriers for Amlodipine (AML) ODFs systematically. The drug-polymer miscibility and the intermolecular interaction were investigated by Flory-Huggins theory, Gordon-Taylor theory, molecular simulation, FTIR, Raman and ¹H NMR. The product attributes of ODFs were also studied. A pharmacokinetic study in rats was then conducted using the film product of PVA5-72, the best performer tested. The results revealed that the drug-polymer miscibility decreased linearly with the increase of hydrolyzed degree of PVA. Hydrogen bonds formed between the drug and the hydrophilic and hydrophobic groups of PVAs were the main intermolecular interaction that caused the differences in drug-polymer miscibility. Furthermore, drug-polymer interaction influenced the product attributes of ODFs, including dissolution profile, mechanical properties and physical stability. The pharmacokinetic study showed the ODFs disintegrated rapidly, and the amorphous AML dissolved and absorbed in the gastrointestinal tract, which was comparable to the commercial product. The research offered a foundation for development scientists in designing and formulating PVA films.

Recent Advances in Amorphous Solid Dispersions: Preformulation, Formulation Strategies, Technological Advancements and Characterization

Amorphous solid dispersions (ASDs) are among the most popular and widely studied solubility enhancement techniques. Since their inception in the early 1960s, the formulation development of ASDs has undergone tremendous progress. For instance, the method of preparing ASDs evolved from solvent-based approaches to solvent-free methods such as hot melt extrusion and Kinetisol^®. The formulation approaches have advanced from employing a single polymeric carrier to multiple carriers with plasticizers to improve the stability and performance of ASDs. Major excipient manufacturers recognized the potential of ASDs and began introducing specialty excipients ideal for formulating ASDs. In addition to traditional techniques such as differential scanning calorimeter (DSC) and X-ray crystallography, recent innovations such as nano-tomography, transmission electron microscopy (TEM), atomic force microscopy (AFM), and X-ray microscopy support a better understanding of the microstructure of ASDs. The purpose of this review is to highlight the recent advancements in the field of ASDs with respect to formulation approaches, methods of preparation, and advanced characterization techniques.

Stabilizing Mechanisms of β-Lactoglobulin in Amorphous Solid Dispersions of Indomethacin

Proteins, and in particular whey proteins, have recently been introduced as a promising excipient class for stabilizing amorphous solid dispersions. However, despite the efficacy of the approach, the molecular mechanisms behind the stabilization of the drug in the amorphous form are not yet understood. To investigate these, we used experimental and computational techniques to study the impact of drug loading on the stability of protein-stabilized amorphous formulations. β-Lactoglobulin, a major component of whey, was chosen as a model protein and indomethacin as a model drug. Samples, prepared by either ball milling or spray drying, formed single-phase amorphous solid dispersions with one glass transition temperature at drug loadings lower than 40-50%; however, a second glass transition temperature appeared at drug loadings higher than 40-50%. Using molecular dynamics simulations, we found that a drug-rich phase occurred at a loading of 40-50% and higher, in agreement with the experimental data. The simulations revealed that the mechanisms of the indomethacin stabilization by β-lactoglobulin were a combination of (a) reduced mobility of the drug molecules in the first drug shell and (b) hydrogen-bond networks. These networks, formed mostly by glutamic and aspartic acids, are situated at the β-lactoglobulin surface, and dependent on the drug loading (>40%), propagated into the second and subsequent drug layers. The simulations indicate that the reduced mobility dominates at low (<40%) drug loadings, whereas hydrogen-bond networks dominate at loadings up to 75%. The computer simulation results agreed with the experimental physical stability data, which showed a significant stabilization effect up to a drug fraction of 70% under dry storage. However, under humid conditions, stabilization was only sufficient for drug loadings up to 50%, confirming the detrimental effect of humidity on the stability of protein-stabilized amorphous formulations.

Modeling Polyzwitterion-Based Drug Delivery Platforms: A Perspective of the Current State-of-the-Art and Beyond

Drug delivery platforms are anticipated to have biocompatible and bioinert surfaces. PEGylation of drug carriers is the most approved method since it improves water solubility and colloid stability and decreases the drug vehicles' interactions with blood components. Although this approach extends their biocompatibility, biorecognition mechanisms prevent them from biodistribution and thus efficient drug transfer. Recent studies have shown (poly)zwitterions to be alternatives for PEG with superior biocompatibility. (Poly)zwitterions are super hydrophilic, mainly stimuli-responsive, easy to functionalize and they display an extremely low protein adsorption and long biodistribution time. These unique characteristics make them already promising candidates as drug delivery carriers. Furthermore, since they have highly dense charged groups with opposite signs, (poly)zwitterions are intensely hydrated under physiological conditions. This exceptional hydration potential makes them ideal for the design of therapeutic vehicles with antifouling capability, i.e., preventing undesired sorption of biologics from the human body in the drug delivery vehicle. Therefore, (poly)zwitterionic materials have been broadly applied in stimuli-responsive "intelligent" drug delivery systems as well as tumor-targeting carriers because of their excellent biocompatibility, low cytotoxicity, insignificant immunogenicity, high stability, and long circulation time. To tailor (poly)zwitterionic drug vehicles, an interpretation of the structural and stimuli-responsive behavior of this type of polymer is essential. To this end, a direct study of molecular-level interactions, orientations, configurations, and physicochemical properties of (poly)zwitterions is required, which can be achieved via molecular modeling, which has become an influential tool for discovering new materials and understanding diverse material phenomena. As the essential bridge between science and engineering, molecular simulations enable the fundamental understanding of the encapsulation and release behavior of intelligent drug-loaded (poly)zwitterion nanoparticles and can help us to systematically design their next generations. When combined with experiments, modeling can make quantitative predictions. This perspective article aims to illustrate key recent developments in (poly)zwitterion-based drug delivery systems. We summarize how to use predictive multiscale molecular modeling techniques to successfully boost the development of intelligent multifunctional (poly)zwitterions-based systems.

Materials informatics approach using domain modelling for exploring structure-property relationships of polymers

In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, ¹H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the ¹H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T₂ relaxation time for domains with different mobility. The average T₂ relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

DPD Modelling of the Self- and Co-Assembly of Polymers and Polyelectrolytes in Aqueous Media: Impact on Polymer Science

This review article is addressed to a broad community of polymer scientists. We outline and analyse the fundamentals of the dissipative particle dynamics (DPD) simulation method from the point of view of polymer physics and review the articles on polymer systems published in approximately the last two decades, focusing on their impact on macromolecular science. Special attention is devoted to polymer and polyelectrolyte self- and co-assembly and self-organisation and to the problems connected with the implementation of explicit electrostatics in DPD numerical machinery. Critical analysis of the results of a number of successful DPD studies of complex polymer systems published recently documents the importance and suitability of this coarse-grained method for studying polymer systems.

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.

Pharmaceutical profiling and molecular dynamics simulations reveal crystallization effects in amorphous formulations

Robust and reliable in vivo performance of medicines based on amorphous solid dispersions (ASDs) depend on maintenance of physical stability and efficient supersaturation. However, molecular drivers of these two kinetic processes are poorly understood. Here we used molecular dynamics (MD) simulations coupled with experimental assessments to explore supersaturation, nucleation, and crystal growth. The effect of drug loading on physical stability and supersaturation potential was highly drug specific. Storage under humid conditions influenced crystallization, but also resulted in morphological changes and particle fusion. This led to increased particle size, which significantly reduced dissolution rate. MD simulations identified the importance of nano-compartmentalization in the crystallization rate of the ASDs. Nucleation during storage did not inherently compromise the ASD. Rather, the poorer performance resulted from a combination of properties of the compound, nanostructures formed in the formulation, and crystallization.

Computational Modeling of Fluidized Beds with a Focus on Pharmaceutical Applications: A Review

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

Analytical and Computational Methods for the Determination of Drug-Polymer Solubility and Miscibility

In the pharmaceutical industry, poorly water-soluble drugs require enabling technologies to increase apparent solubility in the biological environment. Amorphous solid dispersion (ASD) has emerged as an attractive strategy that has been used to market more than 20 oral pharmaceutical products. The amorphous form is inherently unstable and exhibits phase separation and crystallization during shelf life storage. Polymers stabilize the amorphous drug by antiplasticization, reducing molecular mobility, reducing chemical potential of drug, and increasing glass transition temperature in ASD. Here, drug-polymer miscibility is an important contributor to the physical stability of ASDs. The current Review discusses the basics of drug-polymer interactions with the major focus on the methods for the evaluation of solubility and miscibility of the drug in the polymer. Methods for the evaluation of drug-polymer solubility and miscibility have been classified as thermal, spectroscopic, microscopic, solid-liquid equilibrium-based, rheological, and computational methods. Thermal methods have been commonly used to determine the solubility of the drug in the polymer, while other methods provide qualitative information about drug-polymer miscibility. Despite advancements, the majority of these methods are still inadequate to provide the value of drug-polymer miscibility at room temperature. There is still a need for methods that can accurately determine drug-polymer miscibility at pharmaceutically relevant temperatures.