Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies.

Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low- and high-viscosity mAbs.

Machine Learning Models of Antibody-Excipient Preferential Interactions for Use in Computational Formulation Design.

Preferential interactions of formulation excipients govern their impact on the stability properties of proteins in solution. The ability to predict these interactions without the need to perform experiments would enable formulation design to begin early in the development of a new antibody therapeutic. With that in mind, we developed a feature set to numerically describe local regions of an antibody's surface for use in machine learning applications. Then, we used these features to train machine learning models for local antibody-excipient preferential interactions for the excipients sorbitol, sucrose, trehalose, proline, arginine·HCl, and NaCl. Our models had accuracies of up to about 85%. We also used linear (elastic net) models to quantify the contribution of antibody surface features to the preferential interaction coefficients, finding that the carbohydrates and proline tend to have similar important features, while the interactions of arginine·HCl and NaCl are governed by charge features. We present several case studies demonstrating how these machine learning models could be used to predict experimental aggregation and viscosity behavior in solution. Finally, we propose an approach to computational formulation design wherein a panel of excipients may be considered while designing an antibody sequence.

Molecular Computations of Preferential Interaction Coefficients of IgG1 Monoclonal Antibodies with Sorbitol, Sucrose, and Trehalose and the Impact of These Excipients on Aggregation and Viscosity.

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol because of their larger size and their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol. Two of the antibodies had high viscosity in sodium acetate buffer, and for these, we found that sucrose and trehalose tended to have opposite effects on viscosity. The data presented here provide further insight into the mechanisms of interactions of these three carbohydrate excipients with the antibody surface and thus their impact on excipient stabilization of antibody formulations.

Understanding the Role of Preferential Exclusion of Sugars and Polyols from Native State IgG1 Monoclonal Antibodies and its Effect on Aggregation and Reversible Self-Association.

PURPOSE: To investigate differences in the preferential exclusion of trehalose, sucrose, sorbitol and mannitol from the surface of three IgG1 monoclonal antibodies (mAbs) and understand its effect on the aggregation and reversible self-association of mAbs at high-concentrations. METHODS: Preferential exclusion was measured using vapor pressure osmometry. Effect of excipient addition on accelerated aggregation kinetics was quantified using size exclusion chromatography and on reversible self-association was quantified using dynamic light scattering. RESULTS: The doubling of excipient concentration in the 0 to 0.5 m range resulted in a doubling of the mAb transfer free energy for all excipients and antibodies tested in this study. Solution pH and choice of buffering agent did not significantly affect the magnitude of preferential exclusion. We find that aggregation suppression for trehalose, sucrose and sorbitol (but not mannitol) correlates with the magnitude of their preferential exclusion from the native state of the three IgG1 mAbs. We also find that addition of sugars and polyols reduced the tendency for reversible self-association in two mAbs that had weakly repulsive or neutral self-interactions in the presence of buffer alone. CONCLUSIONS: The magnitude of preferential exclusion for trehalose, sucrose and sorbitol correlates well with their partial molar volumes in solution. Mannitol is excluded to a greater extent than that expected from its partial molar volume, suggesting specific interactions of mannitol that might be different than the other sugars and polyols tested in this study. Local interactions play a role in the effect of excipient addition on the reversible self-association of mAbs. These results provide further insights into the stabilization of high-concentration mAb formulations by sugars and polyols.

On computing the solubility of molecular systems subject to constraints using the extended Einstein crystal method.

A method to compute solubilities for molecular systems using atomistic simulations, based on an extension of the Einstein crystal method, has recently been presented [Li et al., J. Chem. Phys. 146, 214110 (2017)]. This methodology is particularly appealing to compute solubilities in cases of practical importance including, but not limited to, solutions where the solute is sparingly soluble and molecules of importance for the pharmaceutical industry, which are often characterized by strong polar interactions and slow relaxation time scales. The mathematical derivation of this methodology hinges on a factorization of the partition function which is not necessarily applicable in the case of a system subject to holonomic molecular constraints. We show here that, although the mathematical procedure to derive it is slightly different, essentially the same mathematical relation for calculating the solubility can be safely applied for computing the solubility of systems subject to constraints, which are the majority of the systems used for practical molecular simulations.

Solubility of paracetamol in ethanol by molecular dynamics using the extended Einstein crystal method and experiments.

Li and co-workers [Li et al., J. Chem. Phys. 146, 214110 (2017)] have recently proposed a methodology to compute the solubility of molecular compounds from first principles, using molecular dynamics simulations. We revise and further explore their methodology that was originally applied to naphthalene in water at low concentration. In particular, we compute the solubility of paracetamol in an ethanol solution at ambient conditions. For the simulations, we used a force field that we previously reparameterized to reproduce certain thermodynamic properties of paracetamol but not explicitly its solubility in ethanol. In addition, we have determined the experimental solubility by performing turbidity measurements using a Crystal16 over a range of temperatures. Our work serves a dual purpose: (i) methodologically, we clarify how to compute, with a relatively straightforward procedure, the solubility of molecular compounds and (ii) applying this procedure, we show that the solubility predicted by our force field (0.085 ± 0.014 in mole ratio) is in good agreement with the experimental value obtained from our experiments and those reported in the literature (average 0.0585 ± 0.004), considering typical deviations for predictions from first principle methods. The good agreement between the experimental and the calculated solubility also suggests that the method used to reparameterize the force field can be used as a general strategy to optimize force fields for simulations in solution.

General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.

Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal's morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques.

Lack of a synergistic effect of arginine-glutamic acid on the physical stability of spray-dried bovine serum albumin.

Improving the physical stability of spray-dried proteins is essential for enabling pulmonary delivery of biotherapeutics as a noninvasive alternative to injections. Recently, a novel combination of two amino acids - l-arginine (l-Arg) and l-glutamic acid (l-Glu), has been reported to have synergistic protein-stabilizing effects on various protein solutions. Using spray-dried bovine serum albumin (BSA) reconstituted in solution as a model protein, we investigated the synergistic effect of these amino acids on the physical stability of proteins. Five BSA solutions were prepared: (1) BSA with no amino acids (control); (2) with 50 mM l-Arg; (3) with 200 mM l-Arg, (4) with 50 mM l-Glu and (5) with 25:25 mM of Arg:Glu. All solutions were spray-dried and accelerated studies at high temperatures were performed. Following accelerated studies, monomer BSA loss was measured using SE-HPLC. We found that l-Arg significantly improved the physical stability of spray-dried BSA even at low concentrations, however, when combined with l-Glu, was ineffective at reducing monomer BSA loss. Our findings demonstrate the limitations in using Arg-Glu for the stabilization of spray-dried BSA. Furthermore, we found that a low concentration of l-Glu enhanced monomer BSA loss. These findings may have significant implications on the design of future biotherapeutic formulations.

Molecular Investigation of the Mechanism of Non-Enzymatic Hydrolysis of Proteins and the Predictive Algorithm for Susceptibility.

A number of potential degradation routes can limit the shelf life of a biotherapeutic. While these are experimentally measurable, the tests to do so require a substantial investment in both time and material, resources rarely available early in the drug development process. To address the potential degradation route of non-enzymatic hydrolysis, we performed a molecular modeling analysis, together with an experimental study, to gain detailed insight into the reaction. On the basis of the mechanism, an algorithm for predicting the likely cleavage sites of a protein has been created. This algorithm measures four key properties during a molecular dynamics simulation, which relate to the key steps of the hydrolysis mechanism, in particular the rate-determining step (which can vary depending on the local environment). The first two properties include the secondary structure and the surface exposure of the amide bond, both of which help detect if the addition of the proton to the amide bond is possible. The second two properties relate to whether the side chain can cyclize and form a furane ring. These two properties are the orientation of the side chain relative to the amide bond and the number of hydrogen bonds between the side chain and the surrounding protein. Overall, the algorithm performs well at identifying reactive versus nonreactive bonds. The algorithm correctly classifies nearly 90% of all amide bonds following an aspartic or glutamic acid residue as reactive or nonreactive.

Mechanistic Insights into Radical-Mediated Oxidation of Tryptophan from ab Initio Quantum Chemistry Calculations and QM/MM Molecular Dynamics Simulations.

An assessment of the mechanisms of (•)OH and (•)OOH radical-mediated oxidation of tryptophan was performed using density functional theory calculations and ab initio plane-wave Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics simulations. For the (•)OH reactions, addition to the pyrrole ring at position 2 is the most favored site with a barrierless reaction in the gas phase. The subsequent degradation of this adduct through a H atom transfer to water was intermittently observed in aqueous-phase molecular dynamics simulations. For the (•)OOH reactions, addition to the pyrrole ring at position 2 is the most favored pathway, in contrast to the situation in the model system ethylene, where concerted addition to the double bond is preferred. From the (•)OOH position 2 adduct QM/MM simulations show that formation of oxy-3-indolanaline occurs readily in an aqueous environment. The observed transformation starts from an initial rupture of the O-O bond followed by a H atom transfer with the accompanying loss of an (•)OH radical to solution. Finally, classical molecular dynamics simulations were performed to equate observed differential oxidation rates of various tryptophan residues in monoclonal antibody fragments. It was found that simple parameters derived from simulation correlate well with the experimental data.

Conformational and Colloidal Stabilities of Isolated Constant Domains of Human Immunoglobulin G and Their Impact on Antibody Aggregation under Acidic Conditions.

Antibody therapeutics are now in widespread use and provide a new approach for treating serious diseases such as rheumatic diseases and cancer. Monoclonal antibodies used as therapeutic agents must be of high quality, and their safety must be guaranteed. Aggregated antibody is a degradation product that may be generated during the manufacturing process. To maintain the high quality and safety of antibody therapeutics, it is necessary to understand the mechanism of aggregation and to develop technologies to strictly control aggregate formation. Here, we extensively investigated the conformational and colloidal characteristics of isolated antibody constant domains, and provided insights into the molecular mechanism of antibody aggregation. Isolated domains (CH2, CH3, CL, and CH1-CL dimer) of human immunoglobulin G were synthesized, solubilized using 49 sets of solution conditions (pH 2-8 and 0-300 mM NaCl), and characterized using circular dichroism, intrinsic tryptophan fluorescence, and dynamic light scattering. Salt-induced conformational changes and oligomer formation were kinetically analyzed by NaCl-jump measurements (from 0 to 300 mM at pH 3). Phase diagrams revealed that the domains have different conformational and colloidal stabilities. The unfolded fractions of CH3 and CH2 at pH 3 were larger than that of CL and CH1-CL dimer. The secondary and tertiary structures and particle sizes of CH3 and CH2 showed that, in non-native states, these domains were sensitive to salt concentration. Kinetic analyses suggest that oligomer formation by CH3 and CH2 proceeds through partially refolded conformations. The colloidal stability of CH3 in non-native states is the lowest of the four domains under the conditions tested. We propose that the impact of IgG constant domains on aggregation follows the order CH3 > CH2 > CH1-CL dimer > CL; furthermore, we suggest that CH3 plays the most critical role in driving intact antibody aggregation under acidic conditions.

A general method for molecular modeling of nucleation from the melt.

Crystallization is one of the fundamental phase transition processes, and it is also important practically, for example, in the chemical, food, and pharmaceutical industries. Despite its importance, however, our basic understanding of crystallization, and especially crystal nucleation, at the molecular level is still incomplete. In this work, we present a general molecular simulation approach that can be used to investigate the nucleation of crystals from a subcooled liquid. Our method combines a previously proposed general method to construct structure-based order parameters [E. E. Santiso and B. L. Trout, J. Chem. Phys. 134, 064109 (2011)] with the string method in collective variables [L. Maragliano et al., J. Chem. Phys. 125, 024106 (2006)] to obtain a minimum free energy path connecting the liquid and solid basins. We then use Markovian milestoning with Voronoi tessellations [E. Vanden-Eijnden and M. Venturoli, J. Chem. Phys. 130, 194101 (2009); L. Maragliano et al., J. Chem. Theory Comput. 5, 2589-2594 (2009)] to obtain the free energy profile along the path and the nucleation kinetics. We illustrate the application of this method to the nucleation of Benzene-I crystals from the melt, and compare the results to those previously found using transition path sampling [M. Shah et al., J. Phys. Chem. B 115, 10400-10412 (2011)].

On the connection between nonmonotonic taste behavior and molecular conformation in solution: The case of rebaudioside-A.

The diterpene steviol glycoside, rebaudioside A, is a natural high potency non-caloric sweetener extracted from the leaves of Stevia rebaudiana. This compound shows a parabolic change in sweet taste intensity with temperature which contrasts with the general finding for other synthetic or natural sweeteners whose sweet taste increases with temperature. The nonmonotonic taste behavior was determined by sensory analysis using large taste panels. The conformational landscape of rebaudioside A was established at a range of temperatures by means of nuclear magnetic resonance and molecular dynamics simulation. The relationship between various conformations and the observed sweetness of rebaudioside A is described.

Reactions of benzene and 3-methylpyrrole with the •OH and •OOH radicals: an assessment of contemporary density functional theory methods.

A high-level quantum chemistry investigation has been carried out for the addition and abstraction reactions by the radicals (•)OH and (•)OOH to and from the model alkenes 3-methylpyrrole and benzene. These models were chosen to reflect the functionalities contained in the side chain of the amino acid tryptophan. The W1BD procedure was used to calculate benchmark barriers and reaction energies for the smaller model system of (•)OOH addition to ethylene. It was found that the CBS-QB3 methodology compares best with the W1BD benchmark, demonstrating a mean absolute deviation (MAD) from W1BD of 3.9 kJ mol(-1). For the reactions involving the (•)OH radical and benzene or 3-methylpyrrole, addition is favored over abstraction in all cases. In particular the CBS-QB3 calculations suggest a barrierless addition reaction of the (•)OH radical to position two of 3-methylpyrrole. For the analogous addition and abstraction reactions involving the (•)OOH radical, the same order of reactivity was found, albeit with higher barriers. A number of other processes involving the addition of the (•)OOH radical were also investigated. The main findings of these studies determined that the initial (•)OOH barrier of stepwise addition to 3-methylpyrrole (+18.8 kJ mol(-1)) is significantly smaller than the concerted addition barrier (+71.5 kJ mol(-1)). This conclusion contrasts starkly with the situation for ethylene in which it is well established that the concerted process has the smaller barrier. A considerable variety of contemporary density functional theory procedures have been tested to examine their accuracy in predicting the CBS-QB3 results. It was found that the best overall performing method was UBMK with an MAD of 7.3 kJ mol(-1). A number of other functionals additionally performed well. They included UM06, RM06, UXYG3 and RXYG3, all of which have MADs of less than 8 kJ mol(-1).

Bézier curve string method for the study of rare events in complex chemical systems.

We present a new string method for finding the most probable transition pathway and optimal reaction coordinate in complex chemical systems. Our approach evolves an analytic parametric curve, known as a Bézier curve, to the most probable transition path between metastable regions in configuration space. In addition, we demonstrate that the geometric properties of the Bézier curve can be used to construct the optimal reaction coordinate near the most probable reaction path, and can further be used to devise a ranking vector capable of identifying precisely which collective variables are most important for governing the transition between metastable states. We discuss the algorithmic details of the Bézier curve string method, analyze its stability, accuracy and efficiency, and illustrate its capabilities using model potential energy functions. In particular, we use the degree elevation property of Bézier curves to develop an algorithm that adaptively learns the degree polynomial necessary to accurately represent the most probable transition path. Subsequently, we apply our method to the isomerization of alanine dipeptide, and demonstrate that the reaction coordinate obtained from the Bézier curve string method is in excellent agreement with the optimal reaction coordinate constructed from an aimless shooting and maximum likelihood procedure. Finally, we apply our method to a large complex system and study the homogenous nucleation of benzene from the melt. In these two examples, we illustrate that the ranking vector correctly identifies which collective variables govern these chemical transitions.

Properties of reactive oxygen species by quantum Monte Carlo.

The electronic properties of the oxygen molecule, in its singlet and triplet states, and of many small oxygen-containing radicals and anions have important roles in different fields of chemistry, biology, and atmospheric science. Nevertheless, the electronic structure of such species is a challenge for ab initio computational approaches because of the difficulties to correctly describe the statical and dynamical correlation effects in presence of one or more unpaired electrons. Only the highest-level quantum chemical approaches can yield reliable characterizations of their molecular properties, such as binding energies, equilibrium structures, molecular vibrations, charge distribution, and polarizabilities. In this work we use the variational Monte Carlo (VMC) and the lattice regularized Monte Carlo (LRDMC) methods to investigate the equilibrium geometries and molecular properties of oxygen and oxygen reactive species. Quantum Monte Carlo methods are used in combination with the Jastrow Antisymmetrized Geminal Power (JAGP) wave function ansatz, which has been recently shown to effectively describe the statical and dynamical correlation of different molecular systems. In particular, we have studied the oxygen molecule, the superoxide anion, the nitric oxide radical and anion, the hydroxyl and hydroperoxyl radicals and their corresponding anions, and the hydrotrioxyl radical. Overall, the methodology was able to correctly describe the geometrical and electronic properties of these systems, through compact but fully-optimised basis sets and with a computational cost which scales as N(3) - N(4), where N is the number of electrons. This work is therefore opening the way to the accurate study of the energetics and of the reactivity of large and complex oxygen species by first principles.

Emerging Applications and Regulatory Strategies for Advanced Medicines Manufacturing - Towards the Development of a Platform Approach.

The last decade has seen Advanced Medicines Manufacturing (AMM) progress from isolated product developments to the creation of industry-academic centres of excellence, regulatory innovation progressing leading to new standards, and product commercialisation across multiple product formats. This paper examines these developments focusing on successful applications and strategies presented at the 2023 Symposium of the International Consortium for Advanced Medicines Manufacturing (ICAMM). Despite these exemplar applications, there remain significant challenges to the sector-wide adoption of AMM technologies. Drawing on Symposium delegate expert responses to open-ended questions, our coding-based thematic analysis suggest three primary enablers drive successful adoption of AMM technologies at scale, namely: the ability to leverage pre-competitive collaborations to challenge-based problem solving; information and knowledge sharing through centres of excellence; and the development of AMM specific regulatory standards. Further analysis of expert responses identified the emergence of a 'Platform creation' approach to AMM innovation; characterised by: i) New collaboration modes; ii) Exploration of common product-process platforms for new dosage forms and therapy areas; iii) Development of modular equipment assets that enable scale-out, and offer more decentralized or distributed manufacturing models; iv) Standards based on product-process platform archetypes; v) Implementation strategies where platform-thinking and AMM technologies can significantly reduce timelines between discovery, approval and GMP readiness. We provide a definition of the Platform creation concept for AMM and discuss the requirements for its systematic development.

Fast and scalable purification of a therapeutic full-length antibody based on process crystallization.

The potential of process crystallization for purification of a therapeutic monoclonal IgG1 antibody was studied. The purified antibody was crystallized in non-agitated micro-batch experiments for the first time. A direct crystallization from clarified CHO cell culture harvest was inhibited by high salt concentrations. The salt concentration of the harvest was reduced by a simple pretreatment step. The crystallization process from pretreated harvest was successfully transferred to stirred tanks and scaled-up from the mL-scale to the 1 L-scale for the first time. The crystallization yield after 24 h was 88-90%. A high purity of 98.5% was reached after a single recrystallization step. A 17-fold host cell protein reduction was achieved and DNA content was reduced below the detection limit. High biological activity of the therapeutic antibody was maintained during the crystallization, dissolving, and recrystallization steps. Crystallization was also performed with impure solutions from intermediate steps of a standard monoclonal antibody purification process. It was shown that process crystallization has a strong potential to replace Protein A chromatography. Fast dissolution of the crystals was possible. Furthermore, it was shown that crystallization can be used as a concentrating step and can replace several ultra-/diafiltration steps. Molecular modeling suggested that a negative electrostatic region with interspersed exposed hydrophobic residues on the Fv domain of this antibody is responsible for the high crystallization propensity. As a result, process crystallization, following the identification of highly crystallizable antibodies using molecular modeling tools, can be recognized as an efficient, scalable, fast, and inexpensive alternative to key steps of a standard purification process for therapeutic antibodies.

Templated nucleation of acetaminophen on spherical excipient agglomerates.

We investigated the effect of spherical agglomeration of heterogeneous crystalline substrates on the nucleation of acetaminophen (AAP). Optical and electron microscopy showed that the surface morphologies of single crystal triclinic lactose and D-mannitol differed significantly from their counterparts formed via spherical agglomeration. Spherical agglomerates of lactose were shown to enhance the nucleation rate of acetaminophen (AAP) by a factor of 11 compared to single crystal lactose; however, no such enhancement was observed for D-mannitol. X-ray powder diffraction identified the presence of new crystal faces of lactose present only in the spherical agglomerates However, D-mannitol did not show any significant change in crystal morphology. The new crystal faces of triclinic lactose were analyzed using geometric lattice matching software and molecular dynamics simulations to establish any new and significant epitaxial matches between lactose and AAP. A coincident lattice match and a large favorable energy interaction from hydrogen bonding were observed between the (141¯) and (001) crystal faces of lactose and AAP, respectively. The enhanced nucleation kinetics, X-ray data, and computational studies indicated that the spherical crystallization of lactose exposed the (141¯) face on the surface of the agglomerates, which subsequently enhanced the nucleation rate of AAP through geometric lattice matching and molecular functionality. This study highlights the importance of exploring different heterogeneous substrate morphologies for enhancing nucleation kinetics.

Electrospun formulations containing crystalline active pharmaceutical ingredients.

PURPOSE: To investigate the use of electrospinning for forming solid dispersions containing crystalline active pharmaceutical ingredients (API) and understand the relevant properties of the resulting materials. METHOD: Free surface electrospinning was used to prepare nanofiber mats of poly(vinyl pyrrolidone) (PVP) and crystalline albendazole (ABZ) or famotidine (FAM) from a suspension of the drug crystals in a polymer solution. SEM and DSC were used to characterize the dispersion, XRD was used to determine the crystalline polymorph, and dissolution studies were performed to determine the influence of the preparation method on the dissolution rate. RESULTS: The electrospun fibers contained 31 wt% ABZ and 26 wt% FAM for the 1:2 ABZ:PVP and 1:2 FAM:PVP formulations, respectively, and both APIs retained their crystalline polymorphs throughout processing. The crystals had an average size of about 10 µm and were well-dispersed throughout the fibers, resulting in a higher dissolution rate for electrospun tablets than for powder tablets. CONCLUSIONS: Previously used to produce amorphous formulations, electrospinning has now been demonstrated to be a viable option for producing fibers containing crystalline API. Due to the dispersion of the crystals in the polymer, tablets made from the fiber mats may also exhibit improved dissolution properties over traditional powder compression.