Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

The Evaluation of Meloxicam Nanocrystals by Oral Administration with Different Particle Sizes

Meloxicam (MLX) is a non-steroidal anti-inflammatory drug used to treat rheumatoid arthritis and osteoarthritis. However, its poor water solubility limits the dissolution process and influences absorption. In order to solve this problem and improve its bioavailability, we prepared it in nanocrystals with three different particle sizes to improve solubility and compare the differences between various particle sizes. The nanocrystal particle sizes were studied through dynamic light scattering (DLS) and laser scattering (LS). Transmission electron microscopy (TEM) was used to characterize the morphology of nanocrystals. The sizes of meloxicam-nanocrystals-A (MLX-NCs-A), meloxicam-nanocrystals-B (MLX-NCs-B), and meloxicam-nanocrystals-C (MLX-NCs-C) were 3.262 ± 0.016 μm, 460.2 ± 9.5 nm, and 204.9 ± 2.8 nm, respectively. Molecular simulation was used to explore the distribution and interaction energy of MLX molecules and stabilizer molecules in water. The results of differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) proved that the crystalline state did not change in the preparation process. Transport studies of the Caco-2 cell model indicated that the cumulative degree of transport would increase as the particle size decreased. Additionally, plasma concentration-time curves showed that the AUC0-∞ of MLX-NCs-C were 3.58- and 2.92-fold greater than those of MLX-NCs-A and MLX-NCs-B, respectively. These results indicate that preparing MLX in nanocrystals can effectively improve the bioavailability, and the particle size of nanocrystals is an important factor in transmission and absorption.

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.

Analytical and Computational Methods for the Estimation of Drug-Polymer Solubility and Miscibility in Solid Dispersions Development

The development of stable solid dispersion formulations that maintain desired improvement of drug dissolution rate during the entire shelf life requires the analysis of drug-polymer solubility and miscibility. Only if the drug concentration is below the solubility limit in the polymer, the physical stability of solid dispersions is guaranteed without risk for drug (re)crystallization. If the drug concentration is above the solubility, but below the miscibility limit, the system is stabilized through intimate drug-polymer mixing, with additional kinetic stabilization if stored sufficiently below the mixture glass transition temperature. Therefore, it is of particular importance to assess the drug-polymer solubility and miscibility, to select suitable formulation (a type of polymer and drug loading), manufacturing process, and storage conditions, with the aim to ensure physical stability during the product shelf life. Drug-polymer solubility and miscibility can be assessed using analytical methods, which can detect whether the system is single-phase or not. Thermodynamic modeling enables a mechanistic understanding of drug-polymer solubility and miscibility and identification of formulation compositions with the expected formation of the stable single-phase system. Advance molecular modeling and simulation techniques enable getting insight into interactions between the drug and polymer at the molecular level, which determine whether the single-phase system formation will occur or not.

Spatiotemporal multistage consensus clustering in molecular dynamics studies of large proteins

The aim of this work is to find semi-rigid domains within large proteins as reference structures for fitting molecular dynamics trajectories. We propose an algorithm, multistage consensus clustering, MCC, based on minimum variation of distances between pairs of Cα-atoms as target function. The whole dataset (trajectory) is split into sub-segments. For a given sub-segment, spatial clustering is repeatedly started from different random seeds, and we adopt the specific spatial clustering with minimum target function: the process described so far is stage 1 of MCC. Then, in stage 2, the results of spatial clustering are consolidated, to arrive at domains stable over the whole dataset. We found that MCC is robust regarding the choice of parameters and yields relevant information on functional domains of the major histocompatibility complex (MHC) studied in this paper: the α-helices and β-floor of the protein (MHC) proved to be most flexible and did not contribute to clusters of significant size. Three alleles of the MHC, each in complex with ABCD3 peptide and LC13 T-cell receptor (TCR), yielded different patterns of motion. Those alleles causing immunological allo-reactions showed distinct correlations of motion between parts of the peptide, the binding cleft and the complementary determining regions (CDR)-loops of the TCR. Multistage consensus clustering reflected functional differences between MHC alleles and yields a methodological basis to increase sensitivity of functional analyses of bio-molecules. Due to the generality of approach, MCC is prone to lend itself as a potent tool also for the analysis of other kinds of big data.

Novel strategies for the formulation and processing of poorly water-soluble drugs

Low aqueous solubility of active pharmaceutical ingredients presents a serious challenge in the development process of new drug products. This article provides an overview on some of the current approaches for the formulation of poorly water-soluble drugs with a special focus on strategies pursued at the Center of Pharmaceutical Engineering of the TU Braunschweig. These comprise formulation in lipid-based colloidal drug delivery systems and experimental as well as computational approaches towards the efficient identification of the most suitable carrier systems. For less lipophilic substances the preparation of drug nanoparticles by milling and precipitation is investigated for instance by means of microsystem-based manufacturing techniques and with special regard to the preparation of individualized dosage forms. Another option to overcome issues with poor drug solubility is the incorporation into nanospun fibers.

The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions

Amorphous solid dispersion (ASD) formulation development is frequently difficult owing to the inherent physical instability of the amorphous form, and limited understanding of the physical and chemical interactions that translate to initial dispersion formation and long-term physical stability. Formulation development for ASDs has been historically accomplished through trial and error or experience with extant systems; however, rational selection of appropriate excipients is preferred to reduce time to market and decrease costs associated with development. Current efforts to develop thermodynamic and computational models attempt to rationally direct formulation and show promise. This review compiles and evaluates important methods used to predict ASD formation and physical stability. Recent literature in which these methods are applied is also reviewed, and limitations of each method are discussed.

A drug-specific nanocarrier design for efficient anticancer therapy

The drug-loading properties of nanocarriers depend on the chemical structures and properties of their building blocks. Here, we customize telodendrimers (linear-dendritic copolymer) to design a nanocarrier with improved in vivo drug delivery characteristics. We do a virtual screen of a library of small molecules to identify the optimal building blocks for precise telodendrimer synthesis using peptide chemistry. With rationally designed telodendrimer architectures, we then optimize the drug binding affinity of a nanocarrier by introducing an optimal drug-binding molecule (DBM) without sacrificing the stability of the nanocarrier. To validate the computational predictions, we synthesize a series of nanocarriers and evaluate systematically for doxorubicin delivery. Rhein-containing nanocarriers have sustained drug release, prolonged circulation, increased tolerated dose, reduced toxicity, effective tumor targeting and superior anticancer effects owing to favourable doxorubicin-binding affinity and improved nanoparticle stability. This study demonstrates the feasibility and versatility of the de novo design of telodendrimer nanocarriers for specific drug molecules, which is a promising approach to transform nanocarrier development for drug delivery.

Insights into pharmaceutical nanocrystal dissolution: a molecular dynamics simulation study on aspirin

The presented molecular dynamics simulations are the first simulations to reveal dynamic dissolution of a pharmaceutical crystal in its experimentally determined shape. Continuous dissolution at constant undersaturation of the surrounding medium is ensured by introducing a plane of sticky dummy atoms into the water slab. These atoms have a strong interaction potential with dissolved aspirin molecules, but interactions with water are excluded from the calculations. Thus, the number of aspirin molecules diffusing freely in solution is kept at a low value and continuous dissolution of the aspirin crystal is monitored. Further insight into face-specific dissolution is drawn. The dissolution mechanism of receding edges is found for the (001) plane. These findings are in good agreement with experimental results. While the proposed dissolution mechanism for the (100) plane is terrace sinking on a rough surface, no pronounced dissolution of the perfectly flat face is seen in the present work. Molecular simulations of pharmaceuticals in their experimentally obtained structure therefore have shown to be especially suited for the investigation of dissolving faces, where the edges have a pronounced effect. In contrast to previous studies a propagation of the dissolution front into the crystal face is reported, and the crystal bulk is stable over the whole simulation time of 150 ns.

Geometric analysis of alloreactive HLA α-helices

Molecular dynamics (MD) is a valuable tool for the investigation of functional elements in biomolecules, providing information on dynamic properties and processes. Previous work by our group has characterized static geometric properties of the two MHC α-helices comprising the peptide binding region recognized by T cells. We build upon this work and used several spline models to approximate the overall shape of MHC α-helices. We applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed us to capture the dynamics of MHC α-helices' steric configurations. Here, we discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines.

Finding semirigid domains in biomolecules by clustering pair-distance variations

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction.

Selective monocationic inhibitors of neuronal nitric oxide synthase. Binding mode insights from molecular dynamics simulations

The reduction of pathophysiologic levels of nitric oxide through inhibition of neuronal nitric oxide synthase (nNOS) has the potential to be therapeutically beneficial in various neurodegenerative diseases. We have developed a series of pyrrolidine-based nNOS inhibitors that exhibit excellent potencies and isoform selectivities (J. Am. Chem. Soc. 2010, 132, 5437). However, there are still important challenges, such as how to decrease the multiple positive charges derived from basic amino groups, which contribute to poor bioavailability, without losing potency and/or selectivity. Here we present an interdisciplinary study combining molecular docking, crystallography, molecular dynamics simulations, synthesis, and enzymology to explore potential pharmacophoric features of nNOS inhibitors and to design potent and selective monocationic nNOS inhibitors. The simulation results indicate that different hydrogen bond patterns, electrostatic interactions, hydrophobic interactions, and a water molecule bridge are key factors for stabilizing ligands and controlling ligand orientation. We find that a heteroatom in the aromatic head or linker chain of the ligand provides additional stability and blocks the substrate binding pocket. Finally, the computational insights are experimentally validated with double-headed pyridine analogues. The compounds reported here are among the most potent and selective monocationic pyrrolidine-based nNOS inhibitors reported to date, and 10 shows improved membrane permeability.

Two birds with one stone? Possible dual-targeting H1N1 inhibitors from traditional Chinese medicine

The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine Database@Taiwan (TCM Database@Taiwan) to screen for compounds that simultaneously target H1 and N1 to overcome current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine. Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well. Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics (MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.