Coarse-grained modeling of crystal growth and polymorphism of a model pharmaceutical molecule

Niosomes: a novel targeted drug delivery system for cancer

Recently, nanotechnology is involved in various fields of science, of which medicine is one of the most obvious. The use of nanoparticles in the process of treating and diagnosing diseases has created a novel way of therapeutic strategies with effective mechanisms of action. Also, due to the remarkable progress of personalized medicine, the effort is to reduce the side effects of treatment paths as much as possible and to provide targeted treatments. Therefore, the targeted delivery of drugs is important in different diseases, especially in patients who receive combined drugs, because the delivery of different drug structures requires different systems so that there is no change in the drug and its effectiveness. Niosomes are polymeric nanoparticles that show favorable characteristics in drug delivery. In addition to biocompatibility and high absorption, these nanoparticles also provide the possibility of reducing the drug dosage and targeting the release of drugs, as well as the delivery of both hydrophilic and lipophilic drugs by Niosome vesicles. Since various factors such as components, preparation, and optimization methods are effective in the size and formation of niosomal structures, in this review, the characteristics related to niosome vesicles were first examined and then the in silico tools for designing, prediction, and optimization were explained. Finally, anticancer drugs delivered by niosomes were compared and discussed to be a suitable model for designing therapeutic strategies. In this research, it has been tried to examine all the aspects required for drug delivery engineering using niosomes and finally, by presenting clinical examples of the use of these nanocarriers in cancer, its clinical characteristics were also expressed.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.

Recent Advances in Understanding the Micro- and Nanoscale Phenomena of Amorphous Solid Dispersions

Many pharmaceutical drugs in the marketplace and discovery pipeline suffer from poor aqueous solubility, thereby limiting their effectiveness for oral delivery. The use of an amorphous solid dispersion (ASD), a mixture of an active pharmaceutical ingredient and a polymer excipient, greatly enhances the aqueous dissolution performance of a drug without the need for chemical modification. Although this method is versatile and scalable, deficient understanding of the interactions between drugs and polymers inhibits ASD rational design. This current Review details recent progress in understanding the mechanisms that control ASD performance. In the solid-state, the use of high-resolution theoretical, computational, and experimental tools resolved the influence of drug/polymer phase behavior and dynamics on stability during storage. During dissolution in aqueous media, novel characterization methods revealed that ASDs can form complex nanostructures, which maintain and improve supersaturation of the drug. The studies discussed here illustrate that nanoscale phenomena, which have been directly observed and quantified, strongly affect the stability and bioavailability of ASD systems, and provide a promising direction for optimizing drug/polymer formulations.

Approaches to increase mechanistic understanding and aid in the selection of precipitation inhibitors for supersaturating formulations – a PEARRL review

Objectives

Supersaturating formulations hold great promise for delivery of poorly soluble active pharmaceutical ingredients (APIs). To profit from supersaturating formulations, precipitation is hindered with precipitation inhibitors (PIs), maintaining drug concentrations for as long as possible. This review provides a brief overview of supersaturation and precipitation, focusing on precipitation inhibition. Trial-and-error PI selection will be examined alongside established PI screening techniques. Primarily, however, this review will focus on recent advances that utilise advanced analytical techniques to increase mechanistic understanding of PI action and systematic PI selection.

Key findings

Advances in mechanistic understanding have been made possible by the use of analytical tools such as spectroscopy, microscopy and mathematical and molecular modelling, which have been reviewed herein. Using these techniques, PI selection can be guided by molecular rationale. However, more work is required to see widespread application of such an approach for PI selection.

Summary

Precipitation inhibitors are becoming increasingly important in enabling formulations. Trial-and-error approaches have seen success thus far. However, it is essential to learn more about the mode of action of PIs if the most optimal formulations are to be realised. Robust analytical tools, and the knowledge of where and how they can be applied, will be essential in this endeavour.

Multiscale Computational Modeling of the Nanostructure of Solid Dispersions of Hydroxypropyl Methylcellulose Acetate Succinate (HPMCAS) and Phenytoin

We recently developed coarse-grained (CG) force fields for hydroxypropyl methylcellulose acetate succinate (HPMCAS) polymers and the model drug molecule phenytoin, and a continuum transport model to study the polymer-drug nanostructures presented during a dissolution test after solvation of solid dispersion particles. We model the polymer-drug interactions that contribute to suppression of drug aggregation, release, and crystal growth during the dissolution process, and we take these as indicators of polymer effectiveness. We find that the size and the intermolecular interaction strength of the functional group and the drug loading concentration are the major factors that impact the effectiveness of the polymeric excipient. The hydroxypropyl acetyl group is the most effective functional group, followed by the acetyl group, while the deprotonated succinyl group is the least effective functional group, except that the deprotonated succinyl group at the 6-position is very effective in slowing down the phenytoin crystal growth. Our simulation results thus suggest HPMCAS with higher acetyl and lower succinyl content is more effective in promoting phenytoin solubility in dissolution media, and polymers become less effective when drug loading becomes high (i.e., 50% of the mass of the polymer/drug solid dispersion), agreeing with previous experimental studies. In addition, our transport model indicates that the drug release time from a solid dispersion particle of 2 μm diameter is less than 10 min, correlating well with the experimental time scale for a typical dissolution profile to reach maximum peak concentration. Our modeling effort, therefore, provides new avenues to understand the dissolution behavior of complex HPMCAS-phenytoin solid dispersions and offers a new design tool to optimize the formulation. Moreover, the systematic and robust approach used in our computational models can be extended to other polymeric excipients and drug candidates.

Efficient estimation of binding free energies between peptides and an MHC class II molecule using coarse-grained molecular dynamics simulations with a weighted histogram analysis method

We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with the weighted histogram analysis method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by inadequate-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remaining peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and reliably estimate the binding free energy between an arbitrary peptide and an MHC class II molecule. © 2017 Wiley Periodicals, Inc.

A framework for multi-scale simulation of crystal growth in the presence of polymers

We present a multi-scale simulation method for modeling crystal growth in the presence of polymer excipients. The method includes a coarse-grained (CG) model for small molecules of known crystal structure whose force field is obtained using structural properties from atomistic simulations. This CG model is capable of stabilizing the molecular crystal structure and capturing the crystal growth from the melt for a wide range of small organic molecules, as demonstrated by application of our method to the molecules isoniazid, urea, sulfamethoxazole, prilocaine, oxcarbazepine, and phenytoin. This CG model can also be used to study the effect of additives, such as polymers, on the inhibition of crystal growth by polymers, as exemplified by our simulation of suppression of the rate of crystal growth of phenytoin, an active pharmaceutical ingredient (API), by a cellulose excipient, functionalized with acetate (Ac), hydroxy-propyl (Hp) and succinate (Su) groups. We show that the efficacy of the cellulosic polymers in slowing crystal growth of small molecules strongly depends on the functional group substitution on the cellulose backbone, with the acetate substituent group slowing crystal growth more than does the deprotonated succinate group, which we confirm by experimental drug supersaturation studies.

Computational Modeling of Hydroxypropyl-Methylcellulose Acetate Succinate (HPMCAS) and Phenytoin Interactions: A Systematic Coarse-Graining Approach

We present coarse-grained (CG) force fields for hydroxypropyl-methylcellulose acetate succinate (HPMCAS) polymers and the drug molecule phenytoin using a bead/stiff spring model, with each bead representing a HPMCAS monomer or monomer side group (hydroxypropyl acetyl, acetyl, or succinyl) or a single phenytoin ring. We obtain the bonded and nonbonded interaction parameters in our CG model using the RDFs from atomistic simulations of short HPMCAS model oligomers (20-mer) and atomistic simulations of phenytoin molecules. The nonbonded interactions are modeled using a LJ 12-6 potential, with separate parameters for each monomer substitution type, which allows heterogeneous polymer chains to be modeled. The cross interaction terms between the polymer and phenytoin CG beads are obtained explicitly from atomistic level polymer-phenytoin simulations, rather than from mixing rules. We study the solvation behavior of 50-mer and 100-mer polymer chains and find chain-length-dependent aggregation. We also compare the phenytoin CG force field developed in this work with that in Mandal et al. (Soft Matter, 2016, 12, 8246-8255) and conclude both are suitable for studying the interaction between polymer and drug in solvated solid dispersion formulation, in the absence of drug crystallization. Finally, we present simulations of heterogeneous HPMCAS model polymer chains and phenytoin molecules. Polymer and drug form a complex in a short period of simulation time due to strong intermolecular interactions. Moreover, the protonated polymer chains are more effective than deprotonated ones in inhibiting the drug aggregation in the polymer-drug complex.