Emerging Landscape of Computational Modeling in Pharmaceutical Development

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.Communicated by Ramaswamy H. Sarma.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization

INTRODUCTION

The screening of multicomponent crystal system (MCC) is a key method for improving physicochemical properties of active pharmaceutical ingredients (APIs). The challenges associated with experimental salt screening include a large number of potential counterions and solvent systems and tendency to undergo disproportionation to produce free form during crystallization. These challenges may be mitigated by a combination of experimental and computational approaches to salt screening. The goal of this study is to evaluate performance of the counterion screening methods and propose and validate novel approaches to virtual solvent screening for MCC crystallization.

METHODS

The actual performance of the ΔpKa > 3 rule for counterion selection was validated using multiple screenings reports. Novel computational models for virtual solvent screening to avoid MCC incongruent crystallization were proposed. Using the ΔpKa rule, 10 acid counterions were selected for experimental aripiprazole (APZ) salt screening using 10 organic solvents. The experimental results were used to validate the proposed novel virtual solvent screen models.

RESULTS

Experimental APZ salt screening resulted in a total of eight MCCs which included glucuronate, mesylate, oxalate, tartrate, salicylate and mandelate. The new model to virtually screen solvents provided a general agreement with APZ experimental findings in terms of selecting the optimal solvent for MCC crystallization.

CONCLUSION

The rational selection of counterions and organic solvents for MCC crystallization was presented using combined novel computational model as well as experimental studies. The current virtual solvent screen model was successfully implemented and validated which can be easily applied to newly discovered APIs.

Predicting crystal form stability under real-world conditions

The physicochemical properties of molecular crystals, such as solubility, stability, compactability, melting behaviour and bioavailability, depend on their crystal form1. In silico crystal form selection has recently come much closer to realization because of the development of accurate and affordable free-energy calculations2-4. Here we redefine the state of the art, primarily by improving the accuracy of free-energy calculations, constructing a reliable experimental benchmark for solid-solid free-energy differences, quantifying statistical errors for the computed free energies and placing both hydrate crystal structures of different stoichiometries and anhydrate crystal structures on the same energy landscape, with defined error bars, as a function of temperature and relative humidity. The calculated free energies have standard errors of 1-2 kJ mol-1 for industrially relevant compounds, and the method to place crystal structures with different hydrate stoichiometries on the same energy landscape can be extended to other multi-component systems, including solvates. These contributions reduce the gap between the needs of the experimentalist and the capabilities of modern computational tools, transforming crystal structure prediction into a more reliable and actionable procedure that can be used in combination with experimental evidence to direct crystal form selection and establish control5.

Vapor Sorption and Halogen-Bond-Induced Solid-Form Rearrangement of a Porous Pharmaceutical

A porous, nonsolvated polymorph of the voltage-gated sodium channel blocker mexiletine hydrochloride absorbs iodine vapor to give a pharmaceutical cocrystal incorporating an I₂Cl^- anion that forms a halogen-π interaction with the mexiletine cations. The most thermodynamically stable form of the compound does not absorb iodine. This example shows that vapor sorption is a potentially useful and underused tool for bringing about changes in pharmaceutical solid form as part of a solid form screening protocol.

Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals

In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability.

Conformational Screening of Arbidol Solvates: Investigation via 2D NOESY

Understanding of the nucleation process's fundamental principles in saturated solutions is an urgent task. To do this task, it is necessary to control the formation of polymorphic forms of biologically active compounds. In certain cases, a compound can exist in a single polymorphic form, but have several solvates which can appear in different crystal forms, depending on the medium and conditions of formation, and show different pharmaceutical activity. In the present paper, we report on the analysis of Arbidol conformational preferences in two solvents of different polarities-deuterated chloroform and dimethyl sulfoxide-at 25 °C, using the 2D NOESY method. The Arbidol molecule has various solvate forms depending on the molecular conformation. The method based on the nuclear Overhauser effect spectroscopy was shown to be efficient in the analysis of complex heterocyclic compounds possessing conformation-dependent pseudo-polymorphism. It is one of the types of polymorphism observed in compounds forming crystal solvates. Combined use of NMR methods and X-ray data allowed determining of conformer populations of Arbidol in CDCl₃ and DMSO-d₆ which were found to be 8/92% and 37/63%, respectively. The preferred conformation in solution is the same that appears in stable crystal solvates of Arbidol.

Accelerating 3D printing of pharmaceutical products using machine learning

Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.