Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning

Insights into Hildebrand Solubility Parameters - Contributions from Cohesive Energies or Electrophilicity Densities?

We introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non- or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability.

Stability Challenges of Amorphous Solid Dispersions of Drugs: A Critical Review on Mechanistic Aspects

The most common drawback of the existing and novel drug molecules is their low bioavailability because of their low solubility. One of the most important approaches to enhance the bioavailability in the enteral route for poorly hydrophilic molecules is amorphous solid dispersion (ASD). The solubility of compounds in amorphous form is comparatively high because of the availability of free energy produced during formulation. This free energy results in the change of crystalline nature of the prepared ASD to the stable crystalline form leading to the reduced solubility of the product. Due to the intrinsic chemical and physical uncertainty and the restricted knowledge about the interactions of active molecules with the carriers making, this ASD is a challenging task. This review focused on strategies to stabilize ASD by considering the various theories explaining the free-energy concept, physical interactions, and thermal properties. This review also highlighted molecular modeling and machine learning computational advancement to stabilize ASD.

Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison

Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles. All-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.

The Utilization of Plant-Material-Loaded Vesicular Drug Delivery Systems in the Management of Pulmonary Diseases

Medicinal plants have been utilized to treat a variety of conditions on account of the bioactive properties that they contain. Most bioactive constituents from plants are of limited effectiveness, due to poor solubility, limited permeability, first-pass metabolism, efflux transporters, chemical instability, and food-drug interactions However, when combined with vesicular drug delivery systems (VDDS), herbal medicines can be delivered at a predetermined rate and can exhibit site-specific action. Vesicular drug delivery systems are novel pharmaceutical formulations that make use of vesicles as a means of encapsulating and transporting drugs to various locations within the body; they are a cutting-edge method of medication delivery that combats the drawbacks of conventional drug delivery methods. Drug delivery systems offer promising strategies to overcome the bioavailability limitations of bioactive phytochemicals. By improving their solubility, protecting them from degradation, enabling targeted delivery, and facilitating controlled release, drug delivery systems can enhance the therapeutic efficacy of phytochemicals and unlock their full potential in various health conditions. This review explores and collates the application of plant-based VDDS with the potential to exhibit protective effects against lung function loss in the interest of innovative and effective treatment and management of respiratory illnesses.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Solubility determination, dissolution properties and solid transformation of resmetirom (form A) in heptane and seven alcohols

In this work, the solubility of resmetirom (form A) was initially measured in heptane and seven alcohol solvents by gravimetric methods. Then, the transformation temperature between form A and ethanol solvate was determined at 333.76 K. Subsequently, some commonly used models were applied to fit the solubility data, and it was found that the modified Apelblat equation and the Jouyban-Acree-van't Hoff (J-A-V) model achieved the highest correlation accuracy for those in mono-solvents and heptane + propanol, respectively. And the average relative deviation (ARD) values of models were less than 0.5%, indicating a good agreement with the experimental results. Additionally, through density functional theory calculation and the analysis of solvent parameters, it was observed that hydrogen-bonding played primary roles in the dissolution process of resmetirom. The multiple factors such as the polarity of solvent, active site interaction, the molecular size and free volume all affect the solubility of resmetirom. Furthermore, by comparing the experimental and simulated infrared spectra of form A and two alcohol solvates, five characteristic bands were selected for quantification. Partial least squares regression (PLSR), a multivariate statistical analysis method, was used to extract quantitative information. The quantitative analysis model was established based on specific wavelength intervals, which were associated with inter-molecular interactions. Combined with PLSR, a new high-precision quantitative method was established to study the solid transformation process between form A and solvates. From 303.15 to 323.15 K, the rate of transformation from form A to methanol solvate or ethanol solvate was decreased with increasing temperature, revealing that the transformation process was driven by the solubility difference between form A and solvates under the studied conditions. This research will definitely afford necessary solubility data and solvent selection for the design of the crystallization process of resmetirom (form A) in industry, and provide basic data for the production of resmetirom (form A) in the pharmaceutical industry.

COSMO Models for the Pharmaceutical Development of Parenteral Drug Formulations

The aqueous solubility of active pharmaceutical ingredients is one of the most important features to be considered during the development of parenteral formulations in the pharmaceutical industry. Computational modelling has become in the last years an integral part of pharmaceutical development. In this context, ab initio computational models, such as COnductor-like Screening MOdel (COSMO), have been proposed as promising tools for the prediction of results without the effective use of resources. Nevertheless, despite the clear evaluation of computational resources, some authors had not achieved satisfying results and new calculations and algorithms have been proposed over the years to improve the outcomes. In the development and production of aqueous parenteral formulations, the solubility of Active Pharmaceutical Ingredients (APIs) in an aqueous and biocompatible vehicle is a decisive step. This work aims to study the hypothesis that COSMO models could be useful in the development of new parenteral formulations, mainly aqueous ones.

Theoretical Exploitation of 1,2,3,4,5,6-Hexachloro- and 1,2,3,4,5,6-Hexafluorocyclohexane Isomers as Biologically Active Compounds

Hexachlorocyclohexanes (HCHs) have been widely explored as biological compounds during the last century. However, most of them were banned due to their potential toxicity in humans, animals, and the environment. Revisiting HCHs to explore their biological activity while improving key features is valuable and may lead to a new class of pesticides that utilizes the biological response of HCHs without their toxic characteristics. In this sense, the fluorine atom can be a possible alternative since a large number of therapeutics and agrochemicals have been developed with this halogen in their structure. We have evaluated herein the conformational behavior of HCHs and their bioisosteric fluorinated compounds, namely, hexafluorocyclohexanes (HFHs), through quantum-chemical calculations. We also explored the potential of the HCH and HFH isomers as biological compounds by docking them inside three possible targets. It was demonstrated that HCH and HFH have similar ligand-protein interactions with three pockets: the picrotoxin and barbiturate sites of the GABA_A receptor and the ryanodine receptor. The results support HFHs as possible alternatives for HCHs since the replacement of Cl with F does not forfeit the main ligand-protein interactions. Finally, we demonstrated that HFHs have a lower log P than HCHs by almost two logarithmic units. This result highlights the role of fluorine in distribution and bioaccumulation.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Molecular Dynamics Simulation of Drug Solubilization Behavior in Surfactant and Cosolvent Injections

Surfactants and cosolvents are often combined to solubilize insoluble drugs in commercially available intravenous formulations to achieve better solubilization. In this study, six marketed parenteral formulations with surfactants and cosolvents were investigated on the aggregation processes of micelles, the structural characterization of micelles, and the properties of solvent using molecular dynamics simulations. The addition of cosolvents resulted in better hydration of the core and palisade regions of micelles and an increase in both radius of gyration (Rg) and the solvent accessible surface area (SASA), causing a rise in critical micelle concentration (CMC), which hindered the phase separation of micelles. At the same time, the presence of cosolvents disrupted the hydrogen bonding structure of water in solution, increasing the solubility of insoluble medicines. Therefore, the solubilization mechanism of the cosolvent and surfactant mixtures was successfully analyzed by molecular dynamics simulation, which will benefit future formulation development for drug delivery.

Biology and medicine in the landscape of quantum advantages

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

The Transport Properties of Semi-Crystalline Polyetherimide BPDA-P3 in Amorphous and Ordered States: Computer Simulations

The effect of polymer chain ordering on the transport properties of the polymer membrane was examined for the semi-crystalline heterocyclic polyetherimide (PEI) BPDA-P3 based on 3,3',4,4'-biphenyltetracarboxylic dianhydride (BPDA) and diamine 1,4-bis [4-(4-aminophenoxy)phenoxy]benzene (P3). All-atom Molecular Dynamics (MD) simulations were used to investigate the gas diffusion process carried through the pores of a free volume several nanometers in size. The long-term (~30 μs) MD simulations of BPDA-P3 were performed at T = 600 K, close to the experimental value of the melting temperature (Tm ≈ 577 K). It was found during the simulations that the transition of the PEI from an amorphous state to an ordered one occurred. We determined a decrease in solubility for both the gases examined (CO2 and CH4), caused by the redistribution of free volume elements occurring during the structural ordering of the polymer chains in glassy state (Tg ≈ 487 K). By analyzing the diffusion coefficients in the ordered state, the presence of gas diffusion anisotropy was found. However, the averaged values of the diffusion coefficients did not differ from each other in the amorphous and ordered states. Thus, permeability in the observed system is primarily determined by gas solubility, rather than by gas diffusion.

Boosting the predictive performance with aqueous solubility dataset curation

Intrinsic solubility is a critical property in pharmaceutical industry that impacts in-vivo bioavailability of small molecule drugs. However, solubility prediction with Artificial Intelligence(AI) are facing insufficient data, poor data quality, and no unified measurements for AI and physics-based approaches. We collect 7 aqueous solubility datasets, and present a dataset curation workflow. Evaluating the curated data with two expanded deep learning methods, improved RMSE scores on all curated thermodynamic datasets are observed. We also compare expanded Chemprop enhanced with curated data and state-of-art physics-based approach using pearson and spearman correlation coefficients. A similar performance on pearson with 0.930 and spearman with 0.947 from expanded Chemprop is achieved. A steadily improved pearson and spearman values with increasing data points are also illustrated. Besides that, the computation advantage of AI models enables quick evaluation of a large set of molecules during the hit identification or lead optimization stages, which helps further decision making within the time cycle at drug discovery stage.

Recent progress in general force fields of small molecules

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018-2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users' convenience.

Overview of nanoparticulate strategies for solubility enhancement of poorly soluble drugs

Poor aqueous solubility and poor bioavailability are major issues with many pharmaceutical industries. By some estimation, 70-90% drug candidates in development stage while up-to 40% of the marketed products are poorly soluble which leads to low bioavailability, reduced therapeutic effects and dosage escalation. That's why solubility is an important factor to consider during design and manufacturing of the pharmaceutical products. To-date, various strategies have been explored to tackle the issue of poor solubility. This review article focuses the updated overview of commonly used macro and nano drug delivery systems and techniques such as micronization, solid dispersion (SD), supercritical fluid (SCF), hydrotropy, co-solvency, micellar solubilization, cryogenic technique, inclusion complex formation-based techniques, nanosuspension, solid lipid nanoparticles, and nanogels/nanomatrices explored for solubility enhancement of poorly soluble drugs. Among various techniques, nanomatrices were found a promising and impeccable strategy for solubility enhancement of poorly soluble drugs. This article also describes the mechanism of action of each technique used in solubilization enhancement.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Study and Computational Modeling of Fatty Acid Effects on Drug Solubility in Lipid-Based Systems

Lipid-based systems have many advantages in formulation of poorly water-soluble drugs but issues of a limited solvent capacity are often encountered in development. One of the possible solubilization approaches of especially basic drugs could be the addition of fatty acids to oils but currently, a systematic study is lacking. Therefore, the present work investigated apparently neutral and basic drugs in medium chain triglycerides (MCT) alone and with added either caproic acid (C6), caprylic acid (C8), capric acid (C10) or oleic acid (C18:1) at different levels (5 - 20%, w/w). A miniaturized solubility assay was used together with X-ray diffraction to analyze the residual solid and finally, solubility data were modeled using the conductor-like screening model for real solvents (COSMO-RS). Some drug bases had an MCT solubility of only a few mg/ml or less but addition of fatty acids provided in some formulations exceptional drug loading of up to about 20% (w/w). The solubility changes were in general more pronounced the shorter the chain length was and the longest oleic acid even displayed a negative effect in mixtures of celecoxib and fenofibrate. The COSMO-RS prediction accuracy was highly specific for the given compounds with root mean square errors (RMSE) ranging from an excellent 0.07 to a highest value of 1.12. The latter was obtained with the strongest model base pimozide for which a new solid form was found in some samples. In conclusion, targeting specific molecular interactions with the solute combined with mechanistic modeling provides new tools to advance lipid-based drug delivery.

Molecular Dynamics Simulations and Experimental Results Provide Insight into Clinical Performance Differences between Sandimmune® and Neoral® Lipid-Based Formulations

OBJECTIVE

Molecular dynamics (MD) simulations provide an in silico method to study the structure of lipid-based formulations (LBFs) and the incorporation of poorly water-soluble drugs within such formulations. In order to validate the ability of MD to effectively model the properties of LBFs, this work investigates the well-known cyclosporine A formulations, Sandimmune® and Neoral®. Sandimmune® exhibits poor dispersibility and its absorption from the gastrointestinal tract is enhanced when administered after food, whereas Neoral® disperses comparatively well and shows no food effect.

METHODS

MD simulations were performed of both LBFs to investigate the differences observed in fasted and fed conditions. These conditions were also tested using an in vitro experimental model of dispersion and digestion.

RESULTS

These MD simulations were able to show that the food effect observed for Sandimmune® can be explained by large changes in drug solubilization on addition of bile. In contrast, Neoral® is well dispersed in water or in simulated fasted conditions, and this dispersion is relatively unchanged on moving to fed conditions. These differences were confirmed using dispersion and digestion in vitro experimental model.

CONCLUSIONS

The current data suggests that MD simulations are a potential method to model the fate of LBFs in the gastrointestinal tract, predict their dispersion and digestion, investigate behaviour of APIs within the formulations, and provide insights into the clinical performance of LBFs.

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBF_Capmul^MC (r² 0.90 vs. 0.56) and sLBF_Maisine^LC (r² 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

Nanoparticulation of Prodrug into Medicines for Cancer Therapy

This article provides a broad spectrum about the nanoprodrug fabrication advances co-driven by prodrug and nanotechnology development to potentiate cancer treatment. The nanoprodrug inherits the features of both prodrug concept and nanomedicine know-how, attempts to solve underexploited challenge in cancer treatment cooperatively. Prodrugs can release bioactive drugs on-demand at specific sites to reduce systemic toxicity, this is done by using the special properties of the tumor microenvironment, such as pH value, glutathione concentration, and specific overexpressed enzymes; or by using exogenous stimulation, such as light, heat, and ultrasound. The nanotechnology, manipulating the matter within nanoscale, has high relevance to certain biological conditions, and has been widely utilized in cancer therapy. Together, the marriage of prodrug strategy which shield the side effects of parent drug and nanotechnology with pinpoint delivery capability has conceived highly camouflaged Trojan horse to maneuver cancerous threats.

Machine learning directed drug formulation development

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

Probe into the Molecular Mechanism of Ibuprofen Interaction with Warfarin Bound to Human Serum Albumin in Comparison to Ascorbic and Salicylic Acids: Allosteric Inhibition of Anticoagulant Release

The release of anticoagulant drugs such as warfarin from human serum albumin (HSA) has been important not only mechanistically but also clinically for patients who take multiple drugs simultaneously. In this study, the role of some commonly used drugs, including s-ibuprofen, ascorbic acid, and salicylic acid, was investigated in the release of warfarin bound to HSA in silico. The effects of the aforementioned drugs on the HSA-warfarin complex were investigated with molecular dynamics (MD) simulations using two approaches; in the first perspective, molecular docking was used to model the interaction of each drug with the HSA-warfarin complex, and in the second approach, drugs were positioned randomly and distant from the binary complex (HSA-warfarin) in a physiologically relevant concentration. The results obtained from both approaches indicated that s-ibuprofen and ascorbic acid both displayed allosteric effects on the release of warfarin from HSA. Although ascorbic acid aided in warfarin release, leading to destabilization of HSA, ibuprofen demonstrated a stabilizing effect on releasing the anticoagulant drug through several noncovalent interactions, including hydrophobic, electrostatic, and hydrogen-bonding interactions with the protein. The calculated binding free energy and energy contribution of involved residues using the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) method, along with root mean square deviation (RMSD) values, protein gyration, and free energy surface (FES) mapping of the protein, provided valuable details on the nature of the interactions of each drug on the release of warfarin from HSA. These results can provide important information on the mechanisms of anticoagulant release that has not been revealed in molecular details previously.

Thermodynamics and Intermolecular Interactions of Nicotinamide in Neat and Binary Solutions: Experimental Measurements and COSMO-RS Concentration Dependent Reactions Investigations

In this study, the temperature-dependent solubility of nicotinamide (niacin) was measured in six neat solvents and five aqueous-organic binary mixtures (methanol, 1,4-dioxane, acetonitrile, DMSO and DMF). It was discovered that the selected set of organic solvents offer all sorts of solvent effects, including co-solvent, synergistic, and anti-solvent features, enabling flexible tuning of niacin solubility. In addition, differential scanning calorimetry was used to characterize the fusion thermodynamics of nicotinamide. In particular, the heat capacity change upon melting was measured. The experimental data were interpreted by means of COSMO-RS-DARE (conductor-like screening model for realistic solvation-dimerization, aggregation, and reaction extension) for concentration dependent reactions. The solute-solute and solute-solvent intermolecular interactions were found to be significant in all of the studied systems, which was proven by the computed mutual affinity of the components at the saturated conditions. The values of the Gibbs free energies of pair formation were derived at an advanced level of theory (MP2), including corrections for electron correlation and zero point vibrational energy (ZPE). In all of the studied systems the self-association of nicotinamide was found to be a predominant intermolecular complex, irrespective of the temperature and composition of the binary system. The application of the COSMO-RS-DARE approach led to a perfect match between the computed and measured solubility data, by optimizing the parameter of intermolecular interactions.

Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Role of surface molecular environment and amorphous content in moisture sorption behavior of milled Terbutaline Sulphate

Milling may cause undesired changes in crystal topology, due to exposure of new facets, their corresponding functional groups and surface amorphization. This study investigated effect of milling induced surface amorphous content and chemical environment on moisture sorption behavior of a model hydrophilic drug, Terbutaline Sulphate (TBS). A Dynamic Vapor Sorption (DVS) based analytical method was developed to detect amorphous content, with LOD and LOQ of 0.41% and 1.24%w/w, respectively. The calibration curve gave a linear regression of 0.999 in a concentration range of 0-16.36%w/w amorphous content plotted against surface area normalized % weight change, due to moisture sorption. TBS was milled using air jet mill at 8 Bars for 3 cycles (D₉₀- 3.46µm) and analyzed using the validated DVS method prior to and post conditioning. The moisture sorption was higher in case of milled unconditioned TBS. Molecular Dynamics Simulation (MDS) was performed to identify the cause for increased moisture sorption due to altered surface environment or amorphous content. The results implied that the new planes and functional groups exposed on milling had negligible contribution to moisture sorption and the higher moisture sorption in milled unconditioned TBS was due to surface amorphization. Conditioning under elevated humidity recrystallized the milling-induced surface amorphous content and led to decreased moisture sorption in milled conditioned TBS.

Computational avenues in oral protein and peptide therapeutics

Proteins and peptides are amongst the most sought-after biomolecules because of their exceptional potential to cater to a vast range of diseases. Although widely studied and researched, the oral delivery of these biomolecules remains a challenge. Alongside formulation strategies, approaches to overcome the inherent barriers for peptide absorption are being designed at the molecular level to establish a sound rationale and to achieve higher bioavailability. Computer-aided drug design (CADD) is a modern in silico approach for developing successful bio-formulations. CADD enables intricate study of the biomolecules in conjunction with their target sites or receptors at the molecular level. Knowledge of the molecular interactions of proteins and peptides makes way for the pre-screening of suitable formulation components and facilitates their delivery.

Novel Physics-Based Ensemble Modeling Approach That Utilizes 3D Molecular Conformation and Packing to Access Aqueous Thermodynamic Solubility: A Case Study of Orally Available Bromodomain and Extraterminal Domain Inhibitor Lead Optimization Series

Drug design with patient centricity for ease of administration and pill burden requires robust understanding of the impact of chemical modifications on relevant physicochemical properties early in lead optimization. To this end, we have developed a physics-based ensemble approach to predict aqueous thermodynamic crystalline solubility, with a 2D chemical structure as the input. Predictions for the bromodomain and extraterminal domain (BET) inhibitor series show very close match (0.5 log unit) with measured thermodynamic solubility for cases with low crystal anisotropy and good match (1 log unit) for high anisotropy structures. The importance of thermodynamic solubility is clearly demonstrated by up to a 4 log unit drop in solubility compared to kinetic (amorphous) solubility in some cases and implications thereof, for instance on human dose. We have also demonstrated that incorporating predicted crystal structures in thermodynamic solubility prediction is necessary to differentiate (up to 4 log unit) between solubility of molecules within the series. Finally, our physics-based ensemble approach provides valuable structural insights into the origins of 3-D conformational landscapes, crystal polymorphism, and anisotropy that can be leveraged for both drug design and development.

Rational Selection of Bio-Enabling Oral Drug Formulations - A PEARRL Commentary

New drug candidates often require bio-enabling formation technologies such as lipid-based formulations, solid dispersions, or nanosized drug formulations. Development of such more sophisticated delivery systems generally requires higher resource investment compared to a conventional oral dosage form, which might slow down clinical development. To achieve the biopharmaceutical objectives while enabling rapid cost effective development, it is imperative to identify a suitable formulation technique for a given drug candidate as early as possible. Hence many companies have developed internal decision trees based mostly on prior organizational experience, though they also contain some arbitrary elements. As part of the EU funded PEARRL project, a number of new decision trees are here proposed that reflect both the current scientific state of the art and a consensus among the industrial project partners. This commentary presents and discusses these, while also going beyond this classical expert approach with a pilot study using emerging machine learning, where the computer suggests formulation strategy based on the physicochemical and biopharmaceutical properties of a molecule. Current limitations are discussed and an outlook is provided for likely future developments in this emerging field of pharmaceutics.

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

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

An Overview of Molecular Dynamic Simulation for Corrosion Inhibition of Ferrous Metals

Molecular dynamics (MD) simulation is a powerful tool to study the molecular level working mechanism of corrosion inhibitors in mitigating corrosion. In the past decades, MD simulation has emerged as an instrument to investigate the interactions at the interface between the inhibitor molecule and the metal surface. Combined with experimental measurement, theoretical examination from MD simulation delivers useful information on the adsorption ability and orientation of the molecule on the surface. It relates the microscopic characteristics to the macroscopic properties which enables researchers to develop high performance inhibitors. Although there has been vast growth in the number of studies that use molecular dynamic evaluation, there is still lack of comprehensive review specifically for corrosion inhibition of organic inhibitors on ferrous metal in acidic solution. Much uncertainty still exists on the approaches and steps in performing MD simulation for corrosion system. This paper reviews the basic principle of MD simulation along with methods, selection of parameters, expected result such as adsorption energy, binding energy and inhibitor orientation, and recent publications in corrosion inhibition studies.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Thermodynamics and Mechanism of the Membrane Permeation of Hv1 Channel Blockers

The voltage-gated proton channel Hv1 mediates efflux of protons from the cell. Hv1 integrally contributes to various physiological processes including pH homeostasis and the respiratory burst of phagocytes. Inhibition of Hv1 may provide therapeutic avenues for the treatment of inflammatory diseases, breast cancer, and ischemic brain damage. In this work, we investigate two prototypical Hv1 inhibitors, 2-guanidinobenzimidazole (2GBI), and 5-chloro-2-guanidinobenzimidazole (GBIC), from an experimentally screened class of guanidine derivatives. Both compounds block proton conduction by binding the same site located on the intracellular side of the channel. However, when added to the extracellular medium, the compounds strongly differ in their ability to inhibit proton conduction, suggesting substantial differences in membrane permeability. Here, we compute the potential of mean force for each compound to permeate through the membrane using atomistic molecular dynamics simulations with the adaptive biasing force method. Our results rationalize the putative distinction between these two blockers with respect to their abilities to permeate the cellular membrane.

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Several approaches to predict and model drug solubility have been used in the drug discovery and development processes during the last decades. Each of these approaches have their own benefits and place, and are typically used as standalone approaches rather than in concert. The synergistic effects of these are often overlooked, partly due to the need of computational experts to perform the modeling and simulations as well as analyzing the data obtained. Here we provide our views on how these different approaches can be used to retrieve more information on drug solubility, ranging from multivariate data analysis over thermodynamic cycle modeling to molecular dynamics simulations. We are discussing aqueous solubility as well as solubility in more complex mixed solvents and media with colloidal structures present. We conclude that the field of computational pharmaceutics is in its early days but with a bright future ahead. However, education of computational formulators with broad knowledge of modeling and simulation approaches is imperative if computational pharmaceutics is to reach its full potential.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SR_MC (r² = 0.81) and SR_LC (r² = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SR_MC r² = 0.74; SR_LC r² = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD_6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs.

Leveraging Extraction Testing to Predict Patient Exposure to Polymeric Medical Device Leachables Using Physics-based Models

Toxicological risk assessment approaches are increasingly being used in lieu of animal testing to address toxicological concerns associated with release of chemical constituents from polymeric medical device components. These approaches currently rely on in vitro extraction testing in aggressive environments to estimate patient exposure to these constituents, but the clinical relevance of the test results is often ambiguous. Physics-based mass transport models can provide a framework to interpret extraction test results to provide more clinically relevant exposure estimates. However, the models require system-specific material properties, such as diffusion (D) and partition coefficients (K), to be established a priori for the extraction conditions. Using systems comprised high-density polyethylene and 4 different additives, we demonstrate that these properties can be quantified through standard extraction testing in hexane and isopropyl alcohol. The values of D and K derived in this manner were consistent with theoretical predictions for these quantities. Based on these results, we discuss both the challenges and benefits to leveraging extraction data to parameterize physics-based exposure models. Our observations suggest that clinically relevant, yet still conservative, exposure dose estimates provided by applying this approach to a single extraction measurement can be more than 100 times lower than would be measured under typical aggressive extraction conditions. However, to apply the framework on a routine basis, limiting values of D and K must be established for device-relevant systems either through the aggregation and analysis of more extensive extraction test data and/or advancements in theoretical and computational modeling efforts to predict these quantities.

Bioavailability Enhancement of Olmesartan Medoxomil Using Hot-Melt Extrusion: In-Silico, In-Vitro, and In-Vivo Evaluation

Olmesartan medoxomil (OLM) an antihypertensive molecule with poor solubility and poor bioavailability (26% when taken orally) was selected as a model drug. Herein, rationale development of amorphous solid dispersion with hot-melt extrusion of poorly bioavailable OLM was carried out with the aid of quality by design (QbD), in-silico, in-vitro, and in-vivo evaluations. Polymer selection commenced with the selection of thermoplastic water-soluble polymers with the compatible processing temperature window as per the thermal behavior of OLM. Molecular dynamics (MD) simulations as well assisted in the selection of a carrier. Promising dissolution enhancement was observed with the help of Kollidon VA-64 (VA-64) as a carrier. Optimization of the formulation was executed using the QbD approach with design of experiment as a statistical optimization tool. Interactions between VA-64 and OLM on the atomic level were studied with the help of atomistic MD simulations. Characterization of the optimized extrudates were carried out with scanning electron microscopy, atomic force microscopy, differential scanning calorimetry, thermogravimetric analysis, Fourier transforms infrared spectroscopy, powder X-ray diffraction, in-vitro dissolution study, and in-vivo pharmacokinetic studies. Molecular-level mixing of OLM with VA-64 resulted into glass solution formation which rapidly dissolves (28 times in-vitro dissolution enhancement) in GI tract fluids and instantly gets absorbed into blood circulation. In-vivo pharmacokinetic studies performed in Sprague-Dawley rats reflected superior bioavailability (201.60%) with a significant increase in the C_max with short T_max through amorphization of OLM. The in-silico results were in agreement with the observed results of in-vitro dissolution studies and in-vivo pharmacokinetic study.

Molecular Mechanism of Gas Solubility in Liquid: Constant Chemical Potential Molecular Dynamics Simulations

Accurate prediction of gas solubility in a liquid is crucial in many areas of chemistry, and a detailed understanding of the molecular mechanism of the gas solvation continues to be an active area of research. Here, we extend the idea of the constant chemical potential molecular dynamics (CμMD) approach to the calculation of the gas solubility in the liquid under constant gas chemical potential conditions. As a representative example, we utilize this method to calculate the isothermal solubility of carbon dioxide in water. Additionally, we provide microscopic insight into the mechanism of solvation that preferentially occurs in areas of the surface where the hydrogen network is broken.