Experimental and Computational Prediction of Glass Transition Temperature of Drugs

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Complex oiling-out behavior of procaine with stable and metastable liquid phases

During the crystallization of a solute from solvent(s), spontaneous liquid-liquid phase separation (LLPS) might occur, under certain conditions. This phenomenon, colloquially referred to as "oiling-out" in the pharmaceutical industry, often leads to undesired outcomes, including undesired particle properties, encrustation, ineffective impurity rejection, and excessively long process time. Therefore, it is critical to understand the thermodynamic driving force and phase boundaries of this phenomenon, such that rational strategies can be developed to avoid oiling-out or minimize its negative impact. In this study, we systematically evaluated the oiling-out behavior of procaine, a low melting point drug, in the solvent systems heptane, and ethanol-heptane as a function of temperature and solvent composition. In the procaine-heptane binary system, we observed a region where the LLPS is metastable with respect to crystallization, which is most commonly observed in the crystallization of modern active pharmaceutical ingredients (APIs); however, we also identified a region of the phase diagram where the LLPS is stable with respect to crystallization, and therefore will persist indefinitely. In the procaine-ethanol-heptane ternary system we identified five different regions, including a homogeneous liquid (L) region, two solid-liquid (SLI and SLII) regions, a liquid-liquid (LILII) region, and a solid-liquid-liquid (SLILII) region. The binary and ternary phase diagrams were also predicted using a state-of-the-art thermodynamic model: the SAFT-γ-Mie equation of state, and the results were compared with experimental data. Our findings highlight the complexity of oiling-out behavior. This work also represents a combined modeling and experimental platform to identify phase boundaries that will enable rational selection of strategies to crystallize active pharmaceutical ingredients with oiling-out risks.

Towards personalized drug delivery via semi-solid extrusion: Exploring poly(vinyl alcohol-co-vinyl acetate) copolymers for hydrochlorothiazide-loaded films

The increasing need for personalized drug delivery requires developing systems with tailorable properties. The copolymer poly(vinyl alcohol-co-vinyl acetate) (PVA/PVAc) allows for adjusting the monomer ratio. This study explored the effect of vinyl alcohol (VA) and vinyl acetate (VAc) monomer ratio on the properties of hydrochlorothiazide (HCT) films. Five copolymers with different VA/VAc ratios were selected and characterized. Semi-solid extrusion was employed as a method for the preparation of HCT-PVA/PVAc films to address the challenges of HCT´s low water solubility, high melting point, and low permeability. All copolymers were suitable for semi-solid extrusion, however, the mechanical properties of films with higher VA proportions were more suitable. The drug was found to be homogeneously distributed on a micrometer level throughout the prepared films. It was found that using different monomer ratios in the copolymer allows for drug release tuning - higher VA proportions showed an increased rate of drug release. Experiments through HT29-MTX cell monolayers revealed differences in HCT permeability between the different formulations. In addition, no cytotoxicity was observed for the tested formulations. The results highlight the effect of monomer ratio on film properties, providing valuable guidance for formulators in selecting PVA/PVAc copolymers for achieving desired high-quality films. In addition, varying the monomer ratio allows tuning of the film properties, and can be applied for personalization, with flexible-dose adjustment and design of appealing shapes of the pharmaceutics, not least attractive for pediatric drug delivery.

Praziquantel Fifty Years on: A Comprehensive Overview of Its Solid State

This review discusses the entire progress made on the anthelmintic drug praziquantel, focusing on the solid state and, therefore, on anhydrous crystalline polymorphs, amorphous forms, and multicomponent systems (i.e., hydrates, solvates, and cocrystals). Despite having been extensively studied over the last 50 years, new polymorphs and the greater part of their cocrystals have only been identified in the past decade. Progress in crystal engineering science (e.g., the use of mechanochemistry as a solid form screening tool and more strategic structure-based methods), along with the development of analytical techniques, including Synchrotron X-ray analyses, spectroscopy, and microscopy, have furthered the identification of unknown crystal structures of the drug. Also, computational modeling has significantly contributed to the prediction and design of new cocrystals by considering structural conformations and interactions energy. Whilst the insights on praziquantel polymorphs discussed in the present review will give a significant contribution to controlling their formation during manufacturing and drug formulation, the detailed multicomponent forms will help in designing and implementing future praziquantel-based functional materials. The latter will hopefully overcome praziquantel's numerous drawbacks and exploit its potential in the field of neglected tropical diseases.

Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding

Constructing porous structures in electromagnetic interference (EMI) shielding materials is a common strategy to decrease the secondary pollution caused by the reflection of electromagnetic waves (EMWs). However, the lack of direct analysis methods makes it difficult to fully understand the effect of porous structures on EMI, hindering EMI composites' development. Furthermore, while deep learning techniques, such as deep convolutional neural networks (DCNNs), have significantly impacted material science, their lack of interpretability limits their applications to property predictions and defect detection tasks. Until recently, advanced visualization techniques provided an approach to reveal the relevant information behind DCNNs' decisions. Inspired by it, a visual approach for porous EMI nanocomposite mechanism studies is proposed. This work combines DCNN visualization with experiments to investigate EMI porous nanocomposites. First, a rapid and straightforward salt-leaked cold-pressing powder sintering method is employed to prepare high-EMI CNTs/PVDF composites with various porosities and filler loadings. Notably, the solid sample with 30 wt % loading maintains an ultrahigh shielding effectiveness of 105 dB. The influence of porosity on the shielding mechanism is discussed macroscopically based on the prepared samples. To determine the shielding mechanism, a modified deep residual network (ResNet) is trained on a dataset of scanning electron microscopy (SEM) images of the samples. The Eigen-CAM visualization of the modified ResNet intuitively shows that the amount and depth of the pores impact the shielding mechanisms and that shallow pore structures contribute less to EMW absorption. This work is instructive for material mechanism studies. Besides, the visualization has the potential as a porous-like structure marking tool.

Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Knowledge of the glass transition temperature of molecular compounds that occur in atmospheric aerosol particles is important for estimating their viscosity, as it directly influences the kinetics of chemical reactions and particle phase state. While there is a great diversity of organic compounds present in aerosol particles, for only a minor fraction of them experimental glass transition temperatures are known. Therefore, we have developed a machine learning model designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The extremely randomized trees (extra trees) procedure was chosen for this purpose. Two approaches using different sets of input variables were followed. The first one uses the number of selected functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. Organic compounds containing carbon, hydrogen, oxygen, nitrogen, and halogen atoms are included. For improved results, both approaches can be combined with the melting temperature of the compound as an additional input variable. The results show that the predictions of both approaches show a similar mean absolute error of about 12-13 K, with the SMILES-based predictions performing slightly better. In general, the model shows good predictive power considering the diversity of the experimental input data. Furthermore, we also show that its performance exceeds that of previous parameterizations developed for this purpose and also performs better than existing machine learning models. In order to provide user-friendly versions of the model for applications, we have developed a web site where the model can be run by interested scientists via a web-based interface without prior technical knowledge. We also provide Python code of the model. Additionally, all experimental input data are provided in form of the Bielefeld Molecular Organic Glasses (BIMOG) database. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science.

Role of Crystal Disorder and Mechanoactivation in Solid-State Stability of Pharmaceuticals

Common energy-intensive processes applied in oral solid dosage development, such as milling, sieving, blending, compaction, etc. generate particles with surface and bulk crystal disorder. An intriguing aspect of the generated crystal disorder is its evolution and repercussion on the physical- and chemical stabilities of drugs. In this review, we firstly examine the existing literature on crystal disorder and its implications on solid-state stability of pharmaceuticals. Secondly, we discuss the key aspects related to the generation and evolution of crystal disorder, dynamics of the disordered/amorphous phase, analytical techniques to measure/quantify them, and approaches to model the disordering propensity from first principles. The main objective of this compilation is to provide special impetus to predict or model the chemical degradation(s) resulting from processing-induced manifestation in bulk solid manufacturing. Finally, a generic workflow is proposed that can be useful to investigate the relevance of crystal disorder on the degradation of pharmaceuticals during stability studies. The present review will cater to the requirements for developing physically- and chemically stable drugs, thereby enabling early and rational decision-making during candidate screening and in assessing degradation risks associated with formulations and processing.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Impact of Simulated Intestinal Fluids on Dissolution, Solution Chemistry, and Membrane Transport of Amorphous Multidrug Formulations

The solution behavior and membrane transport of multidrug formulations were herein investigated in a biorelevant medium simulating fasted conditions. Amorphous multidrug formulations were prepared by the solvent evaporation method. Combinations of atazanavir (ATV) and ritonavir (RTV) and felodipine (FDN) and indapamide (IPM) were prepared and stabilized by a polymer for studying their dissolution (under non-sink conditions) and membrane transport in fasted state simulated intestinal fluid (FaSSIF). The micellar solubilization by FaSSIF enhanced the amorphous solubility of the drugs to different extents. Similar to buffer, the maximum achievable concentration of drugs in combination was reduced in FaSSIF, but the extent of reduction was affected by the degree of FaSSIF solubilization. Dissolution studies of ATV and IPM revealed that the amorphous solubility of these two drugs was not affected by FaSSIF solubilization. In contrast, RTV was significantly affected by FaSSIF solubilization with a 30% reduction in the maximum achievable concentration upon combination to ATV, compared to 50% reduction in buffer. This positive deviation by FaSSIF solubilization was not reflected in the mass transport–time profiles. Interestingly, FDN concentrations remain constant until the amount of IPM added was over 1000 μg/mL. No decrease in the membrane transport of FDN was observed for a 1:1 M ratio of FDN-IPM combination. This study demonstrates the importance of studying amorphous multidrug formulations under physiologically relevant conditions to obtain insights into the performance of these formulations after oral administration.

Structure and Glass Transition Temperature of Amorphous Dispersions of Model Pharmaceuticals with Nucleobases from Molecular Dynamics

Glass transition temperature (Tg) is an important material property, which predetermines the kinetic stability of amorphous solids. In the context of active pharmaceutical ingredients (API), there is motivation to maximize their Tg by forming amorphous mixtures with other chemicals, labeled excipients. Molecular dynamics simulations are a natural computational tool to investigate the relationships between structure, dynamics, and cohesion of amorphous materials with an all-atom resolution. This work presents a computational study, addressing primarily the predictions of the glass transition temperatures of four selected API (carbamazepine, racemic ibuprofen, indomethacin, and naproxen) with two nucleobases (adenine and cytosine). Since the classical non-polarizable simulations fail to reach the quantitative accuracy of the predicted Tg, analyses of internal dynamics, hydrogen bonding, and cohesive forces in bulk phases of pure API and their mixtures with the nucleobases are performed to interpret the predicted trends. This manuscript reveals the method for a systematic search of beneficial pairs of API and excipients (with maximum Tg when mixed). Monitoring of transport and cohesive properties of API-excipients systems via molecular simulation will enable the design of such API formulations more efficiently in the future.

A property-oriented adaptive design framework for rapid discovery of energetic molecules based on small-scale labeled datasets

It remains an important challenge to apply machine learning in material discovery with limited-scale datasets available, in particular for the energetic materials. Motivated by the challenge, we developed a Property-oriented Adaptive Design Framework (PADF) to quickly design new energetic compounds with desired properties. The PADF consists of a search space, machine learning model, optimization algorithm and an evaluator based on quantum mechanical calculations. The effectiveness and generality of the PADF were assessed by two case studies on the heat of formation and heat of explosion as the target properties. 88 compounds were selected as the initial training dataset from the search space containing 84 083 compounds generated. SVR.lin/Trade-off coupled with E-state + SOB and KRR/KG coupled with CDS + E-state + SOB were determined to be the best combination pairs for the heat of formation and the heat of explosion, respectively. Most of the ten compounds selected from the first ten iterations exhibit better properties than the optimal sample in the initial dataset. Besides, the heat of explosion as the target property outperforms the heat of formation in designing energetic compounds with high detonation performance. In particular, a new compound selected at the 3rd iteration exhibits high potential as an explosive. Our strategy could be extended to other domains limited by small-scale datasets labeled.

In this work, we construct a self-adaptive design framework to efficiently screen energetic compounds with the desired heat of formation and heat of explosion from the vast chemical space unexplored.

Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method

Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (T_g) of Ge_xSe_1-x glass transition remains an open challenge. In this paper, a predictive model for the T_g in Ge_xSe_1-x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with T_g from the candidate features of Ge_xSe_1-x glass system. Secondly, in order to simulate the two-stage characteristic of T_g which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for T_g prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of T_g of other glass systems with the multi-stage characteristic.

Exploring the chemical space for freeze-drying excipients

Commonly, a limited number of generally accepted bulking agents and lyoprotectants are used for freeze-drying; predominantly mannitol, glycine, sucrose and trehalose. The purpose of this study was to combine a theoretical approach using molecular descriptors with a large scale experimental screening to evaluate the suitability of a broad range of excipients for freeze-drying. A large selection of sugars, polyols and amino acids was characterized by modulated differential scanning calorimetry (mDSC) and X-ray powder diffraction (XRPD) after well-plate based freeze-drying. The calculated molecular descriptors were investigated with both hierarchical cluster analysis and principal component analysis. A clear clustering of the excipients according to the size-related and weight-related descriptors was observed; however other relevant descriptors could also be identified. From a practical perspective, a trend was observed with regard to a higher likelihood for amorphisation and a higher glass transition temperature of the maximally freeze-concentrated solution with increasing molecular size. A translation of the molecular descriptors on pharmaceutical performance was more successful for lyoprotectants than for bulking agents. Additionally, in the course of the experimental screening, several new potential bulking agents and lyoprotectants were identified.

Design of Inhalable Solid Dosage Forms of Budesonide and Theophylline for Pulmonary Combination Therapy

Corticosteroid resistance poses a major challenge to effective treatment of chronic obstructive pulmonary diseases. However, corticosteroid resistance can be overcome by co-administration of theophylline. The aim of this study was to formulate the corticosteroid budesonide with theophylline into inhalable dry powders intended for pulmonary combination therapy. Four types of spray-dried powders were prepared: (i) budesonide and theophylline co-dissolved and processed using a 2-fluid nozzle spray drier, (ii) budesonide nanocrystals and dissolved theophylline co-dispersed and processed using a 2-fluid nozzle spray drier, (iii) dissolved budesonide and dissolved theophylline processed using a 3-fluid nozzle spray drier, and (iv) budesonide nanocrystals and dissolved theophylline processed using a 3-fluid nozzle spray drier. Spray drying from the solutions resulted in co-amorphous (i) and partially amorphous powders (iii), whereas spray drying of the nanosuspensions resulted in crystalline products (ii and iv). Even though budesonide was amorphous in (i) and (iii), it failed to exhibit any dissolution advantage over the unprocessed budesonide. In contrast, the dissolution of budesonide from its nanocrystalline formulations, i.e., (ii) and (iv), was significantly higher compared to a physical mixture or unprocessed budesonide. Furthermore, the spray-dried powders obtained from the 2-fluid nozzle spray drier, i.e., (i) and (ii), exhibited co-deposition of budesonide and theophylline at the same weight ratio in the aerodynamic assessment using the New Generation Impactor. In contrast, the depositions of budesonide and theophylline deviated from the starting weight ratio in the aerodynamic assessment of spray-dried powders obtained from the 3-fluid nozzle spray drier, i.e., (iii) and (iv). Based on these results, the powders spray-dried from the suspension by using the 2-fluid nozzle spray drier, i.e., (ii), offered the best formulation properties given the physically stable crystalline solid-state properties and the co-deposition profile.

Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions

In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).

Milling and comilling Praziquantel at cryogenic and room temperatures: Assessment of the process-induced effects on drug properties

This study is a comprehensive evaluation of praziquantel (PZQ) behavior upon grinding considering the influence of milling temperature (cryogenic vs room temperature), frequency and time and presence of polymers (milled raw PZQ vs comilled PZQ/povidone and PZQ/crospovidone at 50:50 w/w) on two experimental responses (residual crystallinity and PZQ recovery). To this aim a full factorial design was set up and the responses of the experimental design were statistically assessed. The powder temperature, measured in different milling conditions, was found to increase with increasing milling frequency and time, up to a maximum recorded value of 46.9 °C (after 90 min at R.T.), for all the three powder systems. When PZQ was ground in RT environment, the recovery was 100%, independently from frequency and time of milling. Its residual crystallinity remained pronounced (>70%) upon milling, even if treated at the most severe conditions. Conversely, when the drug was milled in presence of the polymers, it showed a higher tendency to degradation and amorphysation, independently from the choice of the polymer. The use of cryogenic conditions, operating at temperatures lower than PZQ glass transition, permitted to dramatically reduce PZQ residual crystallinity when the drug was ground by itself. In the case of binary mixtures, the switch to a cryogenic environment did not affect significantly the experimental responses, but permitted to obtain a more predictable trend of both drug recovery and residual crystallinity when varying time and frequency of milling.

Gaining Thermodynamic Insight From Distinct Glass Formation Kinetics of Structurally Similar Organic Compounds

Thermodynamic and kinetic aspects of crystallization of 12 structurally similar organic compounds were investigated from the supercooled liquid state by calorimetric and rheologic measurements. Based on their crystallization behaviors, these compounds were divided into 3 categories: stable glass formers, poor glass formers, and good glass formers with poor stability on reheating. Correlation was sought between thermodynamic quantities and glass formation based on nucleation and crystal growth theories. Larger values of enthalpy of fusion and melting point were found to correlate with poor glass-forming ability. Conversely, lower entropy of fusion was found to correlate with glass formation. Examination of kinetic aspects of glass formation revealed 2 important facets of good glass formers, that is, rapid increase in viscosity on supercooling and high melting point viscosity compared with non-glass formers. A broader relationship was sought between entropy of fusion and glass formation by including several glass formers from literature. Our analysis indicated that good glass formers tend to have an entropy of fusion closer to 0.3 J cm^-3 K^-1. The structural similarity of the compounds in this study provides insights regarding the nature of intermolecular interactions responsible for the observed effect on entropy of fusion, viscosity, and crystallization kinetics.

The Need for Restructuring the Disordered Science of Amorphous Drug Formulations

The alarming numbers of poorly soluble discovery compounds have centered the efforts towards finding strategies to improve the solubility. One of the attractive approaches to enhance solubility is via amorphization despite the stability issue associated with it. Although the number of amorphous-based research reports has increased tremendously after year 2000, little is known on the current research practice in designing amorphous formulation and how it has changed after the concept of solid dispersion was first introduced decades ago. In this review we try to answer the following questions: What model compounds and excipients have been used in amorphous-based research? How were these two components selected and prepared? What methods have been used to assess the performance of amorphous formulation? What methodology have evolved and/or been standardized since amorphous-based formulation was first introduced and to what extent have we embraced on new methods? Is the extent of research mirrored in the number of marketed amorphous drug products? We have summarized the history and evolution of amorphous formulation and discuss the current status of amorphous formulation-related research practice. We also explore the potential uses of old experimental methods and how they can be used in tandem with computational tools in designing amorphous formulation more efficiently than the traditional trial-and-error approach.

Computational prediction of formulation strategies for beyond-rule-of-5 compounds

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of 'formulate-ability' during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets.

Isoenergetic Polymorphism: The Puzzle of Tolazamide as a Case Study

In the present case study of tolazamide we illustrate how many seemingly contradictory results that have been obtained from experimental observations and theoretical calculations can finally start forming a consistent picture: a "puzzle put together". For many years, tolazamide was considered to have no polymorphs. This made this drug substance unique among the large family of sulfonylureas, which was known to be significantly more prone to polymorphism than many other organic compounds. The present work employs a broad and in-depth analysis that includes the use of optical microscopy, single-crystal and powder X-ray diffraction, IR and Raman spectroscopies, DSC, semiempirical PIXEL calculations and DFT of three polymorphs of tolazamide. This case study shows how the polymorphs of a molecular crystal can be overlooked even if discovered serendipitously on one of numerous crystallizations, and how very different molecular packings can be practically isoenergetic but still crystallize quite selectively and transform one into another irreversibly upon heating.

Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability

Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (T_g) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12 h at temperatures 20 °C above or below the T_g. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20 °C below the T_g. Fourteen of the Class II compounds crystallized when stored above the T_g whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e.g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the T_g. The use of a large dataset revealed that molecular features related to aromaticity and π–π interactions reduce the inherent physical stability of amorphous drugs.

Comparative Study of Different Methods for the Prediction of Drug-Polymer Solubility

In this study, a comparison of different methods to predict drug-polymer solubility was carried out on binary systems consisting of five model drugs (paracetamol, chloramphenicol, celecoxib, indomethacin, and felodipine) and polyvinylpyrrolidone/vinyl acetate copolymers (PVP/VA) of different monomer weight ratios. The drug-polymer solubility at 25 °C was predicted using the Flory-Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point depression method. These predictions were compared with the solubility in the low molecular weight liquid analogues of the PVP/VA copolymer (N-vinylpyrrolidone and vinyl acetate). The predicted solubilities at 25 °C varied considerably depending on the method used. However, the three thermal analysis methods ranked the predicted solubilities in the same order, except for the felodipine-PVP system. Furthermore, the magnitude of the predicted solubilities from the recrystallization method and melting point depression method correlated well with the estimates based on the solubility in the liquid analogues, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug-polymer solubility.