Influence of Solvent Selection on the Crystallizability and Polymorphic Selectivity Associated with the Formation of the "Disappeared" Form I Polymorph of Ritonavir.

The comparative crystallizability and polymorphic selectivity of ritonavir, a novel protease inhibitor for the treatment of acquired immune-deficiency syndrome, as a function of solvent selection are examined through an integrated and self-consistent experimental and computational molecular modeling study. Recrystallization at high supersaturation by rapid cooling at 283.15 K is found to produce the metastable "disappeared" polymorphic form I from acetone, ethyl acetate, acetonitrile, and toluene solutions in contrast to ethanol which produces the stable form II. Concomitant crystallization of the other known solid forms is not found under these conditions. Isothermal crystallization studies using turbidometric detection based upon classical nucleation theory reveal that, for an equal induction time, the required driving force needed to initiate solution nucleation decreases with solubility in the order of ethanol, acetone, acetonitrile, ethyl acetate, and toluene consistent with the expected desolvation behavior predicted from the calculated solute solvation free energies. Molecular dynamics simulations of the molecular and intermolecular chemistry reveal the presence of conformational interplay between intramolecular and intermolecular interactions within the solution phase. These encompass the solvent-dependent formation of intramolecular O-H...O hydrogen bonding between the hydroxyl and carbamate groups coupled with differing conformations of the hydroxyl's shielding phenyl groups. These conformational preferences and their relative interaction propensities, as a function of solvent selection, may play a rate-limiting role in the crystallization behavior by not only inhibiting to different degrees the nucleation process but also restricting the assembly of the optimal intermolecular hydrogen bonding network needed for the formation of the stable form II polymorph.

Predicting the Strength of Cohesive and Adhesive Interparticle Interactions for Dry Powder Inhalation Blends of Terbutaline Sulfate with α-Lactose Monohydrate.

Grid-based systematic search methods are used to investigate molecule-molecule, molecule-surface, and surface-surface contributions to interparticle interactions in order to identify the crystal faces that most strongly affect particle behavior during powder blend formulation and delivery processes. The model system comprises terbutaline sulfate (TBS) as an active pharmaceutical ingredient (API) and α-form lactose monohydrate (LMH). A combination of systematic molecular modeling and X-ray computed tomography (XCT) is used to determine not only the adhesive and cohesive interparticle energies but, also the agglomeration behavior during manufacturing and de-agglomeration behavior during delivery after inhalation. This is achieved through a detailed examination of the balance between the adhesive and cohesive energies with the XCT results confirming the blend segregation tendencies, through the particle-particle de-agglomeration process. The results reveal that the cohesive interaction energies of TBS-TBS are higher than the adhesive energies between TBS and LMH, but that the cohesive energies of LMH-LMH are the smallest between molecule and molecule, molecule and surface, and surface and surface. This shows how systematic grid-search molecular modeling along with XCT can guide the digital formulation design of inhalation powders in order to achieve optimum aerosolization and efficacy for inhaled medicines. This will lead to faster pharmaceutical design with less variability, higher quality, and enhanced performance.

The structural pathway from its solvated molecular state to the solution crystallisation of the α- and ß-polymorphic forms of para amino benzoic acid.

Para amino benzoic acid (PABA) has two well-characterised α- and ß-polymorphic forms and, whilst both crystallise in the monoclinic space group P21/n, they have quite different crystal chemistry and crystallisability behaviour. Previous work has shown that the molecular conformation deformation energy in the crystalline state is higher for the ß-form than for the α-form and that the lattice energy for the former converges more slowly than for the latter overall. This suggests that not only is there a higher barrier to crystallisation for the ß-form but also that low solution supersaturations might be needed for it to preferentially nucleate. Additionally, solute cluster propensity and solute solvation energetic analysis highlight the importance of an aqueous solvation environment in inhibiting the α-form's strong OH⋯O carboxylic acid hydrogen bond (H-bond) dimer. Despite this, the detailed molecular-scale pathway from solvated molecules to 3D crystallographic structure still remains unclear, most notably regarding how the nucleation process is activated and how, as a result, this mediates the preferential formation of either of the two polymorphic forms. Molecular dynamics (MD) simulations coupled with FTIR studies and intermolecular synthon analysis address this issue through characterisation of the propensity of the incipient bulk synthons that are important in the crystallisation of the two polymorphic forms within the solution state. MD molecular trajectory analysis within crystallisation solutions reveals a greater propensity for OH⋯O synthons (both single H-bonds and homodimers) typical of the α-form and NH⋯O synthons found in both the α- and ß-forms when compared to aqueous solution but much lower propensities for the ß-form's "fingerprinting" OH⋯N and π-π stacking synthons. In contrast, data from the aqueous solution environment reveals a much greater propensity for the ß-form's π-π interaction synthons. IR dilution studies in acetonitrile in the carbonyl region reveal the presence of two CO vibrational stretching bands, whose relative intensities vary as a function of solution dilution. These were assigned to the solvated PABA monomer and a COOH dimer of PABA. Similar data in ethanol shows a main CO stretching band with a shoulder peak suggesting a similar monomer vs. dimer speciation may exist in this solvent. The IR data is consistent with the organic solvent MD data, albeit the corresponding analysis for the aqueous solution was precluded due to the latter's strong OH vibrational mode which restricted validation in aqueous solutions.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Automated Growth Rate Measurement of the Facet Surfaces of Single Crystals of the ß-Form of l-Glutamic Acid Using Machine Learning Image Processing.

Precision measurement of the growth rate of individual single crystal facets (hkl) represents an important component in the design of industrial crystallization processes. Current approaches for crystal growth measurement using optical microscopy are labor intensive and prone to error. An automated process using state-of-the-art computer vision and machine learning to segment and measure the crystal images is presented. The accuracies and efficiencies of the new crystal sizing approach are evaluated against existing manual and semi-automatic methods, demonstrating equivalent accuracy but over a much shorter time, thereby enabling a more complete kinematic analysis of the overall crystallization process. This is applied to measure in situ the crystal growth rates and through this determining the associated kinetic mechanisms for the crystallization of ß-form l-glutamic acid from the solution phase. Growth on the {101} capping faces is consistent with a Birth and Spread mechanism, in agreement with the literature, while the growth rate of the {021} prismatic faces, previously not available in the literature, is consistent with a Burton-Cabrera-Frank screw dislocation mechanism. At a typical supersaturation of σ = 0.78, the growth rate of the {101} capping faces (3.2 × 10-8 m s-1) is found to be 17 times that of the {021} prismatic faces (1.9 × 10-9 m s-1). Both capping and prismatic faces are found to have dead zones in their growth kinetic profiles, with the capping faces (σc = 0.23) being about half that of the prismatic faces (σc = 0.46). The importance of this overall approach as an integral component of the digital design of industrial crystallization processes is highlighted.

The Influence of Solvent Selection upon the Crystallizability and Nucleation Kinetics of Tolfenamic Acid Form II.

The influence of the solution environment on the solution thermodynamics, crystallizability, and nucleation of tolfenamic acid (TFA) in five different solvents (isopropanol, ethanol, methanol, toluene, and acetonitrile) is examined using an integrated workflow encompassing both experimental studies and intermolecular modeling. The solubility of TFA in isopropanol is found to be the highest, consistent with the strongest solvent-solute interactions, and a concomitantly higher than ideal solubility. The crystallizability is found to be highly dependent on the solvent type with the overall order being isopropanol < ethanol < methanol < toluene < acetonitrile with the widest solution metastable zone width in isopropanol (24.49 to 47.41 °C) and the narrowest in acetonitrile (8.23 to 16.17 °C). Nucleation is found to occur via progressive mechanism in all the solvents studied. The calculated nucleation parameters reveal a considerably higher interfacial tension and larger critical nucleus radius in the isopropanol solutions, indicating the higher energy barrier hindering nucleation and hence lowering the nucleation rate. This is supported by diffusion coefficient measurements which are lowest in isopropanol, highlighting the lower molecular diffusion in the bulk of solution compared to the other solutions. The TFA concentration and critical supersaturation at the crystallization onset is found to be directly correlated with TFA/isopropanol solutions having the highest values of solubility and critical supersaturation. Intermolecular modeling of solute-solvent interactions supports the experimental observations of the solubility and crystallizability, highlighting the importance of understanding solvent selection and solution state structure at the molecular level in directing the solubility, solute mass transfer, crystallizability, and nucleation kinetics.

Exploring the CSD Drug Subset: An Analysis of Lattice Energies and Constituent Intermolecular Interactions for the Crystal Structures of Pharmaceuticals.

Intermolecular (synthonic) modelling is used for a statistical analysis of crystal lattice energies, together with their contributing intermolecular interactions for the crystallographic structures selected from the CCDC's Drug Subset (https://doi.org/10.1016/j.xphs.2018.12.011). Analysis of this selected subset reveal similarities in packing compared to other organic crystals in the CSD with linear relationships between molecular weight and unit cell volume, void space, and packing coefficient. Crystal lattice energy calculations converge within a 30 Šintermolecular radius characterised by a mean lattice energy of ca. -36 kcal mol-1 with ca. 85% and 15% due to dispersive and electrostatic interactions, respectively. The distribution of the strongest synthons within the individual structures reveals an average strength of -5.79 kcal mol-1. The diversity of chemical space within the drug molecules is in agreement with the analysis of atom types across the selected subset with phenyl groups being found to contribute the highest mean energy of -11.28 kcal mol-1, highlighting the importance of aromatic interactions within pharmaceutical compounds. Despite an initial focus on Z' = 1 structures, this automated approach enables rapid and consistent quantitative analysis of lattice energy, synthon strength and functional group contributions, providing solid-form informatics for pharmaceutical R&D and a helpful basis for further investigations.

Role of Molecular, Crystal, and Surface Chemistry in Directing the Crystallization of Entacapone Polymorphs on the Au(111) Template Surface.

The pharmaceutical compound entacapone ((E)-2-cyano-3-(3,4-dihydroxy-5-nitrophenyl)-N,N-diethylprop-2-enamide) is important in the treatment of Parkinson's disease, exhibiting interesting polymorphic behavior upon crystallization from solution. It consistently produces its stable form A with a uniform crystal size distribution on the surface of an Au(111) template while concomitantly forming its metastable form D within the same bulk solution. Molecular modeling using empirical atomistic force-fields reveals more complex molecular and intermolecular structures for form D compared to form A, with the crystal chemistry of both polymorphs being dominated by van der Waals and π-π stacking interactions with lower contributions (ca. 20%) from hydrogen bonding and electrostatic interactions. Comparative lattice energies and convergence for the polymorphs are consistent with the observed concomitant polymorphic behavior. Synthon characterization reveals an elongated needle-like morphology for form D crystals in contrast to the more equant form A crystals with the surface chemistry of the latter exposing the molecules' cyano groups on its {010} and {011} habit faces. Density functional theory modeling of surface adsorption reveals preferential interactions between Au and the synthon GA interactions of form A on the Au surface. Molecular dynamics modeling of the entacapone/gold interface reveals the entacapone molecular structure within the first adsorbed layer to show nearly identical interaction distances, for both the molecules within form A or D with respect to the Au surface, while in the second and third layers when entacapone molecule-molecule interactions overtake the interactions between those of molecule-Au, the intermolecular structures are found to be closer to the form A structure than form D. In these layers, synthon GA (form A) could be reproduced with just two small azimuthal rotations (5° and 15°) whereas the closest alignment to a form D synthon requires larger azimuthal rotations (15° and 40°). The cyano functional group interactions with the Au template dominate interfacial interactions with these groups being aligned parallel to the Au surface and with nearest neighbor distances to Au atoms more closely matching those in form A than form D. The overall polymorph direction pathway thus encompasses consideration of molecular, crystal, and surface chemistry factors.

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches.

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models.

Nano(Q)SAR: Challenges, pitfalls and perspectives.

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.