Prediction of hydrate and solvate formation using knowledge-based models

Solvate formation is a phenomenon that has received special attention in solid state chemistry over the past few years. This is due to its potential to both improve and impair pharmaceutical formulations. The reasons for solvate formation aren’t explicitly known. Therefore, there is currently no reliable guide in the literature on what solvents to choose in order to avoid or form a solvate when crystallizing an organic material. In this thesis we address the problem by trying to find the main reasons of solvate formation. A knowledge-based approach was used to link the molecular structure of an organic compound to its ability to form a solvate with five different solvents; these are ethanol, methanol, dichloromethane, chloroform and water. The Cambridge Structural Database (CSD) was used as a source of information for this study. A supervised machine learning method, logistic regression was found to be the optimal method for fitting these knowledge-based models. The result was one predictive model per solvent, with a success rate of 74-80 %. Each model incorporated two molecular descriptors, representing two molecular features of molecules. These are the size and branching in addition to hydrogen bonding ability. The models’ predictive ability was validated via experimental work, in which slurries of 10 pharmaceutically active ingredients were screened for solvate formation with each of the five solvents in the study. During the screening process, a new diflunisal dichloromethane solvate, a diflunisal chloroform solvate and a hymercromone methanol solvate were found. The PXRD patterns of these forms are reported. The thesis also includes SCXRD analysis of a previously known grisoefulvin dichloromethane solvate, a previously known fenofibrate polymorph and a new fenofibrate polymorph

Prediction of Hydrate and Solvate Formation Using Statistical Models

Novel, knowledge based models for the prediction of hydrate and solvate formation are introduced, which require only the molecular formula as input. A data set of more than 19 000 organic, nonionic, and nonpolymeric molecules was extracted from the Cambridge Structural Database. Molecules that formed solvates were compared with those that did not using molecular descriptors and statistical methods, which allowed the identification of chemical properties that contribute to solvate formation. The study was conducted for five types of solvates: ethanol, methanol, dichloromethane, chloroform, and water solvates. The identified properties were all related to the size and branching of the molecules and to the hydrogen bonding ability of the molecules. The corresponding molecular descriptors were used to fit logistic regression models to predict the probability of any given molecule to form a solvate. The established models were able to predict the behavior of ∼80% of the data correctly using only two descriptors in the predictive model

A New Low Melting-point Polymorph of Fenofibrate Prepared via Talc Induced Heterogeneous Nucleation

Fenofibrate is one of the most commonly prescribed hyperlipidemia agents. Despite its high lipophilicity and ultralow aqueous solubility, most commercially available formulations use micronized crystalline fenofibrate form I, which has a low dissolution rate and poor oral bioavailability. Little is known about the crystallization of other polymorphs from supercooled amorphous fenofibrate. This study reports a new fenofibrate polymorph (form III) obtained via a controlled heterogeneous nucleation method using low quantity (1% w/w) of the generally recognized as safe (GRAS) oral pharmaceutical excipient talc. Form III has a low melting point of 50 °C, and crystallization of form I immediately occurs after the melting of form III. The microscopic, thermal, and spectroscopic characterizations of form III confirmed the distinct molecular packing difference between the new form and other known forms. The discovery of this new form will enrich the understanding of the molecular behavior of fenofibrate and bring useful insights into the role pharmaceutical excipients in selective crystallization of pharmaceutical active ingredient