No abstract

An accurate and efficient procedure was developed for performing C NMR chemical shift calculations employing density functional theory with the gauge invariant atomic orbitals (DFT-GIAO). Benchmarking analysis was carried out, incorporating several density functionals and basis sets commonly used for prediction ofC NMR chemical shifts, from which the B3LYP/cc-pVDZ level of theory was found to provide accurate results at low computational cost. Statistical analyses from a large data set of C NMR chemical shifts in DMSO are presented with TMS as the calculated reference and with empirical scaling parameters obtained from a linear regression analysis. Systematic errors were observed locally for key functional groups and carbon types, and correction factors were determined. The application of this process and associated correction factors enabled assignment of the correct structures of therapeutically relevant compounds in cases where experimental data yielded inconclusive or ambiguous results. Overall, the use of B3LYP/cc-pVDZ with linear scaling and correction terms affords a powerful and efficient tool for structure elucidation.

Methods to predict crystallization behavior for active pharmaceutical ingredients (APIs) can serve as an important guide in small molecule pharmaceutical development. Here, we describe solvate formation propensity prediction for pharmaceutical molecules via a machine learning approach. Random forests (RF) and support vector machine (SVM) algorithms were trained and tested with data sets extracted from Cambridge Structural Database (CSD). The machine learning models, requiring only 2D structures as input, were able to predict solvate formation propensity for organic molecules with up to 86% success rate. Performance of the models was demonstrated with a collection of 20 pharmaceutical molecules.

The preparation of 3-(2-chlorethyl)-4-oxo-3H-imidazo[5,1-d]-1,2,3,5- tetrazine-8-carboxylic acid, a key derivative of mitozolomide in our exploration of the structure-activity relationships of this class of antitumor agents, is described. The facile conversion to the 8-carbonyl chloride gave a derivative that reacted preferentially with nucleophiles at the 8-position rather than at the reactive 4-oxo group, allowing the preparation of a wide range of ester, thioester, amide (including an amide derived from an amino acid), hydroxamic acid, hydrazide and sulfoximide, azide and diazoacetyl derivatives. The in vivo activity is presented of a range of these compounds against TLX5 lymphoma and L1210 leukemia cell lines.

The creation of the Enabling Technologies Consortium (ETC) is described. The ETC fosters precompetitive collaborations aimed at the development and evaluation of new enabling technologies for pharmaceutical research and development, with an initial focus on chemistry, manufacturing, and controls. An overview of the structure and function of the new organization, which will carry out its work while remaining mindful of antitrust compliance requirements, is herein presented along with a description of several ongoing development projects.

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.