RESUMEN

Various approaches have been developed to enhance the solubility or dissolution rate for the delivery of poorly water-soluble molecules. In this work, guided by an in silico solubility sensitivity analysis for oral absorption, a comparative assessment of the biopharmaceutical performance of a jet-milled free base, a tosylate salt, and a 50:50 (w/w) amorphous solid dispersion (ASD) with hydroxypropyl methylcellulose acetate succinate (HPMCAS) of a weak base drug candidate, GDC-3280, was conducted. Successful particle size reduction without amorphization or form change was confirmed for the jet-milled free base. The potential of solubility enhancement and desupersaturation risk were identified for tosylate salt and ASD formulation by measurements of tosylate salt solubility product constant (Ksp) and amorphous solubility of GDC-3280. In vitro dissolution testing demonstrated dissolution rate improvement for the jet-milled free base when compared with the unmilled free base and confirmed solubility enhancement followed by desupersaturation for GDC-3280 tosylate salt and ASD formulation. A crystallization inhibitor, hydroxypropyl methylcellulose (HPMC), was found to slow down the desupersaturation of tosylate salt solution, providing general insights for the development of pharmaceutical salts with disproportionation risks. Finally, a pharmacokinetic study in dogs showed that the in vivo exposure increased by 1.7- to 2-fold for the tosylate salt and ASD formulation compared with the jet-milled free base, consistent with the in silico solubility sensitivity analysis for the fraction of drug absorbed. Overall, this work provides insights into the evaluation of multiple formulation approaches for enhancing the biopharmaceutical performance of poorly water-soluble drugs.

Asunto(s)

Productos Biológicos , Animales , Perros , Preparaciones Farmacéuticas/química , Tamaño de la Partícula , Química Farmacéutica , Solubilidad , Agua/química , Liberación de Fármacos

RESUMEN

BACKGROUND: Network connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner Network problems: given a reference "database" graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu. RESULTS AND DISCUSSION: We introduce the condition Steiner Network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C - ϵ , for every C ≥ 2 and ϵ > 0 , and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network. CONCLUSION: Our results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.