Drug Polymer Thermodynamic Phase Diagrams in the Selection of Optimal API-Polymer Compositions for Amorphous Solid Dispersions

Purpose The aim of this work was to examine, for amorphous solid dispersions, how the thermal analysis method selected impacts on the construction of thermodynamic phase diagrams, and to assess the predictive value of such phase diagrams in the selection of optimal, physically stable API-polymer compositions. Methods Thermodynamic phase diagrams for two API/polymer systems (naproxen/HPMC AS LF and naproxen/Kollidon 17 PF) were constructed from data collected using two different thermal analysis methods. The “dynamic” method involved heating the physical mixture at a rate of 1 &[deg]C/minute. In the "static" approach, samples were held at a temperature above the polymer Tg for prolonged periods, prior to scanning at 10 &[deg]C/minute. Subsequent to construction of phase diagrams, solid dispersions consisting of API-polymer compositions representative of different zones in the phase diagrams were spray dried and characterised using DSC, pXRD, TGA, FTIR, DVS and SEM. The stability of these systems was investigated under the following conditions: 25 &[deg]C, desiccated; 25 &[deg]C, 60 % RH; 40 &[deg]C, desiccated; 40 &[deg]C, 60 % RH. Results Endset depression occurred with increasing polymer volume fraction (Figure 1a). In conjunction with this data, Flory-Huggins and Gordon-Taylor theory were applied to construct thermodynamic phase diagrams (Figure 1b). The Flory-Huggins interaction parameter (&[chi]) for naproxen and HPMC AS LF was + 0.80 and + 0.72, for the dynamic and static methods respectively. For naproxen and Kollidon 17 PF, the dynamic data resulted in an interaction parameter of - 1.1 and the isothermal data produced a value of - 2.2. For both systems, the API appeared to be less soluble in the polymer when the dynamic approach was used. Stability studies of spray dried solid dispersions could be used as a means of validating the thermodynamic phase diagrams. Conclusion The thermal analysis method used to collate data has a deterministic effect on the phase diagram produced. This effect should be considered when constructing thermodynamic phase diagrams, as they can be a useful tool in predicting the stability of amorphous solid dispersions.

Novel microtubule-targeting agents, pyrrolo-1,5-benzoxazepines, induce apoptosis in multi-drug-resistant cancer cells

PURPOSE: The development of multi-drug resistance (MDR) due to the expression of members of the ATP binding cassette (ABC) transporter family is a major obstacle in cancer treatment. The broad range of substrate specificities associated with these transporters leads to the efflux of many anti-cancer drugs from tumour cells. Therefore, the development of new chemotherapeutic agents that are not substrates of these transporters is important. We have recently demonstrated that some members of a novel series of pyrrolo-1,5-benzoxazepine (PBOX) compounds are microtubule-depolymerising agents that potently induce apoptosis in several cancer cell lines and impair growth of mouse breast tumours. The aim of this current study was to establish whether PBOXs were capable of inducing apoptosis in cancer cells expressing either P-glycoprotein or breast cancer resistance protein (BCRP), two of the main ABC transporters associated with MDR.

METHODS: We performed in vitro studies to assess the effects of PBOXs on cell proliferation, cell cycle and apoptosis in human cancer cell lines and their drug-resistant substrains expressing either P-glycoprotein or BCRP. In addition, we performed a preliminary molecular docking study to examine interactions between PBOXs and P-glycoprotein.

RESULTS: We established that three representative PBOXs, PBOX-6, -15 and -16 were capable of inducing apoptosis in drug-resistant HL60-MDR1 cells (expressing P-glycoprotein) and HL60-ABCG2 cells (expressing BCRP) with similar potencies as in parental human promyelocytic leukaemia HL60 cells. Likewise, resistance to PBOX-6 and -16 was not evident in P-glycoprotein-expressing A2780-ADR cells in comparison with parent human ovarian carcinoma A2780 cells. Finally, we deduced by molecular docking that PBOX-6 is not likely to form favourable interactions with the substrate binding site of P-glycoprotein.

CONCLUSION: Our results suggest that pro-apoptotic PBOX compounds may be potential candidates for the treatment of P-glycoprotein- or BCRP-associated MDR cancers.

Stoichiometric Molecularly Imprinted Polymers for the Recognition of Anti-Cancer Pro-drug Tegafur

Molecularly Imprinted Polymers (MIPs) targeting tegafur, an anti-cancer 5-fluorouracil pro-drug, have been prepared by stoichiometric imprinting using 2,6-bis(acrylamido)pyridine (BAAPy) as the functional monomer. Solution association between tegafur and BAAPy was studied by 1H NMR titration, which confirmed the formation of 1:1 complexes with an affinity constant of 574±15 M-1 ¬in CDCl3. Evaluation of the synthesised materials by HPLC and equilibrium rebinding experiments revealed high selectivity of the imprinted polymer for the pro-drug versus 5-fluorouracil and other competing analytes, with maximum imprinting factors of 25.3 and a binding capacity of 45.1 μmol g-1. The synthesised imprinted polymer was employed in solid-phase extraction of the pro-drug using an optimised protocol that included a simple wash with the porogen used in the preparation of the material. Tegafur recoveries of up to 96% were achieved from aqueous samples and 92% from urine samples spiked with the template and three competing analytes. The results demonstrate the potential of the prepared polymers in the pre-concentration of tegafur from biological samples, which could be an invaluable tool in the monitoring of patient compliance and drug uptake and excretion.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

Teamwork Efficiency and Company Size

We study how ownership structure and management objectives interact in determining the company size without assuming information constraints or any explicit costs of management. In symmetric agent economies, the optimal company size balances the returns to scale of the production function and the returns to collaboration efficiency. For a general class of payoff functions, we characterize the optimal company size, and we compare the optimal company size across different managerial objectives. We demonstrate the restrictiveness of common assumptions on effort aggregation (e.g., constant elasticity of effort substitution), and we show that common intuition (e.g., that corporate companies are more efficient and therefore will be larger than equal-share partnerships) might not hold in general.